Quantitative assessment of microenvironment characteristics and metabolic activity in glioma via multiphoton microscopy

Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy

Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists' subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards "next-generation diagnostic pathology", prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.

Quantitative Optical Redox Imaging of Melanoma Xenografts with Different Metastatic Potentials

To develop imaging biomarkers for tumors aggressiveness, our previous optical redox imaging (ORI) studies of the reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavoproteins (Fp, containing flavin adenine dinucleotide, i.e., FAD) in tumor xenografts of human melanoma associated the high optical redox ratio (ORR = Fp/(Fp + NADH)) and its heterogeneity to the high invasive/metastatic potential, without having reported quantitative results for NADH and Fp. Here, we implemented a calibration procedure to facilitate imaging the nominal concentrations of tissue NADH and Fp in the mouse xenografts of two human melanoma lines, an indolent less metastatic A375P and a more metastatic C8161. Images of the redox indices (NADH, Fp, ORR) revealed the existence of more oxidized areas (OAs) and more reduced areas (RAs) within individual tumors. ORR was found to be higher and NADH lower in C8161 compared to that of A375P xenografts, both globally for the whole tumors and locally in OAs. The ORR in the OA can differentiate xenografts with a higher statistical significance than the global averaged ORR. H&E staining of the tumors indicated that the redox differences we identified were more likely due to intrinsically different cell metabolism, rather than variations in cell density.

SLAM medical imaging enabled by pre-chirp and gain jointly managed Yb-fiber laser

We demonstrate a pre-chirp and gain jointly managed Yb-fiber laser that drives simultaneous label-free autofluorescence-multiharmonic (SLAM) medical imaging. We show that a gain managed Yb-fiber amplifier produces high-quality compressed pulses when the seeding pulses exhibit proper negative pre-chirp. The resulting laser source can generate 43-MHz, 34-fs pulses centered at 1110 nm with more than 90-nJ energy. We apply this ultrafast source to SLAM imaging of cellular and extracellular components in various human tissues of intestinal adenocarcinoma, lung adenocarcinoma, and liver.

Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence

Introduction

The intrinsic autofluorescence of biological tissues interferes with the detection of fluorophores administered for fluorescence guidance, an emerging auxiliary technique in oncological surgery. Yet, autofluorescence of the human brain and its neoplasia is sparsely examined. This study aims to assess autofluorescence of the brain and its neoplasia on a microscopic level by stimulated Raman histology (SRH) combined with two-photon fluorescence.

Methods

With this experimentally established label-free microscopy technique unprocessed tissue can be imaged and analyzed within minutes and the process is easily incorporated in the surgical workflow. In a prospective observational study, we analyzed 397 SRH and corresponding autofluorescence images of 162 samples from 81 consecutive patients that underwent brain tumor surgery. Small tissue samples were squashed on a slide for imaging. SRH and fluorescence images were acquired with a dual wavelength laser (790 nm and 1020 nm) for excitation. In these images tumor and non-tumor regions were identified by a convolutional neural network that reliably differentiates between tumor, healthy brain tissue and low quality SRH images. The identified areas were used to define regions.of- interests (ROIs) and the mean fluorescence intensity was measured.

Results

In healthy brain tissue, we found an increased mean autofluorescence signal in the gray (11.86, SD 2.61, n=29) compared to the white matter (5.99, SD 5.14, n=11, p<0.01) and in the cerebrum (11.83, SD 3.29, n=33) versus the cerebellum (2.82, SD 0.93, n=7, p<0.001), respectively. The signal of carcinoma metastases, meningiomas, gliomas and pituitary adenomas was significantly lower (each p<0.05) compared to the autofluorescence in the cerebrum and dura, and significantly higher (each p<0.05) compared to the cerebellum. Melanoma metastases were found to have a higher fluorescent signal (p<0.01) compared to cerebrum and cerebellum.

Discussion

In conclusion we found that autofluorescence in the brain varies depending on the tissue type and localization and differs significantly among various brain tumors. This needs to be considered for interpreting photon signal during fluorescence-guided brain tumor surgery.

Application of Multiphoton Microscopic Imaging in Study of Gastric Cancer

Multiphoton microscopy (MPM) imaging relies on the nonlinear interaction between ultrashort optical pulses and the samples to achieve image contrast. Featuring larger penetration depth, less phototoxicity, 3-dimensional sectioning capability, no need for labeling, MPM become a powerful medical imaging technique that can identify structural characteristics of tissues at the cellular and subcellular levels. In this review paper, we introduce the working principle of MPM imaging, present the current results of MPM imaging applied to the study of gastric tumors, and discuss the future prospects of this interdisciplinary research field.

