PT  - JOURNAL ARTICLE
AU  - Nirmal, Ajit J.
AU  - Yapp, Clarence
AU  - Santagata, Sandro
AU  - Sorger, Peter K.
TI  - Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images
AID  - 10.1101/2023.11.15.567196
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.11.15.567196
4099  - http://biorxiv.org/content/early/2023/11/17/2023.11.15.567196.short
4100  - http://biorxiv.org/content/early/2023/11/17/2023.11.15.567196.full
AB  - Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.Competing Interest StatementPKS is a co-founder and member of the BOD of Glencoe Software, member of the BOD for Applied Biomath, and member of the SAB for RareCyte, NanoString, and Montai Health; he holds equity in Glencoe, Applied Biomath, and RareCyte. PKS consults for Merck and the Sorger lab has received research funding from Novartis and Merck in the past five years. The other authors declare no outside interests.