Microcalcification-Based Tumor Malignancy Evaluation in Fresh Breast Biopsies with Hyperspectral Stimulated Raman Scattering

Fast Real-Time Brain Tumor Detection Based on Stimulated Raman Histology and Self-Supervised Deep Learning Model

In intraoperative brain cancer procedures, real-time diagnosis is essential for ensuring safe and effective care. The prevailing workflow, which relies on histological staining with hematoxylin and eosin (H&E) for tissue processing, is resource-intensive, time-consuming, and requires considerable labor. Recently, an innovative approach combining stimulated Raman histology (SRH) and deep convolutional neural networks (CNN) has emerged, creating a new avenue for real-time cancer diagnosis during surgery. While this approach exhibits potential, there exists an opportunity for refinement in the domain of feature extraction. In this study, we employ coherent Raman scattering imaging method and a self-supervised deep learning model (VQVAE2) to enhance the speed of SRH image acquisition and feature representation, thereby enhancing the capability of automated real-time bedside diagnosis. Specifically, we propose the VQSRS network, which integrates vector quantization with a proxy task based on patch annotation for analysis of brain tumor subtypes. Training on images collected from the SRS microscopy system, our VQSRS demonstrates a significant speed enhancement over traditional techniques (e.g., 20-30 min). Comparative studies in dimensionality reduction clustering confirm the diagnostic capacity of VQSRS rivals that of CNN. By learning a hierarchical structure of recognizable histological features, VQSRS classifies major tissue pathological categories in brain tumors. Additionally, an external semantic segmentation method is applied for identifying tumor-infiltrated regions in SRH images. Collectively, these findings indicate that this automated real-time prediction technique holds the potential to streamline intraoperative cancer diagnosis, providing assistance to pathologists in simplifying the process.

Quantitative Real-Time Analysis of Living Materials by Stimulated Raman Scattering Microscopy

Composite materials built in part from living organisms have the potential to exhibit useful autonomous, adaptive, and self-healing behavior. The physicochemical, biological, and mechanical properties of such materials can be engineered through the genetic manipulation of their living components. Successful development of living materials will require not only new methods for design and preparation but also new analytical tools that are capable of real-time noninvasive mapping of chemical compositions. Here, we establish a strategy based on stimulated Raman scattering microscopy to monitor phosphatase-catalyzed mineralization of engineered bacterial films in situ. Real-time label-free imaging elucidates the mineralization process, quantifies both the organic and inorganic components of the material as functions of time, and reveals spatial heterogeneity at multiple scales. In addition, we correlate the mechanical performance of films with the extent of mineralization. This work introduces a promising strategy for quantitatively analyzing living materials, which should contribute to the accelerated development of such materials in the future.

Physicochemical understanding of biomineralization by molecular vibrational spectroscopy: From mechanism to nature

The process and mechanism of biomineralization and relevant physicochemical properties of mineral crystals are remarkably sophisticated multidisciplinary fields that include biology, chemistry, physics, and materials science. The components of the organic matter, structural construction of minerals, and related mechanical interaction, etc., could help to reveal the unique nature of the special mineralization process. Herein, the paper provides an overview of the biomineralization process from the perspective of molecular vibrational spectroscopy, including the physicochemical properties of biomineralized tissues, from physiological to applied mineralization. These physicochemical characteristics closely to the hierarchical mineralization process include biological crystal defects, chemical bonding, atomic doping, structural changes, and content changes in organic matter, along with the interface between biocrystals and organic matter as well as the specific mechanical effects for hardness and toughness. Based on those observations, the special physiological properties of mineralization for enamel and bone, as well as the possible mechanism of pathological mineralization and calcification such as atherosclerosis, tumor micro mineralization, and urolithiasis are also reviewed and discussed. Indeed, the clearly defined physicochemical properties of mineral crystals could pave the way for studies on the mechanisms and applications.

