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Single cell lncRNAs in hematopoiesis
AB
Figure 6. Transcription factor occupancy and epigenetic modification of long noncoding RNAs during hematopoietic differentiation. (A) Cumulative distribution of genes [long noncoding RNA (lncRNA)-encoding, up; protein-coding, down] with or without GATA1 binding at promoters. x axis, log10(P value), indicating the significance of gene expression in MEP versus non-MEP cells; y axis, a cumulative distribution function (CDF) of lncRNAs (%) or messenger RNAs (mRNAs) (%). For both lncRNAs and mRNAs, the lower log10(P value), which means the higher significance of preferential gene expression in MEP cells versus non-MEP cells, indicated the higher GATA1 binding CDF. (B) Distribution of single cell RNA sequencing (scRNA-seq) reads across two MEP-specific lncRNAs (SHG3 and RP11-620J15.3) in MEP and other cell types, and the histone modification marks in the same region. Top tracks are images from the IGV Browser depicting scRNA-seq signals as the density of mapped scRNA-seq reads, and chromatin immunoprecipitation sequencing (ChIP-seq) signals as the density of processed signal enrichment of GATA1. Track 2 shows a lncRNA transcript model. Tracks 3 to 7 represent scRNA-seq signals of two MEP-specific lncRNAs (SHG3 and RP11-620J15.3) in two single cells of each cell type including MEP and others. Tracks 8 to 11 depict the ChIP-sequ signal for active histone modification marks (H3K79me2, H3K27Ac, and H3K4me2) and repressive histone modification mark H3K27me3 in a human erythroleukemia cell line, K562. MEP: megakaryocyte-erythroid progenitor; ETP: earliest thymic progenitor; GMP: granulo- cyte-monocyte progenitor, ProB: pro-B cell; MLP: multilymphoid progenitor.
correlation. We attempted to computationally distinguish lncRNA roles as primary and possibly regulatory from sec- ondary and “epiphenomonal”. To this end, we first deter- mined whether lncRNAs were preferentially expressed in specific cell types; if so, their functions were postulated to relate to lineage-specific protein-coding genes. We then applied pseudotime ordering to reconstruct hematopoietic differentiation in order to examine dynamic gene expres- sion. HSCs are assumed to lose “stemness” and to progres- sively gain restricted lineage commitment gene expression during differentiation. Indeed, we observed repression of stemness genes and activation of the cell proliferation/metabolism gene program, accompanied by activation of specific-lineage genes and repression of alter- native pathway of differentiation genes. By this analysis, we defined lncRNAs that are coordinately expressed in those gene modules and thus have a greater probability of regulatory roles in lineage specification. Our data should assist in narrowing the scope of future efforts including in vitro perturbation and in vivo experiments to study func- tions of individual lncRNAs in hematopoiesis.
The highly ordered expression pattern of lncRNAs dur- ing hematopoiesis implies regulatory constraint. Our analysis and earlier studies8,39,47 indicated that lncRNAs are likely regulated by cell-type specific transcription fac- tors.13,14,16 The observation that lncRNAs exhibited higher expression variability than did mRNAs in the same regu- latory program suggests more diverse and active expres-
sion of lncRNAs. lncRNAs exert regulatory roles transcrip- tionally and post-transcriptionally by a variety of mecha- nisms.1-6 These features of lncRNAs would make them more dynamic participants in cell states and biological processes, facilitating prompt adaptive responses to stim- uli or perturbations, and add another layer of complexity in gene expression regulation and cell fate decision.
Our data indicated considerable stage- and lineage- specificity of lncRNAs in human HSPCs and potential engagement in early priming of cell fate, consistent with tissue- and cell type-specificity observed in previous stud- ies.5,7-9, 13-18 This conclusion was confirmed by extension to an external independent scRNA-seq study of 1,034 sorted single human HSPCs,45 and the reproducible lineage-speci- ficity of 35 lncRNAs in both single cells and sorted bulk samples by quantitative RT-PCR. lncRNAs often form sec- ondary structures and there are sensitive, rapid, low-cost methods readily available for lncRNA quantification, all of which make lncRNAs promising biomarkers for disease detection, diagnosis, and prognosis. One study based on microarray assay of bone marrow mononuclear cells from 176 adult patients with MDS established a four-lncRNA risk-scoring system that correlated with distinctive clinical features, and was an independent prognostic factor for survival and leukemia transformation.51 We also found lncRNAs to be dysregulated in MDS cells, but due to the limited number of patients, lncRNA signatures of MDS patients in the current study should be interpreted with
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