Subcellular level spatial transcriptomics with PHOTON

Shreya Rajachandran, Qianlan Xu, Qiqi Cao, Xin Zhang, Fei Chen, Sarah M. Mangiameli and Haiqi Chen ()
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Shreya Rajachandran: University of Texas Southwestern Medical Center
Qianlan Xu: University of Texas Southwestern Medical Center
Qiqi Cao: University of Texas Southwestern Medical Center
Xin Zhang: University of Texas Southwestern Medical Center
Fei Chen: The Broad Institute of MIT and Harvard
Sarah M. Mangiameli: The Broad Institute of MIT and Harvard
Haiqi Chen: University of Texas Southwestern Medical Center

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract The subcellular localization of RNA is closely linked to its function. Many RNA species are partitioned into organelles and other subcellular compartments for storage, processing, translation, or degradation. Thus, capturing the subcellular spatial distribution of RNA would directly contribute to the understanding of RNA functions and regulation. Here, we present PHOTON, a method which combines high resolution imaging with high throughput sequencing to achieve spatial transcriptome profiling at subcellular resolution. We demonstrate PHOTON as a versatile tool to accurately capture the transcriptome of target cell types in situ at the tissue level such as granulosa cells in the ovary, as well as RNA content within subcellular compartments such as the nucleoli, the mitochondria, and the stress granules. Using PHOTON, we also reveal the functional role of m6A modifications on mRNA partitioning into stress granules. These results collectively demonstrate that PHOTON is a flexible and generalizable platform for understanding subcellular molecular dynamics through the transcriptomic lens.

Date: 2025
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DOI: 10.1038/s41467-025-59801-3

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