Harnessing the power of optical microscopy for visualization and analysis of histopathological images

Histopathology is the foundation and gold standard for identifying diseases, and precise quantification of histopathological images can provide the pathologist with objective clues to make a more convincing diagnosis. Optical microscopy (OM), an important branch of optical imaging technology that provides high-resolution images of tissue cytology and structural morphology, has been used in the diagnosis of histopathology and evolved into a new disciplinary direction of optical microscopic histopathology (OMH). There are a number of ex-vivo studies providing applicability of different OMH approaches, and a transfer of these techniques toward in vivo diagnosis is currently in progress. Furthermore, combined with advanced artificial intelligence algorithms, OMH allows for improved diagnostic reliability and convenience due to the complementarity of retrieval information. In this review, we cover recent advances in OMH, including the exploration of new techniques in OMH as well as their applications, and look ahead to new challenges in OMH. These typical application examples well demonstrate the application potential and clinical value of OMH techniques in histopathological diagnosis.

Imaging Low-Temperature Phases of Ice with Polarization-Resolved Hyperspectral Stimulated Raman Scattering Microscopy

Water freezes into various phases of ice under different cryogenic temperatures and pressure conditions, such as ice I_h and ice XI at normal pressure. Vibrational imaging with high spectral, spatial, and polarization resolutions could provide detailed information on ice, including the phases and crystal orientations at the microscopic level. Here, we report in situ stimulated Raman scattering (SRS) imaging of ice to analyze the vibrational spectral changes of the OH stretching modes associated with the phase transition between ice I_h and ice XI. In addition, polarization-resolved measurements were performed to reveal the microcrystal orientations of the two phases of ice, with the spatial-dependent anisotropy pattern indicating the inhomogeneous distribution of their orientations. Furthermore, the angular patterns were theoretically explained by third-order nonlinear optics with the known crystal symmetries of the ice phases. Our work may provide new opportunities to investigate many intriguing physical chemistry properties of ice under low-temperature conditions.

High-Precision Detection of Cellular Drug Response Based on SERS Spectrum and Multivariate Statistical Analysis

The rapid development of personalized medicine places high demands on the control of drug dose and cellular drug response to provide patients with better curative effects and low side effects. To solve the problem of low detection accuracies of the cell-counting kit-8 (CCK8) method, a detection method based on surface-enhanced Raman spectroscopy (SERS) of cell-secreted proteins was adopted to evaluate the concentration of the anticancer drug cisplatin and the cellular drug response of nasopharyngeal carcinoma. CNE1 and NP69 cell lines were used to evaluate cisplatin response. The results showed that the combination of the SERS spectrum with principal component analysis-linear discriminant analysis could detect the difference in the response of cisplatin with a concentration difference of 1 μg/mL, which considerably exceeded that of CCK8. In addition, the SERS spectral peak intensity of the cell-secreted proteins strongly correlated with the cisplatin concentration. Furthermore, the mass spectrum of the secreted proteins of the nasopharyngeal carcinoma cells was analyzed to verify the results obtained using the SERS spectrum. The results demonstrated that SERS of secreted proteins has great potential for high-precision detection of chemotherapeutic drug response.

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19

BACKGROUND AND OBJECTIVE

Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics.

METHODS

Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset.

RESULTS

For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients.

CONCLUSIONS

Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.

Visualization of a Limonene Synthesis Metabolon Inside Living Bacteria by Hyperspectral SRS Microscopy

Monitoring biosynthesis activity at single-cell level is key to metabolic engineering but is still difficult to achieve in a label-free manner. Using hyperspectral stimulated Raman scattering imaging in the 670-900 cm^-1 region, localized limonene synthesis are visualized inside engineered Escherichia coli. The colocalization of limonene and GFP-fused limonene synthase is confirmed by co-registered stimulated Raman scattering and two-photon fluorescence images. The finding suggests a limonene synthesis metabolon with a polar distribution inside the cells. This finding expands the knowledge of de novo limonene biosynthesis in engineered bacteria and highlights the potential of SRS chemical imaging in metabolic engineering research.

