Single-cell RNAseq reveals seven classes of colonic sensory neuron

ObjectiveIntegration of nutritional, microbial and inflammatory events along the gut-brain axis can alter bowel physiology and organism behaviour. Colonic sensory neurons activate reflex pathways and give rise to conscious sensation, but the diversity and division of function within these neurons is poorly understood. The identification of signalling pathways contributing to visceral sensation is constrained by a paucity of molecular markers. Here we address this by comprehensive transcriptomic profiling and unsupervised clustering of individual mouse colonic sensory neurons.DesignUnbiased single-cell RNA-sequencing was performed on retrogradely traced mouse colonic sensory neurons isolated from both thoracolumbar (TL) and lumbosacral (LS) dorsal root ganglia associated with lumbar splanchnic and pelvic spinal pathways, respectively. Identified neuronal subtypes were validated by single-cell qRT-PCR, immunohistochemistry (IHC) and Ca2+-imaging.ResultsTranscriptomic profiling and unsupervised clustering of 314 colonic sensory neurons revealed seven neuronal subtypes. Of these, five neuronal subtypes accounted for 99% of TL neurons, with LS neurons almost exclusively populating the remaining two subtypes. We identify and classify neurons based on novel subtype-specific marker genes using single-cell qRT-PCR and IHC to validate subtypes derived from RNA-sequencing. Lastly, functional Ca2+-imaging was conducted on colonic sensory neurons to demonstrate subtype-selective differential agonist activation.ConclusionsWe identify seven subtypes of colonic sensory neurons using unbiased single-cell RNA-sequencing and confirm translation of patterning to protein expression, describing sensory diversity encompassing all modalities of colonic neuronal sensitivity. These results provide a pathway to molecular interrogation of colonic sensory innervation in health and disease, together with identifying novel targets for drug development.

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Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

Briefings in Bioinformatics ◽

10.1093/bib/bbz096 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1581-1595 ◽

Cited By ~ 6

Author(s):

Xinlei Zhao ◽

Shuang Wu ◽

Nan Fang ◽

Xiao Sun ◽

Jue Fan

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Reference Data ◽

Predictive Accuracy ◽

Cell Types ◽

Superior Performance ◽

Marker Genes ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

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Unveiling the heterogeneity of NKT cells in the liver through single cell RNA sequencing

Scientific Reports ◽

10.1038/s41598-020-76659-1 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Hao Shen ◽

Chan Gu ◽

Tao Liang ◽

Haifeng Liu ◽

Fan Guo ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Subset ◽

Nkt Cells ◽

Organ Distribution ◽

Marker Genes ◽

Type I ◽

Nkt Cell ◽

Single Cell Rna Sequencing

Abstract CD1d-dependent type I NKT cells, which are activated by lipid antigen, are known to play important roles in innate and adaptive immunity, as are a portion of type II NKT cells. However, the heterogeneity of NKT cells, especially NKT-like cells, remains largely unknown. Here, we report the profiling of NKT (NK1.1+CD3e+) cells in livers from wild type (WT), Jα18-deficient and CD1d-deficient mice by single-cell RNA sequencing. Unbiased transcriptional clustering revealed distinct cell subsets. The transcriptomic profiles identified the well-known CD1d-dependent NKT cells and defined two CD1d-independent NKT cell subsets. In addition, validation of marker genes revealed the differential organ distribution and landscape of NKT cell subsets during liver tumor progression. More importantly, we found that CD1d-independent Sca-1−CD62L+ NKT cells showed a strong ability to secrete IFN-γ after costimulation with IL-2, IL-12 and IL-18 in vitro. Collectively, our findings provide a comprehensive characterization of NKT cell heterogeneity and unveil a previously undefined functional NKT cell subset.

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VASC: dimension reduction and visualization of single cell RNA sequencing data by deep variational autoencoder

10.1101/199315 ◽

2017 ◽

Cited By ~ 6

Author(s):

Dongfang Wang ◽

Jin Gu

Keyword(s):

Dimension Reduction ◽

Single Cell ◽

Rna Sequencing ◽

Original Data ◽

Marker Genes ◽

Single Cell Level ◽

Sequencing Data ◽

Cell Level ◽

Variational Autoencoder ◽

Single Cell Rna Sequencing

AbstractSingle cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities in single cell level. It is an important step for studying cell sub-populations and lineages based on scRNA-seq data by finding an effective low-dimensional representation and visualization of the original data. The scRNA-seq data are much noiser than traditional bulk RNA-Seq: in the single cell level, the transcriptional fluctuations are much larger than the average of a cell population and the low amount of RNA transcripts will increase the rate of technical dropout events. In this study, we proposed VASC (deep Variational Autoencoder for scRNA-seq data), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. It can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on twenty datasets, VASC shows superior performances in most cases and broader dataset compatibility compared with four state-of-the-art dimension reduction methods. Then, for a case study of pre-implantation embryos, VASC successfully re-establishes the cell dynamics and identifies several candidate marker genes associated with the early embryo development.

