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Morasso, M. I.

Publications and source records attributed to Morasso, M. I..

2 recordsLinked to original sources

Multi-omics identification of activated T cells and spatial PD-1/PD-L1 signaling as biomarkers of diabetic foot ulcer healing

Diabetic foot ulcers (DFUs) are a common and debilitating complication of diabetes, and amputations from non-healing ulcers carry high morbidity and mortality. A critical need exists for biomarkers that can identify healing potential early and guide targeted interventions. To address this, we applied an integrated multi-omics approach across four patient cohorts comprising 51 DFUs (29 Healing, 22 Non-healing). Bulk RNA-sequencing revealed marked activation of Th1 and Th2 pathways (activation z-score +4.8, p = 3.8x10-{superscript 1}), and immune cell deconvolution predicted higher proportions of T cell populations in Healers. Spatial proteomics in a second cohort identified elevated CD3 T cell density and selective enrichment of PD-1 and PD-L1 expression in vascular niches of the papillary dermis in Healers (p < 0.001). Flow cytometry in a third cohort further demonstrated higher proportions of CD3PD-1 and CD3PD-L1 T cells in Healers compared with Non-healers. Single-cell RNA-sequencing from a fourth cohort showed upregulation of PD-1 and PD-L1 within CD4 T cells from Healers. Complementary immunofluorescence and serological profiling confirmed that both PD-1 and PD-L1 are elevated in tissue and circulating serum of healing DFUs, supporting their potential use as systemic biomarkers. Taken together, vascular-enriched PD-1/PD-L1 signaling and T cell activation were observed in association with healing DFUs, supporting PD-1/PD-L1 as candidate biomarkers in both tissue and blood with potential translational relevance for predicting DFU outcomes and informing precision therapies.

immunology↗

A data mining paradigm for identifying key factors in biological processes using gene expression data

A large volume of biological data is being generated for studying mechanisms of various biological processes. These precious data enable large-scale computational analyses to gain biological insights. However, it remains a challenge to mine the data efficiently for knowledge discovery. The heterogeneity of these data makes it difficult to consistently integrate them, slowing down the process of biological discovery. We introduce a data processing paradigm to identify key factors in biological processes via systematic collection of gene expression datasets, primary analysis of data, and evaluation of consistent signals. To demonstrate its effectiveness, our paradigm was applied to epidermal development and identified many genes that play a potential role in this process. Besides the known epidermal development genes, a substantial proportion of the identified genes are still not supported by gain- or loss-of-function studies, yielding many novel genes for future studies. Among them, we selected a top gene for loss-of-function experimental validation and confirmed its function in epidermal differentiation, proving the ability of this paradigm to identify new factors in biological processes. In addition, this paradigm revealed many key genes in cold-induced thermogenesis using data from cold-challenged tissues, demonstrating its generalizability. This paradigm can lead to fruitful results for studying molecular mechanisms in an era of explosive accumulation of publicly available biological data.

bioinformatics↗