Open-top Light-sheet Microscopy Research Articles

Abstract PO2-28-12: Rapid diagnosis of breast biopsies with open-top light-sheet microscopy

Abstract Introduction: Breast cancer is typically diagnosed through a diagnostic work-up that involves specialized breast imaging, image-guided biopsy, and pathological assessment. Pathology results require 2 to 7 business days before a diagnosis is rendered, creating anxiety in women presenting with a breast abnormality. Decreasing pathology evaluation lead-time has the potential to reduce anxiety, streamline patient care, and reduce heathcare costs. In this pilot study, we evaluated the potential of open-top light-sheet microscopy to deliver a preliminary breast pathology diagnosis within 30 minutes of biopsy. Methods: Fresh 14-gauge breast biopsies (N=40) in normal saline were received directly from the breast imaging clinic and immediately stained using the nuclear marker SYBR Gold (Invitrogen) and pan-protein marker Atto 655 NHS Ester (Sigma) prepared in dimethyl sulfoxide, washed, and cleared for imaging at a refractive index of 1.46 using 2,2’-Thiodiethanol (Sigma). The full process requires approximately 14 minutes for staining and clearing. After staining, we placed the biopsy in an in-house-built specimen holder and imaged a 100 micron cross section along the full length of the biopsy using our custom open-top light-sheet microscope. Images were subsequently converted computationally to a standard H&E color format using Fiji and Aivia (Leica) software and were ready for evaluation by a pathologist the same day they were collected. mages were assessed by two pathologists with subspecialization in breast pathology for a two-category diagnosis: benign or atypical. After imaging, the biopsy was frozen, a frozen section was cut, and an H&E-stained slide was produced. Results: Using the protocol above, we demonstrated the ability to stain, clear, image, and visualize needle core biopsies within 30 minutes of receiving the tissue sample. Processing and converting the data to an H&E color palette required additional time, surpassing the 30-minute turnaround time goal. The images contained readily identifiable stroma, epithelial cells, immune cells, and duct structures to a depth of 100 microns. 29/40 biopsies yielded interpretable results. 11/40 had insufficient staining, clearing, or imaging quality for diagnosis. The number of non-diagnostic images improved in the second half of the study (5%) compared to the first half of the study (50%) with sample prep and imaging optimization. Agreement between the two breast pathologists on classifying the biopsies as benign vs. atypical was 89.7%. Discussion: We describe a novel method to obtain a microscopic image of breast core biopsy specimens to facilitate preliminary diagnosis within 30 minutes of receipt of tissue. The quality of the images produced by the method shows promise for preliminary diagnosis, especially after protocol optimization for the second half of the study. Additional optimization is needed in data processing to meet the 30-minute turnaround time requirement. This optimization can be achieved by parallelization of the data processing on a cluster or cloud, which is currently under investigation by our team. Citation Format: Brandy Olin Pope, Rebeca Alvarez, Charles Childers, Suzanne Dintzis, Sarah Javid, Nicholas Reder, Habib Rahbar. Rapid diagnosis of breast biopsies with open-top light-sheet microscopy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-12.

Analysis of 3D pathology samples using weakly supervised AI

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Abstract 3516: AI-derived features of 3D glandular networks are associated with likelihood of biochemical recurrence post radical prostatectomy

