Cell-type Specific Ribosomal Tagging Allows for Simultaneous Multi-Tissue Translatomic Sequencing

preprint OA: closed
📄 Open PDF Full text JSON View at publisher

Abstract

In vivo transcriptomic analysis has advanced significantly with the development of single cell technologies. However, bulk RNA sequencing also continues to provide information on critical signaling pathways and cellular responses. The isolation of mRNA by polysome immunoprecipitation can identify genes undergoing active translation. Unfortunately, the inability to profile multiple cell types from the same sample remains a major downfall of the technique and limits broader analysis of the tissue microenvironment. In this study, we demonstrated the feasibility of immunoprecipitating polysome-associated mRNA from different cell types by strategically expressing differently tagged Rpl22 subunits. Using this technique, we isolated two distinct sets of high quality transcripts from intact B16F10 melanomas, endothelial cells and B16F10 tumors cells, for further molecular analysis.
Full text 1,039 characters · extracted from oa-doi-fallback · click to expand
Abstract In vivo transcriptomic analysis has advanced significantly with the development of single cell technologies. However, bulk RNA sequencing also continues to provide information on critical signaling pathways and cellular responses. The isolation of mRNA by polysome immunoprecipitation can identify genes undergoing active translation. Unfortunately, the inability to profile multiple cell types from the same sample remains a major downfall of the technique and limits broader analysis of the tissue microenvironment. In this study, we demonstrated the feasibility of immunoprecipitating polysome-associated mRNA from different cell types by strategically expressing differently tagged Rpl22 subunits. Using this technique, we isolated two distinct sets of high quality transcripts from intact B16F10 melanomas, endothelial cells and B16F10 tumors cells, for further molecular analysis. Competing Interest Statement The authors have declared no competing interest. Footnotes The authors declare no potential conflicts of interest.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00