Application Of Single-cell RNA Sequencing Research Articles

FSCAM: CAM-Based Feature Selection for Clustering scRNA-seq.

Cell type determination based on transcriptome profiles is a key application of single-cell RNA sequencing (scRNA-seq). It is usually achieved through unsupervised clustering. Good feature selection is capable of improving the clustering accuracy and is a crucial component of single-cell clustering pipelines. However, most current single-cell feature selection methods are univariable filter methods ignoring gene dependency. Even the multivariable filter methods developed in recent years only consider "one-to-many" relationship between genes. In this paper, a novel single-cell feature selection method based on convex analysis of mixtures (FSCAM) is proposed, which takes into account "many-to-many" relationship. Compared to the previous "one-to-many" methods, FSCAM selects genes with a combination of relevancy, redundancy and completeness. Pertinent benchmarking is conducted on the real datasets to validate the superiority of FSCAM. Through plugging into the framework of partition around medoids (PAM) clustering, a single-cell clustering algorithm based on FSCAM method (SCC_FSCAM) is further developed. Comparing SCC_FSCAM with existing advanced clustering algorithms, the results show that our algorithm has advantages in both internal criteria (clustering number) and external criteria (adjusted Rand index) and has a good stability.

ScAnalyzeR: A Comprehensive Software Package With Graphical User Interface for Single-Cell RNA Sequencing Analysis and its Application on Liver Cancer.

Introduction: The application of single-cell RNA sequencing to delineate tissue heterogeneity and complexity has become increasingly popular. Given its tremendous resolution and high-dimensional capacity for in-depth investigation, single-cell RNA sequencing offers an unprecedented research power. Although some popular software packages are available for single-cell RNA sequencing data analysis and visualization, it is still a big challenge for their usage, as they provide only a command-line interface and require significant level of bioinformatics skills. Methods: We have developed scAnalyzeR, which is a single-cell RNA sequencing analysis pipeline with an interactive and user-friendly graphical interface for analyzing and visualizing single-cell RNA sequencing data. It accepts single-cell RNA sequencing data from various technology platforms and different model organisms (human and mouse) and allows flexibility in input file format. It provides functionalities for data preprocessing, quality control, basic summary statistics, dimension reduction, unsupervised clustering, differential gene expression, gene set enrichment analysis, correlation analysis, pseudotime cell trajectory inference, and various visualization plots. It also provides default parameters for easy usage and allows a wide range of flexibility and optimization by accepting user-defined options. It has been developed as a docker image that can be run in any docker-supported environment including Linux, Mac, and Windows, without installing any dependencies. Results: We compared the performance of scAnalyzeR with 2 other graphical tools that are popular for analyzing single-cell RNA sequencing data. The comparison was based on the comprehensiveness of functionalities, ease of usage and flexibility, and execution time. In general, scAnalyzeR outperformed the other tested counterparts in various aspects, demonstrating its superior overall performance. To illustrate the usefulness of scAnalyzeR in cancer research, we have analyzed the in-house liver cancer single-cell RNA sequencing dataset. Liver cancer tumor cells were revealed to have multiple subpopulations with distinctive gene expression signatures. Conclusion: scAnalyzeR has comprehensive functionalities and demonstrated usability. We anticipate more functionalities to be adopted in the future development.

Transcription factor network analysis based on single cell RNA-seq identifies that Trichostatin-a reverses docetaxel resistance in prostate Cancer

BackgroundOvercoming drug resistance is critical for increasing the survival rate of prostate cancer (PCa). Docetaxel is the first cytotoxic chemotherapeutical approved for treatment of PCa. However, 99% of PCa patients will develop resistance to docetaxel within 3 years. Understanding how resistance arises is important to increasing PCa survival.MethodsIn this study, we modeled docetaxel resistance using two PCa cell lines: DU145 and PC3. Using the Passing Attributes between Networks for Data Assimilation (PANDA) method to model transcription factor (TF) activity networks in both sensitive and resistant variants of the two cell lines. We identified edges and nodes shared by both PCa cell lines that composed a shared TF network that modeled changes which occur during acquisition of docetaxel resistance in PCa. We subjected the shared TF network to connectivity map analysis (CMAP) to identify potential drugs that could disrupt the resistant networks. We validated the candidate drug in combination with docetaxel to treat docetaxel-resistant PCa in both in vitro and in vivo models.ResultsIn the final shared TF network, 10 TF nodes were identified as the main nodes for the development of docetaxel resistance. CMAP analysis of the shared TF network identified trichostatin A (TSA) as a candidate adjuvant to reverse docetaxel resistance. In cell lines, the addition of TSA to docetaxel enhanced cytotoxicity of docetaxel resistant PCa cells with an associated reduction of the IC50 of docetaxel on the resistant cells. In the PCa mouse model, combination of TSA and docetaxel reduced tumor growth and final weight greater than either drug alone or vehicle.ConclusionsWe identified a shared TF activity network that drives docetaxel resistance in PCa. We also demonstrated a novel combination therapy to overcome this resistance. This study highlights the usage of novel application of single cell RNA-sequencing and subsequent network analyses that can reveal novel insights which have the potential to improve clinical outcomes.

