Biology subjects

Fleck, J. S.

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

12 recordsLinked to original sources

bioRxiv2025

CellFlow enables generative single-cell phenotype modeling with flow matching

High-content phenotypic screens provide a powerful strategy for studying biological systems, but the scale of possible perturbations and cell states makes exhaustive experiments unfeasible. Computational models that are trained on existing data and extrapolate to correctly predict outcomes in unseen contexts have the potential to accelerate biological discovery. Here, we present CellFlow, a flexible framework based on flow matching that can model single cell phenotypes induced by complex perturbations. We apply CellFlow to various phenotypic screens, accurately predicting expression responses to a wide range of perturbations, including cytokine stimulation, drug treatments and gene knockouts. CellFlow successfully modeled developmental perturbations at the whole-embryo scale and guided cell fate and organoid engineering by predicting heterogeneous cell populations arising from combinatorial morphogen treatments and by performing a virtual organoid protocol screen. Taken together, CellFlow has the potential to accelerate discovery from phenotypic screens by learning from existing data and generating phenotypes induced by unseen conditions.

bioinformatics↗

bioRxiv2024

Decoding morphogen patterning of human neural organoids with a multiplexed single-cell transcriptomic screen

Morphogens, secreted signalling molecules that direct cell fate and tissue development, are used to direct neuroepithelial progenitors towards discrete regional identities across the central nervous system. Neural tissues derived from pluripotent stem cells in vitro (neural organoids) provide new models for studying neural regionalization, however, we lack a comprehensive survey of how the developing human neuroepithelium responds to morphogen cues. Here, we produce a detailed map of morphogen-induced effects on the axial and regional specification of human neural organoids using a multiplexed single-cell transcriptomics screen. We find that the timing, concentration, and combination of morphogens strongly influence organoid cell type and regional composition, and that cell line and neural induction method strongly impact the response to a given morphogen condition. We apply concentration gradients in microfluidic chips or a range of static concentrations in multi-well plates to explore how human neuroepithelium interprets morphogen concentrations and observe similar dose-dependent induction of patterned domains in both scenarios. Altogether, we provide a detailed resource that supports the development of new regionalized neural organoid protocols and enhances our understanding of human central nervous system patterning.

developmental biology↗

bioRxiv2023

An integrated transcriptomic cell atlas of human endoderm-derived organoids

Human stem cells can generate complex, multicellular epithelial tissues of endodermal origin in vitro that recapitulate aspects of developing and adult human physiology. These tissues, also called organoids, can be derived from pluripotent stem cells or tissue-resident fetal and adult stem cells. However, it has remained difficult to understand the precision and accuracy of organoid cell states through comparison with primary counterparts, and to comprehensively assess the similarity and differences between organoid protocols. Advances in computational single-cell biology now allow the integration of datasets with high technical variability. Here, we integrate single-cell transcriptomes from 218 samples covering organoids of diverse endoderm-derived tissues including lung, pancreas, intestine, liver, biliary system, stomach, and prostate to establish an initial version of a human endoderm organoid cell atlas (HEOCA). The integration includes nearly one million cells across diverse conditions, data sources and protocols. We align and compare cell types and states between organoid models, and harmonize cell type annotations by mapping the atlas to primary tissue counterparts. To demonstrate utility of the atlas, we focus on intestine and lung, and clarify ontogenic cell states that can be modeled in vitro. We further provide examples of mapping novel data from new organoid protocols to expand the atlas, and showcase how integrating organoid models of disease into the HEOCA identifies altered cell proportions and states between healthy and disease conditions. The atlas makes diverse datasets centrally available, and will be valuable to assess organoid fidelity, characterize perturbed and diseased states, and streamline protocol development.

cell biology↗

bioRxiv2023

An integrated transcriptomic cell atlas of human neural organoids

Neural tissues generated from human pluripotent stem cells in vitro (known as neural organoids) are becoming useful tools to study human brain development, evolution and disease. The characterization of neural organoids using single-cell genomic methods has revealed a large diversity of neural cell types with molecular signatures similar to those observed in primary human brain tissue. However, it is unclear which domains of the human nervous system are covered by existing protocols. It is also difficult to quantitatively assess variation between protocols and the specific cell states in organoids as compared to primary counterparts. Single-cell transcriptome data from primary tissue and neural organoids derived with guided or un-guided approaches and under diverse conditions combined with large-scale integrative analyses make it now possible to address these challenges. Recent advances in computational methodology enable the generation of integrated atlases across many data sets. Here, we integrated 36 single-cell transcriptomics data sets spanning 26 protocols into one integrated human neural organoid cell atlas (HNOCA) totaling over 1.7 million cells. We harmonize cell type annotations by incorporating reference data sets from the developing human brain. By mapping to the developing human brain reference, we reveal which primary cell states have been generated in vitro, and which are under-represented. We further compare transcriptomic profiles of neuronal populations in organoids to their counterparts in the developing human brain. To support rapid organoid phenotyping and quantitative assessment of new protocols, we provide a programmatic interface to browse the atlas and query new data sets, and showcase the power of the atlas to annotate new query data sets and evaluate new organoid protocols. Taken together, the HNOCA will be useful to assess the fidelity of organoids, characterize perturbed and diseased states and facilitate protocol development in the future.

