Journal Article

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

Liang, Rachel, Li, Yuanyuan, Nolan, Garry P., Good, Zinaida, Zhou, Xin, Kong, Christina S., Sunwoo, John B., Samusik, Nikolay, Engleman, Edgar G., Li, Irene, Gentles, Andrew J., Saumyaa, Saumyaa, Plevritis, Sylvia K., Le, Quynh-Thu, Chang, Serena, Reticker-Flynn, Nathan E., Zhang, Weiruo

2022

Overview

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 findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell–cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions. CELESTA identifies cell types in multiplexed imaging datasets based on the expression profiles of cells and their spatial information.

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 631/114/2397 / 631/1647/794 / 631/67/2321 / Algorithms / Bioinformatics / Biological Microscopy / Biological Techniques / Biomedical and Life Sciences / Biomedical Engineering/Biotechnology / Cancer / Cohort Studies / Colorectal cancer / Crosstalk / Gene sequencing / Head & neck cancer / Head and neck carcinoma / Head and Neck Neoplasms / Humans / Imaging / Immunofluorescence / Life Sciences / Lymph nodes / Lymphatic Metastasis / Lymphatic system / Machine learning / Metastases / Metastasis / Multiplexing / Proteomics / Spatial analysis / Spatial data / Squamous cell carcinoma / Squamous Cell Carcinoma of Head and Neck