Deciphering the RNA landscapes on mammalian cell surfaces

Cell surface RNAs, notably glycoRNAs, have been reported, yet the precise compositions of surface RNAs across different primary cell types remain unclear. Here, we introduce a comprehensive suite of methodologies for profiling, imaging, and quantifying specific surface RNAs. We present AMOUR, a method leveraging T7-based linear amplification, to profile surface RNAs while preserving plasma membrane integrity. By integrating fluorescently labeled DNA probes with live primary cells, and employing imaging along with flow cytometry analysis, we can effectively image and quantify representative surface RNAs. Utilizing these techniques, we have identified diverse non-coding RNAs present on mammalian cell surfaces, expanding beyond the known glycoRNAs. We confirm the membrane anchorage and quantify the abundance of several representative surface RNA molecules in cultured HeLa cells and human umbilical cord blood mononuclear cells (hUCB-MNCs). Our imaging and flow cytometry analyses unequivocally confirm the membrane localization of Y family RNAs, spliceosomal snRNA U5, mitochondrial rRNA MTRNR2, mitochondrial tRNA MT-TA, VTRNA1-1, and the long non-coding RNA XIST. Our study not only introduces effective approaches for investigating surface RNAs but also provides a detailed portrayal of the surface RNA landscapes of hUCB-MNCs and murine blood cells, paving the way for future research in the field of surface RNAs.

Authors

Institutions

Publication Details

Journal
Protein & Cell
Published
2025-09-16
DOI
https://doi.org/10.1093/procel/pwaf079
Citations
4
Primary Topic
RNA Research and Splicing
Type
article
Field-Weighted Citation Impact
3.22
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deciphering the RNA landscapes on mammalian cell surfaces

Yanhui Xu, Lulu Hu, Yong Peng, Weizhi He et al.
4 citations
Protein & Cell
RNA Research and Splicing
3.22
article

Deciphering the RNA landscapes on mammalian cell surfaces

Yanhui Xu, Lulu Hu, Yong Peng, Weizhi He, Xinxin Huang, Qiang Shao, Jiang Xiao, Jingwen Si, Shuang He, Xu Chu, Enzhuo Yang, Zhuo Liu, Qiuxiao Chen, Xue Han, Danhua Xu, Chuan He
article en
4 citations

Abstract

Cell surface RNAs, notably glycoRNAs, have been reported, yet the precise compositions of surface RNAs across different primary cell types remain unclear. Here, we introduce a comprehensive suite of methodologies for profiling, imaging, and quantifying specific surface RNAs. We present AMOUR, a method leveraging T7-based linear amplification, to profile surface RNAs while preserving plasma membrane integrity. By integrating fluorescently labeled DNA probes with live primary cells, and employing imaging along with flow cytometry analysis, we can effectively image and quantify representative surface RNAs. Utilizing these techniques, we have identified diverse non-coding RNAs present on mammalian cell surfaces, expanding beyond the known glycoRNAs. We confirm the membrane anchorage and quantify the abundance of several representative surface RNA molecules in cultured HeLa cells and human umbilical cord blood mononuclear cells (hUCB-MNCs). Our imaging and flow cytometry analyses unequivocally confirm the membrane localization of Y family RNAs, spliceosomal snRNA U5, mitochondrial rRNA MTRNR2, mitochondrial tRNA MT-TA, VTRNA1-1, and the long non-coding RNA XIST. Our study not only introduces effective approaches for investigating surface RNAs but also provides a detailed portrayal of the surface RNA landscapes of hUCB-MNCs and murine blood cells, paving the way for future research in the field of surface RNAs.

Protein & CellVol. 17(2)
Tongji University (CN), Howard Hughes Medical Institute (US), Chicago Institute for Psychoanalysis (US), Fudan University (CN), University of Chicago (US), Fudan University Shanghai Cancer Center (CN), Shanghai FRP Research Institute (China) (CN), Shanghai Xuhui Central Hospital (CN), Shanghai Research Institute of Building Sciences (China) (CN), Ministry of Science and Technology (LA), Chengdu University of Traditional Chinese Medicine (CN)
Openalex Percentile: Top 8%
RNA Research and Splicing
3.22
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.