scholarly journals Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the prenatal human hypothalamus

2021 ◽  
Author(s):  
Brian Herb ◽  
Hannah J Glover ◽  
Aparna Bhaduri ◽  
Alex M Casella ◽  
Tracy L Bale ◽  
...  
Keyword(s):  
Developmental Trajectories ◽  
Single Cells ◽  
Neuronal Diversity ◽  
Cell Type ◽  
Human Hypothalamus ◽  
Paired Samples ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
Stem Cell Populations ◽  
Temporal Trajectory

The hypothalamus is critically important for regulating most autonomic, metabolic, and behavioral functions essential for life and species propagation, yet a comprehensive understanding of neuronal subtypes and their development in the human brain is lacking. Here, we characterized the prenatal human hypothalamus by sequencing the transcriptomes of 45,574 single-cells from 12 embryos, spanning gestational weeks 4 through 25. These cells describe a temporal trajectory from proliferative stem cell populations to maturing neurons and glia, including 38 distinct excitatory and inhibitory neuronal subtypes. Merging these data with paired samples from the cortex and ganglionic eminences (GE) revealed two distinct neurogenesis pathways, one shared between GE and hypothalamus and a second unique to cortex. Gene regulatory network modeling predicted that these distinct maturation trajectories involve the activation of region- and cell type-specific transcription factor networks. These results provide the first comprehensive transcriptomic view of human hypothalamus development at cellular resolution.

scholarly journals Improved estimation of cell type-specific gene expression through deconvolution of bulk tissues with matrix completion

2021 ◽  
Author(s):  
Weixu Wang ◽  
Jun Yao ◽  
Yi Wang ◽  
Chao Zhang ◽  
Wei Tao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Matrix Completion ◽  
Superior Performance ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Large Patient ◽  
Cell Type Specific

Cell type-specific gene expression (CSE) brings novel biological insights into both physiological and pathological processes compared with bulk tissue gene expression. Although fluorescence-activated cell sorting (FACS) and single-cell RNA sequencing (scRNA-seq) are two widely used techniques to detect gene expression in a cell type-specific manner, the constraints of cost and labor force make it impractical as a routine on large patient cohorts. Here, we present ENIGMA, an algorithm that deconvolutes bulk RNA-seq into cell type-specific expression matrices and cell type fraction matrices without the need of physical sorting or sequencing of single cells. ENIGMA used cell type signature matrix generated from either FACS RNA-seq or scRNA-seq as reference, and applied matrix completion technique to achieve fast and accurate deconvolution. We demonstrated the superior performance of ENIGMA to previously published algorithms (TCA, bMIND and CIBERSORTx) while requiring much less running time on both simulated and realistic datasets. To prove its value in biological discovery, we applied ENIGMA to bulk RNA-seq from arthritis patients and revealed a pseudo-differentiation trajectory that could reflect monocyte to macrophage transition. We also applied ENIGMA to bulk RNA-seq data of pancreatic islet tissue from type 2 diabetes (T2D) patients and discovered a beta cell-specific gene co-expression module related to senescence and apoptosis that possibly contributed to the pathogenesis of T2D. Together, ENIGMA provides a new framework to improve the CSE estimation by integrating FACS RNA-seq and scRNA-seq with tissue bulk RNA-seq data, and will extend our understandings about cell heterogeneity on population level with no need for experimental tissue disaggregation.

scholarly journals Dynamics of gene expression in single root cells ofA. thaliana

2018 ◽  
Author(s):  
Ken Jean-Baptiste ◽  
José L. McFaline-Figueroa ◽  
Cristina M. Alexandre ◽  
Michael W. Dorrity ◽  
Lauren Saunders ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Root Cells ◽  
Cell Lineages ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTSingle-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach toA. thalianaroot cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.

scholarly journals Predicting gene regulatory networks from cell atlases

2020 ◽  
Author(s):  
Andreas Fønss Møller ◽  
Kedar Nath Natarajan
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Mouse Cell ◽  
Cell Type ◽  
Integrated Network ◽  
Mouse Tissues ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

scholarly journals A human cell atlas of fetal chromatin accessibility

Science ◽  
2020 ◽  
Vol 370 (6518) ◽  
pp. eaba7612 ◽  
Author(s):  
Silvia Domcke ◽  
Andrew J. Hill ◽  
Riza M. Daza ◽  
Junyue Cao ◽  
Diana R. O’Day ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Cell ◽  
Single Cells ◽  
Complex Trait ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Cell Type ◽  
Cell Type Specific

