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

Abstract

Objective and Impact Statement. Identifying benign mimics of prostatic adenocarcinoma remains a significant diagnostic challenge. In this work, we developed an approach based on label-free, high-resolution molecular imaging with multispectral deep ultraviolet (UV) microscopy which identifies important prostate tissue components, including basal cells. This work has significant implications towards improving the pathologic assessment and diagnosis of prostate cancer. Introduction. One of the most important indicators of prostate cancer is the absence of basal cells in glands and ducts. However, identifying basal cells using hematoxylin and eosin (H&E) stains, which is the standard of care, can be difficult in a subset of cases. In such situations, pathologists often resort to immunohistochemical (IHC) stains for a definitive diagnosis. However, IHC is expensive and time-consuming and requires more tissue sections which may not be available. In addition, IHC is subject to false-negative or false-positive stains which can potentially lead to an incorrect diagnosis. Methods. We leverage the rich molecular information of label-free multispectral deep UV microscopy to uniquely identify basal cells, luminal cells, and inflammatory cells. The method applies an unsupervised geometrical representation of principal component analysis to separate the various components of prostate tissue leading to multiple image representations of the molecular information. Results. Our results show that this method accurately and efficiently identifies benign and malignant glands with high fidelity, free of any staining procedures, based on the presence or absence of basal cells. We further use the molecular information to directly generate a high-resolution virtual IHC stain that clearly identifies basal cells, even in cases where IHC stains fail. Conclusion. Our simple, low-cost, and label-free deep UV method has the potential to improve and facilitate prostate cancer diagnosis by enabling robust identification of basal cells and other important prostate tissue components.

Deep learning-enabled virtual histological staining of biological samples

“…c Blind inference of a trained virtual staining model. The virtual histology images are rapidly generated from label-free images using a digital computer smears 39 , as well as H&E 40 and IHC 41 staining on prostate tissue sections; photoacoustic microscopy was also demonstrated to achieve virtual H&E staining of mouse brain 42 and frozen sections of bone tissue 43 . As another example, Mayerich.…”

Deep-ultraviolet microscopy for analysis of bone marrow aspirate adequacy

“…Recently, it has been demonstrated for fast hematology analysis of whole blood smears and blood samples in custom microfluidic cartridges for point-of-care applications enabled by advancements in low-cost UV LEDs and sensors [9][10][11][12][13]. UV microscopy has also been used for multi-spectral, label-free histopathology of tissue samples [14][15][16]. In these prior works, deep neural networks have been required to pseudo-colorize images to mimic conventional biochemical stains (i.e., hematoxylin and eosin, Giemsa) [12,13,15].…”

Characterizing UV-induced photo-damage to improve long-term, dynamic live cell imaging with UV microscopy

“…Early studies demonstrated UV microscopy for biomolecular mass-mapping in live cells without observable photodamage [7,9,10]. Recently applications of UV microscopy have included hematology analysis of whole blood samples and label-free histopathology of prostate cancer sections and fresh brain tissue [11][12][13][14][15][16][17]. In addition, live single-cell imaging with UV microscopy has been demonstrated for analysis of intracellular dynamics in cancer cells and T-cell phenotyping [18].…”