Quantitative IR microscopy and spectromics open the way to 3D digital pathology

Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features

In order to improve the clinical application of hyperspectral technology in the pathological diagnosis of tumor tissue, a joint diagnostic method based on spectral-spatial transfer features was established by simulating the actual clinical diagnosis process and combining micro-hyperspectral imaging with large-scale pathological data. In view of the limited sample volume of medical hyperspectral data, a multi-data transfer model pre-trained on conventional pathology datasets was applied to the classification task of micro-hyperspectral images, to explore the differences in spectral-spatial transfer features in the wavelength of 410-900 nm between tumor tissues and normal tissues. The experimental results show that the spectral-spatial transfer convolutional neural network (SST-CNN) achieved a classification accuracy of 95.46% for the gastric cancer dataset and 95.89% for the thyroid cancer dataset, thus outperforming models trained on single conventional digital pathology and single hyperspectral data. The joint diagnostic method established based on SST-CNN can complete the interpretation of a section of data in 3 min, thus providing a new technical solution for the rapid diagnosis of pathology. This study also explored problems involving the correlation between tumor tissues and typical spectral-spatial features, as well as the efficient transformation of conventional pathological and transfer spectral-spatial features, which solidified the theoretical research on hyperspectral pathological diagnosis.

Spectroscopic Microtomography in the Short-Wave Infrared Wavelength Range

Spectroscopic microtomography provides the ability to perform 4D (3D structural and 1D chemical) imaging of a thick microscopic specimen. Here, we demonstrate spectroscopic microtomography in the short-wave infrared (SWIR) wavelength using digital holographic tomography, which captures both the absorption coefficient and refractive index. A broadband laser in tandem with a tunable optical filter allows us to scan the wavelength from 1100 to 1650 nm. Using the developed system, we measure human hair and sea urchin embryo samples. The resolution estimated with gold nanoparticles is 1.51 μm (transverse) and 1.57 μm (axial) for the field of view of 307 × 246 μm2. The developed technique will enable accurate and efficient analyses of microscopic specimens that have a distinctive absorption or refractive index contrast in the SWIR range.

Extended Multiplicative Signal Correction for Infrared Microspectroscopy of Heterogeneous Samples with Cylindrical Domains

Optical scattering corrections are invoked to computationally distinguish between scattering and absorption contributions to recorded data in infrared (IR) microscopy, with a goal to obtain an absorption spectrum that is relatively free of the effects of sample morphology. Here, we present a modification of the extended multiplicative signal correction (EMSC) approach that allows for spectral recovery from fibers and cylindrical domains in heterogeneous samples. The developed theoretical approach is based on exact Mie theory for infinite cylinders. Although rigorous Mie theory implies utilization of comprehensive and time-consuming calculations, we propose to change the workflow of the original EMSC algorithm to minimize extensive calculations for each recorded spectrum at each iteration step. This makes the modified EMSC approach practical for routine use. First, we tested our approach using synthetic data derived from a rigorous model of scattering from cylinders in an IR microscope. Second, we applied the approach to Fourier transform IR (FT-IR) microspectroscopy data recorded from filamentous fungal and cellulose samples with pronounced fiber-like shapes. While the corrected spectra show greatly reduced baseline offsets and consistency, strongly absorbing regions of the spectrum require further refinement. The modified EMSC algorithm broadly mitigates the effects of scattering, offering a practical approach to more consistent and accurate spectra from cylindrical objects or heterogeneous samples with cylindrical domains.

Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra

The last two decades have seen tremendous progress in the application of two-dimensional correlation spectroscopy (2D-COS) as a versatile analysis method for data series obtained using a large variety of different spectroscopic modalities, including infrared (IR) and Raman spectroscopy. The analysis technique is applicable to a series of spectra recorded under the influence of an external sample perturbation. Two-dimensional COS analysis is not only helpful to decipher correlations, which may exist between distinct spectral features, but can also be utilized to obtain the sequence of individual spectral changes. The focus of this review article is on the application of 2D-COS for analyzing spatially resolved data with special emphasis on hyperspectral imaging (HSI) study. In this review, we briefly introduce the fundamentals of the generalized 2D-COS analysis approach, discuss specific points of 2D-COS application to spatially resolved spectra and demonstrate essential aspects of data pre-processing for 2D-COS analysis of spatially resolved spectra. Based on illustrative examples, we show that 2D-COS is useful for spectral band assignment in HSI applications and demonstrate its utility for detecting subtle correlations between spectra features, or between features from different imaging modalities in the case of heterospectral (multimodal) HSI. Furthermore, a short overview on existing 2D-COS software tools is provided. It is hoped that this article represents not only a useful guideline for 2D-COS analyses of spatially resolved hyperspectral data but supports also further dissemination of the 2D-COS analysis method as a whole.

