Application of single-cell sequencing in human cancer

Microfluidic design in single-cell sequencing and application to cancer precision medicine

Single-cell sequencing (SCS) is a crucial tool to reveal the genetic and functional heterogeneity of tumors, providing unique insights into the clonal evolution, microenvironment, drug resistance, and metastatic progression of cancers. Microfluidics is a critical component of many SCS technologies and workflows, conferring advantages in throughput, economy, and automation. Here, we review the current landscape of microfluidic architectures and sequencing techniques for single-cell omics analysis and highlight how these have enabled recent applications in oncology research. We also discuss the challenges and the promise of microfluidics-based single-cell analysis in the future of precision oncology.

Single-cell RNA-Seq and bulk RNA-Seq reveal reliable diagnostic and prognostic biomarkers for CRC

PURPOSE

The potential role of epithelium-specific genes through the adenoma-carcinoma sequence in the development of colorectal cancer (CRC) remains unknown. Therefore, we integrated single-cell RNA sequencing and bulk RNA sequencing data to select diagnosis and prognosis biomarkers for CRC.

METHODS

The CRC scRNA-seq dataset was used to describe the cellular landscape of normal intestinal mucosa, adenoma and CRC and to further select epithelium-specific clusters. Differentially expressed genes (DEGs) of epithelium-specific clusters were identified between intestinal lesion and normal mucosa in the scRNA-seq data throughout the adenoma-carcinoma sequence. Diagnostic biomarkers and prognostic biomarker (the risk score) for CRC were selected in the bulk RNA-seq dataset based on DEGs shared by the adenoma epithelium-specific cluster and the CRC epithelium-specific cluster (shared-DEGs).

RESULTS

Among the 1063 shared-DEGs, we selected 38 gene expression biomarkers and 3 methylation biomarkers that had promising diagnostic power in plasma. Multivariate Cox regression identified  174 shared-DEGs as prognostic genes for CRC. We combined 1000 times LASSO-Cox regression and two-way stepwise regression to select 10 prognostic shared-DEGs to construct the risk score in the CRC meta-dataset. In the external validation dataset, the 1- and 5-year AUCs of the risk score were higher than those of stage, the pyroptosis-related genes (PRG) score and the cuproptosis-related genes (CRG) score. In addition, the risk score was closely associated with the immune infiltration of CRC.

CONCLUSION

The combined analysis of the scRNA-seq dataset and the bulk RNA-seq dataset in this study provides reliable biomarkers for the diagnosis and prognosis of CRC.

Laser Capture Microdissection: A Gear for Pancreatic Cancer Research

The advancement in molecular techniques has been attributed to the quality and significance of cancer research. Pancreatic cancer (PC) is one of the rare cancers with aggressive behavior and a high mortality rate. The asymptomatic nature of the disease until its advanced stage has resulted in late diagnosis as well as poor prognosis. The heterogeneous character of PC has complicated cancer development and progression studies. The analysis of bulk tissues of the disease was insufficient to understand the disease, hence, the introduction of the single-cell separating technique aided researchers to decipher more about the specific cell population of tumors. This review gives an overview of the Laser Capture Microdissection (LCM) technique, one of the single-cell separation methods used in PC research.

Systematic analysis of circRNA biomarkers for diagnosis, prognosis and therapy in colorectal cancer

As the third most common cancer and the second leading cause of cancer death worldwide, colorectal cancer (CRC) poses a serious threat to people's health. In recent years, circRNA has been widely reported as a new biomarker in CRC, but a comprehensive summary and analysis is lacking. This study aims to evaluate the diagnostic, therapeutic and prognostic significance of circRNAs in CRC by systematically analysing their expression patterns, biological functions and clinical significance in CRC. The literature on circRNA in CRC was searched in the PubMed database and included for analysis after screening according to strict inclusion and exclusion criteria. The UALCAN online tool was used to obtain host gene expression data. The miRTargetLink 2.0 was used to predict target genes for miRNAs action in CRC patients. Cytoscape was used to construct circRNA-miRNA-mRNA interaction networks. From the 236 included papers, we identified 217 circRNAs and their associated 108 host genes and 145 miRNAs. Among the 145 miRNAs, 27 miRNAs had no corresponding target genes. After prediction of target genes and differential analysis, a total of 25 target genes were obtained and a circRNA-miRNA-mRNA interaction network was constructed. Among the 217 circRNAs, 74 were associated with diagnosis, 160 with treatment and 51 with prognosis. And 154 of them function as oncogenes while 58 as tumour suppressor genes. In addition, these circRNAs include 32 exosomal circRNAs, which have unique advantages as biomarkers. In total, we summarize and analyze the expression patterns, biological functions and clinical significance of circRNAs in CRC. In addition, we constructed some new circRNA-miRNA-mRNA regulatory axes based on the miRNAs sponged by circRNAs.

