RNA localization—its specific position within subcellular structures like the nucleus, mitochondria, or stress granules—is closely tied to its function, whether that’s being translated into protein, modified, stored, or degraded. Traditional RNA sequencing methods tell us what RNAs are present, but not where they are.

A team led by researchers at the University of Texas Southwestern Medical Center have developed a new method called PHOTON that provides an exciting leap forward in RNA biology. PHOTON combines high-resolution microscopy with powerful sequencing technologies to map the subcellular spatial distribution of RNA molecules with remarkable precision.

PHOTON enables spatially resolved transcriptome profiling

A Schematic diagram illustrating the PHOTON method. First, in situ RT is performed using photocleavable RT primers. Second, targeted illumination is performed within specific ROIs identified by fluorescence imaging. Exposure of the cDNA molecules to near-UV light modifies the N-POM-caged dTs and breaks the photocleavable linkers on the RT primers, releasing the fluorescent labels and revealing the phosphate groups. Third, nucleic acids are purified, and PCR handles are ligated to previously photocleaved cDNA molecules through splint oligos. Finally, successfully ligated cDNA molecules are pulled down using streptavidin beads, and a template switching step is performed with TSOs. Following PCR amplification, sequencing libraries are generated and read by next-generation sequencing. B Design of the RT primer. C Photocleavage of cDNA molecules in HeLa cells. The color-outlined cells were exposed to 25 mW 405 nm laser light, which cleaved the fluorophores from the RT primers and resulted in an 86.9% ± 4.3% (mean ± s.d.) intensity decrease (n  =  51 cells, 1 experiment). Scale bar, 5 µm. The box represents the range between the first and third quartiles of the data. The line within the box indicates the median. The whiskers extend to the furthest data points. Source data are provided as a Source Data file. D Measuring the efficiency of the PHOTON photocaging mechanism. Left: tapestation gel image of a representative experiment; Right: bar graph showing the photocaging efficiency of PHOTON (n  =  3 independent experiments). On average, 99.1 ± 0.26% (mean ± s.d.) of the photocaged cDNA molecules could not be amplified without near-UV light exposure. Source data are provided as a Source Data file. E SNR of PHOTON as a function of the fraction of cells selected under two different laser power conditions. The shaded areas represent the 95% confidence interval. Source data are provided as a Source Data file.

What makes PHOTON different is its versatility. It works not only in cultured cells but also in tissue samples, like ovarian granulosa cells, offering a window into RNA behavior in a more natural biological environment. PHOTON can identify RNA species within specific compartments such as nucleoli, mitochondria, and stress granules—tiny cellular structures that form in response to stress.

One particularly intriguing finding from the team’s research involved a chemical tag called m6A, a type of RNA modification. PHOTON revealed how m6A influences which RNAs get directed into stress granules, hinting at a deeper regulatory code that cells use to control gene expression under stress.

By offering both high spatial resolution and transcript-level detail, PHOTON gives researchers a powerful tool to study how RNA is organized and regulated inside cells. This has far-reaching implications—from understanding basic cell biology to exploring how diseases like cancer or neurodegenerative disorders disrupt RNA localization and function.

In summary, PHOTON represents a generalizable and flexible platform that brings spatial transcriptomics down to the subcellular level, opening new frontiers in our quest to understand the hidden life of RNA inside cells.

Availability – Custom code is available at https://github.com/HaiqiChenLab/PHOTON.