Biology subjects

Gut, G.

Publications and source records attributed to Gut, G..

6 recordsLinked to original sources

bioRxiv2026

Live single-molecule imaging reveals global shifts in mRNA mobility during human stem cell differentiation

Spatiotemporal regulation of mRNA localisation is fundamental to cell identity specification and function, yet tracking transcript dynamics in living differentiating cells remains technically challenging. Here, we establish a robust pipeline for MS2 tagging of endogenous transcripts in human induced pluripotent stem cells (iPSCs), coupled with single-particle tracking and Hidden Markov Modelling to map mRNA mobility landscapes during differentiation and cell state transitions. Applying this approach to different cytoskeleton-encoding transcripts in diverse contexts -- neural organoids, directly programmed neurons, and vascular organoids -- we reveal a conserved principle: Both, {beta}-actin and {beta}2b-tubulin particle dynamics progressively shift towards constrained, compartmentalised patterns as cells acquire cell type identity. Perturbation experiments demonstrate that microtubule-dependent tethering is a common, conserved mechanism controlling {beta}-actin mRNA localisation in all cell types studied, whereas translation-dependent anchoring and actin filaments contribute in a context-dependent manner. Analysis of particle dynamics in migrating blood vessel progenitors further showed that {beta}-actin mRNAs accumulating at cell edges are highly diffusive, while those in perinuclear regions show constrained movement. Together, our integrated framework provides a scalable foundation for mechanistic dissection of mRNA targeting in human developmental and disease models.

cell biology↗

bioRxiv2023

Morphodynamics of human early brain organoid development

Brain organoids enable the mechanistic study of human brain development, and provide opportunities to explore self-organization in unconstrained developmental systems. Here, we establish long-term, live light sheet microscopy on unguided brain organoids generated from fluorescently labeled human induced pluripotent stem cells, which enables tracking of tissue morphology, cell behaviors, and subcellular features over weeks of organoid development. We provide a novel dual-channel, multi-mosaic and multi-protein labeling strategy combined with a computational demultiplexing approach to enable simultaneous quantification of distinct subcellular features during organoid development. We track Actin, Tubulin, plasma membrane, nucleus, and nuclear envelope dynamics, and quantify cell morphometric and alignment changes during tissue state transitions including neuroepithelial induction, maturation, lumenization, and brain regionalization. Based on imaging and single-cell transcriptome modalities, we find that lumenal expansion and cell morphotype composition within the developing neuroepithelium are associated with modulation of gene expression programs involving extracellular matrix (ECM) pathway regulators and mechanosensing. We show that an extrinsically provided matrix enhances lumen expansion as well as telencephalon formation, and unguided organoids grown in the absence of an extrinsic matrix have altered morphologies with increased neural crest and caudalized tissue identity. Matrixinduced regional guidance and lumen morphogenesis are linked to the WNT and Hippo (YAP1) signaling pathways, including spatially restricted induction of the Wnt Ligand Secretion Mediator (WLS) that marks the earliest emergence of nontelencephalic brain regions. Altogether, our work provides a new inroad into studying human brain morphodynamics, and supports a view that matrix-linked mechanosensing dynamics play a central role during brain regionalization.

developmental biology↗

bioRxiv2022

Multimodal spatiotemporal phenotyping of human organoid development

Organoids generated from human pluripotent stem cells (PSCs) provide experimental systems to study development and disease. However, we lack quantitative spatiotemporal descriptions of organoid development that incorporate measurements across different molecular modalities. Here we focus on the retina and use a single-cell multimodal approach to reconstruct human retinal organoid development. We establish an experimental and computational pipeline to generate multiplexed spatial protein maps over a retinal organoid time course and primary adult human retina, registering protein expression features at the population, cellular, and subcellular levels. We develop an analytical toolkit to segment nuclei, identify local and global tissue units, infer morphology trajectories, and analyze cell neighborhoods from multiplexed imaging data. We use this toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components, and global patterning in each organoid and primary tissue. In addition, we generate a single-cell transcriptome and chromatin accessibility time course dataset and infer a gene regulatory network underlying organoid development. We then integrate genomic data with spatially segmented nuclei into a multi-modal atlas enabling virtual exploration of retinal organoid development. We visualize molecular, cellular, and regulatory dynamics during organoid lamination, and identify regulons associated with neuronal differentiation and maintenance. We use the integrated atlas to explore retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death. Finally, we show that mosaic CRISPR/Cas genetic perturbations in retinal organoids provide insight into cell fate regulation. Altogether, our work is a major advance toward a virtual human retinal organoid, and provides new directions for how to approach disorders of the visual system. More broadly, our approaches can be adapted to many organoid systems.

systems biology↗

bioRxiv2021

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

The ability to understand and predict molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CO_SCPLOWELLC_SCPLOWOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CO_SCPLOWELLC_SCPLOWOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.

bioinformatics↗

bioRxiv2021

pmVAE: Learning Interpretable Single-Cell Representations with Pathway Modules

MotivationDeep learning techniques have yielded tremendous progress in the field of computational biology over the last decade, however many of these techniques are opaque to the user. To provide interpretable results, methods have incorporated biological priors directly into the learning task; one such biological prior is pathway structure. While pathways represent most biological processes in the cell, the high level of correlation and hierarchical structure make it complicated to determine an appropriate computational representation. ResultsHere, we present pathway module Variational Autoencoder (pmVAE). Our method encodes pathway information by restricting the structure of our VAE to mirror gene-pathway memberships. Its architecture is composed of a set of subnetworks, which we refer to as pathway modules. The subnetworks learn interpretable latent representations by factorizing the latent space according to pathway gene sets. We directly address correlation between pathways by balancing a module-specific local loss and a global reconstruction loss. Furthermore, since many pathways are by nature hierarchical and therefore the product of multiple downstream signals, we model each pathway as a multidimensional vector. Due to their factorization over pathways, the representations allow for easy and interpretable analysis of multiple downstream effects, such as cell type and biological stimulus, within the contexts of each pathway. We compare pmVAE against two other state-of-the-art methods on two single-cell RNA-seq case-control data sets, demonstrating that our pathway representations are both more discriminative and consistent in detecting pathways targeted by a perturbation. Availability and implementationhttps://github.com/ratschlab/pmvae

bioinformatics↗

bioRxiv2020

Cytopath: Simulation based inference of differentiation trajectories from RNA velocity fields

AO_SCPLOWBSTRACTC_SCPLOWTrajectory inference from single-cell RNA sequencing data bears the potential to systematically reconstruct complex differentiation processes, however inferring trajectories that accurately model the biological characteristics of varied processes continues to be a challenge, notwithstanding the many available solutions. In general, trajectory and pseudotime inference methods have so far suffered from the ambiguity of static single-cell transcriptome snapshots lacking a concept of directionality and rate of transcriptional activity. We report Cytopath, a method for trajectory inference that takes advantage of transcriptional activity information from RNA velocity of single-cells to perform trajectory inference. Cytopath performs this task by defining a Markov chain model, simulating an ensemble of possible differentiation trajectories and constructs a consensus trajectory. We show that Cytopath can recapitulate the topological and molecular characteristics of the differentiation process under study. In our analysis we include differentiation trajectories with varying bifurcated, circular, convergent and mixed topology studied in single-snapshot as well as time-series single-cell RNA sequencing experiments. We demonstrate superior and enabling capability to reconstruct differentiation trajectories in comparison to state-of-the art trajectory inference approaches.

bioinformatics↗