Abstract Introduction: Prostate cancer is the most common cancer in American men and is mainly diagnosed from two-dimensional histology sections. Recently, three-dimensional (3D) prostate histopathology has emerged as a valuable strategy to enhance the understanding of the disease by increasing microscopic sampling of specimens. This strategy enhances the microscopic examination of specimens by facilitating the analysis of the volumetric shape of cells and glands, as well as the pathway structures through entire biopsies and other morphological features. These aspects hold the potential to be linked with biochemical recurrence (BCR) outcomes. This study highlights the potential of features derived from the 3D structure of glands in samples of patients with prostate cancer for identifying patients who are at higher risk of BCR. Methods: Radical prostatectomy specimens were collected from 50 patients along with 5-year postoperative BCR follow-up from the University of Washington. A total of 118 ex vivo whole-biopsy 3D pathology images were obtained from RP by an open-top light-sheet microscopy platform. Images were converted into a synthetic CK8 immunofluorescence dataset using an image-sequence translation model. From these 3D images, a glandular structure mask was generated by a computer-vision algorithm. The 3D structure of prostate glands was modeled through a Tree-structure Extraction Algorithm for Accurate and Robust Skeletons (TEASER), which provides a simplification of the length of the gland, its shape, and its pathway. 91 features were then extracted from the glandular skeleton architecture. A 3-fold cross-validation approach was used in which patients were divided into training (D1) and test (D2) sets of 33 and 17 patients, respectively. Fisher’s score, used to select features based on their correlation to the outcome, was applied to D1 to identify the top 5 features most correlated with BCR. We then utilized these features to train a K-Nearest Neighbors classifier on D1, aimed at predicting whether a patient would experience BCR. The model’s performance was evaluated on D2 and averaged across all folds. Results: The classifier was able to accurately differentiate between BCR+ and BCR- patients, achieving an average ROCAUC of 0.73 (±0.04) in D2. Significant differences in 3D skeletal features were observed between BCR+ and BCR- groups (p<0.05). Among the five selected features, three were related to the length and diameter of the gland, with the longest glands observed in BCR- patients. The remaining two features pertained to gland curvature and tortuosity, revealing more twisted glands in BCR+ patients. Conclusion: Features derived from a skeleton model of the 3D glandular structure of biopsies showed promise in identifying patients with prostate cancer who are at a higher risk of BCR. Additional, multi-site independent validation of these findings is warranted. Citation Format: Jennifer Salguero, Hisham Abdeltawab, Kamal Hammouda, German Corredor, Sarah Hawley, Pushkar Mutha, Rohan Dhamdhere, Sebastian Medina, Tilak Pathak, Kevin Bishop, Rob Serafin, Tuomas Mirtti, Priti Lal, Lawrence True, Jonathan Liu, Anant Madabhushi. AI-derived features of 3D glandular networks are associated with likelihood of biochemical recurrence post radical prostatectomy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3516.

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

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. 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). 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. 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. 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.

Visual Assessment of 2-Dimensional Levels Within 3-Dimensional Pathology Data Sets of Prostate Needle Biopsies Reveals Substantial Spatial Heterogeneity

Prostate cancer prognostication largely relies on visual assessment of a few thinly sectioned biopsy specimens under a microscope to assign a Gleason grade group (GG). Unfortunately, the assigned GG is not always associated with a patient’s outcome in part because of the limited sampling of spatially heterogeneous tumors achieved by 2-dimensional histopathology. In this study, open-top light-sheet microscopy was used to obtain 3-dimensional pathology data sets that were assessed by 4 human readers. Intrabiopsy variability was assessed by asking readers to perform Gleason grading of 5 different levels per biopsy for a total of 20 core needle biopsies (ie, 100 total images). Intrabiopsy variability (Cohen κ) was calculated as the worst pairwise agreement in GG between individual levels within each biopsy and found to be 0.34, 0.34, 0.38, and 0.43 for the 4 pathologists. These preliminary results reveal that even within a 1-mm-diameter needle core, GG based on 2-dimensional images can vary dramatically depending on the location within a biopsy being analyzed. We believe that morphologic assessment of whole biopsies in 3 dimension has the potential to enable more reliable and consistent tumor grading.

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

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. 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. 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. OTLS microscopy has the feasibility to provide intraoperative guidance of surgical oncology procedures. The reported methods can potentially improve tumor-resection procedures, thereby improving patient outcomes and quality of life.

