• biomarker
• machine learning and deep learning
• multi‐omics
• neurodegenerative disease
• precision medicine

• Natural Science Foundation of China

Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

Summary: We propose quantitative methods to analyze cancer sc-RNAseq data: measures of intra-tumoral heterogeneity, characterization of a hierarchy of gene clusters, and alignment of gene cluster hierarchies from multiple datasets.

• Bulk level RNA-sequencing
• Clustering
• Pan-cancer analysis
• Single-cell RNA-sequencing
• Tumor heterogeneity

• early-stage lung cancer
• immune evasion
• immune-editing
• single-cell sequencing technology
• tumor immunology
• tumorigenesis

Non-small cell lung cancer (NSCLC) is one of the main causes of cancer-related deaths worldwide. Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). Human NSCLC adenocarcinoma cells A549, H1975, and H1650 were studied at single-cell resolution for the expression pattern of 13 markers: GLUT1, MCT4, CA9, TMEM45A, CD66, CD274, CD24, CD326, pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The intra- and inter-cell-line heterogeneity of A549, H1975, and H1650 cells were demonstrated through hypoxic modeling. Additionally, human primary lung adenocarcinoma, and non-involved healthy lung tissue were homogenized to prepare a single-cell suspension for CyTOF analysis. The single-cell heterogeneity was confirmed using unsupervised viSNE and FlowSOM analysis. Our results also show, for the first time, that TMEM45A is expressed in lung adenocarcinoma.

Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). First, we studied the single-cell heterogeneity of frequent NSCLC adenocarcinoma models, such as A549, H1975, and H1650. The intra- and inter-cell-line single-cell heterogeneity is represented in the expression patterns of 13 markers—namely GLUT1, MCT4, CA9, TMEM45A, CD66, CD274 (PD-L1), CD24, CD326 (EpCAM), pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The qRT-PCR and CyTOF analyses revealed that a hypoxic microenvironment and altered metabolism may influence cell-line heterogeneity. Additionally, human primary lung adenocarcinoma and non-involved healthy lung tissue biopsies were homogenized to prepare a single-cell suspension for CyTOF analysis. The CyTOF showed the ITH of human primary lung adenocarcinoma for 14 markers; particularly, the higher expressions of GLUT1, MCT4, CA9, TMEM45A, and CD66 were associated with the lung-tumor tissue. Our single-cell results are the first to demonstrate TMEM45A expression in human lung adenocarcinoma, which was verified by immunohistochemistry.

• Cancer genomics
• Colorectal cancer
• Intra-tumor heterogeneity
• Single-cell sequencing
• Tumorigenesis

• Animals
• Colorectal Neoplasms/genetics
• DNA
• Exome/genetics
• Genetic Heterogeneity
• Mice
• RNA
• Sequence Analysis, RNA
• Tumor Microenvironment

• Ministry of Education, Culture, Sports, Science and Technology
• Japan Agency for Medical Research and Development
• National Cancer Center Research and Development Funds
• the Foundation for the Promotion of Cancer Research
• the Kato Memorial Bioscience Foundation

• ADT
• CRPC-Adeno
• CRPC-NE
• EGFR
• STAT3

• carcinoma
• carcinoma stem cells
• single-cell RNA sequencing
• tumor microenvironment
• tumor resistance

• Circulating tumor cells
• Heterogeneity
• Microenvironment
• Single-cell sequencing

• Animals
• Epigenesis, Genetic
• Gene Expression Profiling/methods
• Genomics/methods
• Humans
• Neoplasms/genetics
• Neoplasms/pathology
• Neoplastic Cells, Circulating/metabolism
• Neoplastic Cells, Circulating/pathology
• Sequence Analysis, DNA/methods
• Sequence Analysis, RNA/methods
• Single-Cell Analysis/methods
• Transcriptome

• National Natural Science Foundation of China
• National Natural Science Foundation of China
• National Science Foundation for Distinguished Young Scholars of China
• Shanghai Sailing Program
• Shanghai Sailing Program
• Shanghai Rising-Star Program
• Shanghai Anticancer Association Young Eagle Program
• Clinical and Scientific Innovation Project of Shanghai Hospital Development Center
• Scientific Innovation Project of Shanghai Education Committee
• Fudan University Shanghai Medical College Qing Feng Scholar Project

• Humans
• Lung Neoplasms/drug therapy
• Lung Neoplasms/genetics
• Precision Medicine
• Sequence Analysis, RNA
• Exome Sequencing

• HIV
• genome
• humanized mouse
• multiomics
• transcriptome

Single-cell sequencing (SCS) which has an unprecedentedly high resolution is an advanced technique for cancer research. Lung cancer still has a high mortality and morbidity. For further understanding the lung cancer, SCS is also been applied to lung cancer research to investigate its heterogeneity, metastasis, drug resistance, tumor microenvironment and many other issues. In this review, we summarized lung cancer research using SCS and their research achievements.