Development of a SERS based cancer diagnosis approach employing cryosectioned thyroid tissue samples on PDMS

An efficient SERS based novel analytical approach named Cryosectioned-PDMS was developed systematically and evaluated applying on 64 thyroid biopsy samples. To utilize thyroid biopsy samples, a 20-μl volume of h-AgNPs suspension was dropped on a 5-μm thick cryosectioned biopsy specimen placed on the PDMS coated glass slide. The SERS spectra from a 10 × 10 points array acquired by mapping 22.5 μm × 22.5 μm sized area from suspended dried droplets placed on the tissue surface. The probability of correctly predicted performance for diagnosis of malignant, benign and healthy tissues was resulted in the accuracy of 100 % for the spectral bands at 667, 724, 920, 960, 1052, 1096, 1315 and 1457 cm^-1 using PCA-fed LDA machine learning. The Cryosectioned-PDMS biophotonic approach with PCA-LDA predictive model demonstrated that the vibrational signatures can accurately recognize the fingerprint of cancer pathology from a healthy one with a simple and fast sample preparation methodology.

Quantitative Stimulated Raman Scattering Microscopy: Promises and Pitfalls

Since its first demonstration, stimulated Raman scattering (SRS) microscopy has become a powerful chemical imaging tool that shows promise in numerous biological and biomedical applications. The spectroscopic capability of SRS enables identification and tracking of specific molecules or classes of molecules, often without labeling. SRS microscopy also has the hallmark advantage of signal strength that is directly proportional to molecular concentration, allowing for in situ quantitative analysis of chemical composition of heterogeneous samples with submicron spatial resolution and subminute temporal resolution. However, it is important to recognize that quantification through SRS microscopy requires assumptions regarding both system and sample. Such assumptions are often taken axiomatically, which may lead to erroneous conclusions without proper validation. In this review, we focus on the tacitly accepted, yet complex, quantitative aspect of SRS microscopy. We discuss the various approaches to quantitative analysis, examples of such approaches, challenges in different systems, and potential solutions. Through our examination of published literature, we conclude that a scrupulous approach to experimental design can further expand the powerful and incisive quantitative capabilities of SRS microscopy.

Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects

Despite significant improvements in the way breast cancer is managed and treated, it continues to persist as a leading cause of death worldwide. If detected and diagnosed early, when tumours are small and localised, there is a considerably higher chance of survival. However, current methods for detection and diagnosis lack the required sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages. Thus, there is a need to develop more rapid and reliable methods, capable of detecting disease earlier, for improved disease management and patient outcome. Raman spectroscopy is a non-destructive analytical technique that can rapidly provide highly specific information on the biochemical composition and molecular structure of samples. In cancer, it has the capacity to probe very early biochemical changes that accompany malignant transformation, even prior to the onset of morphological changes, to produce a fingerprint of disease. This review explores the application of Raman spectroscopy in breast cancer, including discussion on its capabilities in analysing both ex-vivo tissue and liquid biopsy samples, and its potential in vivo applications. The review also addresses current challenges and potential future uses of this technology in cancer research and translational clinical application.

Fiber-Enhanced Stimulated Raman Scattering and Sensitive Detection of Dilute Solutions

Stimulated Raman scattering (SRS) is known to gain coherent amplification of molecular vibrations that allow for rapid and label-free chemical imaging in the microscopy setting. However, the tightly focused laser spot has limited the detection sensitivity, partly due to the tiny interaction volume. Here, we report the use of metal-lined hollow-core fiber (MLHCF) to improve the sensitivity of SRS in sensing dilute solutions by extending the light-matter interaction volume through the fiber waveguide. With a focusing lens (100 mm FL) and 320 μm diameter fiber, we demonstrated an optimum enhancement factor of ~20 at a fiber length of 8.3 cm. More importantly, the MLHCF exhibited a significantly suppressed cross-phase modulation (XPM) background, enabling the detection of ~0.7 mM DMSO in water. Furthermore, the relationship between fiber length and SRS signal could be well explained theoretically. The fiber-enhanced SRS (FE-SRS) method may be further optimized and bears potential in the sensitive detection of molecules in the solution and gas phases.