VarID2 quantifies gene expression noise dynamics and unveils functional heterogeneity of ageing hematopoietic stem cells.

Variability of gene expression due to stochasticity of transcription or variation of extrinsic signals, termed biological noise, is a potential driving force of cellular differentiation. Utilizing single-cell RNA-sequencing, we develop VarID2 for the quantification of biological noise at single-cell resolution. VarID2 reveals enhanced nuclear versus cytoplasmic noise, and distinct regulatory modes stratified by correlation between noise, expression, and chromatin accessibility. Noise levels are minimal in murine hematopoietic stem cells (HSCs) and increase during differentiation and ageing. Differential noise identifies myeloid-biased Dlk1+ long-term HSCs in aged mice with enhanced quiescence and self-renewal capacity. VarID2 reveals noise dynamics invisible to conventional single-cell transcriptome analysis.

Temporal expression of MOF acetyltransferase primes transcription factor networks for erythroid fate.

Self-renewal and differentiation of hematopoietic stem cells (HSCs) are orchestrated by the combinatorial action of transcription factors and epigenetic regulators. Here, we have explored the mechanism by which histone H4 lysine 16 acetyltransferase MOF regulates erythropoiesis. Single-cell RNA sequencing and chromatin immunoprecipitation sequencing uncovered that MOF influences erythroid trajectory by dynamic recruitment to chromatin and its haploinsufficiency causes accumulation of a transient HSC population. A regulatory network consisting of MOF, RUNX1, and GFI1B is critical for erythroid fate commitment. GFI1B acts as a Mof activator which is necessary and sufficient for cell type-specific induction of Mof expression. Plasticity of Mof-depleted HSCs can be rescued by expression of a downstream effector, Gata1, or by rebalancing acetylation via a histone deacetylase inhibitor. Accurate timing and dosage of Mof expression act as a rheostat for the feedforward transcription factor network that safeguards progression along the erythroid fate.

EBF1-deficient bone marrow stroma elicits persistent changes in HSC potential.

Crosstalk between mesenchymal stromal cells (MSCs) and hematopoietic stem cells (HSCs) is essential for hematopoietic homeostasis and lineage output. Here, we investigate how transcriptional changes in bone marrow (BM) MSCs result in long-lasting effects on HSCs. Single-cell analysis of Cxcl12-abundant reticular (CAR) cells and PDGFRα+Sca1+ (PαS) cells revealed an extensive cellular heterogeneity but uniform expression of the transcription factor gene Ebf1. Conditional deletion of Ebf1 in these MSCs altered their cellular composition, chromatin structure and gene expression profiles, including the reduced expression of adhesion-related genes. Functionally, the stromal-specific Ebf1 inactivation results in impaired adhesion of HSCs, leading to reduced quiescence and diminished myeloid output. Most notably, HSCs residing in the Ebf1-deficient niche underwent changes in their cellular composition and chromatin structure that persist in serial transplantations. Thus, genetic alterations in the BM niche lead to long-term functional changes of HSCs.

High-Throughput Single-Cell RNA Sequencing and Data Analysis.

Understanding biological systems at a single cell resolution may reveal several novel insights which remain masked by the conventional population-based techniques providing an average readout of the behavior of cells. Single-cell transcriptome sequencing holds the potential to identify novel cell types and characterize the cellular composition of any organ or tissue in health and disease. Here, we describe a customized high-throughput protocol for single-cell RNA-sequencing (scRNA-seq) combining flow cytometry and a nanoliter-scale robotic system. Since scRNA-seq requires amplification of a low amount of endogenous cellular RNA, leading to substantial technical noise in the dataset, downstream data filtering and analysis require special care. Therefore, we also briefly describe in-house state-of-the-art data analysis algorithms developed to identify cellular subpopulations including rare cell types as well as to derive lineage trees by ordering the identified subpopulations of cells along the inferred differentiation trajectories.