Expanding the coverage of spatial proteomics: a machine learning approach

Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts

The outbreak of COVID-19, led to an ongoing pandemic with devastating consequences for the global economy and human health. With the global spread of SARS-CoV-2, multidisciplinary initiatives were launched to explore new diagnostic, therapeutic, and vaccination strategies. From this perspective, proteomics could help to understand the mechanisms associated with SARS-CoV-2 infection and to identify new therapeutic options. A TMT-based quantitative proteomics and phosphoproteomics analysis was performed to study the proteome remodeling of human lung alveolar cells expressing human ACE2 (A549-ACE2) after infection with SARS-CoV-2. Detectability and the prognostic value of selected proteins was analyzed by targeted PRM. A total of 6802 proteins and 6428 phospho-sites were identified in A549-ACE2 cells after infection with SARS-CoV-2. The differential proteins here identified revealed that A549-ACE2 cells undergo a time-dependent regulation of essential processes, delineating the precise intervention of the cellular machinery by the viral proteins. From this mechanistic background and by applying machine learning modeling, 29 differential proteins were selected and detected in the serum of COVID-19 patients, 14 of which showed promising prognostic capacity. Targeting these proteins and the protein kinases responsible for the reported phosphorylation changes may provide efficient alternative strategies for the clinical management of COVID-19.

MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation

Cyclic Immunofluorescence (CyCIF) can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types.