Biology subjects

Cosenza, M. R.

Publications and source records attributed to Cosenza, M. R..

4 recordsLinked to original sources

bioRxiv2025

Smart Microscopy: Current Implementations and a Roadmap for Interoperability

Smart microscopy is transforming life sciences by automating experimental imaging workflows and enabling real-time adaptation based on feedback from images and other data streams. This shift increases throughput, improves reproducibility, and expands the functional capabilities of microscopes. However, the current landscape is highly fragmented. Academic researchers often develop custom solutions for specific scientific needs, while industry offerings are typically proprietary and tied to specific hardware. This diversity, while fostering innovation, also creates major challenges in interoperability, reproducibility, and standardization, which slows progress and adaption. This article presents a collaborative effort between academic and industry leaders to survey the current state of smart microscopy, highlight representative implementations, and identify common technical and organizational barriers. We propose a framework for greater interoperability based on shared standards, modular software design, and community-driven development. Our goal is to support collaboration across the field and lay the groundwork for a more connected, reusable, and accessible smart microscopy ecosystem. We conclude with a call to action for researchers, hardware developers, and institutions to join in building an open, interoperable foundation that will unlock the full potential of smart microscopy in life science research.

cell biology↗

bioRxiv2024

Origins of de novo chromosome rearrangements unveiled by coupled imaging and genomics

Chromosomal instability results in widespread structural and numerical chromosomal abnormalities (CAs) during cancer evolution1-3. While CAs have been linked to mitotic errors resulting in the emergence of nuclear atypias4-7, the underlying processes and basal rates of spontaneous CA formation in human cells remain under-explored. Here we introduce machine learning-assisted genomics-and-imaging convergence (MAGIC), an autonomously operated platform that integrates automated live-cell imaging of micronucleated cells, machine learning in real-time, and single-cell genomics to investigate de novo CA formation at scale. Applying MAGIC to near-diploid, non-transformed cell lines, we track CA events over successive cell cycles, highlighting the common role of dicentric chromosomes as an initiating event. We determine the baseline CA rate, which approximately doubles in TP53-deficient cells, and show that chromosome losses arise more rapidly than gains. The targeted induction of DNA double-strand breaks along chromosomes triggers distinct CA processes, revealing stable isochromosomes, amplification and coordinated segregation of isoacentric segments in multiples of two, and complex CA outcomes, depending on the break location. Our data contrast de novo CA spectra from somatic mutational landscapes after selection occurred. The large-scale experimentation enabled by MAGIC provides insights into de novo CA formation, paving the way to unravel fundamental determinants of chromosome instability.

genomics↗

bioRxiv2024

Integrating Multi-Modal Cancer Data Using Deep Latent Variable Path Modelling

Cancers are commonly characterised by a complex pathology encompassing genetic, microscopic and macroscopic features, which can be probed individually using imaging and omics technologies. Integrating this data to obtain a full understanding of pathology remains challenging. We introduce a new method called Deep Latent Variable Path Modelling (DLVPM), which combines the representational power of deep learning with the capacity of path modelling to identify relationships between interacting elements in a complex system. To evaluate the capabilities of DLVPM, we initially trained a foundational model to map dependencies between SNV, Methylation, miRNA-Seq, RNA-Seq and Histological data using Breast Cancer data from The Cancer Genome Atlas (TCGA). This method exhibited superior performance in mapping associations between data types compared to classical path modelling. We additionally performed successful applications of the model to: stratify single-cell data, identify synthetic lethal interactions using CRISPR-Cas9 screens derived from cell-lines, and detect histologic-transcriptional associations using spatial transcriptomic data. Results from each of these data types can then be understood with reference to the same holistic model of illness.

cancer biology↗

bioRxiv2023

STIL overexpression shortens lifespan and reduces tumor formation in mice

Centrosomes are the major microtubule organizing centers of animal cells. Supernumerary centrosomes are a common feature of human tumors and associated with karyotype abnormalities and aggressive disease, but whether they are cause or consequence of cancer remains controversial. Here, we analyzed the consequences of centrosome amplification by generating transgenic mice in which centrosome numbers can be increased by overexpression of the structural centrosome protein STIL. We show that STIL overexpression induces centrosome amplification and aneuploidy, leading to senescence, apoptosis, and impaired proliferation in mouse embryonic fibroblasts, and microcephaly with increased perinatal lethality and shortened lifespan in mice. Importantly, both overall tumor formation in mice with constitutive, global STIL overexpression and chemical skin carcinogenesis in animals with inducible, skin-specific STIL overexpression were reduced, an effect that was not rescued by concomitant p53 inactivation. These results suggest that supernumerary centrosomes impair proliferation in vitro as well as in vivo, resulting in reduced lifespan and spontaneous as well as carcinogen-induced tumor formation.

cancer biology↗