Biology subjects

Nzima, M.

Publications and source records attributed to Nzima, M..

5 recordsLinked to original sources

bioRxiv2026

Systematic CRISPRi perturbation of 1,408 autism risk genes maps multilevel transcriptional convergence in human cortical neurons

Genetic heterogeneity in autism spectrum disorder (ASD) complicates the identification of shared molecular pathways amenable to therapeutic intervention. Here, we perform a CRISPRi Perturb-seq screen targeting 1,408 ASD risk genes in human embryonic stem cell-derived immature cortical neurons and profile the resulting transcriptomic effects by single-cell RNA sequencing. We identify 215 ASD risk genes whose repression induces significant global transcriptomic dysregulation. Leveraging this functional atlas, we characterize candidate genes based on transcriptional similarity to established ASD hub genes. We identify gene programs recurrently dysregulated across perturbations, anchored by processes governing microtubule dynamics, neuron differentiation, cell migration, and transmembrane transport. We further identify CHAMP1 as a previously unrecognized regulator of Wnt signaling and define specific ASD risk genes that modulate the rate of cortical neuron differentiation. Analysis of differentially expressed genes reveals both convergent and perturbation-specific downstream transcriptional responses. Together, these findings provide a multilevel map of transcriptomic convergence in ASD and establish a framework for identifying both pathway-level and genotype-specific therapeutic strategies.

neuroscience↗

bioRxiv2026

Perturb-seq resolves physiologic programs and bidirectional regulation of non-canonical NF-κB signaling in epidermal organoids

Regulated keratinocyte differentiation is required for formation of the stratified epidermis and a functional barrier. Understanding genetic drivers of keratinocyte differentiation is crucial for understanding several skin diseases. Perturb-seq is a single-cell CRISPR screen that measures transcriptomic responses to perturbations. To date, Perturb-seq experiments have principally focused on 2-dimensional cell culture models lacking hallmarks of skin development - physiological desmosome formation and barrier function. Here, we leverage Perturb-seq in an epidermal organoid model that recapitulates physiologically relevant differentiation programs. We demonstrate that our perturbations significantly impact diverse differentiation programs and reveal bidirectional function of non-canonical NF-{kappa}B signaling in late keratinocyte differentiation.

genomics↗

bioRxiv2026

pertTF: context-aware AI modeling for genome-scale and cross-system perturbation prediction

Predicting genetic perturbation responses at a single-cell level is central to building models for cell state and disease. However, existing approaches are limited on predicting phenotypic outcomes beyond expression changes and generalizing predictions across genome-scale perturbations in biologically relevant contexts. Here we introduce pertTF, a transformer-based single-cell genetic perturbation model. pertTF was trained from a unique dataset capturing single cell expressions profiles of 30 full gene knockouts across 14 relevant cell types during human pancreatic development and beta-cell differentiation. pertTF outperforms current methods in predicting expression changes of perturbing unseen genes in unseen cellular contexts. In addition, pertTF infers perturbation-induced shifts in cell identity and population composition, an important phenotypic outcome of perturbation in many physiology and disease settings. Through transfer learning, pertTF operates in physiologically relevant systems, including primary human islets, where large-scale perturbation experiments are challenging. The generalizability of pertTF is further demonstrated by in silico pooled and single-cell CRISPR screens, capturing critical regulators of stem cells and early pancreatic cell development. These results establish pertTF as a framework for integrating large-scale single-cell perturbation data with AI models to predict genetic perturbation effects across cellular systems and disease contexts.

bioinformatics↗

bioRxiv2025

Comprehensive perturbation of transcription factors in human cardiomyocytes reveals the regulatory architecture of congenital heart disease

Transcription factors (TFs), including DNA binding proteins and epigenetic co-regulators, control the timing of gene programs during lineage commitment1. However, systematically defining the dynamic activities of the human genomes nearly 2000 TFs during development remains challenging2,3. Here, we use Perturb-Seq to map the transcriptional impact of nearly all human TFs and a subset of enhancers during cardiomyocyte differentiation. Our results suggest that developmental regulatory networks are distributed across highly connected hub TFs4-6 that are dynamic across lineage and time, rather than organizing into top-down hierarchies directed by "master regulators". We also identify distinct TF ensembles that regulate sequential decision points during cardiac cell-fate specification by coordinating lineage-specific activation with alternate lineage repression. Separately, analysis of TF-TF cooperativity uncovers a dynamic interaction between MEF2 family TFs7 and members of the Polycomb Repressive complex 18-10 to execute alternative fate repression. Building on these regulatory interactions, we construct a deep learning transformer model to accurately predict perturbed TFs driving altered regulatory networks in patient-derived transcriptomes. Together, our results define the regulatory network architecture of human lineage specification and provide a platform for predicting TF function and interpreting disease mechanisms.

genomics↗

bioRxiv2025

Benchmarking and optimizing Perturb-seq in differentiating human pluripotent stem cells

Perturb-seq is a powerful approach to systematically assess how genes and enhancers impact the molecular and cellular pathways of development and disease. However, technical challenges have limited its application in stem cell-based systems. Here, we benchmarked Perturb-seq across multiple CRISPRi modalities, on diverse genomic targets, in multiple human pluripotent stem cells, during directed differentiation to multiple lineages, and across multiple sgRNA delivery systems. To ensure cost-effective production of large-scale Perturb-seq datasets as part of the Impact of Genomic Variants on Function (IGVF) consortium, our optimized protocol dynamically assesses experiment quality across the weeks-long procedure. Our analysis of 1,996,260 sequenced cells across benchmarking datasets reveals shared regulatory networks linking disease-associated enhancers and genes with downstream targets during cardiomyocyte differentiation. This study establishes open tools and resources for interrogating genome function during stem cell differentiation.

genomics↗