Mark-Anthony Bray

Human tumors instigate granulin-expressing hematopoietic cells that promote malignancy by activating stromal fibroblasts in mice

Systemic instigation is a process by which endocrine signals sent from certain tumors (instigators) stimulate BM cells (BMCs), which are mobilized into the circulation and subsequently foster the growth of otherwise indolent carcinoma cells (responders) residing at distant anatomical sites. The identity of the BMCs and their specific contribution or contributions to responder tumor growth have been elusive. Here, we have demonstrated that Sca1+ cKit- hematopoietic BMCs of mouse hosts bearing instigating tumors promote the growth of responding tumors that form with a myofibroblast-rich, desmoplastic stroma. Such stroma is almost always observed in malignant human adenocarcinomas and is an indicator of poor prognosis. We then identified granulin (GRN) as the most upregulated gene in instigating Sca1+ cKit- BMCs relative to counterpart control cells. The GRN+ BMCs that were recruited to the responding tumors induced resident tissue fibroblasts to express genes that promoted malignant tumor progression; indeed, treatment with recombinant GRN alone was sufficient to promote desmoplastic responding tumor growth. Further, analysis of tumor tissues from a cohort of breast cancer patients revealed that high GRN expression correlated with the most aggressive triple-negative, basal-like tumor subtype and reduced patient survival. Our data suggest that GRN and the unique hematopoietic BMCs that produce it might serve as novel therapeutic targets.

Identification of Host-Targeted Small Molecules That Restrict Intracellular Mycobacterium tuberculosis Growth

Mycobacterium tuberculosis remains a significant threat to global health. Macrophages are the host cell for M. tuberculosis infection, and although bacteria are able to replicate intracellularly under certain conditions, it is also clear that macrophages are capable of killing M. tuberculosis if appropriately activated. The outcome of infection is determined at least in part by the host-pathogen interaction within the macrophage; however, we lack a complete understanding of which host pathways are critical for bacterial survival and replication. To add to our understanding of the molecular processes involved in intracellular infection, we performed a chemical screen using a high-content microscopic assay to identify small molecules that restrict mycobacterial growth in macrophages by targeting host functions and pathways. The identified host-targeted inhibitors restrict bacterial growth exclusively in the context of macrophage infection and predominantly fall into five categories: G-protein coupled receptor modulators, ion channel inhibitors, membrane transport proteins, anti-inflammatories, and kinase modulators. We found that fluoxetine, a selective serotonin reuptake inhibitor, enhances secretion of pro-inflammatory cytokine TNF-α and induces autophagy in infected macrophages, and gefitinib, an inhibitor of the Epidermal Growth Factor Receptor (EGFR), also activates autophagy and restricts growth. We demonstrate that during infection signaling through EGFR activates a p38 MAPK signaling pathway that prevents macrophages from effectively responding to infection. Inhibition of this pathway using gefitinib during in vivo infection reduces growth of M. tuberculosis in the lungs of infected mice. Our results support the concept that screening for inhibitors using intracellular models results in the identification of tool compounds for probing pathways during in vivo infection and may also result in the identification of new anti-tuberculosis agents that work by modulating host pathways. Given the existing experience with some of our identified compounds for other therapeutic indications, further clinically-directed study of these compounds is merited.

Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes

Significance Genome sequencing of individuals in the population reveals new mutations in almost every protein coding gene; interpreting the consequence of these mutations for human health and disease remains challenging. We sequenced the gene PPARG, a target of antidiabetic drugs, in nearly 20,000 individuals with and without type 2 diabetes (T2D). We identified 49 previously unidentified protein-altering mutations, characterized their cellular function in human cells, and discovered that nine of these mutations cause loss-of-function (LOF). The individuals who carry these nine LOF mutations have a sevenfold increased risk of T2D, whereas individuals carrying mutations we classify as benign have no increased risk of T2D. Peroxisome proliferator-activated receptor gamma (PPARG) is a master transcriptional regulator of adipocyte differentiation and a canonical target of antidiabetic thiazolidinedione medications. In rare families, loss-of-function (LOF) mutations in PPARG are known to cosegregate with lipodystrophy and insulin resistance; in the general population, the common P12A variant is associated with a decreased risk of type 2 diabetes (T2D). Whether and how rare variants in PPARG and defects in adipocyte differentiation influence risk of T2D in the general population remains undetermined. By sequencing PPARG in 19,752 T2D cases and controls drawn from multiple studies and ethnic groups, we identified 49 previously unidentified, nonsynonymous PPARG variants (MAF < 0.5%). Considered in aggregate (with or without computational prediction of functional consequence), these rare variants showed no association with T2D (OR = 1.35; P = 0.17). The function of the 49 variants was experimentally tested in a novel high-throughput human adipocyte differentiation assay, and nine were found to have reduced activity in the assay. Carrying any of these nine LOF variants was associated with a substantial increase in risk of T2D (OR = 7.22; P = 0.005). The combination of large-scale DNA sequencing and functional testing in the laboratory reveals that approximately 1 in 1,000 individuals carries a variant in PPARG that reduces function in a human adipocyte differentiation assay and is associated with a substantial risk of T2D.

Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling

Significance A large compound screening collection is usually constructed to be tested in many distinct assays, each one designed to find modulators of a different biological process. However, it is generally not known to what extent a compound collection actually contains molecules with distinct biological effects (or even any effect) until it has been tested for a couple of years. This study explores a cost-effective way of rapidly assessing the biological performance diversity of a screening collection in a single assay. By simultaneously measuring a large number of cellular features, unbiased profiling assays can distinguish compound effects with high resolution and thus measure performance diversity. We show that this approach could be used as a filtering strategy to build effective screening collections. 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 cell-based 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.

