BRWD1 establishes epigenetic states for germinal center initiation, maintenance, and function.

Germinal center (GC) B cells segregate into three subsets that compartmentalize the antagonistic molecular programs of selection, proliferation, and somatic hypermutation. In bone marrow, the epigenetic reader BRWD1 orchestrates and insulates the sequential stages of cell proliferation and Igk recombination. We hypothesized BRWD1 might play similar insulative roles in the periphery. In Brwd1 -/- follicular B cells, GC initiation and class switch recombination following immunization were inhibited. In contrast, in Brwd1 -/- GC B cells there was admixing of chromatin accessibility across GC subsets and transcriptional dysregulation including induction of inflammatory pathways. This global molecular GC dysregulation was associated with specific defects in proliferation, affinity maturation, and tolerance. These data suggest that GC subset identity is required for some but not all GC-attributed functions. Furthermore, these data demonstrate a central role for BRWD1 in orchestrating epigenetic transitions at multiple steps along B cell developmental and activation pathways.

Towards standardising retinal OCT angiography image analysis with open-source toolbox OCTAVA.

Quantitative assessment of retinal microvasculature in optical coherence tomography angiography (OCTA) images is important for studying, diagnosing, monitoring, and guiding the treatment of ocular and systemic diseases. However, the OCTA user community lacks universal and transparent image analysis tools that can be applied to images from a range of OCTA instruments and provide reliable and consistent microvascular metrics from diverse datasets. We present a retinal extension to the OCTA Vascular Analyser (OCTAVA) that addresses the challenges of providing robust, easy-to-use, and transparent analysis of retinal OCTA images. OCTAVA is a user-friendly, open-source toolbox that can analyse retinal OCTA images from various instruments. The toolbox delivers seven microvascular metrics for the whole image or subregions and six metrics characterising the foveal avascular zone. We validate OCTAVA using images collected by four commercial OCTA instruments demonstrating robust performance across datasets from different instruments acquired at different sites from different study cohorts. We show that OCTAVA delivers values for retinal microvascular metrics comparable to the literature and reduces their variation between studies compared to their commercial equivalents. By making OCTAVA publicly available, we aim to expand standardised research and thereby improve the reproducibility of quantitative analysis of retinal microvascular imaging. Such improvements will help to better identify more reliable and sensitive biomarkers of ocular and systemic diseases.

Pseudo-spectral angle mapping for automated pixel-level analysis of highly multiplexed tissue image data.

The rapid development of highly multiplexed microscopy systems has enabled the study of cells embedded within their native tissue, which is providing exciting insights into the spatial features of human disease [1]. However, computational methods for analyzing these high-content images are still emerging, and there is a need for more robust and generalizable tools for evaluating the cellular constituents and underlying stroma captured by high-plex imaging [2]. To address this need, we have adapted spectral angle mapping - an algorithm used widely in hyperspectral image analysis - to compress the channel dimension of high-plex immunofluorescence images. As many high-plex immunofluorescence imaging experiments probe unique sets of protein markers, existing cell and pixel classification models do not typically generalize well. Pseudospectral angle mapping (pSAM) uses reference pseudospectra - or pixel vectors - to assign each pixel in an image a similarity score to several cell class reference vectors, which are defined by each unique staining panel. Here, we demonstrate that the class maps provided by pSAM can directly provide insight into the prevalence of each class defined by reference pseudospectra. In a dataset of high-plex images of colon biopsies from patients with gut autoimmune conditions, sixteen pSAM class representation maps were combined with instance segmentation of cells to provide cell class predictions. Finally, pSAM detected a diverse set of structure and immune cells when applied to a novel dataset of kidney biopsies imaged with a 43-marker panel. In summary, pSAM provides a powerful and readily generalizable method for evaluating high-plex immunofluorescence image data.

Specific in situ inflammatory states associate with progression to renal failure in lupus nephritis.

