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Supplementary Information (5 sections): Study design; Review of prior studies to validate the technical robustness of REMI; Selection of cell-surface biomarkers for REMI of breast tumors; Spectral demultiplexing to quantify SERS NPs; Cell culture and flow cytometry for the validation of conjugated NPs Supplementary Figures (10 figures): Supplementary Figure S1. Linearity and limit-of-detection of spectral measurements. Supplementary Figure S2. REMI of normal tissue (EGFR-negative) and tumor xenograſts (SkBr3, U251, and A431) that express various levels of EGFR. Supplementary Figure S3. Structure and spectra of SERS NPs. Supplementary Figure S4. Flow-cytometry based validation of the binding of targeted NPs to cell-surface receptors on cultured cells. Supplementary Figure S5. REMI enables the detection of double-positive breast tumor (ER+ and HER2+). Supplementary Figure S6. REMI enables the detection of EGFR in a triple-negative breast tumor. Supplementary Figure S7. REMI of fibroadenoma (benign lesion). Supplementary Figure S8. An illustration of a key difference between staining histological tissue sections and fresh tissue surfaces. Supplementary Figure S9. Comparison of traditional single-targeted-agent imaging vs. ratiometric imaging, in which an untargeted agent is used to normalize for the nonspecific accumulation that occurs for all agents. Supplementary Figure S10. Comparison of ROC curves for identification of biomarker expression using (A) a ratiometric imaging strategy (same as Fig. 5B) and (B) a traditional single-targeted-agent strategy. Supplementary Table S1. Benign lesions. Supplementary Table S2. Molecular profile of patients.

Intraoperative identification of carcinoma at lumpectomy margins would enable reduced re-excision rates, which are currently as high as 20% to 50%. Although imaging of disease-associated biomarkers can identify malignancies with high specificity, multiplexed imaging of such biomarkers is necessary to detect molecularly heterogeneous carcinomas with high sensitivity. We have developed a Raman-encoded molecular imaging (REMI) technique in which targeted nanoparticles are topically applied on excised tissues to enable rapid visualization of a multiplexed panel of cell surface biomarkers at surgical margin surfaces. A first-ever clinical study was performed in which 57 fresh specimens were imaged with REMI to simultaneously quantify the expression of four biomarkers HER2, ER, EGFR, and CD44. Combined detection of these biomarkers enabled REMI to achieve 89.3% sensitivity and 92.1% specificity for the detection of breast carcinoma. These results highlight the sensitivity and specificity of REMI to detect biomarkers in freshly resected tissue, which has the potential to reduce the rate of re-excision procedures in cancer patients. Cancer Res; 77(16); 4506–16. ©2017 AACR.

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.

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. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 16 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

Recent technological advances have demonstrated the feasibility of achieving high-throughput slide-free three-dimensional (3D) pathology of biopsy and surgical specimens. In comparison to conventional slide-based pathology, 3D pathology has the potential to provide a transformative improvement in diagnostic performance for a number of reasons: (1) vastly greater (multiple log orders) sampling of tissue specimens, (2) volumetric imaging of cell distributions and tissue structures that are prognostic and predictive, (3) nondestructive imaging, which allows valuable biopsy specimens to be used for downstream molecular assays, and (4) a simplified process with cost benefits for pathology laboratories. However, due to the immense size of these feature-rich datasets, new challenges exist in terms of data management and computer-aided interpretation. In this forward-looking Perspective, we first provide a brief overview of the imaging technologies that can enable nondestructive 3D pathology, including computational tools needed to support these 3D methods. We then provide a roadmap for how machine learning, which is already being developed within the context of 2D digital pathology, should be leveraged and refined for 3D pathology. Finally, we discuss future challenges and opportunities for the clinical validation, regulatory approval, and clinical adoption of this new paradigm for precision medicine, including for reducing health disparities across populations. This includes learning from, and integrating with, other diagnostic modalities such as radiology and genomics.

For 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.We 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.We 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 HE-like datasets in 3D, with low-resolution screening to identify potential metastases followed by high-resolution localized imaging to confirm malignancy.We 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.Our 3D pathology method may improve clinical management for breast cancer patients by improving staging accuracy of LN metastases.

