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Biology subjects

Grisanti, F.

Publications and source records attributed to Grisanti, F..

2 recordsLinked to original sources

Hippo-deficient cardiac fibroblasts differentiate into osteochondroprogenitors

Cardiac fibrosis, a common pathophysiology associated with various heart diseases, occurs from the excess deposition of extracellular matrix (ECM)1. Cardiac fibroblasts (CFs) are the primary cells that produce, degrade, and remodel ECM during homeostasis and tissue repair2. Upon injury, CFs gain plasticity to differentiate into myofibroblasts3 and adipocyte-like4,5 and osteoblast-like6 cells, promoting fibrosis and impairing heart function7. How CFs maintain their cell state during homeostasis and adapt plasticity upon injury are not well defined. Recent studies have shown that Hippo signalling in CFs regulates cardiac fibrosis and inflammation8-11. Here, we used single-nucleus RNA sequencing (snRNA-seq) and spatially resolved transcriptomic profiling (ST) to investigate how the cell state was altered in the absence of Hippo signaling and how Hippo-deficient CFs interact with macrophages during cardiac fibrosis. We found that Hippo-deficient CFs differentiate into osteochondroprogenitors (OCPs), suggesting that Hippo restricts CF plasticity. Furthermore, Hippo-deficient CFs colocalized with macrophages, suggesting their intercellular communications. Indeed, we identified several ligand-receptor pairs between the Hippo-deficient CFs and macrophages. Blocking the Hippo-deficient CF-induced CSF1 signaling abolished macrophage expansion. Interestingly, blocking macrophage expansion also reduced OCP differentiation of Hippo-deficient CFs, indicating that macrophages promote CF plasticity.

developmental biology↗

Gene panel design for spatial transcriptomics with prioritized gene sets

A fundamental limitation of the emerging single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, an sc-ST dataset can profile only a pre-determined panel of a few hundred genes. This often forces biologists to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or genes in specific pathways. We propose scGIST- a deep neural network that designs sc-ST panels through constrained feature selection. On four datasets, scGIST outperformed alternative methods in terms of cell type detection accuracy. Moreover, unlike other methods, scGIST allows genes of interest to be prioritized for inclusion in the panel while staying within the its size constraint. We demonstrate through diverse use cases that scGIST includes large fractions of prioritized genes without compromising cell type prediction efficacy making it a valuable addition to sc-STs algorithmic toolbox.

bioinformatics↗