Evidence map›Paper›PMID 39031987›Full record

ArticleClinical and translational medicine2024

Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicles.

Hongke Wang, Qing Zhan, Meng Ning, Hongjie Guo, Qian Wang, Jiuliang Zhao, Pengfei Bao, Shaozhen Xing, Shanwen Chen, Shuai Zuo and 4 more

Abstract read
In one paragraph

Article in Clinical and translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Hongke WangMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.ORCID 0009-0008-1773-367X
Qing ZhanMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.
Meng NingTianjin Third Central Hospital, Tianjin, China.
Hongjie GuoDepartment of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China.
Qian WangDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), MST State Key Laboratory of Complex Severe and Rare Diseases, MOE Key Laboratory of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Jiuliang ZhaoDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), MST State Key Laboratory of Complex Severe and Rare Diseases, MOE Key Laboratory of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.ORCID 0000-0001-9308-2858
Pengfei BaoMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.
Shaozhen XingMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.
Shanwen ChenGastrointestinal Surgery, Peking University First Hospital, Beijing, China.
Shuai ZuoGastrointestinal Surgery, Peking University First Hospital, Beijing, China.
Xuefeng XiaGeneplus-Beijing Institute, Beijing, China.
Mengtao LiDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), MST State Key Laboratory of Complex Severe and Rare Diseases, MOE Key Laboratory of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Pengyuan WangGastrointestinal Surgery, Peking University First Hospital, Beijing, China.
Zhi John LuMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.

Funding

Bayer Micro-fundingBio-Computing Platform of Tsinghua University Branch of China National Center for Protein SciencesCapital's Funds for Health Improvement and Research CFH 2022-2-4075Institute of Health and Medicine, Hefei Comprehensive National Science CenterNational Key Research and Development Plan of China 2022ZD0117700National Natural Science Foundation of China 32170671National Natural Science Foundation of China 81902384National Natural Science Foundation of China 82341101National Natural Science Foundation of China 82371855Tsinghua University Guoqiang Institute 2021GQG1020Tsinghua University Initiative Scientific Research Program of Precision Medicine 2022ZLA003
6 · The paper itself

Abstract

backgroundCell-free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature.

methodsTo investigate these fragmented cell-free RNAs (cfRNAs), we developed a cost-effective cfRNA sequencing method called DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing). DETECTOR-seq utilised a meticulously tailored set of customised guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimise plasma requirements.

resultsUsing DETECTOR-seq, we conducted a comprehensive analysis of cell-free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs and viral RNAs, while EVs showed enrichment in messenger RNAs (mRNAs) and signal recognition particle RNAs (srpRNAs). Functional pathway analysis highlighted RNA splicing-related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration and antigen receptor-mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types.

conclusionsOur comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA-based studies. We envision the cost effectiveness and efficiency of DETECTOR-seq will empower transcriptome-wide investigations in the fields of cfRNAs and liquid biopsy. KEYPOINTS: DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing) enabled efficient and specific depletion of sequences derived from fragmented ribosomal and mitochondrial RNAs in plasma. Distinct human and microbial cell-free RNA (cfRNA) signatures in whole Plasma versus extracellular vesicles (EVs) were revealed. Both Plasma and EV cfRNAs were capable of distinguishing cancer patients from normal individuals, while microbial RNAs in Plasma cfRNAs enabled better classification of cancer types than EV cfRNAs.

Indexed as

Cell-Free Nucleic AcidsExtracellular VesiclesSequence Analysis, RNAHumansCell-Free Nucleic Acidscancer classificationcell‐free RNAextracellular vesiclesliquid biopsy

Identifiers

PMID39031987
PMCPMC11259601

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.