Biology subjects

Memi, F.

Publications and source records attributed to Memi, F..

9 recordsLinked to original sources

bioRxiv2026

Multi-scale modeling of human tissues from spatial transcriptomics with TERRA

Spatial transcriptomics maps gene expression at cellular resolution, revealing how cells organize into multicellular niches. Yet computational analyses remain dataset-specific, without a transferable representation of tissue organization that generalizes across datasets, tasks and tissues or predicts how tissues behave under perturbation. We present TERRA, a foundation model pretrained on 112 million human cells profiled by spatial transcriptomics. From a single pretrained backbone, TERRA yields embeddings at the scale of cells, the genes they express and the neighborhoods in which they reside, and supports spatial in silico perturbation, all applied zero-shot to unseen tissues. At the cell level, in newly generated spatial data for developing pancreas, TERRA identified an islet-associated capillary state which we posit represents a developmental precursor of the mature islet microvasculature. At the gene level, in untreated kidney sections, in silico knockout of immune-checkpoint targets predicted a gene program of immune-checkpoint-blockade-associated nephrotoxicity, which we validated in treatment-exposed tissue and recovered in blood. At the neighborhood level, TERRA mapped macrophages across tissues to identify recurring cross-organ niches, which we term archetypes, including a tumor-boundary niche associated with poor prognosis in kidney cancer. Together, TERRA captures the spatial and multicellular logic of human tissue and predicts, in silico, its response to perturbation, providing a multi-scale framework for tissue biology, therapeutic development and clinical application.

genomics↗

bioRxiv2026

A multimodal interrogation of Broca's area in the pediatric and adult human brain

Language is a defining trait of our species, and disruptions in language acquisition can have profound consequences to the individuals affected. Uncovering the neurodevelopmental basis of this complex trait requires detailed molecular and cellular insights into the neocortical areas that support linguistic abilities. Here we performed joint gene expression and chromatin accessibility profiling at single-nucleus resolution (10x Genomics Single cell Multiome) and spatial transcriptomic profiling (Xenium high-plex in situ spatial transcriptomics) of Brocas area alongside adjacent motor cortical areas. We profiled individuals from different ancestries (European and African) and developmental stages (infancy, childhood, adolescence, and adulthood). We provide a high-resolution dissection of the cellular and molecular architecture of Brocas and motor cortical areas across early life stages and anchor the trajectories to the cellular states found in the adult human brain. We identify distinct area- and stage-specific cellular signatures, including a prominent role of glia populations and interneuron subtypes contributing to cytoarchitectonic specializations. Using longitudinal single cell spatial transcriptomic profiling, we orthogonally validate our consensus cell taxonomy and spatially resolve layer enrichment of neuronal and astrocyte subtypes that distinguish Brocas area and motor cortex. We also uncover cell type-specific molecular signatures that distinguish cell developmental trajectories in these cortical areas, including an early molecular code established by differential expression of cadherin genes that might contribute to area-specific intercellular communication. We also identify cell type-specific vulnerabilities to language- related neurodevelopmental and neuropsychiatric disorders, with selective susceptibility of particular somatostatin-positive interneuron subtypes to ASD/ADHD. Finally, evolutionary analysis of differentially accessible regions between Brocas area and motor cortex suggests that genetic mutations that might have contributed to the emergence of linguistic abilities accumulated over the course of million years following the divergence of human and chimpanzee lineages. Together, our study provides a comprehensive molecular, cellular and spatial definition of Brocas area and motor cortex, laying the groundwork for investigations into unique aspects of human cognition and related neurodevelopmental and neuropsychiatric disorders.

neuroscience↗

bioRxiv2026

A human developmental and adult brain atlas benchmarks dopaminergic stem cell models and cell therapy candidates

