Biology subjects

Carraro, C.

Publications and source records attributed to Carraro, C..

5 recordsLinked to original sources

bioRxiv2026

A high-throughput cost-efficient in vitro platform for the screening of immune senomodulators

With advancing age, the immune systems capacity to effectively combat pathogens diminishes. This decline of the overall immune function impacts both the innate and adaptive compartments, contributing in many cases to a systemic state of chronic inflammation which further increases the risk of the most prevalent non-communicable diseases and severe infections. Given the increase in median life expectancy with a demographic development towards a larger number of elderly people, identifying interceptive strategies to mitigate the individual and societal impact of diseases related to immune aging is of paramount importance. We developed a molecularly defined strategy to guide interventions with the aim to reduce immune aging. We introduce an omics-based drug screening platform to identify and characterize the pharmacological profile of immune senomodulators applicable to cross-age human cohorts using human-derived peripheral immune cells. To this aim we developed a robust experimental approach to screen for effective anti-aging compounds directly on human cells. This methodology allows us to quickly screen for drug candidates at different scales: from cost-effective bulk transcriptomics for a broader high-throughput overview of cellular responses, down to single-cell resolution approaches for a more detailed look at gene expression and other molecular data. This in vitro screening method is designed to maximize the clinical relevance of our findings, providing a direct link between preclinical research and patient care. By analyzing how different compounds affect the immune cells of individual persons, we can identify treatments that are most likely to be effective against aging in a subject-specific manner--a key step toward personalized medicine. In short, our approach enables a faster translation of anti-aging immune treatments from the lab to the clinic, tailoring them to each individuals unique biological makeup.

systems biology↗

bioRxiv2026

A reproducible human brain tissue model to study physiological and disease-associated microglia phenotypes

Stem-cell-based in vitro models offer promising potential to elucidate human brain cell functions and interactions under physiological and pathological conditions. However, harnessing this potential is impaired by low reproducibility, maturity, or cell-type diversity of existing models. Especially, prolonged incorporation of mature microglia and studies of neuroinflammation have proven challenging. Here, we developed a 3D cortical brain tissue model (3BTM) containing neurons, astrocytes, and microglia with high reproducibility, maturity, and viability. 3BTMs show morphological, functional, and proteomic maturation of all cell types, leading to high similarity to their in vivo counterparts. Incorporated microglia survive for over 6 months and display mature morphology, functions, and gene expression. Importantly, when engineered to model Alzheimers disease pathology, 3BTMs recapitulate key disease hallmarks including amyloid deposition, increased phospho-Tau levels, and neuroinflammation, with microglia shifting their transcriptional landscape to disease-relevant signatures. Together, our model offers unprecedented possibilities for studying physiological and pathological states of human brain tissue and translational applications.

neuroscience↗

bioRxiv2024

ConvexGating infers gating strategies from clusters in single cell cytometry data

Manual expert gating remains common practice for the definition of specific cell populations in the analysis of flow cytometry data. The increasing number of measured parameters per individual cell and high inter-rater variability makes manual gating inconsistent in many scenarios such as multi-center studies. Here, we propose ConvexGating, an AI tool that automatically learns gating strategies in an unbiased, fully data-driven, yet interpretable manner. ConvexGating scales efficiently with increasing parameter space, creating proficient strategies with low-contamination in the extracted population for previously known and so far unknown or ill-defined cell populations. The inferred strategies are independent of parent populations, for instance, plasmacytoid dendritic cells (pDCs) can be fully identified as CD45RA- CD123+. In addition to flow cytometry data, ConvexGating derives gating strategies for cyTOF (Cytometry by Time of Flight) and CITEseq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) data and supports optimal design of marker panels for cell sorting.

immunology↗

bioRxiv2024

Unveiling the Power of High-Dimensional Cytometry Data with cyCONDOR

High-dimensional cytometry (HDC) is a powerful technology for studying single-cell phenotypes in complex biological systems. Although technological developments and affordability have made HDC broadly available in recent years, technological advances were not coupled with an adequate development of analytical methods that can take full advantage of the complex data generated. While several analytical platforms and bioinformatics tools have become available for the analysis of HDC data, these are either web-hosted with limited scalability or designed for expert computational biologists, making their use unapproachable for wet lab scientists. Additionally, end-to-end HDC data analysis is further hampered due to missing unified analytical ecosystems, requiring researchers to navigate multiple platforms and software packages to complete the analysis. To bridge this data analysis gap in HDC we developed cyCONDOR, an easy-to-use computational framework covering not only all essential steps of cytometry data analysis but also including an array of downstream functions and tools to expand the biological interpretation of the data. The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. Additionally, the advanced analytical features of cyCONDOR, such as pseudotime analysis and batch integration, provide researchers with the tools to extract deeper insights from their data. We used cyCONDOR on a variety of data from different tissues and technologies demonstrating its versatility to assist the analysis of high dimensionality data from preprocessing to biological interpretation.

bioinformatics↗

bioRxiv2022

Decoding mechanism of action and susceptibility to drug candidates from integrated transcriptome and chromatin state

Omics-based technologies are driving major advances in precision medicine but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chloropiperidines (3-CePs), a new class of candidate anticancer agents. Combined analyses of transcriptome and chromatin accessibility elucidated the mechanisms underlying sensitivity to test agents. Further, we implemented a new versatile strategy for the integration of RNA-seq and ATAC-seq data, able to accelerate and extend the standalone analyses of distinct omic layers. This platform guided the construction of a perturbation-informed basal signature able to predict cancer cell lines sensitivity and to further direct compound development against specific tumor types. Overall, this approach offered a scalable pipeline to support the early phases of drug discovery, understanding of mechanism and potentially inform the positioning of therapeutics in the clinic.

systems biology↗