Evidence map›Paper›PMID 32293536›Full record

ArticleSkeletal muscle2020

High-throughput proteomics fiber typing (ProFiT) for comprehensive characterization of single skeletal muscle fibers.

Sebastian Kallabis, Lena Abraham, Stefan Müller, Verena Dzialas, Clara Türk, Janica Lea Wiederstein, Theresa Bock, Hendrik Nolte, Leonardo Nogara, Bert Blaauw and 2 more

Open access · goldAbstract read
In one paragraph

Article in Skeletal muscle, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

22 citing papers in PubMed, 37 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Muscle Proteome Dynamics.Advances in experimental medicine and biology · 2025
    Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics.Journal of the American Society for Mass Spectrometry · 2023
    Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
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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

12 authors at 5 institutions in 2 countries.

Sebastian KallabisCECAD Research Center, Institute for Genetics, University of Cologne, 50931, Cologne, Germany.
Lena AbrahamCECAD Research Center, Institute for Genetics, University of Cologne, 50931, Cologne, Germany.
Stefan MüllerCECAD Research Center, Institute for Genetics, University of Cologne, 50931, Cologne, Germany.
Verena DzialasCECAD Research Center, Institute for Genetics, University of Cologne, 50931, Cologne, Germany.
Clara TürkCECAD Research Center, Institute for Genetics, University of Cologne, 50931, Cologne, Germany.
Janica Lea WiedersteinCECAD Research Center, Institute for Genetics, University of Cologne, 50931, Cologne, Germany.
Theresa BockCECAD Research Center, Institute for Genetics, University of Cologne, 50931, Cologne, Germany.
Hendrik NolteMax Planck Institute for the Biology of Aging, 50931, Cologne, Germany.
Leonardo NogaraVenetian Institute of Molecular Medicine (VIMM), Via Orus 2, 35129, Padova, Italy.
Bert BlaauwVenetian Institute of Molecular Medicine (VIMM), Via Orus 2, 35129, Padova, Italy.
Thomas BraunMax Planck Institute for Heart and Lung Research, 61231, Bad Nauheim, Germany.
Marcus KrügerCECAD Research Center, Institute for Genetics, University of Cologne, 50931, Cologne, Germany. marcus.krueger@uni-koeln.de.
University of Cologne · DECologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases · DEVeneto Institute of Molecular Medicine · ITMax Planck Institute for Biology of Ageing · DEMax Planck Institute for Heart and Lung Research · DE

Funding

Deutsche Forschungsgemeinschaft EXC 2030 - 390661388
6 · The paper itself

Abstract

backgroundSkeletal muscles are composed of a heterogeneous collection of fiber types with different physiological adaption in response to a stimulus and disease-related conditions. Each fiber has a specific molecular expression of myosin heavy chain molecules (MyHC). So far, MyHCs are currently the best marker proteins for characterization of individual fiber types, and several proteome profiling studies have helped to dissect the molecular signature of whole muscles and individual fibers.

methodsHerein, we describe a mass spectrometric workflow to measure skeletal muscle fiber type-specific proteomes. To bypass the limited quantities of protein in single fibers, we developed a Proteomics high-throughput fiber typing (ProFiT) approach enabling profiling of MyHC in single fibers. Aliquots of protein extracts from separated muscle fibers were subjected to capillary LC-MS gradients to profile MyHC isoforms in a 96-well format. Muscle fibers with the same MyHC protein expression were pooled and subjected to proteomic, pulsed-SILAC, and phosphoproteomic analysis.

resultsOur fiber type-specific quantitative proteome analysis confirmed the distribution of fiber types in the soleus muscle, substantiates metabolic adaptions in oxidative and glycolytic fibers, and highlighted significant differences between the proteomes of type IIb fibers from different muscle groups, including a differential expression of desmin and actinin-3. A detailed map of the Lys-6 incorporation rates in muscle fibers showed an increased turnover of slow fibers compared to fast fibers. In addition, labeling of mitochondrial respiratory chain complexes revealed a broad range of Lys-6 incorporation rates, depending on the localization of the subunits within distinct complexes.

conclusionOverall, the ProFiT approach provides a versatile tool to rapidly characterize muscle fibers and obtain fiber-specific proteomes for different muscle groups.

Indexed as

ActininAnimalsCells, CulturedDesminGlycolysisMaleMiceMice, Inbred C57BLMuscle Fibers, SkeletalMyosin Heavy ChainsProteomeProteomicsSingle-Cell AnalysisActininDesminMyosin Heavy ChainsProteomeMuscle fiber proteomicsMyHC profilingPhosphoproteomicsProtein turnover

Identifiers

PMID32293536
PMCPMC7087369
OpenAlexW3013529766

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.