Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA.

ABSTRACT: Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell's marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our fin

SUBMITTER: Zhang W 

PROVIDER: S-EPMC9728133 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature