Comparative analysis of single-cell RNA sequencing data from mouse spermatogonial and mesenchymal stem cells to identify differentially expressed genes and transcriptional regulators of germline cells

2018 ◽  
Vol 233 (7) ◽  
pp. 5231-5242 ◽  
Author(s):  
Sajjad Sisakhtnezhad ◽  
Parvin Heshmati
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Comparative Analysis ◽  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Transcriptional Regulators ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

scholarly journals scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Bobby Ranjan ◽  
Florian Schmidt ◽  
Wenjie Sun ◽  
Jinyu Park ◽  
Mohammad Amin Honardoost ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Differentially Expressed ◽  
Consensus Clustering ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Background Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering approaches have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. Results We present scConsensus, an $${\mathbf {R}}$$ R framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. Conclusions scConsensus combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. scConsensus is implemented in $${\mathbf {R}}$$ R and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

scholarly journals SIDEseq: A Cell Similarity Measure Defined by Shared Identified Differentially Expressed Genes for Single-Cell RNA sequencing Data

2017 ◽  
Vol 9 (1) ◽  
pp. 200-216 ◽  
Author(s):  
Courtney Schiffman ◽  
Christina Lin ◽  
Funan Shi ◽  
Luonan Chen ◽  
Lydia Sohn ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Similarity Measure ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
A Cell

scholarly journals Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

2019 ◽  
Author(s):  
Florian Klimm ◽  
Enrique M. Toledo ◽  
Thomas Monfeuga ◽  
Fang Zhang ◽  
Charlotte M. Deane ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Protein Protein Interaction ◽  
Single Cell Rna Sequencing ◽  
Protein Protein Interaction Networks

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks (PPINs) that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted PPINs, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. scPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, scPPIN also identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.

scholarly journals Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Florian Klimm ◽  
Enrique M. Toledo ◽  
Thomas Monfeuga ◽  
Fang Zhang ◽  
Charlotte M. Deane ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Protein Protein Interaction ◽  
Single Cell Rna Sequencing ◽  
Protein Protein Interaction Networks

Abstract Background Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. Results In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. Conclusions The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.

Novel Insights into the Stemness and Immune Privilege of Mesenchymal Stem Cells from Human Wharton Jelly by Single-Cell RNA Sequencing

2021 ◽  
Vol 28 ◽  
Author(s):  
Zikuan Leng ◽  
Longyu Li ◽  
Xiang Zhou ◽  
Guangyao Dong ◽  
Songfeng Chen ◽  
...  
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Privilege ◽  
Single Cell Rna Sequencing

scholarly journals Single-cell RNA sequencing deconvolutes the in vivo heterogeneity of human bone marrow-derived mesenchymal stem cells

2020 ◽  
Author(s):  
Zun Wang ◽  
Xiaohua Li ◽  
Junxiao Yang ◽  
Yun Gong ◽  
Huixi Zhang ◽  
...  
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Mesenchymal Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Critical Role ◽  
Cellular Heterogeneity ◽  
Bone Homeostasis ◽  
Single Cell Rna Sequencing

AbstractBone marrow-derived mesenchymal stem cells (BM-MSCs) are multipotent stromal cells, which have a critical role in the maintenance of skeletal tissues such as bone, cartilage, and the fat found in bone marrow. In addition to providing microenvironmental support for hematopoietic processes, BM-MSCs can differentiate into various mesodermal lineages including osteoblast/osteocyte, chondrocyte, and adipocyte cells that are crucial for bone metabolism. While BM-MSCs have high cell-to-cell heterogeneity in gene expression, the cell subtypes that contribute to this heterogeneity in vivo in humans have not been characterized. To investigate the transcriptional diversity of BM-MSCs, we applied single-cell RNA sequencing (scRNA-seq) on freshly isolated CD271+ BM-derived mononuclear cells (BM-MNCs) from two human subjects. We successfully identified LEPRhiCD45low BM-MSCs within the CD271+ BM-MNC population, and further codified the BM-MSCs into distinct subpopulations corresponding to the osteogenic, chondrogenic, and adipogenic differentiation trajectories, as well as terminal-stage quiescent cells. Biological functional annotations of transcriptomes suggest that osteoblast precursors may induce angiogenesis coupled with osteogenesis, and chondrocyte precursors may have the potential to differentiate into myocytes. We discovered transcripts for several cluster of differentiation (CD) markers that were highly expressed (e.g., CD167b, CD91, CD130 and CD118) or absent (e.g., CD74, CD217, CD148 and CD68) in BM-MSCs and could be novel markers for human BM-MSC purification. This study is the first systematic in vivo dissection of human BM-MSCs cell subtypes at the single-cell resolution, revealing insight into the extent of their cellular heterogeneity and bone homeostasis.

scholarly journals Single-cell RNA sequencing deconvolutes the in vivo heterogeneity of human bone marrow-derived mesenchymal stem cells

2021 ◽  
Vol 17 (15) ◽  
pp. 4192-4206
Author(s):  
Zun Wang ◽  
Xiaohua Li ◽  
Junxiao Yang ◽  
Yun Gong ◽  
Huixi Zhang ◽  
...  
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Mesenchymal Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Human Bone ◽  
Human Bone Marrow ◽  
Single Cell Rna Sequencing
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