Abstract
The global burden of cancer is rising, with treatment failures often due to the metastatic nature of late-stage malignancies. Circulating tumour cells (CTCs) are metastatic precursors shed from primary tumours, which survive in circulation, extravasate and colonise distant organs. The advent of high-throughput single-cell RNA sequencing (scRNA-seq) has revolutionised the investigation of transcriptomic landscape at single-cell resolution, enabling deep transcriptomic profiling, re-stratifying CTC subtypes and improving the detection of rare new subpopulations. Applications extend to understanding tumour microenvironments, characterising cellular heterogeneity, uncovering metastasis molecular mechanisms and improving prognosis and diagnostic strategies. A timeline of key milestones in CTC scRNA-seq research is also provided. Nevertheless, a knowledge gap remains due to unstandardised protocols and fragmented resources in CTC scRNA-seq research. We address this gap by proposing a 12-step CTC-specific scRNA-seq workflow to overcome methodological inconsistencies. This workflow spans the entire process from enrichment, single-cell sorting and sequencing to data pre-processing and downstream analyses, with a detailed compilation of data analysis tools. An in-depth discussion of the pros and cons of commonly used scRNA-seq tools is also included, specifically evaluating their suitability for CTC research. Additionally, emerging research frontiers, including the discovery of hybrid cells—fusion products of tumour and normal cells—and the integration of machine learning (ML) into scRNA-seq workflows, are explored. Future research should prioritise CTC scRNA-seq workflow standardisation, integrate ML-driven analysis and investigate rare and hybrid populations to advance metastasis research. This review supports these goals by guiding methods, informing tool selection and promoting data sharing for reproducibility.
Keywords: Circulating tumour cells, Single-cell RNA sequencing, Cancer, Hybrid cells, Machine learning integration
Introduction
The landscape of cancer diagnosis has evolved significantly over the past decade, integrating liquid biopsies alongside traditional tissue biopsies [1]. Liquid biopsies have gained popularity due to their minimally invasive sampling procedures, which allow frequent collection and is particularly beneficial when tumour tissue is inaccessible or limited [2]. Moreover, they provide diagnostic and prognostic insights into solid tumours, offering the potential for early, accurate diagnostics and effective disease monitoring across diverse cancer types [3, 4]. Consequently, liquid biopsies are increasingly being integrated into clinical practice [5, 6].
Circulating tumour cells are rare cells released from primary tumour lesions into the bloodstream, serving as the precursors for metastasis [7]. They exhibit phenotypic plasticity, including the ability to undergo epithelial-mesenchymal transition (EMT), interacting dynamically with their microenvironment to enhance survival and metastatic potential [8, 9]. This results in a heterogeneous population that mirrors the complexity of the primary tumour, influencing cancer progression and treatment response [10, 11]. Of particular interest are hybrid cells, resulting from the fusion of neoplastic and immune cell characteristics. They further complicate the understanding of tumour heterogeneity and immune responses within the tumour microenvironment (TME) [12]. These hybrid cells represent a novel frontier in cancer research, and have significant implications for disease progression and therapeutic strategies [13, 14].
Single-cell RNA sequencing (scRNA-seq) is a powerful tool for dissecting metastatic processes driven by CTCs [15]. The transition from bulk to scRNA-seq represents a significant advancement in deciphering intratumoral heterogeneity (ITH) and phenotypic plasticity [16]. Unlike bulk sequencing, scRNA-seq provides insights into individual cell gene expression profiles. It can reveal intricate molecular networks that influence tumour heterogeneity and response to therapy [17–19]. Additionally, the integration of artificial intelligence and machine learning (ML) improves raw data processing for single CTCs, thereby enhancing CTC clustering, cell identification and the analysis of cellular heterogeneity [20, 21]. This knowledge advances precision oncology by enabling earlier detection and more personalized treatment strategies, which will ultimately improve outcomes for cancer patients.
Technological advancements in CTCs scRNA-seq
The use of scRNA-seq to CTCs has evolved rapidly since the introduction of the Smart-seq protocol in 2012 [22]. In the following year, Smart-seq2 improved sensitivity and transcript coverage, sparking interest in CTC transcriptomics [23, 24]. By 2014, Ting and the researchers had identified distinct transcriptomic profiles of CTCs compared to primary tumours and tumour-derived cell lines. CTC clusters were found to exhibit low proliferation, enriched expression of ALDH1A2 (stemness gene), SPARC (stromal ECM gene), LGFBP5 (epithelial-stromal interface marker) and co-expression of epithelial and mesenchymal markers [25]. In 2016, the 10X Genomics Chromium system was introduced [26]. Innovations further included flow cytometry-guided CTC capture and scRNA-seq [27], scalable hydrodynamic Hydro-Seq barcoding system [28]; the adoption of locked nucleic acids in PCR whole transcriptome amplification to increase sensitivity [29]; and the development of SCR-chip—a microfluidic scRNA-seq platform using EpCAM+ immunomagnetic beads [30]. Most recently, the NICHE nanoplatform has enabled real-time, in situ gene expression and immune profiling of live CTCs [31]. Alongside this, the size-based MetaCell® technology provided a label-free approach to enrich viable CTCs from colorectal cancer (CRC) patient blood [32], while Stucky et al. established a robust clinical workflow for isolating CTCs from head and neck squamous cell carcinoma (HNSCC) patients before and during therapy, contributing to standardisation in CTC research [33].
Figure 1 illustrates a chronological timeline of technological advancements and key applications of CTC scRNA-seq, laying the groundwork for the exploration of emerging functional insights—such as the discovery of rare and hybrid CTC populations, as well as the integration of ML in computational analyses.
Fig. 1.
Timeline of technological milestones in CTC scRNA-seq from 2012 to 2024. It highlights foundational breakthroughs such as the introduction of Smart-seq (2012) and Smart-seq2 (2013), which significantly improved transcriptome coverage and sensitivity. Landmark study in 2014 revealed key transcriptomic differences between CTCs and primary tumours, highlighting stemness, EMT and ECM-related gene signatures. The 10X Genomics Chromium system (2016) enabled high-throughput profiling, followed by innovations such as flow cytometry-guided scRNA-seq, Hydro-Seq and whole transcriptomics analysis using locked nucleic acid. Biological insights progressed with studies on TME (2019), CTC identification, CTC subtype stratification (2020) and molecular cancer mechanisms (2021). Recent tools include the NICHE for in situ gene profiling, SCR-chip and label-free enrichment via MetaCell®. The timeline concludes with clinical standardisation efforts, including a validated CTC isolation workflow for HNSCC in 2024. This technological trajectory underpins the field’s readiness to tackle emerging frontiers—such as rare and hybrid cell discovery and machine learning–integrated CTC analysis
Clinical relevance of CTCs
Molecular characterisation and phenotypic analysis
The identification of distinct gene expression signatures within CTCs via scRNA-seq represents the most fundamental level of molecular characterisation, a strategy that has been applied since as early as 2014 [25]. Beyond this, molecular characterisation permits the identification of genetic alterations and broader expression patterns [34, 35], while phenotyping strategies incorporate functional aspects, examining cellular behaviours and responsiveness to treatments [36, 37]. Both of these strategies are integral to defining transcriptomic profiles and enabling accurate clustering of CTC subpopulations. For instance, Grigoryeva et al. identified nine distinct integrin expression profiles from 42,225 CTCs from 81 non-metastatic breast cancer (BC) patients [38]. Similarly, three BC CTC clusters have been identified—estrogen receptor-positive (ER+), human epidermal growth factor receptor 2-positive and triple-negative—each exhibiting distinct expression profiles, including integrins, platelet degranulation markers and key oncogenes [39].
In non-small cell lung cancer (NSCLC), a large-scale study analysed 3363 single-cell CTC transcriptomes, revealing extensive phenotypic heterogeneity. They identified distinct clusters, including epithelial-like and proliferative (Cluster 1), cancer stem cell–like (Cluster 4), mesenchymal with oxidative phosphorylation and immune evasion (Cluster 5) and mesenchymal with invasive and glycolytic features (Cluster 6) [40]. In neuroblastoma, scRNA-seq has identified two CTC subgroups; Subgroup 1 was associated with increased proliferation and cell cycle-related features, whereas Subgroup 2 exhibited overexpression of neuronal injury-related genes (FOS, RHOA and MIF). Notably, higher CTC numbers were observed in patients with advanced-stage neuroblastoma [41]. In CRC, Kozuka et al. performed molecular characterisation on 59 single CTCs, revealing distinct gene expressions related to epithelial, EMT and stem cells within CTCs. The phenotypic classification of CTCs further contributes to a more refined and improved prognosis [42].
Refinement of CTC heterogeneity
Over the past 5 years, CTC scRNA-seq research has surged remarkably, advancing the understanding of tumour heterogeneity. Studies have revealed that distinct CTC clusters often emerge based on patient-specific patterns, mirroring the robust intertumoral heterogeneity (eITH) observed across various cancer types [43, 44]. However, this diversity can mask subtle, yet significant patterns shared among CTCs obtained from a single tumour within a single patient (ITH) [45]. Conversely, there are intricate variations in both molecular and phenotypic differences within CTC clusters. Understanding them might shed light on both inter- and intra-tumoral heterogeneity, providing a comprehensive view of CTC diversity. Herein, Ruan et al. proved that scRNA-seq can quantify eITH and ITH within lung cancer metastases, both between patients and within a given patient. They attributed this variability to spatial differences among metastatic sites, variations in the expression of cell-cycle genes and cancer-testis antigens, as well as mesenchymal and circulating stem cell properties in CTCs. They also demonstrated that scRNA-seq could uncover potential temporal heterogeneity during tumour progression, offering further insight into ITH dynamics [46].
In the same year, Brechbuhl et al. revealed two distinct populations of CTCs from BC patients, differing in proliferative and EMT characteristics. Their study also highlighted immune evasion mechanisms, such as T cell exhaustion and PD-1/PD-L1 pathway activation, driven by CTC interactions with peripheral blood mononuclear cells (PBMCs) within the TME [47]. Pauken et al. identified unique circulating neoplastic cell populations in metastatic BC (mBC), revealing the significant role of genetic and epigenetic changes in ITH [48]. In 2022, clonal RNA expression variations within portal blood samples from each patient were first reported in pancreatic ductal adenocarcinoma (PDAC) [49]. Additionally, ITH characterisation in HNSCC found mutations in key signalling pathways, including CREB, β-Adrenergic receptor signalling and G-protein receptor signalling [33].
Similarly, two distinct CTC hierarchical clusters from two castration-resistant prostate cancer (CRPC) patients were discovered with the upregulation of disease progression pathways (AR, MYC, oxidative phosphorylation) and inflammatory response pathways (chemokine signalling, IL/JAK/STAT signalling) [50]. Collectively, understanding heterogeneity within CTCs unravels the complex landscape of CTC diversity, highlighting how dynamic interactions with the TME shape CTC behaviour, evolution and immune evasion.
Dissecting CTC tumour microenvironment
The TME encompasses a dynamic ecosystem of various cell types, blood vessels, extracellular matrix and signalling molecules [51]. Leveraging scRNA-seq allows the dissection of this ecosystem at single-cell resolution, unravelling the intricate interactions between CTCs and their milieu [52]. Early evidence in BC showed that CTC-neutrophil clusters enriched in cytokine–receptor and cell–cell junction interactions promoted cell cycle progression and metastatic potential, pinpointing potential therapeutic vulnerabilities within the TME [53]. In hepatocellular carcinoma (HCC), spatially resolved CTC analysis across different vascular compartments uncovered transcriptional heterogeneity linked to stress response, immune evasion, and cell cycle signalling. These findings have highlighted CCL5 as a key player in immune escape and that spatial context is essential to dissect TME dynamics [54].
Similarly, Arnoletti et al. demonstrated that CTCs from PDAC contribute to immunosuppression and metastasis by promoting myeloid cell differentiation through CSF1R (colony-stimulating factor 1 receptor) and CXCR2 (CXC motif chemokine receptor 2) signalling pathways, mediated by M-CSF/IL-34 (macrophage colony-stimulating factor/interleukin 34) and IL-8, respectively. These interactions, as validated through ex vivo co-culture with myeloid fibroblasts, enhanced CTC proliferation and clustering, while blockading of these pathways impaired their metastatic potential [49]. Another study uncovered immune evasion mechanisms wherein PDAC CTCs interact with natural killer cells via the HLA-E: CD94-NKG2A checkpoint, aided by platelet-derived RGS18RGS18 (regulators of G-protein signalling 18) [55]. Complementing this, a multi-omics approach (scRNA-seq, WGCNA, CIBERSORT) revealed hallmark TIME features in metastatic PDAC CTCs, including EMT and cytoskeletal activation, elevated Treg and macrophage infiltration, and reduced dendritic cell proportions [56].
Molecular mechanisms decoding EMT, metastasis and resistance
Metastatic progression signifies the final and most complex stage of tumour evolution. Noteworthy, it hinges on the delicate balance of the transition between epithelial and mesenchymal phenotypes, known as EMT [57]. Inherent CTC heterogeneity fuels increased proliferation, motility, and the acquisition of resistance. Exploring these traits at the single-cell level provides a granular understanding of metastatic behaviour and tumour adaptability. For example, a significant CTC subtype with EMT characteristics has been reported. It displayed platelet adhesion, contributing to EMT progression and the development of chemoresistance in metastatic human gastric cancer [58]. In early BC, CTCs with pronounced tumour cell characteristics (activated oxidative stress, proliferation and metastasis) have been subdivided into two subtypes. Subtype 1 exhibits strong metastatic ability due to its partial EMT phenotypes, whereas subtype 2 is linked to anti-apoptosis, EMT and migration properties [59]. Similarly, EMT-related pathways have been enriched in two CTC clusters identified in CRC (Vasantharajan, 2024).
Another scRNA-seq research has revealed heterogeneity in androgen receptor gene mutations and splicing variants among individual prostate CTCs, alongside divergent pathway activations that may underlie treatment failure [60]. In line with this, a recent multi-dataset analysis uncovered nearly 1000 alternative splicing events in single CTCs and CTC clusters, alongside a global 3′ UTR lengthening in BC CTC clusters driven by core polyadenylation factors such as PPP1CA—suggesting an additional layer of transcriptomic regulation shaping CTC phenotypes [61]. Moreover, single-cell profiling of a prospective cohort comprising 55 ER+ mBC women has revealed the presence of oestrogen receptor 1 mutations in isolated CTCs. This detection has further been correlated with the time to metastatic relapse and the duration of aromatase inhibitor therapy following such recurrence—highlighting their role in endocrine therapy resistance [62].
Clinical translation of CTCs
After identifying the CTC subtypes and mechanisms, the subsequent phase is to translate these findings into clinical applications. Correlating specific CTC subtypes and molecular mechanisms with clinical outcomes help predict disease progression, treatment response and overall prognosis [63]. For example, Li and co-researchers identified 18 genes that were closely related to the specific CTC epithelial phenotype in BC, including a novel potential glycyl-tRNA synthetase (GARS) oncogene. They developed a risk score that correlated with high metastasis rates, poor survival, defective immune infiltration and low immunotherapy response. High-risk patients also showed greater sensitivity to AKT-mTOR and cyclin-dependent kinase (CDK) inhibitors [64]. In the same year, Yang et al. demonstrated that hexokinase 2 (HK2)-identified CTCs in NSCLC with EGFR mutations were linked to reduced EGFR inhibitor efficacy, enabling early prognosis and informing personalised therapy [65].
In a prospective melanoma cohort, CTC scRNA-seq revealed the coordinated upregulation of lipogenesis and iron homeostasis pathways, highlighting Sterol Regulatory Element-Binding Protein (SREBP)-driven mechanisms linked to cancer progression, drug resistance and poor prognosis, irrespective of treatment regimen in metastatic melanoma [36]. Moreover, two distinct CTC subgroups were identified, with one subgroup exhibiting elevated gene expressions related to epithelial phenotypes (CHD1, EpCAM, ASGR2, KRT8), EMT (VIM) and stemness (CD133, POU5F1, NOTCH1, STAT3). The presence of these CTC subgroups indicated a poorer prognosis in advanced HCC [66].
Table 1 presents a comprehensive overview of CTC scRNA-seq landscape across various cancer types, highlighting key research trends in enrichment methods, sequencing technologies and key findings. Breast, lung and pancreatic cancers are among the most frequently studied in CTC scRNA-seq research, with growing interest in prostate cancer and mesothelioma. All computational tools used or cited by the authors, as well as the specific scRNA-seq technologies employed in each study, have been included. This provides the foundation for the proposed scRNA-seq workflow detailed in Sect. 4.
Table 1.
