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Page 10 of 19 Shi et al. J Cancer Metastasis Treat 2018;4:47 I http://dx.doi.org/10.20517/2394-4722.2018.32

Table 2. Overview of single-cell studies on analyzing ITH
Tumor type Sample type Method Description Ref.
Colorectal cancer CTC DNA-seq Mutation profiling, clonal evolution [55]
Prostate cancer CTC DNA-seq Genetic lineage [58]
Breast cancer CTC RNA-seq Transcriptome profiling [72]
Breast cancer Primary tumor DNA-seq Clonal diversity [75]
Melanoma CTC RNA-seq Transcriptome profiling [83]
Leukemia Primary tumor DNA-seq Mutation profiling, clonal evolution [97]
Glioblastoma multiforme Primary tumor RNA-seq Clonal evolution [106]
Acute myeloid leukemia Primary tumor DNA-seq Mutation profiling, clonal evolution [105]
Breast cancer Primary tumor DNA-seq Copy number evolution, clonal evolution [74]
Breast cancer Primary tumor DNA-seq Copy number evolution, clonal evolution [77]
Acute myeloid leukemia Primary tumor DNA-seq Clonal evolution [109]
Kidney cancer Primary tumor DNA-seq Mutation profiling [76]
Bladder cancer Primary tumor DNA-seq Mutation profiling, clonal evolution [110]
Colon cancer Primary tumor DNA-seq Clonal evolution [111]
Acute myeloid leukemia Primary tumor DNA-seq Clonal evolution [112]
Chronic lymphocytic leukemia Primary tumor DNA-seq, Genotype-phenotype relationship [113]
RNA-seq clonal evolution, mutation profiling
Lung cancer CTC DNA-seq Copy number evolution [56]
Pancreatic ductal adenocarcinoma CTC RNA-seq Phenotype characterization [115]
Glioblastoma Primary tumor RNA-seq Transcriptional profiling, [43]
phenotype characterization
Glioblastoma Primary tumor DNA-seq EGFR evolution [116]
B cell leukemia Primary tumor DNA-seq Karyotype heterogeneity [117]
Myeloproliferative neoplasm Primary tumor DNA-seq Mutation profiling, clonal evolution [78]
Melanoma CTC DNA-seq Mutation profiling, copy number evolution [118]
Breast cancer CTC RNA-seq Transcriptome profiling [120]
Various cancers Primary tumor RNA-seq TCR repertoire analysis [124,126]
Liver cancer Primary tumor RNA-seq Characterization of T cell functional states [130]
Breast cancer Primary tumor RNA-seq Tumor microenvironment characterization [132]
Prostate cancer CTC RNA-seq Heterogeneity in signaling pathways [136]
Prostate cancer CTC DNA-seq Copy number evolution [137]
Breast cancer Primary tumor DNA-seq, Clonal evolution, transcriptome profiling [32]
RNA-seq

ITH: intratumoral heterogeneity; CTC: circulating tumor cell

plification are ongoing . A novel technique termed Drop-seq uses the microfluidic chamber to isolate single
[99]
cells followed by labeling RNA of individual cells with a different barcode, allowing pooling of cDNA during
sequencing thereby greatly improving the multiplexing efficiency [100] . Applying Drop-seq to mouse retinal
bipolar cells resulted in the identification of different types of neurons by matching molecular expression to
cell morphology [101] . A similar technique was commercialized by 10× Genomics Inc [Figure 4Cii] in 2016.
The 10x platform applies unique barcodes to separately index each cell by partitioning thousands of cells
into Gel Bead-in-Emulsions. Libraries are generated and sequenced and the 10x barcodes are used to associ-
ate individual reads back to the individual cells. The platform can profile up to 10,000 cells from a complex
mixture of different cell types.

APPLICATIONS OF SINGLE-CELL SEQUENCING
Recent technical advances have enabled generation of unprecedented amount of information on genomics
and transcriptomics at the single-cell level [Table 2]. Compared to bulk transcriptomics data obtained from
tumor tissues, single-cell RNA-seq allows capturing of the gene expression profile from individual cells of
heterogenous origin, which is a significant advantage over bulk sequencing that captures the average gene
expression of a sample. Secondly, for the samples with limited amount of material, single-cell analysis is a
good alternative to characterize the genotype. Taking CTCs for an example, mutations identified in CTCs