Evidence map›Paper›PMID 34687543›Full record

ArticleClinical chemistry2022

Standardized Workflow for Precise Mid- and High-Throughput Proteomics of Blood Biofluids.

Angela Mc Ardle, Aleksandra Binek, Annie Moradian, Blandine Chazarin Orgel, Alejandro Rivas, Kirstin E Washington, Conor Phebus, Danica-Mae Manalo, James Go, Vidya Venkatraman and 8 more

Abstract read
In one paragraph

Article in Clinical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
–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

36 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

18 authors.

Angela Mc ArdleSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Aleksandra BinekSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Annie MoradianPrecision Biomarker Laboratories, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Blandine Chazarin OrgelSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Alejandro RivasSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Kirstin E WashingtonSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Conor PhebusSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Danica-Mae ManaloSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
James GoSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Vidya VenkatramanPrecision Biomarker Laboratories, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Casey W Coutelin JohnsonPrecision Biomarker Laboratories, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Qin FuSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Susan ChengSmidt Heart Institute, Barbra Streisand Women's Heart Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Koen RaedscheldersSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Justyna Fert-BoberSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Stephen R PenningtonSchool of Medicine and Medical Sciences, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland.
Christopher I MurraySmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jennifer E Van EykSmidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Funding

Genomic and Proteomic Architecture of AtherosclerosisR01HL111362 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI HERRINGTON, DAVID MCLEOD · 2012 to 2021
$17.2M
Training in Advanced Heart Disease ResearchT32HL116273 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI Joshua I Goldhaber, EDUARDO MARBAN · 2013 to 2026
$5.6M
Post-translational modification and protein quantification of plasma and CSFU01NS115658 · NINDS · CEDARS-SINAI MEDICAL CENTER · PI SOBHANI, KIMIA, VAN EYK, JENNIFER E · 2019 to 2019
$1.9M
NHLBI NIH HHS R01 HL111362NHLBI NIH HHS T32 HL116273NINDS NIH HHS U01 NS115658
6 · The paper itself

Abstract

backgroundAccurate discovery assay workflows are critical for identifying authentic circulating protein biomarkers in diverse blood matrices. Maximizing the commonalities in the proteomic workflows between different biofluids simplifies the approach and increases the likelihood for reproducibility. We developed a workflow that can accommodate 3 blood-based proteomes: naive plasma, depleted plasma and dried blood.

methodsOptimal conditions for sample preparation and data independent acquisition-mass spectrometry analysis were established in plasma then automated for depleted plasma and dried blood. The mass spectrometry workflow was modified to facilitate sensitive high-throughput analysis or deeper profiling with mid-throughput analysis. Analytical performance was evaluated by the linear response of peptides and proteins to a 6- or 7-point dilution curve and the reproducibility of the relative peptide and protein intensity for 5 digestion replicates per day on 3 different days for each biofluid.

resultsUsing the high-throughput workflow, 74% (plasma), 93% (depleted), and 87% (dried blood) displayed an inter-day CV <30%. The mid-throughput workflow had 67% (plasma), 90% (depleted), and 78% (dried blood) of peptides display an inter-day CV <30%. Lower limits of detection and quantification were determined for peptides and proteins observed in each biofluid and workflow. Based on each protein and peptide's analytical performance, we could describe the observable, reliable, reproducible, and quantifiable proteomes for each biofluid and workflow.

conclusionThe standardized workflows established here allows for reproducible and quantifiable detection of proteins covering a broad dynamic range. We envisage that implementation of this standard workflow should simplify discovery approaches and facilitate the translation of candidate markers into clinical use.

Indexed as

BloodProteomicsWorkflowBiomarkersHumansPeptidesReproducibility of ResultsBiomarkersPeptidesdepletiondried bloodmass spectrometryplasmaproteomics

Identifiers

PMID34687543
PMCPMC11175165

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.