Identification of Distinct Tumor Subpopulations in Lung Adenocarcinoma via Single-Cell RNA-seq

Integrated transcriptomic analysis systematically reveals the heterogeneity and molecular characterization of cancer-associated fibroblasts in osteosarcoma

BACKGROUND

Osteosarcoma (OS), with a peak incidence during the adolescent growth spurt, is correlated with poor prognosis for its high malignancy. The tumor microenvironment (TME) is highly complicated, with frequent interactions between tumor and stromal cells. The cancer-associated fibroblasts (CAFs) in the TME have been considered to actively involve in the progression, metastasis, and drug resistance of OS. This study aimed to characterize cellular heterogeneity and molecular characterization in CAFs subtypes and explore the potential targeting therapeutic strategies to improve the prognosis of OS patients.

METHODS

The single-cell atlas of human OS tumor lesions were constructed from the GEO database. Then significant marker genes and potential biological functions for each CAFs subtype were identified and explored using the Seurat R package. Next, by performing the survival analyses and constructing the risk scores for CAFs subtypes, we aimed to identify and characterize the prognostic values of specific marker genes and different CAFs subtypes. Furthermore, we explored the therapeutic targets and innovative drugs targeting different CAFs subtypes based on the GDSC database. Finally, prognoses related CAFs subtypes were further validated through immunohistochemistry (IHC) on clinical OS specimens.

RESULTS

Overall, nine main cell clusters and five subtypes of CAFs were identified. The differentially expressed marker genes for each CAFs clusters were then identified. Moreover, through Gene Ontology (GO) enrichment analysis, we defined the CAFs_2 (upregulated CXCL14 and C3), which was closely related to leukocyte migration and chemotaxis, as inflammatory CAFs (iCAFs). Likewise, we defined the CAFs_4 (upregulated CD74, HLA-DRA and HLA-DRB1), which was closely related to antigen process and presentation, as antigen-presenting CAFs (apCAFs). Furthermore, Kaplan-Meier analyses showed that CAFs_2 and CAFs_4 were correlated with poor clinical prognosis of OS patients. Meanwhile, therapeutic drugs targeting CAFs_2 and CAFs_4, such as 17-AAG/Docetaxel/Bleomycin and PHA-793887/NG-25/KIN001-102, were also explored, respectively. Finally, IHC assay confirmed the abundant CAFs_2 and CAFs_4 subtypes infiltration in the OS microenvironment compared with adjacent tissues.

CONCLUSION

Our study revealed the diversity, complexity, and heterogeneity of CAFs in OS, and complemented the single-cell atlas in OS TME.

A cellular and spatial atlas of TP53 -associated tissue remodeling in lung adenocarcinoma

TP53 is the most frequently mutated gene across many cancers and is associated with shorter survival in lung adenocarcinoma (LUAD). To define how TP53 mutations affect the LUAD tumor microenvironment (TME), we constructed a multi-omic cellular and spatial tumor atlas of 23 treatment-naïve human lung tumors. We found that TP53 -mutant ( TP53 mut ) malignant cells lose alveolar identity and upregulate highly proliferative and entropic gene expression programs consistently across resectable LUAD patient tumors, genetically engineered mouse models, and cell lines harboring a wide spectrum of TP53 mutations. We further identified a multicellular tumor niche composed of SPP1 + macrophages and collagen-expressing fibroblasts that coincides with hypoxic, pro-metastatic expression programs in TP53 mut tumors. Spatially correlated angiostatic and immune checkpoint interactions, including CD274 - PDCD1 and PVR - TIGIT , are also enriched in TP53 mut LUAD tumors, which may influence response to checkpoint blockade therapy. Our methodology can be further applied to investigate mutation-specific TME changes in other cancers.

Function of CD8+, conventional CD4+, and regulatory CD4+ T cell identification in lung cancer

Lung cancer is the malignant tumor with the highest mortality rate in the world. There is obvious heterogeneity within the tumor. Single cell sequencing technology enables scholars to obtain information about the cell type, status, subpopulation distribution and communication behavior between cells in the tumor microenvironment from the cellular level. However, due to the problem of sequencing depth, some genes with low expression cannot be detected, which results in that most of the specific genes of immune cells cannot be recognized, and lead to defects in the functional identification of immune cells. In this paper, we used single cell sequencing data of 12346 T cells in 14 treatment-naïve non-small-cell lung cancer patients to identify immune cell-specific genes and infer the function of three types of T cells. The method, named GRAPH-LC, implemented this function by gene interaction network and graph learning methods. Graph learning methods are used to extract genes feature and dense neural network is used to identify immune cell-specific genes. The experiments on 10-cross validation shows that the AUROC and AUPR reached at least 0.802, 0.815 on identifying cell-specific genes of three types of T cells. And we did functional enrichment analysis on the top 15 expressed genes. By functional enrichment analysis, we got 95 GO terms and 39 KEGG pathways that related to three types of T cells. The use of this technology will help to deeply understand the mechanism of the occurrence and development of lung cancer, find new diagnostic markers and therapeutic targets, and provide a theoretical reference for the precise treatment of lung cancer patients in the future.

Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related deaths worldwide. Identification of gene biomarkers and their regulatory factors and signaling pathways is very essential to reveal the molecular mechanisms of NSCLC initiation and progression. Thus, the goal of this study is to identify gene biomarkers for NSCLC diagnosis and prognosis by using scRNA-seq data through bioinformatics techniques. scRNA-seq data were obtained from the GEO database to identify DEGs. A total of 158 DEGs (including 48 upregulated and 110 downregulated) were detected after gene integration. Gene Ontology enrichment and KEGG pathway analysis of DEGs were performed by FunRich software. A PPI network of DEGs was then constructed using the STRING database and visualized by Cytoscape software. We identified 12 key genes (KGs) including MS4A1, CCL5, and GZMB, by using two topological methods based on the PPI networking results. The diagnostic, expression, and prognostic potentials of the identified 12 key genes were assessed using the receiver operating characteristics (ROC) curve and a web-based tool, SurvExpress. From the regulatory network analysis, we extracted the 7 key transcription factors (TFs) (FOXC1, YY1, CEBPB, TFAP2A, SREBF2, RELA, and GATA2), and 8 key miRNAs (hsa-miR-124-3p, hsa-miR-34a-5p, hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-449a, hsa-miR-24-3p, hsa-let-7b-5p, and hsa-miR-7-5p) associated with the KGs were evaluated. Functional enrichment and pathway analysis, survival analysis, ROC analysis, and regulatory network analysis highlighted crucial roles of the key genes. Our findings might play a significant role as candidate biomarkers in NSCLC diagnosis and prognosis.

Single-Cell Sequencing and Its Applications in Liver Cancer

As one of the most lethal cancers, primary liver cancer (PLC) has high tumor heterogeneity, including the heterogeneity between cancer cells. Traditional methods which have been used to identify tumor heterogeneity for a long time are based on large mixed cell samples, and the research results usually show average level of the cell population, ignoring the heterogeneity between cancer cells. In recent years, single-cell sequencing has been increasingly applied to the studies of PLCs. It can detect the heterogeneity between cancer cells, distinguish each cell subgroup in the tumor microenvironment (TME), and also reveal the clonal characteristics of cancer cells, contributing to understand the evolution of tumor. Here, we introduce the process of single-cell sequencing, review the applications of single-cell sequencing in the heterogeneity of cancer cells, TMEs, oncogenesis, and metastatic mechanisms of liver cancer, and discuss some of the current challenges in the field.

Identification and validation of a seven-gene prognostic marker in colon cancer based on single-cell transcriptome analysis

Colon cancer (CC) is one of the most commonly diagnosed tumours worldwide. Single-cell RNA sequencing (scRNA-seq) can accurately reflect the heterogeneity within and between tumour cells and identify important genes associated with cancer development and growth. In this study, scRNA-seq was used to identify reliable prognostic biomarkers in CC. ScRNA-seq data of CC before and after 5-fluorouracil treatment were first downloaded from the Gene Expression Omnibus database. The data were pre-processed, and dimensionality reduction was performed using principal component analysis and t-distributed stochastic neighbour embedding algorithms. Additionally, the transcriptome data, somatic variant data, and clinical reports of patients with CC were obtained from The Cancer Genome Atlas database. Seven key genes were identified using Cox regression analysis and the least absolute shrinkage and selection operator method to establish signatures associated with CC prognoses. The identified signatures were validated on independent datasets, and somatic mutations and potential oncogenic pathways were further explored. Based on these features, gene signatures, and other clinical variables, a more effective predictive model nomogram for patients with CC was constructed, and a decision curve analysis was performed to assess the utility of the nomogram. A prognostic signature consisting of seven prognostic-related genes, including CAV2, EREG, NGFRAP1, WBSCR22, SPINT2, CCDC28A, and BCL10, was constructed and validated. The proficiency and credibility of the signature were verified in both internal and external datasets, and the results showed that the seven-gene signature could effectively predict the prognosis of patients with CC under various clinical conditions. A nomogram was then constructed based on features such as the RiskScore, patients' age, neoplasm stage, and tumor (T), nodes (N), and metastases (M) classification, and the nomogram had good clinical utility. Higher RiskScores were associated with a higher tumour mutational burden, which was confirmed to be a prognostic risk factor. Gene set enrichment analysis showed that high-score groups were enriched in 'cytoplasmic DNA sensing', 'Extracellular matrix receptor interactions', and 'focal adhesion', and low-score groups were enriched in 'natural killer cell-mediated cytotoxicity', and 'T-cell receptor signalling pathways', among other pathways. A robust seven-gene marker for CC was identified based on scRNA-seq data and was validated in multiple independent cohort studies. These findings provide a new potential marker to predict the prognosis of patients with CC.

Deciphering the Immune-Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology

Lung cancer is the leading cause of cancer-related death worldwide. Cancer immunotherapy has shown great success in treating advanced-stage lung cancer but has yet been used to treat early-stage lung cancer, mostly due to lack of understanding of the tumor immune microenvironment in early-stage lung cancer. The immune system could both constrain and promote tumorigenesis in a process termed immune editing that can be divided into three phases, namely, elimination, equilibrium, and escape. Current understanding of the immune response toward tumor is mainly on the "escape" phase when the tumor is clinically detectable. The detailed mechanism by which tumor progenitor lesions was modulated by the immune system during early stage of lung cancer development remains elusive. The advent of single-cell sequencing technology enables tumor immunologists to address those fundamental questions. In this perspective, we will summarize our current understanding and big gaps about the immune response during early lung tumorigenesis. We will then present the state of the art of single-cell technology and then envision how single-cell technology could be used to address those questions. Advances in the understanding of the immune response and its dynamics during malignant transformation of pre-malignant lesion will shed light on how malignant cells interact with the immune system and evolve under immune selection. Such knowledge could then contribute to the development of precision and early intervention strategies toward lung malignancy.

