PT  - JOURNAL ARTICLE
AU  - Hyunwoo Kim
AU  - Seoungbin Bae
AU  - Junmo Cho
AU  - Hoyeon Nam
AU  - Junyoung Seo
AU  - Seungjae Han
AU  - Euiin Yi
AU  - Eunsu Kim
AU  - Young-Gyu Yoon
AU  - Jae-Byum Chang
TI  - IMPASTO: Multiplexed cyclic imaging without signal removal <em>via</em> self-supervised neural unmixing
AID  - 10.1101/2022.11.22.517463
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.11.22.517463
4099  - http://biorxiv.org/content/early/2022/11/24/2022.11.22.517463.short
4100  - http://biorxiv.org/content/early/2022/11/24/2022.11.22.517463.full
AB  - Spatially resolved proteomics requires a highly multiplexed imaging modality. Cyclic imaging techniques, which repeat staining, imaging, and signal erasure, have been adopted for this purpose. However, due to tissue distortion, it is challenging to obtain high fluorescent signal intensities and complete signal erasure in thick tissue with cyclic imaging techniques. Here, we propose an “erasureless” cyclic imaging method named IMPASTO. In IMPASTO, specimens are iteratively stained and imaged without signal erasure. Then, images from two consecutive rounds are unmixed to retrieve the images of single proteins through self-supervised machine learning without any prior training. Using IMPASTO, we demonstrate 30-plex imaging from brain slices in 10 rounds, and when used in combination with spectral unmixing, in five rounds. We show that IMPASTO causes negligible tissue distortion and demonstrate 3D multiplexed imaging of brain slices. Further, we show that IMPASTO can shorten the signal removal processes of existing cyclic imaging techniques.Competing Interest StatementJ.-B. C., Y.-G. Y., H. K., S. B., H. N., J. S., and J. C. are coinventors of patent applications owned by KAIST covering IMPASTO.