Evidence map›Paper›PMID 41332745›Full record

ArticlebioRxiv : the preprint server for biology2025

Fix or Freeze? Spectral Differences Arising from Tissue Preparation in Chemical Imaging.

Tianyi Zheng, Wihan Adi, Paul J Campagnola, Filiz Yesilkoy

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Tianyi ZhengDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Wihan AdiDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.ORCID 0000-0002-4439-2021
Paul J CampagnolaDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Filiz YesilkoyDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Justine Yang Bruce · 1985 to 2026
$142.6M
A novel multimodal ECM analysis platform for tumor characterization combining morphological and spectrochemical tissue imaging approaches.R61CA281795 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI CAMPAGNOLA, PAUL J, YESILKOY, FILIZ · 2023 to 2025
$615k
Automated Tissue MicroarrayerS10OD023526 · OD · UNIVERSITY OF WISCONSIN-MADISON · PI MATKOWSKYJ, KRISTINA A. · 2018 to 2018
$184k
NCI NIH HHS P30 CA014520NCI NIH HHS R61 CA281795NIH HHS S10 OD023526
6 · The paper itself

Abstract

Spectrochemical imaging has emerged as a powerful, label-free modality for visualizing the biochemical composition of tissues based on intrinsic vibrational signatures. Specifically, mid-infrared spectrochemical imaging (MIRSI) is becoming essential for fundamental biomedical research studying disease mechanisms, identifying biomarkers, and guiding drug development. However, the sensitivity of MIRSI to sample preparation protocols and its impact on spectral data interpretation remain poorly characterized. Here, we systematically compared spectral data collected from rat kidney and liver tissues prepared using standard fresh frozen (FF) and formalin-fixed, paraffin-embedded (FFPE) tissue processing methods using quantum cascade laser (QCL)-based MIRSI. We applied frequently used spectral data processing techniques, including uniform manifold approximation and projection (UMAP), correlation matrices, and second-derivative spectral analysis, to characterize preparation-induced differences. FF samples preserved a broader range of biochemical signals, retaining the innate chemical composition of tissues, while FFPE tissues showed reduced spectral diversity and absorption signal intensity. Moreover, a unique spectral band at 1026 cm

Identifiers

PMID41332745
PMCPMC12667749

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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.