CellProfiler and KNIME: open source tools for high content screening

Optimizing the Cell Painting assay for image-based profiling

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations on the basis of their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells and predicting assay outcomes by using machine learning, among many others. Here, we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum and mitochondria. The original protocol was updated in 2016 on the basis of several years' experience running it at two sites, after optimizing it by visual stain quality. Here, we describe the work of the Joint Undertaking for Morphological Profiling Cell Painting Consortium, to improve upon the assay via quantitative optimization by measuring the assay's ability to detect morphological phenotypes and group similar perturbations together. The assay gives very robust outputs despite various changes to the protocol, and two vendors' dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for typically sized batches of ≤20 plates; feature extraction and data analysis take an additional 1-2 weeks.This protocol is an update to Nat. Protoc. 11, 1757-1774 (2016): https://doi.org/10.1038/nprot.2016.105.

Autophagy profiling in single cells with open source CellProfiler-based image analysis

Single cell-based analysis of macroautophagy/autophagy is largely achieved through the use of fluorescence microscopy to detect autophagy-related proteins that associate with autophagic membranes and therefore can be quantified as fluorescent puncta. In this context, an automated analysis of the number and size of recognized puncta is preferable to a manual count, because more reliable results can be generated in a short time. Here we present a method for open source CellProfiler software-based analysis for quantitative autophagy assessments using GFP-tagged WIPI1 (WD repeat domain, phosphoinositide interacting 1) images acquired with Airyscan or confocal laser-scanning microscopy. The CellProfiler protocol is provided as a ready-to-use software pipeline, and the creation of this pipeline is detailed in both text and video formats. In addition, we provide CellProfiler pipelines for endogenous SQSTM1/p62 (sequestosome 1) or intracellular lipid droplet (LD) analysis, suitable to assess forms of selective autophagy. All protocols and software pipelines can be quickly and easily adapted for the use of alternative autophagy markers or cell types, and can also be used for high-throughput purposes.Abbreviations: AF Alexa Fluor ATG autophagy related BafA1 bafilomycin A1 BSA bovine serum albumin DAPI 4,6-diamidino-2-phenylindole DMEM Dulbecco's modified Eagle's medium DMSO dimethyl sulfoxide EDTA ethylenediaminetetraacetic acid EBSS Earle's balanced salt solution FBS fetal bovine serum GFP green fluorescent protein LD lipid droplet LSM laser scanning microscope MAP1LC3B microtubule associated protein 1 light chain 3 beta MTOR mechanistic target of rapamycin kinase PBS phosphate-buffered saline PIK3C3/VPS34 phosphatidylinositol 3-kinase catalytic subunit type 3 SQSTM1 sequestosome 1 TIFF tagged image file format U2OS U-2 OS cell line WIPI WD repeat domain, phosphoinositide interacting.

Recent Microscopy Advances and the Applications to Huntington’s Disease Research

 Huntingtin is a 3144 amino acid protein defined as a scaffold protein with many intracellular locations that suggest functions in these compartments. Expansion of the CAG DNA tract in the huntingtin first exon is the cause of Huntington’s disease. An important tool in understanding the biological functions of huntingtin is molecular imaging at the single-cell level by microscopy and nanoscopy. The evolution of these technologies has accelerated since the Nobel Prize in Chemistry was awarded in 2014 for super-resolution nanoscopy. We are in a new era of light imaging at the single-cell level, not just for protein location, but also for protein conformation and biochemical function. Large-scale microscopy-based screening is also being accelerated by a coincident development of machine-based learning that offers a framework for truly unbiased data acquisition and analysis at very large scales. This review will summarize the newest technologies in light, electron, and atomic force microscopy in the context of unique challenges with huntingtin cell biology and biochemistry.

