Biology subjects

Bugani, E.

Publications and source records attributed to Bugani, E..

4 recordsLinked to original sources

bioRxiv2026

Generative modeling reveals the connection between cellular morphology and gene expression

The understanding of how transcriptional programs give rise to cellular morphology, and how morphological features reflect and influence cell identity and function remains limited. This is due in part to the lack of large-scale datasets pairing the two modalities as well as the absence of computational frameworks capable of modeling their cross-modal structure. Here, we introduce COSMIC, a bidirectional generative framework that enables quantitative decomposition of transcriptional variance reflected in morphology and morphological variance explained by gene expression. COSMIC builds on a foundation model trained on over 21 million segmented nuclei and couples it with existing transcriptomic embeddings. To enable cross-modal learning, we leveraged a newly generated multimodal dataset acquired using IRIS, a technology that captures high-resolution images and transcriptomes from the same single cells at scale. COSMIC accurately modeled cell type identity, as well as continuous dynamics such as cell-cycle progression, establishing a quantitative link between morphological phenotypes and underlying gene expression. In prostate cancer cells, COSMIC identified morphological and transcriptomic differences between chemotherapy drug treatment-responsive and -resistant cells, and revealed morphology-associated genes linked to tumor state. Together, these results demonstrate that generative modeling powered by paired single-cell measurements can capture the bidirectional flow of information between cellular form and gene expression, opening new avenues for mechanistic discovery and predictive modeling in both basic and translational cell biology.

bioinformatics↗

bioRxiv2025

Single-cell phenomics through integrated imaging and molecular profiling

Single-cell technologies such as transcriptomics, microscopy, and flow cytometry have revolutionized the study of cellular identity and function. While each of these technologies is powerful on its own, their full potential lies in their integration, enabling multimodal profiling of the same cell and revealing how distinct modalities influence one another. Here, we introduce IRIS (Interconnected Robotic Imaging and Single cell transcriptomics), a deterministic single-cell platform technology that seamlessly couples high-resolution microscopy with droplet-based single-cell RNA sequencing. IRIS enables precise cell positioning, multimode imaging across brightfield and fluorescent channels, and subsequent molecular capture from the same cell, directly linking high-resolution morphological features to matched transcriptomes. We validate IRIS by recovering cell cycle progression states and transcriptional programmes associated with canonical morphologies and demonstrate its discovery power by molecularly resolving two nuclear-ER architectures within naive CD8+ T cells, each defined by distinct gene expression profiles and functional markers. IRIS establishes an integrative single-cell phenomics framework, opening new avenues for dissecting how cellular form relates to molecular state and function.

immunology↗

bioRxiv2025

MeCP2 binding and genome-lamina reorganization precede long gene activation during mouse corticogenesis

During corticogenesis, neural gene expression is tightly coordinated by chromatin and epigenetic changes, whose misregulation can lead to neurodevelopmental disorders1-4. The role of spatial genome organization--particularly interactions with the nuclear lamina--during these developmental programs remains poorly understood. Here, we combined in utero electroporation with scDam&T-seq to jointly profile genome-lamina contacts and transcriptomes in single cells of the mouse embryonic cortex. Interestingly, we find extensive genome-lamina reorganization during corticogenesis that is strongly biased towards long neuronal genes ([≥]100 kb), which are associated with neurodevelopmental disorders including autism spectrum disorder. Detachment of these genes frequently precedes transcriptional activation, positioning lamina disengagement as an early gene regulatory event. We identify the methyl CpG binding protein 2 (MeCP2)--mutated in Rett syndrome--as a candidate mediator of this process. MeCP2 binds lamina-associated, hydroxymethylated long genes before their repositioning, suggesting that MeCP2 may play a role in genome-lamina reorganization. These findings suggest a link between prevalent genome-lamina reorganization and MeCP2 regulation to ensure proper spatiotemporal activation of long neuronal genes during corticogenesis.

molecular biology↗

bioRxiv2025

MiTo: tracing the phenotypic evolution of somatic cell lineages via mitochondrial single-cell multi-omics

Mitochondrial single-cell lineage tracing (MT-scLT) has recently emerged as a scalable and non-invasive tool to trace somatic cell lineages. However, the reliability and resolution of MT-scLT remains highly debated. Here, we present MiTo, the first end-to-end framework for robust MT-scLT data analysis. Thanks to highly-optimized algorithms and user-friendly interfaces, this modular toolkit offers unprecedented control across the entire MT-scLT workflow. Benchmarked against novel real-world datasets (375-2,757 cells; 8-216 lentiviral clones), MiTo outperformed state-of-the-art methods and baselines in MT-scLT data pre-processing and clonal inference. Applied to a time-resolved dataset of breast cancer evolution (>2,500 cells), MiTo accurately inferred ground-truth cell lineages (ARI=0.94) and cell state transitions, detected clonal fitness markers, and quantified heritability of gene regulatory networks. Comparing alternative lineage markers, MiTo quantified the resolution limit of existing MT-scLT systems, which currently enable reliable inference of coarse-grained cellular ancestries, but not high-resolution phylogenetic inference. In conclusion, this work provides robust tools and practical guidelines to dissect somatic evolution with single-cell multi-omics.

bioinformatics↗