The chromatin landscape underlying the specification of human cell types is of fundamental interest. We generated human cell atlases of chromatin accessibility and gene expression in fetal tissues. For chromatin accessibility, we devised a three-level combinatorial indexing assay and applied it to 53 samples representing 15 organs, profiling ~800,000 single cells. We leveraged cell types defined by gene expression to annotate these data and cataloged hundreds of thousands of candidate regulatory elements that exhibit cell type–specific chromatin accessibility. We investigated the properties of lineage-specific transcription factors (such as POU2F1 in neurons), organ-specific specializations of broadly distributed cell types (such as blood and endothelial), and cell type–specific enrichments of complex trait heritability. These data represent a rich resource for the exploration of in vivo human gene regulation in diverse tissues and cell types.

scholarly journals Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution

2020 ◽  
Author(s):  
Alexandro E. Trevino ◽  
Fabian Müller ◽  
Jimena Andersen ◽  
Laksshman Sundaram ◽  
Arwa Kathiria ◽  
...  
Keyword(s):  
Single Cell ◽  
Network Models ◽  
Regulatory Elements ◽  
Autism Spectrum ◽  
Cell Type ◽  
Neural Network Models ◽  
Neurodevelopmental Disease ◽  
Gene Regulatory Elements ◽  
Cell Type Specific ◽  
Gene Regulatory

ABSTRACTGenetic perturbations of cerebral cortical development can lead to neurodevelopmental disease, including autism spectrum disorder (ASD). To identify genomic regions crucial to corticogenesis, we mapped the activity of gene-regulatory elements generating a single-cell atlas of gene expression and chromatin accessibility both independently and jointly. This revealed waves of gene regulation by key transcription factors (TFs) across a nearly continuous differentiation trajectory into glutamatergic neurons, distinguished the expression programs of glial lineages, and identified lineage-determining TFs that exhibited strong correlation between linked gene-regulatory elements and expression levels. These highly connected genes adopted an active chromatin state in early differentiating cells, consistent with lineage commitment. Basepair-resolution neural network models identified strong cell-type specific enrichment of noncoding mutations predicted to be disruptive in a cohort of ASD subjects and identified frequently disrupted TF binding sites. This approach illustrates how cell-type specific mapping can provide insights into the programs governing human development and disease.

scholarly journals scTenifoldKnk: a machine learning workflow performing virtual knockout experiments on single-cell gene regulatory networks

2021 ◽  
Author(s):  
Daniel Osorio ◽  
Yan Zhong ◽  
Guanxun Li ◽  
Qian Xu ◽  
Andrew E. Hillhouse ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Gene Function ◽  
Specific Gene ◽  
Cell Type ◽  
Cell Technology ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
The Impact ◽  
Cell Gene

Gene knockout (KO) experiments are a proven approach for studying gene function. A typical KO experiment usually involves the phenotypic characterization of KO organisms. The recent advent of single-cell technology has greatly boosted the resolution of cellular phenotyping, providing unprecedented insights into cell-type-specific gene function. However, the use of single-cell technology in large-scale, systematic KO experiments is prohibitive due to the vast resources required. Here we present scTenifoldKnk, a machine learning workflow that performs virtual KO experiments using single-cell RNA sequencing (scRNA-seq) data. scTenifoldKnk first uses data from wild-type (WT) samples to construct a single-cell gene regulatory network (scGRN). Then, a gene is knocked out from the constructed scGRN by setting weights of the gene's outward edges to zeros. ScTenifoldKnk then compares this "pseudo-KO" scGRN with the original scGRN to identify differentially regulated (DR) genes. These DR genes, also called virtual-KO perturbed genes, are used to assess the impact of the gene KO and reveal the gene's function in analyzed cells. Using existing data sets, we demonstrate that the scTenifoldKnk analysis recapitulates the main findings of three real-animal KO experiments and confirms the functions of genes underlying three Mendelian diseases. We show the power of scTenifoldKnk as a predictive method to successfully predict the outcomes of two KO experiments that involve intestinal enterocytes in Ahr-/- mice and pancreatic islet cells in Malat1-/- mice, respectively. Finally, we demonstrate the use of scTenifoldKnk to perform systematic KO analyses, in which a large number of genes are virtually deleted, allowing gene functions to be revealed in a cell type-specific manner.

