Evidence map›Paper›PMID 34822378›Full record

ArticleMetabolites2021

Opti-nQL: An Optimized, Versatile and Sensitive Nano-LC Method for MS-Based Lipidomics Analysis.

Angela Cattaneo, Giuseppe Martano, Umberto Restuccia, Laura Tronci, Michele Bianchi, Angela Bachi, Vittoria Matafora

Abstract read
In one paragraph

Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
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.

Angela CattaneoCogentech SRL Benefit Corporation, 20139 Milan, Italy.
Giuseppe MartanoInstitute of Neurosciences (CNR), 20133 Milan, Italy.ORCID 0000-0002-5963-4580
Umberto RestucciaThe FIRC Institute of Molecular Oncology (IFOM), 20139 Milan, Italy.
Laura TronciThe FIRC Institute of Molecular Oncology (IFOM), 20139 Milan, Italy.ORCID 0000-0002-1217-4046
Michele BianchiCogentech SRL Benefit Corporation, 20139 Milan, Italy.
Angela BachiThe FIRC Institute of Molecular Oncology (IFOM), 20139 Milan, Italy.ORCID 0000-0003-4842-6556
Vittoria MataforaThe FIRC Institute of Molecular Oncology (IFOM), 20139 Milan, Italy.ORCID 0000-0002-4311-998X

Funding

Associazione Italiana per la Ricerca sul Cancro IG-18607
6 · The paper itself

Abstract

Lipidomics is the comprehensive analysis of lipids in a given biological system. This investigation is often limited by the low amount and high complexity of biological samples, therefore highly sensitive lipidomics methods are required. Nanoflow-LC/MS offers extremely high sensitivity; however, it is challenging as a more demanding maintenance is often needed compared to conventional microflow-LC approaches. Here, we developed a sensitive and reproducible lipidomics LC method, termed Opti-nQL, which can be applied to any biological system. Opti-nQL has been validated with cellular lipid extracts of human and mouse origin and with different lipid extraction methods. Among the resulting 4000 detected features, 700 and even more unique lipid molecular species have been identified covering 16 lipid sub-classes, while 400 lipids were uniquely structure defined by MS/MS. These results were obtained by analyzing an amount of lipids extract equivalent to 40 ng of proteins, being highly suitable for low abundant samples. MS analysis showed that theOpti-nQL method increases the number of identified lipids, which is evidenced by injecting 20 times less material than in microflow based chromatography, being more reproducible and accurate thus enhancing robustness of lipidomics analysis.

Indexed as

lipidomicslipid speciesnano-LC-MS/MSquantitative analysissensitivity

Identifiers

PMID34822378
PMCPMC8623082

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.