scholarly journals Improving gene network inference with graph wavelets and making insights about ageing-associated regulatory changes in lungs

2020 ◽  
Author(s):  
Shreya Mishra ◽  
Divyanshu Srivastava ◽  
Vibhor Kumar
Keyword(s):  
Single Cell ◽  
Gene Network ◽  
Network Inference ◽  
Expression Profiles ◽  
Gene Network Inference ◽  
Regulatory Changes ◽  
Gene Regulatory ◽  
Cell Transcriptome ◽  
Cell Expression ◽  
Single Cell Transcriptome

Abstract Using gene-regulatory-networks-based approach for single-cell expression profiles can reveal unprecedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here, we devise a conceptually different method using graphwavelet filters for improving gene network (GWNet)-based analysis of the transcriptome. Our approach improved the performance of several gene network-inference methods. Most Importantly, GWNet improved consistency in the prediction of gene regulatory network using single-cell transcriptome even in the presence of batch effect. The consistency of predicted gene network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically relevant changes in the influence of pathways and master regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to the effect of novel coronavirus infection in human lung.

scholarly journals Improving gene-network inference with graph-wavelets and making insights about ageing associated regulatory changes in lungs

2020 ◽  
Author(s):  
Shreya Mishra ◽  
Divyanshu Srivastava ◽  
Vibhor Kumar
Keyword(s):  
Single Cell ◽  
Gene Network ◽  
Network Inference ◽  
Expression Profiles ◽  
Batch Effect ◽  
Gene Network Inference ◽  
Regulatory Changes ◽  
Cell Transcriptome ◽  
Cell Expression ◽  
Single Cell Transcriptome

AbstractUsing gene-regulatory-networks based approach for single-cell expression profiles can reveal un-precedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here we devise a conceptually different method using graph-wavelet filters for improving gene-network (GWNet) based analysis of the transcriptome. Our approach improved the performance of several gene-network inference methods. Most Importantly, GWNet improved consistency in the prediction of generegulatory-network using single-cell transcriptome even in presence of batch effect. Consistency of predicted gene-network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically-relevant changes in the influence of pathways and master-regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to effect of novel coronavirus infection in Human Lung.

scholarly journals Single cell transcriptome atlas of mouse mammary epithelial cells across development

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Bhupinder Pal ◽  
Yunshun Chen ◽  
Michael J. G. Milevskiy ◽  
François Vaillant ◽  
Lexie Prokopuk ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Developmental Stages ◽  
Mammary Epithelial Cells ◽  
Expression Profiles ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background Heterogeneity within the mouse mammary epithelium and potential lineage relationships have been recently explored by single-cell RNA profiling. To further understand how cellular diversity changes during mammary ontogeny, we profiled single cells from nine different developmental stages spanning late embryogenesis, early postnatal, prepuberty, adult, mid-pregnancy, late-pregnancy, and post-involution, as well as the transcriptomes of micro-dissected terminal end buds (TEBs) and subtending ducts during puberty. Methods The single cell transcriptomes of 132,599 mammary epithelial cells from 9 different developmental stages were determined on the 10x Genomics Chromium platform, and integrative analyses were performed to compare specific time points. Results The mammary rudiment at E18.5 closely aligned with the basal lineage, while prepubertal epithelial cells exhibited lineage segregation but to a less differentiated state than their adult counterparts. Comparison of micro-dissected TEBs versus ducts showed that luminal cells within TEBs harbored intermediate expression profiles. Ductal basal cells exhibited increased chromatin accessibility of luminal genes compared to their TEB counterparts suggesting that lineage-specific chromatin is established within the subtending ducts during puberty. An integrative analysis of five stages spanning the pregnancy cycle revealed distinct stage-specific profiles and the presence of cycling basal, mixed-lineage, and 'late' alveolar intermediates in pregnancy. Moreover, a number of intermediates were uncovered along the basal-luminal progenitor cell axis, suggesting a continuum of alveolar-restricted progenitor states. Conclusions This extended single cell transcriptome atlas of mouse mammary epithelial cells provides the most complete coverage for mammary epithelial cells during morphogenesis to date. Together with chromatin accessibility analysis of TEB structures, it represents a valuable framework for understanding developmental decisions within the mouse mammary gland.

