Single-cell RNA profiling links ncRNAs to spatiotemporal gene expression during C. elegans embryogenesis

Abstract Recent studies show that non-coding RNAs (ncRNAs) can regulate the expression of protein-coding genes and play important roles in mammalian development. Previous studies have revealed that during C. elegans (Caenorhabditis elegans) embryo development, numerous genes in each cell are spatiotemporally regulated, causing the cell to differentiate into distinct cell types and tissues. We ask whether ncRNAs participate in the spatiotemporal regulation of genes in different types of cells and tissues during the embryogenesis of C. elegans. Here, by using marker-free full-length high-depth single-cell RNA sequencing (scRNA-seq) technique, we sequence the whole transcriptomes from 1031 embryonic cells of C. elegans and detect 20,431 protein-coding genes, including 22 cell-type-specific protein-coding markers, and 9843 ncRNAs including 11 cell-type-specific ncRNA markers. We induce a ncRNAs-based clustering strategy as a complementary strategy to the protein-coding gene-based clustering strategy for single-cell classification. We identify 94 ncRNAs that have never been reported to regulate gene expressions, are co-expressed with 1208 protein-coding genes in cell type specific and/or embryo time specific manners. Our findings suggest that these ncRNAs could potentially influence the spatiotemporal expression of the corresponding genes during the embryogenesis of C. elegans.

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A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution

Science ◽

10.1126/science.aax1971 ◽

2019 ◽

Vol 365 (6459) ◽

pp. eaax1971 ◽

Cited By ~ 65

Author(s):

Jonathan S. Packer ◽

Qin Zhu ◽

Chau Huynh ◽

Priya Sivaramakrishnan ◽

Elicia Preston ◽

...

Keyword(s):

Nervous System ◽

Caenorhabditis Elegans ◽

Single Cell ◽

Molecular Basis ◽

Cell Types ◽

Embryonic Cells ◽

Cell Type ◽

C Elegans ◽

Exact Position ◽

Sister Cells

Caenorhabditis elegans is an animal with few cells but a wide diversity of cell types. In this study, we characterize the molecular basis for their specification by profiling the transcriptomes of 86,024 single embryonic cells. We identify 502 terminal and preterminal cell types, mapping most single-cell transcriptomes to their exact position in C. elegans’ invariant lineage. Using these annotations, we find that (i) the correlation between a cell’s lineage and its transcriptome increases from middle to late gastrulation, then falls substantially as cells in the nervous system and pharynx adopt their terminal fates; (ii) multilineage priming contributes to the differentiation of sister cells at dozens of lineage branches; and (iii) most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state.

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A lineage-resolved molecular atlas of C. elegans embryogenesis at single cell resolution

10.1101/565549 ◽

2019 ◽

Cited By ~ 10

Author(s):

Jonathan S. Packer ◽

Qin Zhu ◽

Chau Huynh ◽

Priya Sivaramakrishnan ◽

Elicia Preston ◽

...

Keyword(s):

Single Cell ◽

Molecular Basis ◽

Cell Types ◽

Early Embryo ◽

Embryonic Cells ◽

Cell Type ◽

C Elegans ◽

Cell Divisions ◽

Exact Position ◽

The Relationship

AbstractC. elegans is an animal with few cells, but a striking diversity of cell types. Here, we characterize the molecular basis for their specification by profiling the transcriptomes of 84,625 single embryonic cells. We identify 284 terminal and pre-terminal cell types, mapping most single cell transcriptomes to their exact position in C. elegans’ invariant lineage. We use these annotations to perform the first quantitative analysis of the relationship between lineage and the transcriptome for a whole organism. We find that a strong lineage-transcriptome correlation in the early embryo breaks down in the final two cell divisions as cells adopt their terminal fates and that most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state. Users can explore our data with a graphical application “VisCello”.

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Single-cell transcriptomics and cell-specific proteomics reveals molecular signatures of sleep

10.1101/2020.12.18.423331 ◽

2020 ◽

Author(s):

Pawan K. Jha ◽

Utham K. Valekunja ◽

Sandipan Ray ◽

Mathieu Nollet ◽

Akhilesh B. Reddy

Keyword(s):

Single Cell ◽

Molecular Mechanisms ◽

Cell Types ◽

Brain Regions ◽

Specific Protein ◽

Cell Type ◽

Transcriptional Responses ◽

A Cell ◽

Cell Type Specific ◽

Different Cell Types

Every day, we sleep for a third of the day. Sleep is important for cognition, brain waste clearance, metabolism, and immune responses. The molecular mechanisms governing sleep are largely unknown. Here, we used a combination of single cell RNA sequencing and cell-type specific proteomics to interrogate the molecular underpinnings of sleep. Different cell types in three important brain regions for sleep (brainstem, cortex, and hypothalamus) exhibited diverse transcriptional responses to sleep need. Sleep restriction modulates astrocyte-neuron crosstalk and sleep need enhances expression of specific sets of transcription factors in different brain regions. In cortex, we also interrogated the proteome of two major cell types: astrocytes and neurons. Sleep deprivation differentially alters the expression of proteins in astrocytes and neurons. Similarly, phosphoproteomics revealed large shifts in cell-type specific protein phosphorylation. Our results indicate that sleep need regulates transcriptional, translational, and post-translational responses in a cell-specific manner.

