Evidence map›Paper›PMID 33742097›Full record

ArticleCommunications biology2021

Fluorescence-detection size-exclusion chromatography utilizing nanobody technology for expression screening of membrane proteins.

Fei Jin, Cheng Shen, Yao Wang, Mengqi Wang, Minxuan Sun, Motoyuki Hattori

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.1field-weighted citation impact, top 23% of its field
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

14 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Article
  3. Advances in Nanobody-Based Platforms for Precision Cancer Diagnosis and Therapy.Polymer science & technology (Washington, D.C.) · 2025
    Review
  4. Article
  5. Review
  6. Cryo-EM structure of the zinc-activated channel (ZAC) in the Cys-loop receptor superfamily.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. The high-throughput production of membrane proteins.Emerging topics in life sciences · 2021
    Article
  13. Review
  14. Article
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

6 authors at 1 institution in 1 country.

Fei Jin *State Key Laboratory of Genetic Engineering, Shanghai Key Laboratory of Bioactive Small Molecules, Collaborative Innovation Center of Genetics and Development, Department of Physiology and Biophysics, School of Life Sciences, Fudan University, Shanghai, China.
Cheng Shen *State Key Laboratory of Genetic Engineering, Shanghai Key Laboratory of Bioactive Small Molecules, Collaborative Innovation Center of Genetics and Development, Department of Physiology and Biophysics, School of Life Sciences, Fudan University, Shanghai, China.
Yao WangState Key Laboratory of Genetic Engineering, Shanghai Key Laboratory of Bioactive Small Molecules, Collaborative Innovation Center of Genetics and Development, Department of Physiology and Biophysics, School of Life Sciences, Fudan University, Shanghai, China.
Mengqi WangState Key Laboratory of Genetic Engineering, Shanghai Key Laboratory of Bioactive Small Molecules, Collaborative Innovation Center of Genetics and Development, Department of Physiology and Biophysics, School of Life Sciences, Fudan University, Shanghai, China.
Minxuan SunState Key Laboratory of Genetic Engineering, Shanghai Key Laboratory of Bioactive Small Molecules, Collaborative Innovation Center of Genetics and Development, Department of Physiology and Biophysics, School of Life Sciences, Fudan University, Shanghai, China.
Motoyuki HattoriState Key Laboratory of Genetic Engineering, Shanghai Key Laboratory of Bioactive Small Molecules, Collaborative Innovation Center of Genetics and Development, Department of Physiology and Biophysics, School of Life Sciences, Fudan University, Shanghai, China. hattorim@fudan.edu.cn.ORCID 0000-0002-5327-5337
Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

GFP fusion-based fluorescence-detection size-exclusion chromatography (FSEC) has been widely employed for membrane protein expression screening. However, fused GFP itself may occasionally affect the expression and/or stability of the targeted membrane protein, leading to both false-positive and false-negative results in expression screening. Furthermore, GFP fusion technology is not well suited for some membrane proteins, depending on their membrane topology. Here, we developed an FSEC assay utilizing nanobody (Nb) technology, named FSEC-Nb, in which targeted membrane proteins are fused to a small peptide tag and recombinantly expressed. The whole-cell extracts are solubilized, mixed with anti-peptide Nb fused to GFP for FSEC analysis. FSEC-Nb enables the evaluation of the expression, monodispersity and thermostability of membrane proteins without the need for purification but does not require direct GFP fusion to targeted proteins. Our results show FSEC-Nb as a powerful tool for expression screening of membrane proteins for structural and functional studies.

Indexed as

Chromatography, GelNanotechnologyAnimalsCryoelectron MicroscopyCysteine Loop Ligand-Gated Ion Channel ReceptorsFish ProteinsGreen Fluorescent ProteinsHEK293 CellsHumansMembrane ProteinsOryziasPeptidesProtein StabilityRecombinant Fusion ProteinsSARS-CoV-2Single-Domain AntibodiesCysteine Loop Ligand-Gated Ion Channel ReceptorsFish ProteinsGreen Fluorescent ProteinsMembrane ProteinsPeptidesRecombinant Fusion ProteinsSingle-Domain AntibodiesViral Proteins

Identifiers

PMID33742097
PMCPMC7979870
OpenAlexW3137366368

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.