• intratumoral heterogeneity
• lung cancer
• lung cancer therapy
• single-cell sequencing
• tumor microenvironment

• Development
• Disease
• Organoids
• Single-cell sequencing

• Heterozygous variant calling
• Human articular chondrocytes
• Red panda
• Single cell sequencing
• Variant calling using scRNAseq

• Animals
• Fibroblasts
• High-Throughput Nucleotide Sequencing
• Mice
• Polymorphism, Single Nucleotide
• Sequence Analysis, RNA
• Single-Cell Analysis
• Software
• Exome Sequencing

• K00 CA223064 NCI NIH HHS
• P20GM103427 NIGMS NIH HHS
• 2P01AG029531 NIA NIH HHS
• P30 CA036727 NCI NIH HHS
• R01 AR070242 NIAMS NIH HHS
• P30CA036727 NCI NIH HHS
• 1P30GM110768 NIGMS NIH HHS
• P30 GM110768 NIGMS NIH HHS
• P30 MH062261 NIMH NIH HHS
• P01 AG029531 NIA NIH HHS
• F99 CA223064 NCI NIH HHS
• National Institute on Aging
• National Institute of General Medical Sciences
• National Cancer Institute

Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.

Combining data from different single-cell sequencing techniques could greatly improve understanding of the molecular profiles associated with disease. Sequencing studies provide valuable insights into diseased and healthy states at a single-cell level, for example the evolutionary paths of brain tumors and cancerous mutations. Ayako Suzuki at the University of Tokyo in Chiba, Japan, and co-workers examined the challenges of integrating data from various experimental and computational single-cell sequencing methods. These methods usually determine the genomic, epigenomic (DNA modifications) or transcriptomic (messenger RNAs) state of a cell, and can be combined to create a detailed picture. Other ‘multiomics’ techniques provide multilayered information from the same cell. The researchers recommend detailed analysis of individual data layers prior to integration, and highlight emerging techniques that analyze larger tissue sections, thus retaining the temporal and spatial information around a cell.

Lung cancer is an extremely heterogeneous disease, and its treatment remains one of the most challenging tasks in medicine. Few existing laboratory lung cancer models can faithfully recapitulate the diversity of the disease and predict therapy response. Here, we establish 12 patient-derived organoids from the most common lung cancer subtype, lung adenocarcinoma (LADC). Extensive gene and histopathology profiling show that the tumor organoids retain the histological architectures, genomic landscapes, and gene expression profiles of their parental tumors. Patient-derived lung cancer organoids are amenable for biomarker identification and high-throughput drug screening in vitro. This study should enable the generation of patient-derived lung cancer organoid lines, which can be used to further the understanding of lung cancer pathophysiology and to assess drug response in personalized medicine.

•

Generation of a living biobank of patient-derived lung adenocarcinoma organoids

• Biotechnology
• Cancer
• Tissue Engineering

• cancer microenvironment
• data integration
• immunophenotyping
• mass cytometry
• multi-omics
• single-cell
• spatial analysis

• Cancer stem cell
• Epigenetic therapeutics
• Glioblastoma multiforme
• Histone deacetylase inhibitors

• Combination drug therapy
• High-throughput screening
• Pharmacogenetics

• cancer immunotherapy
• drug-resistance
• multi-omics
• single-cell
• tumor heterogeneity

The drug response of single polar cells was evaluated via single cell trapping on anisotropic microwells for tumor heterogeneity.

Genome graph is an emerging approach for representing structural variants on genomes with branches. For example, representing structural variants of cancer genomes as a genome graph is more natural than representing such genomes as differences from the linear reference genome. While more and more structural variants are being identified by long-read sequencing, many of them are difficult to visualize using existing structural variants visualization tools. To this end, visualization method for large genome graphs such as human cancer genome graphs is demanded.

We developed MOdular Multi-scale Integrated Genome graph browser, MoMI-G, a web-based genome graph browser that can visualize genome graphs with structural variants and supporting evidences such as read alignments, read depth, and annotations. This browser allows more intuitive recognition of large, nested, and potentially more complex structural variations. MoMI-G has view modules for different scales, which allow users to view the whole genome down to nucleotide-level alignments of long reads. Alignments spanning reference alleles and those spanning alternative alleles are shown in the same view. Users can customize the view, if they are not satisfied with the preset views. In addition, MoMI-G has Interval Card Deck, a feature for rapid manual inspection of hundreds of structural variants. Herein, we describe the utility of MoMI-G by using representative examples of large and nested structural variations found in two cell lines, LC-2/ad and CHM1.

