Plate IV.59 · Publications
DISCO-seq: 3D single-cell transcriptomics of intact biological systems
Preprint in bioRxiv, 13 December 2025.
Abstract
Single-cell transcriptomics has transformed tissue analysis, yet current methods struggle to integrate whole-tissue 3D architecture. Conventional techniques restrict molecular profiling to pre-selected 2D sections, losing systemic context and introducing anatomical bias by sampling less than 0.001% of a whole organism. To overcome these challenges, we developed DISCO-seq, a tissue-clearing chemistry that enables superior RNA accessibility compared to fresh or fixed tissues. DISCO-seq integrates whole-organ or organism 3D imaging with both untargeted and targeted transcriptomics, yielding high-quality RNA from cleared tissues comparable to standard samples. We demonstrate its versatility by investigating tumor heterogeneity in a syngeneic glioblastoma mouse model, using 3D imaging to identify spatially distinct microenvironments and characterize their unique transcriptomic signatures. Moreover, DISCO-seq enabled unbiased, whole-body mapping of SARS-CoV-2 S1 protein deposition in mice, followed by transcriptomic profiling of spatially defined niches. By bridging mesoscale 3D imaging with single-cell transcriptomics, DISCO-seq establishes a paradigm for anatomically contextualized, hypothesis-free tissue interrogation.
Cite
@misc{bhatia2025disco,
title = {{DISCO-seq: 3D single-cell transcriptomics of intact biological systems}},
author = {Bhatia, Harsharan Singh and Simons, Laurent H.A. and Kuemmerle, Louis B. and McCabe, Cristin and Jansen, Sophie and He, Zhisong and Ali, Mayar and Jeridi, Denise and Todorov, Mihail Ivilinov and Hoeher, Luciano and Padmarasu, Sudharsan and Park, Ha Eun and Thevis, Justus Flinn and Bartos, Laura M. and Singh, Inderjeet and Ussar, Siegfried and Gorgulu, Kivanc and Alguel, Hana and Roberts, Kenny and Bayraktar, Omer A. and Brendel, Matthias and Theis, Fabian J. and Treutlein, Barbara and Regev, Aviv and Erturk, Ali},
howpublished = {bioRxiv},
publisher = {openRxiv},
year = {2025},
month = dec,
doi = {10.64898/2025.12.10.693352},
url = {https://doi.org/10.64898/2025.12.10.693352},
note = {Preprint}
}