Evidence map›Paper›PMID 30397757›Full record

ArticleAnalytical and bioanalytical chemistry2019

Nanowell-mediated multidimensional separations combining nanoLC with SLIM IM-MS for rapid, high-peak-capacity proteomic analyses.

Maowei Dou, Christopher D Chouinard, Ying Zhu, Gabe Nagy, Andrey V Liyu, Yehia M Ibrahim, Richard D Smith, Ryan T Kelly

Abstract read
In one paragraph

Article in Analytical and bioanalytical chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Single-cell Proteomics: Progress and Prospects.Molecular & cellular proteomics : MCP · 2020
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

8 authors.

Maowei DouEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
Christopher D ChouinardBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
Ying ZhuEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
Gabe NagyBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
Andrey V LiyuEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
Yehia M IbrahimBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
Richard D SmithBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA. rds@pnnl.gov.
Ryan T KellyEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99352, USA. ryan.kelly@pnnl.gov.ORCID http://orcid.org/0000-0002-3339-4443

Funding

Equipment Supplement Request for a Research Resource for Ultra-sensitive High Throughput ProteomicsP41GM103493 · NIGMS · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI SMITH, RICHARD D · 2012 to 2022
$22.6M
Area A: In-Depth Proteome Mapping of the Tumor Microenvironment with Single-Cell ResolutionR33CA225248 · NCI · BRIGHAM YOUNG UNIVERSITY · PI KELLY, RYAN T · 2017 to 2017
$1.7M
High-throughput multidimensional bioseparations for next-generation proteomicsR21EB020976 · NIBIB · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI KELLY, RYAN T · 2016 to 2017
$400k
NCI NIH HHS R33 CA225248NIBIB NIH HHS R21 EB020976NIGMS NIH HHS P41 GM103493
6 · The paper itself

Abstract

Mass spectrometry (MS)-based analysis of complex biological samples is essential for biomedical research and clinical diagnostics. The separation prior to MS plays a key role in the overall analysis, with separations having larger peak capacities often leading to more identified species and improved confidence in those identifications. High-resolution ion mobility (IM) separations enabled by Structures for Lossless Ion Manipulation (SLIM) can provide extremely rapid, high-resolution separations and are well suited as a second dimension of separation following nanoscale liquid chromatography (nanoLC). However, existing sample handling approaches for offline coupling of separation modes require microliter-fraction volumes and are thus not well suited for analysis of trace biological samples. We have developed a novel nanowell-mediated fractionation system that enables nanoLC-separated samples to be efficiently preconcentrated and directly infused at nanoelectrospray flow rates for downstream analysis. When coupled with SLIM IM-MS, the platform enables rapid and high-peak-capacity multidimensional separations of small biological samples. In this study, peptides eluting from a 100 nL/min nanoLC separation were fractionated into ~ 60 nanowells on a microfluidic glass chip using an in-house-developed robotic system. The dried samples on the chip were individually reconstituted and ionized by nanoelectrospray for SLIM IM-MS analysis. Using model peptides for characterization of the nanowell platform, we found that at least 80% of the peptide components of the fractionated samples were recovered from the nanowells, providing up to ~tenfold preconcentration for SLIM IM-MS analysis. The combined LC-SLIM IM separation peak capacities exceeded 3600 with a measurement throughput that is similar to current one-dimensional (1D) LC-MS proteomic analyses. Graphical abstract A nanowell-mediated multidimensional separation platform that combines nanoLC with SLIM IM-MS enables rapid, high-peak-capacity proteomic analyses.

Indexed as

NanotechnologyChromatography, Reverse-PhasePeptidesProteomicsSpectrometry, Mass, Electrospray IonizationPeptidesIon mobilityMass spectrometryNanoelectrospraynanoPOTS

Identifiers

PMID30397757
PMCPMC6500776

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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.