An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome

Hormones coordinate long-range cell-cell communication in multicellular organisms and play vital roles in normal physiology, metabolism, and health. Using the newly-completed organism-wide single cell transcriptional atlas of a non-human primate, the mouse lemur (Microcebus murinus), we have systematically identified hormone-producing and -target cells for 87 classes of hormones, and have created a browsable atlas for hormone signaling that reveals previously unreported sites of hormone regulation and species-specific rewiring. Hormone ligands and receptors exhibited cell-type-dependent, stereotypical expression patterns, and their transcriptional profiles faithfully classified the discrete cell types defined by the full transcriptome, despite their comprising less than 1% of the transcriptome. Although individual cell types generally exhibited the same characteristic patterns of hormonal gene expression, a number of examples of similar or seemingly-identical cell types (e.g., endothelial cells of the lung versus of other organs) displayed different hormonal gene expression patterns. By linking ligand-expressing cells to the cells expressing the corresponding receptor, we constructed an organism-wide map of the hormonal cell-cell communication network. The hormonal cell-cell network was remarkably densely and robustly connected, and included classical hierarchical circuits (e.g. pituitary → peripheral endocrine gland → diverse cell types) as well as examples of highly distributed control. The network also included both well-known examples of feedback loops and a long list of potential novel feedback circuits. This primate hormone atlas provides a powerful resource to facilitate discovery of regulation on an organism-wide scale and at single-cell resolution, complementing the single-site-focused strategy of classical endocrine studies. The network nature of hormone regulation and the principles discovered here further emphasize the importance of a systems approach to understanding hormone regulation.

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Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuanyuan Li ◽

Ping Luo ◽

Yi Lu ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spectral Clustering ◽

Incidence Matrix ◽

Expression Patterns ◽

Cell Types ◽

Clustering Method ◽

Different Types ◽

Cell Data ◽

Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses

10.1101/2020.08.02.233569 ◽

2020 ◽

Author(s):

Juexin Wang ◽

Anjun Ma ◽

Yuzhou Chang ◽

Jianting Gong ◽

Yuexu Jiang ◽

...

Keyword(s):

Neural Network ◽

Gene Expression ◽

Single Cell ◽

Neural Development ◽

Expression Patterns ◽

Gaussian Model ◽

Postmortem Brain ◽

Differential Mechanism ◽

Disease Study ◽

Cell Cell

ABSTRACTSingle-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell-cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. scGNN integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark scRNA-Seq datasets. In an Alzheimer’s disease study with 13,214 single nuclei from postmortem brain tissues, scGNN successfully illustrated disease-related neural development and the differential mechanism. scGNN provides an effective representation of gene expression and cell-cell relationships. It is also a novel and powerful framework that can be applied to scRNA-Seq analyses.

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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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RNA splicing programs define tissue compartments and cell types at single cell resolution

10.1101/2021.05.01.442281 ◽

2021 ◽

Author(s):

Julia Eve Olivieri ◽

Roozbeh Dehghannasiri ◽

Peter Wang ◽

SoRi Jang ◽

Antoine de Morree ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

High Throughput ◽

Rna Splicing ◽

Single Cells ◽

Cell Types ◽

Mouse Lemur ◽

Cell Type ◽

Multiple Organs ◽

Single Cell Pcr

More than 95% of human genes are alternatively spliced. Yet, the extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach that is agnostic to transcript annotation, to detect cell-type-specific regulated splicing in > 110K carefully annotated single cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type specifically spliced. These results are validated with RNA FISH, single cell PCR, and in high throughput with Smart-seq2. Regulated splicing is found in ubiquitously expressed genes such as actin light chain subunit MYL6 and ribosomal protein RPS24, which has an epithelial-specific microexon. 13% of the statistically most variable splice sites in cell-type specifically regulated genes are also most variable in mouse lemur or mouse. SpliZ analysis further reveals 170 genes with regulated splicing during sperm development using, 10 of which are conserved in mouse and mouse lemur. The statistical properties of the SpliZ allow model-based identification of subpopulations within otherwise indistinguishable cells based on gene expression, illustrated by subpopulations of classical monocytes with stereotyped splicing, including an un-annotated exon, in SAT1, a Diamine acetyltransferase. Together, this unsupervised and annotation-free analysis of differential splicing in ultra high throughput droplet-based sequencing of human cells across multiple organs establishes splicing is regulated cell-type-specifically independent of gene expression.