Overview of published scRNA-seq-related studies on CTCs across cancer types
| Type of cancer | Year | CTC enrichment and single-cell sorting | scRNA-seq technology | Data analysis | Number of CTCs analysed | Finding | Cit |
|---|---|---|---|---|---|---|---|
| Breast cancer | 2024 | SRP131647: Parsortix® PC1 System (ANGLE plc), followed by micromanipulation [53] | Smart-seq2 (Takara Bio) | FastQC, STAR, salmon, outrigger |
SRP131647: 92 single CTCs and 82 CTC clusters from ten BC patients and three xenografts; SRP133387: 66 single CTCs and 64 CTC clusters from matched donors, SRP066632: 41 single CTCs; SRP186111: 31 CTC clusters |
▪ Four scRNA-seq datasets were used: SRP131647 (main), SRP133387, SRP066632, and SRP186111 (three validation sets) ▪ Identification of 994 and 836 alternative splicing events in single CTCs and CTC clusters, respectively ▪ Discovery of a global lengthening of 3′ UTRs in CTC clusters compared to single CTCs, governed by 14 core alternative polyadenylation factors, particularly PPP1CA |
[61] |
| 2024 | Negative enrichment via RosetteSep human CD45 depletion kit, followed by FACS (CD45−, EpCAM+, integrins β3, β4, and αVβ5) | Chromium system (10 × Genomics) | Not stated | 42,225 CTCs from 81 non-metastatic BC patients |
▪ Nine groups: o Group 1: 143 CTCs with non-complementary integrin α- and β-subunits o Group 2: 81 CTCs expressing only integrin α-subunits o Group 3: 8 CTCs with β1-containing heterodimers (no other β-subunits) o Group 4: 2 CTCs with β2-containing heterodimers (no other β-subunits) o Group 5: 11 CTCs with β3-containing heterodimers (no other β-subunits) o Group 6: 38 CTCs with multiple β-subunits and at least one heterodimer o Group 7: 3 3 CTCs with β4-subunit and other subunits/heterodimers o Group 8: 5 CTCs expressing full-fledged integrin α6β4 genes o Group 9: 154 CTCs without integrins gene expression |
[38] | |
| 2023 | Immunomagnetic separation (CellSearch) and identification (CellCelector, Sartorius, Germany), followed by micromanipulation | QIAseq UPX 3′ transcriptome kit (QIAGEN) | CLC Genomics Workbench | 24 pooled CTCs from two patient samples |
▪ Workflow established for CTC enrichment, identification, and isolation without cell fixation/permeabilization, preserving viability and morphology ▪ Discrimination of apoptotic vs. non-apoptotic CTCs for downstream analysis |
[67] | |
| 2023 | Integrated ClearCell FX system (size selection) and Polaris workflow (marker-free negative enrichment); control GSE144494: CTC-iChip microfluidic device (EpCAM+, Cadherin+, HER+), followed by micromanipulation | Fluidigm Polaris system (Fluidigm) | unCTC, FastQC, RSEM, bowtie, limma, DDLK custering, inferCNV | 72 CTCs from 6 patients of three major subtypes: ER−/PR−/HER2–, ER+/PR+/HER2−, and ER−/PR−/HER2+; control GSE144494: 135 single CTCs or CTC clusters from 45 patients with HR+ mBC [68] |
▪ Identification of three CTC clusters: ER+, HER2+, and triple-negative ▪ Molecular characterisation revealed: o Cluster 1: Upregulation of integrins (ITGA2B, ITGB5), platelet degranulation markers (CLU, SPARC), and oncogenes (CDKN1A, TIMP1, PGRMC1) o Cluster 2: Elevation in BC–associated transcripts (IL10, BRIP1, IDO1, POU5F1) o Cluster 3: Upregulation of EPCAM, KRT18, KRT19, SOD1 ▪ Identification of several similar CNV patterns as in [68], including chromosome 1q associated with breast carcinogenesis and housing tumour suppressor genes and oncogenes |
[39] | |
| 2023 | Immunomagnetic nanospheres (EpCAM+, CD45–) | Bead-dd-seq method [69] | Not stated | 39 out of 90 CTCs from eight early BC patients with different lymph node status |
▪ Identification of two CTC clusters: o Subtype 1: strong metastatic ability with partial EMT phenotypes and enriched transcripts in regulating the cell cycle and stemness (TP53, E2F1, BRCA1, ESR1, SP1, MYC, KLF4) o Subtype 2: anti-apoptosis, EMT and migration (MUC4 and MUC16 expression) |
[59] | |
| 2023 | CTC isolation kit (Cytogen, CIKW10) and SMART BIOPSY™ Cell Isolator (Cytogen, CIS030), followed by sorting via size exclusion based HDM (high density microporous) chip | Chromium system (10 × Genomics) | Not stated | Clinical sample: 55,580 and 15,142 single cells from blood and tissues from three BC patients; mouse model: 5,511 cells from primary tumours and 3,006 CTCs |
▪ Identification of 21 EpCAM+ CTCs in blood and 1,488 EpCAM+ cancer cells in tissues from three BC patients ▪ Identification of three CTC clusters (cluster 4, 5, 7) consisting of epithelial and mesenchymal stem cells |
[70, 71] | |
| 2021 | Hydrodynamic CTC sorting chip (EpCAM+, CD45−, CD15–); GSE144494: CTC-iChip microfluidic device (EpCAM+, Cadherin+, HER+), followed by micromanipulation | Smart-seq HT (Takara Bio) | GSEA | 35 single CTCs from patients after endocrine therapy (8 CTCs from 3 patients with ESR1 mutations and 27 CTCs from 8 patients without ESR1 mutations); GSE144494: 135 single CTCs or CTC clusters from 45 patients with HR+ mBC [68] |
▪ Detection of significant enrichment of CARM1-mediated ER signalling in wild type ESR1 CTCs than mutant ESR1 CTCs ▪ Evaluation of endocrine resistance via correlation of ESR1 mutations with time to metastatic relapse and duration of AI therapy following such recurrence |
[62] | |
| 2021 | FACS to obtain single cell suspensions, followed by validation via RareCyte Cytefinder II™ (CD45−, EpCAM+, CK+) | Chromium system (10 × Genomics) | Bcl2fastq, CellRanger, Seurat, DoubletFinder, SCtransform, FindNeighbors, FindCluster, singleR, MAST, ClusterProfiler | Lin− and Lin+ cell populations from three mBC patients with different hormone receptor status (number not stated) |
▪ Identification of 16 cell populations based on gene expression, where cluster 10 is CTC ▪ Upregulation of CLDN4, CLDN7, MGP and CRABP2 in CTCs |
[48] | |
| 2021 | GSE109761: Parsortix® PC1 System (ANGLE plc) (EpCAM+); validation GSE144494: CTC-iChip microfluidic device (EpCAM+, Cadherin+, HER+); followed by micromanipulation | GSE109761: Smart-seq2; GSE144494: smart-seq HT | Seurat, SingleR, limma, glemnet | 116 single-cell CTCs datasets from GEO set GSE109761 [53]; validation GSE144494: 135 single CTCs or CTC clusters from 45 patients with HR+ mBC [68] |
▪ Identification of eighteen genes closely related to the specific CTC epithelial phenotype BC patient prognosis ▪ Identification GARS gene, previously not studied in BC, as a potential oncogene ▪ Establishment of a risk score using the 18 genes as a prognostic and predictive marker |
[64] | |
| 2020 | Filtration of whole blood via CellSieve filter (CREATV MicroTech, Inc), followed by filter backwash method | Chromium system (10 × Genomics) | CellRanger, Seurat | Patient 2 (11 CTC-1, 13 CTC-2), Patient 5 (13 CTC-1, 12 CTC-2), Patient 6 (0 CTC-1, 3 CTC-2), Patient 7 (1 CTC-1, 0 CTC-2), the combined patient samples 9, 10 and 11 (20 CTC-1, 10 CTC-2) and from the unfiltered local BC Patient 1 (7 CTC-1, 3 CTC-2) |
▪ Identification of two populations of CTCs: o CTC-1 cluster: transcripts indicative of oestrogen responsiveness and increased proliferation o CTC-2 cluster: transcripts characteristic of reduced proliferation and EMT ▪ Prediction of increased immune evasion in the CTC population with EMT characteristics |
[47] | |
| 2020 | CTC-iChip microfluidic device (EpCAM+, Cadherin+, HER+), followed by micromanipulation | Smart-seq HT (Takara Bio) | DESeq2 | 135 single CTCs or CTC clusters from 45 patients with HR + mBC; validation: 109 CTCs from 33 mBC patients |
▪ Identification of a CTC subset with strong ribosome and protein synthesis signatures; proliferation and epithelial markers; and correlated with poor clinical outcome ▪ Exploration of CTCs as potential suppressors of metastatic progression |
[68] | |
| 2020 | Microfluidic chip of scRNA-seq (SCR-chip) with immunomagnetic beads (EpCAM+) | Smart-seq v4 (Takara Bio) | PCA, t-SNE | 14 single MCF-7 cells and 12 single white blood cells |
▪ Design of SCR-chip for filtering blood clots, magnetic enrichment of CTCs, screening single CTCs, and obtaining single CTC RNA lysates ▪ scRNA-seq analysis revealed distinct genetic separation of CTCs and WBCs via PCA, tSNE, and marker genes, showing stable tumour-specific expression in CTCs and immune subtype diversity in WBCs, with no cross-contamination |
[30] | |
| 2019 | Label-free microfluidic device (Labyrinth and Celsee PREP100 System) | Drop-seq | STAR, Drop-seq tools, Seurat, SNN, PCA, Enrichr | 666 CTCs from 21 BC patient |
▪ Development of Hydro-Seq, a scalable hydrodynamic scRNA-seq barcoding method, for high-throughput CTC analysis ▪ Identification of BC drug targets for hormone and targeted therapies and tracked individual cells that express markers of cancer stem cells as well as of epithelial/mesenchymal cell state transitions |
[28] | |
| 2019 | Parsortix® PC1 System (ANGLE plc), followed by micromanipulation | Smart-seq2 (Takara Bio) | FastQC, FastQ Screen, MultiQC, Trim Galore, RSeQC, RefSeq, scran. t-SNE, scater, RCA | 262 CTCs, 14 CTC-PBMC clusters and 82 PBMCs from 34 metastatic BC patients |
▪ Identification of cell–cell junctions and cytokine–receptor pairs in CTC–neutrophil clusters as key vulnerabilities in the metastatic process ▪ Association between neutrophils and CTCs promoted cell cycle progression and enhanced the metastatic potential of CTCs, suggesting a therapeutic target for BC treatment |
[53] | |
| 2019 | Parsortix GEN3D6.5 Cell Separation Cassette (Angle Europe), followed by micromanipulation | Smart-seq2 (Takara Bio) | FastQC, Trim Galore, bowtie2, RSeQC, MethylKit, SLIM, LOLA | Matched 48 CTCs and 24 CTC clusters within individual liquid biopsies from six breast cancer patients with progressive metastatic disease; and 49 CTCs and 54 CTC clusters isolated from three xenograft models |
▪ Identification of cluster-specific gene modules linked to proliferation and cell–cell adhesion via transcriptome-wide weighted gene co-expression network analysis ▪ No significant modules were associated with single CTCs ▪ Ki67 immunostaining confirmed elevated proliferative activity in CTC clusters |
[72] | |
| 2018 | CTC-iChip microfluidic device (EpCAM+, Cadherin+, HER+), followed by micromanipulation | ABI SOLiD protocol | DESeq2 | 13 patients with Bone (+) and nine patients with Visceral (+) disease, with a median of 3.5 CTCs per patient | ▪ Identification of increased AR expression in BC with bone metastases | [73] | |
| 2014 | negCTC-iChip, followed by staining (EpCAM+, HER2+, CDH11+, CD45–, CD14–, CD16–) and micromanipulation | ABI SOLiD protocol | Not stated | 29 samples (15 pools of single CTCs and 14 CTC-clusters) isolated from 10 breast cancer patients | ▪ Identification of the cell junction component plakoglobin as a highly expressed component in scRNA-seq profiling of CTC clusters and single CTCs | [74] | |
| Pancreatic cancer | 2023 | Laparoscopy, followed by microfluidic chip to isolate CTC; validation via immunofluorescence staining and FACS (EpCAM+, CD45–) | Chromium system (10 × Genomics) | CellRanger, Seurat, Louvain, t-SNE, sciBet, inferCNV, CopyKAT, SingleCellSignatureExplore, limma, ClueGO, CellPhoneDB | 300,000 to 500,000 FACS-sorted cells from primary, and liver metastatic lesions of six treatment-naive PDAC patients [75] |
▪ Profiling of the cellular ecosystem in primary, CTC, and metastatic lesions ▪ Validation of interaction between CTCs and NK cells via the immune checkpoint molecule pair HLA-E:CD94-NKG2A ▪ Cytoprotection of CTCs from NK-mediated immune surveillance via platelet-derived RGS18 |
[55] |
| 2022 | MoFlo FACS instrument with CyClone robotic adaptor (CD44+ CD147+ EPCAM+ CK+ CD45−) | Chromium system (10 × Genomics) | GeneWiz, limma, randomForest, ClusterProfile, factoextra, ReactomePA | 35,338.46 ± 59,742.90SD/median 11,932 CTCs per million PBMC collected; representative CTC samples from 2 patients |
▪ Identification of clonal RNA expression variation within each patient’s portal blood sample ▪ Identification of high level of CXCL8, indicating involvement of PDAC CTCs in myeloid cell differentiation to support survival and immuno-resistance in portal vein circulation |
[49] | |
| 2022 | GSE114704: AutoMACS Pro Separator using MS Columns (Miltenyi Biotec) (HLA–ABC+), followed by C1™ Single-Cell Auto Prep System (Fluidigm) | GSE114704: Smart-seq | Seurat, PCA, t-SNE, Metascape tool | GSE114704: 10 CTCs, 23 liver metastasis cells, and 37 primary tumour cells derived from PDX mouse model [76] |
▪ Identification of 87 marker genes highly associated with PDAC metastasis via scRNA-seq (GSE114704) ▪ Combination of scRNAseq with bulk RNA-seq (GSE144561) allows pinpointing cell-type-specific expression patterns in CTCs [77] ▪ Multi-omics approach (scRNA-seq + WGCNA + CIBERSORT) revealed high Treg and macrophage infiltration, lower dendritic cell proportions, and enrichment of EMT and cytoskeleton-related pathways in metastatic PDAC CTCs |
[56] | |
| 2020 | CTC-iChip and anti-CD45 magnetic beads, followed by micromanipulation [25] | Modified single-cell amplification and library protocol by Tang et al., 2010 [78] | Seurat, PCA, t-SNE, g:Profiler | GSE51372: 75 CTC from the genetically engineered mouse model, 12 WBCs from a control mouse, 16 single tumour cells from NB508 cell line, 12 white blood cells, 18 GFP-traced CTCs, 20 EGFP-positive primary bulk tumour cells and 34 RNA dilutions from primary pancreatic tumours |
▪ Identification of functionally enriched focal adhesion pathway in pancreatic CTCs ▪ GAS2L1 as a potential surface marker of pancreatic CTCs in combination with EpCAM ▪ Identification of three murine pancreatic CTC clusters (Cluster 0, 1, 2) with different biological functions |
[79] | |
| 2020 | AutoMACS Pro Separator using MS Columns (Miltenyi Biotec) (HLA−ABC+), followed by C1™ Single-Cell Auto Prep System (Fluidigm) | Smart-seq | FastQ Screen, TopHat2, HTSeq-count, SCDE, GSEA | 10 CTCs, 23 liver metastasis cells, and 37 primary tumour cells derived from PDX mouse model |
▪ Identification of distinct expression profiles between CTCs, their matched primary and metastatic tumours ▪ Characterisation of CTCs with low expression of cell-cycle and extracellular matrix-associated genes ▪ Identification of new target: survivin (BIRC5) as a key regulator of mitosis and apoptosis in cancer metastasis |
[76] | |
| 2014 | CTC-iChip and anti-CD45 magnetic beads, followed by micromanipulation | Modified single-cell amplification and library protocol by Tang et al., 2010 [78] | Not stated | 75 CTC from the genetically engineered mouse model, 12 WBCs from a control mouse, 16 single tumour cells from NB508 cell line, 12 white blood cells, 18 GFP-traced CTCs, 20 EGFP-positive primary bulk tumour cells, and 34 RNA dilutions from primary pancreatic tumours |
▪ Identification of distinct transcriptomic profile of CTCs from primary tumours and tumour-derived cell lines ▪ CTC cluster exhibit low proliferation, enrichment of ALDH1A2 (stemness gene), SPARC (stromal ECM gene), LGFBP5 (epithelial-stromal interface marker) and co-express epithelial and mesenchymal markers |
[25] | |
| Lung cancer | 2024 | Negative enrichment via magnetic MACS MicroBeads targeting CD3+, CD16+, CD31+, CD45−, and CD235a+ (Miltenyi Biotec), followed by FACSAria (BD Bioscience) | Chromium system (10 × Genomics) | FastQC, CellRanger, SingleR, scclusteval, Monocle2, Seurat, GSEA, inferCNV | 9659 CTCs from six NSCLC patients |
▪ Identification of 3,363 single cell CTC whole transcriptomes ▪ Identification of high degree of phenotypic heterogeneity and a variety of CTC phenotypes: o CTC cluster 1: epithelial-like, immune responsive and highly proliferative o CTC cluster 4: cancer-stem cell like o CTC cluster 5: mesenchymal, oxidative phosphorylation, and immune evasive o CTC cluster 6: mesenchymal, invasive, and glycolytic |
[40] |
| 2024 | Microfluidic cell trap arrays for WBC removal, CTC capture, and CTC analysis | On-chip single-cell gene expression analysis using fluorescence probes | NA | CTCs from 80 NSCLC patients | ▪ Development of a nanoplatform for interrogating living cell host-gene and (micro-)environment (NICHE) for real-time, in situ CTC analysis, including gene expression and immune response profiling | [31] | |
| 2023 | Leukocyte negative enrichment using magnetic beads, followed by EpCAM+ positive staining and manual picking via mouth pipette | Smart-seq2 (Takara Bio) | Trimmomatic, HISAT2, FeatureCounts, Seurat, scater, KEGGprofile, Clusterprofiler, CytoScape, Monocle v2 | 124 single cells from primary tumour tissue (PTT), primary para-tumour tissue (PTP), metastatic tumour tissue (MTT), para-metastatic tumour normal brain tissue (MTP), and CTCs from a LUAD patient |
▪ Identification of 16 CTCs, 30 MTPs, 43 MTTs. 13 PTPs and 22 PTTs from LUAD-PT1 patient ▪ Elevated levels of LCN2, SAT1, RAC1, IFITM3, VAMP8, RAB13, NFKBIA and S100A4 (related to regulated cell death and apoptosis, promoted macromolecule organisation) in CTCs |
[80] | |
| 2021 | RosetteSep enrichment, followed by staining (CK7/8, HK2+), on-chip imaging and single-cell manipulation | Chromium system (10 × Genomics) | CellRanger, UMAP, inferCNV, fgsea, limma, GSVA | 16 randomly selected CTCs from 70 putative CTCs from patient 1 and 120 putative CTCs from patient 2 |
▪ Confirmation of the malignancy of HK2+ putative CTCs in pleural effusions and cerebrospinal fluids ▪ Prediction of NSCLC patients with poor prognosis before therapy via selective association of CK subtypes in CTCs with EGFR mutation |
[65] | |
| 2020 | Isolation from blood samples on a Ficoll gradient, followed by FACS (HLA–ABC+, Calcein-AM+) | Chromium system (10 × Genomics) | BWA-MEM, VARSCAN2, TOPHAT2, HTSEQ, EdgeR, GSEA, ANNOVAR, Seurat, Cell Ranger, PCA, t-SNE, inferCNV | At least 3,500 cells from four CTC-derived xenografts models (MDA-SC39, MDA-SC68, HCI-008, MDA-SC4) and SCLC patient treatment-naïve (84 CTCs), relapsed samples (627 CTCs), and maximal response (1 CTC) |
▪ SCLC CDX models are predominantly neuroendocrine, exhibiting strong ASCL1 expression in both platinum-sensitive and -resistant cases, with one resistant model showing high NEUROD1 and minimal POU2F3/YAP1 ▪ Identification of five unique patient CTC clusters with increased heterogeneity in cisplatin-treated CDXs—with an EMT-enriched cluster (low ASCL1 and DLL3) and treatment-specific clusters in PARPi and CHKi relapse—indicating varied resistance mechanisms |
[37] | |
| Colorectal cancer | 2024 | Size-based Metacell® technology [81] | NA | NA | CTCs from CRC patients (number not stated) |
▪ Detection of CTCs and CTC subpopulations in 47.6% of CRC patients via MetaCell ▪ Characterisation of CTCs using canonical markers (EpCAM and cytokeratins) ▪ Identification of EMT-related pathways in two CTC clusters out of 14 clusters |
[32] |
| 2022 | FACS to obtain single cell suspensions | Chromium system (10 × Genomics) | CellRanger, scimpute, Seurat, PCA, harmony, SingleCellSignatureExplore, Monocle, GSVA | 54,788 cells from patient-matched tissue samples (17 CTCs from a patient) |
▪ Identification of CTCs with some shared tumour-specific marker genes (TGFB1 and SOX9) with tumour cells in solid lesions ▪ Identification of CTC-specific signatures related to platelet activation, regulation of cell death, and cell adhesion |
[82] | |
| 2021 | CD45-based immunomagnetic negative selection, fluorescence staining (CD45/50–), followed by manual pipetting | Smart-seq v4 (Takara Bio) | Not stated (Quickbiology NGS analysis service) | 59 single CTCs from 27 mCRC patients |
▪ Gene discovery for CTC phenotyping ▪ Classification of CTCs into four main groups by epithelial, EMT and stem cell-related gene expressions for CRC prognosis |
[42] | |
| Skin cancer | 2023 | CTC isolation kit (Cytogen, CIKW10) and SMART BIOPSY™ Cell Isolator (Cytogen, CIS030), followed by sorting via size exclusion based HDM (high density microporous) chip | Chromium system (10 × Genomics) | Not stated | 1129 primary tumour cells, 1139 CTCs and 1630 metastatic tumour cells | ▪ Identification of upregulated AST factors in CTCs, which were absent in the primary tumour cells | [70, 71] |
| 2022 | Ficoll-paque density gradient separation, followed by CD45-based negative enrichment (EaspSep direct human CTC enrichment kit) | Smart-seq2 (Takara Bio) | FASTQC, TrimGalore, bowtie2, HISAT2, FetaureCounts, mixOmics | 182 CTCs from seven patients (20 mL of blood per collection) | ▪ Development of a novel inexpensive pipeline to isolate melanoma CTCs and perform scRNA-seq, demonstrating its feasibility | [83] | |
| 2021 | Microfluidic CTC-iChip isolation, followed by confirmation via melanoma lineage markers and micromanipulation | Modified Smart-seq2 protocol [84] | Linnorm, GSVA, ZINB-WaVE, DEseq2, GSEA, fgSEA | 76 individual CTCs collected from 22 metastatic melanoma patients |
▪ Discovery of two CTC clusters via hierarchical clustering analysis ▪ Identification of upregulated lipogenic programs, iron homeostasis signatures, proliferation, and increased energy production in CTC cluster 2 compared to cluster 1 ▪ Correlation of CTCs with high lipogenic and iron metabolic RNA signatures to adverse clinical outcome, irrespective of treatment regimen |
[36] | |
| Liver cancer | 2022 | Dual filtration system, followed by micromanipulation with micropipette | Smart-seq2 (Takara Bio) | STAR, Subread, Seurat, UMAP, | 38 CTCs and 33 CTC clusters from 6 HCC patients |
▪ Identification of two distinct CTC groups ▪ Upregulation of epithelial phenotypes (CDH1, EPCAM, ASGR2, KRT8), epithelial-mesenchymal transition (VIM), and stemness (CD133, POU5F1, NOTCH1, STAT3) in Group 1 as compared to Group 2 |
[66] |
| 2021 | RosetteSep Human CD45 Depletion kit, followed by staining via EpCAM+, pan-CK+, CK19+, CCL+ and CD45– | Smart-seq2 (Takara Bio) | SOAPnuke, RSEM, Rt-SNE, edgeR, Monocle, HALLMARK | 113 single CTCs from 4 different vascular sites, including hepatic vein, peripheral artery, peripheral vein and portal vein of HCC patients |
▪ Transcriptional dynamics of CTCs were associated with stress response, cell cycle and immune-evasion signalling during hematogeneous transportation ▪ Identification of chemokine CCL5 as an important mediator for CTC immune evasion ▪ Discovery of a previously unappreciated spatial heterogeneity and an immune-escape mechanism of CTC |
[54] | |
| 2018 | Immunodensity CD45 depletion, followed by imaging flow cytometry | Chromium system (10 × Genomics) | Seurat, nUMIs, PCA, t-SNE, CCA, GSEA | 25 CTCs (5 CTCs from patient 1; 5 CTCs from patient 2; 11 CTCs from patient 3; 4 CTCs from patient 4) |
▪ Description of a method that sequentially combines image flow cytometry and high density scRNA-seq to identify CTCs in HCC patients ▪ Identification of HCC driver genes revealed the molecular heterogeneity in CTCs |
[27] | |
| Prostate cancer | 2025 | Microfluidic-based negative depletion strategy (CTC-iChip: inertial separation array and MAGLENS), followed by two-step sorting using SONY SH800 sorter (bulk and single-cell); validation via EpCAM, PSMA, GPC3, ASGPR1, CD45, CD16, and CD66b | Smart-Seq2 (Takara Bio) | TrimGalore, bamToBed, Ginkgo website, samtools, TopHat, HT-Seq, GSEA | 30 CTCs from patient GU-1 and 74 CTCs from patient GU-2 |
