Researchers at the University of Texas at Austin used a method called Ribo-ITP to identify thousands of translated genomic regions, known as translons, from limited input biological samples, according to a study published in Nature Communications.
Conventional ribosome profiling and mass spectrometry previously restricted translon identification to cell lines or large organs because of high sample volume requirements. The research team applied Ribo-ITP to difficult-to-collect samples, including microdissected hippocampal tissues and single preimplantation embryos.
Translons can act as regulatory elements or encode functional micropeptides. To test whether the identified translons could undergo translation, the authors engineered a translon-dependent GFP reporter system. They detected expression of translons initiating at ATG and near-cognate start codons in mouse embryonic stem cells.
The researchers analyzed more than a thousand ribosome profiling datasets across various cell types, revealing distinct expression patterns for translons. Using a machine learning model, the team predicted that specific upstream translons in synaptically enriched mRNAs regulate the translation efficiency of annotated coding regions. The researchers described the work as a proof-of-concept study for identifying non-canonical translation events in low-input cell and tissue types that conventional methods cannot access.
Vighnesh Ghatpande, Uma Paul, Logan Persyn, Yifan Tian, MacKenzie A. Howard, and Can Cenik authored the peer-reviewed paper. The research team received funding from National Institutes of Health grants R35GM150667 and HD110096, alongside Welch Foundation grants F-2027-20230405 and F-2027-20260402. The study was submitted on July 21, 2025, accepted on July 4, 2026, and published on July 31, 2026. An OpenAI large language model suggested edits to improve clarity and grammar in the paper, while figures were generated using BioRender.com.
