Uniform volumetric single-cell processing for organ-scale molecular phenotyping
Name
universal strategy supp videos.zip
Description
10 supplementary videos
Size
957.67 MB
Format
ZIP
Checksum (MD5)
739782417198fcc2eed4d65e78914bf5
Name
AcceptedManuscriptCombined.pdf
Description
Accepted version
Size
86.53 MB
Format
Adobe PDF
Checksum (MD5)
46e5948a9015a027343ad33c32837020
Author(s)
Yun, Dae Hee
Park, Young-Gyun
Cho, Jae Hun
Kamentsky, Lee
Evans, Nicholas B
DiNapoli, Nicholas
Xie, Katherine
Choi, Seo Woo
Albanese, Alexandre
Tian, Yuxuan
Date Issued
January 24, 2025
Journal
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Citation
Yun, D.H., Park, YG., Cho, J.H. et al. Uniform volumetric single-cell processing for organ-scale molecular phenotyping. Nat Biotechnol (2025).
Version
Final published version
Abstract
Extending single-cell analysis to intact tissues while maintaining organ-scale spatial information poses a major challenge due to unequal chemical processing of densely packed cells. Here we introduce Continuous Redispersion of Volumetric Equilibrium (CuRVE) in nanoporous matrices, a framework to address this challenge. CuRVE ensures uniform processing of all cells in organ-scale tissues by perpetually maintaining dynamic equilibrium of the tissue's gradually shifting chemical environment. The tissue chemical reaction environment changes at a continuous, slow rate, allowing redispersion of unevenly distributed chemicals and preserving chemical equilibrium tissue wide at any given moment. We implemented CuRVE to immunologically label whole mouse and rat brains and marmoset and human tissue blocks within 1 day. We discovered highly variable regionalized reduction of parvalbumin immunoreactive cells in wild-type adult mice, a phenotype missed by the commonly used genetic labeling. We envision that our platform will advance volumetric single-cell processing and analysis, facilitating comprehensive single-cell level investigations within their spatial context in organ-scale tissues.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Chemical Engineering
McGovern Institute for Brain Research at MIT
Broad Institute of MIT and Harvard
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41587-024-02533-4