3D open-top light-sheet microscopy and 3D microdissection of neuoadjuvant-treated primary prostate cancer reveals latent subclonal mutations.

e17109 Background: Intratumoral molecular heterogeneity is a well described phenomenon in primary prostate cancer. Increased genomic heterogeneity is associated with more aggressive clinical behavior, including recurrence and metastasis. However, current methods to characterize heterogeneity suffer from tradeoffs between generating high quality morphologic information, correlating morphology with genomic alterations, and workflow considerations. We describe a technology to image prostate samples in 3D, perform 3D microdissection of regions of interest (ROIs), and preserve nucleic acids for DNA sequencing. Methods: Samples (N = 18) were obtained from patients (N = 12) enrolled in an open-label, single-site, Phase II neoadjuvant trial in men with NCCN high to very-high risk PC. Patients received 12 weeks of apalutamide, abiraterone acetate plus prednisone, degarelix, and indomethacin followed by radical prostatectomy (RP). Fresh frozen prostate samples (~2x2x0.4cm) were obtained from RP specimens. Frozen samples were fixed in an ethanol-based fixative, stained with nuclear (TOPRO-3) and general protein (eosin) fluorescent dyes, cleared with ethyl cinnamate, and imaged using 3D open-top light-sheet (OTLS) fluorescence microscopy. Image-guided 3D microdissection was performed on ROIs with distinct morphologies. Microdissected ROIs measured 0.5x0.5x2mm. Microdissected samples were sent for DNA extraction and sequencing using the comprehensive clinically-validated UW-OncoPlex NGS assay. Results: The tissue from neoadjuvant-treated patients contained sparse carcinoma, with cancer volumes ranging from 0.01-0.3 cm2 and cellularity ranging from 2%-30%. Molecular alterations were detected in a sample with only 0.07 cm2 volume of carcinoma with 5% cellularity. The read depth of 3D microdissected samples was 3.9 times greater than the read depth of FFPE samples from the same patients. At least two morphologically distinct ROIs were 3D microdissected from 11 of 12 patients. Four of the 18 samples had molecular alterations that differed between ROIs, i.e. intra-sample molecular heterogeneity. For example, one sample with three microdissected ROIs had a PTEN alteration exclusive to a single ROI. Conclusions: The findings show the potential for 3D microdissection of morphologically distinct subclones in samples from neoadjuvant-treated patient, to enable sequencing of specific cellular populations. The sparsity of carcinoma necessitated a spatially targeted approach for extracting material for sequencing. The increased yield from avoiding FFPE processing and carcinoma enrichment from targeted 3D microdissection of carcinoma ROIs enabled the detection of spatially distinct molecular subclones. Future work will include the analysis of additional samples and implementation of AI-guided ROI detection to streamline workflows.

Quantitative 3D assessment of the lung cancer microenvironment using multi-resolution open-top light-sheet microscopy.

2616 Background: Non-small cell lung cancer (NSCLC) tissue is a valuable resource for diagnosis, treatment planning, and drug development. Current 2D histopathological techniques introduce under-sampling error (i.e., a single 5 µm section represents 0.5% of a 1 mm thick biopsy), interobserver variability, and failure to capture the biology contained within the entire tissue sample. We have developed a suite of technologies to stain, chemically clarify, image, visualize, and analyze entire, intact lung tissue samples. Herein, we present analysis methods for comprehensive 3D assessment of intact NSCLC tissue. Methods: Human NSCLC tissue, stored frozen in optimal cutting temperature (OCT) compound, was fixed in 4% paraformaldehyde, stained with nuclear (TOPRO-3) and general protein (Eosin) fluorescent dyes, and optically cleared using a modified iDISCO protocol with ethyl cinnamate as the refractive index matching solution. Twenty tissue samples measuring 1-5 mm3 in volume were imaged at 2 µm/pixel resolution with a hybrid open-top light-sheet microscope. Smaller regions of interest (ROIs) with interesting pathologic features were re-imaged at higher resolution, 0.17 µm/pixel. Visualization and analysis was performed using Aivia software, custom spatial analysis software and python scripts. Results: All NSCLC tissue samples were successfully imaged in 3D at both low and high resolution. Computational approaches, such as machine learning, were used to segment and classify tumor cells and lymphocytes within the high resolution ROIs. In one example we quantified a 0.0045 mm3 ROI containing 1x109 voxels. We segmented 1,474 unique objects, and classified 683 (46%) of the objects as lymphocytes and 309 (20%) of the cells as tumor cells. We found that on average the lymphocytes were within 12 µm of the nearest tumor cell. Additionally the tissue was broken into separate lymphocyte dense (58% of volume) and tumor dense regions (42% of volume). Further analysis is in progress to quantify more features, such as tertiary lymphoid structures, stromal cells, and fibrosis over the full 20 sample dataset. Conclusions: We successfully implemented 3D machine learning analysis pipelines using images from intact NSCLC tissue samples measuring up to 5 mm3. The imaging was performed using our custom hybrid open-top light-sheet microscope. This novel method enables us to identify and quantify key histological features such as tumor cells and lymphocytes within the tumor microenvironment. In addition, machine learning driven algorithms clustered cells into distinct tissue regions in 3D which enables a more precise assessment of disease state and heterogeneity. Further investigation is ongoing to link these results to patient outcomes.