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

Tumorigenesis refers to the process of clonal dysplasia that occurs due to the collapse of normal growth regulation in cells caused by the action of various carcinogenic factors. These “successful” tumor cells pass on the genetic templates to their generations in evolutionary terms, but they also constantly adapt to ever-changing host environments. A unique peculiarity known as intratumor heterogeneity (ITH) is extensively involved in tumor development, metastasis, chemoresistance, and immune escape. An understanding of ITH is urgently required to identify the diversity and complexity of the tumor microenvironment (TME), but achieving this understanding has been a challenge. Single-cell sequencing (SCS) is a powerful tool that can gauge the distribution of genomic sequences in a single cell and the genetic variability among tumor cells, which can improve the understanding of ITH. SCS provides fundamental ideas about existing diversity in specific TMEs, thus improving cancer diagnosis and prognosis prediction, as well as improving the monitoring of therapeutic response. Herein, we will discuss advances in SCS and review SCS application in tumors based on current evidence.

Single-Cell Transcriptomics Reveals the Complexity of the Tumor Microenvironment of Treatment-Naive Osteosarcoma

Osteosarcoma (OS), which occurs most commonly in adolescents, is associated with a high degree of malignancy and poor prognosis. In order to develop an accurate treatment for OS, a deeper understanding of its complex tumor microenvironment (TME) is required. In the present study, tissues were isolated from six patients with OS, and then subjected to single-cell RNA sequencing (scRNA-seq) using a 10× Genomics platform. Multiplex immunofluorescence staining was subsequently used to validate the subsets identified by scRNA-seq. ScRNA-seq of six patients with OS was performed prior to neoadjuvant chemotherapy, and data were obtained on 29,278 cells. A total of nine major cell types were identified, and the single-cell transcriptional map of OS was subsequently revealed. Identified osteoblastic OS cells were divided into five subsets, and the subsets of those osteoblastic OS cells with significant prognostic correlation were determined using a deconvolution algorithm. Thereby, different transcription patterns in the cellular subtypes of osteoblastic OS cells were reported, and key transcription factors associated with survival prognosis were identified. Furthermore, the regulation of osteolysis by osteoblastic OS cells via receptor activator of nuclear factor kappa-B ligand was revealed. Furthermore, the role of osteoblastic OS cells in regulating angiogenesis through vascular endothelial growth factor-A was revealed. C3_TXNIP⁺ macrophages and C5_IFIT1⁺ macrophages were found to regulate regulatory T cells and participate in CD8⁺ T cell exhaustion, illustrating the possibility of immunotherapy that could target CD8⁺ T cells and macrophages. Our findings here show that the role of C1_osteoblastic OS cells in OS is to promote osteolysis and angiogenesis, and this is associated with survival prognosis. In addition, T cell depletion is an important feature of OS. More importantly, the present study provided a valuable resource for the in-depth study of the heterogeneity of the OS TME.

HLA-DR cancer cells expression correlates with T cell infiltration and is enriched in lung adenocarcinoma with indolent behavior

Lung adenocarcinoma (ADC) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate whether CyTOF identifies cellular and molecular predictors of tumor behavior. We developed and validated a CyTOF panel of 34 antibodies in four ADC cell lines and PBMC. We tested our panel in a set of 10 ADCs, classified into long- (LPS) (n = 4) and short-predicted survival (SPS) (n = 6) based on radiomics features. We identified cellular subpopulations of epithelial cancer cells (ECC) and their microenvironment and validated our results by multiplex immunofluorescence (mIF) applied to a tissue microarray (TMA) of LPS and SPS ADCs. The antibody panel captured the phenotypical differences in ADC cell lines and PBMC. LPS ADCs had a higher proportion of immune cells. ECC clusters (ECCc) were identified and uncovered two ADC groups. ECCc with high HLA-DR expression were correlated with CD4+ and CD8+ T cells, with LPS samples being enriched for those clusters. We confirmed a positive correlation between HLA-DR expression on ECC and T cell number by mIF staining on TMA slides. Spatial analysis demonstrated shorter distances from T cells to the nearest ECC in LPS. Our results demonstrate a distinctive cellular profile of ECC and their microenvironment in ADC. We showed that HLA-DR expression in ECC is correlated with T cell infiltration, and that a set of ADCs with high abundance of HLA-DR+ ECCc and T cells is enriched in LPS samples. This suggests new insights into the role of antigen presenting tumor cells in tumorigenesis.

Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data

MOTIVATION

Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single cell genomic data in order to infer their evolutionary trajectory remains computationally challenging due to the extremely noisy and sparse nature of the data.

RESULTS

Here we describe 'Dhaka', a variational autoencoder method which transforms single cell genomic data to a reduced dimension feature space that is more efficient in differentiating between (hidden) tumor subpopulations. Our method is general and can be applied to several different types of genomic data including copy number variation from scDNA-Seq and gene expression from scRNA-Seq experiments. We tested the method on synthetic and six single cell cancer datasets where the number of cells ranges from 250 to 6000 for each sample. Analysis of the resulting feature space revealed subpopulations of cells and their marker genes. The features are also able to infer the lineage and/or differentiation trajectory between cells greatly improving upon prior methods suggested for feature extraction and dimensionality reduction of such data.

AVAILABILITY AND IMPLEMENTATION

All the datasets used in the paper are publicly available and developed software package and supporting info is available on Github https://github.com/MicrosoftGenomics/Dhaka.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell RNA sequencing in human lung cancer: Applications, challenges, and pathway towards personalized therapy

Lung cancer is one of the most prevalent human cancers, and single-cell RNA sequencing (scRNA-seq) has been widely used to study human lung cancer at the cellular, genetic, and molecular level. Even though there are published reviews, which summarized the applications of scRNA-seq in human cancers like breast cancer, there is lack of a comprehensive review, which could effectively highlight the broad use of scRNA-seq in studying lung cancer. This review, therefore, was aimed to summarize the various applications of scRNA-seq in human lung cancer research based on the findings from different published in vitro, in vivo, and clinical studies. The review would first briefly outline the concept and principle of scRNA-seq, followed by the discussion on the applications of scRNA-seq in studying human lung cancer. Finally, the challenges faced when using scRNA-seq to study human lung cancer would be discussed, and the potential applications and challenges of scRNA-seq to facilitate the development of personalized cancer therapy in the future would be explored.