Intra-tumor heterogeneity and prognostic risk signature for hepatocellular carcinoma based on single-cell analysis

Intra-tumor heterogeneity poses a serious challenge in the treatment of cancer, including hepatocellular carcinoma (HCC). Recent developments in single-cell RNA sequencing (scRNA-seq) make it possible to examine the heterogeneity of tumor cells. The Gene Expression Omnibus (GEO) database was retrieved to obtain scRNA-seq data of 13 HCC and 8 para cancer samples, and the cells were clustered using FindNeighbors and FindClusters functions. Cell subsets were defined using the "Enricher" function of the clusterProfiler package. Monocle was used to predict cell developmental trajectory. The LIMMA package included in the R program was utilized to detect differentially expressed genes (DEGs) between HCC and paracancerous tissues. Univariate Cox analysis and Least Absolute and Selection Operator (Lasso) Cox regression analysis were conducted to establish a risk assessment model. Thirteen cell subpopulations were identified from the sequencing data of 64,634 single cells. Four cell subgroups (dendritic cells, hepatocytes, liver bud hepatic cells, and liver progenitor cells) in tumor tissues were highly enriched. Between HCC and para cancer tissues, 3024 DEGs were identified, and 641 were specific markers of four cell subgroups. To develop a prognostic risk model, 9 genes among the 641 genes were selected. In the training and validation sets, the model demonstrated a higher 5-year AUC and independently served as a prognostic marker as confirmed by multivariate and univariate Cox analyses. This study revealed the characteristics of different cell subpopulations of immune cells and tumor cells from the HCC microenvironment. We established a novel nine-gene prognostic model to determine the death risk of HCC patients. The discoveries in this research improved the current knowledge of HCC heterogeneity and may inspire future research.

Cancer Risk and Mutational Patterns Following Organ Transplantation

The rapid development of medical technology and widespread application of immunosuppressive drugs have improved the success rate of organ transplantation significantly. However, the use of immunosuppressive agents increases the frequency of malignancy greatly. With the prospect of “precision medicine” for tumors and development of next-generation sequencing technology, more attention has been paid to the application of high-throughput sequencing technology in clinical oncology research, which is mainly applied to the early diagnosis of tumors and analysis of tumor-related genes. All generations of cancers carry somatic mutations, meanwhile, significant differences were observed in mutational signatures across tumors. Systematic sequencing of cancer genomes from patients after organ transplantation can reveal DNA damage and repair processes in exposed cancer cells and their precursors. In this review, we summarize the application of high-throughput sequencing and organoids in the field of organ transplantation, the mutational patterns of cancer genomes, and propose a new research strategy for understanding the mechanism of cancer following organ transplantation.

Constructing a Prognostic Gene Signature for Lung Adenocarcinoma Based on Weighted Gene Co-Expression Network Analysis and Single-Cell Analysis

Purpose

Lung adenocarcinoma (LUAD) has a high degree of intratumor heterogeneity. Advanced single-cell RNA sequencing (scRNA-seq) technologies have offered tools to analyze intratumor heterogeneity, which improves the accuracy of identifying biomarkers based on single-cell expression data, and thus helps in predicting prognosis of cancer patients and assisting decision-makings for cancer treatment.

