scholarly journals Understanding of mouse and human bladder at single‐cell resolution: integrated analysis of trajectory and cell‐cell interactive networks based on multiple scRNA‐seq datasets

2021 ◽  
Author(s):  
Bowen Shi ◽  
Yanyuan Wu ◽  
Haojie Chen ◽  
Jie Ding ◽  
Jun Qi
Keyword(s):  
Single Cell ◽  
Integrated Analysis ◽  
Human Bladder ◽  
Interactive Networks ◽  
Cell Cell

scholarly journals Integrated analysis of multimodal single-cell data

Cell ◽  
2021 ◽  
Author(s):  
Yuhan Hao ◽  
Stephanie Hao ◽  
Erica Andersen-Nissen ◽  
William M. Mauck ◽  
Shiwei Zheng ◽  
...  
Keyword(s):  
Single Cell ◽  
Integrated Analysis ◽  
Cell Data

scholarly journals Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics

Cell Reports ◽  
2018 ◽  
Vol 25 (6) ◽  
pp. 1458-1468.e4 ◽  
Author(s):  
Manu P. Kumar ◽  
Jinyan Du ◽  
Georgia Lagoudas ◽  
Yang Jiao ◽  
Andrew Sawyer ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Communication ◽  
Tumor Characteristics ◽  
Rna Seq ◽  
Cell Cell

scholarly journals New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data

2020 ◽  
Vol 11 (12) ◽  
pp. 866-880 ◽  
Author(s):  
Xin Shao ◽  
Xiaoyan Lu ◽  
Jie Liao ◽  
Huajun Chen ◽  
Xiaohui Fan
Keyword(s):  
Single Cell ◽  
Communication Networks ◽  
Cell Communication ◽  
Receptor Interaction ◽  
Communication Studies ◽  
Transcriptomics Data ◽  
Phenotype Heterogeneity ◽  
Multicellular Organisms ◽  
Communication Study ◽  
Cell Cell

AbstractFor multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.

scholarly journals Unsupervised integration of single-cell multi-omics datasets with disparities in cell-type representation

2021 ◽  
Author(s):  
Pinar Demetci ◽  
Rebecca Santorella ◽  
Bjorn Sandstede ◽  
Ritambhara Singh
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
Integrated Analysis ◽  
Omics Data ◽  
Cell Alignment ◽  
Cell Type ◽  
Type Representation ◽  
Cellular Processes ◽  
Self Tuning ◽  
Unsupervised Algorithms

Integrated analysis of multi-omics data allows the study of how different molecular views in the genome interact to regulate cellular processes; however, with a few exceptions, applying multiple sequencing assays on the same single cell is not possible. While recent unsupervised algorithms align single-cell multi-omic datasets, these methods have been primarily benchmarked on co-assay experiments rather than the more common single-cell experiments taken from separately sampled cell populations. Therefore, most existing methods perform subpar alignments on such datasets. Here, we improve our previous work Single Cell alignment using Optimal Transport (SCOT) by using unbalanced optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. We show that our proposed method, SCOTv2, consistently yields quality alignments on five real-world single-cell datasets with varying cell-type proportions and is computationally tractable. Additionally, we extend SCOTv2 to integrate multiple ($M\geq2$) single-cell measurements and present a self-tuning heuristic process to select hyperparameters in the absence of any orthogonal correspondence information.

scholarly journals A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data

2021 ◽  
pp. 100071
Author(s):  
Kodai Minoura ◽  
Ko Abe ◽  
Hyunha Nam ◽  
Hiroyoshi Nishikawa ◽  
Teppei Shimamura
Keyword(s):  
Single Cell ◽  
Generative Model ◽  
Integrated Analysis ◽  
Mixture Of Experts

scholarly journals Adhesion to type I collagen fibrous gels induces E- to N-cadherin switching without other EMT-related phenotypes in lung carcinoma cell A549

2020 ◽  
Author(s):  
Hitomi Fujisaki ◽  
Sugiko Futaki ◽  
Masashi Yamada ◽  
Kiyotoshi Sekiguchi ◽  
Toshihiko Hayashi ◽  
...  
Keyword(s):  
Single Cell ◽  
Type I Collagen ◽  
Transforming Growth Factor ◽  
A549 Cells ◽  
Type I ◽  
Mesenchymal Transition ◽  
Cell Behaviors ◽  
Tgf Β1 ◽  
Cells Cultured ◽  
Cell Cell

