Enhanced resolution 3D digital cytology and pathology with dual-view inverted selective plane illumination microscopy

In situ observation of cartilage matrix based on two-photon fluorescence microscopy

Articular cartilage lesions remain a major challenge for clinicians and researchers. Several techniques, such as histological scoring, magnetic resonance imaging, and tissue section staining, are available for detecting cartilage degeneration and lesions and evaluating cartilage repairs. Nevertheless, these methods are complex and have numerous influencing factors, which may present obstacles to efficient communication between studies. In this study, we developed a fluorescence observation system that integrated a two-photon laser scanning confocal microscope (TPLSCM) with the second-harmonic generation (SHG) of a cartilage matrix. The observation system enabled the detection of autofluorescence emitted by the cartilage matrix without species specificity, facilitating both qualitative and quantitative analyses of the cartilage matrix. Notably, this observation could be applied three-dimensionally to a fresh specimen in situ up to a depth of 300 μm, obviating the need for traditional histological fixation, slicing, or staining. Furthermore, using this observation system, we reconstructed a three-dimensional (3D) image and a 3D model of the cartilage matrix. The utilization of the 3D fluorescence model may serve as a dependable option for the fabrication of cartilage matrix biomimetic scaffolds in future studies.

Towards Artificial Intelligence Applications in Next Generation Cytopathology

Over the last 20 years we have seen an increase in techniques in the field of computational pathology and machine learning, improving our ability to analyze and interpret imaging. Neural networks, in particular, have been used for more than thirty years, starting with the computer assisted smear test using early generation models. Today, advanced machine learning, working on large image data sets, has been shown to perform classification, detection, and segmentation with remarkable accuracy and generalization in several domains. Deep learning algorithms, as a branch of machine learning, are thus attracting attention in digital pathology and cytopathology, providing feasible solutions for accurate and efficient cytological diagnoses, ranging from efficient cell counts to automatic classification of anomalous cells and queries over large clinical databases. The integration of machine learning with related next-generation technologies powered by AI, such as augmented/virtual reality, metaverse, and computational linguistic models are a focus of interest in health care digitalization, to support education, diagnosis, and therapy. In this work we will consider how all these innovations can help cytopathology to go beyond the microscope and to undergo a hyper-digitalized transformation. We also discuss specific challenges to their applications in the field, notably, the requirement for large-scale cytopathology datasets, the necessity of new protocols for sharing information, and the need for further technological training for pathologists.

Rapid On-Site Microscopy and Mapping of Diagnostic Biopsies for See-And-Treat Guidance of Localized Prostate Cancer Therapy

Prostate cancer continues to be the most diagnosed non-skin malignancy in men. While up to one in eight men will be diagnosed in their lifetimes, most diagnoses are not fatal. Better lesion location accuracy combined with emerging localized treatment methods are increasingly being utilized as a treatment option to preserve healthy function in eligible patients. In locating lesions which are generally <2cc within a prostate (average size 45cc), small variance in MRI-determined boundaries, tumoral heterogeneity, patient characteristics including location of lesion and prostatic calcifications, and patient motion during the procedure can inhibit accurate sampling for diagnosis. The locations of biopsies are recorded and are then fully processed by histology and diagnosed via pathology, often days to weeks later. Utilization of real-time feedback could improve accuracy, potentially prevent repeat procedures, and allow patients to undergo treatment of clinically localized disease at earlier stages. Unfortunately, there is currently no reliable real-time feedback process for confirming diagnosis of biopsy samples. We examined the feasibility of implementing structured illumination microscopy (SIM) as a method for on-site diagnostic biopsy imaging to potentially combine the diagnostic and treatment appointments for prostate cancer patients, or to confirm tumoral margins for localized ablation procedures. We imaged biopsies from 39 patients undergoing image-guided diagnostic biopsy using a customized SIM system and a dual-color fluorescent hematoxylin & eosin (H&E) analog. The biopsy images had an average size of 342 megapixels (minimum 78.1, maximum 842) and an average imaging duration of 145 s (minimum 56, maximum 322). Comparison of urologist's suspicion of malignancy based on MRI, to pathologist diagnosis of biopsy images obtained in real time, reveals that real-time biopsy imaging could significantly improve confirmation of malignancy or tumoral margins over medical imaging alone.