Light-sheet microscopy has emerged as the preferred means for high-throughput volumetric imaging of cleared tissues. However, there is a need for a user-friendly system that can address diverse imaging applications with varied requirements in terms of resolution (mesoscopic to sub-micron), sample geometry (size, shape, and number), and compatibility with tissue-clearing protocols of different refractive indices. We present a hybrid system that combines a novel non-orthogonal dual-objective and conventional open-top light-sheet architecture for highly versatile multi-scale volumetric imaging. One sentence summary Glaser et al. describe a hybrid open-top light-sheet microscope to image cleared tissues at mesoscopic to sub-micron resolution and depths of up to 1 cm.

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. In current clinical practice, the microscopic architecture of the prostate glands is what forms the basis for prognostic grading by pathologists. Interpretation of these convoluted 3D glandular structures via visual inspection of a limited number of 2D histology sections is often unreliable, which contributes to the under- and over-treatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for non-destructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analog of standard H&E staining. Our 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 real immunolabeling. To provide evidence of the translational value of a computational 3D pathology approach, we analyzed ex vivo biopsies (n = 300) extracted from archived radical-prostatectomy specimens (N = 50), and found that 3D glandular features are superior to corresponding 2D features for risk stratification of low-to intermediate-risk PCa patients based on their clinical biochemical recurrence (BCR) outcomes.SignificanceWe present an end-to-end pipeline for computational 3D pathology of whole prostate biopsies, showing that non-destructive pathology has the potential to enable superior prognostic stratification for guiding critical oncology decisions.

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.

Recent advances in optical clearing and light-sheet microscopy have provided unprecedented access to structural and molecular information from intact tissues. However, current light-sheet microscopes have imposed constraints on the size, shape, number of specimens, and compatibility with various clearing protocols. Here we present a multi-immersion open-top light-sheet microscope that enables simple mounting of multiple specimens processed with a variety of clearing protocols, which will facilitate wide adoption by preclinical researchers and clinical laboratories. In particular, the open-top geometry provides unsurpassed versatility to interface with a wide range of accessory technologies in the future., Light-sheet microscopes are increasingly used for imaging cleared tissues, but have imposed constraints on sample geometries and protocols. Here the authors present a multi-immersion open-top light-sheet microscope to overcome these limitations and enable high-throughput imaging of samples processed with various clearing protocols.

Context.—Ex vivo microscopy encompasses a range of techniques to examine fresh or fixed tissue with microscopic resolution, eliminating the need to embed the tissue in paraffin or produce a glass slide. One such technique is light-sheet microscopy, which enables rapid 3D imaging. Our pathology-engineering collaboration has resulted in an open-top light-sheet (OTLS) microscope that is specifically tailored to the needs of pathology practice.Objective.—To present an image atlas of OTLS images of prostate core needle biopsies.Design.—Core needle biopsies (N = 9) were obtained from fresh radical prostatectomy specimens. Each biopsy was fixed in formalin, dehydrated in ethanol, stained with TO-PRO3 and eosin, optically cleared, and imaged using OTLS microscopy. The biopsies were then processed, paraffin embedded, and sectioned. Hematoxylin-eosin and immunohistochemical staining for cytokeratin 5 and cytokeratin 8 was performed.Results.—Benign and neoplastic histologic structures showed high fidelity between OTLS and traditional light microscopy. OTLS microscopy had no discernible effect on hematoxylin-eosin or immunohistochemical staining in this pilot study. The 3D histology information obtained using OTLS microscopy enabled new structural insights, including the observation of cribriform and well-formed gland morphologies within the same contiguous glandular structures, as well as the continuity of poorly formed glands with well-formed glands.Conclusions.—Three-dimensional OTLS microscopy images have a similar appearance to traditional hematoxylin-eosin histology images, with the added benefit of useful 3D structural information. Further studies are needed to continue to document the OTLS appearance of a wide range of tissues and to better understand 3D histologic structures.

Light-sheet microscopy has emerged as the preferred means for high-throughput volumetric imaging of cleared tissues. However, there is a need for a user-friendly system that can address diverse imaging applications with varied requirements in terms of resolution (mesoscopic to sub-micron), sample geometry (size, shape, and number), and compatibility with tissue-clearing protocols of different refractive indices. We present a hybrid system that combines a novel non-orthogonal dual-objective and conventional open-top light-sheet architecture for highly versatile multi-scale volumetric imaging.

Glandular architecture is currently the basis for the Gleason grading of prostate biopsies. To visualize and computationally analyze glandular features in large 3D pathology datasets, we developed an annotation-free segmentation method for 3D prostate glands that relies upon synthetic 3D immunofluorescence (IF) enabled by generative adversarial networks. By using a fluorescent analog of H and E (cheap and fast stain) as an input, our strategy allows for accurate glandular segmentation that does not rely upon subjective and tedious human annotations or slow and expensive 3D immunolabeling. We aim to demonstrate that this 3D segmentation will enable improved prostate cancer prognostication.