Update on the value and application of single-cell RNA sequencing in human cardiovascular diseases

近年来转录组测序技术实现了从低通量到高通量的快速发展。但传统的转录组测序技术局限于群体细胞基因表达平均水平，忽视了细胞间的异质性，而单细胞转录组测序可在单个细胞水平得到基因表达图谱，在分析细胞异质性、寻找心血管疾病新的诊断和治疗靶点以及促进心脏再生中发挥重要作用。该文综述了单细胞转录组测序在心血管疾病领域应用的研究进展。.

The Application of Single-Cell RNA Sequencing in Mammalian Meiosis Studies.

Meiosis is a cellular division process that produces gametes for sexual reproduction. Disruption of complex events throughout meiosis, such as synapsis and homologous recombination, can lead to infertility and aneuploidy. To reveal the molecular mechanisms of these events, transcriptome studies of specific substages must be conducted. However, conventional methods, such as bulk RNA-seq and RT-qPCR, are not able to detect the transcriptional variations effectively and precisely, especially for identifying cell types and stages with subtle differences. In recent years, mammalian meiotic transcriptomes have been intensively studied at the single-cell level by using single-cell RNA-seq (scRNA-seq) approaches, especially through two widely used platforms, Smart-seq2 and Drop-seq. The scRNA-seq protocols along with their downstream analysis enable researchers to accurately identify cell heterogeneities and investigate meiotic transcriptomes at a higher resolution. In this review, we compared bulk RNA-seq and scRNA-seq to show the advantages of the scRNA-seq in meiosis studies; meanwhile, we also pointed out the challenges and limitations of the scRNA-seq. We listed recent findings from mammalian meiosis (male and female) studies where scRNA-seq applied. Next, we summarized the scRNA-seq analysis methods and the meiotic marker genes from spermatocytes and oocytes. Specifically, we emphasized the different features of the two scRNA-seq protocols (Smart-seq2 and Drop-seq) in the context of meiosis studies and discussed their strengths and weaknesses in terms of different research purposes. Finally, we discussed the future applications of scRNA-seq in the meiosis field.

Cell-specific epigenetic changes in atherosclerosis.

Atherosclerosis is a disease of large and medium arteries that can lead to life-threatening cerebrovascular and cardiovascular consequences such as heart failure and stroke and is a major contributor to cardiovascular-related mortality worldwide. Atherosclerosis development is a complex process that involves specific structural, functional and transcriptional changes in different vascular cell populations at different stages of the disease. The application of single-cell RNA sequencing (scRNA-seq) analysis has discovered not only disease-related cell-specific transcriptomic profiles but also novel subpopulations of cells once thought as homogenous cell populations. Vascular cells undergo specific transcriptional changes during the entire course of the disease. Epigenetics is the instruction-set-architecture in living cells that defines and maintains the cellular identity by regulating the cellular transcriptome. Although different cells contain the same genetic material, they have different epigenomic signatures. The epigenome is plastic, dynamic and highly responsive to environmental stimuli. Modifications to the epigenome are driven by an array of epigenetic enzymes generally referred to as writers, erasers and readers that define cellular fate and destiny. The reversibility of these modifications raises hope for finding novel therapeutic targets for modifiable pathological conditions including atherosclerosis where the involvement of epigenetics is increasingly appreciated. This article provides a critical review of the up-to-date research in the field of epigenetics mainly focusing on in vivo settings in the context of the cellular role of individual vascular cell types in the development of atherosclerosis.

