Biology subjects

Geller, E.

Publications and source records attributed to Geller, E..

4 recordsLinked to original sources

bioRxiv2026

A ML-framework for the discovery of next-generation IBD targets using a harmonized single-cell atlas of patient tissue

Target discovery for IBD has traditionally relied on genetic associations, which lack the cellular resolution needed to identify novel, actionable, cell type-specific disease pathways. Here, we describe an integrated analytical and experimental framework that leverages harmonized single-cell data to systematically discover novel therapeutic strategies for IBD. We used AMICA DBTM, Immunais harmonized database of single-cell RNA datasets to construct a harmonized 1 million single-cell atlas of the human intestine. We applied a machine learning framework (Immune Patient Representation, IPR) to identify disease-associated transcriptional programs and cell type-specific gene targets. Candidate targets were prioritized using atlas-derived metrics, refined using custom criteria emphasizing translational actionability, and validated across independent clinical cohorts. Select candidates were evaluated in human primary-cell models reflecting the targets cell-type context. The IPR framework identified 85 disease-associated transcriptional programs and ranked 400 cell type-specific target genes across immune and stromal lineages. Disease-associated programs were interpreted using a structured AI-assisted reasoning framework for structured biological reasoning, linking them to IBD-relevant pathways and guiding the identification of novel, promising gene targets. Functional validation of two cell-type-specific candidates, PTGIR in myeloid cells and IL6ST in fibroblasts, confirmed the reduction of inflammatory and fibrotic pathways linked to IBD pathology. Multi-omic profiling and projection of in vitro phenotypes to patient datasets demonstrated the reversal of disease-associated programs via mechanisms distinct from those of existing biologics. Our single-cell anchored, machine-learning framework integrates in silico discovery with experimental validation, revealing new cell type-specific therapeutic opportunities and supporting a scalable approach for precision target discovery in IBD and other immune-mediated diseases.

bioinformatics↗

bioRxiv2020

Scalable pooled CRISPR screens with single-cell chromatin accessibility profiling

Pooled CRISPR screens have been used to identify genes responsible for specific phenotypes and diseases, and, more recently, to connect genetic perturbations with multi-dimensional gene expression profiles. Here, we describe a method to link genome-wide chromatin accessibility to genetic perturbations in single cells. This scalable, cost-effective method combines pooled CRISPR perturbations with a single-cell combinatorial indexing assay for transposase-accessible chromatin (CRISPR-sciATAC). Using a human and mouse species-mixing experiment, we show that CRISPR-sciATAC separates single cells with a low doublet rate. Then, in human myelogenous leukemia cells, we apply CRISPR-sciATAC to target 21 chromatin-related genes that are frequently mutated in cancer and 84 subunits and cofactors of chromatin remodeling complexes, generating chromatin accessibility data for ~30,000 single cells. Using this large-scale atlas, we correlate loss of specific chromatin remodelers with changes in accessibility -- globally and at the binding sites of individual transcription factors. For example, we show that loss of the H3K27 methyltransferase EZH2 leads to increased accessibility at heterochromatic regions involved in embryonic development and triggers expression of multiple genes in the HOXA and HOXD clusters. At a subset of regulatory sites, we also analyze dynamic changes in nucleosome spacing upon loss of chromatin remodelers. CRISPR-sciATAC is a high-throughput, low-cost single-cell method that can be applied broadly to study the role of genetic perturbations on chromatin in normal and disease states.

genomics↗

bioRxiv2019

Massively parallel discovery of human-specific substitutions that alter neurodevelopmental enhancer activity

Genetic changes that altered the function of gene regulatory elements have been implicated in the evolution of the human brain. However, identifying the particular changes that modified regulatory activity during neurodevelopment remains challenging. Here we used massively parallel enhancer assays in human neural stem cells to measure the impact of 32,776 human-specific substitutions on enhancer activity in 1,363 Human Accelerated Regions (HARs) and 3,027 Human Gain Enhancers (HGEs), which include enhancers with novel activities in humans. We found that 31.9% of active HARs and 36.4% of active HGEs exhibited differential activity between human and chimpanzee. This enabled us to isolate the effects of 401 human-specific substitutions from other types of genetic variation in HARs and HGEs. Substitutions acted in both an additive and non-additive manner to alter enhancer activity. Human-specific substitutions altered predicted binding sites for a specific set of human transcription factors (TFs) that were a subset of TF binding sites associated with enhancer activity in our assay. Substitutions within HARs, which are overall highly constrained compared to HGEs, showed smaller effects on enhancer activity, suggesting that the impact of human-specific substitutions may be buffered in enhancers with constrained ancestral functions. Our findings yield insight into the mechanisms by which human-specific genetic changes impact enhancer function and provide a rich set of candidates for experimental studies of regulatory evolution in humans.

genomics↗

bioRxiv2019

Massively parallel disruption of enhancers active during human corticogenesis

Changes in gene regulation have been linked to the expansion of the human cerebral cortex and to neurodevelopmental disorders. However, the biological effects of genetic variation within developmental regulatory elements on human corticogenesis are not well understood. We used sgRNA-Cas9 genetic screens in human neural stem cells (hNSCs) to disrupt 10,674 expressed genes and 2,227 enhancers active in the developing human cortex and determine the resulting effects on cellular proliferation. Gene disruptions affecting proliferation were enriched for genes associated with risk for human neurodevelopmental phenotypes including primary microcephaly and autism spectrum disorder. Although disruptions in enhancers had overall weaker effects on proliferation than gene disruptions, we identified enhancer disruptions that severely perturbed hNSC self-renewal. Disruptions in Human Accelerated Regions and Human Gain Enhancers, regulatory elements implicated in the evolution of the human brain, also perturbed hNSC proliferation, establishing a role for these elements in human neurodevelopment. Integrating proliferation phenotypes with chromatin interaction maps revealed regulatory relationships between enhancers and target genes that contribute to neurogenesis and potentially to human cortical evolution.

genomics↗