A hybrid open-top light-sheet microscope for versatile multi-scale imaging of cleared tissues.

Light-sheet microscopy has emerged as the preferred means for high-throughput volumetric imaging of cleared tissues. However, there is a need for a flexible system that can address imaging applications with varied requirements in terms of resolution, sample size, tissue-clearing protocol, and transparent sample-holder material. Here, we present a 'hybrid' system that combines a unique non-orthogonal dual-objective and conventional (orthogonal) open-top light-sheet (OTLS) architecture for versatile multi-scale volumetric imaging. We demonstrate efficient screening and targeted sub-micrometer imaging of sparse axons within an intact, cleared mouse brain. The same system enables high-throughput automated imaging of multiple specimens, as spotlighted by a quantitative multi-scale analysis of brain metastases. Compared with existing academic and commercial light-sheet microscopy systems, our hybrid OTLS system provides a unique combination of versatility and performance necessary to satisfy the diverse requirements of a growing number of cleared-tissue imaging applications.

Axially swept open-top light-sheet microscopy for densely labeled clinical specimens.

Open-top light-sheet (OTLS) microscopy offers rapid 3D imaging of large optically cleared specimens. This enables nondestructive 3D pathology, which provides key advantages over conventional slide-based histology including comprehensive sampling without tissue sectioning/destruction and visualization of diagnostically important 3D structures. With 3D pathology, clinical specimens are often labeled with small-molecule stains that broadly target nucleic acids and proteins, mimicking conventional hematoxylin and eosin (H&E) dyes. Tight optical sectioning helps to minimize out-of-focus fluorescence for high-contrast imaging in these densely labeled tissues but has been challenging to achieve in OTLS systems due to trade-offs between optical sectioning and field of view. Here we present an OTLS microscope with voice-coil-based axial sweeping to circumvent this trade-off, achieving 2 µm axial resolution over a 750 × 375 µm field of view. We implement our design in a non-orthogonal dual-objective (NODO) architecture, which enables a 10-mm working distance with minimal sensitivity to refractive index mismatches, for high-contrast 3D imaging of clinical specimens.

Direct three-dimensional segmentation of prostate glands with nnU-Net.

Significance: In recent years, we and others have developed non-destructive methods to obtain three-dimensional (3D) pathology datasets of clinical biopsies and surgical specimens. For prostate cancer risk stratification (prognostication), standard-of-care Gleason grading is based on examining the morphology of prostate glands in thin 2D sections. This motivates us to perform 3D segmentation of prostate glands in our 3D pathology datasets for the purposes of computational analysis of 3D glandular features that could offer improved prognostic performance. Aim: To facilitate prostate cancer risk assessment, we developed a computationally efficient and accurate deep learning model for 3D gland segmentation based on open-top light-sheet microscopy datasets of human prostate biopsies stained with a fluorescent analog of hematoxylin and eosin (H&E). Approach: For 3D gland segmentation based on our H&E-analog 3D pathology datasets, we previously developed a hybrid deep learning and computer vision-based pipeline, called image translation-assisted segmentation in 3D (ITAS3D), which required a complex two-stage procedure and tedious manual optimization of parameters. To simplify this procedure, we use the 3D gland-segmentation masks previously generated by ITAS3D as training datasets for a direct end-to-end deep learning-based segmentation model, nnU-Net. The inputs to this model are 3D pathology datasets of prostate biopsies rapidly stained with an inexpensive fluorescent analog of H&E and the outputs are 3D semantic segmentation masks of the gland epithelium, gland lumen, and surrounding stromal compartments within the tissue. Results: nnU-Net demonstrates remarkable accuracy in 3D gland segmentations even with limited training data. Moreover, compared with the previous ITAS3D pipeline, nnU-Net operation is simpler and faster, and it can maintain good accuracy even with lower-resolution inputs. Conclusions: Our trained DL-based 3D segmentation model will facilitate future studies to demonstrate the value of computational 3D pathology for guiding critical treatment decisions for patients with prostate cancer.

An end-to-end workflow for nondestructive 3D pathology.

Recent advances in 3D pathology offer the ability to image orders of magnitude more tissue than conventional pathology methods while also providing a volumetric context that is not achievable with 2D tissue sections, and all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis, however, is not trivial and requires careful attention to a series of details during tissue preparation, imaging and initial data processing, as well as iterative optimization of the entire process. Here, we provide an end-to-end procedure covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. Although 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol focuses on the use of a fluorescent analog of hematoxylin and eosin, which remains the most common stain used for gold-standard pathological reports. We present our guidelines for a broad range of end users (e.g., biologists, clinical researchers and engineers) in a simple format. The end-to-end workflow requires 3-6 d to complete, bearing in mind that data analysis may take longer.