A User-Friendly Approach for Routine Histopathological and Morphometric Analysis of Skeletal Muscle Using CellProfiler Software

Adult skeletal muscle is capable of active and efficient differentiation in the event of injury in both physiological and pathological conditions, such as in Duchenne muscular dystrophy (DMD). DMD is characterized by different features, such as continuous cycles of degeneration/regeneration, fiber heterogeneity, chronic inflammation and fibrosis. A well-defined and standardized approach for histological and morphometric analysis of muscle samples is necessary in order to measure and quantify specific regenerative parameters in myopathies. Indeed, non-automatic methods are time-consuming and prone to error. Here, we describe a simple automatized computational approach to quantify muscle parameters with specific pipelines to be run by CellProfiler software in an open-source and well-defined fashion. Our pipelines consist of running image-processing modules in CellProfiler with the aim of quantifying different histopathological muscle hallmarks in mdx mice compared to their wild-type littermates. Specifically, we quantified the minimum Feret diameter, centrally nucleated fibers and the number of macrophages, starting from multiple images. Finally, for extracellular matrix quantification, we used Sirius red staining. Collectively, we developed reliable and easy-to-use pipelines that automatically measure parameters of muscle histology, useful for research in myobiology. These findings should simplify and shorten the time needed for the quantification of muscle histological properties, avoiding challenging manual procedures.

Segmentation of the Haematoxylin and Eosin Stained Muscle Cell Images—A Comparative Study

The effective detection of muscle cells, the accurate counting of their numbers and the analysis of their morphological features have great importance in biomedical research. At present, the quantification of muscle cell and the computation of their cross-sectional areas (CSA) are still manual or semi-automated, and with the increase of the image number, the manual or semi-automated methods might become intractable. Hence, the automatic methods are very desirable, which motivated the developments of many muscle cell segmentation methods. In this paper, three methods, SDDM, CELLSEGM and SMASH are compared and evaluated with 100 images with over 6000 cells. The Dices computed by SDDM, CELLSEGM and SMASH are 97.38%, 89.85% and 90.08% respectively. The average differences between the calculated cross-sectional areas and the ground truths by SDDM, CELLSEGM and SMASH are 5.14%, 10.76% and 7.97% respectively.

High-content screen in human pluripotent cells identifies miRNA-regulated pathways controlling pluripotency and differentiation

Background

By post-transcriptionally regulating multiple target transcripts, microRNAs (miRNAs or miR) play important biological functions. H1 embryonic stem cells (hESCs) and NTera-2 embryonal carcinoma cells (ECCs) are two of the most widely used human pluripotent model cell lines, sharing several characteristics, including the expression of miRNAs associated to the pluripotent state or with differentiation. However, how each of these miRNAs functionally impacts the biological properties of these cells has not been systematically evaluated.

Methods

We investigated the effects of 31 miRNAs on NTera-2 and H1 hESCs, by transfecting miRNA mimics. Following 3–4 days of culture, cells were stained for the pluripotency marker OCT4 and the G2 cell-cycle marker Cyclin B1, and nuclei and cytoplasm were co-stained with Hoechst and Cell Mask Blue, respectively. By using automated quantitative fluorescence microscopy (i.e., high-content screening (HCS)), we obtained several morphological and marker intensity measurements, in both cell compartments, allowing the generation of a multiparametric miR-induced phenotypic profile describing changes related to proliferation, cell cycle, pluripotency, and differentiation.

Results

Despite the overall similarities between both cell types, some miRNAs elicited cell-specific effects, while some related miRNAs induced contrasting effects in the same cell. By identifying transcripts predicted to be commonly targeted by miRNAs inducing similar effects (profiles grouped by hierarchical clustering), we were able to uncover potentially modulated signaling pathways and biological processes, likely mediating the effects of the microRNAs on the distinct groups identified. Specifically, we show that miR-363 contributes to pluripotency maintenance, at least in part, by targeting NOTCH1 and PSEN1 and inhibiting Notch-induced differentiation, a mechanism that could be implicated in naïve and primed pluripotent states.

Conclusions

We present the first multiparametric high-content microRNA functional screening in human pluripotent cells. Integration of this type of data with similar data obtained from siRNA screenings (using the same HCS assay) could provide a large-scale functional approach to identify and validate microRNA-mediated regulatory mechanisms controlling pluripotency and differentiation.

Electronic supplementary material

The online version of this article (10.1186/s13287-019-1318-6) contains supplementary material, which is available to authorized users.