Patients and Methods

ScRNA-seq data containing two LUAD and two para-cancerous tissue samples were included to identify different cell clusters in tumor tissues. To identify the most relevant modules and important cell subpopulations (clusters) in LUAD tissues, weighted gene co-expression network analysis (WGCNA) was performed. Subsequently, LUAD molecular subtypes were constructed by unsupervised consensus clustering based on genes in key modules. Using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic model of LUAD was established.

Results

A total of 14 cell clusters belonging to 10 cell types in LUAD were identified. The turquoise module was the most relevant to LUAD among all the modules; cluster 10 (C10, lung epithelial cells) was found to be the most strongly associated with the turquoise module. LUAD samples were divided into two groups of distinct molecular subtypes. Based on the 165 shared genes between the turquoise module and C10, 511 DEGs between the two molecular subtypes were obtained, and five of them were selected to construct the gene signature, which was validated to be an independent prognostic marker of LUAD.

Conclusion

Fourteen cell clusters co-existed in LUAD, which contributed to its intratumor heterogeneity. Two molecular subtypes of LUAD were identified and a five-gene signature was developed and validated to be significantly associated with prognostic and clinical characteristics of LUAD patients.

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.

Single-Cell RNA Sequencing with Spatial Transcriptomics of Cancer Tissues

Single-cell RNA sequencing (RNA-seq) techniques can perform analysis of transcriptome at the single-cell level and possess an unprecedented potential for exploring signatures involved in tumor development and progression. These techniques can perform sequence analysis of transcripts with a better resolution that could increase understanding of the cellular diversity found in the tumor microenvironment and how the cells interact with each other in complex heterogeneous cancerous tissues. Identifying the changes occurring in the genome and transcriptome in the spatial context is considered to increase knowledge of molecular factors fueling cancers. It may help develop better monitoring strategies and innovative approaches for cancer treatment. Recently, there has been a growing trend in the integration of RNA-seq techniques with contemporary omics technologies to study the tumor microenvironment. There has been a realization that this area of research has a huge scope of application in translational research. This review article presents an overview of various types of single-cell RNA-seq techniques used currently for analysis of cancer tissues, their pros and cons in bulk profiling of transcriptome, and recent advances in the techniques in exploring heterogeneity of various types of cancer tissues. Furthermore, we have highlighted the integration of single-cell RNA-seq techniques with other omics technologies for analysis of transcriptome in their spatial context, which is considered to revolutionize the understanding of tumor microenvironment.

Application and prospects of single cell sequencing in tumors

Cancer is an intricate disease with inherent intra-tumor heterogeneity at the cellular level because of genetic changes and environmental differences. Cellular heterogeneity exists even within the same tumor type. Small deviations in a genome or transcriptome can lead to significant differences in function. Conventional bulk population sequencing, which produces admixed populations of cells, can only provide an average expression signal for one cell population, ignoring differences between individual cells. Important advances in sequencing have been made in recent years. Single cell sequencing starts in a single cell, thereby increasing our capability to characterize intratumor heterogeneity. This technology has been used to analyze genetic variation, specific metabolic activity, and evolutionary processes in tumors, which may help us understand tumor occurrence and development and improve our understanding of the tumor microenvironment. In addition, it provides a theoretical basis for the development of clinical treatments, especially for personalized medicine. In this article, we briefly introduce Single cell sequencing technology, summarize the application of Single cell sequencing to study the tumor microenvironment, as well as its therapeutic application in different clinical procedures.

Therapeutic Effect of IL-38 on Experimental Autoimmune Uveitis: Reprogrammed Immune Cell Landscape and Reduced Th17 Cell Pathogenicity

Purpose

The purpose of this study was to elucidate the effects of interleukin (IL)-38 on experimental autoimmune uveitis (EAU) and its underlying mechanisms.

Methods

Mice with EAU were treated with IL-38, and the retinas and cervical draining lymph nodes (CDLNs) were analyzed by flow cytometry. Single-cell RNA sequencing (scRNA-seq) was conducted to analyze the immune cell profiles of CDLNs from normal, EAU, and IL-38-treated mice.