Miniature line-scanned dual-axis confocal microscope for versatile clinical use.

A miniature optical-sectioning fluorescence microscope with high sensitivity and resolution would enable non-invasive and real-time tissue inspection, with potential use cases including early disease detection and intraoperative guidance. Previously, we developed a miniature MEMS-based dual-axis confocal (DAC) microscope that enabled video-rate optically sectioned in vivo microscopy of human tissues. However, the device's clinical utility was limited due to a small field of view, a non-adjustable working distance, and a lack of a sterilization strategy. In our latest design, we have made improvements to achieve a 2x increase in the field of view (600 × 300 µm) and an adjustable working distance range of 150 µm over a wide range of excitation/emission wavelengths (488-750 nm), all while maintaining a high frame rate of 15 frames per second (fps). Furthermore, the device is designed to image through a disposable sterile plastic drape for convenient clinical use. We rigorously characterize the performance of the device and show example images of ex vivo tissues to demonstrate the optical performance of our new design, including fixed mouse skin and human prostate, as well as fresh mouse kidney, mouse intestine, and human head and neck surgical specimens with corresponding H&E histology. These improvements will facilitate clinical testing and translation.

Comprehensive Surface Histology of Fresh Resection Margins With Rapid Open-Top Light-Sheet (OTLS) Microscopy.

OBJECTIVE: For tumor resections, margin status typically correlates with patient survival but positive margin rates are generally high (up to 45% for head and neck cancer). Frozen section analysis (FSA) is often used to intraoperatively assess the margins of excised tissue, but suffers from severe under-sampling of the actual margin surface, inferior image quality, slow turnaround, and tissue destructiveness. METHODS: Here, we have developed an imaging workflow to generate en face histologic images of freshly excised surgical margin surfaces based on open-top light-sheet (OTLS) microscopy. Key innovations include (1) the ability to generate false-colored H&E-mimicking images of tissue surfaces stained for < 1 min with a single fluorophore, (2) rapid OTLS surface imaging at a rate of 15 min/cm2 followed by real-time post-processing of datasets within RAM at a rate of 5 min/cm2, and (3) rapid digital surface extraction to account for topological irregularities at the tissue surface. RESULTS: In addition to the performance metrics listed above, we show that the image quality generated by our rapid surface-histology method approaches that of gold-standard archival histology. CONCLUSION: OTLS microscopy has the feasibility to provide intraoperative guidance of surgical oncology procedures. SIGNIFICANCE: The reported methods can potentially improve tumor-resection procedures, thereby improving patient outcomes and quality of life.

An end-to-end workflow for non-destructive 3D pathology.

Recent advances in 3D pathology offer the ability to image orders-of-magnitude more tissue than conventional pathology while providing a volumetric context that is lacking with 2D tissue sections, all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis is non-trivial, requiring careful attention to many details regarding tissue preparation, imaging, and data/image processing in an iterative process. Here we provide an end-to-end protocol covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. While 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol will focus on a fluorescent analog of hematoxylin and eosin (H&E), which remains the most common stain for gold-standard diagnostic determinations. We present our guidelines for a broad range of end-users (e.g., biologists, clinical researchers, and engineers) in a simple tutorial format.

In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment.

SIGNIFICANCE: There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, commercialized products, and publications have resulted from these efforts, mainstream clinical adoption has been relatively slow other than for a few clinical applications (e.g., dermatology). AIM: Here, our goals are threefold: (1) to introduce and motivate the need for in vivo microscopy (IVM) as an adjunctive tool for clinical detection, diagnosis, and treatment, (2) to discuss the key translational challenges facing the field, and (3) to propose best practices and recommendations to facilitate clinical adoption. APPROACH: We will provide concrete examples from various clinical domains, such as dermatology, oral/gastrointestinal oncology, and neurosurgery, to reinforce our observations and recommendations. RESULTS: While the incremental improvement and optimization of IVM technologies should and will continue to occur, future translational efforts would benefit from the following: (1) integrating clinical and industry partners upfront to define and maintain a compelling value proposition, (2) identifying multimodal/multiscale imaging workflows, which are necessary for success in most clinical scenarios, and (3) developing effective artificial intelligence tools for clinical decision support, tempered by a realization that complete adoption of such tools will be slow. CONCLUSIONS: The convergence of imaging modalities, academic-industry-clinician partnerships, and new computational capabilities has the potential to catalyze rapid progress and adoption of IVM in the next few decades.

Quantitative tumor depth determination using dual wavelength excitation fluorescence.

