Biology subjects

Pedrini, E.

Publications and source records attributed to Pedrini, E..

3 recordsLinked to original sources

bioRxiv2024

Glia-enriched stem-cell 3D model of the human brain mimics the glial-immune neurodegenerative phenotypes of multiple sclerosis

The role of central nervous system (CNS) glia in sustaining self-autonomous inflammation and driving clinical progression in multiple sclerosis (MS) is gaining scientific interest. We applied a single transcription factor (SOX10)-based protocol to accelerate oligodendrocyte differentiation from hiPSC-derived neural precursor cells, generating self-organizing forebrain organoids. These organoids include neurons, astrocytes, oligodendroglia, and hiPSC-derived microglia to achieve immunocompetence. Over 8 weeks, organoids reproducibly generated mature CNS cell types, exhibiting single-cell transcriptional profiles similar to the adult human brain. Exposed to inflamed cerebrospinal fluid (CSF) from MS patients, organoids properly mimic macroglia-microglia neuro-degenerative phenotypes and intercellular communication seen in chronic active MS. Oligodendrocyte vulnerability emerged by day 6 post-MS-CSF exposure, with nearly 50% reduction. Temporally-resolved organoid data support and expand on the role of soluble CSF mediators in sustaining downstream events leading to oligodendrocyte death and inflammatory neurodegeneration. Such findings support implementing this organoid model for drug screening to halt inflammatory neurodegeneration.

neuroscience↗

bioRxiv2023

Cell population growth as manifestation of stochastic heterogeneity of cell phenotype

Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized exponential growth. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture the departure from the exponential growth model in the initial growth phase. Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth kinetics and the presence of subpopulations with different growth rates that endured for many generations. Based on the hypothesis of existence of multiple inter-converting subpopulations, we developed a branching process model that captures the experimental observations.

cancer biology↗

bioRxiv2017

Extracting Intercellular Signaling Network Of Cancer Tissues Using Ligand-Receptor Expression Patterns From Whole-Tumor And Single-Cell Transcriptomes

Many behaviors of cancer, such as progression, metastasis and drug resistance etc., cannot be fully understood by genetic mutations or intracellular signaling alone. Instead, they are emergent properties of cell community that forms a tumor. Studies of tumor heterogeneity reveal that many cancer behaviors critically depend on the intercellular communication mediated by secreted signaling ligands and their cognate receptors that take place between cancer cells and stromal cells. Owing to systematic cancer omics efforts, we studied such cell-cell interactions using data from cancer transcriptome database. We curated a list of more than 2,500 ligand-receptor pairs and developed a method to identify their enrichment in tumors from TCGA pancancer data and to build a cell interaction network from single-cell data for the case of melanoma. Using the specificity of the ligand-receptor interaction and their expressions measured in individual cells, we built a map of a cell-cell communication network which indicate what signal is exchanged between which cell types. Such networks establish a new formal phenotypes which are embodied by the cell communication structure - it may offer new opportunities to identify the molecular signatures which may influence cancer cell behaviors by changing cell population dynamics.

systems biology↗