Parkinsons disease (PD) is characterized by the progressive loss of midbrain dopaminergic (mDA) neurons1. Stem cell-derived mDA neurons hold promise for disease modelling2,3 and are currently in clinical trials for cell replacement therapy4-6. However, systematic benchmarking has been limited by the lack of a unified high-resolution reference and methods that quantify incomplete or mixed lineage specification in vitro7. We establish a single-cell and spatial atlas of the human developing diencephalon-midbrain-hindbrain axis resolving 93 cell subtypes, including 39 lacking prior single-cell characterization and 4 entirely novel populations. Using this atlas as a reference, we integrate 19 hPSC-derived mDA datasets, both published2,8-25 and unpublished, to build the Human Dopaminergic Neural Atlas (HDNA) spanning 2D, 3D, and graft models, including those used in clinical trials. To classify cells and quantify lineage fidelity, we develop CapybaraBrain, a marker-driven non-negative decomposition framework that assigns each cell continuous identity scores across all 93 developmental programs, enabling systematic discrimination of discrete, transitioning, and cross-lineage hybrid states26. We uncover a pervasive landscape of off-target populations reflecting relaxed transcriptional boundaries in vitro, including a previously unrecognized TH-PITX2 midbrain neuronal population, and we validate atlas-predicted latent lineage plasticity through inducible genetic fate mapping in mouse models. We further define maturation-associated transcriptional programs by harmonizing adult mDA subtype atlases, revealing that dopaminergic identity and maturation are partially decoupled across protocols. Finally, projecting PD patient-derived tri-cultures onto the HDNA uncovers genotype- and cell-type-specific transcriptional dysregulation. Together, these integrated atlases and computational framework establish a unified standard for benchmarking differentiation fidelity, exposing off-target states, and guiding next-generation PD models and cell therapies.

Developmental Biology↗

bioRxiv2025

A spatial transcriptomic atlas of autism-associated genes identifies convergence in the developing human thalamus

Autism is a highly heritable neurodevelopmental condition that manifests across a wide phenotypic spectrum. Rare and de novo loss-of-function mutations strongly predispose to autism and co-occurring developmental and intellectual disabilities in over 10% of autistic individuals. Understanding whether these variants converge on specific regional brain circuits or widely alter human brain development is crucial to understanding the etiology of profound autism. To date, transcriptomic atlases have mainly implicated the developing cerebral cortex, yet other brain areas have received relatively little attention. Here, we present a single-cell resolution spatial transcriptomic atlas of 250 autism susceptibility genes during human brain development. Profiling over 10 million cells across the midgestation forebrain, we found convergence of these genes across a small number of regional programs. The developing thalamus showed the most prevalent expression of autism susceptibility genes, followed by germinal zones throughout the brain. Within the thalamus, excitatory neurons showed the most enriched expression, which varied across thalamic nuclei harboring distinct circuits. Across the germinal zones, neural progenitors in the medial ganglionic eminences that generate parvalbumin- and somatostanin-positive interneurons showed highest expression. Our findings reveal the prevalent expression of autism associated genes beyond the developing cerebral cortex and implicate the developing human thalamus as a major hub of autism susceptibility.

neuroscience↗

bioRxiv2025

A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow defined cellular trajectories across tumours is poorly understood. Here, we generated a deep single cell and spatial multi-omic atlas of human glioblastoma that pairs transcriptomic, epigenomic and genomic profiling of 12 tumours across multiple regions. We identify that glioblastoma heterogeneity is driven by spatially-patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia. This cellular trajectory regionalises tumours into distinct tissue niches and manifests in a molecularly conserved manner across tumours as well as genetically distinct tumour subclones. Moreover, using a new deep learning framework to map cancer cell states jointly with clones in situ, we show that tumour subclones are finely spatially intermixed through glioblastoma tissue niches. Finally, we show that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments. Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumour heterogeneity into a tractable cellular and tissue framework.

cancer biology↗

bioRxiv2025

Decoding Plasticity Regulators and Transition Trajectories in Glioblastoma with Single-cell Multiomics

Glioblastoma (GB) is one of the most lethal human cancers, marked by profound intratumoral heterogeneity and near-universal treatment resistance. Cellular plasticity, the capacity of cancer cells to transition between phenotypic states, drives GB progression and resistance. However, the regulatory logic that permits or restricts specific state transitions remains poorly understood. Here, we integrated single-nucleus RNA and chromatin accessibility multi-ome profiles from over one million cells across primary IDH-wildtype GBs and developed scDORI, a scalable deep-learning framework to infer enhancer-driven gene regulatory networks (eGRNs) at single-cell resolution. Our analysis revealed a structured hierarchy of GB cell states governed by distinct regulatory programs, with marked variability in epigenetic plasticity that enables or constrains transitions. Neuronal-like tumor cells emerge as a low plasticity state that deploys active repression, in contrast to more permissive progenitor-like and astrocytic states. We identified the neuronal-like state-specific repressor MYT1L as a key regulator that silences master transcription factors of alternative states. MYT1L gain-of-function in patient-derived GB cells reduced chromatin accessibility, induced neuronal-like identity, and restricted proliferation and invasion in vivo, whereas loss-of-function reactivated plasticity and accelerated malignant features. Our findings delineate the epigenetic architecture and associated transcriptional master regulators that shape GB state trajectories, and establish safeguard repressors such as MYT1L as potential therapeutic targets to constrain malignant plasticity.

