Evidence map›Paper›PMID 28419111›Full record

ArticlePloS one2017

Time resolved and label free monitoring of extracellular metabolites by surface enhanced Raman spectroscopy.

Victoria Shalabaeva, Laura Lovato, Rosanna La Rocca, Gabriele C Messina, Michele Dipalo, Ermanno Miele, Michela Perrone, Francesco Gentile, Francesco De Angelis

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives.Metabolomics : Official journal of the Metabolomic Society · 2023
    Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Frontiers in bioengineering and biotechnology · 2020
    Review
  18. Article
  19. Scalable nanolaminated SERS multiwell cell culture assay.Microsystems & nanoengineering · 2020
    Article
  20. ContinuousFrontiers in molecular biosciences · 2019
    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

9 authors.

Victoria ShalabaevaPlasmon Nanotechnologies, Istituto Italiano di Tecnologia, Genoa, Italy.
Laura LovatoPlasmon Nanotechnologies, Istituto Italiano di Tecnologia, Genoa, Italy.ORCID http://orcid.org/0000-0002-0077-2719
Rosanna La RoccaPlasmon Nanotechnologies, Istituto Italiano di Tecnologia, Genoa, Italy.
Gabriele C MessinaPlasmon Nanotechnologies, Istituto Italiano di Tecnologia, Genoa, Italy.
Michele DipaloPlasmon Nanotechnologies, Istituto Italiano di Tecnologia, Genoa, Italy.
Ermanno MielePlasmon Nanotechnologies, Istituto Italiano di Tecnologia, Genoa, Italy.
Michela PerronePlasmon Nanotechnologies, Istituto Italiano di Tecnologia, Genoa, Italy.
Francesco GentileDepartment of Electrical Engineering and Information Technologies (DIETI), University Federico II of Naples, Naples, Italy.
Francesco De AngelisPlasmon Nanotechnologies, Istituto Italiano di Tecnologia, Genoa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolomics is an emerging field of cell biology that aims at the comprehensive identification of metabolite levels in biological fluids or cells in a specific functional state. Currently, the major tools for determining metabolite concentrations are mass spectrometry coupled with chromatographic techniques and nuclear magnetic resonance, which are expensive, time consuming and destructive for the samples. Here, we report a time resolved approach to monitor metabolite dynamics in cell cultures, based on Surface Enhanced Raman Scattering (SERS). This method is label-free, easy to use and provides the opportunity to simultaneously study a broad range of molecules, without the need to process the biological samples. As proof of concept, NIH/3T3 cells were cultured in vitro, and the extracellular medium was collected at different time points to be analyzed with our engineered SERS substrates. By identifying individual peaks of the Raman spectra, we showed the simultaneous detection of several components of the conditioned medium, such as L-tyrosine, L-tryptophan, glycine, L-phenylalanine, L-histidine and fetal bovine serum proteins, as well as their intensity changes during time. Furthermore, analyzing the whole Raman data set with the Principal Component Analysis (PCA), we demonstrated that the Raman spectra collected at different days of culture and clustered by similarity, described a well-defined trajectory in the principal component plot. This approach was then utilized to determine indirectly the functional state of the macrophage cell line Raw 264.7, stimulated with the lipopolysaccharide (LPS) for 24 hours. The collected spectra at different time points, clustered by the PCA analysis, followed a well-defined trajectory, corresponding to the functional change of cells toward the activated pro-inflammatory state induced by the LPS. This study suggests that our engineered SERS surfaces can be used as a versatile tool both for the characterization of cell culture conditions and the functional state of cells over time.

Indexed as

Amino AcidsAnimalsCattleCell LineExtracellular SpaceFibroblastsInterleukin-12 Subunit p40LipopolysaccharidesMacrophagesMetal NanoparticlesMiceMicroscopy, Electron, ScanningNIH 3T3 CellsPrincipal Component AnalysisRhodaminesSerum Albumin, BovineAmino AcidsInterleukin-12 Subunit p40Lipopolysaccharidesrhodamine 6GRhodaminesSerum Albumin, BovineSilver

Identifiers

PMID28419111
PMCPMC5395151

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.