Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy
Date
2021
Authors
Cheng, S.
Fu, S.
Kim, Y.M.
Song, W.
Li, Y.
Xue, Y.
Yi, J.
Tian, Lei
Version
Published version
OA Version
Citation
Cheng, S. Fu, Y.M. Kim, W. Song, Y. Li, Y. Xue, J. Yi, L. Tian. 2021. "Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy." Science Advances, Volume 7, pp. eabe0431 - eabe0431.
https://doi.org/10.1126/sciadv.abe0431.
Abstract
Traditional imaging cytometry uses fluorescence markers to identify specific structures but is limited in throughput
by the labeling process. We develop a label-free technique that alleviates the physical staining and provides
multiplexed readouts via a deep learning–augmented digital labeling method. We leverage the rich structural
information and superior sensitivity in reflectance microscopy and show that digital labeling predicts accurate
subcellular features after training on immunofluorescence images. We demonstrate up to three times improvement
in the prediction accuracy over the state of the art. Beyond fluorescence prediction, we demonstrate that
single cell–level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images,
including Golgi twins, Golgi haze during mitosis, and DNA synthesis. We further show that the multiplexed readouts
enable accurate multiparametric single-cell profiling across a large cell population. Our method can markedly
improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content
screening.
Description
License
Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).