Single-Cell Sequencing, an Advanced Technology in Lung Cancer Research

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.

Single-cell RNA sequencing reveals heterogeneous tumor and immune cell populations in early-stage lung adenocarcinomas harboring EGFR mutations

Lung adenocarcinoma (LUAD) harboring EGFR mutations prevails in Asian population. However, the inter-patient and intra-tumor heterogeneity has not been addressed at single-cell resolution. Here we performed single-cell RNA sequencing (scRNA-seq) of total 125,674 cells from seven stage-I/II LUAD samples harboring EGFR mutations and five tumor-adjacent lung tissues. We identified diverse cell types within the tumor microenvironment (TME) in which myeloid cells and T cells were the most abundant stromal cell types in tumors and adjacent lung tissues. Within tumors, accompanied by an increase in CD1C+ dendritic cells, the tumor-associated macrophages (TAMs) showed pro-tumoral functions without signature gene expression of defined M1 or M2 polarization. Tumor-infiltrating T cells mainly displayed exhausted and regulatory T-cell features. The adenocarcinoma cells can be categorized into different subtypes based on their gene expression signatures in distinct pathways such as hypoxia, glycolysis, cell metabolism, translation initiation, cell cycle, and antigen presentation. By performing pseudotime trajectory, we found that ELF3 was among the most upregulated genes in more advanced tumor cells. In response to secretion of inflammatory cytokines (e.g., IL1B) from immune infiltrates, ELF3 in tumor cells was upregulated to trigger the activation of PI3K/Akt/NF-κB pathway and elevated expression of proliferation and anti-apoptosis genes such as BCL2L1 and CCND1. Taken together, our study revealed substantial heterogeneity within early-stage LUAD harboring EGFR mutations, implicating complex interactions among tumor cells, stromal cells and immune infiltrates in the TME.

High accuracy gene expression profiling of sorted cell subpopulations from breast cancer PDX model tissue

Intratumor Heterogeneity (ITH) is a functionally important property of tumor tissue and may be involved in drug resistance mechanisms. Although descriptions of ITH can be traced back to very early reports about cancer tissue, mechanistic investigations are still limited by the precision of analysis methods and access to relevant tissue sources. PDX models have provided a reproducible source of tissue with at least a partial representation of naturally occurring ITH. We investigated the properties of phenotypically distinct cell populations by Fluorescence activated cell sorting (FACS) tissue derived cells from multiple tumors from a triple negative breast cancer patient derived xenograft (PDX) model. We subsequently subjected each population to in depth gene expression analysis. Our findings suggest that process related gene expression changes (caused by tissue dissociation and FACS sorting) are restricted to Immediate Early Genes (IEGs). This allowed us to discover highly reproducible gene expression profiles of distinct cellular compartments identifiable by cell surface markers in this particular tumor model. Within the context of data from a previously published model our work suggests that gene expression profiles associated with hypoxia, stemness and drug resistance may reside in tumor subpopulations predictably growing in PDX models. This approach provides a novel opportunity for prospective mechanistic studies of ITH.

Breathing fresh air into respiratory research with single-cell RNA sequencing

The complex cellular heterogeneity of the lung poses a unique challenge to researchers in the field. While the use of bulk RNA sequencing has become a ubiquitous technology in systems biology, the technique necessarily averages out individual contributions to the overall transcriptional landscape of a tissue. Single-cell RNA sequencing (scRNA-seq) provides a robust, unbiased survey of the transcriptome comparable to bulk RNA sequencing while preserving information on cellular heterogeneity. In just a few years since this technology was developed, scRNA-seq has already been adopted widely in respiratory research and has contributed to impressive advancements such as the discoveries of the pulmonary ionocyte and of a profibrotic macrophage population in pulmonary fibrosis. In this review, we discuss general technical considerations when considering the use of scRNA-seq and examine how leading investigators have applied the technology to gain novel insights into respiratory biology, from development to disease. In addition, we discuss the evolution of single-cell technologies with a focus on spatial and multi-omics approaches that promise to drive continued innovation in respiratory research.

Single-cell sequencing and its applications in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC), like many tumors, is characterized by significant intra-tumoral heterogeneity, namely transcriptional, genetic, and epigenetic differences that define distinct cellular subpopulations. While it has been established that intra-tumoral heterogeneity may have prognostic significance in HNSCC, we are only beginning to describe and define such heterogeneity at a cellular resolution. Recent advances in single-cell sequencing technologies have been critical in this regard, opening new avenues in our understanding of more nuanced tumor biology by identifying distinct cellular subpopulations, dissecting signaling within the tumor microenvironment, and characterizing cellular genomic mutations and copy number aberrations. The combined effect of these insights is likely to be robust and meaningful changes in existing diagnostic and treatment algorithms through the application of novel biomarkers as well as targeted therapeutics. Here, we review single-cell technological and computational advances at the genomic, transcriptomic, and epigenomic levels, and discuss their applications in cancer research and clinical practice, with a specific focus on HNSCC.

Cytogenetics and Cytogenomics Evaluation in Cancer

The availability of cytogenetics and cytogenomics technologies improved the detection and identification of tumor molecular signatures as well as the understanding of cancer initiation and progression. The use of large-scale and high-throughput cytogenomics technologies has led to a fast identification of several cancer candidate biomarkers associated with diagnosis, prognosis, and therapeutics. The advent of array comparative genomic hybridization and next-generation sequencing technologies has significantly improved the knowledge about cancer biology, underlining driver genes to guide targeted therapy development, drug-resistance prediction, and pharmacogenetics. However, few of these candidate biomarkers have made the transition to the clinic with a clear benefit for the patients. Technological progress helped to demonstrate that cellular heterogeneity plays a significant role in tumor progression and resistance/sensitivity to cancer therapies, representing the major challenge of precision cancer therapy. A paradigm shift has been introduced in cancer genomics with the recent advent of single-cell sequencing, since it presents a lot of applications with a clear benefit to oncological patients, namely, detection of intra-tumoral heterogeneity, mapping clonal evolution, monitoring the development of therapy resistance, and detection of rare tumor cell populations. It seems now evident that no single biomarker could provide the whole information necessary to early detect and predict the behavior and prognosis of tumors. The promise of precision medicine is based on the molecular profiling of tumors being vital the continuous progress of high-throughput technologies and the multidisciplinary efforts to catalogue chromosomal rearrangements and genomic alterations of human cancers and to do a good interpretation of the relation genotype-phenotype.

Single-cell transcriptional profiling reveals the heterogenicity in colorectal cancer

BACKGROUND

Colorectal Cancer (CRC) is a highly heterogeneous disease. RNA profiles of bulk tumors have enabled transcriptional classification of CRC. However, such ways of sequencing can only target a cell colony and obscure the signatures of distinct cell populations. Alternatively, single-cell RNA sequencing (scRNA-seq), which can provide unbiased analysis of all cell types, opens the possibility to map cellular heterogeneity of CRC unbiasedly.

METHODS

In this study, we utilized scRNA-seq to profile cells from cancer tissue of a CRC patient. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to understand the roles of genes within the clusters.

RESULTS AND CONCLUSION

The 2824 cells were analyzed and categorized into 5 distinct clusters by scRNA-seq. For every cluster, specific cell markers can be applied, indicating each 1 of them different from another. We discovered that the tumor of CRC displayed a clear sign of heterogenicity, while genes within each cluster serve different functions. GO term analysis also stated that different cluster's relatedness towards the tumor of CRC differs. Three clusters participate in peripheral works in cells, including, energy transport, extracellular matrix generation, etc; Genes in other 2 clusters participate more in immunology processes. Lastly, trajectory plot analysis also supports the viewpoint, in that some clusters present in different states and pseudo-time, while others present in a single state or pseudo time. Our analysis provides more insight into the heterogeneity of CRC, which can provide assistance to further researches on this topic.

Single-cell RNA sequencing of lung adenocarcinoma reveals heterogeneity of immune response-related genes

Immunotherapy has emerged as a promising approach to treat cancer. However, partial responses across multiple clinical trials support the significance of characterizing intertumor and intratumor heterogeneity to achieve better clinical results and as potential tools in selecting patients for different types of cancer immunotherapies. Yet, the type of heterogeneity that informs clinical outcome and patient selection has not been fully explored. In particular, the lack of characterization of immune response-related genes in cancer cells hinders the further development of metrics to select and optimize immunotherapy. Therefore, we analyzed single-cell RNA-Seq data from lung adenocarcinoma patients and cell lines to characterize the intratumor heterogeneity of immune response-related genes and demonstrated their potential impact on the efficacy of immunotherapy. We discovered that IFN-γ signaling pathway genes are heterogeneously expressed and coregulated with other genes in single cancer cells, including MHC class II (MHCII) genes. The downregulation of genes in IFN-γ signaling pathways in cell lines corresponds to an acquired resistance phenotype. Moreover, analysis of 2 groups of tumor-restricted antigens, namely neoantigens and cancer testis antigens, revealed heterogeneity in their expression in single cells. These analyses provide a rationale for applying multiantigen combinatorial therapies to prevent tumor escape and establish a basis for future development of prognostic metrics based on intratumor heterogeneity.

Technical Advances in Single-Cell RNA Sequencing and Applications in Normal and Malignant Hematopoiesis

Single-cell RNA sequencing (scRNA-seq) has been tremendously developed in the past decade owing to overcoming challenges associated with isolation of massive quantities of single cells. Previously, cell heterogeneity and low quantities of available biological material posed significant difficulties to scRNA-seq. Cell-to-cell variation and heterogeneity are fundamental and intrinsic characteristics of normal and malignant hematopoietic cells; this heterogeneity has often been ignored in omics studies. The application of scRNA-seq has profoundly changed our comprehension of many biological phenomena, including organ development and carcinogenesis. Hematopoiesis, is actually a maturation process for more than ten distinct blood and immune cells, and is thought to be critically involved in hematological homeostasis and in sustaining the physiological functions. However, aberrant hematopoiesis directly leads to hematological malignancy, and a deeper understanding of malignant hematopoiesis will provide deeper insights into diagnosis and prognosis for patients with hematological malignancies. Here, we aim to review the recent technical progress and future prospects for scRNA-seq, as applied in physiological and malignant hematopoiesis, in efforts to further understand the hematopoietic hierarchy and to illuminate personalized therapy and precision medicine approaches used in the clinical treatment of hematological malignancies.

Dissecting LncRNA Roles in Renal Cell Carcinoma Metastasis and Characterizing Genomic Heterogeneity by Single-Cell RNA-seq

Long noncoding RNAs (lncRNA) have recently emerged as important regulators in cancer cell proliferation and metastasis. However, the role of lncRNAs in metastatic clear cell renal cell carcinoma (ccRCC) remains unclear. Here, single-cell RNA sequencing data were analyzed from primary renal cell carcinoma and paired metastatic renal cell carcinoma specimens, and characterized the expression profiles of over 10,000 genes, including 1,874 lncRNAs. Further analysis revealed that lncRNAs exhibit cancer type- and tissue-specific expression across ccRCC cells. Interestingly, a number of lncRNAs (n = 173) associated with ccRCC metastasis, termed ccRCC metastasis-associated lncRNAs (CMAL). Moreover, functional analysis based on a CMAL-PCG coexpression network revealed that CMALs contribute to cell adhesion, immune response, and cell proliferation. In combination with survival analysis, 12 CMALs were identified that participate in TNF and hypoxia-inducible factor 1 signaling to promote ccRCC metastasis. Further investigation on intratumoral heterogeneity showed that some CMALs are selectively expressed in different subpopulations. IMPLICATIONS: To explore ccRCC metastasis, the current study performed a global dissection of lncRNAs and a complex genomic analysis of ccRCC tumor heterogeneity. The data shed light on the discovery of potential lncRNA biomarkers and lncRNA therapeutic targets.

Machine Learning Helps Identify New Drug Mechanisms in Triple-Negative Breast Cancer

This paper demonstrates the ability of mach- ine learning approaches to identify a few genes among the 23,398 genes of the human genome to experiment on in the laboratory to establish new drug mechanisms. As a case study, this paper uses MDA-MB-231 breast cancer single-cells treated with the antidiabetic drug metformin. We show that mixture-model-based unsupervised methods with validation from hierarchical clustering can identify single-cell subpopulations (clusters). These clusters are characterized by a small set of genes (1% of the genome) that have significant differential expression across the clusters and are also highly correlated with pathways with anticancer effects driven by metformin. Among the identified small set of genes associated with reduced breast cancer incidence, laboratory experiments on one of the genes, CDC42, showed that its downregulation by metformin inhibited cancer cell migration and proliferation, thus validating the ability of machine learning approaches to identify biologically relevant candidates for laboratory experiments. Given the large size of the human genome and limitations in cost and skilled resources, the broader impact of this work in identifying a small set of differentially expressed genes after drug treatment lies in augmenting the drug-disease knowledge of pharmacogenomics experts in laboratory investigations, which could help establish novel biological mechanisms associated with drug response in diseases beyond breast cancer.

Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor

Single-cell RNA-sequencing (scRNA-seq) technology provides a new avenue to discover and characterize cell types; however, the experiment-specific technical biases and analytic variability inherent to current pipelines may undermine its replicability. Meta-analysis is further hampered by the use of ad hoc naming conventions. Here we demonstrate our replication framework, MetaNeighbor, that quantifies the degree to which cell types replicate across datasets, and enables rapid identification of clusters with high similarity. We first measure the replicability of neuronal identity, comparing results across eight technically and biologically diverse datasets to define best practices for more complex assessments. We then apply this to novel interneuron subtypes, finding that 24/45 subtypes have evidence of replication, which enables the identification of robust candidate marker genes. Across tasks we find that large sets of variably expressed genes can identify replicable cell types with high accuracy, suggesting a general route forward for large-scale evaluation of scRNA-seq data.

Inhibition of the colony-stimulating-factor-1 receptor affects the resistance of lung cancer cells to cisplatin

In the present work we show that multiple lung cancer cell lines contain cisplatin resistant cell subpopulations expressing the Colony-Stimulating-Factor-Receptor-1 (CSF-1R) and surviving chemotherapy-induced stress. By exploiting siRNA-mediated knock down in vitro and the use of an investigational CSF-1R TKI (JNJ-40346527) in vitro and in vivo, we show that expression and function of the receptor are required for the clonogenicity and chemoresistance of the cell lines. Thus, inhibition of the kinase activity of the receptor reduced the levels of EMT-associated genes, stem cell markers and chemoresistance genes. Additionally, the number of high aldehyde dehydrogenase (ALDH) expressing cells was reduced, consequent to the lack of cisplatin-induced increase of ALDH isoforms. This affected the collective chemoresistance of the treated cultures. Treatment of tumor bearing mice with JNJ-40346527, at pharmacologically relevant doses, produced strong chemo-sensitizing effects in vivo. These anticancer effects correlated with a reduced number of CSF-1Rpos cells, in tumors excised from the treated mice. Depletion of the CD45pos cells within the treated tumors did not, apparently, play a major role in mediating the therapeutic response to the TKI. Thus, lung cancer cells express a functional CSF-1 and CSF-1R duo which mediates pro-tumorigenic effects in vivo and in vitro and can be targeted in a therapeutically relevant way. These observations complement the already known role for the CSF-1R at mediating the pro-tumorigenic properties of tumor-infiltrating immune components.

Application of Single Cell Sequencing in Cancer

Cancer is a heterogenetic disease at both the level of clinical manifestation and the level of the genome. Single-cell sequencing provides an unprecedented means of characterizing the intra-tumor heterogeneity and detecting and analyzing the genomes of cancer cells. These data will help to reconstruct the understanding of the evolutionary lineage of cancer cells. In the future, single-cell technology is believed to be a useful tool in diagnostic and prognostic application in oncology. The application of single cell technology in clinics will make it possible to detect cancer non-invasively at early stages and to develop precision medicine. In this chapter, we review the research and application status of the single cell technology in cancer.

Single-cell sequencing and tumorigenesis: improved understanding of tumor evolution and metastasis

Extensive genomic and transcriptomic heterogeneity in human cancer often negatively impacts treatment efficacy and survival, thus posing a significant ongoing challenge for modern treatment regimens. State-of-the-art DNA- and RNA-sequencing methods now provide high-resolution genomic and gene expression portraits of individual cells, facilitating the study of complex molecular heterogeneity in cancer. Important developments in single-cell sequencing (SCS) technologies over the past 5 years provide numerous advantages over traditional sequencing methods for understanding the complexity of carcinogenesis, but significant hurdles must be overcome before SCS can be clinically useful. In this review, we: (1) highlight current methodologies and recent technological advances for isolating single cells, single-cell whole-genome and whole-transcriptome amplification using minute amounts of nucleic acids, and SCS, (2) summarize research investigating molecular heterogeneity at the genomic and transcriptomic levels and how this heterogeneity affects clonal evolution and metastasis, and (3) discuss the promise for integrating SCS in the clinical care arena for improved patient care.