Results

Administration of IL-38 attenuated EAU symptoms and reduced the proportion of T helper 17 (Th17) and T helper 1 (Th1) cells in the retinas and CDLNs. In scRNA-seq analysis, IL-38 downregulated the IL-17 signaling pathway and reduced the expression of Th17 cell pathogenicity-related genes (Csf2 and Il23r), findings which were also confirmed by flow cytometry. In vitro, IL-38 reduced the granulocyte-macrophage colony-stimulating factor (GM-CSF) stimulation function of IL-23 and inhibited IL-23R expression in Th17 cells. Moreover, when co-cultured with Th17 cells, IL-38 prevented IL-23 production in antigen-presenting cells (APCs).

Conclusions

Our data demonstrate the therapeutic effect of IL-38 on EAU, and suggest that the effect of IL-38 may be caused by dampening of the GM-CSF/IL-23R/IL-23 feedback loop between Th17 cells and APCs.

Aging weakens Th17 cell pathogenicity and ameliorates experimental autoimmune uveitis in mice

Aging-induced changes in the immune system are associated with a higher incidence of infection and vaccination failure. Lymph nodes, which filter the lymph to identify and fight infections, play a central role in this process. However, careful characterization of the impact of aging on lymph nodes and associated autoimmune diseases is lacking. We combined single-cell RNA sequencing (scRNA-seq) with flow cytometry to delineate the immune cell atlas of cervical draining lymph nodes (CDLNs) of both young and old mice with or without experimental autoimmune uveitis (EAU). We found extensive and complicated changes in the cellular constituents of CDLNs during aging. When confronted with autoimmune challenges, old mice developed milder EAU compared to young mice. Within this EAU process, we highlighted that the pathogenicity of T helper 17 cells (Th17) was dampened, as shown by reduced GM-CSF secretion in old mice. The mitigated secretion of GM-CSF contributed to alleviation of IL-23 secretion by antigen-presenting cells (APCs) and may, in turn, weaken APCs’ effects on facilitating the pathogenicity of Th17 cells. Meanwhile, our study further unveiled that aging downregulated GM-CSF secretion through reducing both the transcript and protein levels of IL-23R in Th17 cells from CDLNs. Overall, aging altered immune cell responses, especially through toning down Th17 cells, counteracting EAU challenge in old mice.

Microfluidics applications for high-throughput single cell sequencing

The inherent heterogeneity of individual cells in cell populations plays significant roles in disease development and progression, which is critical for disease diagnosis and treatment. Substantial evidences show that the majority of traditional gene profiling methods mask the difference of individual cells. Single cell sequencing can provide data to characterize the inherent heterogeneity of individual cells, and reveal complex and rare cell populations. Different microfluidic technologies have emerged for single cell researches and become the frontiers and hot topics over the past decade. In this review article, we introduce the processes of single cell sequencing, and review the principles of microfluidics for single cell analysis. Also, we discuss the common high-throughput single cell sequencing technologies along with their advantages and disadvantages. Lastly, microfluidics applications in single cell sequencing technology for the diagnosis of cancers and immune system diseases are briefly illustrated.

Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression-Morphology Analysis in Breast Cancer

Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for prediction of molecular phenotypes. We report here the first transcriptome-wide expression-morphology (EMO) analysis in breast cancer, where individual deep convolutional neural networks were optimized and validated for prediction of mRNA expression in 17,695 genes from hematoxylin and eosin-stained whole slide images. Predicted expressions in 9,334 (52.75%) genes were significantly associated with RNA sequencing estimates. We also demonstrated successful prediction of an mRNA-based proliferation score with established clinical value. The results were validated in independent internal and external test datasets. Predicted spatial intratumor variabilities in expression were validated through spatial transcriptomics profiling. These results suggest that EMO provides a cost-efficient and scalable approach to predict both tumor average and intratumor spatial expression from histopathology images. SIGNIFICANCE: Transcriptome-wide expression morphology deep learning analysis enables prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer.