BACKGROUNDIn human lupus nephritis (LN), tubulointerstitial inflammation (TII) on biopsy predicts progression to end-stage renal disease (ESRD). However, only about half of patients with moderate-to-severe TII develop ESRD. We hypothesized that this heterogeneity in outcome reflects different underlying inflammatory states. Therefore, we interrogated renal biopsies from LN longitudinal and cross-sectional cohorts.METHODSData were acquired using conventional and highly multiplexed confocal microscopy. To accurately segment cells across whole biopsies, and to understand their spatial relationships, we developed computational pipelines by training and implementing several deep-learning models and other computer vision techniques.RESULTSHigh B cell densities were associated with protection from ESRD. In contrast, high densities of CD8+, γδ, and other CD4-CD8- T cells were associated with both acute renal failure and progression to ESRD. B cells were often organized into large periglomerular neighborhoods with Tfh cells, while CD4- T cells formed small neighborhoods in the tubulointerstitium, with frequency that predicted progression to ESRD.CONCLUSIONThese data reveal that specific in situ inflammatory states are associated with refractory and progressive renal disease.FUNDINGThis study was funded by the NIH Autoimmunity Centers of Excellence (AI082724), Department of Defense (LRI180083), Alliance for Lupus Research, and NIH awards (S10-OD025081, S10-RR021039, and P30-CA14599).

Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.

With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating high-throughput analysis of image data in a variety of biomedical imaging disciplines, ranging from radiology and pathology to cancer biology and immunology. Specifically, a growth in research on deep learning has led to the widespread application of computer-visualization techniques for analyzing and mining data from biomedical images. The availability of open-source software packages and the development of novel, trainable deep neural network architectures has led to increased accuracy in cell detection and segmentation algorithms. By automating cell segmentation, it is now possible to mine quantifiable cellular and spatio-cellular features from microscopy images, providing insight into the organization of cells in various pathologies. This mini-review provides an overview of the current state of the art in deep learning- and artificial intelligence-based methods of segmentation and data mining of cells in microscopy images of tissue.

Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis.

SIGNIFICANCE: Lupus nephritis (LuN) is a chronic inflammatory kidney disease. The cellular mechanisms by which LuN progresses to kidney failure are poorly characterized. Automated instance segmentation of immune cells in immunofluorescence images of LuN can probe these cellular interactions. AIM: Our specific goal is to quantify how sample fixation and staining panel design impact automated instance segmentation and characterization of immune cells. APPROACH: Convolutional neural networks (CNNs) were trained to segment immune cells in fluorescence confocal images of LuN biopsies. Three datasets were used to probe the effects of fixation methods on cell features and the effects of one-marker versus two-marker per cell staining panels on CNN performance. RESULTS: Networks trained for multi-class instance segmentation on fresh-frozen and formalin-fixed, paraffin-embedded (FFPE) samples stained with a two-marker panel had sensitivities of 0.87 and 0.91 and specificities of 0.82 and 0.88, respectively. Training on samples with a one-marker panel reduced sensitivity (0.72). Cell size and intercellular distances were significantly smaller in FFPE samples compared to fresh frozen (Kolmogorov-Smirnov, p ≪ 0.0001). CONCLUSIONS: Fixation method significantly reduces cell size and intercellular distances in LuN biopsies. The use of two markers to identify cell subsets showed improved CNN sensitivity relative to using a single marker.

Fluorescence modeling of in vivo optical detection of Mycobacterium tuberculosis.

Tuberculosis is one of the deadliest infectious diseases worldwide. New tools to study pathogenesis and monitor subjects in pre-clinical studies to develop treatment regimens are critical for progress. We developed an improved optical system for detecting bacteria in lungs of mice using internal illumination. We present a computational optical model of the full mouse torso to characterize the optical system. Simulated theoretical limits for the lowest detectable bacterial load support the experimental improvements with an internal illumination source, and suggest that protocol improvements could further lower the detection threshold.

Light scattering by pulmonary alveoli and airway surface liquid using a concentric sphere model.