Users can inspect complex and large structural variations found by long-read analysis in large genomes such as human genomes more smoothly and more intuitively. In addition, users can easily filter out false positives by manually inspecting hundreds of identified structural variants with supporting long-read alignments and annotations in a short time.

MoMI-G is freely available at https://github.com/MoMI-G/MoMI-G under the MIT license.

• Genome browser
• Genome graphs
• Long-read sequencing
• Structural variant
• Variation graphs
• Visualization

DBTSS (Database of Transcriptional Start Sites)/DBKERO (Database of Kashiwa Encyclopedia for human genome mutations in Regulatory regions and their Omics contexts) is the database originally initiated with the information of transcriptional start sites and their upstream transcriptional regulatory regions. In recent years, we updated the database to assist users to elucidate biological relevance of the human genome variations or somatic mutations in cancers which may affect the transcriptional regulation. In this update, we facilitate interpretations of disease associated genomic variation, using the Japanese population as a model case. We enriched the genomic variation dataset consisting of the 13,368 individuals collected for various genome-wide association studies and the reference epigenome information in the surrounding regions using a total of 455 epigenome datasets (four tissue types from 67 healthy individuals) collected for the International Human Epigenome Consortium (IHEC). The data directly obtained from the clinical samples was associated with that obtained from various model systems, such as the drug perturbation datasets using cultured cancer cells. Furthermore, we incorporated the results obtained using the newly developed analytical methods, Nanopore/10x Genomics long-read sequencing of the human genome and single cell analyses. The database is made publicly accessible at the URL (http://dbtss.hgc.jp/).

Esophageal squamous cell carcinoma (ESCC) cells are heterogeneous, easily develop radioresistance, and recur. Single-cell RNA-seq (scRNA-seq) is a next-generation sequencing method that can delineate diverse gene expression profiles of individual cells and mining their heterogeneous behaviors in response to irradiation. Our aim was using scRNA-seq to describe the difference between parental cells and cells that acquired radioresistance, and to investigate the dynamic changes of the transcriptome of cells in response to FIR.

We sequenced ESCC cell lines KYSE180 with and without fractionated irradiation (FIR). A total of 218 scRNA-seq libraries were obtained from 88 cells exposed to 12 Gy (KYSE-180-12 Gy), 89 exposed to 30 Gy (KYSE-180-30 Gy), and 41 parental KYSE-180 cells not exposed to FIR. Dynamic gene expression patterns were determined by comprehensive consideration of genes and pathways. Biological experiments showed that KYSE-180 cells became radioresistant after FIR. PCA analysis of scRNA-seq data showed KYSE-180, KYSE-180-12 Gy and KYSE-180-30 Gy cells were discrete away from each other. Two sub-populations found in KYSE-180-12 Gy and only one remained in KYSE-180-30 Gy. This sub-population genes exposure to FIR through 12 Gy to 30 Gy were relevant to the PI3K-AKT pathway, pathways evading apoptosis, tumor cell migration, metastasis, or invasion pathways, and cell differentiation and proliferation pathways. We validated DEGs, such as CFLAR, LAMA5, ITGA6, ITGB4, and SDC4 genes, in these five pathways as radioresistant genes in bulk cell RNA-seq data from ESCC tissue of a ESCC patient treated with radiotherapy and from KYSE-150 cell lines.

Our results delineated the divergent gene expression patterns of individual ESCC cells exposure to FIR, and displayed genes and pathways related to development of radioresistance.

The online version of this article (10.1186/s12864-019-5970-0) contains supplementary material, which is available to authorized users.

• Esophageal squamous cell carcinoma
• Gene expression
• Radioresistance
• Single-cell RNA-seq
• Transcriptome

• Anther dehiscence
• Arabidopsis thaliana
• Ovule
• mARF6
• mARF8
• miR167

• circulating tumor cell
• omic technologies
• precision medicine
• single cell
• tumor heterogeneity

• Animals
• Drug Resistance, Neoplasm/genetics
• Ecosystem
• Genomics/methods
• Humans
• Neoplasms/genetics
• Precision Medicine/methods
• Transcriptome/genetics