▪ Two distinct hierarchical clusters from patient GU-1: o Cluster-1: Upregulation of FGFR and AR signalling o Cluster-2: Upregulation of pathways associated with inflammatory responses, chemokine signalling and IL/JAK/STAT signalling ▪ Two distinct hierarchical clusters from patient GU-2: o Cluster-1: Upregulation of CRPC disease progression pathways (AR, MYC and oxidative phosphorylation) o Cluster-2: Upregulation of ion channels and neuroendocrine differentiation genes ▪ High CTC yields (mean 10,057 CTCs per patient; range 100 to 58,125) revealed considerable intra-patient heterogeneity |
[50] |
| 2015 | Dynabeads MyOne Streptavidin T1 (Invitrogen), followed by microfluidic CTC-iChip (EpCAM+, CDH11+, CD45–) | Modified ABI SOLiD protocol | TopHat | 77 intact CTCs isolated from 13 patients |
▪ Identification of heterogeneity among single CTCs, including variations in AR gene mutations and splicing variants ▪ Identification of heterogeneity in signalling pathways that could contribute to treatment failure |
[60] | |
| Head and neck cancer | 2024 | Positive enrichment with human CD326/EpCAM MicroBeads (Miltenyi Biotec) | Chromium system (10 × Genomics) | CellRanger, Seurat, UMAP, Ingenuity Pathway Analysis (QIAGEN) | 1,000 EpCAM+ CTCs from four patients with biopsy-proven HNSCC |
▪ Identification of CTC heterogeneity within a single individual ▪ Identification of CTC mutations in the CREB signalling pathway, β-Adrenergic receptor signalling and G-protein receptor signalling pathway ▪ Establishment of a workflow to isolate CTCs from HNSCC patients’ blood samples before and during cancer treatment |
[33] |
| 2024 | Isolation of PBMC fraction via centrifugation, followed by RBC lysis | Chromium system (10 × Genomics) | CellRanger | 4,852 PBMCs, including CTCs |
▪ Identification of a CTC cluster expressing MYB proto-oncogene ▪ Development of a sensitive, cost-effective, and minimally invasive diagnostic test that leverages tumour-specific signatures to screen for metastatic ACC disease |
[34] | |
| Neuroblastoma | 2023 | FACS Aria II cell sorter (BD Bioscience) (GD2+, CD90+, CD45–, CD235a–, DAPI–) | Smart-seq HT (Takara Bio) | CLC Genomics Workbench, Strand NGS | 42 single CTCs from five neuroblastoma patients |
▪ Higher CTCs in patients from advanced stages ▪ Identification of upregulated genes related to angiogenesis and cell cycle ▪ Identification of 2 subgroups from 20 CTCs: o Subgroup 1: proliferative and cell cycle-related o Subgroup 2: overexpression of neuronal injury-related genes (FOS, RHOA, MIF) |
[41] |
| 2021 | FACS Aria II cell sorter (BD Bioscience) (GD2+, CD90+, CD45–, CD235a–, DAPI–) | Smart-seq HT (Takara Bio) | CLC Genomics Workbench, Strand NGS | 1 × 106 TGW human neuroblastoma cells |
▪ Three different whole-transcriptome amplification methods were conducted prior to scRNA-seq ▪ Validation of the use of locked nucleic acid technology in PCR-based WTA as an effective tool to amplify mRNA from a single cell accurately ▪ Introduction of a more dependable and flexible method for profiling CTCs at the single-cell level |
[29] | |
| Mesothelioma | 2025 | Enrichment of MSLN+CD45− CTC from blood via MACS column and a microfluidic chip [85] | Chromium system (10 × Genomics v2) | Loupe Cell Browser v5.0.0, GSEA, WebGestalt V1.0, InteractiVenn, GSVA, GSCA | 489 CTCs, 7662 in vitro mesothelioma cultured cells (CC) and 2,170 peritoneal lavage tumour cells (Lav) |
▪ Identification of unique characteristics based on origin: o CTC: Upregulation of genes cancer cell stemness genes (Ppbp, Gp9, Clec11b) o CC: Upregulation of cell cycle control, proliferation, and apoptosis genes o Lav: Upregulation of microenvironment modulation genes (e.g., EMT and IFN-α/IFN-γ immune responses) ▪ Shared pathways indicated the potential for transitioning between functional states under specific conditions |
[35] |
| Gastric cancer | 2022 | Mounting of PBMCs in a spacer seal affixed at a slide, followed by micro-manipulation of CD45− cells | Quartz-Seq (Illumina) | Trimmomatic, RSeQC, Seurat, DAVID | 49 CTCs from 26 patients and 12 single cells from cell lines (3 AGS, 3 NCI-N87, 6 SNU-1) |
▪ Identification of majority gastric CTCs with EMT in metastatic cancer ▪ Contribution of platelet adhesion toward EMT progression and acquisition of chemoresistance |
[58] |
| Bone cancer | 2020 | Manual pipetting of VIM+ CD45– | Smart-seq v4 (Takara Bio) | Trim Galore, HISAT2, Kallisto, DESeq2, rMATS, STRING, WGCNA | < 500,000 mapped reads in single osteosarcoma CTCs and > 30 million mapped reads in whole tumours |
▪ Implication of a newly discovered, multidimensional MAPK7/MMP9 signalling hub in primary bone cancer metastasis ▪ Identification of single osteosarcoma CTCs positive for cell surface vimentin and negative for CD45 ▪ scRNA-seq revealed mitochondrial enrichment (MT-CO1, MT-CO2, MT-CO3, MT-ND1-4, MT-CYB), stress adaptation (HBB, UBC), stemness (MET, FGF10, FN1, TGFB2, RUNX2), extracellular matrix remodeling, and downregulation of metastasis suppressor (BAP1) |
[86] |
ACC adenoid cystic carcinoma, AI aromatase inhibitor, AML acute myeloid leukemia, AR androgen receptor, BC breast cancer, BIRC5 baculoviral inhibitor of apoptosis repeat-containing 5/survivin, CDX CTC-derived xenografts, CLDN Claudin, CRABP2 cellular retinoic acid binding protein 2, CRC colorectal cancer, CRPC castration-resistant prostate cancer, CTC circulating tumour cells, DTCs disseminated tumour cells, EMT epithelial-mesenchymal transition, EpCAM epithelial cell adhesion molecule, ESC embryonic stem cell, ESR1 oestrogen receptor 1, FGFR fibroblast growth factor receptor, GARS glycyl-tRNA synthetase, HK2 hexokinase-2, HNSCC head and neck squamous cell carcinoma, IFN interferon, LMD leptomeningeal disease, LUAD lung adenocarcinoma, LUAD-LM lung adenocarcinoma leptomeningeal metastases, LUSC lung squamous-cell carcinoma, MAGLENS magnetic lens-based high-throughput cell sorter, mBC metastatic breast cancer, mCRC metastatic CRC, MGP matrix Gla protein, NK natural killer, NSCLC non-small cell lung cancer, PBMC peripheral blood mononuclear cell, PDX patient-derived xenograft, PPP1CA serine/threonine-protein phosphatase PP1-alpha catalytic subunit, RBC red blood cell, SCLC small cell lung cancer, SKCM skin cutaneous melanoma, Smart-seq switch mechanism at the 5′ end of RNA template sequencing, SOLiD sequencing by oligonucleotide ligation and detection, SOX9 SRY-box transcription factor 9, TGFB1 transforming growth factor beta 1, WTA whole transcriptome amplification
General workflow of CTC scRNAseq
Despite advancements in scRNA-seq technologies, most CTC-focused studies have remained confined to basic transcriptomic profiling and subtype identification with limited clinical or functional translation. This is due to the intrinsic rarity of CTCs (approximately 1 CTC per 107–109 haematological cells per mL), low separation efficiency, poor recovery rates and the lack of a universal CTC-specific surface marker/panel [87–92]. Additionally, many studies fail to disclose complete methodological details, making them difficult to reproduce. Even when raw datasets are made publicly available, they frequently lack standardisation—differing in gene lists, file formats, data orientation and annotation—further hindering reuse and cross-study comparisons. These combined technical and biological hurdles have created a bottleneck, restricting most investigations to molecular characterisation and subtyping instead of functional validation or therapeutic explorations. Notably, the number of published scRNA-seq studies on CTCs peaked around 2021 and has since declined, likely due to these unresolved limitations.
To overcome these persistent limitations, a standardised twelve-step workflow for CTC-specific scRNA-seq studies (Fig. 2) is proposed. It spans from sample acquisition to biological interpretation and is grouped into five major phases. Steps 1–5 cover pre-processing of single CTCs, including sample processing, CTC enrichment (marker-dependent or -independent), phenotypic confirmation, multiplexing and single-cell sorting. Steps 6–9 constitute platform-dependent molecular processing, such as single-cell lysis, mRNA molecule capture and barcoding, cDNA synthesis and amplification. Step 10 involves cDNA library preparation, followed by step 11, which uses high-throughput sequencers (e.g. Illumina NextSeq, NovaSeq or HiSeq). Finally, step 12 encompasses bioinformatic analysis—covering quality control, alignment, count matrix generation, doublet detection, correction of technical artifacts (e.g. batch effects, cell cycle) and downstream interpretation including clustering, differential expression and pathway analysis.
Fig. 2.
A practical twelve-step workflow for CTC specific scRNA-seq. Steps 1–5: pre-processing of single CTCs includes sample acquisition, CTC enrichment (marker-dependent or -independent), confirmation using tumour-specific markers, sample multiplexing and single-cell sorting. Steps 6–9: scRNA-seq platform-dependent processing covers single-cell lysis, mRNA capture and barcoding, reverse transcription into cDNA and cDNA amplification. Step 10: library preparation prepares amplified cDNA into sequencing-ready libraries. Step 11: next generation sequencing using high-throughput sequencers such as Illumina NextSeq, NovaSeq and HiSeq. Step 12: bioinformatic analysis involves quality control, alignment, count matrix generation, doublet removal, technical variability corrections and dimensionality reduction as well as biological interpretation (e.g. clustering, differential gene expression, pathway analysis, etc.)
In short, each step—from enrichment to data interpretation—must be supported by reliable methods and platforms to fully unlock the workflow’s potential. The following subsections explore each component in detail.
CTC Enrichment
The first and most important step in CTC scRNA-seq is the effective isolation of viable single CTCs from a patient’s blood. Traditionally, this involves processing whole blood to isolate PBMCs, from which CTCs are subsequently enriched [93]. However, recent approaches increasingly favour direct processing of whole blood, such as microfluidic-based technologies like the CTC-iChip, to reduce cell loss and preserve CTC integrity [94]. Currently, only two FDA-approved methodologies are available for CTC detection and enrichment, namely CellSearch® (Veridex) [89] and Parsortix® PC1 (ANGLE plc) [95]. The CellSearch® system relies on positive enrichment strategy that targets CTCs of epithelial origin (CD45–, EpCAM+ and cytokeratins 8+, 18+ and/or 19+), whereas the latter employs a label-independent, size and deformability-based microfluidic separation strategy. Despite being the most widely adopted platforms in CTC research, CellSearch® may overlook mesenchymal or stem-like CTC subtypes due to its reliance on epithelial markers [89], while Parsortix® can be constrained by CTC size variability, potentially affecting capture consistency [95].
As a result, there has been a shift toward negative enrichment strategies, which aim to isolate CTCs by depleting CD45+ haematopoietic cells, thereby enabling the broader capture of phenotypically diverse CTC populations [96].
CTC enrichment techniques can be categorised into three main approaches: biophysical isolation, positive enrichment and negative enrichment. Biophysical isolation is label-free and is applicable to various cancer types, but may miss smaller or deformable CTCs. Positive enrichment offers high specificity by targeting specific CTC surface markers but may overlook other subtypes. Negative enrichment depletes immune cells, enabling broader CTC capture, though it often suffers from higher contamination and reduced purity. Therefore, researchers must carefully select an enrichment strategy based on both their specific research objectives and the phenotypic characteristics of the CTCs they wish to isolate.
Table 2 provides an overview of the three primary CTC enrichment strategies prior to scRNA-seq, their associated surface markers, applicable cancer types and current methodologies. Notably, there is often overlap among these strategies, as certain markers and techniques may be relevant across multiple cancer types or enrichment protocols. Following enrichment, choosing the appropriate scRNA-seq platform becomes critical to ensure transcriptomic fidelity and sensitivity, particularly for rare CTC populations.
Table 2.
Blood-based CTC enrichment techniques for scRNA-seq studies
| CTC enrichment | Type of cancer | Surface marker | Method |
|---|---|---|---|
| Biophysical isolation | Breast | – | Size (Parsortix® PC1; microfluidic; filtration-CellSieve; ClearCell FX system), density (high density microporous chip) |
| Lung | – | Density gradient centrifugation | |
| Colorectal | – | Size (Metacell®) | |
| Skin | – | Density gradient centrifugation | |
| Liver | – | Density gradient centrifugation, size/dual filtration | |
| Prostate | – | Microfluidic CTC-ichip (inertial separation array and MAGLENS) | |
| Positive enrichment (CTC cell surface marker) | Breast | EpCAM, cadherin, HER | Immunomagnetic beads/nanospheres (CellSearch; SCR-chip), microfluidic CTC-ichip, hydrodynamic CTC sorting chip |
| Pancreas | CD44, CD147, CK, EpCAM, ABC | FACS, immunomagnetic beads (MACS) | |
| Lung | EpCAM, CD3, CD16, CD31, CD325a, CK7, CK8, HK2, ABC | Immunomagnetic beads (RosetteSep), Immunomagnetic beads (MACS) | |
| Prostate | EpCAM, CDH11 | Microfluidic CTC-ichip | |
| Head and neck | EpCAM | Immunomagnetic beads (MicroBeads) | |
| Neuroblastoma | GD2, CD90 | FACS | |
| Mesothelioma | MLSN | Immunostaining and manual pipetting | |
| Bone | VIM | Immunostaining and manual pipetting | |
| Negative enrichment (immune cells depletion) | Breast | CD45, CD15 | Immunomagnetic beads/nanospheres (RosetteSep), microfluidic CTC-ichip, hydrodynamic CTC sorting chip immunostaining and manual pipetting |
| Pancreas | HLA, CD45 | Immunomagnetic beads (MACS) | |
| Lung | HLA | Immunomagnetic beads, microfluidic CTC-ichip | |
| Colorectal | CD45 | Immunomagnetic beads | |
| Skin | CD45 | Microfluidic CTC-ichip | |
| Liver | CD45 | Immunomagnetic beads (RosetteSep) | |
| Head and neck | CD45 | Density gradient centrifugation | |
| Gastric | CD45 | Immunostaining and manual pipetting |
ABC ATP-binding cassette, CD cluster of differentiation, CK cytokeratin, CTC circulating tumour cells, EpCAM epithelial cell adhesion molecule, FACS fluorescence-activated cell sorting, GD2 disialoganglioside 2, HER human epidermal growth factor receptor, HK2 hexokinase 2, HLA human leukocyte antigen, MACS magnetic-activated cell sorting, MLSN mesothelin, VIM vimentin
scRNA-seq platform comparison
Following CTC enrichment and validation using tumour-specific markers, single-cell sorting is a critical prelude to scRNA-seq. While most commercial platforms incorporate this within their workflows, methods such as Quartz-Seq [97, 98] and Smart-seq [22–24, 99] typically require pre-isolated single cells, which are typically prepared via manual pipetting or fluorescence-activated cell sorting (FACS). Depending on the experimental needs, researchers may also opt for alternatives such as automated micromanipulation, flow cytometry-based cell sorting, droplet microfluidics, and microfluidic or nanowell-based systems. Selecting the appropriate scRNA-seq platform hinges on key factors including throughput, capture rate, sensitivity, methods for Unique Molecular Identifier (UMI), cell barcoding, as well as compatibility with rare cell types like CTCs.
Full-length transcript method
Quartz-Seq and its updated version Quartz-Seq2 combine template-switching PCR with high UMI conversion efficiency (in the latter). They can also achieve full-length coverage and high sensitivity. Quartz-Seq2 is suitable for large-scale transcriptomics, with high reproducibility and detection of rare populations. However, these methods do not integrate single-cell sorting and are prone to PCR bias, preferentially amplifying shorter transcripts. Additionally, they require separate library prep kits such as Nextera XT [97, 98].
Like Quartz-Seq, Smart-seq protocols provide full-length transcript coverage, high sensitivity and low dropout rates—ideal for profiling rare cells and detecting splice variants or mutations. Smart-seq2 offers high mappability and transcript coverage from as little as 50 ng of starting RNA, with good reproducibility. Smart-seq3 adds 5′-end counting with UMI for improved quantification. However, these protocols are low-throughput, labour-intensive, and more expensive per cell. They also suffer from amplification bias and lack strand specificity, limiting scalability for large cohorts [22–24, 99].
Manual pipetting or micromanipulation may be incorporated to offer nearly perfect (~ 100%) capture accuracy, and is ideal for extremely rare cell types, but it is labour-intensive and time-consuming, and thus lacks scalability. These techniques are often paired with Smart-seq protocols for targeted applications requiring maximum resolution [22–24, 99].
Droplet-based platform
10 × Genomics Chromium is a droplet-based system which supports high throughput (up to > 20,000 cells with GEM-X technology), robust UMI-based quantification, and automated barcoding workflows. It offers a moderate capture efficiency (~ 30–80%) and is ideal for profiling larger populations. However, its reliance on 3′-end counting can lead to dropouts and limited detection of low-RNA content cells, making it less than optimal for rare or low-input cell types like CTCs without prior enrichment [26].
Drop-seq, developed by McCarroll lab, is another droplet-based system offering an economical, scalable option with the ability to process ~ 10,000 cells/day at a very low cost per cell (~ $0.07). Like 10× Chromium, Drop-seq incorporates barcoded beads and UMIs to individually label transcripts during cell lysis within nanoliter droplets. However, it suffers from a low capture efficiency (< 10%), which limits its standalone use for rare-cell studies. In addition, it offers lower per-cell gene detection sensitivity, captures only the 3′ end of transcripts, and requires custom-built microfluidics, posing technical barrier for clinical labs or multi-site studies. Drop-seq is unlikely to recover sufficient rare cells for meaningful analysis without upstream CTC enrichment. The use of external RNA controls (ERCC spike-ins), while useful for normalisation, increases sequencing costs and may introduce further noise in low-input samples [100–102].
Bead-dd-seq is a droplet-based platform with 3′ end counting and poly(A)-primed PCR amplification. It supports thousands of cells and provides unbiased gene expression profiles with relatively simple workflows. Although scalable and effective, it often requires protocol modifications to ensure robust amplicon yields for sequencing, which may limit reproducibility across labs [69].
Nanowell-based platform
Nanowell-based platforms, such as ICELL8 (Takara Bio) [26, 103] and BD Rhapsody single-cell analysis platforms [104] offer a compromise between throughput and control. For instance, BD Rhapsody integrates real-time imaging and barcoding with high single-cell purity (~ 80%), and its ability to reuse archived beads enhances experimental flexibility and reproducibility. It incorporates UMIs and supports targeted gene panels, offering flexibility for targeted CTC profiling. However, it shows a bias towards highly expressed housekeeping genes and uses random primer extension, which limits transcriptomic breadth unless complemented by whole-transcriptome amplification. Despite this, its high capture rate and multiplexing make it promising for low-input, targeted studies. It uses 3′-end counting and has been used in immune profiling, but has not yet been widely adopted for CTC analysis [104].
On the other hand, ICELL8 captures up to 5184 single cells and supports image-guided selection to avoid doublets. It accommodates a wide range of cell sizes (3–500 µm) and offers both 3′ and full-length chemistries. However, its singlet capture efficiency is modest (24–39%), and UMI quantification is unreliable because cDNA is amplified in the presence of barcoding primers, potentially inflating UMI counts. The alternative ICELL8 3′ DE-UMI protocol is more robust for UMI counting, since reverse transcription and cDNA amplification are uncoupled by an exonuclease digestion of barcoding primers [26, 103].
Microfluidic platform
Fluidigm Polaris system is one of the earliest commercial microfluidic systems designed for single-cell transcriptomics. It offers automated single-cell capture, lysis, reverse transcription, and cDNA amplification in an integrated chip-based workflow, supporting both 3′-end and full-length transcript profiling. It is well-suited for rare cell applications due to its image-based cell selection, which reduces doublet rates and allows users to exclude unwanted cell types before sequencing. However, it suffers from low throughput (typically up to 96 cells per run) and high per-cell cost. These constraints have contributed to its declining use in favour of more scalable platforms. Nonetheless, Fluidigm remains valuable in studies prioritising high data quality over quantity, especially when visual verification and full-length coverage are critical such as in characterising transcriptomic heterogeneity among CTC subpopulations or detecting splice variants [105].
Selecting the best approach: Amplification strategy and technical consideration
In terms of amplification, 3′ end methods—employed by 10 × Chromium [26], Drop-seq [100–102], and BD Rhapsody [104]—are cost-effective and scalable, but offer limited transcript coverage, potentially missing biologically relevant isoforms or mutation sites. Full-length approaches, as used in Smart-seq [22–24, 99] and Quartz-Seq [97, 98], deliver more comprehensive transcript data, but demand higher input quality and are more resource-intensive.
Notably, CTC analysis presents unique challenges not directly addressed by most commercial platforms: low RNA content, high contamination risk from leukocytes and the need for precise isolation of ultra-rare cells from large blood volumes. While some platforms achieve sensitivity or throughput, no single technology yet fully satisfies all requirements for CTC scRNA-seq. Therefore, platform selection should be driven by the specific goals of the study—whether that be high-resolution profiling of a few cells or population-scale discovery—and must be carefully balanced against technical limitations in terms of sensitivity, throughput and data quality.
Table 3 outlines the technical features, strengths, and limitations of each scRNA-seq technology, in order to aid researchers in making informed decisions tailored to CTC analysis and other rare-cell applications.
