The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidation of how genome variation contributes to disease1–3. Here, we present BioPlex 2.0 (Biophysical Interactions of ORFEOME-derived complexes), which employs robust affinity purification-mass spectrometry (AP-MS) methodology4 to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein coding genes from the human genome, and constitutes the largest such network to date. With >56,000 candidate interactions, BioPlex 2.0 contains >29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering (MCL)5 of interacting proteins identified more than 1300 protein communities representing diverse cellular activities. Genes essential for cell fitness6,7 are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

Vascular plants appeared ~410 million years ago then diverged into several lineages of which only two survive: the euphyllophytes (ferns and seed plants) and the lycophytes (1). We report here the genome sequence of the lycophyte Selaginella moellendorffii (Selaginella), the first non-seed vascular plant genome reported. By comparing gene content in evolutionary diverse taxa, we found that the transition from a gametophyte- to sporophyte-dominated life cycle required far fewer new genes than the transition from a non-seed vascular to a flowering plant, while secondary metabolic genes expanded extensively and in parallel in the lycophyte and angiosperm lineages. Selaginella differs in post-transcriptional gene regulation, including small RNA regulation of repetitive elements, an absence of the tasiRNA pathway and extensive RNA editing of organellar genes.

Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner. Recent approaches to single view depth estimation explore the possibility of learning without full supervision via minimizing photometric error. In this paper, we explore the use of stereo sequences for learning depth and visual odometry. The use of stereo sequences enables the use of both spatial (between left-right pairs) and temporal (forward backward) photometric warp error, and constrains the scene depth and camera motion to be in a common, realworld scale. At test time our framework is able to estimate single view depth and two-view odometry from a monocular sequence. We also show how we can improve on a standard photometric warp loss by considering a warp of deep features. We show through extensive experiments that: (i) jointly training for single view depth and visual odometry improves depth prediction because of the additional constraint imposed on depths and achieves competitive results for visual odometry; (ii) deep feature-based warping loss improves upon simple photometric warp loss for both single view depth estimation and visual odometry. Our method outperforms existing learning based methods on the KITTI driving dataset in both tasks. The source code is available at https://github.com/Huangying-Zhan/ Depth-VO-Feat.

BackgroundLarge-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications.FindingsThis microscopy dataset includes 919 265 five-channel fields of view, representing 30 616 tested compounds, available at “The Cell Image Library” (CIL) repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied.ConclusionsBecause computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The dataset can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics.

High-throughput screening has become a mainstay of small-molecule probe and early drug discovery. The question of how to build and evolve efficient screening collections systematically for cellbased and biochemical screening is still unresolved. It is often assumed that chemical structure diversity leads to diverse biological performance of a library. Here, we confirm earlier results showing that this inference is not always valid and suggest instead using biological measurement diversity derived from multiplexed profiling in the construction of libraries with diverse assay performance patterns for cell-based screens. Rather than using results from tens or hundreds of completed assays, which is resource intensive and not easily extensible, we use high-dimensional image-based cell morphology and gene expression profiles. We piloted this approach using over 30,000 compounds. We show that small-molecule profiling can be used to select compound sets with high rates of activity and diverse biological performance.chemical diversity | biological performance diversity | biological activity | chemical similarity P rofiling small molecules based on multiple biological activity measurements can illuminate mechanisms of action by comparing profiles with compounds whose mechanisms of action are known (1-5). Here, we describe a previously unidentified use of small-molecule profiling-enabling the creation of activityenriched and performance-diverse compound libraries for smallmolecule probe and drug discovery.Biochemical and cell-based high-throughput screening (HTS) is routinely used to discover novel bioactive molecules through unbiased testing of up to several million compounds per screen (6). However, despite ongoing advances in throughput, compound libraries will always represent only a small fraction of all relevant compounds theoretically accessible through chemical synthesis (a concept often referred to as "chemical space") (7). Library composition therefore presents a strong source of bias and potential limitation for any screening endeavor.There is little dissent about the notion that a good screening collection should yield many high-quality hits for a wide range of biological targets or phenotypes. In other words, it should be enriched for bioactive compounds and have high biological performance diversity. A high percentage of compounds lacking any activity will contribute to high cost and low performance of a high-throughput screen. A practical example is a compound collection containing a high percentage of compounds that fail to penetrate cell membranes-such a library will be unlikely to perform effectively in a cell-based HTS exploring an intracellular process. Similarly, a screening collection of compounds with highly redundant biological activities will be less efficient than an equally sized library with diverse performance (Fig. 1). A systematic path to reach these goals, however, remains elusive. One common practice is analyzing structural features of compounds to maximize chemical structural diversity. Ho...