SABER enables highly multiplexed and amplified detection of DNA and RNA in cells and tissues, bioRxiv, 2018-08-28

SUMMARYFluorescent in situ hybridization (FISH) reveals the abun-dance and positioning of nucleic acid sequences in fixed sam-ples and can be combined with cell segmentation to produce a powerful single cell gene expression assay. However, it re-mains difficult to label more than a few targets and to visu-alize nucleic acids in environments such as thick tissue sam-ples using conventional FISH technologies. Recently, meth-ods have been developed for multiplexed amplification of FISH signals, yet it remains challenging to achieve high lev-els of simultaneous multiplexing combined with high sam-pling efficiency and simple workflows. Here, we introduce signal amplification by exchange reaction (SABER), which endows oligo-based FISH probes with long, single-stranded DNA concatemers that serve as targets for sensitive fluores-cent detection. We establish that SABER effectively ampli-fies the signal of probes targeting nucleic acids in fixed cells and tissues, can be deployed against at least 17 targets si-multaneously, and detects mRNAs with high efficiency. As a demonstration of the utility of SABER in assays involv-ing genetic manipulations, we apply multiplexed FISH of reporters and cell type markers to the identification of en-hancers with cell type-specific activity in the mouse retina. SABER represents a simple and versatile molecular toolkit to allow rapid and cost effective multiplexed imaging.

biorxiv genetics 200-500-users 2018

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media, bioRxiv, 2018-08-21

AbstractBackgroundPathologists are responsible for rapidly providing a diagnosis on critical health issues, from infection to malignancy. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to (i) improve diagnostic accuracy and (ii) generate broader consensus on next steps in patient care.Methods and findingsFrom Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. For deep learning, we derive novel regularization and activation functions for set representations related to set cardinality and the Heaviside step function. Area Under Receiver Operating Characteristic is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k=1 = 0.701±0.003 (chance 0.397±0.004, mean±stdev). The classifiers find texture and tissue are important clinico-visual features of disease. For search, deep features and cell nuclei features are less important.We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. The bot activates when mentioned in a social media post containing pathology text and images. The bot generates quantitative predictions of disease state (normalartifact infectioninjurynontumor, pre-neoplasticbenignlow-grade-malignant-potential, or malignant) and provides a ranked list of similar cases across social media and PubMed.ConclusionsOur project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e. from infections to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.Author summaryWhy was this study done?<jatslist list-type=bullet><jatslist-item>No publicly available pan-tissue pan-disease dataset exists for computational pathology. This limits the general application of machine learning in histopathology.<jatslist-item><jatslist-item>Pathologists use social media to obtain both (i) opinions for challenging patient cases and (ii) continuing education. Connecting pathologists and linking to similar cases leads to more informative exchanges than computational predictions – e.g. to diagnose best, pathologists may discuss patient history and next tests to order. Additionally, pathologists seek the most interesting rare cases and new articles.<jatslist-item>What did the researchers do and find?<jatslist list-type=bullet><jatslist-item>We generated a pan-tissue, pan-disease dataset comprising 10,000+ images from social media and 100,000+ images from PubMed. Classifiers applied to social media data suggest texture and tissue are important clinico-visual features of disease. Learning from both clinical covariates (e.g. tissue type or marker mentions) and visual features (e.g. local binary patterns or deep learning image features), these classifiers are multimodal.<jatslist-item><jatslist-item>These data and classifiers power the first social media bot for pathology. It responds to pathologists in real time, searches for similar cases, and encourages collaboration.<jatslist-item>What do these findings mean?<jatslist list-type=bullet><jatslist-item>This diverse dataset will be a critical test for machine learning in computational pathology, e.g. search for cures of rare diseases.<jatslist-item><jatslist-item>Interpretable real-time classifiers can be successfully applied to images on social media and PubMed to find similar diseases and generate disease predictions. Going forward, similar methods may elucidate important clinico-visual features of specific diseases.<jatslist-item>

biorxiv pathology 100-200-users 2018

 

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