Biology subjects

Herman, J. S.

Publications and source records attributed to Herman, J. S..

4 recordsLinked to original sources

bioRxiv2022

Gene expression noise dynamics unveil 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. While unicellular organisms exploit noise as a bet-hedging strategy, its role during multilineage differentiation of stem cells is underexplored. Utilizing single-cell RNA-sequencing to reconstruct cell state manifolds, we developed VarID2, a method for the quantification of biological noise at single-cell resolution. VarID2 reveals enhanced nuclear versus cytoplasmic noise across cell types of the peripheral blood, and distinct regulatory modes stratified by correlation between noise, expression, and chromatin accessibility. Noise levels are minimal in murine hematopoietic stem cells and increase during both differentiation and ageing. Differential noise identified myeloid-biased Dlk1+ long-term-HSCs in aged mice with enhanced quiescence and self-renewal capacity. VarID2 reveals fundamental properties of noise across cellular compartments, during stem cell differentiation and ageing, and uncovers distinct cellular sub-states invisible to conventional gene expression analysis.

systems biology↗

bioRxiv2021

Single-cell transcriptomic resolution of stem cells and their developmental trajectories in the hippocampus reveals epigenetic control of cell state perseverance

Despite conceptual research on hippocampus development and the application of single-cell-resolved technologies, the nature and maturation of its diverse progenitor populations are unexplored. The chromatin modifier DOT1L balances progenitor proliferation and differentiation, and conditional loss-of-function mice featured impaired hippocampus development. We applied single-cell RNA sequencing on DOT1L-mutant mice and explored cell trajectories in the E16.5 hippocampus. We resolved in our data five distinct neural stem cell populations with the developmental repertoire to specifically generate the cornu ammonis (CA) 1 field and the dentate gyrus (DG). Within the two developing CA1- and CA3-fields, we identified two distinct maturation states and we thus propose CA1- and CA3-differentiation along the radial axis. In the developing hippocampus, DOT1L is primarily involved in the proper development of CA3 and the DG, and it serves as a state-preserving epigenetic factor that orchestrates the expression of several important transcription factors that impact neuronal differentiation and maturation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/452775v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@d611caorg.highwire.dtl.DTLVardef@2a0e3corg.highwire.dtl.DTLVardef@10c9082org.highwire.dtl.DTLVardef@1e358c8_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIThe developing hippocampus contains distinct and spatially separated NSC populations that differ in expression of a specific set of firstly described marker genes. C_LIO_LICA pyramidal neurons mature along the radial axis and pass through distinct maturation states. C_LIO_LIDOT1L preserves the dentate granule cell lineage in the developing hippocampus and limits maturation in the CA1- and CA3-fields development. C_LIO_LIDOT1L gates cell maturation as upstream regulator of transcription factor expression that confer instrumental roles in hippocampus development. C_LI

neuroscience↗

bioRxiv2018

Deciphering the Regulatory Landscape of γδ T Cell Development by Single-Cell RNA-Sequencing

Recent studies have established{gamma}{delta} T cells as critical players in a broad range of infections, antitumor surveillance, autoimmune diseases and tissue homeostasis. However, differentiation of{gamma}{delta} T cells in the adult thymus remains poorly understood, due to the rare frequency of this lineage. Here, we infer high-resolution developmental trajectories of this rare population by single-cell RNA-sequencing. We reveal previously unknown subtypes and identify the transcription factor c-MAF as a novel key regulator of IL-17-producing{gamma}{delta} T cell ({gamma}{delta}T17) differentiation. c-MAF knockout mice exhibit a complete block in{gamma}{delta} T17 differentiation, absence of these cells from peripheral organs, and protection from an autoimmune phenotype in a psoriasis model. Single-cell RNA-sequencing of Sox13 and Rorc knockout mice pinpoints c-MAF as an essential missing link between these lineage-specifying factors. These findings significantly enhance our understanding of{gamma}{delta} T cell ontogeny. Our experimental strategy provides a blueprint for deciphering differentiation of rare cell types.

immunology↗

bioRxiv2017

FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data

Differentiation of multipotent cells is a complex process governed by interactions of thousands of genes subject to substantial expression fluctuations. Resolving cell state heterogeneity arising during this process requires quantification of gene expression within individual cells. However, computational methods linking this heterogeneity to biases towards distinct cell fates are not well established. Here, we perform deep single-cell transcriptome sequencing of ~2,000 bone-marrow derived mouse hematopoietic progenitors enriched for lymphoid lineages. To resolve subtle transcriptome priming indicative of distinct lineage biases, we developed FateID, an iterative supervised learning algorithm for the probabilistic quantification of cell fate bias. FateID delineates domains of fate bias within progenitor populations and permits the derivation of high-resolution differentiation trajectories, revealing a common progenitor population of B cells and plasmacytoid dendritic cells, which we validated by in vitro differentiation assays. We expect that FateID will enhance our understanding of the process of cell fate choice in complex multi-lineage differentiation systems.

bioinformatics↗