Emerging clinical applications in oncology for non-invasive multi- and hyperspectral imaging of cell and tissue autofluorescence

Multispectral imaging for characterizing autofluorescent tissues

Selective Plane Illumination Microscopy (SPIM) has become an emerging technology since its first application for 3D in-vivo imaging of the development of a living organism. An extensive number of works have been published, improving both the speed of acquisition and the resolution of the systems. Furthermore, multispectral imaging allows the effective separation of overlapping signals associated with different fluorophores from the spectrum over the whole field-of-view of the analyzed sample. To eliminate the need of using fluorescent dyes, this technique can also be applied to autofluorescence imaging. However, the effective separation of the overlapped spectra in autofluorescence imaging necessitates the use of mathematical tools. In this work, we explore the application of a method based on Principal Component Analysis (PCA) that enables tissue characterization upon spectral autofluorescence data without the use of fluorophores. Thus, enabling the separation of different tissue types in fixed and living samples with no need of staining techniques. Two procedures are described for acquiring spectral data, including a single excitation based method and a multi-excitation scanning approach. In both cases, we demonstrate the effective separation of various tissue types based on their unique autofluorescence spectra.

Towards label-free non-invasive autofluorescence multispectral imaging for melanoma diagnosis

This study focuses on the use of cellular autofluorescence which visualizes the cell metabolism by monitoring endogenous fluorophores including NAD(P)H and flavins. It explores the potential of multispectral imaging of native fluorophores in melanoma diagnostics using excitation wavelengths ranging from 340 nm to 510 nm and emission wavelengths above 391 nm. Cultured immortalized cells are utilized to compare the autofluorescent signatures of two melanoma cell lines to one fibroblast cell line. Feature analysis identifies the most significant and least correlated features for differentiating the cells. The investigation successfully applies this analysis to pre-processed, noise-removed images and original background-corrupted data. Furthermore, the applicability of distinguishing melanomas and healthy fibroblasts based on their autofluorescent characteristics is validated using the same evaluation technique on patient cells. Additionally, the study tentatively maps the detected features to underlying biological processes. This research demonstrates the potential of cellular autofluorescence as a promising tool for melanoma diagnostics.

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning

Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter-patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single-sample modelling to detect necrotic sites in small intestinal tissue .

Quantitative melanoma diagnosis using spectral phasor analysis of hyperspectral imaging from label-free slices

Introduction

Melanoma diagnosis traditionally relies on microscopic examination of hematoxylin and eosin (H&E) slides by dermatopathologists to search for specific architectural and cytological features. Unfortunately, no single molecular marker exists to reliably differentiate melanoma from benign lesions such as nevi. This study explored the potential of autofluorescent molecules within tissues to provide molecular fingerprints indicative of degenerated melanocytes in melanoma.

Methods

Using hyperspectral imaging (HSI) and spectral phasor analysis, we investigated autofluorescence patterns in melanoma compared to intradermal nevi. Using UV excitation and a commercial spectral confocal microscope, we acquired label-free HSI data from the whole-slice samples.

Results

Our findings revealed distinct spectral phasor distributions between melanoma and intradermal nevi, with melanoma displaying a broader phasor phase distribution, signifying a more heterogeneous autofluorescence pattern. Notably, longer wavelengths associated with larger phases correlated with regions identified as melanoma by expert dermatopathologists using H&E staining. Quantitative analysis of phase and modulation histograms within the phasor clusters of five melanomas (with Breslow thicknesses ranging from 0.5 mm to 6 mm) and five intradermal nevi consistently highlighted differences between the two groups. We further demonstrated the potential for the discrimination of several melanocytic lesions using center-of-mass comparisons of phase and modulation variables. Remarkably, modulation versus phase center of mass comparisons revealed strong statistical significance among the groups. Additionally, we identified the molecular endogenous markers responsible for tissue autofluorescence, including collagen, elastin, NADH, FAD, and melanin. In melanoma, autofluorescence is characterized by a higher phase contribution, indicating an increase in FAD and melanin in melanocyte nests. In contrast, NADH, elastin, and collagen dominate the autofluorescence of the nevus.

Discussion

This work underscores the potential of autofluorescence and HSI-phasor analysis as valuable tools for quantifying tissue molecular fingerprints, thereby supporting more effective and quantitative melanoma diagnosis.