scholarly journals Mesenchymal Stromal Cells in the Bone Marrow Niche Consist of Multi-Populations With Distinct Transcriptional and Epigenetic Properties

2021 ◽  
Author(s):  
Sanshiro Kanazawa ◽  
Hironori Hojo ◽  
Shinsuke Ohba ◽  
Junichi Iwata ◽  
Makoto Komura ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Single Cell ◽  
Expression Profiles ◽  
Marker Genes ◽  
Mouse Bone Marrow ◽  
Bone Marrow Niche ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Although multiple studies have investigated the mesenchymal stem and progenitor cells (MSCs) that give rise to mature bone marrow, high heterogeneity in their morphologies and properties causes difficulties in molecular separation of their distinct populations. In this study, by taking advantage of the resolution of the single cell transcriptome, we analyzed Sca-1 and PDGFR-α fraction in the mouse bone marrow tissue. The single cell transcriptome enabled us to further classify the population into seven populations according to their gene expression profiles. We then separately obtained the seven populations based on candidate marker genes, and specified their gene expression properties and epigenetic landscape by ATAC-seq. Our findings will enable to elucidate the stem cell niche signal in the bone marrow microenvironment, reconstitute bone marrow in vitro, and shed light on the potentially important role of identified subpopulation in various clinical applications to the treatment of bone- and bone marrow-related diseases.

scholarly journals Single-Cell Transcriptome Analysis Profile of Meniscal Tissue Macrophages in Human Osteoarthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jingbin Zhou ◽  
Zhihong Zhao ◽  
Chen He ◽  
Feng Gao ◽  
Yu Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Vital Role ◽  
Low Grade ◽  
Sequencing Analysis ◽  
Human Knee ◽  
Meniscal Tissue ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Osteoarthritis (OA) has long been considered as a degenerative disease, but growing evidence suggests that inflammation plays a vital role in its pathogenesis. Unlike rheumatoid arthritis and other autoimmune diseases, inflammation in OA is chronic and, in relatively low grade, mainly mediated by the innate immune system, especially macrophages. However, due to its low abundance, there is a lack of systematic studies on macrophages in the OA condition. Here, we have used single-cell RNA sequencing analysis to gain insight into the heterogeneity and functional specialization of human knee macrophages. We also compared the gene expression profiles of macrophages in healthy people and OA patients and found the characteristic changes of special macrophages in the OA knee. We believe that this in-depth understanding of the basis of OA inflammation will bring hope for the development of new therapies.

scholarly journals Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision

2019 ◽  
Author(s):  
Jiang Xie ◽  
Fuzhang yang ◽  
Jiamin Sun ◽  
Jiao Wang
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Cell Fate ◽  
Neural Stem Cell ◽  
Integrative Analysis ◽  
Cell Fate Decisions ◽  
Multi Stage ◽  
Gene Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background Neural stem cell (NSC) differentiation is one of many multi-stage lineage systems that require multiple cell fate decisions. Recent single-cell transcriptome datasets became available at individual differentiation, however, a systematic and integrative analysis of multiple datasets at multiple temporal points of NSC differentiation is lacking. Results Here we investigate five NSC differentiation paths by analyzing and comparing four different single-cell transcriptome datasets. By constructing gene regulatory networks for each cell type, we delineate their regulatory patterns via analyses of differential topology and network diffusion. Among the five NSC differentiation paths, we find 12 common differentially expressed genes, with one common three-gene regulatory pattern shared by all paths. The identified regulatory pattern, partly supported by previous experimental evidence, is found to be essential to all differentiation paths, however, plays a different role in each path when regulating other genes. Conclusions Together, our integrative analysis provides both common and specific regulatory mechanisms for each of the five NSC differentiation paths, and the approach can be applied to analyzing other complex multi-stage lineage systems.

scholarly journals A single-cell catalogue of regulatory states in the ageing Drosophila brain

2017 ◽  
Author(s):  
Kristofer Davie ◽  
Jasper Janssens ◽  
Duygu Koldere ◽  
Uli Pech ◽  
Sara Aibar ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Neuronal Cell ◽  
Cell Types ◽  
Disease Mutations ◽  
Drosophila Brain ◽  
Gene Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
The Brain

