Noninvasive Detection of Differential Water Content Inside Biological Samples Using Deep Raman Spectroscopy

Fabrication and characterization of multi-biomarker optimized tissue-mimicking phantoms for multi-modal optical spectroscopy

Rapid advancement of novel optical spectroscopy and imaging systems relies on the availability of well-characterised and reproducible protocols for phantoms as a standard for the validation of the technique. The tissue-mimicking phantoms are also used to investigate photon transport in biological samples before clinical trials that require well-characterized phantoms with known optical properties (reduced scattering (μ's) and absorption (μa) coefficients). However, at present, there is limited literature available providing well-characterized phantom recipes considering various biomarkers and tested over a wide range of optical properties covering most of the human organs and applicable to multimodal optical spectroscopy. In this study, gelatin-based phantoms were designed to simulate tissue optical properties where India ink and Intralipid were used as absorbing and scattering agents, respectively. Multiple biomarkers were simulated by varying the gelatin concentration to mimic the change in tissue hydration and hydroxyapatite concentration to mimic bone signature. The recipe along with biomarkers were optimized and characterised over a wide range of optical properties (μa from 0.1 to 0.5 cm-1; μ's from 5 to 15 cm-1) relevant to human tissue using a broadband time-domain diffuse optical spectrometer. The data collected showed a linear relationship between the concentration of ink/lipids and μa/μ's values with negligible coupling between μa and μ's values. While being stored in a refrigerator post-fabrication, the μa and μ's did not change significantly (<4% coefficient of variation, 'CV') over three weeks. The reproducibility in three different sets was validated experimentally and found to be strong with a variation of ≤6% CV in μa and ≤9% CV in μ's. From the 3 × 3 data of μa and μ's matrices, one can deduce the recipe for any target absorption or reduced scattering coefficient. The applicability of the phantoms was tested using diffuse reflectance and Raman spectrometers. A use case application was demonstrated for Raman spectroscopy where hydration and hydroxyapatite phantoms were designed to characterize the Raman instrument. The Raman instrument could detect the change in 1% of HA and 5% of hydration. This study presents a first-of-its-kind robust, well-characterized, multi-biomarker phantom recipe for calibration and benchmarking of multimodal spectroscopy devices assisting in their clinical translation.

Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning

The most commonly occurring malignant brain tumors are gliomas, and among them is glioblastoma multiforme. The main idea of the paper is to estimate dependency between glioma tissue and blood serum biomarkers using Raman spectroscopy. We used the most common model of human glioma when continuous cell lines, such as U87, derived from primary human tumor cells, are transplanted intracranially into the mouse brain. We studied the separability of the experimental and control groups by machine learning methods and discovered the most informative Raman spectral bands. During the glioblastoma development, an increase in the contribution of lactate, tryptophan, fatty acids, and lipids in dried blood serum Raman spectra were observed. This overlaps with analogous results of glioma tissues from direct Raman spectroscopy studies. A non-linear relationship between specific Raman spectral lines and tumor size was discovered. Therefore, the analysis of blood serum can track the change in the state of brain tissues during the glioma development.