Table 3.
Comparison of scRNA-seq technologies—features, strengths and limitations
| Technology | Single-cell sorting | Number of cells | Capture rate | Cell barcode | UMI | cDNA coverage | Amp. method | Library prep | Strength | Limitation | Cit | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Quartz-Seq/Quartz-Seq2 (Laboratory of Bioinformatics Research: open-source) | None (Requires pre-isolated single cells) | Up to 96–384 single cells per plate | ~ 100%a | No | Quartz-Seq: No; Quartz-Seq2: Yes | Full-length | Template switching-based PCR | No; require another kit (e.g. Nextera XT) |
▪ Single-tube reaction suitable for automation ▪ Quartz-Seq2: High UMI conversion efficiency and gene count detection, ▪ High sensitivity and reproducibility ▪ Suitable for large-scale transcriptome analysis and identifying rare cell populations |
▪ PCR biases and amplification errors ▪ Preferential amplification of short targets (< 500bp) ▪ Higher noise level ▪ Does not perform single-cell sorting |
[97, 98] | |||
| Smart-seq/Smart-seq2/Smart-seq3/Smart-seq4 (Takara Bio) | None (Requires pre-isolated single cells) | Up to 96–384 single cells per plate | ~ 100%a | No | No | Full-length | Template switching-based PCR | No; require another kit (e.g. Nextera XT) |
▪ Smart-seq: Higher sensitivity for detecting low-abundance transcripts; low dropout rate, suitable for rare single cells ▪ Smart-seq2: Require only 50 ng as starting material; mRNA sequence does not have to be known; high transcript coverage; high mappability; thermal stability of LNA-DNA base pairs ▪ Smartseq-3: No limit on cell size; no need to keep cells fresh for transport; cheaper than droplet-based methods for small sample sizes |
▪ Smart-seq: low throughput, expensive per cell, amplification bias ▪ Smart-seq2: Not strand-specific; no multiplexing; transcript length bias (> 4kb); prefer high-abundance transcripts; strand-invasion bias ▪ Smart-seq3: Limited cell capacity; requires FACS sorting; labour-intensive |
[22–24, 99] | |||
| Bead-dd-seq | Droplet-based microfluidic system, | 100–10,000 cells | < 2% | Yes | Yes | 3′ end counting | PCR-based amplification (Poly(A)-primed PCR) | Yes; require modification |
▪ Sensitive and simple for single-cell library construction ▪ Unbiased characterisation of gene expression and gene regulation ▪ Unbiased profiling of diverse cell populations ▪ Scalable solution |
▪ Require protocol modification to ensure sufficient amplicon yield for downstream sequencing | [69] | |||
| Drop-seq (McCarroll Lab: open-source) | Droplet-based microfluidic for single-cell encapsulation | Up to 10,000 cells | < 10% | Yes | Yes | 3′ end counting | Template switching-based PCR | No; require another kit (e.g., Nextera XT) |
▪ High throughput scRNA-seq ▪ Cost effective: $0.07 per cell ($653 per 10,000 cells) and fast library prep (10,000 cells per day) |
▪ Requires custom microfluidics for droplet separation ▪ Lower gene-per-cell sensitivity ▪ ERCC spike-ins increase sequencing costs |
[100–102] | |||
| Fluidigm Polaris system (Fluidigm) | Microfluidic-based single cell capture and processing | Up to 48 single cells per plate (capacity to scale up to 96 cells based on specific workflow) | 65–80% | Yes | Yes | Full-length | Template switching-based PCR | No; require another kit (e.g., Nextera XT) |
▪ Integrated workflow for single-cell captures and live-cell manipulation ▪ High precision and reproducibility ▪ Supports routine functional single-cell studies ▪ Imaging data via Visiopharm® analysis software since 2021 |
▪ Low capture efficiency and cell damage ▪ High cost ▪ Low throughput (only 96 cells per run) |
[105] | |||
| Chromium system (10 × Genomics) | Droplet-based microfluidic for single-cell encapsulation | 500–10,000 cells; > 20,000 cells for new GEM-X technology | ~ 30% to ~ 80% | Yes | Yes | 3′ end counting | PCR-based amplification (T7-based IVT or Poly(A)-primed PCR) | Included |
▪ High throughput due to 10 × barcoding ▪ Cost effective and time saving for large-scale single-cell analysis ▪ Supports single-cell ATAC-seq ▪ Detection of rare cell types by analysing a large number of cells ▪ Capable of encapsulate single cells into droplets |
▪ Only 3′ terminal fragments can be used for sequencing ▪ High number of cells requirement (not suitable for pure rare cell suspension) ▪ Low capture efficiency and high dropout rates |
[26] | |||
| Clontech ICELL8 single-cell system (Takara Bio, formerly Wafergen) | Nanowell-based single cell capture and processing | Up to 5,184 single cells per nanowell chip | 24–39% | Yes | Yesb | Full-length | Template switching-based PCR | No; require another kit (e.g., Nextera XT) |
▪ Image-based cell selection for viability and singlet detection ▪ High throughput with traceable sequencing data ▪ Flexible chemistry (3′ end or full-length, SE or PE mode) ▪ Compatibility with live, fixed, and various cell sizes (3–500 µm) ▪ Removal of abnormal cells and doublets |
▪ Cell limit of more than 1000 single cells ▪ Low singlet capture efficiency ▪ UMI count is unreliable as cDNA is amplified without barcode primers (use alternative ICELL8 3′ DE-UMI instead) |
[26, 103] | |||
| BD Rhapsody single-cell capture and analysis system (BD Biosciences) | Nanowell-based single cell capture and processing | 100–40,000 single cells per cartridge | ~ 80% | Yes | Yes | 3′ end counting | Random primer extension (RPE) PCR | Included |
▪ Multitier molecular barcoding and multiplexing ▪ High single-cell purity (0% multiplet for ~ 1000 cells) ▪ Low inter-cell noise ▪ High capture rate ▪ Ability to visualise single-cell capture ▪ Archived beads reusable for future experiments ▪ Compatible with various downstream workflows |
▪ Cell limit of more than 100 single cells ▪ Bias for housekeeping genes detection due to high expression but a targeted approach enables amplification of a selected set of genes of interest |
[104] | |||
ATAC-seq assay for transposase-accessible chromatin using sequencing, DNA deoxyribonucleic acid, Drop-seq droplet sequencing, LNA locked nucleic acid, mRNA messenger ribonucleic acid, PCR polymerase chain reaction, PE pair-end, SE single-end, TCR T cell receptor
aSince cells are manually pipetted; b when used with specific kit
Bioinformatic analysis
After selecting an appropriate CTC enrichment method and sequencing platform, the next critical phase lies in computational analysis. This begins with the pre-processing of raw sequencing data to generate a high-quality count matrix, followed by post-processing steps such as normalisation, dimensionality reduction and clustering. Downstream analyses uncover biologically and clinically relevant insights, supporting the translational potential of CTC scRNA-seq.
To support these analyses, a wide range of computational resources have been developed, typically categorized as tools, frameworks, toolkits or pipelines. A tool refers to a single-purpose algorithm designed for a specific task (e.g. UMAP for dimensionality reduction). A framework is a broader environment that integrates multiple tools and provides built-in workflows or functions across several stages of analysis (e.g. scran). A toolkit is typically modular and customisable, allowing users to flexibly combine multiple functions, often written within the same programming ecosystem (e.g. Seurat in R). A pipeline, on the other hand, is a more rigid, end-to-end workflow (e.g. Cell Ranger) that automates a predefined series of steps from raw data to processed output.
Pre-processing
Pre-processing of CTC scRNA-seq data involves several key steps to convert raw sequencing files into usable expression matrices. It begins with demultiplexing (e.g. bcl2fastq), where raw BCL files are split into FASTQ files. Next, quality control and read trimming tools like FastQC [106], Trim Galore [107] or Trimmomatic [108] remove low-quality bases and adapter sequences. Species confirmation and contamination checks (e.g. FastQ Screen [109]) can be used to validate data origin. Barcode and UMI processing (e.g. alevin, zUMIs) assigns reads to individual cells. Reads are then aligned and quantified against a reference genome using tools such as STAR [110], Salmon [111] or kallisto [112]. After assigning reads to genomic features using HTSeq [113] or FeatureCounts [114], the count matrix is generated. This matrix, representing expression levels per gene per cell, forms the basis for all post-processing and downstream analyses. Integrated pipelines such as Cell Ranger can be used to streamline these steps.
Post-processing
Post-processing refines a raw count matrix to prepare it for meaningful biological analysis. First, doublet detection tools like Scrublet [115] or DoubletFinder [116] identify and remove droplets containing more than one cell. Normalisation (e.g. SCTransform [117, 118]) corrects for differences in sequencing depth across cells, while batch effect correction (e.g. fastMNN [119], Scanorama [120]) adjusts for technical variability between experiments. Cell cycle correction (e.g. scLVM [121]) removes gene expression noise caused by cell division stages. Imputation tools like scImpute help recover missing values due to dropouts [122]. Dimensionality reduction (e.g. PCA [123], UMAP [124, 125]) simplifies data for visualisation and pattern recognition, followed by clustering (e.g. Louvain [126], scclusteval [127]) to group similar cells. Finally, cell type annotation tools such as Monocle [128], SingleR [129] and RCA [130] assign identities to each cluster based on known markers. Comprehensive scran [131] framework integrate many of these functions.
A comprehensive summary of pre- and post-processing tools, along with their functions, computational environments and sources, is provided in Table 4.
Table 4.
Commonly used tools for pre- and post-processing analyses of CTC scRNA-seq data
p scRNA-seq pipeline; f framework, t toolkit
Downstream analysis
Downstream analysis explores biological insights after post-processing. Differential gene expression tools like Limma [132], clusterProfiler [133] and RSEM [134] are used to identify genes that vary across conditions or clusters. For biological interpretation, pathway and enrichment analysis tools (e.g. GSEA, GSVA [135], ReactomePA [136]) highlight functional pathways and gene sets involved. Cell–cell communication platforms like CellPhoneDB infer intercellular signalling [137], while copy number variation tools (inferCNV, CopyKAT) detect genomic alterations. Alternative splicing and polyadenylation are profiled using tools like rMATS [138] and Outrigger [139]. For studying developmental processes, trajectory inference tools such as Monocle3 [140] reconstruct cell lineage dynamics. Additionally, tool like scDIOR [144] and SeuratDisk enable cross-platform data conversion.
Several open-source and freely available full-featured pipelines, including Seurat [141], MAST [142] and Pagoda2 [143], integrate multiple steps of downstream analysis. Others, such as Loupe Cell Browser (10X Genomics), Strand NGS (Strand Life Sciences) and CLC Genomics Workbench (QIAGEN Digital Insights), are proprietary or accessible only through specific commercial platforms.
Table 5 presents a comprehensive summary of key tools used for downstream analyses in CTC-specific scRNA-seq. It outlines the primary function of each tool along with their compatible computational environments (e.g., R, Python), and access sources (e.g., GitHub, CRAN, Bioconductor). To contextualise their use, Fig. 3 maps these tools across the entire computational pipeline—from pre-processing to downstream analysis. While general scRNA-seq workflows remain broadly applicable, CTC-specific analyses require additional considerations due to the extreme rarity and heterogeneity of these cells. Key refinements, such as barcode and UMI processing, batch effect and cell cycle correction and more nuanced clustering, are crucial for ensuring accurate interpretation. These CTC-specific refinements are marked with asterisks (*) in Fig. 3.
Table 5.
Tools commonly used for downstream analyses of CTC scRNA-seq data
p scRNA-seq pipeline; f framework, t toolkit
Fig. 3.
Workflow and commonly used tools in CTC scRNA-seq data analyses. 1. Pre-processing of scRNA-seq data begins with demultiplexing raw data into single CTCs (bcl2fastq), followed by quality control (FastQC, RSeQC), trimming (Trim Galore, Trimmomatic), contamination checks (FastQ Screen) and barcode/UMI processing (alevin, UMI-tools, zUMIs). Reads are aligned (TopHat, bowtie, STAR) and assigned to features (HTSeq, FeatureCounts) to generate count matrices. Cell Ranger is a commonly used pre-processing pipeline. 2. Post-processing includes double removal (doubletfinder, scrublet), normalization (SCTransform, linnorm), batch effect correction (fastMNN, scanorama, scLVM), imputation (scimpute) and dimensionality reduction (PCA, t-SNE, UMAP). 3. Downstream analysis covers clustering (scclusteval, Leiden, Louivain, SC3), cell type identification (Monocle, Harmony, Garnett, Single R), differential gene expression (edgeR, limma, RSEM) and pathway analysis (GSEA, GSVA, gsea). Other biological interpretations include cell–cell communication (CellPhone DB), copy number variation (inferCNV, copyKAT), alternative splicing (Outrigger, rMATS) and trajectory analysis (Monocle3). End-to-end scRNA-seq analysis platforms include Seurat, MAST, Pagoda2, unCTC, ICSVA, Strand NGS, CLC Genomics Workbench, Loupe Cell Browser and Monocle3. CTC-specific refinements, which address the unique characteristics of CTC data, are marked with asterisks (*)
Emerging research frontiers
CTC-related new/novel rare cells
Recent advances in single-cell RNA sequencing have expanded the scope of CTC research, revealing novel subtypes, interactions and functional states that were previously inaccessible or unrecognised. Several new CTC populations were discovered in 2022 and 2025. In 2022, a preclinical model using patient-derived cerebrospinal fluid (CSF)-CTCs in melanoma uncovered IGF1R as a potential therapeutic target, with consistent gene expression profiles across in vitro and in vivo expansions [180]. TERT+, PSMA-high CTCs were identified in prostate cancer, enriched for genes associated with proliferation and metastasis, and showing distinct gene expression patterns between metastatic and local cases [181].
CTC–platelet adhesion complexes were identified in 2025, highlighting CD155 upregulation in immune evasion via TIGIT interaction, regulated through the FAK/JNK/c-Jun pathway [182]. Another study discovered CTCs with increased genomic content (CTC-IGCs), exhibiting polyploidisation and clonal relationships with typical CTCs, suggesting new dimensions of CTC biology [183]. Lastly, two distinct CTC populations, namely platelet-positive and platelet-negative, have been characterized, with differential expression of MYC targets and stemness markers. This has led to new insights into CTC-mediated metastasis [184].
Rare cells
Other studies have uncovered important rare cell populations via scRNA-seq. For instance, Yang et al. developed a tumour self-seeding (TSC) mouse model using CRC (HCT116) and HCC (PLC/PRF/5) cell lines and identified a TM4SF1+ TSC population with a metastatic profile in liver and CRC patients from two scRNA-seq datasets [185]. In the same year, a common ‘starter’ subpopulation driving tumour initiation and metastasis was identified through evolutionary trajectory analysis, revealing upregulated cell cycle-related genes crucial for neuroblastoma progression and observing a partial EMT transition along the metastatic route to bone marrow [186].
In NSCLC, patient-derived CTCs were used to create xenograft models, which identified a regenerative alveolar epithelial type II-like cell population shared with non-xenografted NSCLC metastases. Single-cell transcriptomic analysis has uncovered distinct drug responses and cellular heterogeneity within the CTC-derived xenografts tumours validated against patient metastases tissues [187]. Similarly, Zheng and the co-researchers successfully isolated circulating tumour-initiating cells (CTICs) from HCC patients and confirmed molecular heterogeneity among individual CTICs via scRNA-seq profiling. They further characterised these subsets into distinct phenotypes (Zheng et al., 2022). Another scRNA-seq study of pancreatic cancer demonstrated the efficacy of adjuvant HGF/c-Met inhibition, providing the first confirmation of circulating human pancreatic stellate cells and accurately distinguishing human from mouse cells using UMI counts, orthologous gene expression and canonical markers [189].
Hybrid cells
Concurrently, hybrid tumour cells have been implicated in metastasis, though their molecular landscape and detection remain unclear [190]. Nevertheless, the high sensitivity of scRNA-seq permits the identification and profiling of these hybrid cells. For instance, Anderson and the co-researchers identified neoplastic-immune hybrid cells in uveal melanoma, which exhibited properties such as enhanced cell motility, immune evasion and altered metabolism, with ligand-receptor pathways potentially driving metastasis [191]. In CRC and BC, tumour hybrid cells co-expressed epithelial and macrophage markers (EpCAM and KRT8), with distinct transcriptomic profiles were reported to be linked to tumour progression [192].
In prostate cancer bone metastasis, scRNA-seq analysis identified a unique cancer cell cluster with myeloid cell markers, suggesting that cell fusion between disseminated tumour cells and bone marrow cells could be a potential source of myeloid-like hybrid cells. Multi-omics analysis revealed that hybrid cells displayed an enhanced EMT phenotype, increased tumorigenicity, and resistance to docetaxel and ferroptosis, although they remained sensitive to radiotherapy [193]. Conversely, hybrid cells from primary melanoma expressing melanoma antigens correlated with poorer responses to immune checkpoint blockade [194].
In another attempt, Menyailo et al. applied scRNA-seq to profile CD45-negative and CD45-positive circulating epithelial cells (CECs) in non-mBC patients. These CECs comprise distinct populations of both aneuploid and diploid cells, as demonstrated by their transcriptional profiles. Notably, cancer-associated signalling pathways were abundant in only one aneuploid CD45– CEC population, possibly representing an aggressive subset of CTC. Therefore, CD45– and CD45+ CECs are highly heterogeneous and include aneuploid cells, which are most likely circulating tumour and hybrid cells, respectively and diploid cells [195]. In kidney cancer, scRNA-seq of c-Kit+ and DBA+ cells identified four groups: progenitor cells (PCs), immune cells (ICs), hybrid cells and non-classified cells. Hybrid cells expressed both PC and IC markers or Aqp2 [196]. Therefore, investigating hybrid cells through scRNA-seq shows promise for advancing cancer biology research, and could potentially revolutionise clinical cancer management.
Table 6 summarises key studies related to rare cells, CTC-related and hybrid subpopulations, highlighting the CTC enrichment methods, scRNA-seq platforms, number of cells analysed, and cancer type. In parallel, Fig. 4 visually maps these rare and hybrid populations, their associated surface markers, and how ML has been integrated into CTC scRNA-seq analyses to enhance cell classification and functional insight.
Table 6.
