ImJoy: an open-source computational platform for the deep learning era

International audienceDeep learning methods have shown extraordinary potential for analyzing very diverse biomedical data, but their dissemination beyond developers is hindered by important computational hurdles. We introduce ImJoy (https://imjoy.io/), a flexible and open-source browser-based platform designed to facilitate widespread reuse of deep learning solutions in biomedical research. We highlight ImJoy's main features and illustrate its functionalities with deep learning plugins for mobile and interactive image analysis and genomics. Deep learning methods, which use artificial neural networks to learn complex mappings between numerical data, have enabled recent breakthroughs in a wide range of biomedical data analysis tasks. Examples for imaging data include image segmentation 1,2 and medical diagnosis, where deep learning vastly outperforms more traditional methods and often exceeds human expert performance 3,4 , or methods to enhance microscopy images, e.g. for denoising or

Antibody Validation in Bioimaging Applications Based on Endogenous Expression of Tagged Proteins

Antibodies are indispensible research tools, yet the scientific community has not adopted standardized procedures to validate their specificity. Here we present a strategy to systematically validate antibodies for immuno­fluorescence (IF) applications using gene tagging. We have assessed the on- and off-target binding capabilities of 197 antibodies using 108 cell lines expressing EGFP-tagged target proteins at endogenous levels. Furthermore, we assessed batch-to-batch effects for 35 target proteins, showing that both the on- and off-target binding patterns vary significantly between antibody batches and that the proposed strategy serves as a reliable procedure for ensuring reproducibility upon production of new antibody batches. In summary, we present a systematic scheme for antibody validation in IF applications using endogenous expression of tagged proteins. This is an important step toward a reproducible approach for context- and application-specific antibody validation and improved reliability of antibody-based experiments and research data

A subcellular map of the human proteome

Mapping the proteome Proteins function in the context of their environment, so an understanding of cellular processes requires a knowledge of protein localization. Thul et al. used immunofluorescence microscopy to map 12,003 human proteins at a single-cell level into 30 cellular compartments and substructures (see the Perspective by Horwitz and Johnson). They validated their results by mass spectroscopy and used them to model and refine protein-protein interaction networks. The cellular proteome is highly spatiotemporally regulated. Many proteins localize to multiple compartments, and many show cell-to-cell variation in their expression patterns. Presented as an interactive database called the Cell Atlas, this work provides an important resource for ongoing efforts to understand human biology. Science , this issue p. eaal3321 ; see also p. 806 </jats:p

Deep learning is combined with massive-scale citizen science to improve large-scale image classification

Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.QC 20181001</p