• Bioinformatics
• Immunology
• Immunotherapy
• Oncology

• Adenocarcinoma of Lung/drug therapy
• Adenocarcinoma of Lung/genetics
• Adenocarcinoma of Lung/immunology
• Adenocarcinoma of Lung/mortality
• Antineoplastic Agents, Immunological/pharmacology
• Antineoplastic Agents, Immunological/therapeutic use
• Antineoplastic Combined Chemotherapy Protocols/pharmacology
• Antineoplastic Combined Chemotherapy Protocols/therapeutic use
• Cell Line, Tumor
• Datasets as Topic
• Down-Regulation
• Female
• Gene Expression Regulation, Neoplastic/drug effects
• Gene Expression Regulation, Neoplastic/immunology
• Genes, MHC Class II/immunology
• Genetic Heterogeneity
• Humans
• Interferon-gamma/immunology
• Interferon-gamma/metabolism
• Kaplan-Meier Estimate
• Lung Neoplasms/drug therapy
• Lung Neoplasms/genetics
• Lung Neoplasms/immunology
• Lung Neoplasms/mortality
• Male
• Middle Aged
• Precision Medicine/methods
• Prognosis
• RNA-Seq
• Signal Transduction/drug effects
• Signal Transduction/genetics
• Signal Transduction/immunology
• Single-Cell Analysis/methods
• Treatment Outcome
• Tumor Escape/drug effects
• Tumor Escape/genetics
• Tumor Microenvironment/genetics
• Tumor Microenvironment/immunology
• Exome Sequencing

• R00 AG040149 NIA NIH HHS
• T32 EB007507 NIBIB NIH HHS
• R33 CA225539 NCI NIH HHS
• Welch Foundation
• National Natural Science Foundation of China

• Chromium
• Computational approach
• Imputation
• Single-cell sequencing
• scRNA-seq

• C1 system
• Cancer
• Fusion gene
• MDA
• SNP
• SNV
• Smart-Seq
• scExome-Seq
• scRNA-Seq
• scWGS

• Drug Resistance, Neoplasm
• Neoplasms/chemistry
• Neoplasms/genetics
• Sequence Analysis
• Single-Cell Analysis
• Tumor Microenvironment

• Key Technologies R&D Program (China)
• National Natural Science Foundation of China

Biomarker-driven individualized treatment in oncology has made tremendous progress through technological developments, new therapeutic modalities and a deeper understanding of the molecular biology for tumors, cancer stem cells and tumor-infiltrating immune cells. Recent technical developments have led to the establishment of a variety of cancer-related diagnostic, prognostic and predictive biomarkers. In this regard, different modern OMICs approaches were assessed in order to categorize and classify prognostically different forms of neoplasia. Despite those technical advancements, the extent of molecular heterogeneity at the individual cell level in human tumors remains largely uncharacterized. Each tumor consists of a mixture of heterogeneous cell types. Therefore, it is important to quantify the dynamic cellular variations in order to predict clinical parameters, such as a response to treatment and or potential for disease recurrence. Recently, single-cell based methods have been developed to characterize the heterogeneity in seemingly homogenous cancer cell populations prior to and during treatment. In this review, we highlight the recent advances for single-cell analysis and discuss the challenges and prospects for molecular characterization of cancer cells, cancer stem cells and tumor-infiltrating immune cells.

• Cancer cells
• Cancer stem cells
• Cancer biomarkers
• Tumor-infiltrating lymphocytes
• Single-cell analysis

• Biomarkers, Tumor
• Genomics/methods
• Humans
• Molecular Targeted Therapy/methods
• Precision Medicine/methods
• Surgical Oncology/methods

Cancers acquire resistance to systemic treatment with platinum‐based chemotherapy (eg, cisplatin [CDDP]) as a result of a dynamic intratumoral heterogeneity (ITH) and clonal repopulation. However, little is known about the influence of chemotherapy on ITH at the single‐cell level. Here, mapping the transcriptome of cancers treated with CDDP by scRNA‐seq, we uncovered a novel gene, COX7B, associated with platinum‐resistance, and surrogate marker, CD63. Knockdown of COX7B in cancer cells decreased the sensitivity of CDDP whereas overexpression recovered the sensitivity of CDDP. Low COX7B levels correlated with higher mortality rates in patients with various types of cancer and were significantly associated with poor response to chemotherapy in urinary bladder cancer. Tumor samples from patients, who underwent CDDP therapy, showed decreased COX7B protein levels after the treatment. Analyzing scRNA‐seq data from platinum‐naïve cancer cells demonstrated a low‐COX7B subclone that could be sorted out from bulk cancer cells by assaying CD63. This low‐COX7B subclone behaved as cells with acquired platinum‐resistance when challenged to CDDP. Our results offer a new transcriptome landscape of platinum‐resistance that provides valuable insights into chemosensitivity and drug resistance in cancers, and we identify a novel platinum resistance gene, COX7B, and a surrogate marker, CD63.

