Table 1 |.

Selected immuno-oncology insights from scRNA-seq-based analyses

Cancer type	Key findings	Single-cell technologies used	Cell types characterized	Ref.
Oligodendroglioma and astrocytoma	Microglia/macrophages are the predominant non-malignant cells in the TME with increased infiltration of these cells in IDH-mutant astrocytomas compared with IDH-mutant oligodendrogliomas	Smart-seq2 (plate-based)	Tumour cells, microglia and TAMs	22
Ovarian ascites	TCGA-based mesenchymal and immunoreactive subtypes of ovarian carcinoma might reflect the relative abundances of CAFs and macrophages, respectively, rather than differences in malignant cells	Smart-seq2, 10× Genomics (droplet-based)	Tumour cells, CAFs and immune cells	20
	Elements of the JAK/STAT signalling pathway are highly expressed in tumour cells and CAFs
Breast cancer	The presence of gene-expression signatures of three myofibroblastic CAF subpopulations at diagnosis is associated with resistance to anti-PD-1 antibodies	10× Genomics	CAFs	126
Pancreatic cancer	Used a published scRNA-seq dataset to characterize CAFs expressing LRRC15, then correlated the LRRC15+ CAF signature with a poor response to anti-PD-L1 antibodies	10× Genomics	CAFs	127
Breast cancer	Identified 38 distinct T cell, 27 myeloid cell, 9 B cell and 9 NK cell clusters	inDrop (droplet-based), 10× Genomics	CD45+ immune cells	28
	Immune cells in the TME have an increased ‘phenotypic volume’ compared with immune cells in non-malignant breast tissues, indicating increased levels of phenotypic heterogeneity
	Trajectory analysis revealed a continuum of T cell states along axes of activation, hypoxia and terminal differentiation
NSCLC	Based on CD8+ T cell phenotypes, higher ratios of progenitor exhausted cells to terminally exhausted cells were associated with a better prognosis	Smart-seq2	T cells	128
HCC	Increased expression of LAYN associated with exhausted tumour-infiltrating CD8+ T cells and a poor prognosis	Smart-seq2, 10× Genomics	T cells	129
Colon cancer	Defined trajectory from CD14+ monocytes through FCN1+ monocyte-like cells to two distinct TAM subpopulations: complement-enriched C1QC+ and pro-angiogenic SPP1+	Smart-seq2, 10× Genomics	TAMs and dendritic cells	130
	High SPP1+/low C1QC+ TAM signature associated with a poor prognosis
B-ALL	Characterized remodelling of the myeloid compartment at diagnosis including an increase in non-classic CD16− monocyte subpopulations associated with unfavourable survival	10× Genomics	Myeloid cells (all immune cells included in sequencing)	131
Melanoma, HNSCC, NSCLC	Identified TAMs as the predominant cell type responsible for producing the CXCR3 ligands CXCL9, CXCL10 and CXCL11	Published data (Smart-seq2, 10× Genomics, inDrop)	TAMs	132
	Implicated macrophages with elevated levels of CXCL10 and CXCL11 at baseline with response to immune-checkpoint inhibitors in patients with melanoma using published scRNA-seq datasets

The capabilities of single-cell RNA sequencing (scRNA-seq) technologies that use end counting (such as 10× Genomics) versus whole-transcript sequencing (such as Smart-seq2) have been reviewed in detail elsewhere^3,12,133. B-ALL, B cell acute lymphoblastic leukaemia; CAFs, cancer-associated fibroblasts; HCC, hepatocellular carcinoma; HNSCC, head and neck squamous cell carcinoma; NK, natural killer; NSCLC, non-small-cell lung cancer; TAMs, tumour-associated macrophages; TCGA, The Cancer Genome Atlas; TME, tumour microenvironment.