Visualization of Parameter Space for Image Analysis

Image analysis algorithms are often highly parameterized and much human input is needed to optimize parameter settings. This incurs a time cost of up to several days. We analyze and characterize the conventional parameter optimization process for image analysis and formulate user requirements. With this as input, we propose a change in paradigm by optimizing parameters based on parameter sampling and interactive visual exploration. To save time and reduce memory load, users are only involved in the first step - initialization of sampling - and the last step - visual analysis of output. This helps users to more thoroughly explore the parameter space and produce higher quality results. We describe a custom sampling plug-in we developed for CellProfiler - a popular biomedical image analysis framework. Our main focus is the development of an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. We implemented this in a prototype called Paramorama. It provides users with a visual overview of parameters and their sampled values. User-defined areas of interest are presented in a structured way that includes image-based output and a novel layout algorithm. To find optimal parameter settings, users can tag high- and low-quality results to refine their search. We include two case studies to illustrate the utility of this approach.

Workflow and metrics for image quality control in large-scale high-content screens

Automated microscopes have enabled the unprecedented collection of images at a rate that precludes visual inspection. Automated image analysis is required to identify interesting samples and extract quantitative information for high-content screening (HCS). However, researchers are impeded by the lack of metrics and software tools to identify image-based aberrations that pollute data, limiting experiment quality. The authors have developed and validated approaches to identify those image acquisition artifacts that prevent optimal extraction of knowledge from high-content microscopy experiments. They have implemented these as a versatile, open-source toolbox of algorithms and metrics readily usable by biologists to improve data quality in a wide variety of biological experiments.

Using CellProfiler for Automatic Identification and Measurement of Biological Objects in Images

Visual analysis is required to perform many biological experiments, from counting colonies to measuring the size or fluorescence intensity of individual cells or organisms. This unit outlines the use of CellProfiler, a free, open‐source image analysis tool that extracts quantitative information from biological images. It includes a step‐by‐step protocol for automated analysis of the number, color, and size of yeast colonies growing on agar plates, but the methods can be adapted to identify and measure many other types of objects in images. The flexibility of the software allows experimenters to create pipelines of adjustable modules to fit different biological experiments and to generate accurate measurements from dozens or even hundreds of thousands of images.

Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

In morphological profiling, quantitative data are extracted from microscopy images of cells to identify biologically relevant similarities and differences among samples based on these profiles. This protocol describes the design and execution of experiments using Cell Painting, which is a morphological profiling assay that multiplexes six fluorescent dyes, imaged in five channels, to reveal eight broadly relevant cellular components or organelles. Cells are plated in multiwell plates, perturbed with the treatments to be tested, stained, fixed, and imaged on a high-throughput microscope. Next, an automated image analysis software identifies individual cells and measures ∼1,500 morphological features (various measures of size, shape, texture, intensity, and so on) to produce a rich profile that is suitable for the detection of subtle phenotypes. Profiles of cell populations treated with different experimental perturbations can be compared to suit many goals, such as identifying the phenotypic impact of chemical or genetic perturbations, grouping compounds and/or genes into functional pathways, and identifying signatures of disease. Cell culture and image acquisition takes 2 weeks; feature extraction and data analysis take an additional 1–2 weeks.

High- and low-throughput scoring of fat mass and body fat distribution in C. elegans

Fat accumulation is a complex phenotype affected by factors such as neuroendocrine signaling, feeding, activity, and reproductive output. Accordingly, the most informative screens for genes and compounds affecting fat accumulation would be those carried out in whole living animals. Caenorhabditis elegans is a well-established and effective model organism, especially for biological processes that involve organ systems and multicellular interactions, such as metabolism. Every cell in the transparent body of C. elegans is visible under a light microscope. Consequently, an accessible and reliable method to visualize worm lipid-droplet fat depots would make C. elegans the only metazoan in which genes affecting not only fat mass but also body fat distribution could be assessed at a genome-wide scale. Here we present a radical improvement in oil red O worm staining together with high-throughput image-based phenotyping. The three-step sample preparation method is robust, formaldehyde-free, and inexpensive, and requires only 15min of hands-on time to process a 96-well plate. Together with our free and user-friendly automated image analysis package, this method enables C. elegans sample preparation and phenotype scoring at a scale that is compatible with genome-wide screens. Thus we present a feasible approach to small-scale phenotyping and large-scale screening for genetic and/or chemical perturbations that lead to alterations in fat quantity and distribution in whole animals.

Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects.

RNA interference and morphological profiling—the measurement of thousands of phenotypes from individual cells by microscopy and image analysis—are a potentially powerful combination. We show that morphological profiles of RNAi-induced knockdown using the Cell Painting assay are in fact highly sensitive and reproducible. However, we find that the magnitude and prevalence of off-target effects via the RNAi seed-based mechanism make morphological profiles of RNAi reagents targeting the same gene look no more similar than reagents targeting different genes. Pairs of RNAi reagents that share the same seed sequence produce image-based profiles that are much more similar to each other than profiles from pairs designed to target the same gene, a phenomenon previously observed in small-scale gene-expression profiling experiments. Various strategies have been used to enrich on-target versus off-target effects in the context of RNAi screening where a narrow set of phenotypes are measured, mostly based on comparing multiple sequences targeting the same gene; however, new approaches will be needed to make RNAi morphological profiling (that is, comparing multi-dimensional phenotypes) viable. We have shared our raw data and computational pipelines to facilitate research.