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Information Theoretic Feature Selection Methods for Single Cell RNA-Sequencing

10.1101/646919 ◽

2019 ◽

Author(s):

Umang Varma ◽

Justin Colacino ◽

Anna Gilbert

Keyword(s):

Feature Selection ◽

Single Cell ◽

Rna Sequencing ◽

Complex Mixture ◽

Cell Types ◽

Marker Genes ◽

Selection Methods ◽

Information Theoretic ◽

Single Cell Rna Sequencing ◽

Information Theoretic Methods

AbstractSingle cell RNA-sequencing (scRNA-seq) technologies have generated an expansive amount of new biological information, revealing new cellular populations and hierarchical relationships. A number of technologies complementary to scRNA-seq rely on the selection of a smaller number of marker genes (or features) to accurately differentiate cell types within a complex mixture of cells. In this paper, we benchmark differential expression methods against information-theoretic feature selection methods to evaluate the ability of these algorithms to identify small and efficient sets of genes that are informative about cell types. Unlike differential methods, that are strictly binary and univariate, information-theoretic methods can be used as any combination of binary or multiclass and univariate or multivariate. We show for some datasets, information theoretic methods can reveal genes that are both distinct from those selected by traditional algorithms and that are as informative, if not more, of the class labels. We also present detailed and principled theoretical analyses of these algorithms. All information theoretic methods in this paper are implemented in our PicturedRocks Python package that is compatible with the widely used scanpy package.

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SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation

10.1101/2020.01.13.904649 ◽

2020 ◽

Cited By ~ 2

Author(s):

Zhirui Hu ◽

Songpeng Zu ◽

Jun S. Liu

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Stem Cell Differentiation ◽

Marker Genes ◽

Cell Type ◽

Imputation Methods ◽

Cell Clusters ◽

Main Challenge ◽

Single Cell Rna Sequencing ◽

Gene Modules

AbstractA main challenge in analyzing single-cell RNA sequencing (scRNASeq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called “dropout”), the gene expression matrix has substantial zero read counts. Existing imputation methods either treat each cell or each gene identically and independently, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs, which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering. Optionally, SIMPLEs can integrate bulk RNASeq data for estimating dropout rates. In simulations, SIMPLEs performed significantly better than prevailing scRNASeq imputation methods by various metrics. By applying SIMPLEs to several real data sets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.

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scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

BMC Bioinformatics ◽

10.1186/s12859-021-04028-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Bobby Ranjan ◽

Florian Schmidt ◽

Wenjie Sun ◽

Jinyu Park ◽

Mohammad Amin Honardoost ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Differentially Expressed Genes ◽

Cell Types ◽

Unsupervised Clustering ◽

Differentially Expressed ◽

Consensus Clustering ◽

Cell Type ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Abstract Background Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering approaches have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. Results We present scConsensus, an $${\mathbf {R}}$$ R framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. Conclusions scConsensus combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. scConsensus is implemented in $${\mathbf {R}}$$ R and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

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Identification of silkworm hemocyte subsets and analysis of their response to BmNPV infection based on single-cell RNA sequencing

10.1101/2020.10.18.344127 ◽

2020 ◽

Author(s):

Min Feng ◽

Junming Xia ◽

Shigang Fei ◽

Xiong Wang ◽

Yaohong Zhou ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Current Knowledge ◽

Early Stage ◽

Expression Profiles ◽

Marker Genes ◽

Potential Marker ◽

Wide Range ◽

Single Cell Rna Sequencing

AbstractA wide range of hemocyte types exist in insects but a full definition of the different subclasses is not yet established. The current knowledge of the classification of silkworm hemocytes mainly comes from morphology rather than specific markers, so our understanding of the detailed classification, hemocyte lineage and functions of silkworm hemocytes is very incomplete. Bombyx mori nucleopolyhedrovirus (BmNPV) is a representative member of the baculoviruses, which are a major pathogens that specifically infects silkworms and cause serious loss in sericulture industry. Here, we performed single-cell RNA sequencing (scRNA-seq) of silkworm hemocytes in BmNPV and mock-infected larvae to comprehensively identify silkworm hemocyte subsets and determined specific molecular and cellular characteristics in each hemocyte subset before and after viral infection. A total of 19 cell clusters and their potential marker genes were identified in silkworm hemocytes. Among these hemocyte clusters, clusters 0, 1, 2, 5 and 9 might be granulocytes (GR); clusters 14 and 17 were predicted as plasmatocytes (PL); cluster 18 was tentatively identified as spherulocytes (SP); and clusters 7 and 11 could possibly correspond to oenocytoids (OE). In addition, all of the hemocyte clusters were infected by BmNPV and some infected cells carried high viral-load in silkworm larvae at 3 day post infection (dpi). Interestingly, BmNPV infection can cause severe and diverse changes in gene expression in hemocytes. Cells belonging to the infection group mainly located at the early stage of the pseudotime trajectories. Furthermore, we found that BmNPV infection suppresses the immune response in the major hemocyte types. In summary, our scRNA-seq analysis revealed the diversity of silkworm hemocytes and provided a rich resource of gene expression profiles for a systems-level understanding of their functions in the uninfected condition and as a response to BmNPV.