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Comprehensive integration of single cell data

10.1101/460147 ◽

2018 ◽

Cited By ~ 74

Author(s):

Tim Stuart ◽

Andrew Butler ◽

Paul Hoffman ◽

Christoph Hafemeister ◽

Efthymia Papalexi ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Patterns ◽

Cell Types ◽

Chromatin Accessibility ◽

Substantial Improvement ◽

Single Cell Protein ◽

Cell Protein ◽

Spatial Positioning ◽

Cell Data

Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to “anchor” diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets.Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

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Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

10.1101/2020.09.15.299123 ◽

2020 ◽

Author(s):

Timothy J. Durham ◽

Riza M. Daza ◽

Louis Gevirtzman ◽

Darren A. Cusanovich ◽

William Stafford Noble ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Patterns ◽

Cell Types ◽

Chromatin Accessibility ◽

Gene Expression Patterns ◽

Rna Seq ◽

Cell Type ◽

Tissue Specific ◽

C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

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Kidney Single-cell Transcriptomes Predict Spatial Corticomedullary Gene Expression and Tissue Osmolality Gradients

Journal of the American Society of Nephrology ◽

10.1681/asn.2020070930 ◽

2020 ◽

pp. ASN.2020070930

Author(s):

Christian Hinze ◽

Nikos Karaiskos ◽

Anastasiya Boltengagen ◽

Katharina Walentin ◽

Klea Redo ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Collecting Duct ◽

Expression Patterns ◽

Cell Types ◽

Spatial Position ◽

Sufficient Information ◽

Additional Information ◽

Spatial Reconstruction ◽

Tissue Osmolality

BackgroundSingle-cell transcriptomes from dissociated tissues provide insights into cell types and their gene expression and may harbor additional information on spatial position and the local microenvironment. The kidney’s cells are embedded into a gradient of increasing tissue osmolality from the cortex to the medulla, which may alter their transcriptomes and provide cues for spatial reconstruction.MethodsSingle-cell or single-nuclei mRNA sequencing of dissociated mouse kidneys and of dissected cortex, outer, and inner medulla, to represent the corticomedullary axis, was performed. Computational approaches predicted the spatial ordering of cells along the corticomedullary axis and quantitated expression levels of osmo-responsive genes. In situ hybridization validated computational predictions of spatial gene-expression patterns. The strategy was used to compare single-cell transcriptomes from wild-type mice to those of mice with a collecting duct–specific knockout of the transcription factor grainyhead-like 2 (Grhl2CD−/−), which display reduced renal medullary osmolality.ResultsSingle-cell transcriptomics from dissociated kidneys provided sufficient information to approximately reconstruct the spatial position of kidney tubule cells and to predict corticomedullary gene expression. Spatial gene expression in the kidney changes gradually and osmo-responsive genes follow the physiologic corticomedullary gradient of tissue osmolality. Single-nuclei transcriptomes from Grhl2CD−/− mice indicated a flattened expression gradient of osmo-responsive genes compared with control mice, consistent with their physiologic phenotype.ConclusionsSingle-cell transcriptomics from dissociated kidneys facilitated the prediction of spatial gene expression along the corticomedullary axis and quantitation of osmotically regulated genes, allowing the prediction of a physiologic phenotype.

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P020 Mapping the changing intercellular communication and its downstream effect in Ulcerative Colitis

Journal of Crohn s and Colitis ◽

10.1093/ecco-jcc/jjab076.149 ◽

2021 ◽

Vol 15 (Supplement_1) ◽

pp. S138-S139

Author(s):

L Potari-gul ◽

D Modos ◽

D Turei ◽

A Valdeolivas ◽

M Madgwick ◽

...

Keyword(s):

Ulcerative Colitis ◽

T Cells ◽

Regulatory T Cells ◽

Single Cell ◽

Intercellular Communication ◽

Cell Types ◽

Cell Junctions ◽

Target Cells ◽

Downstream Effects ◽

Cell Cell

Abstract Background Intercellular communication is essential for growing and differentiating in multicellular organisms by transducing the signal from cell to cell. Despite its importance, the molecular background is less discovered due to the lack of data. This gap has started to be addressed with the appearance of single-cell omics approaches providing an insight among others into the gene expression of individual cells. Methods We have developed a method to predict and compare cell-cell signalling interactions using single-cell RNAseq data from colon biopsies. Transcriptomic data alone is not capable of connecting the cells, a reliable network resource is needed to mediate the signal via protein-protein interactions between the source and target cells. Here we used OmniPath - a resource providing not only intra- and intercellular interactions but also annotations of proteins involved in the interplay of cells - to reconstruct signalling networks. We examined intercellular communication among five cell-types (regulatory T cell, macrophage, dendritic cell, goblet cell and myofibroblast) in healthy colon and during Ulcerative Colitis. Results Our analysis shows that there are significant differences in the type of cell-cell communication (ligand-receptor connections, adherens junctions, etc.) between the healthy and Ulcerative Colitis (UC) conditions, and these differences lead to altered downstream effects in the signal receiving cell. In both conditions, the ligand-receptor and adhesion connections were overrepresented, however cell junctions were less abundant in UC. Regarding the communication among the five cell-types, in healthy condition, cells are tightly connected to dendritic cells while in diseased condition to regulatory T cells. Focusing on ligand-receptor interactions between myofibroblasts and regulatory T cells, our pipeline identified the MAPK, Toll-like receptor (TLR) 2/6 and TLR 7/8 pathways enriched downstream in healthy conditions. In contrast, TLR3 and TLR4 pathways were affected by the myofibroblast in Ulcerative Colitis. Conclusion We found key intercellular mechanisms leading to well-defined differential pathway activation profiles. We showed that in uninflamed UC condition myofibroblasts disrupt the anti-inflammatory effect of regulatory T cells. Our pipeline is able to predict and analyse cell-cell interactions and their downstream effects and to highlight the differences in healthy and diseased states.

