AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics

Aanchal Mongia, Fatema Tuz Zohora, Noah G. Burget, Yeqiao Zhou, Diane C. Saunders, Yue J. Wang, Marcela Brissova, Alvin C. Powers, Klaus H. Kaestner, Golnaz Vahedi, Ali Naji, Gregory W. Schwartz () and Robert B. Faryabi ()
Additional contact information
Aanchal Mongia: University of Pennsylvania Perelman School of Medicine
Fatema Tuz Zohora: University Health Network
Noah G. Burget: University of Pennsylvania Perelman School of Medicine
Yeqiao Zhou: University of Pennsylvania Perelman School of Medicine
Diane C. Saunders: Vanderbilt University Medical Center
Yue J. Wang: University of Pennsylvania Perelman School of Medicine
Marcela Brissova: Vanderbilt University Medical Center
Alvin C. Powers: Vanderbilt University Medical Center
Klaus H. Kaestner: University of Pennsylvania Perelman School of Medicine
Golnaz Vahedi: University of Pennsylvania Perelman School of Medicine
Ali Naji: University of Pennsylvania Perelman School of Medicine
Gregory W. Schwartz: University Health Network
Robert B. Faryabi: University of Pennsylvania Perelman School of Medicine

Nature Communications, 2024, vol. 15, issue 1, 1-19

Abstract: Abstract 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 for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop 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 shows the higher 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 recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-47334-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47334-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-47334-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().