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In vivo single-cell profiling of lncRNAs during Ebola virus infection

10.1101/2022.01.12.476002 ◽

2022 ◽

Author(s):

Luisa Santus ◽

Raquel García-Pérez ◽

Maria Sopena-Rios ◽

Aaron E Lin ◽

Gordon C Adams ◽

...

Keyword(s):

Viral Infection ◽

Single Cell ◽

Ebola Virus ◽

Cell Type ◽

Protein Coding ◽

Expression Variation ◽

Lncrna Expression ◽

Ebov Infection ◽

Cell Type Specific

Long non-coding RNAs (lncRNAs) are pivotal mediators of systemic immune response to viral infection, yet most studies concerning their expression and functions upon immune stimulation are limited to in vitro bulk cell populations. This strongly constrains our understanding of how lncRNA expression varies at single-cell resolution, and how their cell-type specific immune regulatory roles may differ compared to protein-coding genes. Here, we perform the first in-depth characterization of lncRNA expression variation at single-cell resolution during Ebola virus (EBOV) infection in vivo. Using bulk RNA-sequencing from 119 samples and 12 tissue types, we significantly expand the current macaque lncRNA annotation. We then profile lncRNA expression variation in immune circulating single-cells during EBOV infection and find that lncRNAs' expression in fewer cells is a major differentiating factor from their protein-coding gene counterparts. Upon EBOV infection, lncRNAs present dynamic and mostly cell-type specific changes in their expression profiles especially in monocytes, the main cell type targeted by EBOV. Such changes are associated with gene regulatory modules related to important innate immune responses such as interferon response and purine metabolism. Within infected cells, several lncRNAs have positively and negatively correlated expression with viral load, suggesting that expression of some of these lncRNAs might be directly hijacked by EBOV to attack host cells. This study provides novel insights into the roles that lncRNAs play in the host response to acute viral infection and paves the way for future lncRNA studies at single-cell resolution.

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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

10.1101/2020.10.06.327601 ◽

2020 ◽

Author(s):

Mohit Goyal ◽

Guillermo Serrano ◽

Ilan Shomorony ◽

Mikel Hernaez ◽

Idoia Ochoa

Keyword(s):

Single Cell ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Rna Seq ◽

Batch Effects ◽

Cell Type ◽

Latent Space ◽

Cell Type Specific ◽

Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

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CellO: Comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

10.1101/634097 ◽

2019 ◽

Cited By ~ 1

Author(s):

Matthew N. Bernstein ◽

Zhongjie Ma ◽

Michael Gleicher ◽

Colin N. Dewey

Keyword(s):

Single Cell ◽

Web Application ◽

Cell Types ◽

Rna Seq ◽

Cell Type ◽

Training Set ◽

Sequence Read Archive ◽

Cell Ontology ◽

Cell Type Specific ◽

Type Classification

SummaryCell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical structure of known cell types, a source of prior knowledge that is not utilized by existing methods. Furthemore, CellO comes pre-trained on a novel, comprehensive dataset of human, healthy, untreated primary samples in the Sequence Read Archive, which to the best of our knowledge, is the most diverse curated collection of primary cell data to date. CellO’s comprehensive training set enables it to run out-of-the-box on diverse cell types and achieves superior or competitive performance when compared to existing state-of-the-art methods. Lastly, CellO’s linear models are easily interpreted, thereby enabling exploration of cell type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO’s models across the ontology.HighlightWe present CellO, a tool for hierarchically classifying cell type from single-cell RNA-seq data against the graph-structured Cell OntologyCellO is pre-trained on a comprehensive dataset comprising nearly all bulk RNA-seq primary cell samples in the Sequence Read ArchiveCellO achieves superior or comparable performance with existing methods while featuring a more comprehensive pre-packaged training setCellO is built with easily interpretable models which we expose through a novel web application, the CellO Viewer, for exploring cell type-specific signatures across the Cell OntologyGraphical Abstract

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ICTD: A semi-supervised cell type identification and deconvolution method for multi-omics data

10.1101/426593 ◽

2018 ◽

Cited By ~ 2

Author(s):

Wennan Chang ◽

Changlin Wan ◽

Xiaoyu Lu ◽

Szu-wei Tu ◽

Yifan Sun ◽

...