AbstractIn culture system, environmental factors, such as increasing exogenous growth factors and adhesion to type I collagen (Col-I) induce epithelial-to-mesenchymal transition (EMT) in cells. Col-I molecules maintain a non-fibril form under acidic conditions, and they reassemble into fibrils under physiological conditions. Col-I fibrils often assemble to form three-dimensional gels. The gels and non-gel-form of Col-I can be utilized as culture substrates and different gel-forming state often elicit different cell behaviors. However, gel-form dependent effects on cell behaviors, including EMT induction, remain unclear. EMT induction in lung cancer cell line A549 has been reported via adhesion to Col-I but the effects of gel form dependency are unelucidated. This study investigated the changes in EMT-related behaviors in A549 cells cultured on Col-I gels.We examined cell morphology, proliferation, single-cell migration and expression of EMT-related features in A549 cells cultured on gels or non-gel form of Col-I and non-treated dish with or without transforming growth factor (TGF)-β1. On Col-I gels, some cells kept cell–cell contacts and formed clusters, others maintained single-cell form. In cell–cell contact regions, E-cadherin expression was downregulated, whereas that of N-cadherin was upregulated. Vimentin and integrins α2 and β1 expression were not increased. In TGF-β1-treated A549 cells, cadherin switched from E- to N-cadherin. Their morphology changed to a mesenchymal form and cells scattered with no cluster formation. Vimentin, integrins α2 and β1 expression were upregulated. Thus, we concluded that culture on Col-I fibrous gels induced E- to N-cadherin switching without other EMT-related phenotypes in A549 cells.

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

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.

scholarly journals Integrated analysis of single-cell embryo data yields a unified transcriptome signature for the human pre-implantation epiblast

Development ◽  
2018 ◽  
Vol 145 (3) ◽  
pp. dev158501 ◽  
Author(s):  
Giuliano G. Stirparo ◽  
Thorsten Boroviak ◽  
Ge Guo ◽  
Jennifer Nichols ◽  
Austin Smith ◽  
...  
Keyword(s):  
Single Cell ◽  
Integrated Analysis ◽  
Cell Embryo ◽  
Transcriptome Signature

scholarly journals SAT-298 Integrative Single-Cell Transcriptomic and Epigenomic Landscape of Mouse Anterior Pituitary Cell Types

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Frederique Murielle Ruf-Zamojski ◽  
Michel A Zamojski ◽  
German Nudelman ◽  
Yongchao Ge ◽  
Natalia Mendelev ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Anterior Pituitary ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Pituitary Cell ◽  
Integrated Analysis ◽  
Pituitary Cells ◽  
Rna Seq ◽  
Cell Type

Abstract The pituitary gland is a critical regulator of the neuroendocrine system. To further our understanding of the classification, cellular heterogeneity, and regulatory landscape of pituitary cell types, we performed and computationally integrated single cell (SC)/single nucleus (SN) resolution experiments capturing RNA expression, chromatin accessibility, and DNA methylation state from mouse dissociated whole pituitaries. Both SC and SN transcriptome analysis and promoter accessibility identified the five classical hormone-producing cell types (somatotropes, gonadotropes (GT), lactotropes, thyrotropes, and corticotropes). GT cells distinctively expressed transcripts for Cga, Fshb, Lhb, Nr5a1, and Gnrhr in SC RNA-seq and SN RNA-seq. This was matched in SN ATAC-seq with GTs specifically showing open chromatin at the promoter regions for the same genes. Similarly, the other classically defined anterior pituitary cells displayed transcript expression and chromatin accessibility patterns characteristic of their own cell type. This integrated analysis identified additional cell-types, such as a stem cell cluster expressing transcripts for Sox2, Sox9, Mia, and Rbpms, and a broadly accessible chromatin state. In addition, we performed bulk ATAC-seq in the LβT2b gonadotrope-like cell line. While the FSHB promoter region was closed in the cell line, we identified a region upstream of Fshb that became accessible by the synergistic actions of GnRH and activin A, and that corresponded to a conserved region identified by a polycystic ovary syndrome (PCOS) single nucleotide polymorphism (SNP). Although this locus appears closed in deep sequencing bulk ATAC-seq of dissociated mouse pituitary cells, SN ATAC-seq of the same preparation showed that this site was specifically open in mouse GT, but closed in 14 other pituitary cell type clusters. This discrepancy highlighted the detection limit of a bulk ATAC-seq experiment in a subpopulation, as GT represented ~5% of this dissociated anterior pituitary sample. These results identified this locus as a candidate for explaining the dual dependence of Fshb expression on GnRH and activin/TGFβ signaling, and potential new evidence for upstream regulation of Fshb. The pituitary epigenetic landscape provides a resource for improved cell type identification and for the investigation of the regulatory mechanisms driving cell-to-cell heterogeneity. Additional authors not listed due to abstract submission restrictions: N. Seenarine, M. Amper, N. Jain (ISMMS).

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