developmental biology↗

bioRxiv2023

Single-cell epigenomic reconstruction of developmental trajectories in human neural organoid systems from pluripotency

Human cell type diversity emerges through a highly regulated series of fate restrictions from pluripotent progenitors. Fate restriction is orchestrated in part through epigenetic modifications at genes and regulatory elements, however it has been difficult to study these mechanisms in humans. Here, we use organoid models of the human central nervous system and establish single-cell profiling of histone modifications (H3K27ac, H3K27me3, H3K4me3) in organoid cells over a time course to reconstruct epigenomic trajectories governing cell identity acquisition from human pluripotency. We capture transitions from pluripotency through neuroepithelium, to retinal and brain region specification, as well as differentiation from progenitors to neuronal and glial terminal states. We find that switching of repressive and activating epigenetic modifications can precede and predict decisions at each stage, providing a temporal census of gene regulatory elements and transcription factors that we characterize in a gene regulatory network underlying human cerebral fate acquisition. We use transcriptome and chromatin accessibility measurements in the same cell from a human developing brain to validate this regulatory mode in a primary tissue. We show that abolishing histone 3 lysine 27 trimethylation (H3K27me3) through inhibition of the polycomb group protein Embryonic Ectoderm Development (EED) at the neuroectoderm stage disrupts fate restriction and leads to aberrant cell fate acquisition, ultimately influencing cell type composition in brain organoids. Altogether, our single-cell genome wide map of histone modifications during human neural organoid development serves as a blueprint (https://episcape.ethz.ch) to explore human cell fate decisions in normal physiology and in neurodevelopmental disorders. More broadly, this approach can be used to study human epigenomic trajectory mechanisms in any human organoid system. SummaryUnguided neural organoids reveal widespread and dynamic switching of epigenetic modifications during development and recapitulate fate restriction from pluripotency to terminally differentiated cells of the human central nervous system.

developmental biology↗

bioRxiv2022

Single-cell brain organoid screening identifies developmental defects in autism

Development of the human brain involves processes that are not seen in many other species, but can contribute to neurodevelopmental disorders (1-4). Cerebral organoids can be used to investigate neurodevelopmental disorders in a human context but are limited by variability and low throughput. We have developed the CRISPR-human organoids-scRNA-seq (CHOOSE) system that utilizes verified pairs of gRNAs, inducible CRISPR/Cas9-based genetic disruption, and single-cell transcriptomics for pooled loss-of-function screening in mosaic organoids. Genetic perturbations of 36 high-risk autism spectrum disorder (ASD) genes related to transcriptional regulation allowed us to identify their effects on cell fate determination and discover developmental stages susceptible to ASD gene perturbations. We construct a developmental gene regulatory network (GRN) of cerebral organoids from single-cell multiomic data including transcriptome and chromatin modalities and identify ASD-associated and perturbation-enriched regulatory modules. We show that perturbing members of the BAF chromatin remodeling complex leads to an expanded population of ventral telencephalon progenitors. Specifically, the BAF subunit ARID1B controls the fate transitions of progenitors to oligodendrocyte precursor cells and interneurons, which we confirmed in patient-specific induced pluripotent stem cell (iPSC) derived organoids. Our study paves the way for phenotypically characterizing disease susceptibility genes in human organoid models with cell type, developmental trajectory, and gene regulatory network readouts.

developmental biology↗

bioRxiv2022

Single-cell analyses of axolotl forebrain organization, neurogenesis, and regeneration

Salamanders are important tetrapod models to study brain organization and regeneration, however the identity and evolutionary conservation of brain cell types is largely unknown. Here, we delineate cell populations in the axolotl telencephalon during homeostasis and regeneration, representing the first single-cell genomic and spatial profiling of an anamniote tetrapod brain. We identify glutamatergic neurons with similarities to amniote neurons of hippocampus, dorsal and lateral cortex, and conserved GABAergic neuron classes. We infer transcriptional dynamics and gene regulatory relationships of postembryonic, region-specific direct and indirect neurogenesis, and unravel conserved signatures. Following brain injury, ependymoglia activate an injury-specific state before reestablishing lost neuron populations and axonal connections. Together, our analyses yield key insights into the organization, evolution, and regeneration of a tetrapod nervous system.