Open-top light-sheet (OTLS) microscopy has recently been developed as a high-throughput, easy-to-use 3D microscopy technique for large specimens [1-2]. The oblique angle of the optical beams relative to the sample plate introduces challenges however, and previous solutions (such as a solid immersion lens) have been limiting [1]. Therefore we have developed a solid immersion meniscus lens (SIMlens), which enables optical beams to transition from air to a higher-index immersion medium without introducing aberrations [3]. A SIMlens is compatible with a turret of air objectives, enabling efficient multi-resolution workflows [4]. We present the first multi-resolution OTLS microscope, enabled by a SIMlens.

A current challenge is providing an accurate diagnosis in a timely manner for patients at risk of having prostate cancer. We developed and demonstrated a non-destructive procedure in which 12 biopsies can be cleared, fluorescently labeled, imaged with an open-top light-sheet (OTLS) microscope, and then diagnosed by a pathologist within an hour of biopsy. Using conventional histology as the gold standard, the accuracy, sensitivity, and specificity of 1Hr2Dx were all >90%. Such a method could potentially provide patients with a preliminary on-site diagnosis after a biopsy procedure, thereby alleviating anxiety and potentially expediting treatments.

Fluorescence-based slide-free digital pathology techniques, including 3D microscopy, are gaining interest as alternatives to traditional slide-based histology. Since pathologists are accustomed to the appearance of standard histology stains, the ability to render grayscale fluorescent images with color palettes that mimic traditional histology is valuable. We present FalseColor-Python, an open-source rapid digital-staining package that renders two-channel fluorescence images to mimic standard histology. Our package offers consistent color-space representations that are robust to both intra-specimen and inter-specimen variations in staining and imaging conditions, along with GPU-accelerated methods to process large datasets efficiently.

Light-sheet microscopy (LSM) is a powerful technique for rapid volumetric imaging of optically cleared specimens. Given the range of possible LSM configurations, designers would benefit from a systematic evaluation of the tradeoffs between different architectures. We present a simulation-based analysis of single- and dual-objective LSM designs for open-top, cleared-tissue imaging. System resolution and contrast are evaluated as functions of the crossing angle between the illumination and collection beams and each beam’s numerical aperture (NA). Our analysis reveals several key tradeoffs to guide designers in addition to potential advantages of a non-orthogonal dual-objective (NODO) architecture for moderate resolution imaging.

Light-sheet microscopy (LSM) has emerged as the technique of choice for many biologists imaging large cleared tissues due to its speed and optical efficiency, which make it possible to generate massive datasets of large specimens at high resolution. Here, we build on several recent innovations in LSM to present a non-orthogonal dual-objective (NODO) LSM system with axial sweeping in an open-top configuration. This system is specifically designed to image large cleared brain tissues, such as for axonal connectomics, and provides subcellular resolution (0.3 µm lateral, 2 µm axial) of large cleared samples up to 8 mm thick.

Fluorescence microscopy is a vital tool in biomedical research but faces considerable challenges in achieving uniform or bright labeling. For instance, fluorescent proteins are limited to model organisms, and antibody conjugates can be inconsistent and difficult to use with thick specimens. To partly address these challenges, we developed a labeling protocol that can rapidly visualize many well-contrasted key features and landmarks on biological specimens in both thin and thick tissues or cultured cells. This approach uses established reactive fluorophores to label a variety of biological specimens for cleared-tissue microscopy or expansion super-resolution microscopy and is termed FLARE (fluorescent labeling of abundant reactive entities). These fluorophores target chemical groups and reveal their distribution on the specimens; amine-reactive fluorophores such as hydroxysuccinimidyl esters target accessible amines on proteins, while hydrazide fluorophores target oxidized carbohydrates. The resulting stains provide signals analogous to traditional general histology stains such as HE or periodic acid-Schiff but use fluorescent probes that are compatible with volumetric imaging. In general, the stains for FLARE are performed in the order of carbohydrates, amine and DNA, and the incubation time for the stains varies from 1 h to 1 d depending on the combination of stains and the type and thickness of the biological specimens. FLARE is powerful, robust and easy to implement in laboratories that already routinely do fluorescence microscopy.

