Evidence map›Paper›PMID 28749931›Full record

ArticleNature protocols2017

Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry.

Hasmik Keshishian, Michael W Burgess, Harrison Specht, Luke Wallace, Karl R Clauser, Michael A Gillette, Steven A Carr

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature protocols, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
91citing papers in PubMed, 1 pooled it
8.2field-weighted citation impact, top 1% of its field
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

91 citing papers in PubMed, 1 synthesis or guideline pooled it, 192 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Differences in blood levels of neuroligin-derived peptides in a cohort for early detection of Alzheimer's disease.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  12. Article
  13. Observational
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

31 more citing papers are in PubMed but not listed here.

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 at 1 institution in 1 country.

Hasmik KeshishianBroad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Michael W BurgessBroad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Harrison SpechtBroad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Luke WallaceBroad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Karl R ClauserBroad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Michael A GilletteBroad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Steven A CarrBroad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Broad Institute · US

Funding

Proteo-genomic Discovery, Prioritization and Verification of Cancer BiomarkersU24CA160034 · NCI · BROAD INSTITUTE, INC. · PI CARR, STEVEN A, PAULOVICH, AMANDA G · 2011 to 2016
$16.2M
Proteomic, Genomic, and Longitudinal Pathways to Ovarian Cancer Biomarker DiscoveryU01CA152990 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI BIRRER, MICHAEL, SKATES, STEVEN J · 2010 to 2021
$8.9M
Discovery & Application of Proteomics-Based Biomarkers of Early Myocardial InjuryR01HL096738 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI CARR, STEVEN A, GERSZTEN, ROBERT E · 2009 to 2012
$2.9M
NCI NIH HHS U01 CA152990NCI NIH HHS U24 CA160034NHLBI NIH HHS HHSN268201000033CNHLBI NIH HHS R01 HL096738
6 · The paper itself

Abstract

Proteomic characterization of blood plasma is of central importance to clinical proteomics and particularly to biomarker discovery studies. The vast dynamic range and high complexity of the plasma proteome have, however, proven to be serious challenges and have often led to unacceptable tradeoffs between depth of coverage and sample throughput. We present an optimized sample-processing pipeline for analysis of the human plasma proteome that provides greatly increased depth of detection, improved quantitative precision and much higher sample analysis throughput as compared with prior methods. The process includes abundant protein depletion, isobaric labeling at the peptide level for multiplexed relative quantification and ultra-high-performance liquid chromatography coupled to accurate-mass, high-resolution tandem mass spectrometry analysis of peptides fractionated off-line by basic pH reversed-phase (bRP) chromatography. The overall reproducibility of the process, including immunoaffinity depletion, is high, with a process replicate coefficient of variation (CV) of <12%. Using isobaric tags for relative and absolute quantitation (iTRAQ) 4-plex, >4,500 proteins are detected and quantified per patient sample on average, with two or more peptides per protein and starting from as little as 200 μl of plasma. The approach can be multiplexed up to 10-plex using tandem mass tags (TMT) reagents, further increasing throughput, albeit with some decrease in the number of proteins quantified. In addition, we provide a rapid protocol for analysis of nonfractionated depleted plasma samples analyzed in 10-plex. This provides ∼600 quantified proteins for each of the ten samples in ∼5 h of instrument time.

Indexed as

BiomarkersBlood ProteinsHumansMass SpectrometryPlasmaProteomicsWorkflowBiomarkersBlood Proteins

Identifiers

PMID28749931
PMCPMC6057147
OpenAlexW2736913410

What OpenQuestion holds

Textmetadata
LicenceTDM
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.