Protein Methyltransferase Inhibition Decreases Endocrine Specification Through the Upregulation of Aldh1b1 Expression

Understanding the mechanisms that promote the specification of pancreas progenitors and regulate their self-renewal and differentiation will help to maintain and expand pancreas progenitor cells derived from human pluripotent stem (hPS) cells. This will improve the efficiency of current differentiation protocols of hPS cells into β-cells and bring such cells closer to clinical applications for the therapy of diabetes. Aldehyde dehydrogenase 1b1 (Aldh1b1) is a mitochondrial enzyme expressed specifically in progenitor cells during mouse pancreas development, and we have shown that its functional inactivation leads to accelerated differentiation and deficient β-cells. In this report, we aimed to identify small molecule inducers of Aldh1b1 expression taking advantage of a mouse embryonic stem (mES) cell Aldh1b1 lacZ reporter line and a pancreas differentiation protocol directing mES cells into pancreatic progenitors. We identified AMI-5, a protein methyltransferase inhibitor, as an Aldh1b1 inducer and showed that it can maintain Aldh1b1 expression in embryonic pancreas explants. This led to a selective reduction in endocrine specification. This effect was due to a downregulation of Ngn3, and it was mediated through Aldh1b1 since the effect was abolished in Aldh1b1 null pancreata. The findings implicated methyltransferase activity in the regulation of endocrine differentiation and showed that methyltransferases can act through specific regulators during pancreas differentiation. Stem Cells 2019;37:640-651.

Defining host-pathogen interactions employing an artificial intelligence workflow

For image-based infection biology, accurate unbiased quantification of host-pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentation. Host protein recruitment to pathogens is often refractory to accurate automated assessment due to its heterogeneous nature. An intuitive intelligent image analysis program to assess host protein recruitment within general cellular pathogen defense is lacking. We present HRMAn (Host Response to Microbe Analysis), an open-source image analysis platform based on machine learning algorithms and deep learning. We show that HRMAn has the capacity to learn phenotypes from the data, without relying on researcher-based assumptions. Using Toxoplasma gondii and Salmonella enterica Typhimurium we demonstrate HRMAn's capacity to recognize, classify and quantify pathogen killing, replication and cellular defense responses. HRMAn thus presents the only intelligent solution operating at human capacity suitable for both single image and high content image analysis.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Arrayed functional genetic screenings in pluripotency reprogramming and differentiation

Thoroughly understanding the molecular mechanisms responsible for the biological properties of pluripotent stem cells, as well as for the processes involved in reprograming, differentiation, and transition between Naïve and Primed pluripotent states, is of great interest in basic and applied research. Although pluripotent cells have been extensively characterized in terms of their transcriptome and miRNome, a comprehensive understanding of how these gene products specifically impact their biology, depends on gain- or loss-of-function experimental approaches capable to systematically interrogate their function. We review all studies carried up to date that used arrayed screening approaches to explore the function of these genetic elements on those biological contexts, using focused or genome-wide genetic libraries. We further discuss the limitations and advantages of approaches based on assays with population-level primary readouts, derived from single-parameter plate readers, or cell-level primary readouts, obtained using multiparametric flow cytometry or quantitative fluorescence microscopy (i.e., high-content screening). Finally, we discuss technical limitation and future perspectives, highlighting how the integration of screening data may lead to major advances in the field of stem cell research and therapy.

Phenotypic Screening Using Mouse and Human Stem Cell-Based Models of Neuroinflammation and Gene Expression Analysis to Study Drug Responses

High-throughput phenotypic screening enables the identification of new therapeutic targets even when the molecular mechanism underlying the disease is unknown. In the case of neurodegenerative disease, there is a dire need to identify new targets that can ameliorate, halt, or reverse degeneration. Stem cell-based disease models are particularly powerful tools for phenotypic screening because they use the same cell type affected in patients. Here, we describe the expansion of mouse stem cells and human induced pluripotent stem cells as well as the differentiation of these cells into neural lineages that, when exposed to neuroinflammatory stress, can be used for compound screening followed by hit identification, validation, and target deconvolution.