Keyword(s):

Single Cell ◽

Cell Types ◽

Training Data ◽

Marker Genes ◽

Cell Detection ◽

Omics Data ◽

Deconvolution Method ◽

Cell Type ◽

Data Set ◽

Cell Type Specific

AbstractWe developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.

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Single-cell RNA sequencing reveals cell type- and artery type-specific vascular remodelling in male spontaneously hypertensive rats

Cardiovascular Research ◽

10.1093/cvr/cvaa164 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jun Cheng ◽

Wenduo Gu ◽

Ting Lan ◽

Jiacheng Deng ◽

Zhichao Ni ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Spontaneously Hypertensive Rats ◽

Cell Types ◽

Vascular Remodelling ◽

Cell Type ◽

Hypertensive Rats ◽

Spontaneously Hypertensive ◽

Single Cell Rna Sequencing ◽

Cell Type Specific

Abstract Aims Hypertension is a major risk factor for cardiovascular diseases. However, vascular remodelling, a hallmark of hypertension, has not been systematically characterized yet. We described systematic vascular remodelling, especially the artery type- and cell type-specific changes, in hypertension using spontaneously hypertensive rats (SHRs). Methods and results Single-cell RNA sequencing was used to depict the cell atlas of mesenteric artery (MA) and aortic artery (AA) from SHRs. More than 20 000 cells were included in the analysis. The number of immune cells more than doubled in aortic aorta in SHRs compared to Wistar Kyoto controls, whereas an expansion of MA mesenchymal stromal cells (MSCs) was observed in SHRs. Comparison of corresponding artery types and cell types identified in integrated datasets unravels dysregulated genes specific for artery types and cell types. Intersection of dysregulated genes with curated gene sets including cytokines, growth factors, extracellular matrix (ECM), receptors, etc. revealed vascular remodelling events involving cell–cell interaction and ECM re-organization. Particularly, AA remodelling encompasses upregulated cytokine genes in smooth muscle cells, endothelial cells, and especially MSCs, whereas in MA, change of genes involving the contractile machinery and downregulation of ECM-related genes were more prominent. Macrophages and T cells within the aorta demonstrated significant dysregulation of cellular interaction with vascular cells. Conclusion Our findings provide the first cell landscape of resistant and conductive arteries in hypertensive animal models. Moreover, it also offers a systematic characterization of the dysregulated gene profiles with unbiased, artery type-specific and cell type-specific manners during hypertensive vascular remodelling.

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Cell-type-specific analysis of alternative polyadenylation using single-cell transcriptomics data

Nucleic Acids Research ◽

10.1093/nar/gkz781 ◽

2019 ◽

Vol 47 (19) ◽

pp. 10027-10039 ◽

Cited By ~ 11

Author(s):

Eldad David Shulman ◽

Ran Elkon

Keyword(s):

Gene Regulation ◽

Single Cell ◽

Alternative Polyadenylation ◽

Cell Types ◽

Protein Coding ◽

Multiple Datasets ◽

Transcriptomics Data ◽

Cell Type Specific ◽

Transcriptional Gene Regulation ◽

Different Cell Types

AbstractAlternative polyadenylation (APA) is emerging as an important layer of gene regulation because the majority of mammalian protein-coding genes contain multiple polyadenylation (pA) sites in their 3′ UTR. By alteration of 3′ UTR length, APA can considerably affect post-transcriptional gene regulation. Yet, our understanding of APA remains rudimentary. Novel single-cell RNA sequencing (scRNA-seq) techniques allow molecular characterization of different cell types to an unprecedented degree. Notably, the most popular scRNA-seq protocols specifically sequence the 3′ end of transcripts. Building on this property, we implemented a method for analysing patterns of APA regulation from such data. Analyzing multiple datasets from diverse tissues, we identified widespread modulation of APA in different cell types resulting in global 3′ UTR shortening/lengthening and enhanced cleavage at intronic pA sites. Our results provide a proof-of-concept demonstration that the huge volume of scRNA-seq data that accumulates in the public domain offers a unique resource for the exploration of APA based on a very broad collection of cell types and biological conditions.

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A single cell brain atlas in human Alzheimer’s disease

10.1101/628347 ◽

2019 ◽

Cited By ~ 4

Author(s):

Alexandra Grubman ◽

Gabriel Chew ◽

John F. Ouyang ◽

Guizhi Sun ◽

Xin Yi Choo ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Single Cell ◽

Cell Fate ◽

Expression Patterns ◽

Cell Types ◽

Gene Expression Patterns ◽

Cell Type ◽

Web Resource ◽

Cell Type Specific

AbstractAlzheimer’s disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimer’s patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.HighlightsWe generated the first human single cell transcriptome in AD patient brainsOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)Our analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categoriesOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clustersWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clustersFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.Data resource web interface:http://adsn.ddnetbio.com

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