Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

Chen, Geng; Ning, Baitang; Shi, Tieliu

doi:10.3389/fgene.2019.00317

Cited by 708 publications

(593 citation statements)

References 127 publications

Supporting

Mentioning

544

Contrasting

Unclassified

Order By: Relevance

“…In consideration of the trade-offs among the large number of methods evaluated with our limited time, computational resources, and manpower, we carefully selected realworld datasets for the benchmarking. The latest scRNA-seq methods are divided into two categories, namely, full-length scRNA-seq methods and high-throughput scRNA-seq methods with specific cell dissociation and cellular/molecular barcoding technologies such as droplet-based and split-and-pool experiments [34,35]. Because the number of cells measured by scRNA-seq has been increased by the latter technology, we selected the following four datasets generated by such technologies: human peripheral blood mononuclear cells (PBMCs), human pancreatic cells (Pancreas), mouse brain and spinal cord (BrainSpinalCord), and mouse cells from the cortex, hippocampus, and ventricular zone (Brain) ( Table 2).…”

Section: Real-world Datasetsmentioning

confidence: 99%

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

Tsuyuzaki

Sato

et al. 2019

Preprint

View full text Add to dashboard Cite

Principal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but large-scale scRNA-seq datasets require long computational times and a large memory capacity.In this work, we review 21 fast and memory-efficient PCA implementations (10 algorithms) and evaluate their application using 4 real and 18 synthetic datasets. Our benchmarking showed that some PCA algorithms are faster, more memory efficient, and more accurate than others. In consideration of the differences in the computational environments of users and developers, we have also developed guidelines to assist with selection of appropriate PCA implementations.

show abstract

Section: Real-world Datasetsmentioning

confidence: 99%

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

Tsuyuzaki

Sato

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…However, these techniques pose further technical and economic challenges (88,89). Specifically, a large number of organoids must be sequenced to mitigate cellular complexity and batch heterogeneity and powerful, reproducible and accurate computational pipelines are required to analyse such data (90).…”

Section: Discussionmentioning

confidence: 99%

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

Treveil

Sudhakar

Matthews

et al. 2019

Preprint

View full text Add to dashboard Cite

The epithelial lining of the small intestine consists of multiple cell types, including Paneth cells and goblet cells, that work in cohort to maintain gut health. 3D in vitro cultures of human primary epithelial cells, called organoids, have become a key model to study the functions of Paneth cells and goblet cells in normal and diseased conditions. Advances in these models include the ability to skew differentiation to particular lineages, providing a useful tool to study cell type specific function/dysfunction in the context of the epithelium. Here, we use comprehensive profiling of mRNA, microRNA and long noncoding RNA expression to confirm that Paneth cell and goblet cell enrichment of murine small intestinal organoids (enteroids) establishes a physiologically accurate model. We employ network analysis to infer the regulatory landscape altered by skewing differentiation, and using knowledge of cell type specific markers, we predict key regulators of cell type specific functions: Cebpa, Jun, Nr1d1 and Rxra specific to Paneth cells, Gfi1b and Myc specific for goblet cells and Ets1, Nr3c1 and Vdr shared between them. Links identified between these regulators and cellular phenotypes of inflammatory bowel disease (IBD) suggest that global regulatory rewiring during or after differentiation of Paneth cells and goblet cells could contribute to IBD aetiology. Future application of cell type enriched enteroids combined with the presented computational workflow can be used to disentangle multifactorial mechanisms of these cell types and propose regulators whose pharmacological targeting could be advantageous in treating IBD patients with Crohn's disease or ulcerative colitis.

show abstract

“…The advent of scRNA-seq has been heralded as a revolution promising new approaches to classification of myeloid heterogeneity [35,157,158]. Single cell (sc) RNA-seq is intrinsically noisy, non-quantitative stochastic sampling of a subset of the most abundant mRNAs in individual cells ( [159,160]). Algorithms that support non-linear dimensional reduction (e.g.…”

Section: The Relationship Between Single Cell and Bulk Rna-seq Datamentioning

confidence: 99%

Transcriptional network analysis of transcriptomic diversity in resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system

Hume

2020

Preprint

View full text Add to dashboard Cite

The mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages and dendritic cells (DC) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected from NCBI-GEO >500 quality RNA-seq datasets generated from mouse MPS cells isolated from multiple tissues. The primary data were randomly down-sized to a depth of 10 million reads and requantified. The resulting dataset was clustered using the network analysis tool Graphia. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1 and cDC2, and lacking Fcgr1 (CD64), were centrally-located within the MPS cluster and no more distinct than other MPS cell types. A gene-to-gene correlation matrix identified large generic co-expression clusters associated with MPS maturation and innate immune function. Smaller co-expression clusters including the transcription factors that drive them showed higher expression within defined isolated cells, including macrophages and DC from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell homeostasis, a cluster of transcripts enriched in intestinal macrophages and a generic cDC cluster associated with Ccr7. However, transcripts encoding many other putative MPS subset markers including Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Retnla and H2-A/E (class II MHC) clustered idiosyncratically and were not correlated with underlying functions. The data provide no support for the concept of markers of M2 polarization or the specific adaptation of DC to present antigen to T cells. Co-expression of immediate early genes (e.g. Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (Tnf, Il1b, Ccl3/4) indicated that all tissue disaggregation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of public RNA-seq and single cell RNA-seq data from the same lung cell populations showed that the extensive heterogeneity implied by the global cluster analysis may be even greater at a single cell level with few markers strongly correlated with each other. This analysis highlights the power of large datasets to identify the diversity of MPS cellular phenotypes, and the limited predictive value of surface markers to define lineages, functions or subpopulations.

show abstract

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

Cited by 708 publications

References 127 publications

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

Transcriptional network analysis of transcriptomic diversity in resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system

Contact Info

Product

Resources

About