Evidence map›Paper›PMID 35796025›Full record

ArticleAnalytical chemistry2022

Seamlessly Integrated Miniaturized Filter-Aided Sample Preparation Method to Fractionation Techniques for Fast, Loss-Less, and In-Depth Proteomics Analysis of 1 μg of Cell Lysates at Low Cost.

Zhenbin Zhang, Norman J Dovichi

Abstract read
In one paragraph

Article in Analytical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Bottom-Up Proteomics: Advancements in Sample Preparation.International journal of molecular sciences · 2023
    Review
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

2 authors.

Zhenbin ZhangInstitute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China.ORCID 0000-0002-8617-6386
Norman J DovichiDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States.ORCID 0000-0002-1331-3191

Funding

Advances in bioanalysisR35GM136334 · NIGMS · UNIVERSITY OF NOTRE DAME · PI DOVICHI, NORMAN J · 2020 to 2024
$2.0M
NIGMS NIH HHS R35 GM136334
6 · The paper itself

Abstract

We report an integrated platform that enabled a seamlessly coupling miniaturized filter-aided sample preparation (MICROFASP) method to high-pH reversed phase (RP) or strong cation exchange (SCX) microreactors for low-loss sample preparation and fractionation of 1 μg of cell lysates prior to LC-ESI-MS/MS analysis. Due to the reduced size of the microreactor, only 5 μL of buffer volume is required to generate each fraction, which speeds both elution and lyophilization. The fraction was directly eluted into an autosampler insert vial for LC-MS analysis to reduce sample transfer steps and minimize sample loss as well as contamination. The flow-through sample generated during the loading step was also collected and analyzed. The integrated platform generated 48,890 unique peptides and 4723 protein groups from 1 μg of a K562 cell lysate using MICROFASP and C18 microreactor-based high-pH RP fractionation methods, which are comparable with the state-of-the-art result using in-StageTip sample preparation and nanoflow RPLC-based fractionation methods but with a significant reduction in cost and time. Both pH gradient elution and salt gradient elution approaches provide high reproducibility for the SCX microreactor-based fractionation method. This integrated platform has significant potential in deep proteomics analysis of mass-limited samples with reduced time and equipment requirements.

Indexed as

ProteomicsTandem Mass SpectrometryChromatography, LiquidPeptidesReproducibility of ResultsPeptides

Identifiers

PMID35796025
PMCPMC9897233

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.