Systematic RNA-interference in primary human monocyte-derived macrophages: A high-throughput platform to study foam cell formation

Macrophage-derived foam cells are key regulators of atherogenesis. They accumulate in atherosclerotic plaques and support inflammatory processes by producing cytokines and chemokines. Identifying factors that regulate macrophage lipid uptake may reveal therapeutic targets for coronary artery disease (CAD). Here, we establish a high-throughput screening workflow to systematically identify genes that impact the uptake of DiI-labeled low-density lipoprotein (LDL) into monocyte-derived primary human macrophages. For this, monocytes isolated from peripheral blood were seeded onto 384-well plates, solid-phase transfected with siRNAs, differentiated in vitro into macrophages, and LDL-uptake per cell was measured by automated microscopy and quantitative image analysis. We applied this workflow to study how silencing of 89 genes impacts LDL-uptake into cells from 16 patients with CAD and 16 age-matched controls. Silencing of four novel genes (APOC1, CMTM6, FABP4, WBP5) reduced macrophage LDL-uptake. Additionally, knockdown of the chemokine receptor CXCR4 reduced LDL-uptake, most likely through a G-protein coupled mechanism that involves the CXCR4 ligand macrophage-induced factor (MIF), but is independent of CXCL12. We introduce a high-throughput strategy to systematically study gene function directly in primary CAD-patient cells. Our results propose a function for the MIF/CXCR4 signaling pathway, as well as several novel candidate genes impacting lipid uptake into human macrophages.

Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress

From bacteria to humans, individual cells within isogenic populations can show significant variation in stress tolerance, but the nature of this heterogeneity is not clear. To investigate this, we used single-cell RNA sequencing to quantify transcript heterogeneity in single Saccharomyces cerevisiae cells treated with and without salt stress to explore population variation and identify cellular covariates that influence the stress-responsive transcriptome. Leveraging the extensive knowledge of yeast transcriptional regulation, we uncovered significant regulatory variation in individual yeast cells, both before and after stress. We also discovered that a subset of cells appears to decouple expression of ribosomal protein genes from the environmental stress response in a manner partly correlated with the cell cycle but unrelated to the yeast ultradian metabolic cycle. Live-cell imaging of cells expressing pairs of fluorescent regulators, including the transcription factor Msn2 with Dot6, Sfp1, or MAP kinase Hog1, revealed both coordinated and decoupled nucleocytoplasmic shuttling. Together with transcriptomic analysis, our results suggest that cells maintain a cellular filter against decoupled bursts of transcription factor activation but mount a stress response upon coordinated regulation, even in a subset of unstressed cells.

Genetically identical cells growing in the same environment can vary in their cellular state and behavior. Such heterogeneity may explain why some cells in an isogenic population can survive sudden severe environmental stress whereas other cells succumb. Cell-to-cell variation in gene expression has been linked to variable stress survival, but how and why transcript levels vary across the transcriptome in single cells is only beginning to emerge. Here, we used single-cell RNA sequencing (scRNA-seq) to measure cell-to-cell heterogeneity in the transcriptome of budding yeast (Saccharomyces cerevisiae). We find surprising patterns of variation across known sets of transcription factor targets, indicating that cells vary in their transcriptome profile both before and after stress exposure. scRNA-seq analysis combined with live-cell imaging of transcription factor activation dynamics revealed some cells in which the stress response was coordinately activated and other cells in which the traditional response was decoupled, suggesting unrecognized regulatory nuances that expand our understanding of stress response and survival.

TRAPPC13 modulates autophagy and the response to Golgi stress

Tether complexes play important roles in endocytic and exocytic trafficking of lipids and proteins. In yeast, the multisubunit transport protein particle (TRAPP) tether regulates endoplasmic reticulum (ER)-to-Golgi and intra-Golgi transport and is also implicated in autophagy. In addition, the TRAPP complex acts as a guanine nucleotide exchange factor (GEF) for Ypt1, which is homologous to human Rab1a and Rab1b. Here, we show that human TRAPPC13 and other TRAPP subunits are critically involved in the survival response to several Golgi-disrupting agents. Loss of TRAPPC13 partially preserves the secretory pathway and viability in response to brefeldin A, in a manner that is dependent on ARF1 and the large GEF GBF1, and concomitant with reduced caspase activation and ER stress marker induction. TRAPPC13 depletion reduces Rab1a and Rab1b activity, impairs autophagy and leads to increased infectivity to the pathogenic bacterium Shigella flexneri in response to brefeldin A. Thus, our results lend support for the existence of a mammalian TRAPPIII complex containing TRAPPC13, which is important for autophagic flux under certain stress conditions.

