Evidence map›Paper›PMID 24761338›Full record

ArticleAdvances in wound care2013

Bottom-Up Mass Spectrometry-Based Proteomics as an Investigative Analytical Tool for Discovery and Quantification of Proteins in Biological Samples.

Ravi Amunugama, Richard Jones, Michael Ford, David Allen

Abstract read
In one paragraph

Article in Advances in wound care, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

4 authors.

Ravi AmunugamaMS Bioworks, LLC , Ann Arbor, Michigan.
Richard JonesMS Bioworks, LLC , Ann Arbor, Michigan.
Michael FordMS Bioworks, LLC , Ann Arbor, Michigan.
David AllenMS Bioworks, LLC , Ann Arbor, Michigan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe objective of this overview is to introduce bottom-up mass spectrometry (MS)-based proteomics approaches and strategies, widely used in other biomedical research fields, to the wound-healing research community. APPROACHES: TWO MAJOR PROTEOMICS WORKFLOWS ARE DISCUSSED: gel-based and gel-free chromatographic separation to reduce the complexity of the sample at protein and peptide level, respectively, prior to nano-liquid chromatography-tandem mass spectrometry analysis. Other strategies to discover less abundant proteins present in the sample, are also briefly discussed along with label-free and label-incorporated methods for protein quantification. Overall, the experimental workflows are designed and continually improved to increase the number of proteins identifiable and quantifiable. DISCUSSION: Recent advances and improvements in all areas of proteomics workflow from sample preparation, to acquisition of massive amounts of data, to bioinformatics analysis have made this technology an indispensable tool for in-depth large-scale characterization of complex proteomes. This technology has been successfully applied in studies focusing on biomarker discovery, differential protein expression, protein-protein interactions, and post-translational modifications in complex biological samples such as cerebrospinal fluid, serum and plasma, and urine from patients. The publications from these studies have reported greater number of identified proteins, novel biomarker candidates, and post-translational modifications previously unknown.

conclusionsThe qualitative and quantitative protein analysis of the protein population of wound tissues or fluids at different stages is important in wound healing research. Given the complexities and analytical challenges of these samples, MS-based proteomic workflows further improved with recent advances offer a powerful and attractive technology for this purpose.

Identifiers

PMID24761338
PMCPMC3842888

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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.