Biology subjects

Giansanti, V.

Publications and source records attributed to Giansanti, V..

3 recordsLinked to original sources

bioRxiv2024

Development of a high-throughput 3D culture microfluidic platform for multi-parameter phenotypic and omics profiling of patient-derived organoids

Patient-derived organoids (PDOs) are poised to become central tools both in clinical practice, to preemptively identify patient optimal treatments, and in drug discovery, overcoming the limitations of cancer cell lines. However, the use of PDOs in these settings has been hampered by several bottlenecks, including sample requirements, assay time, and handling in the context of high-throughput assays. We developed a Microfluidic Platform for Organoids culture (MPO) that miniaturises and simplifies PDOs cultures in a 384-plate format. Both retrospective and prospective clinical studies demonstrate MPO predictive value and the straightforward implementation in the clinical setting. MPO allows subcellular phenotypic screenings, as imaging-based applications like Cell Painting, target engagement analyses, alongside the comprehensive definition of PDOs genomic, transcriptomic, proteomic, lipidomic, and metabolomic landscapes. Harnessing the pleiotropic capabilities of MPO, we uncovered the role of EZH2 inhibitors to prevent the long-term emergence of resistance to RAS inhibitors in metastatic colon cancer. In all, we demonstrate the potential of MPO to impact clinical practice, alongside exploration of the mechanisms underlying compound response and resistance.

cancer biology↗

bioRxiv2023

Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks

Single cell profiling has become a common practice to investigate the complexity of tissues, organs and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or from the very same cells. Despite development of computational methods for data integration is an active research field, most of the available strategies have been devised for the joint analysis of two modalities and cannot accommodate a high number of them. To solve this problem, we here propose a multiomic data integration framework based on Wasserstein Generative Adversarial Networks (MOWGAN) suitable for the analysis of paired or unpaired data with high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated. Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.

bioinformatics↗

bioRxiv2020

Nested Stochastic Block Models Applied to the Analysis of Single Cell Data

Single cell profiling has been proven to be a powerful tool in molecular biology to understand the complex behaviours of heterogeneous system. The definition of the properties of single cells is the primary endpoint of such analysis, cells are typically clustered to underpin the common determinants that can be used to describe functional properties of the cell mixture under investigation. Several approaches have been proposed to identify cell clusters; while this is matter of active research, one popular approach is based on community detection in neighbourhood graphs by optimisation of modularity. In this paper we propose an alternative and principled solution to this problem, based on Stochastic Block Models. We show that such approach not only is suitable for identification of cell groups, it also provides a solid framework to perform other relevant tasks in single cell analysis, such as label transfer. To encourage the use of Stochastic Block Models, we developed a python library, schist, that is compatible with the popular scanpy framework.

bioinformatics↗