Quantifying solid tumor margins with fluorescence-guided surgery approaches is a challenge, particularly when using near infrared (NIR) wavelengths due to increased penetration depths. An NIR dual wavelength excitation fluorescence (DWEF) approach was developed that capitalizes on the wavelength-dependent attenuation of light in tissue to determine fluorophore depth. A portable dual wavelength excitation fluorescence imaging system was built and tested in parallel with an NIR tumor-targeting fluorophore in tissue mimicking phantoms, chicken tissue, and in vivo mouse models of breast cancer. The system showed high accuracy in all experiments. The low cost and simplicity of this approach make it ideal for clinical use.

Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis.

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation-assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning-based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. SIGNIFICANCE: An end-to-end pipeline for deep learning-assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

Calcium carbonate nanoparticles stimulate cancer cell reprogramming to suppress tumor growth and invasion in an organ-on-a-chip system.

The acidic microenvironment of solid tumors induces the propagation of highly invasive and metastatic phenotypes. However, simulating these conditions in animal models present challenges that confound the effects of pH modulators on tumor progression. To recapitulate the tumor microenvironment and isolate the effect of pH on tumor viability, we developed a bifurcated microfluidic device that supports two different cell environments for direct comparison. RFP-expressing breast cancer cells (MDA-MB-231) were cultured in treatment and control chambers surrounded by fibrin, which received acid-neutralizing CaCO3 nanoparticles (nanoCaCO3) and cell culture media, respectively. Data analysis revealed that nanoCaCO3 buffered the pH within the normal physiological range and inhibited tumor cell proliferation compared to the untreated control (p < 0.05). Co-incubation of cancer cells and fibroblasts, followed by nanoCaCO3 treatment showed that the nanoparticles selectively inhibited the growth of the MDA-MB-231 cells and reduced cellular migration of these cells with no impact on the fibroblasts. Sustainable decrease in the intracellular pH of cancer cells treated with nanoCaCO3 indicates that the extracellular pH induced cellular metabolic reprogramming. These results suggest that the nanoCaCO3 can restrict the aggressiveness of tumor cells without affecting the growth and behavior of the surrounding stromal cells.

Microdissected "cuboids" for microfluidic drug testing of intact tissues.

As preclinical animal tests often do not accurately predict drug effects later observed in humans, most drugs under development fail to reach the market. Thus there is a critical need for functional drug testing platforms that use human, intact tissues to complement animal studies. To enable future multiplexed delivery of many drugs to one small biopsy, we have developed a multi-well microfluidic platform that selectively treats cuboidal-shaped microdissected tissues or "cuboids" with well-preserved tissue microenvironments. We create large numbers of uniformly-sized cuboids by semi-automated sectioning of tissue with a commercially available tissue chopper. Here we demonstrate the microdissection method on normal mouse liver, which we characterize with quantitative 3D imaging, and on human glioma xenograft tumors, which we evaluate after time in culture for viability and preservation of the microenvironment. The benefits of size uniformity include lower heterogeneity in future biological assays as well as facilitation of their physical manipulation by automation. Our prototype platform consists of a microfluidic circuit whose hydrodynamic traps immobilize the live cuboids in arrays at the bottom of a multi-well plate. Fluid dynamics simulations enabled the rapid evaluation of design alternatives and operational parameters. We demonstrate the proof-of-concept application of model soluble compounds such as dyes (CellTracker, Hoechst) and the cancer drug cisplatin. Upscaling of the microfluidic platform and microdissection method to larger arrays and numbers of cuboids could lead to direct testing of human tissues at high throughput, and thus could have a significant impact on drug discovery and personalized medicine.

Performance tradeoffs for single- and dual-objective open-top light-sheet microscope designs: a simulation-based analysis.

Light-sheet microscopy (LSM) has emerged as a powerful tool for high-speed volumetric imaging of live model organisms and large optically cleared specimens. When designing cleared-tissue LSM systems with certain desired imaging specifications (e.g. resolution, contrast, and working distance), various design parameters must be taken into consideration. In order to elucidate some of the key design tradeoffs for LSM systems, we present a diffraction-based analysis of single- and dual-objective LSM configurations using simulations of LSM point spread functions. We assume Gaussian illumination is utilized. Specifically, we analyze the effects of the illumination and collection numerical aperture (NA), as well as their crossing angle, on spatial resolution and contrast. Assuming an open-top light-sheet (OTLS) architecture, we constrain these parameters based on fundamental geometric considerations as well as those imposed by currently available microscope objectives. In addition to revealing the performance tradeoffs of various single- and dual-objective LSM configurations, our analysis showcases the potential advantages of a novel, non-orthogonal dual-objective (NODO) architecture, especially for moderate-resolution imaging applications (collection NA of 0.5 to 0.8).