A Glycolysis-Related Five-Gene Signature Predicts Biochemical Recurrence-Free Survival in Patients With Prostate Adenocarcinoma

Prostate cancer (PCa) is one of the most frequently diagnosed cancers in males worldwide. Approximately 25% of all patients experience biochemical recurrence (BCR) after radical prostatectomy (RP) and BCR indicates increased risk for metastasis and castration resistance. PCa patients with highly glycolytic tumors have a worse prognosis. Thus, this study aimed to explore glycolysis-based predictive biomarkers for BCR. Expression data and clinical information of PCa samples were retrieved from three publicly available datasets. One from The Cancer Genome Atlas (TCGA) dataset was used as the training cohort, and two from the Gene Expression Omnibus (GEO) dataset (GSE54460 and GSE70769) were used as validation cohorts. Using the training cohort, univariate Cox regression survival analysis, robust likelihood-based survival model, and stepwise multiply Cox analysis were sequentially applied to explore predictive glycolysis-related candidates. A five-gene risk score was then constructed based on the Cox coefficient as the following: (−0.8367*GYS2) + (0.3448*STMN1) + (0.3595*PPFIA4) + (−0.1940*KDELR3) + (0.4779*ABCB6). Receiver operating characteristic curve (ROC) analysis was used to identify the optimal cut-off point, and patients were divided into low risk and high risk groups. Kaplan–Meier analysis revealed that high risk group had significantly shorter BCR free survival time as compared with that in low risk group in training and validation cohorts. In conclusion, our data support the glycolysis-based five-gene signature as a novel and robust signature for predicting BCR of PCa patients.

Novel Biomarkers in the Diagnosis of Benign and Malignant Gastrointestinal Diseases

BACKGROUND

Various noninvasive biomarkers have been used in the diagnosis, prognosis, and treatment of different gastrointestinal (GI) diseases for years. Novel technological developments and profound perception of molecular processes related to GI diseases over the last decade have allowed researchers to evaluate genetic, epigenetic, and many other potential molecular biomarkers in different diseases and clinical settings. Here, we present a review of recent and most relevant articles in order to summarize major findings on novel biomarkers in the diagnosis of benign and malignant GI diseases.

SUMMARY

Genetic variations, noncoding RNAs (ncRNAs), cell-free DNA (cfDNA), and microbiome-based biomarkers have been extensively analyzed as potential biomarkers in benign and malignant GI diseases. Multiple single-nucleotide polymorphisms have been linked with a number of GI diseases, and these observations are further being used to build up disease-specific genetic risk scores. Micro-RNAs and long ncRNAs have a large potential as noninvasive biomarkers in the management of inflammatory bowel diseases and GI tumors. Altered microbiome profiles were observed in multiple GI diseases, but most of the findings still lack translational clinical application. As of today, cfDNA appears to be the most potent biomarker for early detection and screening of GI cancers. Key Messages: Novel noninvasive molecular biomarkers show huge potential as useful tools in the diagnostics and management of different GI diseases. However, the use of these biomarkers in real-life clinical practice still remains limited, and further large studies are needed to elucidate the ultimate role of these potential noninvasive clinical tools.

Single-cell sequencing of genomic DNA resolves sub-clonal heterogeneity in a melanoma cell line

We performed shallow single-cell sequencing of genomic DNA across 1475 cells from a cell-line, COLO829, to resolve overall complexity and clonality. This melanoma tumor-line has been previously characterized by multiple technologies and is a benchmark for evaluating somatic alterations. In some of these studies, COLO829 has shown conflicting and/or indeterminate copy number and, thus, single-cell sequencing provides a tool for gaining insight. Following shallow single-cell sequencing, we first identified at least four major sub-clones by discriminant analysis of principal components of single-cell copy number data. Based on clustering, break-point and loss of heterozygosity analysis of aggregated data from sub-clones, we identified distinct hallmark events that were validated within bulk sequencing and spectral karyotyping. In summary, COLO829 exhibits a classical Dutrillaux’s monosomic/trisomic pattern of karyotype evolution with endoreduplication, where consistent sub-clones emerge from the loss/gain of abnormal chromosomes. Overall, our results demonstrate how shallow copy number profiling can uncover hidden biological insights.

