Cellular community detection for tissue phenotyping in colorectal cancer histology images

Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development of computational pathology tools for systematic digital profiling of the spatial tumor microenvironment. Most existing methods for tissue phenotyping are limited to the classification of tumor and stroma and require large amount of annotated histology images which are often not available. In the current work, we pose the problem of identifying distinct tissue phenotypes as finding communities in cellular graphs or networks. First, we train a deep neural network for cell detection and classification into five distinct cellular components. Considering the detected nuclei as nodes, potential cell-cell connections are assigned using Delaunay triangulation resulting in a cell-level graph. Based on this cell graph, a feature vector capturing potential cell-cell connection of different types of cells is computed. These feature vectors are used to construct a patch-level graph based on chi-square distance. We map patch-level nodes to the geometric space by representing each node as a vector of geodesic distances from other nodes in the network and iteratively drifting the patch nodes in the direction of positive density gradients towards maximum density regions. The proposed algorithm is evaluated on a publicly available dataset and another new large-scale dataset consisting of 280K patches of seven tissue phenotypes. The estimated communities have significant biological meanings as verified by the expert pathologists. A comparison with current state-of-the-art methods reveals significant performance improvement in tissue phenotyping

Deciphering the Mechanisms of Developmental Disorders (DMDD): a new programme for phenotyping embryonic lethal mice

International efforts to test gene function in the mouse by the systematic knockout of each gene are creating many lines in which embryonic development is compromised. These homozygous lethal mutants represent a potential treasure trove for the biomedical community. Developmental biologists could exploit them in their studies of tissue differentiation and organogenesis; for clinical researchers they offer a powerful resource for investigating the origins of developmental diseases that affect newborns. Here, we outline a new programme of research in the UK aiming to kick-start research with embryonic lethal mouse lines. The 'Deciphering the Mechanisms of Developmental Disorders' (DMDD) programme has the ambitious goal of identifying all embryonic lethal knockout lines made in the UK over the next 5 years, and will use a combination of comprehensive imaging and transcriptomics to identify abnormalities in embryo structure and development. All data will be made freely available, enabling individual researchers to identify lines relevant to their research. The DMDD programme will coordinate its work with similar international efforts through the umbrella of the International Mouse Phenotyping Consortium [see accompanying Special Article (Adams et al., 2013)] and, together, these programmes will provide a novel database for embryonic development, linking gene identity with molecular profiles and morphology phenotypes

Bloomsbury report on mouse embryo phenotyping:recommendations from the IMPC workshop on embryonic lethal screening

Identifying genes that are important for embryo development is a crucial first step towards understanding their many functions in driving the ordered growth, differentiation and organogenesis of embryos. It can also shed light on the origins of developmental disease and congenital abnormalities. Current international efforts to examine gene function in the mouse provide a unique opportunity to pinpoint genes that are involved in embryogenesis, owing to the emergence of embryonic lethal knockout mutants. Through internationally coordinated efforts, the International Knockout Mouse Consortium (IKMC) has generated a public resource of mouse knockout strains and, in April 2012, the International Mouse Phenotyping Consortium (IMPC), supported by the EU InfraCoMP programme, convened a workshop to discuss developing a phenotyping pipeline for the investigation of embryonic lethal knockout lines. This workshop brought together over 100 scientists, from 13 countries, who are working in the academic and commercial research sectors, including experts and opinion leaders in the fields of embryology, animal imaging, data capture, quality control and annotation, high-throughput mouse production, phenotyping, and reporter gene analysis. This article summarises the outcome of the workshop, including (1) the vital scientific importance of phenotyping embryonic lethal mouse strains for basic and translational research; (2) a common framework to harmonise international efforts within this context; (3) the types of phenotyping that are likely to be most appropriate for systematic use, with a focus on 3D embryo imaging; (4) the importance of centralising data in a standardised form to facilitate data mining; and (5) the development of online tools to allow open access to and dissemination of the phenotyping data

SHIRAZ: an automated histology image annotation system for zebrafish phenomics

Histological characterization is used in clinical and research contexts as a highly sensitive method for detecting the morphological features of disease and abnormal gene function. Histology has recently been accepted as a phenotyping method for the forthcoming Zebrafish Phenome Project, a large-scale community effort to characterize the morphological, physiological, and behavioral phenotypes resulting from the mutations in all known genes in the zebrafish genome. In support of this project, we present a novel content-based image retrieval system for the automated annotation of images containing histological abnormalities in the developing eye of the larval zebrafish

Spectral imaging in preclinical research and clinical pathology.

Spectral imaging methods are attracting increased interest from researchers and practitioners in basic science, pre-clinical and clinical arenas. A combination of better labeling reagents and better optics creates opportunities to detect and measure multiple parameters at the molecular and cellular level. These tools can provide valuable insights into the basic mechanisms of life, and yield diagnostic and prognostic information for clinical applications. There are many multispectral technologies available, each with its own advantages and limitations. This chapter will present an overview of the rationale for spectral imaging, and discuss the hardware, software and sample labeling strategies that can optimize its usefulness in clinical settings

Research Techniques Made Simple: Use of Imaging Mass Cytometry for Dermatological Research and Clinical Applications

Traditional immunohistochemistry (IHC) is inherently limited by its ability to analyze only several markers within a histological tissue section at a given time, which hinders in-depth characterization and phenotyping of tissues. Imaging mass cytometry (IMC), which combines IHC using metal-labeled antibodies with laser ablation and detection using mass cytometry by time-of-flight, overcomes this limitation with the capability to simultaneously analyze up to 40 protein markers to generate high-dimensional images from a single tissue section. IMC analysis preserves tissue architecture and spatial cellular relationships that would otherwise be lost or significantly altered in applications requiring tissue dissociation, such as flow cytometry or single-cell RNA sequencing. Resulting high-dimensional histological images permit spatially conserved analysis to identify unique cell populations, cellular interactions and avoidances, and insight into activation and behavioral status based on tissue location. IMC can be performed on both frozen and formalin-fixed paraffin-embedded tissue, allowing for previously banked samples to be analyzed and correlated with known clinical outcomes. Expectedly, IMC will change the landscape of investigative pathology, particularly when used in coordination with multiomic platforms to combine transcriptomic and proteomic data at a single-cell resolution. Here, we aim to highlight the potential utility of IMC within dermatologic research and clinical applications