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

Inferring and perturbing cell fate regulomes in human cerebral organoids

Self-organizing cerebral organoids grown from pluripotent stem cells combined with single-cell genomic technologies provide opportunities to explore gene regulatory networks (GRNs) underlying human brain development. Here we acquire single-cell transcriptome and accessible chromatin profiling data over a dense time course covering multiple phases of organoid development including neuroepithelial formation, patterning, brain regionalization, and neurogenesis. We identify temporally dynamic and brain region-specific regulatory regions, and cell interaction analysis reveals emergent patterning centers associated with regionalization. We develop Pando, a flexible linear model-based framework that incorporates multi-omic data and transcription binding site predictions to infer a global GRN describing organoid development. We use pooled genetic perturbation with single-cell transcriptome readout to assess transcription factor requirement for cell fate and state regulation in organoid. We find that certain factors regulate the abundance of cell fates, whereas other factors impact neuronal cell states after differentiation. We show that the zinc finger protein GLI3 is required for cortical fate establishment in humans, recapitulating previous work performed in mammalian model systems. We measure transcriptome and chromatin accessibility in normal or GLI3-perturbed cells and identify a regulome central to the dorsoventral telencephalic fate decision. This regulome suggests that Notch effectors HES4/5 are direct GLI3 targets, which together coordinate cortex and ganglionic eminence diversification. Altogether, we provide a framework for how multi-brain region model systems and single-cell technologies can be leveraged to reconstruct human brain developmental biology.

developmental biology↗

bioRxiv2021

Accurate de novo identification of biosynthetic gene clusters with GECCO

Biosynthetic gene clusters (BGCs) are enticing targets for (meta)genomic mining efforts, as they may encode novel, specialized metabolites with potential uses in medicine and biotechnology. Here, we describe GECCO (GEne Cluster prediction with COnditional random fields; https://gecco.embl.de), a high-precision, scalable method for identifying novel BGCs in (meta)genomic data using conditional random fields (CRFs). Based on an extensive evaluation of de novo BGC prediction, we found GECCO to be more accurate and over 3x faster than a state-of-the-art deep learning approach. When applied to over 12,000 genomes, GECCO identified nearly twice as many BGCs compared to a rule-based approach, while achieving higher accuracy than other machine learning approaches. Introspection of the GECCO CRF revealed that its predictions rely on protein domains with both known and novel associations to secondary metabolism. The method developed here represents a scalable, interpretable machine learning approach, which can identify BGCs de novo with high precision.

bioinformatics↗

bioRxiv2021

Caprin-1 binding to the critical stress granule protein G3BP1 is regulated by pH

G3BP is the central hub within the protein-RNA interaction network of stress-induced bio-molecular condensates known as stress granules (SG). The SG-associated proteins Caprin-1 and USP10 exhibit mutually exclusive binding to the structured NTF2-domain of G3BP1, thereby regulating G3BP1-mediated condensation, but with opposite effects: Caprin-1 promotes but USP10 inhibits SG formation. Herein, we present the crystal structure of G3BP1-NTF2 in complex with a Caprin-1 derived short linear motif (SLiM), which provides a molecular understanding for the mutually exclusive binding of USP10 and Caprin-1 to G3BP1. Caprin-1 but not USP10 contacts two G3BP1-NTF2 histidine residues, which was confirmed using biochemical, biophysical and cellular biological binding assays. G3BP1/Caprin-1 interactions disrupted via point mutations resulted in fewer and smaller SG condensates. In addition, biochemical binding assays demonstrated reduced binding of Caprin-1 to G3BP1 at lower pH values. Finally, ratiometric pH sensitive measurements of SGs revealed a substantial drop in pH compared to the adjacent cytosol, suggesting that reduced pH can fine-tune and regulate the G3BP1-mediated interaction network via a NTF2-mediated pH-sensitive SLiM-selection mechanism.

biochemistry↗

bioRxiv2020

Resolving brain organoid heterogeneity by mapping single cell genomic data to a spatial reference

Self-organizing tissues resembling brain regions grown in vitro from human stem cells (so-called organoids or spheroids) offer exciting possibilities to study human brain development, disease, and evolution. Brain organoids or spheroids are complex and can contain cells at various stages of differentiation from different brain structures. Single-cell genomic methods provide powerful approaches to explore cell composition, differentiation trajectories, gene regulation, and genetic perturbations in brain organoid systems. However, it remains a major challenge to understand the cellular heterogeneity observed within and between individual organoids. Here, we have developed a computational approach (VoxHunt) to assess brain organoid patterning, developmental state, and cell composition through systematic comparisons to three-dimensional in situ hybridization data from the Allen Brain Atlas. Cellular transcriptomes as well as accessible chromatin landscapes can be compared to spatial transcript patterns in the developing mammalian brain, which enables characterization and visualization of organoid cell compositions. VoxHunt will be useful to assess novel organoid engineering protocols and to annotate cell fates that emerge in organoids during genetic and environmental perturbation experiments.

bioinformatics↗