Biology subjects

Pizzini, L.

Publications and source records attributed to Pizzini, L..

3 recordsLinked to original sources

bioRxiv2026

TGF-β1-induced differentiation enhances chemotherapy response in metastatic colorectal cancer organoids

BackgroundIn metastatic colorectal cancer, systemic therapies frequently fail, partly due to underlying phenotypic plasticity rooted in pre-existing multi-type cell populations. Intratumoral lineage hierarchies within colorectal tumors require renovated efforts to decode growth principles, design rational therapeutic approaches, and accurately interpret drug response. Understanding the cell-state dynamics of untreated tumors and the degree of cell responsiveness to exogenous stimuli is therefore crucial to improving currently underwhelming therapeutic outcomes. MethodsHere, we leveraged patient-derived organoids established from hepatic metastases of colorectal cancer patients to deconstruct population hierarchies by combining single-cell transcriptomics with single-molecule RNA fluorescent in situ hybridization. Computational frameworks were used to identify independent gene modules. We then employed flow cytometry analysis to track cytokine-induced population shifts using a cell surface marker, validating our findings through bulk RNA analysis, functional assays, and viability assays in response to oxaliplatin. ResultsOur data substantiate the existence of a dual population configuration within untreated metastatic colorectal cancer organoids with diverse genetic backgrounds. Gene modules detected via single-cell transcriptomics delineate a stem-like (LGR5+) and a differentiated-like (KRT20+) population that fluctuate dynamically over time. Spatially and temporally resolved, single-cell level analysis through single molecule FISH captures the inherent stochasticity in cell fate decisions revealing surprising phenotypic variability even across different organoids derived from the same patient. By using GABRA2 as a surface marker we track the emergence of differentiated cells over the course of time and investigate the respective roles of TGF-{beta}1 and IL-6 in the differentiation of organoids. Our findings indicate that IL-6 exerts no major effect within our cell autonomous setting. In stark contrast, TGF-{beta}1 triggered cell cycle arrest and differentiation, while simultaneously reducing clonogenic capacity and significantly amplifying the cytotoxic potency of oxaliplatin. ConclusionsOur findings provide evidence of the dual Stem and Differentiated population hierarchy in metastatic colorectal cancer organoids, and demonstrate how this axis can be effectively hijacked by TGF-{beta}1 to suppress tumor growth. Our results suggest that further mechanistic exploitation of this cell-autonomous, tumor-suppressive arm of TGF-{beta}1 signalling could open unappreciated therapeutic windows in advanced colorectal cancer.

cancer biology↗

bioRxiv2025

Inference of lineage hierarchies, growth and drug response mechanisms in cancer cell populations without tracking

Lineage hierarchies and plasticity regulate development and tissue homeostasis, while diverted lineage dynamics and aberrant phenotypic plasticity are among the causes of incomplete drug response and secondary resistance in cancer. Knowing the dynamics of phenotypically plastic populations is therefore central to understand growth regulation principles and to rationally design therapeutic approaches that might anticipate drug-tolerant states. Lineage inference however largely relies on single-cell tracking techniques, which are notoriously difficult in complex biological models. To overcome these limitations, we developed a method to infer active phenotypic transitions in a multi-lineage tumor or clone and to quantify them, solely relying on counting lineage abundances with no pedigree. We demonstrate the effectiveness of our approach to cancer cell plasticity and drug treatment in silico. We then perform experiments on cancer cell populations and show that our method correctly predicts growth mechanisms and transition probabilities.

cancer biology↗

bioRxiv2024

Topic Modeling analysis of the Allen Human Brain Atlas

The human brain is a complex interconnected structure controlling all elementary and high-level cognitive tasks. It is composed of many regions that exhibit specific distributions of cell types and distinct patterns of functional connections. This complexity is rooted in differential transcription. The constituent cell types of different brain regions express distinctive combinations of genes as they develop and mature, ultimately shaping their functional state in adulthood. How precisely the genetic information of anatomical structures is connected to their underlying biological functions remains an open question in modern neuroscience. A major challenge is the identification of "universal patterns", which do not depend on the particular individual, but are instead basic structural properties shared by all brains. Despite the vast amount of gene expression data available at both the bulk and single-cell levels, this task remains challenging, mainly due to the lack of suitable data mining tools. In this paper, we propose an approach to address this issue based on a hierarchical version of Stochastic Block Modeling. Thanks to its specific choice of priors, the method is particularly effective in identifying these universal features. We use as a laboratory to test our algorithm a dataset obtained from six independent human brains from the Allen Human Brain Atlas. We show that the proposed method is indeed able to identify universal patterns much better than more traditional algorithms such as Latent Dirichlet Allocation or Weighted Correlation Network Analysis. The probabilistic association between genes and samples that we find well represents the known anatomical and functional brain organization. Moreover, leveraging the peculiar "fuzzy" structure of the gene sets obtained with our method, we identify examples of transcriptional and post-transcriptional pathways associated with specific brain regions, highlighting the potential of our approach.

biophysics↗