Prudent application of single-cell RNA sequencing in understanding cellular features and functional phenotypes in cancer studies.

This decade has seen remarkable advances in the field of high-throughput single cell techniques. Single-cell RNA sequencing (scRNA-seq) has proven to be a powerful strategy to study the heterogeneity in clinical samples, providing an unbiased approach to uncover the characteristics in different cell subsets. To ensure the reproducibility and robustness of biological discoveries, researchers need to be aware of hidden caveats in tissue dissociation, cell capturing and transcripts measurement which may affect cell composition assessment and cellular function annotation. With measured interpretation of data and innovations in experimental and technical approaches, scRNA-seq can greatly unravel the heterogeneity in complex system and improve our understandings in tissue homeostasis and cancer biology.

Research advances on application of single-cell RNA sequencing in islet cell biology

After the first single-cell RNA sequencing of human islet in 2016, a large number of analysis of RNA sequencing at single-cell level on mouse and human islets emerged, which has brought new progress to the research of islet cell biology. It has been proved that single-cell RNA sequencing can characterize rare types of endocrine cells, find the cellular heterogeneity of typical endocrine cells and new cell subtypes, analyze the differences of endocrine cells among species, and describe different states of various types of islet cells in developmental processes and metabolic diseases more accurately. Although the detection efficiency of single-cell RNA sequencing for low abundance transcripts is relatively low at present, with the improvement of technology and the progress of analytical methods, this technology will undoubtedly become a powerful tool to explore the cellular heterogeneity, development, and biological characteristics of metabolic diseases.

Lung megakaryocytes display distinct transcriptional and phenotypic properties.

Megakaryocytes (MKs) are responsible for platelet biogenesis, which is believed to occur canonically in adult bone marrow (BM) and in the fetal liver during development. However, emerging evidence highlights the lung as a previously underappreciated residence for MKs that may contribute significantly to circulating platelet mass. Although a diversity of cells specific to the BM is known to promote the maturation and trafficking of MKs, little investigation into the impact of the lung niche on the development and function of MKs has been done. Here, we describe the application of single-cell RNA sequencing, coupled with histological, ploidy, and flow cytometric analyses, to profile primary MKs derived from syngeneic mouse lung and hematopoietic tissues. Transcriptional profiling demonstrated that lung MKs have a unique signature distinct from their hematopoietic counterparts, with lung MKs displaying enrichment for maturation markers, potentially indicating a propensity for more efficient platelet production. Reciprocally, fetal lung MKs also showed the robust expression of cytokines and growth factors that are known to promote lung development. Lastly, lung MKs possess an enrichment profile skewed toward roles in immunity and inflammation. These findings highlight the existence of a lung-specific MK phenotype and support the notion that the lung plays an independent role in the development and functional maturation of MKs. The immune phenotype displayed by lung MKs also introduces their potential role in microbial surveillance and antigen presentation.

The Application of Single-Cell RNA Sequencing in Studies of Autoimmune Diseases: a Comprehensive Review.

Complex composition is one of the most important features of the immune system, involving many types of organs, tissues, cells, and molecules that perform immune functions. The normal function of each component of the immune system is the guarantee for maintaining the relatively stable immune function of the body. When the self-immune tolerance mechanism of the body is unregulated or destroyed, the immune system reacts to autoantigens, resulting in damage to self-tissues and organs or an immunopathological state with abnormal functions. Autoimmune diseases are diverse, and their pathogenesis is complicated. Various immune cells and their interactions play significant roles in the occurrence and development of diseases. The solution to heterogeneity of immune cells is the basic science and translational understanding of how genes and the environment interact to induce disease so that we can develop personalized medicine, a goal that has to this point eluded scientists. Single-cell RNA sequencing (scRNA-Seq) refers to a new technique allowing high-throughput sequencing analysis of the whole transcriptome to reveal the gene expression status of individual cells. It has emerged as an indispensable tool in the field of life science research, and can help identify the complex mechanism of cell heterogeneity, discover new cell subsets, and help uncover the molecular mechanisms of pathogenesis, the evolution of disorders, and drug resistance. This information can provide us with new strategies for diagnosis and prognostic evaluation, as well as monitoring treatment responses. In this review, we summarize the crucial experimental procedures used for single-cell RNA sequencing, and the current applications of this technique to study autoimmune diseases are described in detail. This technique will be widely used in more in-depth studies of autoimmune diseases and will contribute to the diagnosis and therapies of these disorders.

