Evidence map›Paper›PMID 41143708›Full record

ArticleAnalytical chemistry2025

Posthandling Spectral Information Enhancement for Single Cell Raman Molecular Mapping Analysis.

Ankit Raj, Nungnit Wattanavichean, Makoto Kawamukai, Tatsuyuki Yamamoto, Hiro-O Hamaguchi

Abstract read
In one paragraph

Article in Analytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

5 authors.

Ankit RajDepartment of Chemistry and Institute of Molecular Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan.ORCID 0000-0002-2495-3354
Nungnit WattanavicheanDepartment of Chemistry and Institute of Molecular Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan.
Makoto KawamukaiDepartment of Life Sciences, Shimane University, 1060 Nishikawatsucho, Matsue, Shimane 690-8504, Japan.
Tatsuyuki YamamotoDepartment of Life Sciences, Shimane University, 1060 Nishikawatsucho, Matsue, Shimane 690-8504, Japan.
Hiro-O HamaguchiDepartment of Chemistry and Institute of Molecular Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biochemical analysis of living systems such as single cells benefits greatly from the label-free and low-invasive molecular mapping with Raman microspectroscopy. Sets of Raman spectra at different spatial points are analyzed to generate Raman molecular maps corresponding to specific chemical species. However, human error and subjective data analysis can be technical issues that limit interpretation and its validity. Here, we present an objective data analysis scheme for postprocessing large data sets of Raman spectra for molecular mapping of living cells. The process comprises three steps: (i) Denoising the spectral data set using low-rank approximation; (ii) obtaining an objective background from data points outside the target cell; (iii) subtracting the thus obtained background using Hypothetical Addition Multivariate Analysis with Numerical Differentiation (HAMAND) via an automatically determined coefficient. Through the present analysis, minor Raman peaks, as indiscernible as they are, can be identified and precisely mapped. We demonstrate a quantitative discussion of cellular components after extracting contributions only from a single target cell from a Raman mapping image where multiple cells or parts of other cells are present. This work opens an improved analysis workflow for accurate spectroscopic analysis of living cells with the advantage of identifying minor Raman peaks unambiguously.

Indexed as

Single-Cell AnalysisSpectrum Analysis, RamanHumans

Identifiers

PMID41143708
PMCPMC12631732

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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.