Evidence map›Paper›PMID 31477762›Full record

ArticleScientific reports2019

New perspectives for viability studies with high-content analysis Raman spectroscopy (HCA-RS).

Abdullah Saif Mondol, Natalie Töpfer, Jan Rüger, Ute Neugebauer, Jürgen Popp, Iwan W Schie

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 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

6 authors.

Abdullah Saif MondolLeibniz Institute of Photonic Technology and Leibniz Health Technologies, Albert Einstein Str. 9, 07745, Jena, Germany.
Natalie TöpferLeibniz Institute of Photonic Technology and Leibniz Health Technologies, Albert Einstein Str. 9, 07745, Jena, Germany.
Jan RügerLeibniz Institute of Photonic Technology and Leibniz Health Technologies, Albert Einstein Str. 9, 07745, Jena, Germany.
Ute NeugebauerLeibniz Institute of Photonic Technology and Leibniz Health Technologies, Albert Einstein Str. 9, 07745, Jena, Germany.
Jürgen PoppLeibniz Institute of Photonic Technology and Leibniz Health Technologies, Albert Einstein Str. 9, 07745, Jena, Germany.
Iwan W SchieLeibniz Institute of Photonic Technology and Leibniz Health Technologies, Albert Einstein Str. 9, 07745, Jena, Germany. iwan.schie@leibniz-ipht.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Raman spectroscopy has been widely used in clinical and molecular biological studies, providing high chemical specificity without the necessity of labels and with little-to-no sample preparation. However, currently performed Raman-based studies of eukaryotic cells are still very laborious and time-consuming, resulting in a low number of sampled cells and questionable statistical validations. Furthermore, the approach requires a trained specialist to perform and analyze the experiments, rendering the method less attractive for most laboratories. In this work, we present a new high-content analysis Raman spectroscopy (HCA-RS) platform that overcomes the current challenges of conventional Raman spectroscopy implementations. HCA-RS allows sampling of a large number of cells under different physiological conditions without any user interaction. The performance of the approach is successfully demonstrated by the development of a Raman-based cell viability assay, i.e., the effect of doxorubicin concentration on monocytic THP-1 cells. A statistical model, principal component analysis combined with support vector machine (PCA-SVM), was found to successfully predict the percentage of viable cells in a mixed population and is in good agreement to results obtained by a standard cell viability assay. This study demonstrates the potential of Raman spectroscopy as a standard high-throughput tool for clinical and biological applications.

Indexed as

Spectrum Analysis, RamanCell DifferentiationCell SurvivalDoxorubicinHumansPrincipal Component AnalysisSupport Vector MachineTHP-1 CellsDoxorubicin

Identifiers

PMID31477762
PMCPMC6718400

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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.