Emerging research frontiers in rare CTC subtypes and hybrid cells
| Research frontier | Type of cancer | Enrichment method | scRNA-seq technology | Number of cells analysed | Finding | Cit |
|---|---|---|---|---|---|---|
| CTC-related rare cells | Lung cancer | ChimeraX-i120 platform | Smart-seq2 (Takara Bio) | CTC-platelet adhesion complexes |
▪ Identification of CTC-platelet adhesion complexes via scRNA-seq ▪ Detection of CD155 upregulation, with functional assays confirming its role in immune evasion via TIGIT interaction, suggesting FAK/JNK/c-Jun cascade as a regulatory pathway |
[182] |
| Prostate cancer | Micro-manipulation of fluorescence-stained cells (CK+, VIM+, CD45/CD31−) | Modified Smart-Seq2 (Takara Bio) | Matched bone marrow and peripheral blood samples from 31 advanced prostate cancer patients (NCT01505868) (number not stated) |
▪ Identification of CTCs with increased genomic content (CTC-IGC), with nuclear diameter at least twice the average CTC size, detected in 9.7% of peripheral blood samples and 80.6% of bone marrow samples ▪ Single cell copy number profiling of CTC-IGC revealed clonal origin with typical CTCs, suggesting complete polyploidisation |
[183] | |
| Skin cancer | Ficoll-paque density gradient separation, followed by negative enrichment (EaspSep direct human CTC enrichment kit) | Smart-seq2 (Takara Bio) | scRNA-seq dataset GSE255299: 75 CTCs from seven SKCM patients [83]; scRNA-seq dataset GSE72056: 4645 cells from 19 metastases of SKCM [197] |
▪ Comparison of CTC (GSE255299) and solid metastases (GSE72056) revealed upregulation of 11 platelet activation genes (THBS1, PF4, TIMP3, VWF, ITGB3, F2, COMP, GP1BA, PLEK, RAPGEF3, and GNB2) ▪ F3 and SERPINE1 were expressed at lower levels in CTCs, with no significant change in PLAT expression ▪ UMAP analysis identified two CTC clusters (60% vs. 40%), corresponding to Platelet-Positive and Platelet-Negative CTCs ▪ Platelet-Positive CTCs showed enrichment in MYC TARGETS v1 and increased expression of stemness markers (ALDH1A3, BSG, NGFR, SOX2) |
[184] | |
| Skin cancer | Separation by centrifugation, followed by adapting CellSearch (Janssen Diagnostics) via the CELLTRACKS circulating melanoma cell kit | Chromium system (10 × Genomics) | 148 CTCs from patient-9; 149 CTCs from patient-12; non-cultured CSF-CTCs from patient-8, 10, and 11 (number not stated) |
▪ Development of a preclinical model of patient-derived CSF-CTCs for experimental therapeutics in melanoma with LMD ▪ scRNA-seq revealed IGF1R as a potential therapeutic target in melanoma LMD, along with MLANA, SOX9, and ERBB3 ▪ Despite heterogeneity between patients, 96 to 97.7% of genes were retained after in vitro and in vivo expansion, confirming that CTCs resembled original patient samples |
[180] | |
| Prostate cancer | TERT-based CTC detection, followed by imaging flow cytometry (CD45−, PSMA+); GSE67980: Dynabeads MyOne Streptavidin T1 (Invitrogen), followed by microfluidic CTC-iChip (EpCAM+, CDH11+, CD45−) | GSE67980: Modified ABI SOLiD protocol | GSE67980: 77 prostate CTCs [60] |
▪ Identification of telomerase (TERT) positive CTCs with PSMA high expression associated with prostate cancer metastasis ▪ The mean ‘TERT+ CTCs’ number was 6.11 ± 9.63 in the metastatic group and 4.09 ± 3.41 in the local group ▪ scRNA-seq of 77 prostate CTCs showed enrichment of proliferation-related terms and high metastasis-related gene expression in PSMA-high CTCs |
[181] | |
| Rare cells | Liver and colorectal cancer | GSE132257: tumour dissociation kit protocol (Miltenyi Biotec) [198]; GSE125449: MACS (Miltenyi Biotech) [199] | Chromium system (10 × Genomics) | GSE132257: 18,409 CRC cells; GSE125449: 5,115 liver cancer cells |
▪ Development of tumour self-seeding mouse model using CRC (HCT116) and HCC (PLC/PRF/5) cell lines ▪ Identification of TM4SF1+ tumour self-seeded (TSC) population with a metastatic profile in liver and CRC patients from two scRNAseq datasets |
[185] |
| Neuroblastoma | Magnetic-activated cell sorting via tumour cell isolation kit (Miltenyi Biotec) (CD45−, DAPI−) | Chromium system (10 × Genomics) | 15,447 neuroblastoma cells from eight NB samples, including paired samples of primary tumours and bone marrow metastases |
▪ Identification of a common ‘starter’ subpopulation driving tumour initiation and metastasis through evolutionary trajectory analysis ▪ Discovery of upregulated cell cycle-related genes in the ‘starter’ subpopulation, crucial for neuroblastoma progression ▪ Observation of partial epithelial-to-mesenchymal transition along the metastatic route to bone marrow |
[186] | |
| Lung cancer |
GSE123904: Tissue dissociation via Gentle MACS Octo Dissociator and filtering [200]; snRNA-seq: Immunostaining (pan-CK+, CD45–), followed by single nuclei isolation |
Chromium system (10 × Genomics) | GSE123904: 40,505 single cells from 17 freshly resected human tissue samples, including normal lung (n = 4), primary LUAD (7 untreated, 1 post-chemotherapy), and LUAD metastases (brain n = 3, bone n = 1, adrenal n = 1) |
▪ Development of NSCLC CDX mouse models with ptPDX‑derived CTCs ▪ Identification of an additional regenerative alveolar epithelial type II (AT2)-like cell population in CDX tumours that was also identified in non-xenografted NSCLC patients’ metastases tissues ▪ Identification of distinct drug responses and cell heterogeneities in CDX tumours that can be validated in NSCLC metastases tissues |
[187] | |
| Liver cancer | Integrated four channel immunomagnetic-microfluidic platform (iMAC) (EpCAM+, Cd133+, CD90+, CD24+) | NA | CTICs from blood samples of 33 HCC patients (number not stated) |
▪ Identification of distinct phenotypes in four CTICs subsets ▪ Distinguishing primary HCC, recurrent HCC, and TACE-resistant HCC via distinct stem-related markers’ expression of CTICs ▪ Development of a novel and informative method for accurate CTICs detection and characterisation |
[188] | |
| Pancreas cancer | Negative immunomagnetic AutoMACS Pro cell separator and Mouse Cell Depletion Microbead cocktail kit (Miltenyi Biotec) | Chromium system (10 × Genomics) | Primary and metastatic tumour cells from mice; CTCs from blood; cultured pancreatic cancer-associated and AsPC-1 cells (number not stated) |
▪ Demonstration of the efficacy of adjuvant HGF/c-Met inhibition for pancreatic cancer and first confirmation of the existence of circulating human pancreatic stellate cells ▪ Accurate identification and classification of circulating human pancreatic stellate cells and pancreas cancer cells, distinguishing them from mouse cells using UMI counts, orthologous gene expression and canonical markers |
[189] | |
| Lung cancer | FACS to obtain single cell suspensions | Smart-seq2 (Takara Bio) | 1776 candidate CTCs from five LUAD-LM patients |
▪ Definition of CSF-CTCs via epithelial markers (EPCAM, CDH1, KRT7, KRT8, KRT18, MUC1), proliferation markers (CCND1, TOP2A) and genes with lung origin (SFTPA1, SFTPA2, SFTPB, NAPSA) ▪ Identification of metastatic-CTC signature genes crucial for the survival and metastasis (CEACAM6) ▪ Quantification of the degree of heterogeneity ▪ Identification of biomarkers during the progression of a liver metastases patient with cancer of unknown primary site |
[46] | |
| Hybrid cells | Eye cancer | GSE139829: Slightly modified Miltenyi tumour dissociation kit protocol [201, 202] | GSE139829: Chromium system (10 × Genomics) | GSE139829: 59,915 single cells from eight primary and three metastatic tumours |
▪ Identification of neoplastic-immune hybrid cells with metastatic properties in cyclic immunofluorescence images and uveal melanoma scRNA-seq dataset ▪ Identification of hybrid cells properties: enhanced cell motility and cytoskeleton rearrangement, immune evasion, and altered metabolism ▪ Identification of potential drivers of metastasis in hybrid cells including ligand-receptor pathways related to IGF1-IGFR1, GAS6-AXL, LGALS9-P4HB, APP-CD74 and CXCL12-CXCR4 |
[191] |
| Colorectal and breast cancer | GSE178341: tissue processing, followed by CD45 enrichment; BC dataset: human tumour dissociation kit (Miltenyi Biotec) |
GSE178341: Chromium system (10 × Genomics) BC dataset: Chromium system (10 × Genomics) |
GSE178341: 257,251 tumour cells and 112,861 adjacent normal cells from 64 tumours of 62 patients and normal tissue from 36 CRC patients[203]; BC dataset: 100,064 cells (GSE number not stated) [204] |
▪ Identification of tumour hybrid cells co-expressing epithelial (EpCAM and KRT8) and macrophage markers (CD163 and CD14) and expressing a distinct transcriptome in CRC and BC via single-cell and spatial transcriptomics ▪ Establishment of a framework for hybrid cell identification in large datasets ▪ Rare hybrid cells were present in normal healthy tissue but had a distinct gene expression profile from tumour hybrid cells, which showed features linked to tumour progression |
[192] | |
| Prostate cancer | Flow cytometry | Chromium system (10 × Genomics) | GSE143791: solid metastatic tissue, liquid bone marrow at the site of the metastasis and liquid bone marrow from a vertebral body distant from the tumour site (Distal) (number not stated) [205]; 17,602 tumour hybrid cells from the bone metastasis model |
▪ scRNAseq GSE143791 dataset identified a unique cancer cell cluster in PCa bone metastases with myeloid cell marker, linked to immune regulation and tumour progression pathway ▪ Development of a bone metastasis model through caudal artery injection of tumour cells and sorted the tumour hybrid cells by flow cytometry ▪ Detection of cell fusion between disseminated tumour cells and bone marrow cells as a potential source of myeloid-like hybrid cells ▪ Multi-omics analysis revealed cell adhesion and proliferation pathways in hybrid cells ▪ scRNAseq and CyTOF showed tumour-associated neutrophils/monocytes/macrophages enriched in hybrid cells-induced immunosuppressive microenvironment ▪ Hybrid cells exhibited an enhanced EMT phenotype, higher tumorigenicity, resistance to docetaxel and ferroptosis, but sensitive to radiotherapy |
[193] | |
| Skin cancer | Not stated | Not stated | Not stated |
▪ Establishment of hybrid clones of A375 cells and type 2 macrophages in co-culture ▪ scRNA-seq dataset identified hybrid cells in primary melanoma, with macrophages expressing melanoma antigens (melan A, tyrosinase, premelanosome protein), and their presence correlated with poorer response to immune checkpoint blockade |
[194] | |
| Breast cancer | RosettSep negative selection | Chromium system (10 × Genomics) | 13,741 CD45-negative and CD45-positive CECs from 20 BC patients and one healthy donor |
▪ Identification of 16 cell clusters ▪ Identification and profiling of CD45-negative and CD45-positive CECs ▪ Identification of aneuploid CTCs and hybrid cells via analysing DNA ploidy |
[195] | |
| Kidney cancer | Centrifugation, followed by FACS (DBA+ and c-Kit+) and C1™ Single-Cell Auto Prep System (Fluidigm) | Smart-seq (Takara Bio) | Four runs: 66 cells derived from a 1:1 mixture of c-Kit+ and DBA+ populations; 74 cells from the same mixture; 43 c-Kit+ cells; and 58 c-Kit+ cells |
▪ Identification of four groups individually: o Group 1: Genes associated with PCs o Group 2: Genes associated with ICs o Group 3: Genes associated with both PCs and ICs (hybrid cells) o Group 4: Do not contain markers for either cell type ▪ Combination of four scRNAseq datasets: o Group 1: 74 PCs o Group 2: 87 A-ICs o Group 3: 23 B-ICs ▪ Identification of hybrid IC/PCs expressing either the IC-marker transcripts or Aqp2 |
[196] |
A-ICs type A intercalated cells, AsPC-1 luciferase-tagged human pancreatic cancer cells, B-ICs type B intercalated cells, CD45 lymphocyte common antigen, CDX CTC-derived xenografts, CECs circulating epithelial cells, CR conditional reprogramming, CSF cerebrospinal fluid, CTCs circulating tumour cells, CTICs circulating tumour-initiating cells, EpCAM epithelial cell adhesion molecule, F3 tissue factor, HCC hepatocellular carcinoma, IC intercalated cells, ISCVA interactive single cell visual analytics, KRT keratin, MEL melanoma, NM normal mammary, PBMC peripheral blood mononuclear cell, PCa prostate cancer, PC principal cell, ptPDX primary tumour patient-derived xenograft, RNA ribonucleic acid, scRNA-seq single-cell RNA-sequencing, Smart-seq switch mechanism at the 5′ end of RNA template sequencing, snRNA-seq single nuclear RNA sequencing, TIGIT T cell immunoreceptor with immunoglobulin and ITIM domain, UMI unique molecular identifiers, VIM vimentin
Fig. 4.
Emerging research frontiers in CTC scRNA-seq: rare and hybrid cells, and machine learning-integrated CTC analyses. 1. Rare cell type discovery identifies rare and CTC-related rare subpopulations with unique transcriptomes that may evade standard enrichment methods. 2. Hybrid cell discovery enables detection of fusion between tumour and immune cells that drive tumour plasticity, immune evasion and metastasis. 3. Integration of machine learning enhances CTC classification, uncover hidden cell states, predict treatment response and improve cell-type annotation accuracy
Artificial intelligence integration
With the increasing application of scRNA-seq in larger-scale studies, the challenge of clustering and analysing the vast amounts of data generated has become a major bottleneck. To address this, ML techniques have been leveraged to streamline data analysis and improve the identification of rare cell populations including CTCs and hybrid cells. For example, Iyer et al. employed ML techniques on publicly available single-cell expression profiles, enabling the detection of various CTCs across different cancer types and opening avenues for discovering consistent pan-cancer CTC surface proteins beyond the widely used epithelial cell adhesion molecule (EpCAM) [20]. Building on this concept, Pastuszak et al. developed ML-based classifiers using four tree-based models trained and tested on Smart-Seq2 data from primary tumour sections of BC patients and PBMCs, along with a public dataset featuring annotated CTC expression profiles from 34 mBC patients, including those with triple-negative BC. Their top-performing models achieved approximately 95% balanced accuracy on the CTC test set per cell, accurately identifying 133 out of 138 CTCs and CTC-PBMC clusters [21].
Beyond CTC identification, ML has been applied to tackle the complexity of cellular heterogeneity. Wang et al. introduced a mixture exponential graph and Markov random field model for cell heterogeneity analysis. This method helps overcome the high-dimensional challenges of scRNA-seq data by identifying key marker genes related to cancer and the immune system, aiding in the discovery of potential therapeutic targets [206]. In the context of hybrid cells, Thong et al. combined scRNA-seq with ML-based dataset alignment to explore phenotypic similarities between mammary stem cells and BC cells. They revealed that normal mammary stem cells exhibit epithelial, mesenchymal, and hybrid epithelial/mesenchymal states—linking hybrid phenotypes with stemness and tumour progression [207].
Deep learning approaches have further advanced rare cell analysis. For example, unCTC, an R package developed by Poonia et al., provides an unbiased framework for identifying and characterizing CTCs using deep dictionary learning, clustering and expression-based copy number variation inference [39]. In parallel, Guo et al. introduced CTC-Tracer, a deep transfer learning-based algorithm that corrects for distributional shifts between primary cancer cells and CTCs, allowing accurate lesion label transfer across diverse RNA-seq datasets [208].
Altogether, the integration of AI and scRNA-seq is opening new frontiers in rare cell analysis, offering unprecedented opportunities for characterizing CTCs and tumour hybrid cells. Future research directions should focus on developing AI models for real-time, automated interpretation of molecular and phenotypic features. Such advancements hold the potential to transform clinical oncology by delivering rapid, actionable insights for personalized treatment decisions, ultimately improving patient outcomes and accelerating cancer research [209].
Table 7 outlines emerging research frontiers in scRNA-seq, with a focus on AI-driven approaches for CTC identification and hybrid cell characterisation.
Table 7.
Emerging research frontiers in machine learning integration in CTC scRNA-seq
| Research frontier | Type of cancer | Number of cells analysed | Finding | Cit |
|---|---|---|---|---|
| Hybrid cells and machine learning | Breast cancer | 200 random cells per sample from three normal mammary NM-CR cell pairs |
▪ Quantified cell state distributions and identification of hybrid epithelial/mesenchymal states (KRT14, KRT18, VIM, and EPCAM) ▪ Analysis of human/mouse scRNA-seq data from mammary glands, incorporating bulk TCGA tumour data, other studies (NM, CR, Bach, Nguyen) and referencing the Giraddi Mouse Mammary Transcriptome Atlas (all via ML) |
[207] |
| Machine learning | Breast cancer | GSE109761: 262 CTCs, 14 CTC-PBMC clusters and 82 PBMCs from 34 metastatic breast cancer patients [53]; GSE118389: 1534 TNBC cells; Single Cell Expression Atlas: 27,620 PBMCs from 6 healthy donors [210] |
▪ Detection of CTCs via ML using Smart‑Seq2 sequencing ▪ Analysis of two feature selection methods and four ML algorithms: Extreme Gradient Boosting, Light Gradient Boosting Machine, Random Forest and Balanced Random Forest ▪ Validation on real CTC data achieved nearly 96% balanced accuracy, outperforming EpCAM-based classification for identifying CTCs in metastatic breast cancer patients |
[21] |
| Pancreatic and breast cancer | GSE51372: 75 CTC-enriched blood cells, 12 fibroblast cells, 16 pancreatic cancer cells, 12 white blood cells, 18 GFP-traced CTCs, 20 GFP-traced primary tumour cells, and 34 RNA dilutions from pancreatic tumours [25]; GSE118389: 1534 cells from six fresh TNBC tumours [210] |
▪ Introduction of a mixture exponential graph and Markov random field model for cell heterogeneity analysis and overcoming high-dimensional challenges ▪ Identification of hub nodes in cell–cell networks and incorporation of Rank Product for robust differential gene expression analysis ▪ Identification of seven key marker genes in GSE118389, which play important roles in the immune system and are closely linked to generic cancer genes |
[206] | |
| Breast, prostate, skin, lung, and pancreas cancer | 558 single CTCs from 10 different studies (6 breast studies and 1 each for other cancer types) [211] |
▪ Design and training of an ML classifier to identify CTC phenotypes based on their gene expression profiles ▪ Validation of the ML classifier via breast CTCs captured using a newly developed microfluidic system for label-free enrichment of CTCs |
[20] | |
| Machine learning and deep transfer learning | Breast cancer | 72 CTCs from 6 patients of three major subtypes: ER−/PR−/HER2−, ER+/PR+/HER2− and ER−/PR−/HER2+ |
▪ Development of unCTC, an R package for unbiased identification and characterisation of CTCs from scRNA-seq ▪ unCTC includes a novel method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost, expression-based copy number variation inference, and combinatorial, marker-based verification of the malignant phenotypes |
[39] |
| Skin, liver, breast, and prostate cancer | Primary dataset: 372 CTCs four cancer CTC scRNA-seq datasets (CNP0000095, GSE109761 [53], GSE67980 [60], GSE157745 [36]) with two blood datasets (400 PBMCs and 800 blood cells from 32 immunophenotypic cell types); validation dataset: 451 CTCs from MEL and BRCA cancers (GSE75367, PRJNA471754, GSE51827 [74], GSE38495 [22] |
▪ Design of CTC-Tracer, a deep transfer learning-based algorithm with an average accuracy of ~ 99% (on test samples) ▪ Integration of two ML modes: transductive and inductive learning ▪ CTC-tracer improves CTC analysis by: o Correcting for data differences between primary tumours and CTCs o Transferring tumour origin information from large cancer cell atlases to CTCs |
[208] |
CTCs circulating tumour cells, EpCAM epithelial cell adhesion molecule, ER estrogen receptor, GFP green fluorescent protein, HER2 human epidermal growth factor receptor 2, KRT keratin, ML machine learning, PBMC peripheral blood mononuclear cell, PR progesterone receptor, scRNA-seq single-cell RNA-sequencing, TCGA The Cancer Genome Atlas, TNBC triple-negative breast cancer, VIM vimentin
Conclusion
ScRNA-seq is a powerful tool for profiling rare cell populations like CTCs and hybrid cells, revealing transcriptomic diversity, novel subtypes, and therapeutic targets. It facilitates patient re-stratification and advances metastasis research. Therefore, we propose a comprehensive workflow for CTC scRNA-seq, covering enrichment, single-cell sorting, sequencing, and data pre-processing. By integrating ML, data analysis and interpretability are enhanced, while focusing on hybrid cells could provide new insights into cancer immunobiology. Researchers should follow this proposed workflow to standardise approaches and enhance the utility of scRNA-seq in precision oncology.
Acknowledgements
The authors thank the University of Malaya for supporting the article processing charges through the KONSEPt transformative agreement with Springer Nature.
Author contributions
F.T.Y.F. contributed to conceptualisation, wrote the original draft, prepared visualisations, manuscript review and editing, validation, coordinated project administration and secured funding. N.-S.A.M. contributed to conceptualisation, manuscript review and editing and validation. L.-H.L. participated in manuscript review and editing, and validation. All authors reviewed and approved the final submitted version of the manuscript.
Funding
Open access funding provided by The Ministry of Higher Education Malaysia and Universiti Malaya. This study is supported by the University of Malaya-National Yang Ming Chiao Tung University matching grant.
Declarations
Declaration of generative AI and AI-assisted technologies in the writing process
During the preparation of this work, the authors used ChatGPT 4.0 (https://chatgpt.com/) to proofread the final manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
Ethical approval and consent to participate
Not applicable.
Consent for publication
All authors have given consent for publication.
Conflict of interest
The authors declare no conflict of interest.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Qi, W., Tian, L., Xu, J., Li, Z., & Wang, T. (2025). Multimodal framework in lung cancer management: Integrating liquid biopsy with traditional diagnostic techniques. Cancer Management And Research,17, 461–481. 10.2147/CMAR.S506630 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Wang, P., Yang, R. K., Jelloul, F. Z., Luthra, R., Routbort, M. J., Chen, H., Loghavi, S., Ok, C. Y., Kanagal-Shamanna, R., Roy-Chowdhuri, S., Medeiros, L. J., & Patel, K. P. (2025). Routine clinical liquid biopsy testing for solid tumors delivers the promise of minimally invasive detection of genomic variants with a faster turnaround time. JCO Precision Oncology. 10.1200/PO.24.00299 [DOI] [PubMed] [Google Scholar]
- 3.Liang, X., Tang, Q., Chen, J., Wei, Y. (2025). Liquid biopsy: A breakthrough technology in early cancer screening. Cancer Screening and Prevention 0 0–0. 10.14218/CSP.2024.00031.
