A versatile information retrieval framework for evaluating profile strength and similarity.

In profiling assays, thousands of biological properties are measured in a single test, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles' high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise. Here, we developed a statistical framework that uses the well-established mean average precision (mAP) as a single, data-driven metric to bridge this gap. We validated the mAP framework against established metrics through simulations and real-world data applications, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we used mAP to assess both phenotypic activity for a given perturbation (or a sample) as well as consistency within groups of perturbations (or samples) across diverse high-dimensional datasets. We evaluated the framework on different profile types (image, protein, and mRNA profiles), perturbation types (CRISPR gene editing, gene overexpression, and small molecules), and profile resolutions (single-cell and bulk). Our open-source software allows this framework to be applied to identify interesting biological phenomena and promising therapeutics from large-scale profiling data.

Assessment of community efforts to advance network-based prediction of protein-protein interactions.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

BioProfiling.jl: profiling biological perturbations with high-content imaging in single cells and heterogeneous populations.

MOTIVATION: High-content imaging screens provide a cost-effective and scalable way to assess cell states across diverse experimental conditions. The analysis of the acquired microscopy images involves assembling and curating raw cellular measurements into morphological profiles suitable for testing biological hypotheses. Despite being a critical step, general-purpose and adaptable tools for morphological profiling are lacking and no solution is available for the high-performance Julia programming language. RESULTS: Here, we introduce BioProfiling.jl, an efficient end-to-end solution for compiling and filtering informative morphological profiles in Julia. The package contains all the necessary data structures to curate morphological measurements and helper functions to transform, normalize and visualize profiles. Robust statistical distances and permutation tests enable quantification of the significance of the observed changes despite the high fraction of outliers inherent to high-content screens. This package also simplifies visual artifact diagnostics, thus streamlining a bottleneck of morphological analyses. We showcase the features of the package by analyzing a chemical imaging screen, in which the morphological profiles prove to be informative about the compounds' mechanisms of action and can be conveniently integrated with the network localization of molecular targets. AVAILABILITY AND IMPLEMENTATION: The Julia package is available on GitHub: https://github.com/menchelab/BioProfiling.jl. We also provide Jupyter notebooks reproducing our analyses: https://github.com/menchelab/BioProfilingNotebooks. The data underlying this article are available from FigShare, at https://doi.org/10.6084/m9.figshare.14784678.v2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Morphological profiling of human T and NK lymphocytes by high-content cell imaging.

The immunological synapse is a complex structure that decodes stimulatory signals into adapted lymphocyte responses. It is a unique window to monitor lymphocyte activity because of development of systematic quantitative approaches. Here we demonstrate the applicability of high-content imaging to human T and natural killer (NK) cells and develop a pipeline for unbiased analysis of high-definition morphological profiles. Our approach reveals how distinct facets of actin cytoskeleton remodeling shape immunological synapse architecture and affect lytic granule positioning. Morphological profiling of CD8+ T cells from immunodeficient individuals allows discrimination of the roles of the ARP2/3 subunit ARPC1B and the ARP2/3 activator Wiskott-Aldrich syndrome protein (WASP) in immunological synapse assembly. Single-cell analysis further identifies uncoupling of lytic granules and F-actin radial distribution in ARPC1B-deficient lymphocytes. Our study provides a foundation for development of morphological profiling as a scalable approach to monitor primary lymphocyte responsiveness and to identify complex aspects of lymphocyte micro-architecture.

Environmental arginine controls multinuclear giant cell metabolism and formation.

Multinucleated giant cells (MGCs) are implicated in many diseases including schistosomiasis, sarcoidosis and arthritis. MGC generation is energy intensive to enforce membrane fusion and cytoplasmic expansion. Using receptor activator of nuclear factor kappa-Β ligand (RANKL) induced osteoclastogenesis to model MGC formation, here we report RANKL cellular programming requires extracellular arginine. Systemic arginine restriction improves outcome in multiple murine arthritis models and its removal induces preosteoclast metabolic quiescence, associated with impaired tricarboxylic acid (TCA) cycle function and metabolite induction. Effects of arginine deprivation on osteoclastogenesis are independent of mTORC1 activity or global transcriptional and translational inhibition. Arginine scarcity also dampens generation of IL-4 induced MGCs. Strikingly, in extracellular arginine absence, both cell types display flexibility as their formation can be restored with select arginine precursors. These data establish how environmental amino acids control the metabolic fate of polykaryons and suggest metabolic ways to manipulate MGC-associated pathologies and bone remodelling.

Systematic characterization of BAF mutations provides insights into intracomplex synthetic lethalities in human cancers.

Aberrations in genes coding for subunits of the BRG1/BRM associated factor (BAF) chromatin remodeling complexes are highly abundant in human cancers. Currently, it is not understood how these mostly loss-of-function mutations contribute to cancer development and how they can be targeted therapeutically. The cancer-type-specific occurrence patterns of certain subunit mutations suggest subunit-specific effects on BAF complex function, possibly by the formation of aberrant residual complexes. Here, we systematically characterize the effects of individual subunit loss on complex composition, chromatin accessibility and gene expression in a panel of knockout cell lines deficient for 22 BAF subunits. We observe strong, specific and sometimes discordant alterations dependent on the targeted subunit and show that these explain intracomplex codependencies, including the synthetic lethal interactions SMARCA4-ARID2, SMARCA4-ACTB and SMARCC1-SMARCC2. These data provide insights into the role of different BAF subcomplexes in genome-wide chromatin organization and suggest approaches to therapeutically target BAF-mutant cancers.

Mutational landscape of the transcriptome offers putative targets for immunotherapy of myeloproliferative neoplasms.

Ph-negative myeloproliferative neoplasms (MPNs) are hematological cancers that can be subdivided into entities with distinct clinical features. Somatic mutations in JAK2, CALR, and MPL have been described as drivers of the disease, together with a variable landscape of nondriver mutations. Despite detailed knowledge of disease mechanisms, targeted therapies effective enough to eliminate MPN cells are still missing. In this study of 113 MPN patients, we aimed to comprehensively characterize the mutational landscape of the granulocyte transcriptome using RNA sequencing data and subsequently examine the applicability of immunotherapeutic strategies for MPN patients. Following implementation of customized workflows and data filtering, we identified a total of 13 (12/13 novel) gene fusions, 231 nonsynonymous single nucleotide variants, and 21 insertions and deletions in 106 of 113 patients. We found a high frequency of SF3B1-mutated primary myelofibrosis patients (14%) with distinct 3' splicing patterns, many of these with a protein-altering potential. Finally, from all mutations detected, we generated a virtual peptide library and used NetMHC to predict 149 unique neoantigens in 62% of MPN patients. Peptides from CALR and MPL mutations provide a rich source of neoantigens as a result of their unique ability to bind many common MHC class I molecules. Finally, we propose that mutations derived from splicing defects present in SF3B1-mutated patients may offer an unexplored neoantigen repertoire in MPNs. We validated 35 predicted peptides to be strong MHC class I binders through direct binding of predicted peptides to MHC proteins in vitro. Our results may serve as a resource for personalized vaccine or adoptive cell-based therapy development.