Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data.

New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data.

Multiplexed tissue imaging facilitates the diagnosis and understanding of complex disease traits. However, the analysis of such digital images heavily relies on the experience of anatomical pathologists for the review, annotation and description of tissue features. In addition, the wider use of data from tissue atlases in basic and translational research and in classrooms would benefit from software that facilitates the easy visualization and sharing of the images and the results of their analyses. In this Perspective, we describe the ecosystem of software available for the analysis of tissue images and discuss the need for interactive online guides that help histopathologists make complex images comprehensible to non-specialists. We illustrate this idea via a software interface (Minerva), accessible via web browsers, that integrates multi-omic and tissue-atlas features. We argue that such interactive narrative guides can effectively disseminate digital histology data and aid their interpretation.

Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data.

Inspection of tissues using a light microscope is the primary method of diagnosing many diseases, notably cancer. Highly multiplexed tissue imaging builds on this foundation, enabling the collection of up to 60 channels of molecular information plus cell and tissue morphology using antibody staining. This provides unique insight into disease biology and promises to help with the design of patient-specific therapies. However, a substantial gap remains with respect to visualizing the resulting multivariate image data and effectively supporting pathology workflows in digital environments on screen. We, therefore, developed Scope2Screen, a scalable software system for focus+context exploration and annotation of whole-slide, high-plex, tissue images. Our approach scales to analyzing 100GB images of 109 or more pixels per channel, containing millions of individual cells. A multidisciplinary team of visualization experts, microscopists, and pathologists identified key image exploration and annotation tasks involving finding, magnifying, quantifying, and organizing regions of interest (ROIs) in an intuitive and cohesive manner. Building on a scope-to-screen metaphor, we present interactive lensing techniques that operate at single-cell and tissue levels. Lenses are equipped with task-specific functionality and descriptive statistics, making it possible to analyze image features, cell types, and spatial arrangements (neighborhoods) across image channels and scales. A fast sliding-window search guides users to regions similar to those under the lens; these regions can be analyzed and considered either separately or as part of a larger image collection. A novel snapshot method enables linked lens configurations and image statistics to be saved, restored, and shared with these regions. We validate our designs with domain experts and apply Scope2Screen in two case studies involving lung and colorectal cancers to discover cancer-relevant image features.

Minerva: a light-weight, narrative image browser for multiplexed tissue images.

Advances in highly multiplexed tissue imaging are transforming our understanding of human biology by enabling detection and localization of 10-100 proteins at subcellular resolution (Bodenmiller, 2016). Efforts are now underway to create public atlases of multiplexed images of normal and diseased tissues (Rozenblatt-Rosen et al., 2020). Both research and clinical applications of tissue imaging benefit from recording data from complete specimens so that data on cell state and composition can be studied in the context of overall tissue architecture. As a practical matter, specimen size is limited by the dimensions of microscopy slides (2.5 × 7.5 cm or ~2-8 cm2 of tissue depending on shape). With current microscopy technology, specimens of this size can be imaged at sub-micron resolution across ~60 spectral channels and ~106 cells, resulting in image files of terabyte size. However, the rich detail and multiscale properties of these images pose a substantial computational challenge (Rashid et al., 2020). See Rashid et al. (2020) for an comparison of existing visualization tools targeting these multiplexed tissue images.

Screening and therapy for malnutrition and related gastro-intestinal disorders in systemic sclerosis: recommendations of a North American expert panel.

OBJECTIVES: To develop a set of recommendations for clinicians caring for patients with systemic sclerosis (SSc) to guide their approach to the patient with malnutrition and possible malabsorption. METHODS: The Canadian Scleroderma Research Group convened a meeting of experts in the areas of nutrition, speech pathology, oral health in SSc, SSc and gastroenterology to discuss the nutrition-GI paradigm in SSc. This meeting generated a set of recommendations based on expert opinion. RESULTS: Physicians should screen ALL patients with SSc for malnutrition. The physician should ask a series of questions that pertain to GI involvement. Patients who screen positive for malnutrition should be referred to a dietitian and gastroenterologist. Referral to a patient support group should be considered and if screening reveals oral health problems, referral to a dentist, preferably with expertise in treating patients with SSc, should be done. All SSc patients should weigh themselves monthly and report any sudden significant changes in weight. They should be assessed by a rheumatologist once a year for signs of malnutrition. CONCLUSIONS: Malnutrition may be common in SSc and a multidisciplinary approach is important.

Noninvasive detection of urinary sulfur for measuring the catabolism of sulfur amino acid / 中国微创外科杂志

Objective To test whether urinary sulfur excretion can be used as an accurate indicator of the catabolism of sulfur amino acid in growing newborn piglets. Methods Using a well-nourished enteral nutrition piglet model,we tested whether intravenous inorganic sulfate and methionine were fully excreted as sulfur in the urine. Results Recovery rate of inorganic sulfate and methionine as total sulfur in urine were 95.6% and 105.5%, respectively. Conclusions Detection of urinary sulfur, as a non-tracer and noninvasive method, may be employed to accurately measure the catabolism of sulfur amino acid in the growing piglet model.