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Updated: Feb 11, 2026

Spatial Molecular Imaging of the Glycome Using Mass Spectrometry

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Published on: November 28, 2025

Cohort-Scale Spatial Autocorrelation for Tumor Prediction in Mid-Infrared Pathology and Spatial Biomarker Discovery

Miriam F Rittel^1,2, Nikolas Ebert³, Denis Abu Sammour¹

¹CeMOS Research and Transfer Center, Mass Spectrometry and Optical Spectroscopy, Technische Hochschule Mannheim, Mannheim, Germany.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|February 9, 2026

Summary

This study introduces a new method using spatial autocorrelation on mid-infrared (MIR) imaging to accurately classify cancer tissue types. This approach enhances automated annotation and biomarker discovery in heterogeneous cancers.

Keywords:

MALDI imaging colorectal cancer liver metastasis mass spectrometry imaging mid‐infrared imaging multimodal correlative imaging spatial autocorrelation

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Sample Preparation for Metabolic Profiling using MALDI Mass Spectrometry Imaging

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Published on: December 22, 2020

Area of Science:

Biomedical Imaging
Computational Pathology
Cancer Research

Background:

Mid-infrared (MIR) imaging offers label-free tissue classification but struggles with accurate annotation of heterogeneous cancers.
Current methods neglect spatial information and rely on time-intensive manual pathology for tumor identification.

Purpose of the Study:

To develop and validate a computational method for accurate tissue type annotation using MIR imaging.
To improve the efficiency and scalability of MIR imaging analysis for cancer diagnostics.
To explore multimodal imaging for novel cancer biomarker discovery.

Main Methods:

Spatial autocorrelation analysis applied to MIR projection images.
Random forest ranking to select relevant wavenumbers.
Interdependent data processing and hyperspectral tissue database referencing for annotation.
Correlation of MIR imaging with mass spectrometry imaging (MSI).

Main Results:

The method achieved high accuracy in computational tissue type annotation, matching manual pathology in a double-blind study of colorectal cancer liver metastases.
Spatial autocorrelation analysis improved annotation accuracy by considering spatial vicinity.
Multimodal imaging identified sphingomyelin isoforms as potential lipidomic tumor markers.

Conclusions:

Spatial autocorrelation analysis on MIR imaging data enhances automated annotation of heterogeneous cancer tissue morphologies.
This approach supports efficient and scalable analysis of large patient cohorts.
The study demonstrates the potential for discovering novel spatial cancer biomarkers through integrated imaging techniques.