Nondestructive 3D Pathology with Light-Sheet Fluorescence Microscopy for Translational Research and Clinical Assays

Abstract

In recent years, there has been a revived appreciation for the importance of spatial context and morphological phenotypes for both understanding disease progression and guiding treatment decisions. Compared with conventional 2D histopathology, which is the current gold standard of medical diagnostics, nondestructive 3D pathology offers researchers and clinicians the ability to visualize orders of magnitude more tissue within their natural volumetric context. This has been enabled by rapid advances in tissue-preparation methods, high-throughput 3D microscopy instrumentation, and computational tools for processing these massive feature-rich data sets. Here, we provide a brief overview of many of these technical advances along with remaining challenges to be overcome. We also speculate on the future of 3D pathology as applied in translational investigations, preclinical drug development, and clinical decision-support assays.

Keywords: computational pathology; digital pathology; optical-sectioning microscopy; precision medicine; spatial biology.

Figure 1.. Key advantages of 3D vs. 2D pathology.

(1) Destructive sectioning of tissues in conventional 2D histology methods can impede downstream molecular assays that require ample tissue material, unlike non-destructive 3D pathology methods. (2) Due to the destructiveness and time-consuming nature of physically sectioning tissues onto glass slides, there are severe sampling limitations for conventional 2D histology. With non-destructive 3D pathology, large specimens can often be imaged in toto, which enables improved diagnostic sensitivity, the ability to identity “rare events” that are often missed with 2D sections, and most importantly: (3) the ability to accurately characterize complex 3D structures and cell distributions, which can improve investigational and diagnostic certainty.

Figure 2.. Light-sheet fluorescence microscopy (LSFM) and data handling. (a-g) Architectures of traditional, inverted, open-top, single-objective, light-sheet theta, and hybrid light-sheet microscopy systems.

Relevant characteristics of each microscope architecture, including lateral constraints on specimen size and usable imaging depth are highlighted. (h) Typical data acquisitions associated with any light-sheet microscopy system are listed. This includes raw data generation from the microscope itself at 1+ GB/sec, storage of the data on high-speed drives, transfer of the datasets to network storage, and optional storage of these often multi-TB datasets in the cloud.

Figure 3.. Clearing, expansion, and labeling of tissues.

A-D) Whole human kidney cleared via SHANEL, labeled with a dextran (magenta) and nuclear stain (green), and imaged by light sheet microscopy to reveal blood vessels and glomeruli. E-F) Human kidney sections imaged by confocal microscopy after hydrogel expansion and revealing differences between E) a healthy patient and F) a patient with minimal change disease. The specimens were antibody stained for vimentin (blue), actinin-4 (green), and collagen IV (red), and counterstained for nuclei (white). G-I) Cleared mouse kidney tissue that was labeled via FLARE (amines (red), carbohydrates (green), and nuclei (blue)) and imaged by open-top light-sheet microscopy. Panels A-D adapted from reference (95) (CC BY 4.0). Panels E-F adapted from reference (101) [**Note to Annual Reviews: Permission to be obtained prior to publication]. Panels G-I used from reference(113). [**Note to Annual Reviews: The panels were created by the authors and the publisher grants authors the right to reuse their own figures without permission.]

Figure 4.. Translational applications in pathology.

A) Human lymph node studied by IBEX at 64 channels and imaged by confocal microscopy (scale bars = 500 μm and 50 μm). B) Cleared human prostate biopsies that have been stained with a fluorescent H&E analog and imaged by open-top light-sheet (OTLS) microscopy (scale bars 1 mm, 25 μm, 10 μm). C) A 3D pathology dataset of a prostate biopsy stained with a fluorescent analogue of H&E (left). Deep learning-based image translation was used to convert the H&E dataset into a synthetic dataset that looks like it has been immunolabeled to highlight a cytokeratin biomarker (brown) that is expressed by the epithelial cells in all prostate glands. In turn, this synthetically immunolabeled dataset allows for accurate 3D segmentation of the prostate gland epithelium (yellow) and lumen spaces (red). Quantitative features derived from these segmented 3D structures are used to train a machine classifier to stratify between aggressive (recurrent) versus indolent (non-recurrent) cancer (82) D) Breast cancer tumor tissue section stained with H&E. Cancerous regions are marked in red, cancer-like region marked in black, and stromal tissue marked in yellow. E) Spatial transcriptomics data corresponding to D) with transcriptomic-based clustering indicated by color of dot. F) Heat map of highly differentially expressed genes for clusters in E). Panel A used from reference(117) with permission. Panel B from reference (45) (CC BY 4.0). Panels D-F adapted from reference (125) (CC BY 4.0).

Figure 5.. Microscopy from cells and organoids to organisms.

There are clear advantages in terms of biological realism and insights when imaging whole organisms or 3D cultures vs. traditional cell monolayers on petri dishes. However, there are exponentially greater challenges in terms of imaging speed/throughput and dataset sizes. With 3D pathology methods, the computational hurdles represent the next frontier for many biological and clinical applications.

Figure 6.. Examples of key considerations and steps in 3D pathology.

Various applications of 3D pathology will each have unique technical requirements and time scales for tissue preparation, high-throughput 3D microscopy, and data handling/analysis. A few examples of technical steps are listed.