Evaluation of initial prostate cancer biopsies utilizing 3D open-top light-sheet microscopy for detection of early disease.

Open-top light-sheet (OTLS) microscopy is a technique that allows for high-resolution 3D imaging of tissue specimens and can therefore provide a more detailed assessment of tissue architecture. Given that Gleason grading is based on tissue architecture, we hypothesized that OTLS microscopy would enable us to survey a larger amount of tissue and detect occult prostate cancers in men who had prostate core biopsy specimens initially classified as being benign-appearing who later developed clinically significant prostate cancers. Benign appearing tissue (based on routine pathologic evaluation) from 20 patients who subsequently developed a clinically significant prostate cancer (experimental group) was evaluated with OTLS microscopy and compared to tissue from 20 patients who underwent prostate biopsy and never developed a clinically significant prostate cancer (control group). We compared the incidence of detectable prostate cancer between groups. Baseline clinical characteristics were similar between the experimental and control groups. Three patients (15%) in the control group and one (5%) in the experimental group had suspicious findings on low-resolution OTLS microscopy. Higher resolution OTLS imaging revealed two patients (10%) in the control group had an occult prostate cancer, while no occult cancers were found in the experimental group. In spite of a high pretest probability for the presence of an occult prostate cancer, we did not identify cancer in our experimental group. This may be due to under-sampling at the time of prostate needle biopsy.

Abstract 6624: 3D assessment of the lung cancer microenvironment using multi-resolution open-top light-sheet microscopy

Abstract Background: Non-small cell lung cancer (NSCLC) tissue is a valuable resource for diagnosis, treatment planning, and drug development. Current 2D histopathological techniques introduce under-sampling error (i.e., a single 5 um section represents 0.5% of a 1 mm thick biopsy), interobserver variability, and fail to capture the biology contained within the entire tissue sample. We have developed a suite of technologies to stain, chemically clarify, image, visualize, and analyze entire intact NSCLC tissue samples. Methods: Human NSCLC tissue, stored frozen in OCT, was fixed in 4% paraformaldehyde, stained with nuclear (TOPRO-3) and general protein (Eosin) fluorescent dyes, and optically cleared using a modified iDISCO protocol with ethyl cinnamate as the refractive index matching solution. Whole, intact tissue samples, roughly 1-5mm3 in volume, were imaged at 2 microns/pixel resolution with an open-top light-sheet microscope (3Di, Alpenglow Biosciences). Smaller regions of interest (ROIs) with key pathologic features were reimaged at higher resolution, 0.17 microns/pixel, to reveal subnuclear features and for cell typing. Visualization was performed using Aivia software. Results: NSCLC tissue samples were successfully imaged in 3D. Low resolution images (2 microns/pixel) were obtained within 4-31 minutes, depending on the tissue volume. The 3D distribution of cancer cells, immune cells, vessels, and fibrosis varied substantially throughout the volume of the tissue. Recognizable histologic features, including nests of tumor cells surrounded by vasculature and immune cells, were readily visualized. Squamous and adenocarcinoma with its subtypes (solid, acinar, lepidic, and micropapillary) morphologies were recognizable in 2D optical sections of the 3D datasets. Imaging quality degraded in tissue deeper than 1 mm due to light scattering. Conclusion: We assessed intact NSCLC tissue samples measuring up to 5 mm3 using our custom light-sheet microscope and tissue clearing techniques. This novel method enables us to visualize key features of NSCLC such as the tumor interfaces, tertiary lymphoid structures, vessels and fibrosis in the entire tissue sample, preventing under sampling error, and potentially enabling new biologic insights. Next steps include segmentation and quantification of key tissue structures such as tumor volume, immune cell distribution, and fibrosis/immune cell exclusion. This proof-of-concept study provides motivation for further investigation into the significance of 3D tissue features in NSCLC tissue samples. Citation Format: Alexandra Alvarsson, Carl Storey, Brandy Olin Pope, Caleb Stoltzfus, Robert Vierkant, Jessica Tufariello, Aaron Bungum, Julia Naso, Cheuk Ki Chan, Eric Edell, Christopher Hartley, Janani Reisenauer, Nicholas Reder. 3D assessment of the lung cancer microenvironment using multi-resolution open-top light-sheet microscopy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6624.