- 4.Shegekar, T., Vodithala, S., & Juganavar, A. (2023). The emerging role of liquid biopsies in revolutionising cancer diagnosis and therapy. Cureus,15, Article e43650. 10.7759/cureus.43650 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Pandey, S., & Yadav, P. (2025). Liquid biopsy in cancer management: Integrating diagnostics and clinical applications. Practical Laboratory Medicine,43, Article e00446. 10.1016/j.plabm.2024.e00446 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Song, J., Ye, X., & Xiao, H. (2025). Liquid biopsy entering clinical practice: Past discoveries, current insights, and future innovations. Critical Reviews in Oncology/Hematology,207, Article 104613. 10.1016/j.critrevonc.2025.104613 [DOI] [PubMed] [Google Scholar]
- 7.Castro-Giner, F., & Aceto, N. (2020). Tracking cancer progression: From circulating tumor cells to metastasis. Genome Medicine,12, Article 31. 10.1186/s13073-020-00728-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Krog, B. L., & Henry, M. D. (2018). Biomechanics of the circulating tumor cell microenvironment. Advances in Experimental Medicine and Biology,1092, 209–233. 10.1007/978-3-319-95294-9_11 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Lowes, L. E., & Allan, A. L. (2018). Circulating tumor cells and implications of the epithelial-to-mesenchymal transition. Advances in Clinical Chemistry,83, 121–181. 10.1016/bs.acc.2017.10.004 [DOI] [PubMed] [Google Scholar]
- 10.Rossi, T., Gallerani, G., Martinelli, G., Maltoni, R., & Fabbri, F. (2021). Circulating tumor cells as a tool to untangle the breast cancer heterogeneity issue. Biomedicines,9, Article 1242. 10.3390/biomedicines9091242 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Yu, T., Wang, C., Xie, M., Zhu, C., Shu, Y., Tang, J., & Guan, X. (2021). Heterogeneity of CTC contributes to the organotropism of breast cancer. Biomedicine & Pharmacotherapy,137, Article 111314. 10.1016/j.biopha.2021.111314 [DOI] [PubMed] [Google Scholar]
- 12.Whalen, R. M., Anderson, A. N., Jones, J. A., Sims, Z., Chang, Y. H., Nederlof, M. A., Wong, M. H., & Gibbs, S. L. (2024). Ultra high content analyses of circulating and tumor associated hybrid cells reveal phenotypic heterogeneity. Scientific Reports,14, Article 7350. 10.1038/s41598-024-57381-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Gauck, D., Keil, S., Niggemann, B., Zänker, K. S., & Dittmar, T. (2017). Hybrid clone cells derived from human breast epithelial cells and human breast cancer cells exhibit properties of cancer stem/initiating cells. BMC Cancer,17, Article 515. 10.1186/s12885-017-3509-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Hass, R., von der Ohe, J., & Dittmar, T. (2021). Hybrid formation and fusion of cancer cells in vitro and in vivo. Cancers,13(17), Article 4496. 10.3390/cancers13174496 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Orrapin, S., Thongkumkoon, P., Udomruk, S., Moonmuang, S., Sutthitthasakul, S., Yongpitakwattana, P., Pruksakorn, D., & Chaiyawat, P. (2023). Deciphering the biology of circulating tumor cells through single-cell RNA sequencing: Implications for precision medicine in cancer. International Journal of Molecular Sciences,24, Article 12337. 10.3390/ijms241512337 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Tieng, F. Y. F., Baharudin, R., Abu, N., Mohd Yunos, R.-I., Lee, L.-H., & Ab Mutalib, N.-S. (2020). Single cell transcriptome in colorectal cancer—current updates on its application in metastasis, chemoresistance and the roles of circulating tumor cells. Frontiers in Pharmacology,11, Article 135. 10.3389/fphar.2020.00135 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Armingol, E., Officer, A., Harismendy, O., & Lewis, N. E. (2021). Deciphering cell–cell interactions and communication from gene expression. Nature Reviews Genetics,22, 71–88. 10.1038/s41576-020-00292-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021). Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nature Reviews Genetics,22, 627–644. 10.1038/s41576-021-00370-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Meng, G., Tang, W., Huang, E., Li, Z., & Feng, H. (2023). A comprehensive assessment of cell type-specific differential expression methods in bulk data. Briefings in Bioinformatics,24, Article bbac516. 10.1093/bib/bbac516 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Iyer, A., Gupta, K., Sharma, S., Hari, K., Lee, Y. F., Ramalingam, N., Yap, Y. S., West, J., Bhagat, A. A., Subramani, B. V., Sabuwala, B., Tan, T. Z., Thiery, J. P., Jolly, M. K., Ramalingam, N., & Sengupta, D. (2020). Integrative analysis and machine learning based characterization of single circulating tumor cells. Journal of Clinical Medicine,9, Article 1206. 10.3390/jcm9041206 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Pastuszak, K., Sieczczyński, M., Dzięgielewska, M., Wolniak, R., Drewnowska, A., Korpal, M., Zembrzuska, L., Supernat, A., & Żaczek, A. J. (2024). Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing. Scientific Reports,14, Article 11057. 10.1038/s41598-024-61378-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., Schroth, G. P., & Sandberg, R. (2012). Full-length mRNA-seq from single cell levels of RNA and individual circulating tumor cells. Nature Biotechnology,30, 777–782. 10.1038/nbt.2282 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013). Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nature Methods,10, 1096–1098. 10.1038/nmeth.2639 [DOI] [PubMed] [Google Scholar]
- 24.Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014). Full-length RNA-seq from single cells using Smart-seq2. Nature Protocols,9, 171–181. 10.1038/nprot.2014.006 [DOI] [PubMed] [Google Scholar]
- 25.Ting, D. T., Wittner, B. S., Ligorio, M., Jordan, N. V., Shah, A. M., Miyamoto, D. T., Aceto, N., Bersani, F., Brannigan, B. W., Xega, K., Ciciliano, J. C., Zhu, H., MacKenzie, O. C., Trautwein, J., Arora, K. S., Shahid, M., Ellis, H. L., Qu, N., Bardeesy, N., … Haber, D. A. (2014). Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Reports,8, 1905–1918. 10.1016/j.celrep.2014.08.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., Gregory, M. T., Shuga, J., Montesclaros, L., Underwood, J. G., Masquelier, D. A., Nishimura, S. Y., Schnall-Levin, M., Wyatt, P. W., Hindson, C. M., … Bielas, J. H. (2017). Massively parallel digital transcriptional profiling of single cells. Nature Communications,8, Article 14049. 10.1038/ncomms14049 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.D’Avola, D., Villacorta-Martin, C., Martins-Filho, S. N., Craig, A., Labgaa, I., von Felden, J., Kimaada, A., Bonaccorso, A., Tabrizian, P., Hartmann, B. M., Sebra, R., Schwartz, M., & Villanueva, A. (2018). High-density single cell mRNA sequencing to characterize circulating tumor cells in hepatocellular carcinoma. Scientific Reports,8, Article 11570. 10.1038/s41598-018-30047-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Cheng, Y.-H., Chen, Y.-C., Lin, E., Brien, R., Jung, S., Chen, Y.-T., Lee, W., Hao, Z., Sahoo, S., Kang, H. M., Cong, J., Burness, M., Nagrath, S., Wicha, M. S., & Yoon, E. (2019). Hydro-seq enables contamination-free high-throughput single-cell RNA-sequencing for circulating tumor cells. Nature Communications,10, Article 2163. 10.1038/s41467-019-10122-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kojima, M., Harada, T., Fukazawa, T., Kurihara, S., Saeki, I., Takahashi, S., & Hiyama, E. (2021). Single-cell DNA and RNA sequencing of circulating tumor cells. Scientific Reports,11, Article 22864. 10.1038/s41598-021-02165-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Shi, F., Jia, F., Wei, Z., Ma, Y., Fang, Z., Zhang, W., & Hu, Z. (2021). A microfluidic chip for efficient circulating tumor cells enrichment, screening, and single-cell RNA sequencing. Proteomics,21, Article e2000060. 10.1002/pmic.202000060 [DOI] [PubMed] [Google Scholar]
- 31.Dong, Z., Wang, Y., Xu, G., Liu, B., Wang, Y., Reboud, J., Jajesniak, P., Yan, S., Ma, P., Liu, F., Zhou, Y., Jin, Z., Yang, K., Huang, Z., Zhuo, M., Jia, B., Fang, J., Zhang, P., Wu, N., & Chang, L. (2024). Genetic and phenotypic profiling of single living circulating tumor cells from patients with microfluidics. Proceedings of the National Academy of Sciences of the United States of America,121, Article e2315168121. 10.1073/pnas.2315168121 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Vasantharajan, S.S. (2024). Characterisation of circulating tumour cells isolated by a size-based method and identification of circulating tumour cell-specific transcriptomic signatures to understand colorectal cancer metastasis, University of Otago. https://ourarchive.otago.ac.nz/esploro/outputs/doctoral/Characterisation-of-circulating-tumour-cells-isolated/9926522380201891 (Accessed November 29, 2024).
- 33.Stucky, A., Viet, C. T., Aouizerat, B. E., Ye, Y., Doan, C., Mundluru, T., Sedhiazadeh, P., Sinha, U. K., Chen, X., Zhang, X., Li, S. C., Cai, J., & Zhong, J. F. (2024). Single-cell molecular profiling of head and neck squamous cell carcinoma reveals five dysregulated signaling pathways associated with circulating tumor cells. Cancer Control,31, 10732748241251572. 10.1177/10732748241251571 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Moeyersoms, A. H. M., Knechtel, K. W., Rong, A. J., Gallo, R. A., Zhang, M., Marsh, H. M., Sargi, Z. B., Leibowitz, J. M., Civantos, F. J., Weed, D. T., Dubovy, S. R., Tse, D. T., & Pelaez, D. (2024). Circulating adenoid cystic carcinoma associated MYB transcripts enable rapid and sensitive detection of metastatic disease in blood liquid biopsies. The Journal of Liquid Biopsy. 10.1016/j.jlb.2024.100276 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Wu, L., Wang, Z., Zia, A., Kelley, S. O., & de Perrot, M. (2025). Mesothelioma cell heterogeneity identified by single cell RNA sequencing. Scientific Reports,15, 8725. 10.1038/s41598-025-92542-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Hong, X., Roh, W., Sullivan, R. J., Wong, K. H. K., Wittner, B. S., Guo, H., Dubash, T. D., Sade-Feldman, M., Wesley, B., Horwitz, E., Boland, G. M., Marvin, D. L., Bonesteel, T., Lu, C., Aguet, F., Burr, R., Freeman, S. S., Parida, L., Calhoun, K., & Haber, D. A. (2021). The lipogenic regulator SREBF2 induces Transferrin in circulating melanoma cells and suppresses ferroptosis. Cancer Discovery,11, 678–695. 10.1158/2159-8290.CD-19-1500 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Stewart, C. A., Gay, C. M., Xi, Y., Sivajothi, S., Sivakamasundari, V., Fujimoto, J., Bolisetty, M., Hartsfield, P. M., Balasubramaniyan, V., Chalishazar, M. D., Moran, C., Kalhor, N., Stewart, J., Tran, H., Swisher, S. G., Roth, J. A., Zhang, J., de Groot, J., Glisson, B., … Byers, L. A. (2020). Single-cell analyses reveal increased intratumoral heterogeneity after the onset of therapy resistance in small-cell lung cancer. Nature Cancer,1, 423–436. 10.1038/s43018-019-0020-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Grigoryeva, E., Tashireva, L., Alifanov, V., Savelieva, O., Zavyalova, M., Menyailo, M., Khozyainova, A., Denisov, E. V., Bragina, O., Popova, N., Cherdyntseva, N. V., & Perelmuter, V. (2024). Integrin-associated transcriptional characteristics of circulating tumor cells in breast cancer patients. PeerJ,12, Article e16678. 10.7717/peerj.16678 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Poonia, S., Goel, A., Chawla, S., Bhattacharya, N., Rai, P., Lee, Y. F., Yap, Y. S., West, J., Bhagat, A. A., Tayal, J., Mehta, A., Ahuja, G., Majumdar, A., Ramalingam, N., & Sengupta, D. (2023). Marker-free characterization of full-length transcriptomes of single live circulating tumor cells. Genome Research,33, 80–95. 10.1101/gr.276600.122 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Rieckmann, L.-M., Spohn, M., Ruff, L., Agorku, D., Becker, L., Borchers, A., Krause, J., O’Reilly, R., Hille, J., Velthaus-Rusik, J.-L., Beumer, N., Günther, A., Willnow, L., Imbusch, C. D., Iglauer, P., Simon, R., Franzenburg, S., Winter, H., Thomas, M., … Janning, M. (2024). Diagnostic leukapheresis reveals distinct phenotypes of NSCLC circulating tumor cells. Molecular Cancer,23, Article 93. 10.1186/s12943-024-01984-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Kojima, M., Harada, T., Fukazawa, T., Kurihara, S., Touge, R., Saeki, I., Takahashi, S., & Hiyama, E. (2023). Single-cell next-generation sequencing of circulating tumor cells in patients with neuroblastoma. Cancer Science,114, 1616–1624. 10.1111/cas.15707 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Kozuka, M., Battaglin, F., Jayachandran, P., Wang, J., Arai, H., Soni, S., Zhang, W., Hirai, M., Matsusaka, S., & Lenz, H.-J. (2021). Clinical significance of circulating tumor cell induced epithelial-mesenchymal transition in patients with metastatic colorectal cancer by single-cell RNA-sequencing. Cancers,13, Article 4862. 10.3390/cancers13194862 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Zolotovskaia, M. A., Sorokin, M. I., Petrov, I. V., Poddubskaya, E. V., Moiseev, A. A., Sekacheva, M. I., Borisov, N. M., Tkachev, V. S., Garazha, A. V., Kaprin, A. D., Shegay, P. V., Giese, A., Kim, E., Roumiantsev, S. A., & Buzdin, A. A. (2020). Disparity between inter-patient molecular heterogeneity and repertoires of target drugs used for different types of cancer in clinical oncology. International Journal of Molecular Sciences,21, 1580. 10.3390/ijms21051580 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Hamelin, B., Obradović, M. M. S., Sethi, A., Kloc, M., Münst, S., Beisel, C., Eschbach, K., Kohler, H., Soysal, S., Vetter, M., Weber, W. P., Stadler, M. B., & Bentires-Alj, M. (2023). Single-cell analysis reveals inter- and intratumour heterogeneity in metastatic breast cancer. Journal of Mammary Gland Biology and Neoplasia,28, 26. 10.1007/s10911-023-09551-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Goldman, S. L., MacKay, M., Afshinnekoo, E., Melnick, A. M., Wu, S., Mason, C.E. (2019). The impact of heterogeneity on single-cell sequencing, Frontiers in Genetics 10. https://www.frontiersin.org/articles/10.3389/fgene.2019.00008 (Accessed January 9, 2024). [DOI] [PMC free article] [PubMed]
- 46.Ruan, H., Zhou, Y., Shen, J., Zhai, Y., Xu, Y., Pi, L., Huang, R., Chen, K., Li, X., Ma, W., Wu, Z., Deng, X., Wang, X., Zhang, C., & Guan, M. (2020). Circulating tumor cell characterization of lung cancer brain metastases in the cerebrospinal fluid through single-cell transcriptome analysis. Clinical and Translational Medicine,10, Article e246. 10.1002/ctm2.246 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Brechbuhl, H. M., Vinod-Paul, K., Gillen, A. E., Kopin, E. G., Gibney, K., Elias, A. D., Hayashi, M., Sartorius, C. A., & Kabos, P. (2020). Analysis of circulating breast cancer cell heterogeneity and interactions with peripheral blood mononuclear cells. Molecular Carcinogenesis,59, 1129–1139. 10.1002/mc.23242 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Pauken, C. M., Kenney, S. R., Brayer, K. J., Guo, Y., Brown-Glaberman, U. A., & Marchetti, D. (2021). Heterogeneity of circulating tumor cell neoplastic subpopulations outlined by single-cell transcriptomics. Cancers,13, Article 4885. 10.3390/cancers13194885 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Arnoletti, J. P., Reza, J., Rosales, A., Monreal, A., Fanaian, N., Whisner, S., Srivastava, M., Rivera-Otero, J., Yu, G., Phanstiel, O., IV., Altomare, D. A., Tran, Q., & Litherland, S. A. (2022). Pancreatic ductal adenocarcinoma (PDAC) circulating tumor cells influence myeloid cell differentiation to support their survival and immunoresistance in portal vein circulation. PLoS One,17, e0265725. 10.1371/journal.pone.0265725 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Mishra, A., Huang, S.-B., Dubash, T., Burr, R., Edd, J. F., Wittner, B. S., Cunneely, Q. E., Putaturo, V. R., Deshpande, A., Antmen, E., Gopinathan, K. A., Otani, K., Miyazawa, Y., Kwak, J. E., Guay, S. Y., Kelly, J., Walsh, J., Nieman, L. T., Galler, I., … Toner, M. (2025). Tumor cell-based liquid biopsy using high-throughput microfluidic enrichment of entire leukapheresis product. Nature Communications,16, Article 32. 10.1038/s41467-024-55140-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Arneth, B. (2019). Tumor microenvironment. Medicina (Kaunas),56, Article 15. 10.3390/medicina56010015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Tieng, F. Y. F., Lee, L.-H., Ab Mutalib, N.-S. (2022). Deciphering colorectal cancer immune microenvironment transcriptional landscape on single cell resolution—A role for immunotherapy, Frontiers in Immunology 13. https://www.frontiersin.org/articles/10.3389/fimmu.2022.959705 (Accessed September 5, 2022). [DOI] [PMC free article] [PubMed]
- 53.Szczerba, B. M., Castro-Giner, F., Vetter, M., Krol, I., Gkountela, S., Landin, J., Scheidmann, M. C., Donato, C., Scherrer, R., Singer, J., Beisel, C., Kurzeder, C., Heinzelmann-Schwarz, V., Rochlitz, C., Weber, W. P., Beerenwinkel, N., & Aceto, N. (2019). Neutrophils escort circulating tumour cells to enable cell cycle progression. Nature,566, 553–557. 10.1038/s41586-019-0915-y [DOI] [PubMed] [Google Scholar]
- 54.Sun, Y.-F., Wu, L., Liu, S.-P., Jiang, M.-M., Hu, B., Zhou, K.-Q., Guo, W., Xu, Y., Zhong, Y., Zhou, X.-R., Zhang, Z.-F., Liu, G., Liu, S., Shi, Y.-H., Ji, Y., Du, M., Li, N.-N., Li, G.-B., Zhao, Z.-K., … Yang, X.-R. (2021). Dissecting spatial heterogeneity and the immune-evasion mechanism of CTCs by single-cell RNA-seq in hepatocellular carcinoma. Nature Communications,12, Article 4091. 10.1038/s41467-021-24386-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Liu, X., Song, J., Zhang, H., Liu, X., Zuo, F., Zhao, Y., Zhao, Y., Yin, X., Guo, X., Wu, X., Zhang, H., Xu, J., Hu, J., Jing, J., Ma, X., & Shi, H. (2023). Immune checkpoint HLA-E: CD94-NKG2A mediates evasion of circulating tumor cells from NK cell surveillance. Cancer Cell,41, 272-287.e9. 10.1016/j.ccell.2023.01.001 [DOI] [PubMed] [Google Scholar]
- 56.Tang, H.-D., Wang, Y., Xie, P., Tan, S.-Y., Li, H.-F., Shen, H., Zhang, Z., Lei, Z.-Q., & Zhou, J.-H. (2022). The crosstalk between immune infiltration, circulating tumor cells, and metastasis in pancreatic cancer: Identification of HMGB3 from a multiple omics analysis. Frontiers in Genetics,13, Article 892177. 10.3389/fgene.2022.892177 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Celià-Terrassa, T., & Kang, Y. (2024). How important is EMT for cancer metastasis? PLoS Biology,22, Article e3002487. 10.1371/journal.pbio.3002487 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Negishi, R., Yamakawa, H., Kobayashi, T., Horikawa, M., Shimoyama, T., Koizumi, F., Sawada, T., Oboki, K., Omuro, Y., Funasaka, C., Kageyama, A., Kanemasa, Y., Tanaka, T., Matsunaga, T., & Yoshino, T. (2022). Transcriptomic profiling of single circulating tumor cells provides insight into human metastatic gastric cancer. Communications Biology,5, 20. 10.1038/s42003-021-02937-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Pang, S., Xu, S., Wang, L., Wu, H., Chu, Y., Ma, X., Li, Y., Zou, B., Wang, S., & Zhou, G. (2023). Molecular profiles of single circulating tumor cells from early breast cancer patients with different lymph node statuses. Thoracic Cancer,14, 156–167. 10.1111/1759-7714.14728 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Miyamoto, D. T., Zheng, Y., Wittner, B. S., Lee, R. J., Zhu, H., Broderick, K. T., Desai, R., Fox, D. B., Brannigan, B. W., Trautwein, J., Arora, K. S., Desai, N., Dahl, D. M., Sequist, L. V., Smith, M. R., Kapur, R., Wu, C.-L., Shioda, T., Ramaswamy, S., … Haber, D. A. (2015). RNA-seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science,349, 1351–1356. 10.1126/science.aab0917 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Wu, Q., Gu, Z., Shang, B., Wan, D., Zhang, Q., Zhang, X., Xie, P., Cheng, S., Zhang, W., & Zhang, K. (2024). Circulating tumor cell clustering modulates RNA splicing and polyadenylation to facilitate metastasis. Cancer Letters,588, Article 216757. 10.1016/j.canlet.2024.216757 [DOI] [PubMed] [Google Scholar]
