Single-Cell RNA-Sequencing Reveals a Continuous Spectrum of Differentiation in Hematopoietic Cells

Abstract

SummaryThe transcriptional programs that govern hematopoiesis have been investigated primarily by population-level analysis of hematopoietic stem and progenitor cells, which cannot reveal the continuous nature of the differentiation process. Here we applied single-cell RNA-sequencing to a population of hematopoietic cells in zebrafish as they undergo thrombocyte lineage commitment. By reconstructing their developmental chronology computationally, we were able to place each cell along a continuum from stem cell to mature cell, refining the traditional lineage tree. The progression of cells along this continuum is characterized by a highly coordinated transcriptional program, displaying simultaneous suppression of genes involved in cell proliferation and ribosomal biogenesis as the expression of lineage specific genes increases. Within this program, there is substantial heterogeneity in the expression of the key lineage regulators. Overall, the total number of genes expressed, as well as the total mRNA content of the cell, decreases as the cells undergo lineage commitment.

Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML

“…[17][18][19][20][21][22][23] In leukemia, single-cell methods additionally offer the opportunity to discriminate between leukemic and healthy cells, thereby allowing for specific characterization of the infrequent residual LSC population even months into treatment. Here we have dissected the heterogeneity of the CML LSC population both at diagnosis and following 3 months of TKI treatment.…”

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types

“…The simultaneous expression of thousands of genes can be measured in each cell, thereby providing an unbiased view of transcriptional activity at the cellular level and avoiding the averaging effect of bulk gene expression studies (Shapiro et al 2013). Cells can then be grouped into biologically relevant clusters based on the similarity of their gene expression profiles rather than a handful of cell surface markers (Grün et al 2015;Trapnell 2015;Macaulay et al 2016). Therefore, despite technical and biological noise and the computational challenges associated with this variability (Brennecke et al 2013;Buettner et al 2015), scRNA-seq has the potential to uncover new immune cell types that cannot be studied using traditional approaches.…”

cycleX: multi-dimensional pseudotime reveals cell cycle and differentiation relationship of dendritic cell progenitors

“…An alternative technique, Bayesian GPLVM has been used to depict 'pseudo time' of cell differentiation. (Macaulay et al, 2016) One way to improve the visualisation of the method is to order the cells by their 'pseudo time' of differentiation and cell cycle progression, and look at the points in time where the cell cycle and the transition of MDP to CDP, CDP to PreDCs occur. Using this method we can find out at which cell cycle stage the cells will differentiate.…”

“…http://dx.doi.org/10.1101/222372 doi: bioRxiv preprint first posted online Nov. 20, 2017; previous studies that used GPLVM to model the order of haematopoietic cells differentiation in a continuous spectrum (Macaulay, et al, 2016). The GPfates model also uses GPLVM to reconstruct CD4+ T cells differentiation fates into Th1/Tfh.…”