Abstract P6-04-18: Rapid diagnosis of breast biopsies with open-top light-sheet microscopy

Abstract Intro: Detection of breast cancer is achieved through a diagnostic work-up that involves specialized breast imaging, image-guided biopsy, and pathological assessment. Pathology results typically require ~ 3 business days before a diagnosis is rendered, creating avoidable anxiety in women presenting with a breast abnormality. We describe a solution to deliver a rapid preliminary diagnosis within 30 minutes of biopsy. This rapid preliminary diagnosis has the potential to reduce anxiety, streamline patient care workflows, and reduce healthcare costs. Methods: Fresh 14 gauge breast biopsies in normal saline were received directly from the breast imaging clinic and immediately stained using the nuclear marker SYBR Gold (Invitrogen) and pan-protein marker Atto 655 NHS Ester (Sigma) prepared in dimethyl sulfoxide, washed, and cleared for imaging at a refractive index of 1.46 using 2,2’-Thiodiethanol (Sigma). The full process requires approximately 14 minutes for staining and clearing. After staining, we placed the biopsy in a custom-built specimen holder and imaged a 100-micron cross section along the full length of the biopsy using our custom open-top light-sheet microscope. Images were subsequently converted computationally to a standard hematoxylin and eosin (H&amp;E) color format using Fiji and Aivia (Leica) software and were ready for evaluation by a pathologist on the same day they were collected. Results: Using the protocol above, we demonstrated the ability to stain, clear, image, and visualize needle core biopsies within 30 minutes of receiving the tissue sample. Processing the data and converting to the H&amp;E color palette required additional time, often requiring 60-90 minutes, surpassing the overall 30 minute turnaround time goal. The images contained identifiable stroma, epithelial cells, immune cells, and duct structures to a depth of 100 microns. Discussion: We describe a method to obtain a microscopic image for preliminary diagnosis within 30 minutes of receipt of tissue. The quality of the images produced by the method shows promise for preliminary diagnosis. Additional optimization is needed in sample preparation and data processing to meet the 30 minute turnaround time requirement. This optimization can be achieved by parallelization of the data processing on a cluster or cloud to reduce the time by an order of magnitude, which is currently under investigation by our team. The imaging and data processing will also be accelerated by multi-resolution imaging, which will decrease the time of imaging and dataset size for processing. A diagnostic study, comparing the preliminary light-sheet-based diagnosis to the final formalin fixed paraffin embedded (FFPE) pathology, is underway. Citation Format: Brandy E. Olin Pope, Suzanne Dintzis, Elizabeth U. Parker, Rebeca Alvarez, Alexandra Alvarsson, Habib Rahbar, Nicholas Reder. Rapid diagnosis of breast biopsies with open-top light-sheet microscopy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-18.

Nondestructive 3D Pathology Image Atlas of Barrett Esophagus With Open-Top Light-Sheet Microscopy.