Single-cell RNA sequencing reveals developmental heterogeneity among early lymphoid progenitors

Single-cell RNA sequencing is a powerful technology for assessing heterogeneity within defined cell populations. Here, we describe the heterogeneity of a B220⁺CD117^intCD19^-NK1.1^- uncommitted hematopoietic progenitor having combined lymphoid and myeloid potential. Phenotypic and functional assays revealed four subpopulations within the progenitor with distinct lineage developmental potentials. Among them, the Ly6D⁺SiglecH^-CD11c^- fraction was lymphoid-restricted exhibiting strong B-cell potential, whereas the Ly6D^-SiglecH^-CD11c^- fraction showed mixed lympho-myeloid potential. Single-cell RNA sequencing of these subsets revealed that the latter population comprised a mixture of cells with distinct lymphoid and myeloid transcriptional signatures and identified a subgroup as the potential precursor of Ly6D⁺SiglecH^-CD11c^- Subsequent functional assays confirmed that B220⁺CD117^intCD19^-NK1.1^- single cells are, with rare exceptions, not bipotent for lymphoid and myeloid lineages. A B-cell priming gradient was observed within the Ly6D⁺SiglecH^-CD11c^- subset and we propose a herein newly identified subgroup as the direct precursor of the first B-cell committed stage. Therefore, the apparent multipotency of B220⁺CD117^intCD19^-NK1.1^- progenitors results from underlying heterogeneity at the single-cell level and highlights the validity of single-cell transcriptomics for resolving cellular heterogeneity and developmental relationships among hematopoietic progenitors.

Single-Cell mRNA Sequencing in Cancer Research: Integrating the Genomic Fingerprint

Critical cancer mutations are often regional and mosaic, confounding the efficacy of targeted therapeutics. Single cell mRNA sequencing (scRNA-seq) has enabled unprecedented studies of intra-tumor heterogeneity and its role in cancer progression, metastasis, and treatment resistance. When coupled with DNA sequencing, scRNA-seq allows one to infer the in vivo impact of genomic alterations on gene expression. This combination can be used to reliably distinguish neoplastic from non-neoplastic cells, to correlate paracrine-signaling pathways between neoplastic cells and stroma, and to map expression signatures to inferred clones and phylogenies. Here we review recent advances in scRNA-seq, with a special focus on cancer. We discuss the challenges and prospects of combining scRNA-seq with DNA sequencing to assess intra-tumor heterogeneity.

Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer

We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was applied to MDA-MB-231 breast cancer cells treated with the antidiabetic drug metformin, which is being repurposed for treatment of triple-negative breast cancer. Unsupervised learning identified a cluster of metformin-treated cells characterized by a significant suppression of 230 genes (p-value < 2E-16). This analysis corroborates known studies of metformin action: a) pathway analysis indicated known mechanisms related to metformin action, including the citric acid (TCA) cycle, oxidative phosphorylation, and mitochondrial dysfunction (p-value < 1E-9); b) 70% of these 230 genes were functionally implicated in metformin response; c) among remaining lesser functionally-studied genes for metformin-response was CDC42, down-regulated in breast cancer treated with metformin. However, CDC42's mechanisms in metformin response remained unclear. Our functional studies showed that CDC42 was involved in metformin-induced inhibition of cell proliferation and cell migration mediated through an AMPK-independent mechanism. Our results points to 230 genes that might serve as metformin response signatures, which needs to be tested in patients treated with metformin and, further investigation of CDC42 and AMPK-independence's role in metformin's anticancer mechanisms.

PRODUCTION OF A PRELIMINARY QUALITY CONTROL PIPELINE FOR SINGLE NUCLEI RNA-SEQ AND ITS APPLICATION IN THE ANALYSIS OF CELL TYPE DIVERSITY OF POST-MORTEM HUMAN BRAIN NEOCORTEX

Next generation sequencing of the RNA content of single cells or single nuclei (sc/nRNA-seq) has become a powerful approach to understand the cellular complexity and diversity of multicellular organisms and environmental ecosystems. However, the fact that the procedure begins with a relatively small amount of starting material, thereby pushing the limits of the laboratory procedures required, dictates that careful approaches for sample quality control (QC) are essential to reduce the impact of technical noise and sample bias in downstream analysis applications. Here we present a preliminary framework for sample level quality control that is based on the collection of a series of quantitative laboratory and data metrics that are used as features for the construction of QC classification models using random forest machine learning approaches. We've applied this initial framework to a dataset comprised of 2272 single nuclei RNA-seq results and determined that ~79% of samples were of high quality. Removal of the poor quality samples from downstream analysis was found to improve the cell type clustering results. In addition, this approach identified quantitative features related to the proportion of unique or duplicate reads and the proportion of reads remaining after quality trimming as useful features for pass/fail classification. The construction and use of classification models for the identification of poor quality samples provides for an objective and scalable approach to sc/nRNA-seq quality control.

nc886, a non-coding RNA and suppressor of PKR, exerts an oncogenic function in thyroid cancer

nc886 is a recently identified cellular non-coding RNA and its depletion leads to acute cell death via PKR (Protein Kinase RNA-activated) activation. nc886 expression is increased in some malignancies, but silenced in others. However, the precise role of nc886/PKR is controversial: is it a tumor suppressor or an oncogene? In this study, we have clarified the role of nc886 in thyroid cancer by sequentially generating PKR knockout (KO) and PKR/nc886 double KO cell lines from Nthy-ori 3-1, a partially transformed thyroid cell line. Compared to the wildtype, PKR KO alone does not exhibit any significant phenotypic changes. However, nc886 KO cells are less proliferative, migratory, and invasive than their parental PKR KO cells. Importantly, the requirement of nc886 in tumor phenotypes is totally independent of PKR. In our microarray data, nc886 KO suppresses some genes whose elevated expression is associated with poor survival confirmed by data from total of 505 thyroid cancer patients in the Caner Genome Atlas project. Also, the nc886 expression level tends to be elevated and in more aggressively metastatic tumor specimens from thyroid cancer patients. In summary, we have discovered nc886's tumor-promoting role in thyroid cancer which has been concealed by the PKR-mediated acute cell death.