Through shallow single-cell sequencing of genomic DNA followed by clustering analysis, Velazquez-Villarreal et al. reveal sub-clones of the melanoma cell line COLO829 and further identify and validate chromosome translocations and copy number changes. This study illustrates how copy number variation analysis can provide insights into cancer cell heterogeneity.

Building de novo reference genome assemblies of complex eukaryotic microorganisms from single nuclei

The advent of novel sequencing techniques has unraveled a tremendous diversity on Earth. Genomic data allow us to understand ecology and function of organisms that we would not otherwise know existed. However, major methodological challenges remain, in particular for multicellular organisms with large genomes. Arbuscular mycorrhizal (AM) fungi are important plant symbionts with cryptic and complex multicellular life cycles, thus representing a suitable model system for method development. Here, we report a novel method for large scale, unbiased nuclear sorting, sequencing, and de novo assembling of AM fungal genomes. After comparative analyses of three assembly workflows we discuss how sequence data from single nuclei can best be used for different downstream analyses such as phylogenomics and comparative genomics of single nuclei. Based on analysis of completeness, we conclude that comprehensive de novo genome assemblies can be produced from six to seven nuclei. The method is highly applicable for a broad range of taxa, and will greatly improve our ability to study multicellular eukaryotes with complex life cycles.

Emerging next-generation sequencing-based discoveries for targeted osteosarcoma therapy

Osteosarcoma (OS) is the most common primary bone malignancy and is frequently lethal via metastasis to the lung. While surgical techniques and adjuvant chemotherapies have emerged to combat this deadly cancer, the 5-year survival rate has plateaued over the past four decades. Therapeutic progress has been notably poor because past technologies have not been able to reveal obscured OS biomarkers and targets. With the advent and implementation of large-scale next-generation sequencing (NGS) studies, various somatic mutations and copy number changes involved in OS progression and metastasis have surfaced. These findings have significantly expanded the amount of genome-informed pathways and candidate genes suitable for targeting in pre-clinical models. Furthermore, NGS analyses comparing primary and matched pulmonary metastatic tumor tissues have catalogued previously unknown prognostic biomarkers in OS. In this review, we delineate the most recent findings in NGS for OS therapy and how this technology has advanced personalized therapy.

Non-templated addition and template switching by Moloney murine leukemia virus (MMLV)-based reverse transcriptases co-occur and compete with each other

Single-cell RNA-Seq (scRNA-Seq) has led to an unprecedented understanding of gene expression and regulation in individual cells. Many scRNA-Seq approaches rely upon the template switching property of Moloney murine leukemia virus (MMLV)-type reverse transcriptases. Template switching is believed to happen in a sequential process involving nontemplated addition of three protruding nucleotides (+CCC) to the 3′-end of the nascent cDNA, which can then anneal to the matching rGrGrG 3′-end of the template-switching oligo (TSO), allowing the reverse transcriptase (RT) to switch templates and continue copying the TSO sequence. In this study, we present a detailed analysis of template switching biases with respect to the RNA template, specifically of the role of the sequence and nature of its 5′-end (capped versus noncapped) in these biases. Our findings confirmed that the presence of a 5′-m⁷G cap enhances template switching efficiency. We also profiled the composition of the nontemplated addition in the absence of TSO and observed that the 5′-end of RNA template influences the terminal transferase activity of the RT. Furthermore, we found that designing new TSOs that pair with the most common nontemplated additions did little to improve template switching efficiency. Our results provide evidence suggesting that, in contrast to the current understanding of the template switching process, nontemplated addition and template switching are concurrent and competing processes.

Isoform-level gene expression patterns in single-cell RNA-sequencing data

Motivation

RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study, we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data.

Results

We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16 562 isoform-pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. Finally, the effects of drop-out events and expression levels of isoforms on ISOP's performances were investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoform-level preference, commitment and heterogeneity in single-cell RNA-sequencing data.