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SMaSH: A scalable, general marker gene identification framework for single-cell RNA sequencing and Spatial Transcriptomics

10.1101/2021.04.08.438978 ◽

2021 ◽

Author(s):

Michael E Nelson ◽

Simone G Riva ◽

Ann Cvejic

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Marker Gene ◽

Marker Genes ◽

Sequencing Data ◽

Computational Framework ◽

Data Set ◽

Spatially Resolved ◽

Single Cell Rna Sequencing ◽

The Given

Spatial transcriptomics is revolutionising the study of single-cell RNA and tissue-wide cell heterogeneity, but few robust methods connecting spatially resolved cells to so-called marker genes from single-cell RNA sequencing, which generate significant insight gleaned from spatial methods, exist. Here we present SMaSH, a general computational framework for extracting key marker genes from single-cell RNA sequencing data for spatial transcriptomics approaches. SMaSH extracts robust and biologically well-motivated marker genes, which characterise the given data-set better than existing and limited computational approaches for global marker gene calculation.

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Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma

Oncogene ◽

10.1038/s41388-021-02054-3 ◽

2021 ◽

Author(s):

Philip Bischoff ◽

Alexandra Trinks ◽

Benedikt Obermayer ◽

Jan Patrick Pett ◽

Jennifer Wiederspahn ◽

...

Keyword(s):

T Cells ◽

Dendritic Cells ◽

Tumor Microenvironment ◽

Lung Adenocarcinoma ◽

Single Cell ◽

Rna Sequencing ◽

Histological Grade ◽

Cell Populations ◽

Marker Genes ◽

Single Cell Rna Sequencing

AbstractRecent developments in immuno-oncology demonstrate that not only cancer cells, but also the tumor microenvironment can guide precision medicine. A comprehensive and in-depth characterization of the tumor microenvironment is challenging since its cell populations are diverse and can be important even if scarce. To identify clinically relevant microenvironmental and cancer features, we applied single-cell RNA sequencing to ten human lung adenocarcinomas and ten normal control tissues. Our analyses revealed heterogeneous carcinoma cell transcriptomes reflecting histological grade and oncogenic pathway activities, and two distinct microenvironmental patterns. The immune-activated CP²E microenvironment was composed of cancer-associated myofibroblasts, proinflammatory monocyte-derived macrophages, plasmacytoid dendritic cells and exhausted CD8+ T cells, and was prognostically unfavorable. In contrast, the inert N³MC microenvironment was characterized by normal-like myofibroblasts, non-inflammatory monocyte-derived macrophages, NK cells, myeloid dendritic cells and conventional T cells, and was associated with a favorable prognosis. Microenvironmental marker genes and signatures identified in single-cell profiles had progonostic value in bulk tumor profiles. In summary, single-cell RNA profiling of lung adenocarcinoma provides additional prognostic information based on the microenvironment, and may help to predict therapy response and to reveal possible target cell populations for future therapeutic approaches.

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Temporal single-cell transcriptomes of zebrafish spinal cord pMN progenitors reveal distinct neuronal and glial progenitor populations

10.1101/2021.04.28.441874 ◽

2021 ◽

Author(s):

Kayt Scott ◽

Rebecca O’Rourke ◽

Caitlin C. Winkler ◽

Christina A. Kearns ◽

Bruce Appel

Keyword(s):

Spinal Cord ◽

Transcription Factor ◽

Single Cell ◽

Progenitor Cells ◽

Rna Sequencing ◽

Motor Neurons ◽

Marker Genes ◽

List Type ◽

Fate Mapping ◽

Single Cell Rna Sequencing

AbstractVentral spinal cord progenitor cells, which express the basic helix loop helix transcription factor Olig2, sequentially produce motor neurons and oligodendrocyte precursor cells (OPCs). Following specification some OPCs differentiate as myelinating oligodendrocytes while others persist as OPCs. Though a considerable amount of work has described the molecular profiles that define motor neurons, OPCs, and oligodendrocytes, less is known about the progenitors that produce them. To identify the developmental origins and transcriptional profiles of motor neurons and OPCs, we performed single-cell RNA sequencing on isolated pMN cells from embryonic zebrafish trunk tissue at stages that encompassed motor neurogenesis, OPC specification, and initiation of oligodendrocyte differentiation. Downstream analyses revealed two distinct pMN progenitor populations: one that appears to produce neurons and one that appears to produce OPCs. This latter population, called Pre-OPCs, is marked by expression of GS Homeobox 2 (gsx2), a gene that encodes a homeobox transcription factor. Using fluorescent in situ hybridizations, we identified gsx2-expressing Pre-OPCs in the spinal cord prior to expression of canonical OPC marker genes. Our data therefore reveal heterogeneous gene expression profiles among pMN progenitors, supporting prior fate mapping evidence.HighlightsSingle-cell RNA sequencing reveals the developmental trajectories of neurons and glia that arise from spinal cord pMN progenitor cells in zebrafish embryosTranscriptionally distinct subpopulations of pMN progenitors are the apparent sources of neurons or oligodendrocytes, consistent with fate mapping datagsx2 expression marks pMN progenitors that produce oligodendrocyte lineage cells

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