Biology subjects

Barile, M.

Publications and source records attributed to Barile, M..

9 recordsLinked to original sources

bioRxiv2026

Hierarchical TBX6-FOXC Regulatory Logic Controls Human Trunk Mesoderm Diversification

Mesoderm forms during gastrulation and diversifies to generate many embryonic tissues, yet the regulatory logic governing early human mesodermal lineage decisions remains poorly defined. We use human iPSC-derived 3D trunk-like structures (hTLS) to model lineage specification during early post-gastrulation development, a stage experimentally inaccessible in humans. Developing hTLS generate neural and mesodermal lineages and recapitulate progressive mesodermal diversification into paraxial (somitic) and intermediate (renal) identities. Using genetic perturbations and temporally controlled transcription factor activation, we identify duration-dependent TBX6 activity as a critical determinant of mesodermal diversification. Downstream, the forkhead box transcription factors FOXC1 and FOXC2 stabilise somitic identity and are required for sclerotome differentiation. These findings reveal a duration-dependent hierarchical regulatory logic in early human mesoderm, in which competence for diversification is established before downstream lineage identities are stabilised, positioning hTLS as a powerful platform to experimentally dissect human developmental programs. HighlightsO_LIHuman trunk-like structures model mesodermal diversification during early post-gastrulation human trunk development C_LIO_LITBX6 duration acts as a temporal gate controlling mesodermal fate potential C_LIO_LIFOXC1/2 function downstream of TBX6 to lock in somitic identity and restrict alternative fates C_LIO_LIDegTrace enables lineage tracing following transient transcription factor activation C_LI

developmental biology↗

bioRxiv2025

Pseudodynamics+: Reconstructing Population Dynamics from Time-Resolved Single Cell Landscapes with Physics Informed Neural Networks

Single-cell profiling provides snapshots of the heterogeneous states that characterise developmental processes, organ regeneration and progression towards disease in a complex landscape. The underlying trajectories are of pivotal interest, but existing methods for reconstructing cell state trajectories commonly neglect population sizes. However, snapshot experiments make it difficult to interpret cell flux because the observed trajectories are confounded by changes in overall population size. This ambiguity can lead to misinterpreting changes in proliferation or death rates as changes in cellular migration. We introduce pseudodynamics+, a physics-informed neural network framework that solves high-dimensional flow equations on complex, branching landscapes. By integrating single-cell genomics with population dynamics, pseudodynamics+ estimates state- and time-dependent parameters of growth, differentiation, and diffusion. The model recapitulates proliferation bursts during T-cell maturation and, when applied to LARRY-barcoded data, predicts differentiation rates consistent with clonal behaviour. When applied to time-resolved persistent-labelling datasets of in vivo mouse bone marrow haematopoiesis, pseudodynamics+ reconstructs continuous tissue flows with dynamic parameters aligned with known molecular signatures. Notably, simulations revealed a previously unrecognised shift from megakaryocyte-biased to balanced progenitor output, explained by evolving fate preferences of progenitor states, as revealed by simulations leveraging our estimated dynamic parameters. Pseudodynamics+ therefore establishes a population-aware framework for reconstructing single-cell population dynamics and is available at https://github.com/Gottgens-lab/pseudodynamics_plus.

bioinformatics↗

bioRxiv2025

A non-catalytic role for MLL2 in controlling chromatin organisation and mobility during the priming of pluripotent cells for differentiation

How the chromatin regulator MLL2 (KMT2B) influences cell differentiation remains poorly understood. MLL2 is the main histone 3 lysine 4 (H3K4) trimethyltransferase acting at bivalent promoters in embryonic stem cells (ESCs) and is required for ESCs to differentiate into neuroectoderm. We show here that this requirement occurs during exit from naive pluripotency, days before neuroectoderm differentiation is impaired. Although MLL2 knockout has only a subtle effect on transcription during exit, reducing the expression of a few important neuroectodermal transcription factors, it substantially remodels chromatin architecture, disrupting 3D chromatin loops associated with bivalent promoters. The enzymatic activity of MLL2 is not needed for stabilising these loops or for neuroectoderm differentiation. This non-catalytic function of MLL2 in stabilising 3D chromatin loops has implications for lineage specification. Because MLL2 shares features with all four MLLs, chromatin tethering, rather than H3K4 methylation, may represent the primary function of MLL proteins during lineage commitment.

cell biology↗

bioRxiv2024

diffGEK: Differential Gene Expression Kinetics

A defining characteristic of all metazoan organisms is the existence of different cell states or cell types, driven by changes in gene expression kinetics, principally transcription, splicing and degradation rates. The RNA velocity framework utilizes both spliced and unspliced reads in single cell mRNA preparations to predict future cellular states and estimate transcriptional kinetics. However, current models assume either constant kinetic rates, rates equal for all genes, or rates completely independent of progression through differentiation. Consequently, current models for rate estimation are either underparametrised or overparametrised. Here we developed a new method (diffGEK) which overcomes this issue, and allows comparison of transcriptional rates across different biological conditions. diffGEK assumes that rates can vary over a trajectory, but are smooth functions of the differentiation process. Analysing Jak2 V617F mutant versus wild type mice for erythropoiesis, and Ezh2 KO versus wild type mice in myelopoiesis, revealed which genes show altered transcription, splicing or degradation rates between different conditions. Moreover, we observed that, for some genes, compensatory changes between different rates can result in comparable overall mRNA levels, thereby masking highly dynamic changes in gene expression kinetics in conventional expression analysis. Collectively, we report a robust pipeline for comparative expression analysis based on altered transcriptional kinetics to discover mechanistic differences missed by conventional approaches, with broad applicability across any biomedical research question where single cell expression data are available for both wild type and treatment/mutant conditions.

