Evidence map›Paper›PMID 39716004›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

A Multi-enzyme Protocol Improves Total Proteome Coverage in Extracellular Matrix.

Ying Xi, Leonard B Collins, Hongxia Bai, Andreea Biehl, Camilo Mora-Navarro, Donald Freytes, Taufika Islam Williams

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Ying XiDepartment of Chemistry, North Carolina State University, Raleigh, NC, USA.
Leonard B CollinsMolecular Education, Technology and Research Innovation Center (METRIC), North Carolina State University, Raleigh, NC, USA.
Hongxia BaiDepartment of Chemistry, North Carolina State University, Raleigh, NC, USA.
Andreea BiehlJoint Department of Biomedical Engineering, North Carolina State University & University of North Carolina at Chapel Hill, Raleigh, NC, USA.
Camilo Mora-NavarroDepartment of Chemical Engineering, University of Puerto Rico-Mayaguez, Mayaguez, PR, USA.
Donald FreytesJoint Department of Biomedical Engineering, North Carolina State University & University of North Carolina at Chapel Hill, Raleigh, NC, USA.
Taufika Islam WilliamsDepartment of Chemistry, North Carolina State University, Raleigh, NC, USA. Taufika_Williams@ncsu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracellular matrix (ECM) from decellularized mammalian tissues has been used in many therapeutic applications. The tissue-specific composition of the ECM is critically associated with therapeutic performance. However, ECM translation needs to be improved because of the complex composition and limited understanding of ECM repairing mechanisms due partly to incomplete proteomic interrogation of ECM samples. In this chapter, we describe a multi-enzyme, bottom-up proteomics workflow employing trypsin, Lys-C, collagenase, and elastase to enhance the digestion of ECM and increase total protein coverage. The outcomes from the reported approach, in a standardized manner, enable users to pinpoint changes in the ECM composition, thereby facilitating the establishment of mechanistic correlations between ECM composition and its effects.

Indexed as

Extracellular MatrixProteomeProteomicsAnimalsCollagenasesExtracellular Matrix ProteinsHumansProteolysisTrypsinCollagenasesExtracellular Matrix ProteinsProteomeTrypsinBottom-up proteomicsCollagenaseDecellularized extracellular matrix (ECM)ElastaseHydrogelMass spectrometryTrypsin/Lys-C

Identifiers

PMID39716004

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.