Exploring viral infection using single-cell sequencing

Single-cell sequencing (SCS) has emerged as a valuable tool to study cellular heterogeneity in diverse fields, including virology. By studying the viral and cellular genome and/or transcriptome, the dynamics of viral infection can be investigated at single cell level. Most studies have explored the impact of cell-to-cell variation on the viral life cycle from the point of view of the virus, by analyzing viral sequences, and from the point of view of the cell, mainly by analyzing the cellular host transcriptome. In this review, we will focus on recent studies that use single-cell sequencing to explore viral diversity and cell variability in response to viral replication.

Exploiting single-cell expression to characterize co-expression replicability

Background

Co-expression networks have been a useful tool for functional genomics, providing important clues about the cellular and biochemical mechanisms that are active in normal and disease processes. However, co-expression analysis is often treated as a black box with results being hard to trace to their basis in the data. Here, we use both published and novel single-cell RNA sequencing (RNA-seq) data to understand fundamental drivers of gene-gene connectivity and replicability in co-expression networks.

Results

We perform the first major analysis of single-cell co-expression, sampling from 31 individual studies. Using neighbor voting in cross-validation, we find that single-cell network connectivity is less likely to overlap with known functions than co-expression derived from bulk data, with functional variation within cell types strongly resembling that also occurring across cell types. To identify features and analysis practices that contribute to this connectivity, we perform our own single-cell RNA-seq experiment of 126 cortical interneurons in an experimental design targeted to co-expression. By assessing network replicability, semantic similarity and overall functional connectivity, we identify technical factors influencing co-expression and suggest how they can be controlled for. Many of the technical effects we identify are expression-level dependent, making expression level itself highly predictive of network topology. We show this occurs generally through re-analysis of the BrainSpan RNA-seq data.

Conclusions

Technical properties of single-cell RNA-seq data create confounds in co-expression networks which can be identified and explicitly controlled for in any supervised analysis. This is useful both in improving co-expression performance and in characterizing single-cell data in generally applicable terms, permitting cross-laboratory comparison within a common framework.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-016-0964-6) contains supplementary material, which is available to authorized users.

Intratumoral Heterogeneity of the Epigenome

Investigation into intratumoral heterogeneity (ITH) of the epigenome is in a formative stage. The patterns of tumor evolution inferred from epigenetic ITH and genetic ITH are remarkably similar, suggesting widespread co-dependency of these disparate mechanisms. The biological and clinical relevance of epigenetic ITH are becoming more apparent. Rare tumor cells with unique and reversible epigenetic states may drive drug resistance, and the degree of epigenetic ITH at diagnosis may predict patient outcome. This perspective presents these current concepts and clinical implications of epigenetic ITH, and the experimental and computational techniques at the forefront of ITH exploration.

Single-cell transcriptome sequencing: recent advances and remaining challenges

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

Single-cell profiling approaches to probing tumor heterogeneity

Tumor heterogeneity is a major hindrance in cancer classification, diagnosis and treatment. Recent technological advances have begun to reveal the true extent of its heterogeneity. Single-cell analysis (SCA) is emerging as an important approach to detect variations in morphology, genetic or proteomic expression. In this review, we revisit the issue of inter- and intra-tumor heterogeneity, and list various modes of SCA techniques (cell-based, nucleic acid-based, protein-based, metabolite-based and lipid-based) presently used for cancer characterization. We further discuss the advantages of SCA over pooled cell analysis, as well as the limitations of conventional techniques. Emerging trends, such as high-throughput sequencing, are also mentioned as improved means for cancer profiling. Collectively, these applications have the potential for breakthroughs in cancer treatment.

Single-cell Transcriptome Study as Big Data

The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies.

Expressional Subpopulation of Cancers Determined by G64, a Co-regulated Module

Studies of cancer heterogeneity have received considerable attention recently, because the presence or absence of resistant sub-clones may determine whether or not certain therapeutic treatments are effective. Previously, we have reported G64, a co-regulated gene module composed of 64 different genes, can differentiate tumor intra- or inter-subpopulations in lung adenocarcinomas (LADCs). Here, we investigated whether the G64 module genes were also expressed distinctively in different subpopulations of other cancers. RNA sequencing-based transcriptome data derived from 22 cancers, except LADC, were downloaded from The Cancer Genome Atlas (TCGA). Interestingly, the 22 cancers also expressed the G64 genes in a correlated manner, as observed previously in an LADC study. Considering that gene expression levels were continuous among different tumor samples, tumor subpopulations were investigated using extreme expressional ranges of G64-i.e., tumor subpopulation with the lowest 15% of G64 expression, tumor subpopulation with the highest 15% of G64 expression, and tumor subpopulation with intermediate expression. In each of the 22 cancers, we examined whether patient survival was different among the three different subgroups and found that G64 could differentiate tumor subpopulations in six other cancers, including sarcoma, kidney, brain, liver, and esophageal cancers.