Biologically Relevant Heterogeneity: Metrics and Practical Insights

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

Lipid droplet subset targeting of the Drosophila protein CG2254/dmLdsdh1

Lipid droplets (LDs) are the principal organelles of lipid storage. They consist of a hydrophobic core of storage lipids, surrounded by a phospholipid monolayer with proteins attached. While some of these proteins are essential to regulate cellular and organismic lipid metabolism, key questions concerning LD protein function, such as their targeting to LDs, are still unanswered. Intriguingly, some proteins are restricted to LD subsets by an as yet unknown mechanism. This finding makes LD targeting even more complex.

Here, we characterize the Drosophila protein CG2254 which targets LD subsets in cultured cells and different larval Drosophila tissues, where the prevalence of LD subsets appears highly dynamic. We find that an amphipathic amino acid stretch mediates CG2254 LD localization. Additionally, we identified a juxtaposed sequence stretch limiting CG2254 localization to LD subsets. This sequence is sufficient to restrict a chimeric protein - consisting of the subset targeting sequence introduced to an otherwise pan LD localized protein sequence - to LD subsets. Based on its subcellular localization and annotated function, we suggest to rename CG2254 to Lipid droplet subset dehydrogenase 1 (Ldsdh1).

Jenkins-CI, an Open-Source Continuous Integration System, as a Scientific Data and Image-Processing Platform

High-throughput screening generates large volumes of heterogeneous data that require a diverse set of computational tools for management, processing, and analysis. Building integrated, scalable, and robust computational workflows for such applications is challenging but highly valuable. Scientific data integration and pipelining facilitate standardized data processing, collaboration, and reuse of best practices. We describe how Jenkins-CI, an “off-the-shelf,” open-source, continuous integration system, is used to build pipelines for processing images and associated data from high-content screening (HCS). Jenkins-CI provides numerous plugins for standard compute tasks, and its design allows the quick integration of external scientific applications. Using Jenkins-CI, we integrated CellProfiler, an open-source image-processing platform, with various HCS utilities and a high-performance Linux cluster. The platform is web-accessible, facilitates access and sharing of high-performance compute resources, and automates previously cumbersome data and image-processing tasks. Imaging pipelines developed using the desktop CellProfiler client can be managed and shared through a centralized Jenkins-CI repository. Pipelines and managed data are annotated to facilitate collaboration and reuse. Limitations with Jenkins-CI (primarily around the user interface) were addressed through the selection of helper plugins from the Jenkins-CI community.

5'-Inositol phosphatase SHIP2 recruits Mena to stabilize invadopodia for cancer cell invasion

Invadopodia are membrane protrusions used by cancer cells to remodel and invade the extracellular matrix. Here, Rajadurai et al. show that the lipid phosphatase SHIP2 recruits the Ena/VASP-family actin regulatory protein Mena to stabilize invadopodia membrane protrusions and promote cell invasion.

Invadopodia are specialized membrane protrusions that support degradation of extracellular matrix (ECM) by cancer cells, allowing invasion and metastatic spread. Although early stages of invadopodia assembly have been elucidated, little is known about maturation of invadopodia into structures competent for ECM proteolysis. The localized conversion of phosphatidylinositol(3,4,5)-triphosphate and accumulation of phosphatidylinositol(3,4)-bisphosphate at invadopodia is a key determinant for invadopodia maturation. Here we investigate the role of the 5′-inositol phosphatase, SHIP2, and reveal an unexpected scaffold function of SHIP2 as a prerequisite for invadopodia-mediated ECM degradation. Through biochemical and structure-function analyses, we identify specific interactions between SHIP2 and Mena, an Ena/VASP-family actin regulatory protein. We demonstrate that SHIP2 recruits Mena, but not VASP, to invadopodia and that disruption of SHIP2–Mena interaction in cancer cells leads to attenuated capacity for ECM degradation and invasion in vitro, as well as reduced metastasis in vivo. Together, these findings identify SHIP2 as a key modulator of carcinoma invasiveness and a target for metastatic disease.

A semi-automated, KNIME-based workflow for biofilm assays

Background

A current focus of biofilm research is the chemical interaction between microorganisms within the biofilms. Prerequisites for this research are bioassay systems which integrate reliable tools for the planning of experiments with robot-assisted measurements and with rapid data processing. Here, data structures that are both human- and machine readable may be particularly useful.