Availability and implementation

The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions

A number of specialized clustering methods have been developed so far for the accurate analysis of single-cell RNA-sequencing (scRNA-seq) expression data, and several reports have been published documenting the performance measures of these clustering methods under different conditions. However, to date, there are no available studies regarding the systematic evaluation of the performance measures of the clustering methods taking into consideration the sample size and cell composition of a given scRNA-seq dataset. Herein, a comprehensive performance evaluation study of 11 selected scRNA-seq clustering methods was performed using synthetic datasets with known sample sizes and number of subpopulations, as well as varying levels of transcriptome complexity. The results indicate that the overall performance of the clustering methods under study are highly dependent on the sample size and complexity of the scRNA-seq dataset. In most of the cases, better clustering performances were obtained as the number of cells in a given expression dataset was increased. The findings of this study also highlight the importance of sample size for the successful detection of rare cell subpopulations with an appropriate clustering tool.

Label-free Mass Cytometry for Unveiling Cellular Metabolic Heterogeneity

Comprehensive analysis of single-cell metabolites is critical since differences in cellular chemical compositions give rise to specialized biological functions. Herein, we propose a label-free mass cytometry by coupling flow cytometry to ESI-MS (named CyESI-MS) for high-coverage and high-throughput detection of cellular metabolites. Cells in suspension were isolated, online extracted by sheath fluid, and lysed during gas-assisted electrospray, followed by real-time MS analysis. Hundreds of metabolites, including nucleotides, amino acids, peptides, carbohydrates, fatty acyls, glycerolipids, glycerophospholipids, and sphingolipids, were detected and identified from one single cell. Discrimination of four types of cancer cell lines and even three subtypes of breast cancer cells was readily achieved using their distinct metabolic profiles. Furthermore, we screened out 102 characteristic ions from 615 detected peak signals for distinguishing breast cancer cell subtypes and identified 40 characteristic molecules which exhibited significant differences among these subtypes and would be potential metabolic markers for clinical diagnosis. CyESI-MS is expected to be a new-generation mass cytometry for studying cell heterogeneity on the metabolic level.

Whole genome amplification of single epithelial cells dissociated from snap-frozen tissue samples in microfluidic platform

Single cell sequencing is a technology capable of analyzing the genome of a single cell within a population. This technology is mostly integrated with microfluidics for precise cell manipulation and fluid handling. So far, most of the microfluidic-based single cell genomic studies have been focused on lab-cultured species or cell lines that are relatively easy to handle following standard microfluidic-based protocols without additional adjustments. The major challenges for performing single cell sequencing on clinical samples is the complex nature of the samples which requires additional sample processing steps to obtain intact single cells of interest without using amplification-inhibitive agents. Fluorescent-activated cell sorting is a common option to obtain single cells from clinical samples for single cell applications but requires >100 000 viable cells in suspension and the need for specialized laboratory and personnel. In this work, we present a protocol that can be used to obtain intact epithelial cells from snap-frozen postsurgical human endometrial tissues for single cell whole genome amplification. Our protocol includes sample thawing, cell dissociation, and labeling for genome amplification of targeted cells. Between 80% and 100% of single cell replicates lead to >25 ng of DNA after amplification with no measurable contamination, sufficient for downstream sequencing.

Viability Assessment Following Anticancer Treatment Requires Single-Cell Visualization

A subset of cells within solid tumors become highly enlarged and enter a state of dormancy (sustained proliferation arrest) in response to anticancer treatment. Although dormant cancer cells might be scored as “dead” in conventional preclinical assays, they remain viable, secrete growth-promoting factors, and can give rise to progeny with stem cell-like properties. Furthermore, cancer cells exhibiting features of apoptosis (e.g., caspase-3 activation) following genotoxic stress can undergo a reversal process called anastasis and survive. Consistent with these observations, single-cell analysis of adherent cultures (solid tumor-derived cell lines with differing p53 status) has demonstrated that virtually all cells—irrespective of their size and morphology—that remain adherent to the culture dish for a long time (weeks) after treatment with anticancer agents exhibit the ability to metabolize 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl- tetrazolium bromide (MTT). The purpose of this commentary is to briefly review these findings and discuss the significance of single-cell (versus population averaged) observation methods for assessment of cancer cell viability and metabolic activity.