Biology subjects

Ancheta, S.

Publications and source records attributed to Ancheta, S..

6 recordsLinked to original sources

bioRxiv2026

Temporal AI model predicts drivers of cell state trajectories across human aging

Foundational AI models have recently shown promise for predicting the impact of perturbations on cell states. However, current models typically consider only one cell state at a time, limiting their ability to learn how cellular responses unfold over time, particularly across long trajectories such as diseases of aging. Here, we develop a temporal AI model, MaxToki, trained on nearly 1 trillion gene tokens including cell state trajectories across the human lifespan to generate cell states across long timelapses of human aging. MaxToki generalized to unseen trajectories through in-context learning and predicted novel age-modulating targets that were experimentally verified to influence age-related gene programs and functional decline in vivo. MaxToki represents a promising strategy for temporal modeling to accelerate the discovery of interventions for programming therapeutic cellular trajectories.

bioinformatics↗

bioRxiv2025

Neuronal APOE4 drives damaging lipid accumulation via contact-dependent neuron-oligodendrocyte-microglia interaction in Alzheimer's disease

Apolipoprotein E4 (APOE4) confers the greatest genetic risk for developing Alzheimers disease (AD). With APOE4 broadly expressed in the brain, its cell-type-specific roles in AD pathogenesis are only beginning to be defined. Here, we show that neuronal APOE4 expression drives damaging lipid accumulation in hippocampal neurons, oligodendrocytes, and microglia, with preferential buildup of peroxidized lipids in microglia in a tauopathy mouse model. Neuron-specific removal of APOE4 abolished this lipid phenotype, whereas neuron-specific expression of APOE4 was sufficient to recapitulate it, demonstrating that neuronal APOE4 is both necessary and sufficient for lipid accumulation. Strikingly, the association between lipid burden, microgliosis, and neurodegeneration was strongest in mice with neuron-specific APOE4 expression. Single-nucleus RNA sequencing revealed neuronal APOE4-vulnerable neuron populations, as well as enrichment of disease-associated microglia and oligodendrocytes, all promoting lipid pathology. Primary mouse co-culture experiments showed that neuronal APOE4 drives microglial lipid accumulation via contact-dependent mechanisms involving uptake of lipids from neurons and oligodendrocytes. These findings establish neuronal APOE4 as a key driver of lipid accumulation via neuron-oligodendrocyte-microglia interactions, providing mechanistic insight into APOE4-driven lipid pathology in AD.

neuroscience↗

bioRxiv2025

Network-aware self-supervised learning enables high-content phenotypic screening for genetic modifiers of neuronal activity dynamics

High-throughput phenotypic screening has historically relied on manually selected features, limiting our ability to capture complex cellular processes, particularly neuronal activity dynamics. While recent advances in self-supervised learning have revolutionized the ability to study cellular morphology and transcriptomics, dynamic cellular processes have remained challenging to phenotypically profile. To address this limitation, we developed Plexus, a self-supervised model specifically designed to capture and quantify network-level neuronal activity dynamics. Unlike existing phenotyping tools that focus on static readouts, Plexus leverages a network-level cell encoding method, which enables it to efficiently encode dynamic neuronal activity data into rich representational embeddings. In turn, Plexus achieves state of the art performance in detecting phenotypic changes in neuronal activity. We validated Plexus using a comprehensive GCaMP6m simulation framework and demonstrated its enhanced ability to classify distinct neuronal activity phenotypes compared to traditional signal-processing approaches. To enable practical application, we integrated Plexus with a scalable experimental system utilizing human iPSC-derived neurons equipped with the GCaMP6m calcium indicator and CRISPR interference machinery. This integrated platform successfully identified nearly seventeen times as many distinct phenotypic changes in response to genetic perturbations compared to conventional signal processing methods, as demonstrated in a 52-gene CRISPRi screen across multiple iPSC lines. Using this framework, we identified potential genetic modifiers of aberrant neuronal activity in frontotemporal dementia, illustrating its utility for understanding complex neurological disorders.

bioinformatics↗

bioRxiv2024

Zebrahub-Multiome: Uncovering Gene Regulatory Network Dynamics During Zebrafish Embryogenesis

During embryonic development, gene regulatory networks (GRNs) drive molecular differentiation of cell types. However, the temporal dynamics of these networks remain poorly understood. Here, we present Zebrahub-Multiome, a single-cell multiomic atlas that captures chromatin accessibility and gene expression from 94,562 cells across six stages of zebrafish embryogenesis (10-24 hours post-fertilization), capturing key developmental stages from the end of gastrulation to the onset of organogenesis. By measuring regulatory element activity alongside transcriptional output from the same cells, we identify 640,000 cis-regulatory elements organized into 402 hierarchically structured modules corresponding to specific developmental pathways. Early embryonic stages employ broadly shared regulatory programs that progressively fragment into lineage-specific modules. Timeresolved gene regulatory network inference reveals that transcription factors undergo functional transitions - from multilineage regulators to specialized, lineage-committed factors. These quantitative measurements reveal the regulatory network rewiring that drives cell fate specification. Our interactive web portal (zebrahub.org/epigenomics) enables exploration of gene dynamics, regulatory networks, and perturbation predictions, providing a quantitative framework for understanding vertebrate developmental regulation.

developmental biology↗

bioRxiv2024

Challenges and Progress in RNA Velocity: Comparative Analysis Across Multiple Biological Contexts

Single-cell RNA sequencing is revolutionizing our understanding of cell state dynamics, allowing researchers to observe the progression of individual cells transcriptomic profiles over time. Among the computational techniques used to predict future cellular states, RNA velocity has emerged as a predominant tool for modeling transcriptional dynamics. RNA velocity leverages the mRNA maturation process to generate velocity vectors that predict the likely future state of a cell, offering insights into cellular differentiation, aging, and disease progression. Although this technique has shown promise across biological fields, the performance accuracy varies depending on the RNA velocity method and dataset. We established a comparative pipeline and analyzed the performance of five RNA velocity methods on three datasets based on local consistency, method agreement, identification of driver genes, and robustness to sequencing depth. This benchmark provides a resource for scientists to understand the strengths and limitations of different RNA velocity methods.

bioinformatics↗

bioRxiv2023

Zebrahub - Multimodal Zebrafish Developmental Atlas Reveals the State Transition Dynamics of Late Vertebrate Pluripotent Axial Progenitors

Elucidating the developmental processes of organisms requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by trajectory reconstructions. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate mapping experiments. To demonstrate the versatility of our multi-modal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of Neuro-Mesodermal Progenitors (NMPs). Our publicly accessible web-based platform, Zebrahub, is a foundational resource for studying developmental processes at both transcriptional and spatiotemporal levels, providing researchers with an integrated approach to exploring and analyzing the complexities of cellular lineages during zebrafish embryogenesis.

developmental biology↗