Table 2.

Overview of available single-cell technologies used to study drug resistance in heterogeneous disorders.

Technology	Purpose	Ref
Targeted drugs
cDNA-seq	Single-cell DNA sequencing (scDNA-seq) was performed on 510 circulating tumor cells and 189 leukocytes. Microheterogeneity analysis of individual CTCs discerned there existed cells prior to drug exposure that was resistant to ERBB2-targeted therapies.	[40]
scRNA-seq bulk ATAC-seq	Single-cell RNA-sequencing (scRNA-seq) of paired drug naïve and resistant acute myeloid leukemia patient samples highlighted regulators of enhancer function as important modulators of the resistant cell state. The inhibition of Lsd1 facilitated the binding of the pioneer factor and cofactor to nucleate new enhancers, overcoming stable epigenetic-derived resistance.	[45]
CROP-seq	Pooled CRISPR screening was combined with single-cell RNA sequencing to facilitate high-throughput functional dissection of complex regulatory mechanisms and heterogeneous cell populations. ETS1, RUNX1, and GATA3 were found to be essential for Jurkat T-cell function.	[66]
Single-cell FISH	Single-cell FISH visualized transcriptional variability at the single-cell level which was used to predict drug resistance development. It was found that the addition of drugs induces epigenetic reprogramming in certain cells, converting a transient transcriptional state to a stable one. Reprogramming began with a loss of SOX70-mediated differentiation followed by activation of new signaling pathways, partially mediated by Jun-AP-1 and TEAD transcription factors.	[43]
sci-Plex scRNA-seq	‘Nuclear hashing’ was used to quantify global transcriptional responses in thousands of independent perturbations at a single-cell resolution. sci-Plex was employed to screen three cancer cell lines exposed to 188 compounds. Approximately 650,000 single-cell transcriptomes across ~5,000 independent samples were profiled in one experiment. The similarity in single-cell transcriptomes treated with distinct compounds highlighted drugs that target convergent molecular pathways.	[35]
scRNA-seq	A candidate tumor cell subgroup associated with anti-cancer drug resistance was identified using scRNA-seq on viable patient-derived xenograft (PDX) cells. 50 tumor-specific single-nucleotide variations were observed to be heterogeneous in individual PDX cells after performing scRNA-seq on 34 PDX tumor cells from a lung adenocarcinoma patient. PDX cells that survived in vitro anti-cancer drug treatment displayed transcriptome signatures consistent with the group characterized by KRAS(G12D).	[39]
scRNA-seq	4645 cells isolated from 19 patients, including malignant, immune, stromal, and endothelial cells, were profiled using scRNA-seq. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with drug-resistance. Analysis of tumor-infiltrating T-cells revealed exhaustion mechanisms were connected to T cell activation and clonal expansion, and their variability across patients.	[41]
RT–qPCR	Single-cell RT-qPCR of the luminal-type breast cancer cell line MCF7 and its derivatives, including docetaxel-resistant cells identified that in the drug-resistant cells, epithelial-to-mesenchymal transition and stemness-related genes were upregulated and cell-cycle-related genes were downregulated. Both were primarily regulated by LEF1.	[46]
Microfluidic Platform	An integrated microfluidic platform was built to construct single-cell arrays that could analyze drug resistance. A proof-of-concept experiment was implemented by determining the vincristine resistance of single glioblastoma cells with different biomechanical properties. The results indicated that the biomechanics of tumor cells has significant implications for cell drug resistance	[87]
MULTI-seq	MULTI-seq: multiplexing using lipid-tagged indices for single-cell and single-nucleus RNA sequencing. MULTI-seq reagents were shown to be able to barcode any cell type or nucleus from any species so long as there was an accessible plasma membrane.	[88]
Single-cell barcoding	Transient transfection with short barcode oligonucleotides simultaneously analyzed multiple samples with scRNA-seq. The accuracy of the method was validated and its ability to identify multiplets and negatives was confirmed by analyzing samples from a 48-plex drug treatment experiment.	[74]
Immunotherapy
TCR-seq scRNA-seq	By performing single-cell RNA sequencing on 5,063 single T-cells and coupled TCR-seq, 11 T-cell subsets were distinguished based on their molecular and functional properties which also delineated their developmental trajectory. The gene layilin, which was found to be upregulated on both activated CD8+ T-cells and Tregs, represses the CD8+ T-cell functions in vitro.	[58]
LIBRA-seq (scBCR-seq)	The antigen specificity of thousands of B-cells from two HIV-infected subjects was mapped. The predicted antigen specificities were confirmed for a number of HIV- and influenza-specific antibodies.	[60]
RAGE-seq (scTCR-seq+scBCR-seq)	7138 cells sampled from the primary tumor and draining lymph node of breast cancer were used to infer B-cell clonal evolution and identify alternatively spliced BCR transcripts.	[61]
Perturb-seq	Perturb-seq was performed on 200,000 immune cells, identifying transcription factors that regulate the response of dendritic cells to lipopolysaccharide. Perturb-seq was shown to accurately identify individual gene targets, gene signatures, and cell states affected by both individual perturbations and their genetic interactions.	[67]
scRNA-seq	By combing single-cell RNA-seq and T cell receptor (TCR) analysis, TCR signal intensity was found to not affect resting/activated Treg proportions but activated Treg programs	[57]