• CD63
• COX7B
• platinum resistance
• single-cell RNA-seq
• tumor heterogeneity

Recent technological advances in single-cell genomics make it possible to analyze cellular heterogeneity of tumor samples. Here, we applied single-cell RNA-seq to measure the transcriptomes of 307 single cells cultured from three biopsies of three different patients with a BRAF/NRAS wild type, BRAF mutant/NRAS wild type and BRAF wild type/NRAS mutant melanoma metastasis, respectively. Analysis based on self-organizing maps identified sub-populations defined by multiple gene expression modules involved in proliferation, oxidative phosphorylation, pigmentation and cellular stroma. Gene expression modules had prognostic relevance when compared with gene expression data from published melanoma samples and patient survival data. We surveyed kinome expression patterns across sub-populations of the BRAF/NRAS wild type sample and found that CDK4 and CDK2 were consistently highly expressed in the majority of cells, suggesting that these kinases might be involved in melanoma progression. Treatment of cells with the CDK4 inhibitor palbociclib restricted cell proliferation to a similar, and in some cases greater, extent than MAPK inhibitors. Finally, we identified a low abundant sub-population in this sample that highly expressed a module containing ABC transporter ABCB5, surface markers CD271 and CD133, and multiple aldehyde dehydrogenases (ALDHs). Patient-derived cultures of the BRAF mutant/NRAS wild type and BRAF wild type/NRAS mutant metastases showed more homogeneous single-cell gene expression patterns with gene expression modules for proliferation and ABC transporters. Taken together, our results describe an intertumor and intratumor heterogeneity in melanoma short-term cultures which might be relevant for patient survival, and suggest promising targets for new treatment approaches in melanoma therapy.

• melanoma
• single cell transcriptome sequencing
• stem cells

• PIMT
• endoplasmic reticulum stress
• epithelial mesenchymal transition
• hypoxia-inducible factor 1α
• lung adenocarcinoma

• Aurora Kinase A/genetics
• Cell Line, Tumor
• Dual Specificity Phosphatase 1/genetics
• Gene Expression Regulation, Neoplastic
• High-Throughput Nucleotide Sequencing
• Humans
• Neoplasms/diagnosis
• Neoplasms/genetics
• Neoplasms/pathology
• Single-Cell Analysis/methods
• Transcriptome/genetics

• Breast cancer
• HER2 positive
• Logistic regression
• Single cell sequencing

• COPD
• Cancer
• Diagnosis
• Lung
• Single cell

• Cancer
• Cancer stem cells
• Circulating tumor cells
• Single-cell sequencing
• Tumor heterogeneity
• Whole-genome amplification

• U.S. Department of Defense

• Cell Separation/methods
• Clinical Laboratory Techniques/methods
• Humans
• Neoplasms/diagnosis
• Neoplastic Cells, Circulating

• R01 CA166011 NCI NIH HHS
• R01 CA187027 NCI NIH HHS
• R01 CA196639 NCI NIH HHS

Unlike population-level approaches, single-cell RNA sequencing enables transcriptomic analysis of an individual cell. Through the combination of high-throughput sequencing and bioinformatic tools, single-cell RNA-seq can detect more than 10,000 transcripts in one cell to distinguish cell subsets and dynamic cellular changes. After several years’ development, single-cell RNA-seq can now achieve massively parallel, full-length mRNA sequencing as well as in situ sequencing and even has potential for multi-omic detection. One appealing area of single-cell RNA-seq is cancer research, and it is regarded as a promising way to enhance prognosis and provide more precise target therapy by identifying druggable subclones. Indeed, progresses have been made regarding solid tumor analysis to reveal intratumoral heterogeneity, correlations between signaling pathways, stemness, drug resistance, and tumor architecture shaping the microenvironment. Furthermore, through investigation into circulating tumor cells, many genes have been shown to promote a propensity toward stemness and the epithelial-mesenchymal transition, to enhance anchoring and adhesion, and to be involved in mechanisms of anoikis resistance and drug resistance. This review focuses on advances and progresses of single-cell RNA-seq with regard to the following aspects:

1. Methodologies of single-cell RNA-seq

2. Single-cell isolation techniques

3. Single-cell RNA-seq in solid tumor research

4. Single-cell RNA-seq in circulating tumor cell research

5. Perspectives

• RNA sequencing
• circulating tumor cell
• single cell
• tumor

• Individualized treatment
• Intratumor heterogeneity
• Rare tumor cells
• Single-cell technology
• Tumor epigenetics