- 62.Sundaresan, T. K., Dubash, T. D., Zheng, Z., Bardia, A., Wittner, B. S., Aceto, N., Silva, E. J., Fox, D. B., Liebers, M., Kapur, R., Iafrate, J., Toner, M., Maheswaran, S., & Haber, D. A. (2021). Evaluation of endocrine resistance using Esr1 genotyping of circulating tumor cells and plasma Dna. Breast Cancer Research and Treatment,188, 43–52. 10.1007/s10549-021-06270-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Capuozzo, M., Ferrara, F., Santorsola, M., Zovi, A., & Ottaiano, A. (2023). Circulating tumor cells as predictive and prognostic biomarkers in solid tumors. Cells,12, 2590. 10.3390/cells12222590 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Li, X., Sun, H., Liu, Q., Liu, Y., Hou, Y., & Jin, W. (2021). Conjoint analysis of circulating tumor cells and solid tumors for exploring potential prognostic markers and constructing a robust novel predictive signature for breast cancer. Cancer Cell International,21, 708. 10.1186/s12935-021-02415-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Yang, L., Yan, X., Chen, J., Zhan, Q., Hua, Y., Xu, S., Li, Z., Wang, Z., Dong, Y., Zuo, D., Xue, M., Tang, Y., Herschman, H. R., Lu, S., Shi, Q., & Wei, W. (2021). Hexokinase 2 discerns a novel circulating tumor cell population associated with poor prognosis in lung cancer patients. Proceedings of the National Academy of Sciences of the United States of America,118, Article e2012228118. 10.1073/pnas.2012228118 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Chen, V. L., Huang, Q., Harouaka, R., Du, Y., Lok, A. S., Parikh, N. D., Garmire, L. X., & Wicha, M. S. (2022). A dual-filtration system for single-cell sequencing of circulating tumor cells and clusters in HCC. Hepatology Communications,6, 1482–1491. 10.1002/hep4.1900 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Abramova, A., Rivandi, M., Yang, L., Stamm, N., Cieslik, J.-P., Honisch, E., Niederacher, D., Fehm, T., Neubauer, H., & Franken, A. (2023). A workflow for the enrichment, the identification, and the isolation of non-apoptotic single circulating tumor cells for RNA sequencing analysis. Cytometry Part A,105, 242–251. 10.1002/cyto.a.24816 [DOI] [PubMed] [Google Scholar]
- 68.Ebright, R. Y., Lee, S., Wittner, B. S., Niederhoffer, K. L., Nicholson, B. T., Bardia, A., Truesdell, S., Wiley, D. F., Wesley, B., Li, S., Mai, A., Aceto, N., Vincent-Jordan, N., Szabolcs, A., Chirn, B., Kreuzer, J., Comaills, V., Kalinich, M., Haas, W., … Micalizzi, D. S. (2020). Deregulation of ribosomal protein expression and translation promotes breast cancer metastasis. Science,367, 1468–1473. 10.1126/science.aay0939 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Matsunaga, H., Goto, M., Arikawa, K., Shirai, M., Tsunoda, H., Huang, H., & Kambara, H. (2015). A highly sensitive and accurate gene expression analysis by sequencing (“bead-seq”) for a single cell. Analytical Biochemistry,471, 9–16. 10.1016/j.ab.2014.10.011 [DOI] [PubMed] [Google Scholar]
- 70.Huh, H. D., Sub, Y., Oh, J., Kim, Y. E., Lee, J. Y., Kim, H.-R., Lee, S., Lee, H., Pak, S., Amos, S. E., Vahala, D., Park, J. H., Shin, J. E., Park, S. Y., Kim, H. S., Roh, Y. H., Lee, H.-W., Guan, K.-L., Choi, Y. S., … Park, H. W. (2023). Reprogramming anchorage dependency by adherent-to-suspension transition promotes metastatic dissemination. Molecular Cancer,22, Article 63. 10.1186/s12943-023-01753-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Huh, H. D., Sub, Y., Oh, J., Kim, Y. E., Lee, J. Y., Kim, H., Lee, S., Lee, H., Pak, S., Amos, S. E., Vahala, D., Park, J. H., Shin, J. E., Park, S. Y., Kim, H. S., Roh, Y. H., Lee, H., Guan, K., Choi, Y. S., … Park, H. W. (2023). Reprogramming anchorage dependency by adherent-to-suspension transition promotes metastatic dissemination. Molecular Cancer,22, 135. 10.1186/s12943-023-01838-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Gkountela, S., Castro-Giner, F., Szczerba, B. M., Vetter, M., Landin, J., Scherrer, R., Krol, I., Scheidmann, M. C., Beisel, C., Stirnimann, C. U., Kurzeder, C., Heinzelmann-Schwarz, V., Rochlitz, C., Weber, W. P., & Aceto, N. (2019). Circulating tumor cell clustering shapes DNA methylation to enable metastasis seeding. Cell,176, 98-112.e14. 10.1016/j.cell.2018.11.046 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Aceto, N., Bardia, A., Wittner, B. S., Donaldson, M. C., O’Keefe, R., Engstrom, A., Bersani, F., Zheng, Y., Comaills, V., Niederhoffer, K., Zhu, H., Mackenzie, O., Shioda, T., Sgroi, D., Kapur, R., Ting, D. T., Moy, B., Ramaswamy, S., Toner, M., … Maheswaran, S. (2018). AR expression in breast cancer CTCs associates with bone metastases. Molecular Cancer Research,16, 720–727. 10.1158/1541-7786.MCR-17-0480 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Aceto, N., Bardia, A., Miyamoto, D. T., Donaldson, M. C., Wittner, B. S., Spencer, J. A., Yu, M., Pely, A., Engstrom, A., Zhu, H., Brannigan, B. W., Kapur, R., Stott, S. L., Shioda, T., Ramaswamy, S., Ting, D. T., Lin, C. P., Toner, M., Haber, D. A., & Maheswaran, S. (2014). Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell,158, 1110–1122. 10.1016/j.cell.2014.07.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Liu, X., Song, J., Liu, X., Zhang, H., Wang, X., Li, Y., Yang, Z., Jing, J., Ma, X., & Shi, H. (2023). Protocol for identifying immune checkpoint on circulating tumor cells of human pancreatic ductal adenocarcinoma by single-cell RNA sequencing. Star Protocols,4, Article 102539. 10.1016/j.xpro.2023.102539 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Dimitrov-Markov, S., Perales-Patón, J., Bockorny, B., Dopazo, A., Muñoz, M., Baños, N., Bonilla, V., Menendez, C., Duran, Y., Huang, L., Perea, S., Muthuswamy, S. K., Al-Shahrour, F., Lopez-Casas, P. P., & Hidalgo, M. (2020). Discovery of new targets to control metastasis in pancreatic cancer by single-cell transcriptomics analysis of circulating tumor cells. Molecular Cancer Therapeutics,19, 1751–1760. 10.1158/1535-7163.MCT-19-1166 [DOI] [PubMed] [Google Scholar]
- 77.Franses, J. W., Philipp, J., Missios, P., Bhan, I., Liu, A., Yashaswini, C., Tai, E., Zhu, H., Ligorio, M., Nicholson, B., Tassoni, E. M., Desai, N., Kulkarni, A. S., Szabolcs, A., Hong, T. S., Liss, A. S., Fernandez-del Castillo, C., Ryan, D. P., Maheswaran, S., … Ting, D. T. (2020). Pancreatic circulating tumor cell profiling identifies LIN28B as a metastasis driver and drug target. Nature communications,11, 3303. 10.1038/s41467-020-17150-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Tang, F., Barbacioru, C., Nordman, E., Xu, N., Bashkirov, V. I., Lao, K., & Surani, M. A. (2010). RNA-seq analysis to capture the transcriptome landscape of a single cell. Nature Protocols,5, Article 10.1038/nprot.2009.236. 10.1038/nprot.2009.236 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.Zhu, L., Kan, K.-J., Grün, J. L., Hissa, B., Yang, C., Győrffy, B., Loges, S., Reißfelder, C., & Schölch, S. (2020). GAS2L1 is a potential biomarker of circulating tumor cells in pancreatic cancer. Cancers,12, Article E3774. 10.3390/cancers12123774 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Chen, M., Li, H., Xu, X., Bao, X., Xue, L., Ai, X., Xu, J., Xu, M., Shi, Y., Zhen, T., Li, J., Yang, Y., Ji, Y., Fu, Z., Xing, K., Qing, T., Wang, Q., Zhong, P., & Zhu, S. (2023). Identification of RAC1 in promoting brain metastasis of lung adenocarcinoma using single-cell transcriptome sequencing. Cell Death & Disease,14, Article 330. 10.1038/s41419-023-05823-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Vasantharajan, S. S., Barnett, E., Gray, E. S., McCall, J. L., Rodger, E. J., Eccles, M. R., Munro, F., Pattison, S., & Chatterjee, A. (2022). Assessment of a size-based method for enriching circulating tumour cells in colorectal cancer. Cancers,14, Article 3446. 10.3390/cancers14143446 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Zheng, X., Song, J., Yu, C., Zhou, Z., Liu, X., Yu, J., Xu, G., Yang, J., He, X., Bai, X., Luo, Y., Bao, Y., Li, H., Yang, L., Xu, M., Song, N., Su, X., Xu, J., Ma, X., & Shi, H. (2022). Single-cell transcriptomic profiling unravels the adenoma-initiation role of protein tyrosine kinases during colorectal tumorigenesis. Signal Transduction and Targeted Therapy,7, 1–14. 10.1038/s41392-022-00881-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Fankhauser, R., Chang, M., Garrison, Z., Berryman, R., Lucero, O. M., Fuiten, A., DePatie, N., Seifert, H., & Kulkarni, R. P. (2022). Single-cell identification of melanoma biomarkers in circulating tumor cells. Cancers,14, Article 4921. 10.3390/cancers14194921 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Sade-Feldman, M., Yizhak, K., Bjorgaard, S. L., Ray, J. P., de Boer, C. G., Jenkins, R. W., Lieb, D. J., Chen, J. H., Frederick, D. T., Barzily-Rokni, M., Freeman, S. S., Reuben, A., Hoover, P. J., Villani, A.-C., Ivanova, E., Portell, A., Lizotte, P. H., Aref, A. R., Eliane, J.-P., … Hacohen, N. (2018). Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell,175, 998-1013.e20. 10.1016/j.cell.2018.10.038 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Wang, Z., Ahmed, S., Labib, M., Wang, H., Wu, L., Bavaghar-Zaeimi, F., Shokri, N., Blanco, S., Karim, S., Czarnecka-Kujawa, K., Sargent, E. H., McGray, A. J. R., de Perrot, M., & Kelley, S. O. (2023). Isolation of tumour-reactive lymphocytes from peripheral blood via microfluidic immunomagnetic cell sorting. Nature Biomedical Engineering,7, 1188–1203. 10.1038/s41551-023-01023-3 [DOI] [PubMed] [Google Scholar]
- 86.Green, D., Eyre, H., Singh, A., Taylor, J. T., Chu, J., Jeys, L., Sumathi, V., Coonar, A., Rassl, D., Babur, M., Forster, D., Alzabin, S., Ponthan, F., McMahon, A., Bigger, B., Reekie, T., Kassiou, M., Williams, K., Dalmay, T., … Finegan, K. G. (2020). Targeting the MAPK7/MMP9 axis for metastasis in primary bone cancer. Oncogene,39, 5553–5569. 10.1038/s41388-020-1379-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87.Hayes, D. F., Smerage, J. B. (2010). Circulating tumor cells, in: Progress in molecular biology and translational science, Elsevier pp. 95–112. 10.1016/B978-0-12-385071-3.00005-8. [DOI] [PubMed]
- 88.Krebs, M. G., Hou, J.-M., Ward, T. H., Blackhall, F. H., & Dive, C. (2010). Circulating tumour cells: Their utility in cancer management and predicting outcomes. Therapeutic Advances in Medical Oncology,2, 351–365. 10.1177/1758834010378414 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89.Miller, M. C., Doyle, G. V., & Terstappen, L. W. M. M. (2010). Significance of circulating tumor cells detected by the CellSearch system in patients with metastatic breast colorectal and prostate cancer. Journal Of Oncology. 10.1155/2010/617421 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 90.Andree, K. C., van Dalum, G., & Terstappen, L. W. M. M. (2016). Challenges in circulating tumor cell detection by the Cell Search System. Molecular Oncology,10, 395–407. 10.1016/j.molonc.2015.12.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.van der Toom, E. E., Verdone, J. E., Gorin, M. A., & Pienta, K. J. (2016). Technical challenges in the isolation and analysis of circulating tumor cells. Oncotarget,7, Article 62754–62766. 10.18632/oncotarget.11191 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.Zou, D., & Cui, D. (2018). Advances in isolation and detection of circulating tumor cells based on microfluidics. Cancer Biology & Medicine,15, 335–353. 10.20892/j.issn.2095-3941.2018.0256 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 93.Ashworth, T. R. (1869). A case of cancer in which cells similar to those in the tumors were seen in the blood after death. Australasian Medical Journal,14, 146–149. [Google Scholar]
- 94.Wong, K. H. K., Tessier, S. N., Miyamoto, D. T., Miller, K. L., Bookstaver, L. D., Carey, T. R., Stannard, C. J., Thapar, V., Tai, E. C., Vo, K. D., Emmons, E. S., Pleskow, H. M., Sandlin, R. D., Sequist, L. V., Ting, D. T., Haber, D. A., Maheswaran, S., Stott, S. L., & Toner, M. (2017). Whole blood stabilization for the microfluidic isolation and molecular characterization of circulating tumor cells. Nature Communications,8, 1733. 10.1038/s41467-017-01705-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 95.Miller, M., Robinson, P., Wagner, C., O’Shannessy, D. (2018). The ParsortixTM cell separation system—A versatile liquid biopsy platform, Cytometry Part A 93. 10.1002/cyto.a.23571. [DOI] [PMC free article] [PubMed]
- 96.Tieng, F. Y. F., Abu, N., Nasir, S. N., Lee, L.-H., & Ab Mutalib, N.-S. (2021). Liquid biopsy-based colorectal cancer screening via surface markers of circulating tumor cells. Diagnostics,11, Article 2136. 10.3390/diagnostics11112136 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 97.Sasagawa, Y., Nikaido, I., Hayashi, T., Danno, H., Uno, K. D., Imai, T., & Ueda, H. R. (2013). Quartz-Seq: A highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biology,14, R31. 10.1186/gb-2013-14-4-r31 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 98.Sasagawa, Y., Danno, H., Takada, H., Ebisawa, M., Tanaka, K., Hayashi, T., Kurisaki, A., & Nikaido, I. (2018). Quartz-seq2: A high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads. Genome Biology,19, 29. 10.1186/s13059-018-1407-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99.Hagemann-Jensen, M., Ziegenhain, C., Chen, P., Ramsköld, D., Hendriks, G.-J., Larsson, A. J. M., Faridani, O. R., & Sandberg, R. (2020). Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nature Biotechnology,38, 708–714. 10.1038/s41587-020-0497-0 [DOI] [PubMed] [Google Scholar]
- 100.Bageritz, J., & Raddi, G. (2019). Single-cell RNA sequencing with Drop-Seq. In V. Proserpio (Ed.), [DOI] [PubMed]
- 101.Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., Trombetta, J. J., Weitz, D. A., Sanes, J. R., Shalek, A. K., Regev, A., & McCarroll, S. A. (2015). Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell,161, 1202–1214. 10.1016/j.cell.2015.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 102.Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017). Comparative analysis of single-cell RNA sequencing methods. Molecular Cell,65, 631-643.e4. 10.1016/j.molcel.2017.01.023 [DOI] [PubMed] [Google Scholar]
- 103.Shomroni, O., Sitte, M., Schmidt, J., Parbin, S., Ludewig, F., Yigit, G., Zelarayan, L. C., Streckfuss-Bömeke, K., Wollnik, B., & Salinas, G. (2022). A novel single-cell RNA-sequencing approach and its applicability connecting genotype to phenotype in ageing disease. Scientific Reports,12, Article 4091. 10.1038/s41598-022-07874-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 104.Shum, E. Y., Walczak, E. M., Chang, C., & Christina Fan, H. (2019). Quantitation of mRNA transcripts and proteins using the BD RhapsodyTM single-cell analysis system. Advances in Experimental Medicine and Biology,1129, 63–79. 10.1007/978-981-13-6037-4_5 [DOI] [PubMed] [Google Scholar]
- 105.Ramalingam, N., Fowler, B., Szpankowski, L., Leyrat, A. A., Hukari, K., Maung, M. T., Yorza, W., Norris, M., Cesar, C., Shuga, J., Gonzales, M. L., Sanada, C. D., Wang, X., Yeung, R., Hwang, W., Axsom, J., Devaraju, N. S. G. K., Angeles, N. D., Greene, C., … West, J. A. A. (2016). Fluidic logic used in a systems approach to enable integrated single-cell functional analysis. Front Bioeng Biotechnol,4, 70. 10.3389/fbioe.2016.00070 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 106.LaMar, D. (2015). FastQC, https://qubeshub.org/resources/fastqc.
- 107.Krueger, F. (2015). Trim Galore!: A wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data, Babraham Institute. https://cir.nii.ac.jp/crid/1370294643762929691 (Accessed March 30, 2025).
- 108.Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics,30, 2114–2120. 10.1093/bioinformatics/btu170 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 109.Wingett, S. W., & Andrews, S. (2018). FastQ screen: A tool for multi-genome mapping and quality control. F1000Research,7, Article 1338. 10.12688/f1000research.15931.2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 110.Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: Ultrafast universal RNA-seq aligner. Bioinformatics,29, 15–21. 10.1093/bioinformatics/bts635 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 111.Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods,14, 417–419. 10.1038/nmeth.4197 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 112.Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology,34(3), 525–527. 10.1038/nbt.3519 [DOI] [PubMed] [Google Scholar]
- 113.Putri, G. H., Anders, S., Pyl, P. T., Pimanda, J. E., & Zanini, F. (2022). Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics,38, 2943–2945. 10.1093/bioinformatics/btac166 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 114.Liao, Y., Smyth, G. K., & Shi, W. (2014). FeatureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics,30, 923–930. 10.1093/bioinformatics/btt656 [DOI] [PubMed] [Google Scholar]
- 115.Wolock, S. L., Lopez, R., & Klein, A. M. (2019). Scrublet: Computational identification of cell doublets in single-cell transcriptomic data. Cell Systems,8, 281-291.e9. 10.1016/j.cels.2018.11.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 116.McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019). DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Systems,8, 329-337.e4. 10.1016/j.cels.2019.03.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 117.Choudhary, S., & Satija, R. (2022). Comparison and evaluation of statistical error models for scRNA-seq. Genome Biology,23, Article 27. 10.1186/s13059-021-02584-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 118.Hafemeister, C., & Satija, R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology,20, 296. 10.1186/s13059-019-1874-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 119.Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018). Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nature Biotechnology,36, 421–427. 10.1038/nbt.4091 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 120.Hie, B., Bryson, B., & Berger, B. (2019). Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nature Biotechnology,37, 685–691. 10.1038/s41587-019-0113-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 121.Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., Teichmann, S. A., Marioni, J. C., & Stegle, O. (2015). Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature Biotechnology,33, 155–160. 10.1038/nbt.3102 [DOI] [PubMed] [Google Scholar]
- 122.Li, W. V., & Li, J. J. (2018). An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nature Communications,9, 997. 10.1038/s41467-018-03405-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 123.Maćkiewicz, A., & Ratajczak, W. (1993). Principal components analysis (PCA). Computers & Geosciences,19, 303–342. 10.1016/0098-3004(93)90090-R [Google Scholar]
- 124.Healy, J., & McInnes, L. (2024). Uniform manifold approximation and projection. Nature Reviews Methods Primers,4, 1–15. 10.1038/s43586-024-00363-x [Google Scholar]
- 125.McInnes, L., Healy, J., Melville, J. (2020). UMAP: Uniform manifold approximation and projection for dimension reduction. 10.48550/arXiv.1802.03426.