A reliable strategy for single-cell RNA sequencing analysis using cryoconserved primary cortical cells

BackgroundThe application of single-cell RNA sequencing (scRNASeq) represents a unique approach to identify hundreds to millions of cells in mammalian cortical multilayers at different stages of embryogenesis. ScRNASeq technology applied to neurological studies requires the use of fresh starting materials because standard cryopreservation methods do not guarantee high viability of cortical primary cells derived from dissected brain areas. New methodHere we set up and validate an innovative strategy to perform scRNASeq studies in cryopreserved primary cortical cells isolated from E15.5 mouse embryo. In order to freeze cortical primary cells, we have employed Neurostore, a medium able to guarantee high viability and cell composition of embryonic cortex after thawing. Comparison with existing methodsWe showed for the first time the possibility to run scRNASeq experiments on primary cortical cells in an off-line set-up, ensuring cellular integrity and diversity. ResultsBy trypan blue assay and flow cytometry analysis, we found that Neurostore-cryopreserved cortical cells showed approximately 95 % of viability. Satisfactory RNA recovery and cDNA libraries were achieved. Transcriptome sequencing of 35,763 cryoconserved single cells yielded a robust data-set, identifying 25 cell clusters in three biological samples. Prevalence of peculiar neural populations before and after the cryopreservation-resuscitation procedure was verified by marker gene expression and immunofluorescence analysis. ConclusionsOur findings support the evidence that frozen primary cortical cells can be successfully employed in scRNASeq experiments allowing an unprecedented flexibility in experimental procedures, such as sample preparation and subsequent processing steps performed in different locations.

The Application of Single-Cell RNA Sequencing in Vaccinology.

Single-cell RNA sequencing allows highly detailed profiling of cellular immune responses from limited-volume samples, advancing prospects of a new era of systems immunology. The power of single-cell RNA sequencing offers various opportunities to decipher the immune response to infectious diseases and vaccines. Here, we describe the potential uses of single-cell RNA sequencing methods in prophylactic vaccine development, concentrating on infectious diseases including COVID-19. Using examples from several diseases, we review how single-cell RNA sequencing has been used to evaluate the immunological response to different vaccine platforms and regimens. By highlighting published and unpublished single-cell RNA sequencing studies relevant to vaccinology, we discuss some general considerations how the field could be enriched with the widespread adoption of this technology.

Autoimmunity in 2019.

Based on the PubMed data, we have been performing a yearly evaluation of the publications related to autoimmune diseases and immunology to ascertain the relative weight of the former in the scientific literature. It is particularly intriguing to observe that despite the numerous new avenues of immune-related mechanisms, such as cancer immunotherapy, the proportion of immunology manuscripts related to autoimmunity continues to increase and has been approaching 20% in 2019. As in the previous 13years, we performed an arbitrary selection of the peer-reviewed articles published by the major dedicated Journals and discussed the common themes which continue to outnumber peculiarites in autoimmune diseases. The investigated areas included systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), psoriatic arthritis (PsA), autoantibodies (autoAbs), and common therapeutic avenues and novel pathogenic mechanisms for autoimmune conditions. Some examples include new pathogenetic evidence which is well represented by IL21 or P2X7 receptor (P2X7R) in SLE or the application of single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing (RNA-seq), and flow cytometry for the analysis of different cellular populations in RA. Cumulatively and of interest to the clinicians, a large number of findings continue to underline the importance of a strict relationship between basic and clinical science to define new pathogenetic and therapeutic developments. The therapeutic pipeline in autoimmunity continues to grow and maintain a constant flow of new molecules, as well illustrated in RA and PsA, and this is most certainly derived from the new basic evidence and the high-throughput tools applied to autoimmune diseases.

Application of Single-cell RNA Sequencing in Research on Tumor Immune Microenvironment

Tumors are highly complex systems. Understanding the compositions and functions of the tumor immune microenvironment is a prerequisite for effective tumor immunotherapy. Single-cell RNA sequencing can detect the transcriptome of a cell at the resolution of single-cell level,describe its functional status,and thus deepen the understanding of the composition and function of different cell clusters in tumor immune microenvironment. This article reviews the application of single-cell RNA sequencing in research on tumor immune microenvironment.