We employ a concentric sphere Mie scattering model to describe light scattering by pulmonary alveoli and airway surface liquid (ASL). Using this layered sphere model, we compare alveolar scattering at different points along the respiratory cycle and observe the effect of ASL thickness on light scattering in the lung. We have also extrapolated the model to investigate alveolar scattering in various animal models of pulmonary disease. This model of pulmonary light scattering can estimate in vivo optical properties for normal and pathological states, potentially aiding the design of optical systems for diagnosis and investigation of pulmonary pathologies.

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure.

The rapid development of new optical imaging techniques is dependent on the availability of low-cost, customizable, and easily reproducible standards. By replicating the imaging environment, costly animal experiments to validate a technique may be circumvented. Predicting and optimizing the performance of in vivo and ex vivo imaging techniques requires testing on samples that are optically similar to tissues of interest. Tissue-mimicking optical phantoms provide a standard for evaluation, characterization, or calibration of an optical system. Homogenous polymer optical tissue phantoms are widely used to mimic the optical properties of a specific tissue type within a narrow spectral range. Layered tissues, such as the epidermis and dermis, can be mimicked by simply stacking these homogenous slab phantoms. However, many in vivo imaging techniques are applied to more spatially complex tissue where three dimensional structures, such as blood vessels, airways, or tissue defects, can affect the performance of the imaging system. This protocol describes the fabrication of a tissue-mimicking phantom that incorporates three-dimensional structural complexity using material with optical properties of tissue. Look-up tables provide India ink and titanium dioxide recipes for optical absorption and scattering targets. Methods to characterize and tune the material optical properties are described. The phantom fabrication detailed in this article has an internal branching mock airway void; however, the technique can be broadly applied to other tissue or organ structures.

Optical model of the murine lung to optimize pulmonary illumination.

We describe a Monte Carlo model of the mouse torso to optimize illumination of the mouse lung for fluorescence detection of low levels of pulmonary pathogens, specifically Mycobacterium tuberculosis. After validation of the simulation with an internally illuminated optical phantom, the entire mouse torso was simulated to compare external and internal illumination techniques. Measured optical properties of deflated mouse lungs were scaled to mimic the diffusive properties of inflated lungs in vivo. Using the full-torso model, a 2 × to 3 × improvement in average fluence rate in the lung was seen for dorsal compared with ventral positioning of the mouse with external illumination. The enhancement in average fluence rate in the lung using internal excitation was 40 × to 60 × over external illumination in the dorsal position. Parameters of the internal fiber optic source were manipulated in the model to guide optimization of the physical system and experimental protocol for internal illumination and whole-body detection of fluorescent mycobacteria in a mouse model of infection.

Intravital excitation increases detection sensitivity for pulmonary tuberculosis by whole-body imaging with ß-lactamase reporter enzyme fluorescence.

Tuberculosis is a pulmonary disease with an especially high mortality rate in immuno-compromised populations, specifically children and HIV positive individuals. The causative agent, Mycobacterium tuberculosis (Mtb), is a very slow growing and difficult organism to work with, making both diagnosis and development of effective treatments cumbersome. We utilize a fiber-optic fluorescence microendoscope integrated with a whole-body imaging system for in vivo Mtb detection. The system exploits an endogenous enzyme of Mtb (ß-lactamase, or BlaC) using a BlaC-specific NIR fluorogenic substrate. In the presence of BlaC, this substrate is cleaved and becomes fluorescent. Using intravital illumination of the lung to excite this probe, sensitivity of the optical system increases over trans- and epi-illumination methods of whole-body fluorescence imaging. We demonstrate that integration of these imaging technologies with BlaC-specific fluorescent reporter probe improves the level of detection to ∼100 colony forming units, a 100× increase in sensitivity in comparison to epi-illumination and a 10× increase in sensitivity in comparison to previous work in intravital excitation of tdTomato-expressing Mtb. This lower detection threshold enables the study of early stage bacterial infections with clinical strains of Mtb and longitudinal studies of disease pathogenesis and therapeutic efficacy with multiple time points in a single animal.