Anatomic pathologists render diagnosis on tissue samples sectioned onto glass slides and viewed under a bright-field microscope. This approach is destructive to the sample, which can limit its use for ancillary assays that can inform patient management. Furthermore, the subjective interpretation of a relatively small number of 2D tissue sections per sample contributes to low interobserver agreement among pathologists for the assessment (diagnosis and grading) of various lesions. To evaluate 3D pathology data sets of thick formalin-fixed Barrett esophagus specimens imaged nondestructively with open-top light-sheet (OTLS) microscopy. Formalin-fixed, paraffin-embedded Barrett esophagus samples (N = 15) were deparaffinized, stained with a fluorescent analog of hematoxylin-eosin, optically cleared, and imaged nondestructively with OTLS microscopy. The OTLS microscopy images were subsequently compared with archived hematoxylin-eosin histology sections from each sample. Barrett esophagus samples, both small endoscopic forceps biopsies and endoscopic mucosal resections, exhibited similar resolvable structures between OTLS microscopy and conventional light microscopy with up to a ×20 objective (×200 overall magnification). The 3D histologic images generated by OTLS microscopy can enable improved discrimination of cribriform and well-formed gland morphologies. In addition, a much larger amount of tissue is visualized with OTLS microscopy, which enables improved assessment of clinical specimens exhibiting high spatial heterogeneity. In esophageal specimens, OTLS microscopy can generate images comparable in quality to conventional light microscopy, with the advantages of providing 3D information for enhanced evaluation of glandular morphologies and enabling much more of the tissue specimen to be visualized nondestructively.

Hybrid Open-Top Light-Sheet Microscopy for Multi-Scale 3D Imaging of Cleared and Expanded Tissues

Hybrid Open-Top Light-Sheet Microscopy for Multi-Scale 3D Imaging of Cleared and Expanded Tissues

Open-top light-sheet imaging of CLEAR emulsion for high-throughput loss-free analysis of massive fluorescent droplets

Digital droplet PCR (ddPCR) is classified as the third-generation PCR technology that enables absolute quantitative detection of nucleic acid molecules and has become an increasingly powerful tool for clinic diagnosis. We previously established a CLEAR-dPCR technique based on the combination of CLEAR droplets generated by micro-centrifuge-based microtubule arrays (MiCA) and insitu 3D readout by light-sheet fluorescence imaging. This CLEAR-dPCR technique attains very high readout speed and dynamic range. Meanwhile, it is free from sample loss and contamination, showing its advantages over commercial d-PCR technologies. However, a conventional orthogonal light-sheet imaging setup in CLEAR d-PCR cannot image multiple centrifuge tubes, thereby limiting its widespread application to large-scale, high-speed dd-PCR assays. Herein, we propose an in-parallel 3D dd-PCR readout technique based on an open-top light-sheet microscopy setup. This approach can continuously scan multiple centrifuge tubes which contain CLEAR emulsions with highly diverse concentrations, and thus further boost the scale and throughput of our 3D dd-PCR technique.

Multiresolution nondestructive 3D pathology of whole lymph nodes for breast cancer staging.

.SignificanceFor breast cancer patients, the extent of regional lymph node (LN) metastasis influences the decision to remove all axillary LNs. Metastases are currently identified and classified with visual analysis of a few thin tissue sections with conventional histology that may underrepresent the extent of metastases.AimWe sought to enable nondestructive three-dimensional (3D) pathology of human axillary LNs and to develop a practical workflow for LN staging with our method. We also sought to evaluate whether 3D pathology improves staging accuracy in comparison to two-dimensional (2D) histology.ApproachWe developed a method to fluorescently stain and optically clear LN specimens for comprehensive imaging with multiresolution open-top light-sheet microscopy. We present an efficient imaging and data-processing workflow for rapid evaluation of H&E-like datasets in 3D, with low-resolution screening to identify potential metastases followed by high-resolution localized imaging to confirm malignancy.ResultsWe simulate LN staging with 3D and 2D pathology datasets from 10 metastatic nodes, showing that 2D pathology consistently underestimates metastasis size, including instances in which 3D pathology would lead to upstaging of the metastasis with important implications on clinical treatment.ConclusionsOur 3D pathology method may improve clinical management for breast cancer patients by improving staging accuracy of LN metastases.

