RT Journal Article SR Electronic T1 AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics JF bioRxiv FD Cold Spring Harbor Laboratory SP 2023.01.15.524135 DO 10.1101/2023.01.15.524135 A1 Mongia, Aanchal A1 Saunders, Diane C. A1 Wang, Yue J. A1 Brissova, Marcela A1 Powers, Alvin C. A1 Kaestner, Klaus H. A1 Vahedi, Golnaz A1 Naji, Ali A1 Schwartz, Gregory W. A1 Faryabi, Robert B. YR 2023 UL http://biorxiv.org/content/early/2023/01/18/2023.01.15.524135.abstract AB Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs, we developed AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX show the superior performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.Competing Interest StatementThe authors have declared no competing interest.