Evidence map›Paper›PMID 30081599›Full record

ArticleMetabolites2018

Validation and Automation of a High-Throughput Multitargeted Method for Semiquantification of Endogenous Metabolites from Different Biological Matrices Using Tandem Mass Spectrometry.

Jatin Nandania, Gopal Peddinti, Alberto Pessia, Meri Kokkonen, Vidya Velagapudi

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.0field-weighted citation impact, top 24% 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

20 citing papers in PubMed, 27 citations in OpenAlex.

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  11. Metabolomics as a Prospective Tool for Soybean (Current issues in molecular biology · 2022
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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

5 authors at 1 institution in 2 countries.

Jatin NandaniaMetabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, HiLIFE, Tukholmankatu 8, Biomedicum 2U, 00290 Helsinki, Finland. jatinnandania@gmail.com.
Gopal PeddintiComputational Systems Medicine group, University of Helsinki, 00290 Helsinki, Finland. cugopal@gmail.com.
Alberto PessiaMetabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, HiLIFE, Tukholmankatu 8, Biomedicum 2U, 00290 Helsinki, Finland. alberto.pessia@helsinki.fi.ORCID 0000-0001-8607-9191
Meri KokkonenMetabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, HiLIFE, Tukholmankatu 8, Biomedicum 2U, 00290 Helsinki, Finland. meri.kokkonen@gmail.com.
Vidya VelagapudiMetabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, HiLIFE, Tukholmankatu 8, Biomedicum 2U, 00290 Helsinki, Finland. vidya.velagapudi@helsinki.fi.
University of Helsinki · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation and data processing using an inhouse developed R-package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated an excellent repeatability of retention times (CV < 4%), calibration curves (R² ≥ 0.980) in their respective wide dynamic concentration ranges (CV < 3%), and concentrations (CV < 25%) of quality control samples interspersed within 25 batches analyzed over a period of one year. The robustness was demonstrated through a high correlation between metabolite concentrations measured using our method and the NIST reference values (R² = 0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R² = 0.975) and NMR analyses (R² = 0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.

Indexed as

automationbiomarkerscross-platform comparabilityhigh-throughputLC-MSmetabolomicsmultianalyte methodsemiquantitationtargetedvalidation

Identifiers

PMID30081599
PMCPMC6161248
OpenAlexW2809353180

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.