Application of Single-Cell RNA Sequencing in Pancreatic Cancer and the Endocrine Pancreas.

The pancreas is a complex organ composed of an endocrine (pancreatic islets) and an exocrine portion. This mixed cell population has resulted in an implacable barrier to exploring the detailed mechanism and function of each cell type in previous investigative approaches. In recent years, single-cell RNA sequencing (scRNA-seq) technologies have provided in-depth analysis of cell heterogeneity in the pancreas and in pancreatic cancer. It is especially effective in cell-type-specific molecule identification and detection of interactions between cancer cells and the stromal microenvironment. To date, numerous reports have described the application of scRNA-seq in studies of pancreatic islets and pancreatic cancer. The aim of this paper is to review recent advances of pancreatic transcriptomics and pancreatic cancer using scRNA-seq strategies.

Design and application of single-cell RNA sequencing to study kidney immune cells in lupus nephritis.

The immune mechanisms that cause tissue injury in lupus nephritis have been challenging to define. The advent of high-dimensional cellular analyses, such as single-cell RNA sequencing, has enabled detailed characterization of the cell populations present in small biopsy samples of kidney tissue. In parallel, the development of methods that cryopreserve kidney biopsyspecimens in a manner that preserves intact, viable cells, has enabled the uniform analysis of tissue samples collected at multiple sites and across many geographic areas and demographic cohorts with high-dimensional platforms. The application of these methods to kidney biopsy samples from patients with lupus nephritis has begun to define the phenotypes of both infiltrating and resident immune cells, as well as parenchymal cells, present in nephritic kidneys. The detection of similar immune cell populations in urine suggests that it might be possible to non-invasively monitor immune activation in kidneys. Once applied to large patient cohorts, these high-dimensional studies might enable patient stratification according to patterns of immune cell activation in the kidney or identify disease features that can be used as surrogate measures of efficacy in clinical trials. Applied broadly across multiple inflammatory kidney diseases, these studies promise to enormously expand our understanding of renal inflammation in the next decade.

Using single-cell technologies to map the human immune system - implications for nephrology.

Advances in single-cell technologies are transforming our understanding of cellular identity. For instance, the application of single-cell RNA sequencing and mass cytometry technologies to the study of immune cell populations in blood, secondary lymphoid organs and the renal tract is helping researchers to map the complex immune landscape within the kidney, define cell ontogeny and understand the relationship of kidney-resident immune cells with their circulating counterparts. These studies also provide insights into the interactions of immune cell populations with neighbouring epithelial and endothelial cells in health, and across a range of kidney diseases and cancer. These data have translational potential and will aid the identification of drug targets and enable better prediction of off-target effects. The application of single-cell technologies to clinical renal biopsy samples, or even cells within urine, will improve diagnostic accuracy and assist with personalized prognostication for patients with various kidney diseases. A comparison of immune cell types in peripheral blood and secondary lymphoid organs in healthyindividuals and in patients with systemic autoimmune diseases that affect the kidney will also help to unravel the mechanisms that underpin the breakdown in self-tolerance and propagation of autoimmune responses. Together, these immune cell atlases have the potential to transform nephrology.

Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma.

BackgroundIntratumoral heterogeneity hampers the success of marker-based anticancer treatment because the targeted therapy may eliminate a specific subpopulation of tumor cells while leaving others unharmed. Accordingly, a rational strategy minimizing survival of the drug-resistant subpopulation is essential to achieve long-term therapeutic efficacy.ResultsUsing single-cell RNA sequencing (RNA-seq), we examine the intratumoral heterogeneity of a pair of primary renal cell carcinoma and its lung metastasis. Activation of drug target pathways demonstrates considerable variability between the primary and metastatic sites, as well as among individual cancer cells within each site. Based on the prediction of multiple drug target pathway activation, we derive a combinatorial regimen co-targeting two mutually exclusive pathways for the metastatic cancer cells. This combinatorial strategy shows significant increase in the treatment efficacy over monotherapy in the experimental validation using patient-derived xenograft platforms in vitro and in vivo.ConclusionsOur findings demonstrate the investigational application of single-cell RNA-seq in the design of an anticancer regimen. The approach may overcome intratumoral heterogeneity which hampers the success of precision medicine.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0945-9) contains supplementary material, which is available to authorized users.