Clelia Corridori’s research while affiliated with University of Padua and other places

Physiological and pathological processes are governed by a network of genes called gene regulatory networks (GRNs). By reconstructing GRNs, we can accurately model how cells behave in their natural state and predict how genetic changes will affect them. Transcriptomic data of single cells are now available for a wide range of cellular processes in multiple species. Thus, a method building predictive GRNs from single-cell RNA sequencing (scRNA-seq) data, without any additional prior knowledge, could have a great impact on our understanding of biological processes and the genes playing a key role in them. To this aim, we developed IGNITE (Inference of Gene Networks using Inverse kinetic Theory and Experiments), an unsupervised machine learning framework designed to infer directed, weighted, and signed GRNs directly from unperturbed single-cell RNA sequencing data. IGNITE uses the GRNs to generate gene expression data upon single and multiple genetic perturbations. IGNITE is based on the inverse problem for a kinetic Ising model, a model from statistical physics that has been successfully applied to biological networks. We tested IGNITE on murine pluripotent stem cells (PSCs) transitioning from the naive to formative states. Using as input only scRNA-seq data of unperturbed PSCs, IGNITE simulated single and triple gene knockouts. Comparison with experimental data revealed high accuracy, up to 74%, outperforming currently available methods. In sum, IGNITE identifies predictive GRNs from scRNA-seq data without additional prior knowledge and faithfully simulates single and multiple gene perturbations. Applications of IGNITE range from studying cell differentiation to identifying genes specifically active under pathological conditions.

In human embryos, naive pluripotent cells of the inner cell mass (ICM) generate epiblast, primitive endoderm and trophectoderm (TE) lineages, whence trophoblast cells derive. In vitro, naive pluripotent stem cells (PSCs) retain this potential and efficiently generate trophoblast stem cells (TSCs), while conventional PSCs form TSCs at low efficiency. Transient histone deacetylase and MEK inhibition combined with LIF stimulation is used to chemically reset conventional to naive PSCs. Here, we report that chemical resetting induces the expression of both naive and TSC markers and of placental imprinted genes. A modified chemical resetting protocol allows for the fast and efficient conversion of conventional PSCs into TSCs, entailing shutdown of pluripotency genes and full activation of the trophoblast master regulators, without induction of amnion markers. Chemical resetting generates a plastic intermediate state, characterised by co-expression of naive and TSC markers, after which cells steer towards one of the two fates in response to the signalling environment. The efficiency and rapidity of our system will be useful to study cell fate transitions and to generate models of placental disorders.