Evidence map›Paper›PMID 36865126›Full record

ArticlebioRxiv : the preprint server for biology2023

Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics.

Amanda Momenzadeh, Yuming Jiang, Simion Kreimer, Laura E Teigen, Carlos S Zepeda, Ali Haghani, Mitra Mastali, Yang Song, Alexandre Hutton, Sarah J Parker and 3 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Amanda MomenzadehDepartment of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California, USA.
Yuming JiangDepartment of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California, USA.
Simion KreimerAdvanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA.
Laura E TeigenDepartment of Physical Therapy, Marquette University, Milwaukee, Wisconsin, USA.
Carlos S ZepedaDepartment of Physical Therapy, Marquette University, Milwaukee, Wisconsin, USA.
Ali HaghaniAdvanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA.
Mitra MastaliAdvanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA.
Yang SongAdvanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA.
Alexandre HuttonDepartment of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California, USA.
Sarah J ParkerAdvanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA.
Jennifer E Van EykAdvanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA.
Christopher W SundbergDepartment of Physical Therapy, Marquette University, Milwaukee, Wisconsin, USA.
Jesse G MeyerDepartment of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California, USA.

Funding

Genomic and Proteomic Architecture of AtherosclerosisR01HL111362 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI HERRINGTON, DAVID MCLEOD · 2012 to 2021
$17.2M
Fatigability of Limb Muscle in Older Adults: Protective Effects of ExerciseR01AG048262 · NIA · MARQUETTE UNIVERSITY · PI FITTS, ROBERT H, HUNTER, SANDRA K · 2015 to 2024
$6.1M
Democratizing Multi-Omics to Expedite Discovery of Hidden Metabolic PathwaysR35GM142502 · NIGMS · MEDICAL COLLEGE OF WISCONSIN · PI MEYER, JESSE · 2021 to 2025
$2.2M
Drug Discovery for Alzheimer’s Disease Enabled by Multi-Omics and Artificial IntelligenceR21AG074234 · NIA · MEDICAL COLLEGE OF WISCONSIN · PI MEYER, JESSE · 2021 to 2022
$443k
NHLBI NIH HHS R01 HL111362NIA NIH HHS R01 AG048262NIA NIH HHS R21 AG074234NIGMS NIH HHS R35 GM142502
6 · The paper itself

Abstract

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious requiring two hours of mass spectrometry time per single muscle fiber; 50 fibers would take approximately four days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 minutes total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 hours. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Sixty-five proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, muscle structure and regulation. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

Identifiers

PMID36865126
PMCPMC9980124

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.