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Single-cell RNA sequencing reveals molecular and functional platelet bias of aged haematopoietic stem cells.

ABSTRACT: Aged haematopoietic stem cells (HSCs) generate more myeloid cells and fewer lymphoid cells compared with young HSCs, contributing to decreased adaptive immunity in aged individuals. However, it is not known how intrinsic changes to HSCs and shifts in the balance between biased HSC subsets each contribute to the altered lineage output. Here, by analysing HSC transcriptomes and HSC function at the single-cell level, we identify increased molecular platelet priming and functional platelet bias as the predominant age-dependent change to HSCs, including a significant increase in a previously unrecognized class of HSCs that exclusively produce platelets. Depletion of HSC platelet programming through loss of the FOG-1 transcription factor is accompanied by increased lymphoid output. Therefore, increased platelet bias may contribute to the age-associated decrease in lymphopoiesis.

SUBMITTER: Grover A

PROVIDER: S-EPMC4820843 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Single-cell RNA sequencing reveals molecular and functional platelet bias of aged haematopoietic stem cells.

Grover Amit A Sanjuan-Pla Alejandra A Thongjuea Supat S Carrelha Joana J Giustacchini Alice A Gambardella Adriana A Macaulay Iain I Mancini Elena E Luis Tiago C TC Mead Adam A Jacobsen Sten Eirik W SE Nerlov Claus C

Nature communications 20160324

Aged haematopoietic stem cells (HSCs) generate more myeloid cells and fewer lymphoid cells compared with young HSCs, contributing to decreased adaptive immunity in aged individuals. However, it is not known how intrinsic changes to HSCs and shifts in the balance between biased HSC subsets each contribute to the altered lineage output. Here, by analysing HSC transcriptomes and HSC function at the single-cell level, we identify increased molecular platelet priming and functional platelet bias as t ...[more]

PMID: 27009448

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Project description:Background: Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include unique molecular identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene, with PCR duplicates resulting in copies of the UMI not included in expression estimates. Methods: Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets that differ in terms of capture technology, library preparation, cell types and species. Results: We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, with a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. Conclusions: We find that the choice of scRNA-seq protocol influences the detection rate of genes, and that full-length datasets exhibit gene-length bias. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal the underlying biology influencing expression of mESCs.

Project description:BackgroundIntra-tumor heterogeneity stems from genetic, epigenetic, functional, and environmental differences among tumor cells. A major source of genetic heterogeneity comes from DNA sequence differences and/or whole chromosome and focal copy number variations (CNVs). Whole chromosome CNVs are caused by chromosomal instability (CIN) that is defined by a persistently high rate of chromosome mis-segregation. Accordingly, CIN causes constantly changing karyotypes that result in extensive cell-to-cell genetic heterogeneity. How the genetic heterogeneity caused by CIN influences gene expression in individual cells remains unknown.MethodsWe performed single-cell RNA sequencing on a chromosomally unstable glioblastoma cancer stem cell (CSC) line and a control normal, diploid neural stem cell (NSC) line to investigate the impact of CNV due to CIN on gene expression. From the gene expression data, we computationally inferred large-scale CNVs in single cells. Also, we performed copy number adjusted differential gene expression analysis between NSCs and glioblastoma CSCs to identify copy number dependent and independent differentially expressed genes.ResultsHere, we demonstrate that gene expression across large genomic regions scales proportionally to whole chromosome copy number in chromosomally unstable CSCs. Also, we show that the differential expression of most genes between normal NSCs and glioblastoma CSCs is largely accounted for by copy number alterations. However, we identify 269 genes whose differential expression in glioblastoma CSCs relative to normal NSCs is independent of copy number. Moreover, a gene signature derived from the subset of genes that are differential expressed independent of copy number in glioblastoma CSCs correlates with tumor grade and is prognostic for patient survival.ConclusionsThese results demonstrate that CIN is directly responsible for gene expression changes and contributes to both genetic and transcriptional heterogeneity among glioblastoma CSCs. These results also demonstrate that the expression of some genes is buffered against changes in copy number, thus preserving some consistency in gene expression levels from cell-to-cell despite the continuous change in karyotype driven by CIN. Importantly, a gene signature derived from the subset of genes whose expression is buffered against copy number alterations correlates with tumor grade and is prognostic for patient survival that could facilitate patient diagnosis and treatment.

Dataset Information

Single-cell RNA sequencing reveals molecular and functional platelet bias of aged haematopoietic stem cells.

Publications

Single-cell RNA sequencing reveals molecular and functional platelet bias of aged haematopoietic stem cells.

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