High-Resolution Multiscale Imaging Enabled by Hybrid Open-Top Light-Sheet Microscopy.

High-Resolution Multiscale Imaging Enabled by Hybrid Open-Top Light-Sheet Microscopy.

Open-top axially swept light-sheet microscopy.

Open-top light-sheet microscopy (OT-LSM) is a specialized microscopic technique for high throughput cellular imaging of large tissue specimens including optically cleared tissues by having the entire optical setup below the sample stage. Current OT-LSM systems had relatively low axial resolutions by using weakly focused light sheets to cover the imaging field of view (FOV). In this report, open-top axially swept LSM (OTAS-LSM) was developed for high-throughput cellular imaging with improved axial resolution. OTAS-LSM swept a tightly focused excitation light sheet across the imaging FOV using an electro tunable lens (ETL) and collected emission light at the focus of the light sheet with a camera in the rolling shutter mode. OTAS-LSM was developed by using air objective lenses and a liquid prism and it had on-axis optical aberration associated with the mismatch of refractive indices between air and immersion medium. The effects of optical aberration were analyzed by both simulation and experiment, and the image resolutions were under 1.6µm in all directions. The newly developed OTAS-LSM was applied to the imaging of optically cleared mouse brain and small intestine, and it demonstrated the single-cell resolution imaging of neuronal networks. OTAS-LSM might be useful for the high-throughput cellular examination of optically cleared large tissues.

Diagnosing 12 prostate needle cores within an hour of biopsy via open-top light-sheet microscopy.

.Significance: Processing and diagnosing a set of 12 prostate biopsies using conventional histology methods typically take at least one day. A rapid and accurate process performed while the patient is still on-site could significantly improve the patient’s quality of life.Aim: We develop and assess the feasibility of a one-hour-to-diagnosis (1Hr2Dx) method for processing and providing a preliminary diagnosis of a set of 12 prostate biopsies.Approach: We developed a fluorescence staining, optical clearing, and 3D open-top light-sheet microscopy workflow to enable 12 prostate needle core biopsies to be processed and diagnosed within an hour of receipt. We analyzed 44 biopsies by the 1Hr2Dx method, which does not consume tissue. The biopsies were then processed for routine, slide-based 2D histology. Three pathologists independently evaluated the 3D 1Hr2Dx and 2D slide-based datasets in a blinded, randomized fashion. Turnaround times were recorded, and the accuracy of our method was compared with gold-standard slide-based histology.Results: The average turnaround time for tissue processing, imaging, and diagnosis was 44.5 min. The sensitivity and specificity of 1Hr2Dx in diagnosing cancer were both .Conclusions: The 1Hr2Dx method has the potential to improve patient care by providing an accurate preliminary diagnosis within an hour of biopsy.

Rapid pathology of lumpectomy margins with open-top light-sheet (OTLS) microscopy.

Open-top light-sheet microscopy is a technique that can potentially enable rapid ex vivo inspection of large tissue surfaces and volumes. Here, we have optimized an open-top light-sheet (OTLS) microscope and image-processing workflow for the comprehensive examination of surgical margin surfaces, and have also developed a novel fluorescent analog of H&E staining that is robust for staining fresh unfixed tissues. Our tissue-staining method can be achieved within 2.5 minutes followed by OTLS microscopy of lumpectomy surfaces at a rate of up to 1.5 cm2/minute. An image atlas is presented to show that OTLS image quality surpasses that of intraoperative frozen sectioning and can approximate that of gold-standard H&E histology of formalin-fixed paraffin-embedded (FFPE) tissues. Qualitative evidence indicates that these intraoperative methods do not interfere with downstream post-operative H&E histology and immunohistochemistry. These results should facilitate the translation of OTLS microscopy for intraoperative guidance of lumpectomy and other surgical oncology procedures.