In recent years light-sheet microscopy has emerged as a powerful technique for fast volumetric imaging of live specimens [1] . In light-sheet microscopy, a thin "sheet" of light optically (rather than physically) sections a specimen, such that fluorescence is only excited within one "optical section". By using weakly-focused illumination light, the sheet maintains its sectioning ability over hundreds of microns, enabling high-speed capture of 2D images. And by scanning the specimen (or light sheet) a 3D dataset is quickly generated [2] . In most systems, the illumination and collection objectives are oriented orthogonally, such that the light sheet is co-planar with the focal plane of the collection objective [3] . However, more recently a class of light-sheet microscopes which use a single objective for both illumination and collection have been proposed. These non-orthogonal designs, known as oblique-plane microscopy (OPM), swept-confocally aligned planar excitation (SCAPE), epi-illumination selective-plane illumination microscopy (eSPIM), and scanned oblique-plane illumination microscopy (SOPi), illuminate samples with a light sheet which is imaged in a non-orthogonal fashion, which is rectified at a tilted remote image focus [4] - [8] . While this non-orthogonal arrangement results in slightly worse imaging performance (compared to the orthogonal arrangement), the use of a single objective enables more convenient and less physically restricted imaging geometries. To date, these non-orthogonal light-sheet microscopes have primarily been explored for dynamic imaging of live specimens, and more recently water-based expanded tissues. Here we combine this non-orthogonal architecture with a more traditional orthogonal light-sheet microscope. The non-orthogonal optical path providing micron to sub-micron resolution, and the orthogonal system providing lower magnification mesoscale imaging. Combined, the single system is able to provide omniscale 3D imaging.

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 >90%. Conclusions: The 1Hr2Dx method has the potential to improve patient care by providing an accurate preliminary diagnosis within an hour of biopsy.

FLARE enables rapid, feature-rich stains of chemical groups in biological samples for super-resolution and cleared tissue imaging., Fluorescence microscopy is a workhorse tool in biomedical imaging but often poses substantial challenges to practitioners in achieving bright or uniform labeling. In addition, while antibodies are effective specific labels, their reproducibility is often inconsistent, and they are difficult to use when staining thick specimens. We report the use of conventional, commercially available fluorescent dyes for rapid and intense covalent labeling of proteins and carbohydrates in super-resolution (expansion) microscopy and cleared tissue microscopy. This approach, which we refer to as Fluorescent Labeling of Abundant Reactive Entities (FLARE), produces simple and robust stains that are modern equivalents of classic small-molecule histology stains. It efficiently reveals a wealth of key landmarks in cells and tissues under different fixation or sample processing conditions and is compatible with immunolabeling of proteins and in situ hybridization labeling of nucleic acids.

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).

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 trade-offs for LSM systems, we present a diffraction-based analysis of single- and dual-objective LSM configurations where 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).

Light-sheet microscopy has emerged as the preferred means for high-throughput volumetric imaging of cleared tissues. However, there is a need for a user-friendly system that can address diverse imaging applications with varied requirements in terms of resolution (mesoscopic to sub-micron), sample geometry (size, shape, and number), and compatibility with tissue-clearing protocols of different refractive indices. We present a hybrid system that combines a novel non-orthogonal dual-objective and conventional open-top light-sheet architecture for highly versatile multi-scale volumetric imaging. One sentence summary Glaser et al. describe a hybrid open-top light-sheet microscope to image cleared tissues at mesoscopic to sub-micron resolution and depths of up to 1 cm.

Handheld and endoscopic optical-sectioning microscopes are being developed for noninvasive screening and intraoperative consultation. Imaging a large extent of tissue is often desired, but miniature in vivo microscopes tend to suffer from limited fields of view. To extend the imaging field during clinical use, we have developed a real-time video mosaicking method, which allows users to efficiently survey larger areas of tissue. Here, we modified a previous post-processing mosaicking method so that real-time mosaicking is possible at >30 frames/second when using a device that outputs images that are 400 × 400 pixels in size. Unlike other real-time mosaicking methods, our strategy can accommodate image rotations and deformations that often occur during clinical use of a handheld microscope. We perform a feasibility study to demonstrate that the use of real-time mosaicking is necessary to enable efficient sampling of a desired imaging field when using a handheld dual-axis confocal microscope.