Assessment of photoacoustic tomography contrast for breast tissue imaging using 3D correlative virtual histology

Current breast tumor margin detection methods are destructive, time-consuming, and result in significant reoperative rates. Dual-modality photoacoustic tomography (PAT) and ultrasound has the potential to enhance breast margin characterization by providing clinically relevant compositional information with high sensitivity and tissue penetration. However, quantitative methods that rigorously compare volumetric PAT and ultrasound images with gold-standard histology are lacking, thus limiting clinical validation and translation. Here, we present a quantitative multimodality workflow that uses inverted Selective Plane Illumination Microscopy (iSPIM) to facilitate image co-registration between volumetric PAT-ultrasound datasets with histology in human invasive ductal carcinoma breast tissue samples. Our ultrasound-PAT system consisted of a tunable Nd:YAG laser coupled with a 40 MHz central frequency ultrasound transducer. A linear stepper motor was used to acquire volumetric PAT and ultrasound breast biopsy datasets using 1100 nm light to identify hemoglobin-rich regions and 1210 nm light to identify lipid-rich regions. Our iSPIM system used 488 nm and 647 nm laser excitation combined with Eosin and DRAQ5, a cell-permeant nucleic acid binding dye, to produce high-resolution volumetric datasets comparable to histology. Image thresholding was applied to PAT and iSPIM images to extract, quantify, and topologically visualize breast biopsy lipid, stroma, hemoglobin, and nuclei distribution. Our lipid-weighted PAT and iSPIM images suggest that low lipid regions strongly correlate with malignant breast tissue. Hemoglobin-weighted PAT images, however, correlated poorly with cancerous regions determined by histology and interpreted by a board-certified pathologist. Nuclei-weighted iSPIM images revealed similar cellular content in cancerous and non-cancerous tissues, suggesting malignant cell migration from the breast ducts to the surrounding tissues. We demonstrate the utility of our nondestructive, volumetric, region-based quantitative method for comprehensive validation of 3D tomographic imaging methods suitable for bedside tumor margin detection.

Maximizing content across scales: Moving multimodal microscopy and mesoscopy toward molecular imaging

Molecular imaging aims to depict the molecules in living patients. However, because this aim is still far beyond reach, patchworks of different solutions need to be used to tackle this overarching goal. From the vast toolbox of imaging techniques, we focus on those recent advances in optical microscopy that image molecules and cells at the submicron to centimeter scale. Mesoscopic imaging covers the "imaging gap" between techniques such as confocal microscopy and magnetic resonance imagingthat image entire live samples but with limited resolution. Microscopy focuses on the cellular level; mesoscopy visualizes the organization of molecules and cells into tissues and organs. The correlation between these techniques allows us to combine disciplines ranging from whole body imaging to basic research of model systems. We review current developments focused on improving microscopic and mesoscopic imaging technologies and on hardware and software that push the current sensitivity and resolution boundaries.

Tissue dynamics spectroscopic imaging: functional imaging of heterogeneous cancer tissue

SIGNIFICANCE

Tumor heterogeneity poses a challenge for the chemotherapeutic treatment of cancer. Tissue dynamics spectroscopy captures dynamic contrast and can capture the response of living tissue to applied therapeutics, but the current analysis averages over the complicated spatial response of living biopsy samples.

AIM

To develop tissue dynamics spectroscopic imaging (TDSI) to map the heterogeneous spatial response of tumor tissue to anticancer drugs.

APPROACH

TDSI is applied to tumor spheroids grown from cell lines and to ex vivo living esophageal biopsy samples. Doppler fluctuation spectroscopy is performed on a voxel basis to extract spatial maps of biodynamic biomarkers. Functional images and bivariate spatial maps are produced using a bivariate color merge to represent the spatial distribution of pairs of signed drug-response biodynamic biomarkers.

RESULTS

We have mapped the spatial variability of drug responses within biopsies and have tracked sample-to-sample variability. Sample heterogeneity observed in the biodynamic maps is associated with histological heterogeneity observed using inverted selective-plane illumination microscopy.

CONCLUSION

We have demonstrated the utility of TDSI as a functional imaging method to measure tumor heterogeneity and its potential for use in drug-response profiling.