Deep UV Microscopy Identifies Prostatic Basal Cells: An Important Biomarker for Prostate Cancer Diagnostics

Rapid, Point-of-Care Bone Marrow Aspirate Adequacy Assessment Via Deep Ultraviolet Microscopy

Bone marrow aspirations are pivotal for diagnosing and monitoring various hematological conditions, including cancers. However, a significant portion (10%-50%) of aspirations yield suboptimal or inadequate diagnostic material. The difficulty and scarcity of bedside adequacy assessment strategies further exacerbate the challenges in this procedure, which can consequently lead to delays in diagnosis and treatment, among other complications. To address this unmet clinical need, we apply deep UV microscopy, a real-time, low-cost, label-free molecular imaging technology that recapitulates the appearance of Giemsa stains. We present results from a prospective clinical study comprising 51 pediatric oncology patients, where the deep UV images of unstained bone marrow aspirate smears are evaluated and compared with the clinical standard of care (a hematopathologist inspection of the same slides after Giemsa staining). Results show that both real-time visual UV inspection and an automated classification algorithm applied to the unstained deep UV images achieve accurate adequacy assessment, with accuracies of 94.1% and 95.7%, respectively. Additionally, we demonstrate whole-slide imaging of bone marrow aspirate smears using a compact and low-cost deep UV microscope that is well suited for point-of-care use. Together, this work has significant implications for improving bone marrow aspirations and the clinical management of many hematological patients.

Quantifying UV-induced photodamage for longitudinal live-cell imaging applications of deep-UV microscopy

Deep-UV microscopy enables high-resolution, label-free molecular imaging by leveraging biomolecular absorption properties in the UV spectrum. Recent advances in UV-imaging hardware have renewed interest in this technique for quantitative live cell imaging applications. However, UV-induced photodamage remains a concern for longitudinal dynamic imaging studies. Here, we quantify UV phototoxicity with several cell types at notable UV wavelengths. We find that the fluence required for cell death via UV phototoxicity with continuous UV exposure varies with cell type and wavelength from ∼0.5µJ/µm2 to 2µJ/µm2, but is independent of typical illumination power/radiant flux of UV microscopy (e.g., 0.1-20 nW/µm2). We also show results from fractionation studies that reveal cell repair following UV exposure, which increases the tolerance to UV radiation by a factor of 2 or more, depending on the fractionation paradigm. Results further show that UV tolerance exceeds ANSI guidelines for maximum permissible exposure. Finally, we calculate imaging limits for a typical application of UV microscopy, such as hematology analysis. Together, this work provides UV fluence thresholds that can serve as guidelines for nondestructive, longitudinal, and dynamic deep-UV microscopy experiments.

Deep learning-assisted low-cost autofluorescence microscopy for rapid slide-free imaging with virtual histological staining

Slide-free imaging techniques have shown great promise in improving the histological workflow. For example, computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP) has achieved high resolution with a long depth of field, which, however, requires a costly ultraviolet laser. Here, simply using a low-cost light-emitting diode (LED), we propose a deep learning-assisted framework of enhanced widefield microscopy, termed EW-LED, to generate results similar to CHAMP (the learning target). Comparing EW-LED and CHAMP, EW-LED reduces the cost by 85×, shortening the image acquisition time and computation time by 36× and 17×, respectively. This framework can be applied to other imaging modalities, enhancing widefield images for better virtual histology.

Virtual histological staining of unlabeled autopsy tissue

Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.

Advances in Prostate Cancer Biomarkers and Probes

Prostate cancer is one of the most prevalent malignant tumors in men worldwide, and early diagnosis is essential to improve patient survival. This review provides a comprehensive discussion of recent advances in prostate cancer biomarkers, including molecular, cellular, and exosomal biomarkers. The potential of various biomarkers such as gene fusions (TMPRSS2-ERG), noncoding RNAs (SNHG12), proteins (PSA, PSMA, AR), and circulating tumor cells (CTCs) in the diagnosis, prognosis, and targeted therapies of prostate cancer is emphasized. In addition, this review systematically explores how multi-omics data and artificial intelligence technologies can be used for biomarker discovery and personalized medicine applications. In addition, this review provides insights into the development of specific probes, including fluorescent, electrochemical, and radionuclide probes, for sensitive and accurate detection of prostate cancer biomarkers. In conclusion, this review provides a comprehensive overview of the status and future directions of prostate cancer biomarker research, emphasizing the potential for precision diagnosis and targeted therapy.

Deep learning-enabled virtual histological staining of biological samples

Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and trained histotechnologists, making it expensive, time-consuming, and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, and accurate alternatives to standard chemical staining methods. These techniques, broadly referred to as virtual staining, were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples; similar approaches were also used for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this Review, we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques. The basic concepts and the typical workflow of virtual staining are introduced, followed by a discussion of representative works and their technical innovations. We also share our perspectives on the future of this emerging field, aiming to inspire readers from diverse scientific fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications.

Compact and low-cost deep-ultraviolet microscope system for label-free molecular imaging and point-of-care hematological analysis

Deep-ultraviolet (UV) microscopy enables label-free, high-resolution, quantitative molecular imaging and enables unique applications in biomedicine, including the potential for fast hematological analysis at the point-of-care. UV microscopy has been shown to quantify hemoglobin content and white blood cells (five-part differential), providing a simple alternative to the current gold standard, the hematological analyzer. Previously, however, the UV system comprised a bulky broadband laser-driven plasma light source along with a large and expensive camera and 3D translation stage. Here, we present a modified deep-UV microscope system with a compact footprint and low-cost components. We detail the novel design with simple, inexpensive optics and hardware to enable fast and accurate automated imaging. We characterize the system, including a modified low-cost web-camera and custom automated 3D translation stage, and demonstrate its ability to scan and capture large area images. We further demonstrate the capability of the system by imaging and analyzing blood smears, using previously trained networks for automatic segmentation, classification (including 5-part white blood cell differential), and colorization. The developed system is approximately 10 times less expensive than previous configurations and can serve as a point-of-care hematology analyzer, as well as be applied broadly in biomedicine as a simple compact, low-cost, quantitative molecular imaging system.