- 126.Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment,2008, Article P10008. 10.1088/1742-5468/2008/10/P10008 [Google Scholar]
- 127.Tang, M., Kaymaz, Y., Logeman, B. L., Eichhorn, S., Liang, Z. S., Dulac, C., & Sackton, T. B. (2021). Evaluating single-cell cluster stability using the Jaccard similarity index. Bioinformatics,37, 2212–2214. 10.1093/bioinformatics/btaa956 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 128.Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology,32, 381–386. 10.1038/nbt.2859 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 129.Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., Butte, A. J., & Bhattacharya, M. (2019). Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology,20, 163–172. 10.1038/s41590-018-0276-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 130.Li, H., Courtois, E. T., Sengupta, D., Tan, Y., Chen, K. H., Goh, J. J. L., Kong, S. L., Chua, C., Hon, L. K., Tan, W. S., Wong, M., Choi, P. J., Wee, L. J. K., Hillmer, A. M., Tan, I. B., Robson, P., & Prabhakar, S. (2017). Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nature Genetics,49, 708–718. 10.1038/ng.3818 [DOI] [PubMed] [Google Scholar]
- 131.Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016). A step-by-step workflow for low-level analysis of single-cell RNA-seq data with bioconductor. F1000research,5, Article 2122. 10.12688/f1000research.9501.2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 132.Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research,43, Article e47. 10.1093/nar/gkv007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 133.Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012). ClusterProfiler: An R package for comparing biological themes among gene clusters. Omics : A Journal of Integrative Biology,16, 284–287. 10.1089/omi.2011.0118 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 134.Li, B., & Dewey, C. N. (2011). Rsem: Accurate transcript quantification from rna-seq data with or without a reference genome. Bmc Bioinformatics,12, 323. 10.1186/1471-2105-12-323 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 135.Hänzelmann, S., Castelo, R., & Guinney, J. (2013). GSVA: Gene set variation analysis for microarray and RNA-Seq data. Bmc Bioinformatics,14, 7. 10.1186/1471-2105-14-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 136.Yu, G., & He, Q.-Y. (2016). ReactomePA: An R/Bioconductor package for reactome pathway analysis and visualization. Molecular Biosystems,12, 477–479. 10.1039/c5mb00663e [DOI] [PubMed] [Google Scholar]
- 137.Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020). Cell PhoneDB: Inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nature Protocols,15, 1484–1506. 10.1038/s41596-020-0292-x [DOI] [PubMed] [Google Scholar]
- 138.Shen, S., Park, J. W., Lu, Z., Lin, L., Henry, M. D., Wu, Y. N., Zhou, Q., & Xing, Y. (2014). rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proceedings of the National Academy of Sciences,111, E5593–E5601. 10.1073/pnas.1419161111 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 139.Song, Y., Botvinnik, O. B., Lovci, M. T., Kakaradov, B., Liu, P., Xu, J. L., & Yeo, G. W. (2017). Single-cell alternative splicing analysis with expedition reveals splicing dynamics during neuron differentiation. Molecular Cell,67, 148-161.e5. 10.1016/j.molcel.2017.06.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 140.Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., Trapnell, C., & Shendure, J. (2019). The single-cell transcriptional landscape of mammalian organogenesis. Nature,566, 496–502. 10.1038/s41586-019-0969-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 141.Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology,33, 495–502. 10.1038/nbt.3192 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 142.Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., Linsley, P. S., & Gottardo, R. (2015). MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biology,16, 278. 10.1186/s13059-015-0844-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 143.Zhang, Y., Ma, Y., Huang, Y., Zhang, Y., Jiang, Q., Zhou, M., & Su, J. (2020). Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data. Computational and Structural Biotechnology Journal,18, 2953–2961. 10.1016/j.csbj.2020.10.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 144.Feng, H., Lin, L., & Chen, J. (2022). ScDIOR: Single cell RNA-seq data IO software. BMC Bioinformatics,23, 16. 10.1186/s12859-021-04528-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 145.Wang, L., Wang, S., & Li, W. (2012). RSeQC: Quality control of RNA-seq experiments. Bioinformatics,28, 2184–2185. 10.1093/bioinformatics/bts356 [DOI] [PubMed] [Google Scholar]
- 146.McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017). Scater: Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics,33, 1179–1186. 10.1093/bioinformatics/btw777 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 147.Chen, Y., Chen, Y., Shi, C., Huang, Z., Zhang, Y., Li, S., Li, Y., Ye, J., Yu, C., Li, Z., Zhang, X., Wang, J., Yang, H., Fang, L., & Chen, Q. (2018). SOAPnuke: A MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience,7, Article gix120. 10.1093/gigascience/gix120 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 148.Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M., & Li, H. (2021). Twelve years of SAMtools and BCFtools. Gigascience,10, Article giab008. 10.1093/gigascience/giab008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 149.Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics,32, 3047–3048. 10.1093/bioinformatics/btw354 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 150.Smith, T., Heger, A., & Sudbery, I. (2017). UMI-tools: Modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Research,27, 491–499. 10.1101/gr.209601.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 151.Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., & Hellmann, I. (2018). zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs. Gigascience,7, Article giy059. 10.1093/gigascience/giy059 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 152.Wang, Z., Hu, J., Johnson, W. E., & Campbell, J. D. (2019). Scruff: An R/Bioconductor package for preprocessing single-cell RNA-sequencing data. Bmc Bioinformatics,20, 222. 10.1186/s12859-019-2797-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 153.Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013). Tophat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology,14, R36. 10.1186/gb-2013-14-4-r36 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 154.Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat. Bioinformatics,25, 1105–1111. 10.1093/bioinformatics/btp120 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 155.Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009). Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology,10, R25. 10.1186/gb-2009-10-3-r25 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 156.Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods,9, 357–359. 10.1038/nmeth.1923 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 157.Kim, D., Paggi, J. M., Park, C., Bennett, C., & Salzberg, S. L. (2019). Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology,37, 907–915. 10.1038/s41587-019-0201-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 158.Yip, S. H., Wang, P., Kocher, J.-P.A., Sham, P. C., & Wang, J. (2017). Linnorm: Improved statistical analysis for single cell RNA-seq expression data. Nucleic Acids Research,45, Article e179. 10.1093/nar/gkx828 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 159.van der Maaten, L., & Hinton, G. (2008). Viualizing data using t-SNE. Journal of Machine Learning Research,9, 2579–2605. [Google Scholar]
- 160.Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S., & Vert, J.-P. (2018). A general and flexible method for signal extraction from single-cell RNA-seq data. Nature Communications,9, 284. 10.1038/s41467-017-02554-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 161.Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: Guaranteeing well-connected communities. Scientific Reports,9, 5233. 10.1038/s41598-019-41695-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 162.Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., & Hemberg, M. (2017). SC3: Consensus clustering of single-cell RNA-seq data. Nature Methods,14, 483–486. 10.1038/nmeth.4236 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 163.Heaton, H., Talman, A. M., Knights, A., Imaz, M., Gaffney, D. J., Durbin, R., Hemberg, M., & Lawniczak, M. K. N. (2020). Souporcell: Robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nature Methods,17, 615–620. 10.1038/s41592-020-0820-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 164.Liu, Y., Wang, T., Zhou, B., & Zheng, D. (2021). Robust integration of multiple single-cell RNA sequencing datasets using a single reference space. Nature Biotechnology,39, 877–884. 10.1038/s41587-021-00859-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 165.Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019). Fast, sensitive and accurate integration of single-cell data with harmony. Nature Methods,16, 1289–1296. 10.1038/s41592-019-0619-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 166.Pliner, H. A., Shendure, J., & Trapnell, C. (2019). Supervised classification enables rapid annotation of cell atlases. Nature Methods,16, 983–986. 10.1038/s41592-019-0535-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 167.Li, C., Liu, B., Kang, B., Liu, Z., Liu, Y., Chen, C., Ren, X., & Zhang, Z. (2020). SciBet as a portable and fast single cell type identifier. Nature Communications,11, 1818. 10.1038/s41467-020-15523-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 168.Chen, Y., Chen, L., Lun, A. T. L., Baldoni, P. L., & Smyth, G. K. (2025). edgeR v4: Powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets. Nucleic Acids Research,53, gkaf018. 10.1093/nar/gkaf018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 169.Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., McDermott, M. G., Monteiro, C. D., Gundersen, G. W., & Ma’ayan, A. (2016). Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research,44, 90–97. 10.1093/nar/gkw377 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 170.Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 10.1186/s13059-014-0550-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 171.Langfelder, P., & Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics,9, 559. 10.1186/1471-2105-9-559 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 172.Fan, J., Salathia, N., Liu, R., Kaeser, G. E., Yung, Y. C., Herman, J. L., Kaper, F., Fan, J.-B., Zhang, K., Chun, J., & Kharchenko, P. V. (2016). Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nature Methods,13, 241–244. 10.1038/nmeth.3734 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 173.Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014). Bayesian approach to single-cell differential expression analysis. Nature Methods,11, 740–742. 10.1038/nmeth.2967 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 174.Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications,10, 1523. 10.1038/s41467-019-09234-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 175.Korotkevich, G., Sukhov, V., Budin, N., Shpak, B., Artyomov, M. (2016) A. Sergushichev, Fast gene set enrichment analysi. 10.1101/060012.
- 176.Zhang, B., Kirov, S., & Snoddy, J. (2005). WebGestalt: An integrated system for exploring gene sets in various biological contexts. Nucleic Acids Research,33, W741-748. 10.1093/nar/gki475 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 177.Ji, Z., Vokes, S. A., Dang, C. V., & Ji, H. (2016). Turning publicly available gene expression data into discoveries using gene set context analysis. Nucleic Acids Research,44, Article e8. 10.1093/nar/gkv873 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 178.Sherman, B. T., Hao, M., Qiu, J., Jiao, X., Baseler, M. W., Lane, H. C., Imamichi, T., & Chang, W. (2022). DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Research,50(W1), W216–W221. 10.1093/nar/gkac194 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 179.Jiang, A., Snell, R. G., & Lehnert, K. (2024). ICARUS v3, a massively scalable web server for single-cell RNA-seq analysis of millions of cells. Bioinformatics,40, Article btae167. 10.1093/bioinformatics/btae167 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 180.Law, V., Chen, Z., Vena, F., Smalley, I., Macaulay, R., Evernden, B. R., Tran, N., Pina, Y., Puskas, J., Caceres, G., Bayle, S., Johnson, J., Liu, J. K. C., Etame, A., Vogelbaum, M., Rodriguez, P., Duckett, D., Czerniecki, B., Chen, A., … Forsyth, P. A. (2022). A preclinical model of patient-derived cerebrospinal fluid circulating tumor cells for experimental therapeutics in leptomeningeal disease from melanoma. Neuro-Oncology. 10.1093/neuonc/noac054 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 181.Yang, Z., Bai, H., Wu, Q., Hu, L., Li, G., Kong, D., Zhang, Q., Wan, D., Gu, Z., Zhao, C., Zhang, K., Zhang, W., & Shou, J. (2022). Telomerase positive CTCs with PSMA high expression associated with prostate cancer metastasis. Translational Andrology and Urology,11, 803–813. 10.21037/tau-21-1140 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 182.Sun, Y., Li, T., Ding, L., Wang, J., Chen, C., Liu, T., Liu, Y., Li, Q., Wang, C., Huo, R., Wang, H., Tian, T., Zhang, C., Pan, B., Zhou, J., Fan, J., Yang, X., Yang, W., Wang, B., & Guo, W. (2025). Platelet-mediated circulating tumor cell evasion from natural killer cell killing through immune checkpoint CD155-TIGIT. Hepatology,81, 791–807. 10.1097/HEP.0000000000000934 [DOI] [PubMed] [Google Scholar]
- 183.Schmidt, M. J., Naghdloo, A., Prabakar, R. K., Kamal, M., Cadaneanu, R., Garraway, I. P., Lewis, M., Aparicio, A., Zurita-Saavedra, A., Corn, P., Kuhn, P., Pienta, K. J., Amend, S. R., & Hicks, J. (2025). Polyploid cancer cells reveal signatures of chemotherapy resistance. Oncogene,44, 439–449. 10.1038/s41388-024-03212-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 184.Arnault, J.-P., Chemmama, K., Ferroudj, K., Demagny, J., Panicot-Dubois, L., Galmiche, A., & Saidak, Z. (2025). The dynamic landscape of the coagulome of metastatic malignant melanoma. International Journal of Molecular Sciences,26, Article 1435. 10.3390/ijms26041435 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 185.Yang, H., Wang, H., He, Y., Yang, Y., Thompson, E. W., Xia, D., Burke, L. J., Cao, L., Hooper, J. D., Roberts, M. S., Crawford, D. H. G., & Liang, X. (2024). Identification and characterization of TM4SF1+ tumor self-seeded cells. Cell Reports. 10.1016/j.celrep.2024.114512 [DOI] [PubMed] [Google Scholar]
- 186.Nian, Z., Wang, D., Wang, H., Liu, W., Ma, Z., Yan, J., Cao, Y., Li, J., Zhao, Q., & Liu, Z. (2024). Single-cell RNA-seq reveals the transcriptional program underlying tumor progression and metastasis in neuroblastoma. Frontiers in Medicine,18, 690–707. 10.1007/s11684-024-1081-7 [DOI] [PubMed] [Google Scholar]
- 187.Suvilesh, K. N., Nussbaum, Y. I., Radhakrishnan, V., Manjunath, Y., Avella, D. M., Staveley-O’Carroll, K. F., Kimchi, E. T., Chaudhuri, A. A., Shyu, C.-R., Li, G., Pantel, K., Warren, W. C., Mitchem, J. B., & Kaifi, J. T. (2022). Tumorigenic circulating tumor cells from xenograft mouse models of non-metastatic NSCLC patients reveal distinct single cell heterogeneity and drug responses. Molecular Cancer,21, Article 73. 10.1186/s12943-022-01553-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 188.Zheng, W.-J., Wang, P.-X., Sun, Y.-F., Cheng, J.-W., Zhong, Y.-C., Xu, Y., Guo, W., Hu, B., Zhou, J., Fan, J., Chen, X., & Yang, X.-R. (2022). Uncovering the heterogeneity and clinical relevance of circulating tumor-initiating cells in hepatocellular carcinoma using an integrated immunomagnetic-microfluidic platform. ACS Applied Materials & Interfaces,14, 36425–36437. 10.1021/acsami.2c09085 [DOI] [PubMed] [Google Scholar]
- 189.Pang, T. C. Y., Xu, Z., Mekapogu, A. R., Pothula, S., Becker, T., Corley, S., Wilkins, M. R., Goldstein, D., Pirola, R., Wilson, J., & Apte, M. (2021). HGF/c-Met inhibition as adjuvant therapy improves outcomes in an orthotopic mouse model of pancreatic cancer. Cancers,13, Article 2763. 10.3390/cancers13112763 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 190.Sutton, T. L., Patel, R. K., Anderson, A. N., Bowden, S. G., Whalen, R., Giske, N. R., & Wong, M. H. (2022). Circulating cells with macrophage-like characteristics in cancer: The importance of circulating neoplastic-immune hybrid cells in cancer. Cancers,14, Article 3871. 10.3390/cancers14163871 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 191.Anderson, A. N., Conley, P., Klocke, C. D., Sengupta, S. K., Pang, A., Farley, H. C., Gillingham, A. R., Dawson, A. D., Fan, Y., Jones, J. A., Gibbs, S. L., Skalet, A. H., Wu, G., & Wong, M. H. (2024). Detection of neoplastic-immune hybrid cells with metastatic properties in uveal melanoma. Biomarker Research,12, 67. 10.1186/s40364-024-00609-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 192.Ali, A. M., & Raza, A. (2024). Scrnaseq and high-throughput spatial analysis of tumor and normal microenvironment in solid tumors reveal a possible origin of circulating tumor hybrid cells. Cancers,16, Article 1444. 10.3390/cancers16071444 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 193.Ye, X., Huang, X., Fu, X., Zhang, X., Lin, R., Zhang, W., Zhang, J., & Lu, Y. (2023). Myeloid-like tumor hybrid cells in bone marrow promote progression of prostate cancer bone metastasis. Journal of Hematology & Oncology,16, 46. 10.1186/s13045-023-01442-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 194.Minowa, T., Hirohashi, Y., Murata, K., Sasaki, K., Handa, T., Nakatsugawa, M., Mizue, Y., Murai, A., Kubo, T., Kanaseki, T., Tsukahara, T., Iwabuchi, S., Hashimoto, S., Ishida-Yamamoto, A., Uhara, H., & Torigoe, T. (2023). Fusion with type 2 macrophages induces melanoma cell heterogeneity that potentiates immunological escape from cytotoxic T lymphocytes. The Journal of Pathology,260, 304–316. 10.1002/path.6083 [DOI] [PubMed] [Google Scholar]
- 195.Menyailo, M. E., Zainullina, V. R., Khozyainova, A. A., Tashireva, L. A., Zolotareva, SYu. ., Gerashchenko, T. S., Alifanov, V. V., Savelieva, O. E., Grigoryeva, E. S., Tarabanovskaya, N. A., Popova, N. O., Choinzonov, E. L., Cherdyntseva, N. V., Perelmuter, V. M., & Denisov, E. V. (2023). Heterogeneity of circulating epithelial cells in breast cancer at single-cell resolution: Identifying tumor and hybrid cells. Advanced Biology,7, Article 2200206. 10.1002/adbi.202200206 [DOI] [PubMed] [Google Scholar]
- 196.Chen, L., Lee, J. W., Chou, C.-L., Nair, A. V., Battistone, M. A., Păunescu, T. G., Merkulova, M., Breton, S., Verlander, J. W., Wall, S. M., Brown, D., Burg, M. B., & Knepper, M. A. (2017). Transcriptomes of major renal collecting duct cell types in mouse identified by single-cell RNA-seq. Proceedings of the National Academy of Sciences of the United States of America,114, E9989–E9998. 10.1073/pnas.1710964114 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 197.Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., Fallahi-Sichani, M., Dutton-Regester, K., Lin, J.-R., Cohen, O., Shah, P., Lu, D., Genshaft, A. S., Hughes, T. K., Ziegler, C. G. K., … Garraway, L. A. (2016). Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-Seq. Science,352, 189–196. 10.1126/science.aad0501 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 198.Lee, H.-O., Hong, Y., Etlioglu, H. E., Cho, Y. B., Pomella, V., Van den Bosch, B., Vanhecke, J., Verbandt, S., Hong, H., Min, J.-W., Kim, N., Eum, H. H., Qian, J., Boeckx, B., Lambrechts, D., Tsantoulis, P., De Hertogh, G., Chung, W., Lee, T., … Park, W.-Y. (2020). Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. Nature Genetics,52, 594–603. 10.1038/s41588-020-0636-z [DOI] [PubMed] [Google Scholar]
- 199.Ma, L., Hernandez, M. O., Zhao, Y., Mehta, M., Tran, B., Kelly, M., Rae, Z., Hernandez, J. M., Davis, J. L., Martin, S. P., Kleiner, D. E., Hewitt, S. M., Ylaya, K., Wood, B. J., Greten, T. F., & Wang, X. W. (2019). Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer. Cancer Cell,36, 418-430.e6. 10.1016/j.ccell.2019.08.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 200.Laughney, A. M., Hu, J., Campbell, N. R., Bakhoum, S. F., Setty, M., Lavallée, V.-P., Xie, Y., Masilionis, I., Carr, A. J., Kottapalli, S., Allaj, V., Mattar, M., Rekhtman, N., Xavier, J. B., Mazutis, L., Poirier, J. T., Rudin, C. M., Pe’er, D., & Massagué, J. (2020). Regenerative lineages and immune-mediated pruning in lung cancer metastasis. Nature Medicine,26(2), 259–269. 10.1038/s41591-019-0750-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 201.Durante, M. A., Rodriguez, D. A., Kurtenbach, S., Kuznetsov, J. N., Sanchez, M. I., Decatur, C. L., Snyder, H., Feun, L. G., Livingstone, A. S., & Harbour, J. W. (2020). Single-cell analysis reveals new evolutionary complexity in uveal melanoma. Nature Communications,11, Article 496. 10.1038/s41467-019-14256-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 202.Hu, D. N., McCormick, S. A., Ritch, R., & Pelton-Henrion, K. (1993). Studies of human uveal melanocytes in vitro: Isolation, purification and cultivation of human uveal melanocytes. Investigative Ophthalmology & Visual Science,34, 2210–2219. [PubMed] [Google Scholar]
- 203.Pelka, K., Hofree, M., Chen, J. H., Sarkizova, S., Pirl, J. D., Jorgji, V., Bejnood, A., Dionne, D., Ge, W. H., Xu, K. H., Chao, S. X., Zollinger, D. R., Lieb, D. J., Reeves, J. W., Fuhrman, C. A., Hoang, M. L., Delorey, T., Nguyen, L. T., Waldman, J., … Hacohen, N. (2021). Spatially organized multicellular immune hubs in human colorectal cancer. Cell,184, 4734-4752.e20. 10.1016/j.cell.2021.08.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 204.Wu, S. Z., Al-Eryani, G., Roden, D., Junankar, S., Harvey, K., Andersson, A., Thennavan, A., Wang, C., Torpy, J., Bartonicek, N., Wang, T., Larsson, L., Kaczorowski, D., Weisenfeld, N. I., Uytingco, C. R., Chew, J. G., Bent, Z. W., Chan, C.-L., Gnanasambandapillai, V., … Swarbrick, A. (2021). A single-cell and spatially resolved atlas of human breast cancers. Nature Genetics,53, 1334–1347. 10.1038/s41588-021-00911-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 205.Kfoury, Y., Baryawno, N., Severe, N., Mei, S., Gustafsson, K., Hirz, T., Brouse, T., Scadden, E. W., Igolkina, A. A., Kokkaliaris, K., Choi, B. D., Barkas, N., Randolph, M. A., Shin, J. H., Saylor, P. J., Scadden, D. T., Sykes, D. B., & Kharchenko, P. V. (2021). Human prostate cancer bone metastases have an actionable immunosuppressive microenvironment. Cancer Cell,39, 1464-1478.e8. 10.1016/j.ccell.2021.09.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 206.Wang, Y., Tian, X., & Ai, D. (2021). Cell heterogeneity analysis in single-cell RNA-seq data using mixture exponential graph and Markov random field model. BioMed Research International,2021, Article 9919080. 10.1155/2021/9919080 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 207.Thong, T., Wang, Y., Brooks, M. D., Lee, C. T., Scott, C., Balzano, L., Wicha, M. S., & Colacino, J. A. (2020). Hybrid stem cell states: Insights into the relationship between mammary development and breast cancer using single-cell transcriptomics. Frontiers in Cell and Developmental Biology,8, 288. 10.3389/fcell.2020.00288 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 208.Guo, X., Lin, F., Yi, C., Song, J., Sun, D., Lin, L., Zhong, Z., Wu, Z., Wang, X., Zhang, Y., Li, J., Zhang, H., Liu, F., Yang, C., & Song, J. (2022). Deep transfer learning enables lesion tracing of circulating tumor cells. Nature Communications,13, 7687. 10.1038/s41467-022-35296-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 209.Del Giudice, M., Peirone, S., Perrone, S., Priante, F., Varese, F., Tirtei, E., Fagioli, F., & Cereda, M. (2021). Artificial intelligence in bulk and single-cell RNA-sequencing data to foster precision oncology. International Journal of Molecular Sciences,22(9), Article 4563. 10.3390/ijms22094563 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 210.Karaayvaz, M., Cristea, S., Gillespie, S. M., Patel, A. P., Mylvaganam, R., Luo, C. C., Specht, M. C., Bernstein, B. E., Michor, F., & Ellisen, L. W. (2018). Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq. Nature Communications,9, Article 3588. 10.1038/s41467-018-06052-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 211.Lee, Y., Guan, G., & Bhagat, A. A. (2018). ClearCell® FX, a label-free microfluidics technology for enrichment of viable circulating tumor cells. Cytometry Part A,93, 1251–1254. 10.1002/cyto.a.23507 [DOI] [PubMed] [Google Scholar]