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

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, we observed a consistent spectral band at 1026 cm⁻¹ primarily in FFPE samples, likely arising from fixation-induced chemical modifications and/or structural rearrangements. Our findings demonstrate that tissue preparation substantially alters chemical and morphological information captured by MIRSI, necessitating careful consideration of processing protocols in workflows involving chemical imaging and artificial intelligence (AI)-based spectral analysis.

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Article information

Article type
Paper
Submitted
04 Feb 2026
Accepted
07 May 2026
First published
12 May 2026
This article is Open Access
Creative Commons BY license

Analyst, 2026, Accepted Manuscript

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

T. Zheng, W. Adi, P. Campagnola and F. Yesilkoy, Analyst, 2026, Accepted Manuscript , DOI: 10.1039/D6AN00137H

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