scholarly journals Single-cell analysis resolves the genetic program of primary vascular system formation in hybrid poplar

2021 ◽  
Author(s):  
Daniel Conde ◽  
Paolo M. Triozzi ◽  
Wendell J. Pereira ◽  
Henry W. Schmidt ◽  
Kelly M. Balmant ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Shoot Apex ◽  
Vascular Tissue ◽  
Vascular System ◽  
Developmental Trajectories ◽  
Specific Gene ◽  
Cell Type ◽  
Woody Perennials ◽  
Cell Type Specific

Despite the enormous potential of novel approaches to explore gene expression at a single-cell level, we lack a high-resolution and cell type-specific gene expression map of the shoot apex in woody perennials. We use single-nuclei RNA sequencing to determine the cell type-specific transcriptome of the Populus vegetative shoot apex. We identified highly heterogeneous cell populations clustered into seven broad groups represented by 18 transcriptionally distinct cell clusters. Next, we established the developmental trajectories of epidermal cells, leaf mesophyll, and vascular tissue. Motivated by the high similarities between Populus and Arabidopsis cell population in the vegetative apex, we created and applied a pipeline for interspecific single-cell expression data integration. We contrasted the developmental trajectories of primary phloem and xylem formation in both species, establishing the first comparison of primary vascular development between a model annual herbaceous and a woody perennial plant species. Our results offer a valuable resource for investigating the basic principles underlying cell division and differentiation conserved between herbaceous and perennial species, which also allows the evaluation of the divergencies at single-cell resolution.

scholarly journals Automated population identification and sorting algorithms for high-dimensional single-cell data

2016 ◽  
Author(s):  
Benedict Anchang ◽  
Sylvia K. Plevritis
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Intracellular Signaling ◽  
Expert Knowledge ◽  
Single Cells ◽  
Cell Type ◽  
Experimental Conditions ◽  
Cell Type Specific ◽  
Cell Subpopulations ◽  
Cell Data

AbstractCell sorting or gating homogenous subpopulations from single-cell data enables cell-type specific characterization, such as cell-type genomic profiling as well as the study of tumor progression. This highlight summarizes recently developed automated gating algorithms that are optimized for both population identification and sorting homogeneous single cells in heterogeneous single-cell data. Data-driven gating strategies identify and/or sort homogeneous subpopulations from a heterogeneous population without relying on expert knowledge thereby removing human bias and variability. We further describe an optimized cell sorting strategy called CCAST based on Clustering, Classification and Sorting Trees which identifies the relevant gating markers, gating hierarchy and partitions that define underlying cell subpopulations. CCAST identifies more homogeneous subpopulations in several applications compared to prior sorting strategies and reveals simultaneous intracellular signaling across different lineage subtypes under different experimental conditions.

scholarly journals PESCA: A scalable platform for the development of cell-type-specific viral drivers

2019 ◽  
Author(s):  
Sinisa Hrvatin ◽  
Christopher P. Tzeng ◽  
M. Aurel Nagy ◽  
Hume Stroud ◽  
Charalampia Koutsioumpa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heterologous Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Functional Evaluation ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Enhancer Activity ◽  
Cell Type Specific

AbstractEnhancers are the primary DNA regulatory elements that confer cell type specificity of gene expression. Recent studies characterizing individual enhancers have revealed their potential to direct heterologous gene expression in a highly cell-type-specific manner. However, it has not yet been possible to systematically identify and test the function of enhancers for each of the many cell types in an organism. We have developed PESCA, a scalable and generalizable method that leverages ATAC- and single-cell RNA-sequencing protocols, to characterize cell-type-specific enhancers that should enable genetic access and perturbation of gene function across mammalian cell types. Focusing on the highly heterogeneous mammalian cerebral cortex, we apply PESCA to find enhancers and generate viral reagents capable of accessing and manipulating a subset of somatostatin-expressing cortical interneurons with high specificity. This study demonstrates the utility of this platform for developing new cell-type-specific viral reagents, with significant implications for both basic and translational research.One sentence summaryHighly paralleled functional evaluation of enhancer activity in single cells generates new cell-type-specific tools with broad medical and scientific applications.

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