Evidence map›Paper›PMID 39877263›Full record

ReviewFrontiers in bioengineering and biotechnology2024

Deciphering cellular complexity: advances and future directions in single-cell protein analysis.

Qirui Zhao, Shan Li, Leonard Krall, Qianyu Li, Rongyuan Sun, Yuqi Yin, Jingyi Fu, Xu Zhang, Yonghua Wang, Mei Yang

Abstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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

10 authors.

Qirui ZhaoYunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China.
Shan LiYunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China.
Leonard KrallYunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China.
Qianyu LiCenter for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China.
Rongyuan SunCenter for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China.
Yuqi YinCenter for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China.
Jingyi FuCenter for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China.
Xu ZhangYunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China.
Yonghua WangYunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China.
Mei YangYunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell protein analysis has emerged as a powerful tool for understanding cellular heterogeneity and deciphering the complex mechanisms governing cellular function and fate. This review provides a comprehensive examination of the latest methodologies, including sophisticated cell isolation techniques (Fluorescence-Activated Cell Sorting (FACS), Magnetic-Activated Cell Sorting (MACS), Laser Capture Microdissection (LCM), manual cell picking, and microfluidics) and advanced approaches for protein profiling and protein-protein interaction analysis. The unique strengths, limitations, and opportunities of each method are discussed, along with their contributions to unraveling gene regulatory networks, cellular states, and disease mechanisms. The importance of data analysis and computational methods in extracting meaningful biological insights from the complex data generated by these technologies is also highlighted. By discussing recent progress, technological innovations, and potential future directions, this review emphasizes the critical role of single-cell protein analysis in advancing life science research and its promising applications in precision medicine, biomarker discovery, and targeted therapeutics. Deciphering cellular complexity at the single-cell level holds immense potential for transforming our understanding of biological processes and ultimately improving human health.

Indexed as

conventional approachesprotein-protein interactionproteomicssingle-cell analysissingle-cell isolation

Identifiers

PMID39877263
PMCPMC11772399

What OpenQuestion holds

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Registered trials

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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.