Biology subjects

Tocci, T.

Publications and source records attributed to Tocci, T..

3 recordsLinked to original sources

bioRxiv2025

Clonal memory of cell division in humans diverges between healthy haematopoiesis and acute myeloid leukaemia

Clonal memory, a cellular property inherited across at least two divisions, has emerged as a key driver of cell heterogeneity. To uncover its roles in human haematopoiesis, we developed high-resolution ex vivo tools that track both division and fate commitment of individual primary human haematopoietic stem and progenitor cells (HSPCs). We show that human HSPCs display a clonal memory of division, as cells descending from the same ancestor cell divide synchronously over multiple generations. In parallel, HSPCs inherit a clonal memory of fate commitment, independently of lineage identity. Both forms of clonal memory persist over at least two divisions, across different HSPC commitment stages and cell culture conditions. In contrast, malignant haematopoiesis exhibits lower synchronicity, revealing a disruption of clonal memory in leukemic cells. Epigenetic remodelling using a bromodomain inhibitor partially restores the clonal memory in division in leukemic HSPCs, highlighting the plasticity of this trait and its potential for therapeutic modulation. Our findings position clonal memory as a key regulator of human haematopoietic stem cell behaviour. Demonstrating that clonal memory can be modulated opens new avenues for tuning cell heterogeneity in healthy and pathological tissues.

cell biology↗

bioRxiv2025

Label-free Machine Learning Prediction of Chemotherapy on Tumor Spheroids using a Microfluidics Droplet Platform

To rapidly evaluate the effects of anticancer treatments in 3D models, an integrated approach is proposed, combining a droplet-based microfluidic platform for spheroid formation and single-spheroid chemotherapy application, label-free morphological analysis, and machine learning to assess treatment response. Morphological features of spheroids, such as size and color intensity, are extracted and selected using the Multivariate Information-based Inductive Causation (MIIC) algorithm, and used to train a neural network for spheroid classification into viability classes, derived from metabolic assays performed within the same platform as a benchmark. The model was tested on Ewing sarcoma cell line and patient-derived xenograft (PDX) cells, demonstrating robust performance across datasets. It accurately predicts spheroid viability, used to generate dose-response curves and to determine half-maximal inhibitory concentration (IC50) values comparable to traditional biochemical assays. Notably, a model trained on cell line spheroids successfully classifies PDX spheroids, highlighting its adaptability. Compared to convolutional neural network-based approaches, this method works with smaller training datasets and provides greater interpretability by identifying key morphological features. The droplet platform further reduces cell requirements, while single spheroid confinement enhances classification quality. Overall, this label-free experimental and analytical platform is confirmed as a scalable, efficient, and dynamic tool for drug screening.

bioengineering↗

bioRxiv2024

CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data

Live-cell microscopy routinely provides massive amount of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell-cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer associated fibroblasts directly inhibit cancer cell apoptosis, independently from anti-cancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.

systems biology↗