Biology subjects

Morgan, M. D.

Publications and source records attributed to Morgan, M. D..

10 recordsLinked to original sources

bioRxiv2025

KDM5-driven transcriptional noise fuels plasticity-led awakening and relapse in paediatric cancer

How drug-tolerant persister (DTP) cells escape quiescence to drive tumour relapse is a central unresolved question in cancer evolution. Here, we identify transcriptional noise (TN), defined as the stochastic variability in gene expression, as a latent property of paediatric cancer cells that becomes a driver of adaptive regrowth after treatment withdrawal. Using functional assays, lineage tracing, single-cell transcriptomics, and multiscale landscape modelling, we show that therapy enriches mesenchymal-like tolerant states in neuroblastoma without clonal selection, while post-treatment awakening is a stochastic process fuelled by noise-enabled plasticity in cell-identity programmes. The histone demethylase KDM5A relocates to noisy cell-state genes during awakening, promoting H3K4me3 removal and chromatin remodelling at these loci. KDM5 inhibition abrogates this process, and suppresses transcriptional noise, halts DTP exit, and prevents tumour recovery in both neuroblastoma and hepatoblastoma models. These results establish DTP as an exploitable evolutionary bottleneck, positioning KDM5-mediated transcriptional noise as an actionable therapeutic target to limit cancer adaptation and relapse GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/682004v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@edba70org.highwire.dtl.DTLVardef@16b1bd9org.highwire.dtl.DTLVardef@f153acorg.highwire.dtl.DTLVardef@1ac5a4e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Ly6d expression delineates two putative postnatal thymus epithelial progenitor cells that are differentially affected by ageing

The thymus is a primary lymphoid organ which provides essential structural and functional support for the development of naive T cells. Thymic epithelial cells (TECs), key components of the thymic stroma, are classified into cortical (cTEC) and medullary (mTEC) lineages based on their distinct molecular, structural, transcriptional, and functional characteristics. Advances in single-cell RNA sequencing (scRNA-seq) have revealed significant TEC heterogeneity, including the identification of intertypical TECs that share properties of both cTEC and mTEC and have been postulated to play a role in the development and maintenance of thymic function. To date, the identity and maintenance of postnatal TEPCs remain unclear, with debates on whether bipotent TEPCs persist after birth or if lineage-restricted progenitors independently maintain TEC compartments. Using an inducible lineage-tracing system based on {beta}5t expression, we explored the early dynamics of the relationships between TEPC and mTEC progenitors and their progeny. Our results identified two potential lineage-biassed TEPC subpopulations, distinguished by Ly6d expression. Additionally, we observed that ageing disproportionately affects Ly6d-compared to Ly6d+ TEPCs, with implications for the rejuvenation of the ageing thymic epithelium. This study provides insights into the developmental pathways of TEC lineages and their maintenance, contributing to strategies for enhancing thymic function in ageing and disease.

immunology↗

bioRxiv2025

Deciphering Cell-Type and Temporal-Specific Matrisome Expression Signatures in Human Cortical Development and Neurodevelopmental Disorders via scRNA-Seq Meta-Analysis

Human cortical development is a complex process involving the proliferation, differentiation, and migration of progenitor cells, all coordinated within a dynamic extracellular matrix (ECM). ECM plays a crucial role in guiding these processes, yet its specific contributions and the implications of its dysregulation in neurodevelopmental disorders (NDDs) remain underexplored. In this study, we conducted a meta-analysis of single-cell RNA sequencing (scRNA-seq) data from 37 donors, gestational weeks (GWs) 8 to 26 across six independent studies to elucidate cell type-specific matrisome gene expression signatures and their dynamics in the developing human cortex. Our analysis identified distinct matrisome gene signatures across various cell types, with significant temporal changes during cortical development. Notably, a substantial proportion of matrisome genes are associated with NDDs, exhibiting cell type, temporal and disease specificity. These findings highlight the critical role of cell type-specific matrisome regulation in cortical development and its potential involvement in NDD pathogenesis. This study provides a comprehensive map of cell type-specific matrisome signatures in the developing human cortex and highlights the importance of ECM in both normal development and the pathogenesis of NDDs.

