CellSighter: a neural network to classify cells in highly multiplexed images

Yael Amitay, Yuval Bussi, Ben Feinstein, Shai Bagon, Idan Milo and Leeat Keren ()
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Yael Amitay: Weizmann Institute of Science
Yuval Bussi: Weizmann Institute of Science
Ben Feinstein: Weizmann Institute of Science
Shai Bagon: Weizmann Institute of Science
Idan Milo: Weizmann Institute of Science
Leeat Keren: Weizmann Institute of Science

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we present CellSighter, a deep-learning based pipeline to accelerate cell classification in multiplexed images. Given a small training set of expert-labeled images, CellSighter outputs the label probabilities for all cells in new images. CellSighter achieves over 80% accuracy for major cell types across imaging platforms, which approaches inter-observer concordance. Ablation studies and simulations show that CellSighter is able to generalize its training data and learn features of protein expression levels, as well as spatial features such as subcellular expression patterns. CellSighter’s design reduces overfitting, and it can be trained with only thousands or even hundreds of labeled examples. CellSighter also outputs a prediction confidence, allowing downstream experts control over the results. Altogether, CellSighter drastically reduces hands-on time for cell classification in multiplexed images, while improving accuracy and consistency across datasets.

Date: 2023
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DOI: 10.1038/s41467-023-40066-7

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