cancer biology↗

bioRxiv2024

A multiomic atlas of human early skeletal development

Bone and joint formation in the developing skeleton rely on co-ordinated differentiation of progenitors in the nascent developing limbs and joints. The cell states, epigenetic processes and key regulatory factors underlying their lineage commitment to osteogenic and other mesenchymal populations during ossification and joint formation remain poorly understood and are largely unexplored in human studies. Here, we apply paired single-nuclei transcriptional and epigenetic profiling of 336,000 droplets, in addition to spatial transcriptomics, to construct a comprehensive atlas of human bone, cartilage and joint development in the shoulder, hip, knee and cranium from 5 to 11 post-conception weeks. Spatial mapping of cell clusters to our highly multiplexed in situ sequencing (ISS) data using our newly developed tool ISS-Patcher revealed new cellular mechanisms of zonation during bone and joint formation. Combined modelling of chromatin accessibility and RNA expression allowed the identification of the transcriptional and epigenetic regulatory landscapes that drive differentiation of mesenchymal lineages including osteogenic and chondrogenic lineages, and novel chondrocyte cell states. In particular, we define regionally distinct limb and cranial osteoprogenitor populations and trajectories across the fetal skeleton and characterise differential regulatory networks that govern intramembranous and endochondral ossification. We also introduce SNP2Cell, a tool to link cell-type specific regulatory networks to numerous polygenic traits such as osteoarthritis. We also conduct in silico perturbations of genes that cause monogenic craniosynostosis and implicate potential pathogenic cell states and disease mechanisms involved. This work forms a detailed and dynamic regulatory atlas of human fetal skeletal maturation and advances our fundamental understanding of cell fate determination in human skeletal development.

developmental biology↗

bioRxiv2024

Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning

Spatial omics allow us to identify and analyze communities of cells coordinating specific functions within a tissue. While these communities, defined as cell niches, are fundamentally shaped by interactions between spatially neighboring cells, we lack computational frameworks that can leverage spatial omics data to quantitatively characterize niches based on cell interaction events. To address this, we introduce NicheCompass, a graph deep learning method designed based on the principles of cellular communication. NicheCompass not only identifies cell niches, but also learns and informs about the signaling events shaping the identity of these niches. Unlike existing methods, it uniquely characterizes niches by quantifying their activity of spatial gene programs which represent diverse mechanisms of cell-cell communication and transcriptional regulation, thereby uncovering the underlying cellular processes constituting each niche. We showcase a comprehensive workflow encompassing data integration, niche identification, and functional interpretation, and demonstrate that, with its biologically informed design, NicheCompass outperforms existing methods. NicheCompass is broadly applicable to spatial transcriptomics data, which we illustrate by mapping the architecture of diverse tissues during mouse embryonic development, and delineating basal (KRT14) and luminal (KRT8) tumor niches in human breast cancer. We further introduce fine-tuning-based spatial reference mapping, revealing an SPP1+ macrophage-dominated tumor niche in non-small cell lung cancer patients. Additionally, we extend NicheCompass to multimodal spatial profiling of gene expression and chromatin accessibility, identifying and characterizing distinct white matter niches in the mouse brain. Finally, we apply NicheCompass to a whole mouse brain spatial atlas with 8.4 million cells demonstrating its scalability and ability to build foundational, interpretable spatial representations for entire organs. Overall, NicheCompass provides a novel approach to the challenge of identifying and analyzing niches, and suggests a more rigorous niche definition grounded in the quantitative characterization of underlying cellular processes.

bioinformatics↗

bioRxiv2023

Model-based inference of RNA velocity modules improves cell fate prediction

RNA velocity is a powerful paradigm that exploits the temporal information contained in spliced and unspliced RNA counts to infer transcriptional dynamics. Existing velocity models either rely on coarse biophysical simplifications or require extensive numerical approximations to solve the underlying differential equations. This results in loss of accuracy in challenging settings, such as complex or weak transcription rate changes across cellular trajectories. Here, we present cell2fate, a formulation of RNA velocity based on a linearization of the velocity ODE, which allows solving a biophysically accurate model in a fully Bayesian fashion. As a result, cell2fate decomposes the RNA velocity solutions into modules, which provides a new biophysical connection between RNA velocity and statistical dimensionality reduction. We comprehensively benchmark cell2fate in real-world settings, demonstrating enhanced interpretability and increased power to reconstruct complex dynamics and weak dynamical signals in rare and mature cell types. Finally, we apply cell2fate to a newly generated dataset from the developing human brain, where we spatially map RNA velocity modules onto the tissue architecture, thereby connecting the spatial organisation of tissues with temporal dynamics of transcription.

genomics↗