neuroscience↗

bioRxiv2023

Dynamic Plasticity Systems Direct Early Adaptation to Treatment in Neuroblastoma

Neuroblastoma, like many aggressive cancers, exhibits phenotypic heterogeneity, contributing to therapy resistance and disease progression. However, direct evidence of how phenotypic plasticity influences tumour evolution remains limited. Using a multi-resolution quantitative approach, we define the principles, types, and dynamics of plasticity in neuroblastoma at unprecedented resolution. We demonstrate that intrinsic plasticity is a model-dependent process that enables the coexistence of drug-sensitive and drug-resistant states in environmentally stable conditions, positioning plasticity as a bet-hedging strategy that allows tumours to anticipate environmental changes. Additionally, we show plasticity varies across lineages and single-cell-derived clones, establishing that it is not merely an induced response but a heritable and selectable trait. By simultaneously mapping plasticity at and clonal dynamics in evolving neuroblastoma populations, we show plasticity is shaped by selective pressures, reinforcing its role as a fundamental driver of neuroblastoma evolution in a treatment- and genetic background-dependent manner. We define three distinct modes of plasticityled adaptation. In the selection of the plasticity model, strong selective pressure temporarily constrains phenotypic transitions, but they reemerge with greater dynamics once the stressor is removed, favouring the selection of highly plastic clones. In the adaptive plasticity model, phenotypic transitions actively reshape tumour heterogeneity, allowing for rapid adaptation to treatment while minimising the impact of clonal selection. Finally, in the plasticity equilibrium model, phenotypic transitions persist at baseline, maintaining a state of phenotypic fluidity, with clonal selection ultimately dictating tumour evolution. These findings highlight the diverse, context-dependent strategies that neuroblastoma populations employ to navigate selective pressures and therapy resistance, emphasizing the need for plasticity-targeting therapeutic approaches to disrupt tumour adaptation and improve treatment outcomes. TeaserNatural selection and phenotypic transitions shape adaptive evolution, guiding neuroblastoma survival under treatment pressure.

cancer biology↗

bioRxiv2023

Milo2.0 unlocks population genetic analyses of cell state abundance using a count-based mixed model

Cell type proportions vary between individuals and are heritable, as demonstrated by statistical genetic analysis of flow cytometry data [1,2]. Higher-resolution cell states can be identified by single-cell RNA-sequencing, the scalability of which now makes it applicable to population-scale cohorts. However, the integration of statistical genetic analysis of cell states using cohort-scale single-cell data requires appropriate algorithms to account for and model the genetic relationships and complex batch-processing inherent to these studies. We describe Milo2.0, which enables the discovery of cell state quantitative trait loci (csQTL), scaling to millions of cells across hundreds of individuals. We identify > 500 csQTLs across peripheral blood immune states and investigate their relationship with the genetic regulation of gene expression. Moreover, we colocalise immune csQTLs with human traits and identify links between immune regulators, cell state abundance and immune-mediated disease.

genetics↗

bioRxiv2022

Origin and segregation of the human germline

Human germline-soma segregation occurs during weeks 2-3 in gastrulating embryos. While direct studies are hindered, here we investigate the dynamics of human primordial germ cell (PGCs) specification using in vitro models with temporally resolved single-cell transcriptomics and in-depth characterisation to in vivo datasets from human and non-human primates, including a 3D marmoset reference atlas. We elucidate the molecular signature for the transient gain of competence for germ cell fate during peri-implantation epiblast development. Further, we show that both the PGCs and amnion arise from transcriptionally similar TFAP2A positive progenitors at the posterior end of the embryo. Notably, genetic loss of function experiments show that TFAP2A is crucial for initiating the PGC fate without detectably affecting the amnion, and its subsequently replaced by TFAP2C as an essential component of the genetic network for PGC fate. Accordingly, amniotic cells continue to emerge from the progenitors in the posterior epiblast, but importantly, this is also a source of nascent PGCs.