bioinformatics↗

bioRxiv2024

Unveiling Clonal Cell Fate and Differentiation Dynamics: A Hybrid NeuralODE-Gillespie Approach

Recent lineage tracing single-cell techniques (LT-scSeq), e.g., the Lineage And RNA RecoverY (LARRY) barcoding system, have enabled clonally resolved interpretation of differentiation trajectories. However, the heterogeneity of clone-specific kinetics remains understudied, both quantitatively and in terms of interpretability, thus limiting the power of bar-coding systems to unravel how heterogeneous stem cell clones drive overall cell population dynamics. Here, we present CLADES, a NeuralODE-based framework to faithfully estimate clone-specific kinetics of cell states from newly generated and publicly available human cord blood LARRY LT-scSeq data. By incorporating a stochastic simulation algorithm (SSA) and differential expression gene (DEGs) analysis, CLADES yields cell division dynamics across differentiation timecourses and fate bias predictions for the early progenitor cells. Moreover, clone-level quantitative behaviours can be grouped into characteristic types by pooling individual clones into meta-clones. By benchmarking with CoSpar, we found that CLADES improves fate bias prediction accuracy at the meta-clone level. In conclusion, we report a broadly applicable approach to robustly quantify differentiation kinetics using meta-clones while providing valuable insights into the fate bias of cellular populations for any organ system maintained by a pool of heterogeneous stem and progenitor cells.

bioinformatics↗

bioRxiv2022

A time and single-cell resolved model of hematopoiesis

The paradigmatic tree model of hematopoiesis is increasingly recognized to be limited as it is based on heterogeneous populations and largely inferred from non-homeostatic cell fate assays. Here, we combine persistent labeling with time-series single-cell RNA-Seq to build the first real- time, quantitative model of in vivo tissue dynamics for any mammalian organ. We couple cascading single-cell expression patterns with dynamic changes in differentiation and growth speeds. The resulting explicit linkage between single cell molecular states and cellular behavior reveals widely varying self-renewal and differentiation properties across distinct lineages. Transplanted stem cells show strong acceleration of neutrophil differentiation, illustrating how the new model can quantify the impact of perturbations. Our reconstruction of dynamic behavior from snapshot measurements is akin to how a Kinetoscope allows sequential images to merge into a movie. We posit that this approach is broadly applicable to empower single cell genomics to reveal important tissue scale dynamics information. HighlightsO_LICell flux analysis reveals high-resolution kinetics of native bone marrow hematopoiesis C_LIO_LIQuantitative model simulates cell behavior in real-time and connects it with gene expression patterns C_LIO_LIDistinct lineage-affiliated progenitors have unique self-renewal and differentiation properties C_LIO_LITransplanted HSCs display accelerated stage- and lineage-specific differentiation C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/506735v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@244ad6org.highwire.dtl.DTLVardef@ad632borg.highwire.dtl.DTLVardef@149daf9org.highwire.dtl.DTLVardef@1c7183f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

A highly efficient reporter system for identifying and characterizing in vitro expanded hematopoietic stem cells

Hematopoietic stem cells (HSCs) cultured outside the body are the fundamental component of a wide range of cellular and gene therapies. Recent efforts have achieved more than 200-fold expansion of functional HSCs, but their molecular characterization has not been possible due to the substantial majority of cells being non-HSCs and single cell-initiated cultures displaying substantial clone-to-clone variability. Using the Fgd5 reporter mouse in combination with the EPCR surface marker, we report exclusive identification of HSCs from non-HSCs in expansion cultures. Linking single clone functional transplantation data with single clone gene expression profiling, we show that the molecular profile of expanded HSCs is similar to actively cycling fetal liver HSCs and shares a gene expression signature with functional HSCs from all sources, including Prdm16, Fstl1 and Palld. This new tool can now be applied to a wide-range of functional screening and molecular experiments previously not possible due to limited HSC numbers.

cell biology↗

bioRxiv2020

Coordinated Changes in Gene Expression Kinetics Underlie both Mouse and Human Erythroid Maturation

BackgroundSingle cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single cell RNA-Seq permits prediction of future expression states. Here we applied this RNA velocity concept to an extended timecourse dataset covering mouse gastrulation and early organogenesis. ResultsIntriguingly, RNA velocity correctly identified epiblast cells as the starting point, but several trajectory predictions at later stages were inconsistent with both real time ordering and existing knowledge. The most striking discrepancy concerned red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes revealed a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be up-regulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1- chimera dataset revealed induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression. ConclusionsBy identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.

developmental biology↗

bioRxiv2020

Hematopoietic stem cells self-renew symmetrically or gradually proceed to differentiation

It is not known whether hematopoietic stem cells (HSCs) undergo symmetric or asymmetric cell divisions in the unperturbed bone marrow. Here, we integrate data from HSC fate mapping and cell-cycle-dependent labeling through mathematical inference and thus gain insight into how HSCs coordinate self-renewal with differentiation. We find that most HSC divisions in adult mice are symmetric self-renewing, replacing HSCs lost by direct differentiation and death, and slowly expanding the HSC population. This expansion maintains constant HSC output to multipotent progenitors (MPPs), despite declining HSC differentiation rate with age. We identify a linear hierarchy of differentiation states between tip HSCs and MPPs, where Tie2-driven HSC fate mapping fully covers the progression of the differentiating cells. A turning point from self-renewal to accelerated cell differentiation occurs between early-stage and late-stage MPPs, just before lineage differentiation becomes manifest in single-cell transcriptomes. This stem cell hierarchy precedes lineage differentiation and may limit mutation accumulation in the hematopoietic system.

systems biology↗