Results

In this report, we present several simplification and robotisation options for an assay of bacteria-induced biofilm formation by the freshwater diatom Achnanthidium minutissimum. We also tested several proof-of-concept robotisation methods for pipetting, as well as for measuring the biofilm absorbance directly in the multi-well plates. Furthermore, we exemplify the implementation of an improved data processing workflow for this assay using the Konstanz Information Miner (KNIME), a free and open source data analysis environment. The workflow integrates experiment planning files and absorbance read-out data, towards their automated processing for analysis.

Conclusions

Our workflow lead to a substantial reduction of the measurement and data processing workload, while still reproducing previously obtained results in the A. minutissimum biofilm assay. The methods, scripts and files we designed are described here, offering adaptable options for other medium-throughput biofilm screenings.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-016-0676-9) contains supplementary material, which is available to authorized users.

The development of high-content screening (HCS) technology and its importance to drug discovery

INTRODUCTION

High-content screening (HCS) was introduced about twenty years ago as a promising analytical approach to facilitate some critical aspects of drug discovery. Its application has spread progressively within the pharmaceutical industry and academia to the point that it today represents a fundamental tool in supporting drug discovery and development.

AREAS COVERED

Here, the authors review some of significant progress in the HCS field in terms of biological models and assay readouts. They highlight the importance of high-content screening in drug discovery, as testified by its numerous applications in a variety of therapeutic areas: oncology, infective diseases, cardiovascular and neurodegenerative diseases. They also dissect the role of HCS technology in different phases of the drug discovery pipeline: target identification, primary compound screening, secondary assays, mechanism of action studies and in vitro toxicology.

EXPERT OPINION

Recent advances in cellular assay technologies, such as the introduction of three-dimensional (3D) cultures, induced pluripotent stem cells (iPSCs) and genome editing technologies (e.g., CRISPR/Cas9), have tremendously expanded the potential of high-content assays to contribute to the drug discovery process. Increasingly predictive cellular models and readouts, together with the development of more sophisticated and affordable HCS readers, will further consolidate the role of HCS technology in drug discovery.

Recent advances in quantitative high throughput and high content data analysis

INTRODUCTION

High throughput screening has become a basic technique with which to explore biological systems. Advances in technology, including increased screening capacity, as well as methods that generate multiparametric readouts, are driving the need for improvements in the analysis of data sets derived from such screens.

AREAS COVERED

This article covers the recent advances in the analysis of high throughput screening data sets from arrayed samples, as well as the recent advances in the analysis of cell-by-cell data sets derived from image or flow cytometry application. Screening multiple genomic reagents targeting any given gene creates additional challenges and so methods that prioritize individual gene targets have been developed. The article reviews many of the open source data analysis methods that are now available and which are helping to define a consensus on the best practices to use when analyzing screening data.

EXPERT OPINION

As data sets become larger, and more complex, the need for easily accessible data analysis tools will continue to grow. The presentation of such complex data sets, to facilitate quality control monitoring and interpretation of the results will require the development of novel visualizations. In addition, advanced statistical and machine learning algorithms that can help identify patterns, correlations and the best features in massive data sets will be required. The ease of use for these tools will be important, as they will need to be used iteratively by laboratory scientists to improve the outcomes of complex analyses.

Apocynin and Diphenyleneiodonium Induce Oxidative Stress and Modulate PI3K/Akt and MAPK/Erk Activity in Mouse Embryonic Stem Cells

Reactive oxygen species (ROS) are important regulators of cellular functions. In embryonic stem cells, ROS are suggested to influence differentiation status. Regulated ROS formation is catalyzed primarily by NADPH-dependent oxidases (NOXs). Apocynin and diphenyleneiodonium are frequently used inhibitors of NOXs; however, both exhibit uncharacterized effects not related to NOXs inhibition. Interestingly, in our model of mouse embryonic stem cells we demonstrate low expression of NOXs. Therefore we aimed to clarify potential side effects of these drugs. Both apocynin and diphenyleneiodonium impaired proliferation of cells. Surprisingly, we observed prooxidant activity of these drugs determined by hydroethidine. Further, we revealed that apocynin inhibits PI3K/Akt pathway with its downstream transcriptional factor Nanog. Opposite to this, apocynin augmented activity of canonical Wnt signaling. On the contrary, diphenyleneiodonium activated both PI3K/Akt and Erk signaling pathways without affecting Wnt. Our data indicates limits and possible unexpected interactions of NOXs inhibitors with intracellular signaling pathways.