SNAP25 Inhibits Glioma Progression by Regulating Synapse Plasticity via GLS-Mediated Glutaminolysis

Background

Neuronal activity regulated by synaptic communication exerts an important role in tumorigenesis and progression in brain tumors. Genes for soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) annotated with the function ‘vesicle’ about synaptic connectivity were identified, and synaptosomal-associated protein 25 (SNAP25), one of those proteins, was found to have discrepant expression levels in neuropathies. However, the specific mechanism and prognostic value of SNAP25 during glioma progression remain unclear.

Methods

Using RNA sequencing data from The Cancer Genome Atlas (TCGA) database, the differential synaptosis-related genes between low grade glioma (LGG) and glioblastoma (GBM) were identified as highly correlated. Cox proportional hazards regression analysis and survival analysis were used to differentiate the outcome of low- and high-risk patients, and the Chinese Glioma Genome Atlas (CGGA) cohort was used for validation of the data set. RT-qPCR, western blot, and immunohistochemistry assays were performed to examine the expression level of SNAP25 in glioma cells and samples. Functional assays were performed to identify the effects of SNAP25 knockdown and overexpression on cell viability, migration, and invasion. Liquid chromatography-high resolution mass spectrometry (LC-MS)-based metabolomics approach was presented for identifying crucial metabolic disturbances in glioma cells. In situ mouse xenograft model was used to investigate the role of SNAP25 in vivo. Then, an immunofluorescence assay of the xenograft tissue was applied to evaluate the expression of the neuronal dendron formation marker-Microtubule Associated Protein 2 (MAP2).

Results

SNAP25 was decreased in level of expression in glioma tissues and cell lines, and low-level SNAP25 indicated an unfavorable prognosis of glioma patients. SNAP25 inhibited cell proliferation, migration, invasion and fostered glutamine metabolism of glioma cells, exerting a tumor suppressor role. Overexpressed SNAP25 exerted a lower expression level of MAP2, indicating poor neuronal plasticity and connectivity. SNAP25 could regulate glutaminase (GLS)-mediated glutaminolysis, and GLS knockdown could rescue the anti-tumor effect of SNAP25 in glioma cells. Moreover, upregulated SNAP25 also decreased tumor volume and prolonged the overall survival (OS) of the xenograft mouse.

Conclusion

SNAP25, a tumor suppressor inhibited carcinogenesis of glioma via limiting glutamate metabolism by regulating GLS expression, as well as inhibiting dendritic formation, which could be considered as a novel molecular therapeutic target for glioma.

Label-free imaging of human brain tissue at subcellular resolution for potential rapid intra-operative assessment of glioma surgery

Background: Frozen section and smear preparation are the current standard for intraoperative histopathology during cancer surgery. However, these methods are time-consuming and subject to limited sampling. Multiphoton microscopy (MPM) is a high-resolution non-destructive imaging technique capable of optical sectioning in real time with subcellular resolution. In this report, we systematically investigated the feasibility and translation potential of MPM for rapid histopathological assessment of label- and processing-free surgical specimens. Methods: We employed a customized MPM platform to capture architectural and cytological features of biological tissues based on two-photon excited NADH and FAD autofluorescence and second harmonic generation from collagen. Infiltrating glioma, an aggressive disease that requires subcellular resolution for definitive characterization during surgery, was chosen as an example for this validation study. MPM images were collected from resected brain specimens of 19 patients and correlated with histopathology. Deep learning was introduced to assist with image feature recognition. Results: MPM robustly captures diagnostic features of glioma including increased cellularity, cellular and nuclear pleomorphism, microvascular proliferation, necrosis, and collagen deposition. Preliminary application of deep learning to MPM images achieves high accuracy in distinguishing gray from white matter and cancer from non-cancer. We also demonstrate the ability to obtain such images from intact brain tissue with a multiphoton endomicroscope for intraoperative application. Conclusion: Multiphoton imaging correlates well with histopathology and is a promising tool for characterization of cancer and delineation of infiltration within seconds during brain surgery.

Diagnosis of Glioma Molecular Markers by Terahertz Technologies

This review considers glioma molecular markers in brain tissues and body fluids, shows the pathways of their formation, and describes traditional methods of analysis. The most important optical properties of glioma markers in the terahertz (THz) frequency range are also presented. New metamaterial-based technologies for molecular marker detection at THz frequencies are discussed. A variety of machine learning methods, which allow the marker detection sensitivity and differentiation of healthy and tumor tissues to be improved with the aid of THz tools, are considered. The actual results on the application of THz techniques in the intraoperative diagnosis of brain gliomas are shown. THz technologies’ potential in molecular marker detection and defining the boundaries of the glioma’s tissue is discussed.