Intraoperative identification of carcinoma at lumpectomy margins would enable reduced re-excision rates, which are currently as high as 20% to 50%. Although imaging of disease-associated biomarkers can identify malignancies with high specificity, multiplexed imaging of such biomarkers is necessary to detect molecularly heterogeneous carcinomas with high sensitivity. We have developed a Raman-encoded molecular imaging (REMI) technique in which targeted nanoparticles are topically applied on excised tissues to enable rapid visualization of a multiplexed panel of cell surface biomarkers at surgical margin surfaces. A first-ever clinical study was performed in which 57 fresh specimens were imaged with REMI to simultaneously quantify the expression of four biomarkers HER2, ER, EGFR, and CD44. Combined detection of these biomarkers enabled REMI to achieve 89.3% sensitivity and 92.1% specificity for the detection of breast carcinoma. These results highlight the sensitivity and specificity of REMI to detect biomarkers in freshly resected tissue, which has the potential to reduce the rate of re-excision procedures in cancer patients. Cancer Res; 77(16); 4506–16. ©2017 AACR.

Slide-free digital pathology techniques, including nondestructive 3D microscopy, are gaining interest as alternatives to traditional slide-based histology. In order to facilitate clinical adoption of these fluorescence-based techniques, software methods have been developed to convert grayscale fluorescence images into color images that mimic the appearance of standard absorptive chromogens such as hematoxylin and eosin (H&E). However, these false-coloring algorithms often require manual and iterative adjustment of parameters, with results that can be inconsistent in the presence of intensity nonuniformities within an image and/or between specimens (intra- and inter-specimen variability). Here, we present an open-source (Python-based) rapid intensity-leveling and digital-staining package that is specifically designed to render two-channel fluorescence images (i.e. a fluorescent analog of H&E) to the traditional H&E color space for 2D and 3D microscopy datasets. However, this method can be easily tailored for other false-coloring needs. Our package offers (1) automated and uniform false coloring in spite of uneven staining within a large thick specimen, (2) consistent color-space representations that are robust to variations in staining and imaging conditions between different specimens, and (3) GPU-accelerated data processing to allow these methods to scale to large datasets. We demonstrate this platform by generating H&E-like images from cleared tissues that are fluorescently imaged in 3D with open-top light-sheet (OTLS) microscopy, and quantitatively characterizing the results in comparison to traditional slide-based H&E histology.

In recent years we have developed open-top light-sheet microscopes for a variety of clinical applications. Here we present a new light-sheet micro-dissection microscope for improved molecular analysis of intact tissue specimens.

An ~45-minute workflow, consisting of fluorescence labeling, tissue clearing, and 3D non-destructive, open-top light-sheet (OTLS) microscopy, has been developed for diagnosis of 12 human prostate needle cores within an hour after biopsy.

One of the main challenges for laser-scanning microscopy of biological tissues with refractive heterogeneities is the degradation in spatial resolution that occurs as a result of beam steering and distortion. This challenge is particularly significant for dual-axis confocal (DAC) microscopy, which achieves improved spatial-filtering and optical-sectioning performance over traditional confocal microscopy through off-axis illumination and collection of light with low-numerical aperture (NA) beams that must intersect precisely at their foci within tissues. DAC microscope image quality is sensitive to positional changes and distortions of these illumination- and collection-beam foci. Previous studies have shown that Bessel beams display improved positional stability and beam quality than Gaussian beams when propagating through tissues with refractive heterogeneities, which suggests that Bessel-beam illumination may enhance DAC microscopy of such tissues. Here, we utilize both Gaussian and Bessel illumination in a point-scanned DAC microscope and quantify the resultant degradation in resolution when imaging within heterogeneous optical phantoms and fresh tissues. Results indicate that DAC microscopy with Bessel illumination exhibits reduced resolution degradation from microscopic tissue heterogeneities compared to DAC microscopy with conventional Gaussian illumination.

The imaging of dysregulated cell-surface receptors (or biomarkers) is a potential means of identifying the presence of cancer with high sensitivity and specificity. However, due to heterogeneities in the expression of protein biomarkers in tumors, molecular imaging technologies should ideally be capable of visualizing a multiplexed panel of cancer biomarkers. Recently, surface-enhanced Raman-scattering (SERS) nanoparticles (NPs) have attracted wide interest due to their potential for sensitive and multiplexed biomarker detection. In this review, we focus on the most recent advances in tumor imaging using SERS-coded NPs. A brief introduction of the structure and optical properties of SERS NPs is provided, followed by a detailed discussion of key imaging issues such as the administration of NPs in tissue (topical versus systemic), the optical configuration and imaging approach of Raman imaging systems, spectral demultiplexing methods for quantifying NP concentrations, and the disambiguation of specific versus nonspecific sources of contrast through ratiometric imaging of targeted and untargeted (control) NP pairs. Finally, future challenges and directions are briefly outlined.