The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker proteins is currently the main clinical focus. Today, spatial omics imaging using MSI or IRI is an emerging diagnostic technology for the identification and classification of various cancer types. However, to conserve tissue-specific metabolomic states, fast, reliable, and precise methods for the preparation of fresh-frozen (FF) tissue sections are crucial. Such methods are often incompatible with clinical practice, since spatial metabolomics and the routine histopathology of needle biopsies currently require two biopsies for FF and FFPE sampling, respectively. Therefore, we developed a device and corresponding laboratory and computational workflows for the multimodal spatial omics analysis of fresh-frozen, longitudinally sectioned needle biopsies to accompany standard FFPE histopathology of the same biopsy core. As a proof-of-concept, we analyzed surgical human liver cancer specimens using IRI and MSI with precise co-registration and, following FFPE processing, by sequential clinical pathology analysis of the same biopsy core. This workflow allowed for a spatial comparison between different spectral profiles and alterations in tissue histology, as well as a direct comparison for histological diagnosis without the need for an extra biopsy.

The fast and reliable detection, segmentation and visualization of latent fingerprints are the main tasks in forensics. Currently, conventional fingerprints are searched for, recorded and subsequently analyzed via traditional destructive physical and chemical methods. For firmly defined crime objects and undefined crime scenes, the forensic process is very time-consuming and can take several hours for a single fingerprint. In this context, a laser-based measurement technique that records complete latent fingerprints under fifteen seconds in a non-destructive manner was developed that digitizes the fingerprint for postprocessing steps. The optical system is based on confocal measurements in the mid-infrared wavelength range (2 μm–4 μm) to analyze specific chemical substances at crime scenes. The resulting chemical segmentation allows molecule-dependent analysis of latent and visually invisible fingerprints, providing clear conclusions about the perpetrator or the course of the crime. In this study, the application of the developed measurement system (MIR scanner) to capture fingerprints in a molecule-dependent manner within few seconds is demonstrated, compared with reference methods such as FTIR (Fourier transform infrared spectroscopy) imaging, and extended to real crime objects.

The impact of microbial biofilm growth poses a threat to both human health and the performance of industrial systems, manifesting as a global crisis with noteworthy economic implications for modern society. Exploring new methods and alternative approaches for the detection of biofilm signatures are imperative for developing optimized and cost-effective strategies that can help to identify early-stage biofilm formation. Clinical diagnostic technologies are constantly looking for more affordable, practical and faster methods of prevention and detection of chronic infections in periprosthetic joint infections (PJIs), which are often characterized by biofilm formation on implant surfaces. Staphylococcus epidermidis (SE) is especially known for its strong biofilm production and is considered a leading cause of biomaterial-associated infections, including PJIs. Implant-associated infections are severe and difficult to treat, therefore it is crucial to continue identifying bacterial biomarkers that contribute to its structural stability and attachment to implant surfaces. This study presents a pioneering approach for fast spectral detection of biofilm formation with a novel mid-infrared (MIR) scanning system. To highlight the advantages of our MIR system, we performed a comparative analysis with measurements from a commercially available Fourier-transform infrared (FTIR) scanner. We have assessed SE biofilms grown for 3 days comparing the processing times between a commercially available infrared (IR) scanning system (∼8 h/cm²), and our innovative scanning approach with rapid self-built MIR detection, achieving a reduction in scanning time to seconds. K-means clustering analysis identified pronounced differences in distribution of clusters, representing a significant variation between biofilm producing (RP62A) and non-biofilm producing (ATCC 12228) bacterial strains. The distribution serves as a critical tool for identifying biofilm phenotypes, particularly where poly-N-acetylglucosamine (PNAG), a key constituent of extracellular polymeric substances (EPS) in S. epidermidis, represents the dominant mass fraction in the samples analyzed by our infrared (IR) scanning systems. In addition to faster processing times, our novel MIR system demonstrated significantly higher sensitivity compared to FTIR, enabling clear differentiation between the chemical signatures of biofilm and planktonic strains. The corresponding novel approach integrates advanced data analytics with a newly designed rapid MIR prototype, enabling optimized and swift detection of biofilm signatures. These signatures, now recognized as critical targets in diagnosing complex infections, provide an alternative to traditional microbial detection methods in clinical diagnostics.

Accurate and detailed tissue characterization is a central goal in medical diagnostics, often requiring the combination of multiple imaging modalities. This study presents a multimodal imaging system that integrates mid-infrared (MIR) scanning with fluorescence imaging to enhance the chemical specificity and spatial resolution in biological samples. A motorized mirror allows rapid switching between MIR and fluorescence modes, enabling efficient, co-registered data acquisition. The MIR modality captures label-free chemical maps based on molecular vibrations, while the fluorescence channel records endogenous autofluorescence for additional biochemical contrast. Applied to mouse brain tissue, the system enabled the clear differentiation of gray matter and white matter, supported by the clustering analysis of spectral features. The addition of autofluorescence imaging further improved anatomical segmentation and revealed fine structural details. In mouse skin, the approach allowed the precise mapping of the layered tissue architecture. These results demonstrate that combining MIR scanning and fluorescence imaging provides complementary, label-free insights into tissue morphology and chemistry. The findings support the utility of this approach as a powerful tool for biomedical research and diagnostic applications, offering a more comprehensive understanding of tissue composition without relying on staining or external markers.

Automated and comprehensive processing of hyperspectral image data is increasingly important in academic research and medical technology. This study presents an automated processing pipeline that integrates hyperspectral image acquisition, analysis, multimodal fusion, and centralized data management to improve the interpretability of spectral information for biological tissue analysis. The pipeline supports modular hyperspectral data processing, fusion of complementary wavelength ranges, and scalable data storage, and was implemented in Python 3.13.3. The pipeline was evaluated using hyperspectral imaging data acquired from a coronal mouse brain section. Clustering-based analysis and spectral correlation metrics were applied to assess the impact of multimodal data fusion on spectral representation. Clustering of individual modalities yielded silhouette coefficients of 0.5879 for near-infrared data, 0.6020 for mid-infrared data, and 0.6715 for RGB data. Multimodal fusion reduced the silhouette coefficient to 0.5420 and enabled the identification of anatomical structures that were not distinguishable in any single modality. High spectral correlation coefficients exceeding 0.98 confirmed that spectral fidelity was preserved during fusion. These results demonstrate that automated multimodal hyperspectral data fusion can enhance the interpretability of biological tissue despite reduced clustering compactness. The proposed pipeline provides a structured framework for preclinical hyperspectral imaging workflows and supports exploratory biological analysis in medical imaging contexts.

Three-dimensional (3D) cellular models, such as spheroids, serve as pivotal systems for understanding complex biological phenomena in histology, oncology, and tissue engineering. In response to the growing need for advanced imaging capabilities, we present a novel multi-modal Raman light sheet microscope designed to capture elastic (Rayleigh) and inelastic (Raman) scattering, along with fluorescence signals, in a single platform. By leveraging a shorter excitation wavelength (532 nm) to boost Raman scattering efficiency and incorporating robust fluorescence suppression, the system achieves label-free, high-resolution tomographic imaging without the drawbacks commonly associated with near-infrared modalities. An accompanying Deep Image Prior (DIP) seamlessly integrates with the microscope to provide unsupervised denoising and resolution enhancement, preserving critical molecular details and minimizing extraneous artifacts. Altogether, this synergy of optical and computational strategies underscores the potential for in-depth, 3D imaging of biomolecular and structural features in complex specimens and sets the stage for future advancements in biomedical research, diagnostics, and therapeutics.