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.

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.

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.

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.

Energetics of cyclic AMP binding to HCN channel C terminus reveal negative cooperativity.

Cyclic AMP binds to the HCN channel C terminus and variably stabilizes its open state. Using isothermal titration calorimetry, we show that cAMP binds to one subunit of tetrameric HCN2 and HCN4 C termini with high affinity (∼0.12 µM) and subsequently with low affinity (∼1 µM) to the remaining three subunits. Changes induced by high affinity binding already exist in both a constrained HCN2 tetramer and the unconstrained HCN1 tetramer. Natural "preactivation" of HCN1 may explain both the smaller effect of cAMP on stabilizing its open state and the opening of unliganded HCN1, which occurs as though already disinhibited.

In situ co-distribution and functional interactions of SAP97 with sinoatrial isoforms of HCN channels.

The sinoatrial node is a region of specialized cardiomyocytes that is responsible for the repetitive activity of the adult heart. The sinoatrial node is heavily innervated compared to the other regions of the heart, and the specialized cardiomyocytes of this region receive neural and hormonal input from the autonomic nervous system, which leads to changes in heart rate. A key regulator of sinoatrial beating frequency in response to autonomic input is the hyperpolarization-activated cyclic nucleotide gated (HCN) channel, a mixed cationic channel whose activity is increased by the binding of cAMP to its cytoplasmic side. HCN channels localize to distinct regions or "hot spots" on the cell surface of sinoatrial myocytes, but how these regions are formed, whether they correspond to specific signaling domains and the specific HCN isoforms and other proteins therein are not known. In this paper, we show that both HCN2 and HCN4 isoforms co-distribute with the adapter protein SAP97, an important component of distinct punctae in the sinoatrial node of the rabbit heart. HCN4, but not HCN2, also co-distributes with the post-synaptic marker beta-catenin, thus identifying diverse organized domains within this tissue. Furthermore, we show, using heterologous expression systems, whole-cell patch clamp electrophysiology and imaging, that SAP97 interacts functionally with HCN in a manner that depends upon the PDZ compatible binding motif of the C-terminus, but that its effects on I(f) behaviour are HCN isoform and context dependent. Together, the data suggest that SAP97 contributes to isoform specific organization of HCN channels within specific domains in the sinoatrial node of the rabbit.

Regulation of cell surface expression of functional pacemaker channels by a motif in the B-helix of the cyclic nucleotide-binding domain.

Previous studies have suggested that a portion of the cyclic nucleotide-binding domain (CNBD) of the hyperpolarization-activated cyclic nucleotide-gated channel 2 (HCN2) "pacemaker" channel, composed of the A- and B-helices and the interceding beta-barrel, confers two functions: inhibition of channel opening in response to hyperpolarization and promotion of cell surface expression. The sequence determinants required for each of these functions are unknown. In addition, the mechanism underlying plasma membrane targeting by this subdomain has been limitedly explored. Here we identify a four-amino acid motif (EEYP) in the B-helix that strongly promotes channel export from the endoplasmic reticulum (ER) and cell surface expression but does not contribute to the inhibition of channel opening. This motif augments a step in the trafficking pathway and/or the efficiency of correct folding and assembly.