SummaryThe diversity of cell types and regulatory states in the brain, and how these change during ageing, remains largely unknown. Here, we present a single-cell transcriptome catalogue of the entire adult Drosophila melanogaster brain sampled across its lifespan. Both neurons and glia age through a process of “regulatory erosion”, characterized by a strong decline of RNA content, and accompanied by increasing transcriptional and chromatin noise. We identify more than 50 cell types by specific transcription factors and their downstream gene regulatory networks. In addition to neurotransmitter types and neuroblast lineages, we find a novel neuronal cell state driven by datilografo and prospero. This state relates to neuronal birth order, the metabolic profile, and the activity of a neuron. Our single-cell brain catalogue reveals extensive regulatory heterogeneity linked to ageing and brain function and will serve as a reference for future studies of genetic variation and disease mutations.

scholarly journals Single-cell transcriptome atlas of Drosophila gastrula 2.0

2021 ◽  
Author(s):  
Shunta Sakaguchi ◽  
Yasushi Okochi ◽  
Chiharu Tanegashima ◽  
Osamu Nishimura ◽  
Tadashi Uemura ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Single Cell ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Mrna Level ◽  
Gene Expression Profiles ◽  
Genome Wide ◽  
A Genome ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

During development, positional information directs cells to specific fates, leading them to differentiate with their own transcriptomes and express specific behaviors and functions. However, the mechanisms underlying these processes in a genome-wide view remain ambiguous, partly because the single-cell transcriptomic data of early developing embryos containing both accurate spatial and lineage information is still lacking. Here, we report a new single-cell transcriptome atlas of Drosophila gastrulae, divided into 65 transcriptomically distinct clusters. We found that the expression profiles of plasma-membrane-related genes, but not those of transcription factor genes, represented each germ layer, supporting the nonequivalent contribution of each transcription factor mRNA level to effector gene expression profiles at the transcriptome level. We also reconstructed the spatial expression patterns of all genes at the single-cell stripe level as the smallest unit. This atlas is an important resource for the genome-wide understanding of the mechanisms by which genes cooperatively orchestrate Drosophila gastrulation.

scholarly journals Mesenchymal stromal cells in the bone marrow niche consist of multi-populations with distinct transcriptional and epigenetic properties

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sanshiro Kanazawa ◽  
Hiroyuki Okada ◽  
Hironori Hojo ◽  
Shinsuke Ohba ◽  
Junichi Iwata ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Single Cell ◽  
Expression Profiles ◽  
Marker Genes ◽  
Mouse Bone Marrow ◽  
Bone Marrow Niche ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractAlthough multiple studies have investigated the mesenchymal stem and progenitor cells (MSCs) that give rise to mature bone marrow, high heterogeneity in their morphologies and properties causes difficulties in molecular separation of their distinct populations. In this study, by taking advantage of the resolution of the single cell transcriptome, we analyzed Sca-1 and PDGFR-α fraction in the mouse bone marrow tissue. The single cell transcriptome enabled us to further classify the population into seven populations according to their gene expression profiles. We then separately obtained the seven populations based on candidate marker genes, and specified their gene expression properties and epigenetic landscape by ATAC-seq. Our findings will enable to elucidate the stem cell niche signal in the bone marrow microenvironment, reconstitute bone marrow in vitro, and shed light on the potentially important role of identified subpopulation in various clinical applications to the treatment of bone- and bone marrow-related diseases.

scholarly journals scRNASeqDB: a database for gene expression profiling in human single cell by RNA-seq

2017 ◽  
Author(s):  
Yuan Cao ◽  
Junjie Zhu ◽  
Guangchun Han ◽  
Peilin Jia ◽  
Zhongming Zhao
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Or Groups ◽  
Cell Transcriptome ◽  
Almost All ◽  
Single Cell Transcriptome ◽  
Different Cell Types ◽  
Online Web

AbstractSummary: Single-cell RNA sequencing (scRNA-Seq) is quickly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. So far, there is no database that comprehensively collects and curates scRNA-Seq data in humans. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (n= 36) covering 71 human cell lines or types and 8910 samples. Our online web interface allows user to query and visualize expression profiles of the gene(s) of interest, search for genes that are expressed in different cell types or groups, or retrieve differentially expressed genes between cell types or groups. The scRNASeqDB is a valuable resource for single cell transcriptional studies.Availability: The database is available at https://bioinfo.uth.edu/scrnaseqdb/.Contact: [email protected]

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