Nanogroove-Enhanced Hydrogel Scaffolds for 3D Neuronal Cell Culture: An Easy Access Brain-on-Chip Model

In order to better understand the brain and brain diseases, in vitro human brain models need to include not only a chemically and physically relevant microenvironment, but also structural network complexity. This complexity reflects the hierarchical architecture in brain tissue. Here, a method has been developed that adds complexity to a 3D cell culture by means of nanogrooved substrates. SH-SY5Y cells were grown on these nanogrooved substrates and covered with Matrigel, a hydrogel. To quantitatively analyze network behavior in 2D neuronal cell cultures, we previously developed an automated image-based screening method. We first investigated if this method was applicable to 3D primary rat brain cortical (CTX) cell cultures. Since the method was successfully applied to these pilot data, a proof of principle in a reductionist human brain cell model was attempted, using the SH-SY5Y cell line. The results showed that these cells also create an aligned network in the 3D microenvironment by maintaining a certain degree of guidance by the nanogrooved topography in the z-direction. These results indicate that nanogrooves enhance the structural complexity of 3D neuronal cell cultures for both CTX and human SH-SY5Y cultures, providing a basis for further development of an easy access brain-on-chip model.

Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma

Image-based analysis is an increasingly important tool to characterize the effect of drugs in large-scale chemical screens. Herein, we present image and data analysis methods to investigate population cell-cycle dynamics in patient-derived brain tumor cells. Images of glioblastoma cells grown in multiwell plates were used to extract per-cell descriptors, including nuclear DNA content. We reduced the DNA content data from per-cell descriptors to per-well frequency distributions, which were used to identify compounds affecting cell-cycle phase distribution. We analyzed cells from 15 patient cases representing multiple subtypes of glioblastoma and searched for clusters of cell-cycle phase distributions characterizing similarities in response to 249 compounds at 11 doses. We show that this approach applied in a blind analysis with unlabeled substances identified drugs that are commonly used for treating solid tumors as well as other compounds that are well known for inducing cell-cycle arrest. Redistribution of nuclear DNA content signals is thus a robust metric of cell-cycle arrest in patient-derived glioblastoma cells.

A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay

Background

Large-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.

Findings

This 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.

Conclusions

Because 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.

Imaging-Based Screen Identifies Laminin 411 as a Physiologically Relevant Niche Factor with Importance for i-Hep Applications

Use of hepatocytes derived from induced pluripotent stem cells (i-Heps) is limited by their functional differences in comparison with primary cells. Extracellular niche factors likely play a critical role in bridging this gap. Using image-based characterization (high content analysis; HCA) of freshly isolated hepatocytes from 17 human donors, we devised and validated an algorithm (Hepatocyte Likeness Index; HLI) for comparing the hepatic properties of cells against a physiological gold standard. The HLI was then applied in a targeted screen of extracellular niche factors to identify substrates driving i-Heps closer to the standard. Laminin 411, the top hit, was validated in two additional induced pluripotent stem cell (iPSC) lines, primary tissue, and an in vitro model of α1-antitrypsin deficiency. Cumulatively, these data provide a reference method to control and screen for i-Hep differentiation, identify Laminin 411 as a key niche protein, and underscore the importance of combining substrates, soluble factors, and HCA when developing iPSC applications.

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iPSC-derived hepatocytes (i-Heps) are functionally limited compared with primary cells

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Factors within the extracellular niche likely play a role in bridging this gap

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Laminin 411 was shown to be an important niche factor for i-Heps

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High content image analysis (HCA) can help development of i-Hep applications

RNA Interference (RNAi) Screening in Drosophila

In the last decade, RNA interference (RNAi), a cellular mechanism that uses RNA-guided degradation of messenger RNA transcripts, has had an important impact on identifying and characterizing gene function. First discovered in Caenorhabditis elegans, RNAi can be used to silence the expression of genes through introduction of exogenous double-stranded RNA into cells. In Drosophila, RNAi has been applied in cultured cells or in vivo to perturb the function of single genes or to systematically probe gene function on a genome-wide scale. In this review, we will describe the use of RNAi to study gene function in Drosophila with a particular focus on high-throughput screening methods applied in cultured cells. We will discuss available reagent libraries and cell lines, methodological approaches for cell-based assays, and computational methods for the analysis of high-throughput screens. Furthermore, we will review the generation and use of genome-scale RNAi libraries for tissue-specific knockdown analysis in vivo and discuss the differences and similarities with the use of genome-engineering methods such as CRISPR/Cas9 for functional analysis.

Optimization of Cell-Based cDNA Microarray Conditions for Gene Functional Studies in HEK293 Cells

Since the cell-based cDNA microarray (CBCM) technique has been a useful tool for gain-of-function studies, many investigators have used CBCMs to identify interesting genes. However, this method requires better-established conditions to ensure high reverse transfection efficiency without cross-contamination. Therefore, we optimized CBCM techniques through various means. We determined that Lipofectamine 2000 was the most appropriate transfection reagent by evaluating eight commercialized reagents, and we determined that the most effective concentrations for printing solution constituents were 0.2 M sucrose (to yield a final concentration of 32 mM) and 0.2% gelatin (to yield a final concentration 0.075%). After examining various combinations, we also determined that the best concentrations of cDNA and transfection reagent for optimal reverse transfection efficiency were 1.5 µg/5 µL of cDNA and 5.5 µL of Lipofectamine 2000. Finally, via a time course, we determined that 72 h was the most effective reaction duration for reverse transfection, and we confirmed the stability of cDNA spot activity of CBCMs for various storage periods. In summary, the CBCM conditions that we have identified can provide more effective outcomes for cDNA reverse transfection on microarrays.

Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries

Traditional natural products discovery using a combination of live/dead screening followed by iterative bioassay-guided fractionation affords no information about compound structure or mode of action until late in the discovery process. This leads to high rates of rediscovery and low probabilities of finding compounds with unique biological and/or chemical properties. By integrating image-based phenotypic screening in HeLa cells with high-resolution untargeted metabolomics analysis, we have developed a new platform, termed Compound Activity Mapping, that is capable of directly predicting the identities and modes of action of bioactive constituents for any complex natural product extract library. This new tool can be used to rapidly identify novel bioactive constituents and provide predictions of compound modes of action directly from primary screening data. This approach inverts the natural products discovery process from the existing "grind and find" model to a targeted, hypothesis-driven discovery model where the chemical features and biological function of bioactive metabolites are known early in the screening workflow, and lead compounds can be rationally selected based on biological and/or chemical novelty. We demonstrate the utility of the Compound Activity Mapping platform by combining 10,977 mass spectral features and 58,032 biological measurements from a library of 234 natural products extracts and integrating these two datasets to identify 13 clusters of fractions containing 11 known compound families and four new compounds. Using Compound Activity Mapping we discovered the quinocinnolinomycins, a new family of natural products with a unique carbon skeleton that cause endoplasmic reticulum stress.

Identifying Small Molecules which Inhibit Autophagy: a Phenotypic Screen Using Image-Based High-Content Cell Analysis

Autophagy plays an important role in cancer and it has been suggested that it functions not only as a tumor suppressor pathway to prevent tumor initiation, but also as a pro-survival pathway that helps tumor cells endure metabolic stress and resist death triggered by chemotherapeutic agents, including acquired resistance. We aimed to identify small-molecule autophagy inhibitors using a HTS/HCA approach through a phenotypic, cell image-based assay, in order to screen multiple biological targets simultaneously and to screen compounds in a physiologically relevant environment. LC3 is a component of the autophagosome, which undergoes a cytoplasmic redistribution from diffuse to punctate dots during autophagy. We employed HeLa cells stably expressing EGFP-LC3 in a primary phenotypic screen. As a first step, a “Validation Library” of about 8,000 pre-selected compounds, about 25% of which had known biological activity and the others representing a range of chemical structures, was run in duplicate both to assess screening suitability and likely hit rate, and to give a valuable preview of possible active structures or biological targets. The primary screen of about 0.25 million compounds yielded around 10,500 positive compounds. These were tested in a suite of further cellular assays designed to eliminate unwanted positives, together with the application of chemi- and bioinformatics to pick out compounds with known biological activity. These processes enabled the selection of compounds that were the most promisingly active and specific. The screening “tree” identified, amongst others with as yet unidentified targets, chemical series active against autophagy-relevant biological targets ULK or Vsp34, validating the phenotypic screening methods selected. Finally, about 400 compounds were fully qualified after following this triage. The development of the assays, compound screening process and the compound triage is described.

Discovery and biological characterization of the auromomycin chromophore as an inhibitor of biofilm formation in Vibrio cholerae

Bacterial biofilms pose a significant challenge in clinical environments due to their inherent lack of susceptibility to antibiotic treatment. It is widely recognized that most pathogenic bacterial strains in the clinical setting persist in the biofilm state, and are the root cause of many recrudescent infections. The discovery and development of compounds capable of either inhibiting biofilm formation or initiating biofilm dispersal might provide new therapeutic avenues for reducing the number of hospital-acquired, biofilm-mediated infections. We detail here the application of our recently reported image-based, high-throughput screen to the discovery of microbially derived natural products with inhibitory activity against Vibrio cholerae biofilm. Examination of a prefractionated library of microbially derived marine natural products has led to the identification of a new biofilm inhibitor that is structurally unrelated to previously reported inhibitors and is one of the most potent inhibitors of V. cholerae reported to date. Combination of this compound with sub-MIC concentrations of a number of clinically relevant antibiotics was shown to improve the inhibitory efficacy of this new compound compared to monotherapy treatments, and provides evidence for the potential therapeutic benefit of biofilm inhibitors in treating persistent biofilm-mediated infections.

Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment

Quantitative microscopy has proven a versatile and powerful phenotypic screening technique. Recently, image-based profiling has shown promise as a means for broadly characterizing molecules' effects on cells in several drug-discovery applications, including target-agnostic screening and predicting a compound's mechanism of action (MOA). Several profiling methods have been proposed, but little is known about their comparative performance, impeding the wider adoption and further development of image-based profiling. We compared these methods by applying them to a widely applicable assay of cultured cells and measuring the ability of each method to predict the MOA of a compendium of drugs. A very simple method that is based on population means performed as well as methods designed to take advantage of the measurements of individual cells. This is surprising because many treatments induced a heterogeneous phenotypic response across the cell population in each sample. Another simple method, which performs factor analysis on the cellular measurements before averaging them, provided substantial improvement and was able to predict MOA correctly for 94% of the treatments in our ground-truth set. To facilitate the ready application and future development of image-based phenotypic profiling methods, we provide our complete ground-truth and test data sets, as well as open-source implementations of the various methods in a common software framework.