Single-cell RNA-seq of cultured human adipose-derived mesenchymal stem cells

Liu, Xuanyu; Xiang, Qinqin; Xu, Fengming; Huang, Jiuzuo; Yu, Nanze; Zhang, Qixu; Long, Xiao; Zhou, Zhou

doi:10.1038/sdata.2019.31

Cited by 63 publications

(76 citation statements)

References 23 publications

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“…Using scRNAseq from Ad-MSCs [19], we observed that our markers are not expressed in all cells but constitute different subpopulations with different levels of rarity in Ad-MSCs. FANTOM6 and single-cell analysis could permit tracing three components of these states : stress inducible cells, lineage commited osteogenic progenitors and proliferating cells.…”

Section: Discussionmentioning

confidence: 95%

“…The public RNAseq datasets (in fastq format) have been assessed using the ENCODE, EBI Ar-rayExpress service or SRA database at each step of the pipeline: i) lncRNAs prediction and first differential analysis (Table S1), ii) k-mer search in ENCODE data to refine lncRNAs' specificity (Table S2), iii) k-mer search in FANTOM6 CAGE dataset and single cell analysis (scRNAseq) from Adipose MSCs by X. Liu et al raw data [19] for functional investigations (Table S3), iv) k-mer search in MSCs in different conditions (Table S4).…”

Section: Data Collection and Basic Processingmentioning

confidence: 99%

“…After normalisation, Inter-donor batch effect was corrected with ComBat method in sva R package [35] (Combat function, prior.plots=FALSE, par.prior=TRUE). Cell cycle scoring was made by CellCycleScoring Seurat function, using gene set used by the initial authors [19]. Finally, other sources of unnecessary variability as percent of mitochondrial genes, cell cycle and number of UMIs were regressed using ScaleData Seurat function.…”

Section: Single-cell Analysismentioning

confidence: 99%

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Detailed analysis of public RNAseq data and long non-coding RNA: a proposed enhancement to mesenchymal stem cell characterisation

Riquier

Mathieu

Boureux

et al. 2020

Preprint

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The development of RNA sequencing (RNAseq) and corresponding emergence of public datasets have created new avenues of transcriptional marker search. The long non-coding RNAs (lncRNAs) constitute an emerging class of transcripts with a potential for high tissue specificity and function. Using a dedicated bioinformatics pipeline, we propose to construct a cell-specific catalogue of unannotated lncRNAs and to identify the strongest cell markers. This pipeline uses ab initio transcript identification, pseudoalignment and new methodologies such as a specific k-mer approach for naive quantification of expression in numerous RNAseq data.For an application model, we focused on Mesenchymal Stem Cells (MSCs), a type of adult multipotent stem-cells of diverse tissue origins. Frequently used in clinics, these cells lack extensive characterisation. Our pipeline was able to highlight different lncRNAs with high specificity for MSCs. In silico methodologies for functional prediction demonstrated that each candidate represents one specific state of MSCs biology. Together, these results suggest an approach that can be employed to harness lncRNA as cell marker, showing different candidates as potential actors in MSCs biology, while suggesting promising directions for future experimental investigations.

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Section: Discussionmentioning

confidence: 95%

Section: Data Collection and Basic Processingmentioning

confidence: 99%

Section: Single-cell Analysismentioning

confidence: 99%

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Detailed analysis of public RNAseq data and long non-coding RNA: a proposed enhancement to mesenchymal stem cell characterisation

Riquier

Mathieu

Boureux

et al. 2020

Preprint

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“…We used publicly available scRNA-seq data from 26,640 human cells from three healthy donors (N8, N7 and N5); the scRNA-seq data was generated on 10x Genomics Chromium v2 platform [31]. The library preparation and sequencing are described in detail elsewhere [31]. Briefly, cells were partitioned using 10x Genomics Single Cell 3ʹ Chips, and barcodes to index cells (14 bp) as well as transcripts (10 bp UMI) were incorporated.…”

Section: Datamentioning

confidence: 99%

“…Here, we demonstrate a pipeline to estimate VAFRNA from scRNA-seq data obtained from 10x Genomics Chromium platform [31]. We have selected this platform due to its growing popularity along with: (1) high throughput (our analysis includes 26,640 cells obtained from three healthy donors), (2) high depth of sequencing (~150,000 sequencing reads per cell), and, (3) support for unique molecular identifiers (UMI) for removal of PCR-related sequencing bias.…”

Section: Introductionmentioning

confidence: 99%

Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

Liu

Bousounis

et al. 2019

Preprint

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With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10x Genomics platform. We include in the analysis 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), with an average sequencing reads over 120K/cell (more than 4 billion scRNA-seq reads total). High quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimate the expressed Variant Allele Fraction (VAFRNA) from SNV-aware alignments and analyze its variance and distribution (mono-and bi-allelic) at different cutoffs for required minimal number of sequencing reads. Our analysis shows that when assessing SNV loci covered by a minimum of 3 unique sequencing reads, over 50% of the heterozygous SNVs show biallelic expression, while at minimum of 10 reads, nearly 90% of the SNVs are bi-allelic. Consistent with single cell studies on RNA velocity and models of transcriptional burst kinetics, we observe a substantially higher rate of monoallelic expression among intronic SNVs, signifying the usefulness of scVAFRNA to assess dynamic cellular processes. Our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3'-based library generation protocol of 10x Genomics scRNA-seq data can be highly informative in SNV-based analyses.

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In‐cytoplasm mitochondrial transplantation for mesenchymal stem cells engineering and tissue regeneration

Yao

Zhou

et al. 2021

Bioengineering & Transla Med

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Single-cell RNA-seq of cultured human adipose-derived mesenchymal stem cells

Cited by 63 publications

References 23 publications

Detailed analysis of public RNAseq data and long non-coding RNA: a proposed enhancement to mesenchymal stem cell characterisation

Detailed analysis of public RNAseq data and long non-coding RNA: a proposed enhancement to mesenchymal stem cell characterisation

Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

In‐cytoplasm mitochondrial transplantation for mesenchymal stem cells engineering and tissue regeneration

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