Evidence map›Paper›PMID 30470915›Full record

ArticleAnalytical and bioanalytical chemistry2019

Asymmetrical flow field-flow fractionation for improved characterization of human plasma lipoproteins.

Carmen R M Bria, Farsad Afshinnia, Patrick W Skelly, Thekkelnaycke M Rajendiran, Pradeep Kayampilly, Thommey P Thomas, Victor P Andreev, Subramaniam Pennathur, S Kim Ratanathanawongs Williams

Abstract read
PubMed Full text at PMC Publisher
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 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

9 authors.

Carmen R M BriaLaboratory for Advanced Separations Technologies, Department of Chemistry, Colorado School of Mines, 1500 Illinois Street, Golden, CO, 80401, USA.
Farsad AfshinniaDepartment of Internal Medicine - Nephrology, University of Michigan, Brehm Tower, 1000 Wall Street, Ann Arbor, MI, 48105, USA.
Patrick W SkellyLaboratory for Advanced Separations Technologies, Department of Chemistry, Colorado School of Mines, 1500 Illinois Street, Golden, CO, 80401, USA.
Thekkelnaycke M RajendiranDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA.
Pradeep KayampillyDepartment of Internal Medicine - Nephrology, University of Michigan, Brehm Tower, 1000 Wall Street, Ann Arbor, MI, 48105, USA.
Thommey P ThomasDepartment of Internal Medicine - Nephrology, University of Michigan, Brehm Tower, 1000 Wall Street, Ann Arbor, MI, 48105, USA.
Victor P AndreevArbor Research Collaborative for Health, 340 East Huron Street, Suite 300, Ann Arbor, MI, 48104, USA.
Subramaniam PennathurDepartment of Internal Medicine - Nephrology, University of Michigan, Brehm Tower, 1000 Wall Street, Ann Arbor, MI, 48105, USA.
S Kim Ratanathanawongs WilliamsLaboratory for Advanced Separations Technologies, Department of Chemistry, Colorado School of Mines, 1500 Illinois Street, Golden, CO, 80401, USA. krwillia@mines.edu.

Funding

Integrated Systems Biology Approach to Diabetic Microvascular ComplicationsR24DK082841 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABCOUWER, STEVEN F, BROSIUS, FRANK C · 2008 to 2020
$13.1M
University of Michigan O'Brien Kidney Translational Core CenterP30DK081943 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KRETZLER, MATTHIAS · 2008 to 2022
$12.9M
Statistics and Bioinformatics Core - WangU24DK097153 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BURANT, CHARLES F · 2012 to 2017
$10.1M
Prognostic Biomarkers in Chronic Kidney DiseaseK08DK106523 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI AFSHINNIA, FARSAD · 2015 to 2019
$789k
National Institutes of Health U24DK097153NIDDK NIH HHS K08 DK106523NIDDK NIH HHS P30 DK081943NIDDK NIH HHS R24 DK082841NIDDK NIH HHS U24 DK097153NSF CHE1508827
6 · The paper itself

Abstract

High- and low-density lipoproteins (HDL and LDL) are attractive targets for biomarker discovery. However, ultracentrifugation (UC), the current methodology of choice for isolating HDL and LDL, is tedious, requires large sample volume, results in sample loss, and does not readily provide information on particle size. In this work, human plasma HDL and LDL are separated and collected using semi-preparative asymmetrical flow field-flow fractionation (SP-AF4) and UC. The SP-AF4 and UC separation conditions, sample throughput, and liquid chromatography/mass spectrometry (LC/MS) lipidomic results are compared. Over 600 μg of total proteins is recovered in a single SP-AF4 run, and Western blot results confirm apoA1 pure and apoB100 pure fractions, consistent with HDL and LDL, respectively. The SP-AF4 separation requires ~ 60 min per sample, thus providing a marked improvement over UC which can span hours to days. Lipidome analysis of SP-AF4-prepared HDL and LDL fractions is compared to UC-prepared HDL and LDL samples. Over 270 lipids in positive MS mode and over 140 lipids in negative MS mode are identified by both sample preparation techniques with over 98% overlap between the lipidome. Additionally, lipoprotein size distributions are determined using analytical scale AF4 coupled with multiangle light scattering (MALS) and dynamic light scattering (DLS) detectors. These developments position SP-AF4 as a sample preparation method of choice for lipoprotein biomarker characterization and identification. Graphical abstract ᅟ.

Indexed as

Blotting, WesternChromatography, LiquidDynamic Light ScatteringFractionation, Field FlowHumansLipoproteins, HDLLipoproteins, LDLParticle SizeSpecimen HandlingTandem Mass SpectrometryUltracentrifugationLipoproteins, HDLLipoproteins, LDLAsymmetrical flow field-flow fractionationLipidomicsLipoproteinsMass spectrometryUltracentrifugation

Identifiers

PMID30470915
PMCPMC6451313

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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.