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Microfluidic system-based time-course tracking of physical proximity between cells and its effect on gene expression for elucidating live single cancer-immune cell interactions

10.1101/2021.11.13.468447 ◽

2021 ◽

Author(s):

Bianca C.T Flores ◽

Smriti Chawla ◽

Ning Ma ◽

Chad Sanada ◽

Praveen Kumar Kujur ◽

...

Keyword(s):

Gene Expression ◽

Time Course ◽

Immune Cell ◽

Cell Communication ◽

Time Lapse ◽

Cell Types ◽

Temporal Variations ◽

Microfluidic System ◽

Cell Interactions ◽

Cell Cell

Cell-cell communication and physical interactions play a vital role in cancer initiation, homeostasis, progression, and immune response. Here, we report a system that combines live capture of different cell types, co-incubation, time-lapse imaging, and gene expression profiling of doublets using a microfluidic integrated fluidic circuit (IFC) that enables measurement of physical distances between cells and the associated transcriptional profiles due to cell-cell interactions. The temporal variations in natural killer (NK) - triple-negative breast cancer (TNBC) cell distances were tracked and compared with terminally profiled cellular transcriptomes. The results showed the time-bound activities of regulatory modules and alluded to the existence of transcriptional memory. Our experimental and bioinformatic approaches serve as a proof of concept for interrogating live cell interactions at doublet resolution, which can be applied across different cancers and cell types.

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Single-Cell Analysis of the Gene Expression Effects of Developmental Lead (Pb) Exposure on the Mouse Hippocampus

Toxicological Sciences ◽

10.1093/toxsci/kfaa069 ◽

2020 ◽

Vol 176 (2) ◽

pp. 396-409

Author(s):

Kelly M Bakulski ◽

John F Dou ◽

Robert C Thompson ◽

Christopher Lee ◽

Lauren Y Middleton ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Cell Analysis ◽

Expression Patterns ◽

Cell Types ◽

Cell Cluster ◽

Marker Genes ◽

Cell Type ◽

Cell Clusters ◽

Pb Exposure

Abstract Lead (Pb) exposure is ubiquitous with permanent neurodevelopmental effects. The hippocampus brain region is involved in learning and memory with heterogeneous cellular composition. The hippocampus cell type-specific responses to Pb are unknown. The objective of this study is to examine perinatal Pb treatment effects on adult hippocampus gene expression, at the level of individual cells. In mice perinatally exposed to control water or a human physiologically relevant level (32 ppm in maternal drinking water) of Pb, 2 weeks prior to mating through weaning, we tested for hippocampus gene expression and cellular differences at 5 months of age. We sequenced RNA from 5258 hippocampal cells to (1) test for treatment gene expression differences averaged across all cells, (2) compare cell cluster composition by treatment, and (3) test for treatment gene expression and pathway differences within cell clusters. Gene expression patterns revealed 12 hippocampus cell clusters, mapping to major expected cell types (eg, microglia, astrocytes, neurons, and oligodendrocytes). Perinatal Pb treatment was associated with 12.4% more oligodendrocytes (p = 4.4 × 10−21) in adult mice. Across all cells, Pb treatment was associated with expression of cell cluster marker genes. Within cell clusters, Pb treatment (q < 0.05) caused differential gene expression in endothelial, microglial, pericyte, and astrocyte cells. Pb treatment upregulated protein folding pathways in microglia (p = 3.4 × 10−9) and stress response in oligodendrocytes (p = 3.2 × 10−5). Bulk tissue analysis may be influenced by changes in cell type composition, obscuring effects within vulnerable cell types. This study serves as a biological reference for future single-cell toxicant studies, to ultimately characterize molecular effects on cognition and behavior.

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