A new age in functional genomics using CRISPR/Cas9 in arrayed library screening

CRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development. What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA. Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions. For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene. In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening. In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening.

The myImageAnalysis project: a web-based application for high-content screening

A major challenge faced by screening centers developing image-based assays is the wide range of assays needed compared to the limited resources that are available to effectively analyze and manage them. To overcome this limitation, we have developed the web-based myImageAnalysis (mIA) application, integrated with an open database connectivity compliant database and powered by Pipeline Pilot (PLP) that incorporates dataset tracking, scheduling and archiving, image analysis, and data reporting. For system administrators, mIA provides automated methods for managing and archiving data. For the biologist, this application allows those without any programming or image analysis experience to quickly develop, validate, and share results of complex image-based assays. Further, the structure of the application within PLP allows those with experience in PLP programming to easily add additional analysis tools as required. The tools within mIA allow users to assess basic (cell count, protein per cell, protein subcellular localization) and more advanced (engineered cell lines analysis, cell toxicity) biological image-based assays that employ advanced statistics and provides key assay performance metrics.

A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs

We exploit the recent availability of a community reconstruction of the human metabolic network ('Recon2') to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) 'nearest' human metabolite. This suggests a 'rule of 0.5' mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This 'rule of 0.5' may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.

Heritability and genetic basis of protein level variation in an outbred population

The genetic basis of heritable traits has been studied for decades. Although recent mapping efforts have elucidated genetic determinants of transcript levels, mapping of protein abundance has lagged. Here, we analyze levels of 4084 GFP-tagged yeast proteins in the progeny of a cross between a laboratory and a wild strain using flow cytometry and high-content microscopy. The genotype of trans variants contributed little to protein level variation between individual cells but explained >50% of the variance in the population’s average protein abundance for half of the GFP fusions tested. To map trans-acting factors responsible, we performed flow sorting and bulk segregant analysis of 25 proteins, finding a median of five protein quantitative trait loci (pQTLs) per GFP fusion. Further, we find that cis-acting variants predominate; the genotype of a gene and its surrounding region had a large effect on protein level six times more frequently than the rest of the genome combined. We present evidence for both shared and independent genetic control of transcript and protein abundance: More than half of the expression QTLs (eQTLs) contribute to changes in protein levels of regulated genes, but several pQTLs do not affect their cognate transcript levels. Allele replacements of genes known to underlie trans eQTL hotspots confirmed the correlation of effects on mRNA and protein levels. This study represents the first genome-scale measurement of genetic contribution to protein levels in single cells and populations, identifies more than a hundred trans pQTLs, and validates the propagation of effects associated with transcript variation to protein abundance.

TLM-Quant: an open-source pipeline for visualization and quantification of gene expression heterogeneity in growing microbial cells

Gene expression heterogeneity is a key driver for microbial adaptation to fluctuating environmental conditions, cell differentiation and the evolution of species. This phenomenon has therefore enormous implications, not only for life in general, but also for biotechnological applications where unwanted subpopulations of non-producing cells can emerge in large-scale fermentations. Only time-lapse fluorescence microscopy allows real-time measurements of gene expression heterogeneity. A major limitation in the analysis of time-lapse microscopy data is the lack of fast, cost-effective, open, simple and adaptable protocols. Here we describe TLM-Quant, a semi-automatic pipeline for the analysis of time-lapse fluorescence microscopy data that enables the user to visualize and quantify gene expression heterogeneity. Importantly, our pipeline builds on the open-source packages ImageJ and R. To validate TLM-Quant, we selected three possible scenarios, namely homogeneous expression, highly 'noisy' heterogeneous expression, and bistable heterogeneous expression in the Gram-positive bacterium Bacillus subtilis. This bacterium is both a paradigm for systems-level studies on gene expression and a highly appreciated biotechnological 'cell factory'. We conclude that the temporal resolution of such analyses with TLM-Quant is only limited by the numbers of recorded images.