developmental biology↗

bioRxiv2020

Milo: differential abundance testing on single-cell data using k-NN graphs

Single-cell omic protocols applied to disease, development or mechanistic studies can reveal the emergence of aberrant cell states or changes in differentiation. These perturbations can manifest as a shift in the abundance of cells associated with a biological condition. Current computational workflows for comparative analyses typically use discrete clusters as input when testing for differential abundance between experimental conditions. However, clusters are not always an optimal representation of the biological manifold on which cells lie, especially in the context of continuous differentiation trajectories. To overcome these barriers to discovery, we present Milo, a flexible and scalable statistical framework that performs differential abundance testing by assigning cells to partially overlapping neighbourhoods on a k-nearest neighbour graph. Our method samples and refines neighbourhoods across the graph and leverages the flexibility of generalized linear models, making it applicable to a wide range of experimental settings. Using simulations, we show that Milo is both robust and sensitive, and can reveal subtle but important cell state perturbations that are obscured by discretizing cells into clusters. We illustrate the power of Milo by identifying the perturbed differentiation during ageing of a lineage-biased thymic epithelial precursor state and by uncovering extensive perturbation to multiple lineages in human cirrhotic liver. Milo is provided as an open-source R software package with documentation and tutorials at https://github.com/MarioniLab/miloR.

bioinformatics↗

bioRxiv2020

Ageing compromises mouse thymus function and remodels epithelial cell differentiation

Ageing is characterised by cellular senescence, leading to imbalanced tissue maintenance, cell death and compromised organ function. This is first observed in the thymus, the primary lymphoid organ that generates and selects T cells. However, the molecular and cellular mechanisms underpinning these ageing processes remain unclear. Here, we show that mouse ageing leads to less efficient T cell selection, decreased self-antigen representation and increased T cell receptor repertoire diversity. Using a combination of single-cell RNA-seq and lineage-tracing, we find that progenitor cells are the principal targets of ageing, whereas the function of mature thymic epithelial cells is compromised only modestly. Specifically, an early-life precursor cell population, retained in the mouse cortex postnatally, is virtually extinguished at puberty. Concomitantly, a medullary precursor cell quiesces, thereby impairing maintenance of the medullary epithelium. Thus, ageing disrupts thymic progenitor differentiation and impairs the core immunological functions of the thymus.

immunology↗

bioRxiv2018

The genetic architecture of hair colour in the UK population

We have extensively mapped the genes responsible for hair colour in the UK population. MC1R mutations are well established as the principal genetic cause of red hair colour, but with variable penetrance. We find variation at genes encoding its agonist (POMC), inverse agonist (ASIP) and other loci contribute to red hair and demonstrate epistasis between MC1R and some of these loci. Blonde hair is associated with over 200 loci, and we find a genetic continuum from black through dark and light brown to blonde. Many of the associated genes are involved in hair growth or texture, emphasising the cellular connections between keratinocytes and melanocytes in the determination of hair colour.

genetics↗

bioRxiv2017

Correcting batch effects in single-cell RNA sequencing data by matching mutual nearest neighbours.

The presence of batch effects is a well-known problem in experimental data analysis, and single- cell RNA sequencing (scRNA-seq) is no exception. Large-scale scRNA-seq projects that generate data from different laboratories and at different times are rife with batch effects that can fatally compromise integration and interpretation of the data. In such cases, computational batch correction is critical for eliminating uninteresting technical factors and obtaining valid biological conclusions. However, existing methods assume that the composition of cell populations are either known or the same across batches. Here, we present a new strategy for batch correction based on the detection of mutual nearest neighbours in the high-dimensional expression space. Our approach does not rely on pre-defined or equal population compositions across batches, only requiring that a subset of the population be shared between batches. We demonstrate the superiority of our approach over existing methods on a range of simulated and real scRNA-seq data sets. We also show how our method can be applied to integrate scRNA-seq data from two separate studies of early embryonic development.

bioinformatics↗