3D Digital Pathology for a Chemical-Functional Analysis of Glomeruli in Health and Pathology

Determining the filtration function and biochemical status of kidney at the single glomerulus level remains hardly accessible, even from biopsies. Here, we provide evidence that IR spectro-microscopy is a suitable method to account for the filtration capacity of individual glomeruli along with related physio-pathological condition. A ∼4 μm voxel resolution 3D IR image reconstruction is built from consecutive tissue sections, thus, providing a 3D IR spectrum matrix of an individual glomerulus. The filtration capacity of glomeruli was quantitatively determined after BaSO₄ perfusion, and additional chemical data could be used to determined oxidative stress effects and fibrosis, thus, combining functional and biochemical information from the same 3D IR spectrum matrix. This analytical approach was applied on mice with unilateral ureteral obstruction (UUO) inducing chronic kidney disease. Compared to the healthy condition, UUO induced a significant drop in glomeruli filtration capacity (-17 ± 8% at day 4 and -48 ± 14% at day 14) and volume (36 ± 10% at day 4 and 67 ± 13% at day 14), along a significant increase of oxidative stress (+61 ± 19% at day 4 and +84 ± 17% at day 14) and a change in the lipid-to-protein ratio (-8.2 ± 3.6% at day 4 and -18.1 ± 5.9% at day 14). Therefore, IR spectro-microscopy might be developed as a new 3D pathology resource for analyzing functional and biochemical parameters of glomeruli.

3D chemical imaging of the brain using quantitative IR spectro-microscopy

Three-dimensional (3D) histology is the next frontier for modern anatomo-pathology. Characterizing abnormal parameters in a tissue is essential to understand the rationale of pathology development. However, there is no analytical technique, in vivo or histological, that is able to discover such abnormal features and provide a 3D distribution at microscopic resolution. Here, we introduce a unique high-throughput infrared (IR) microscopy method that combines automated image correction and subsequent spectral data analysis for 3D-IR image reconstruction. We performed spectral analysis of a complete organ for a small animal model, a mouse brain with an implanted glioma tumor. The 3D-IR image is reconstructed from 370 consecutive tissue sections and corrected using the X-ray tomogram of the organ for an accurate quantitative analysis of the chemical content. A 3D matrix of 89 × 106 IR spectra is generated, allowing us to separate the tumor mass from healthy brain tissues based on various anatomical, chemical, and metabolic parameters. We demonstrate that quantitative metabolic parameters can be extracted from the IR spectra for the characterization of the brain vs. tumor metabolism (assessing the Warburg effect in tumors). Our method can be further exploited by searching for the whole spectral profile, discriminating tumor vs. healthy tissue in a non-supervised manner, which we call 'spectromics'.

Dielectric Sphere Clusters as a Model to Understand Infrared Spectroscopic Imaging Data Recorded from Complex Samples

Understanding the infrared (IR) spectral response of materials as a function of their morphology is not only of fundamental importance but also of contemporary practical need in the analysis of biological and synthetic materials. While significant work has recently been reported in understanding the spectra of particles with well-defined geometries, we report here on samples that consist of collections of particles. First, we theoretically model the importance of multiple scattering effects and computationally predict the impact of local particles' environment on the recorded IR spectra. Both monodisperse and polydisperse particles are considered in clusters with various degrees of packing. We show that recorded spectra are highly dependent on the cluster morphology and size of particles but the origin of this dependence is largely due to the scattering that depends on morphology and not absorbance that largely depends on the volume of material. The effect of polydispersity is to reduce the fine scattering features in the spectrum, resulting in a closer resemblance to bulk spectra. Fourier transform-IR (FT-IR) spectra of clusters of electromagnetically coupled poly(methyl methacrylate) (PMMA) spheres with wavelength-scale diameters were recorded and compared to simulated results. Measured spectra agreed well with those predicted. Of note, when PMMA spheres occupy a volume greater than 18% of the focal volume, the recorded IR spectrum becomes almost independent of the cluster's morphological changes. This threshold, where absorbance starts to dominate the signal, exactly matches the percolation threshold for hard spheres and quantifies the transition between the single particle and bulk behavior. Our finding enables an understanding of the spectral response of structured samples and points to appropriate models for recovering accurate chemical information from in IR microspectroscopy data.