Table 2.

Summary table of main results from selected scRNA-seq studies classifying cell types from different tumors.

Source of Cells Assayed	Seq. Method	Number of Cells	Key Results	Ref.
Circulating tumor cells (CTCs) from breast cancer patient	Hydro-seq	666	Identified the cells based on expression of ER, PR, and HER2 which could act as biomarkers	[19]
Human renal tumors and normal tissue from fetal, pediatric, and adult kidneys	-	72,501	Identified total 110 subtypes of cells	[21]
Primary glioblastomas cells from patients	SMART-seq	430	Cells from each tumor patients demonstrate higher overall intratumoral coherence, and several cells showed positive correlations with cells from other tumors	[20]
Breast cancer cells from patients	Tru-seq	515	Identified 11 clusters, mixture of tumor cells and immune cells	[61]
T-cells that were isolated from peripheral blood, tumor tissue, and adjacent healthy tissue from hepatocellular carcinoma patients	Smart-seq2	5063	Eleven subpopulations of T-cells were identified based on their molecular and functional properties	[65]
Primary PDAC tumors and control pancreases	-	57,530	Identified 10 main clusters (type 1 ductal, type 2 ductal, acinar, endocrine, endothelial, fibroblast, stellate, macrophage, and T and B cells)	[66]
Neuroblastoma cells from donor patients and cell lines	ChIP-seq	-	Three heterogeneous cell types in neuroblastoma cell lines: (i) sympathetic noradrenergic cells, (ii) neural crest cells, and (iii) a mixed type	[67]
Bone marrow aspirates from AML patients and healthy donors	Seq-Well	38,410	Differentiated monocyte-like AML cells expressed diverse immunomodulatory genes	[68]