Cell Painting unravels insecticidal modes of action on Spodoptera frugiperda insect cells.

The "Cell Painting" technology utilizes multiplexed fluorescent staining of various cell organelles, to produce high-content microscopy images of cells for multidimensional phenotype assessment. The phenotypic profiles extracted from those images can be analyzed upon perturbations with biologically active molecules to annotate the mode of action or biological activity by comparison with reference profiles of already known mechanisms of action, ultimately enabling the determination of on-target and off-target effects. This approach is already described in various human cell cultures, the most commonly used being the U2OS cell line, yet allows broad applications in additional areas of chemical-biological research. Here we describe for the first time the application and adaptation of Cell Painting to an insect cell line, the Sf9 cells from Spodoptera frugiperda. By adjusting image acquisition and analysis models, specific phenotypic profiles were obtained in a dose-dependent manner for 20 reference compounds, including representatives for the most relevant insecticidal modes of action categories (nerve & muscle, respiration and growth & development). Through a dimensionality-reduction method, both calculations of phenotypic half maximal inhibition concentration (IC50) values as well as similarity analysis of the obtained profiles by hierarchical clustering were performed. By Cell Painting effects on the phenotype could be obtained at higher sensitivity than in other assay formats, such as cytotoxicity assessments. More importantly, these analyses provide insight into mechanistic determinants of biological activity. Compounds with similar modes of action showed a high degree of proximity in a hierarchical clustering analysis while being distinct from actives with an unrelated mode of action. In essence, we provide strong evidence on the impact of Cell Painting mechanistic understanding of insecticides with regards to determinants of efficacy and safety utilizing an insect cell model system.

Systematic analysis of drug combinations against Gram-positive bacteria.

Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled ~8,000 combinations of 65 antibacterial drugs against the model species Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface ( https://apps.embl.de/combact/ ).

Comparing the value of mono- vs coculture for high-throughput compound screening in hematological malignancies.

Large-scale compound screens are a powerful model system for understanding variability of treatment response and discovering druggable tumor vulnerabilities of hematological malignancies. However, as mostly performed in a monoculture of tumor cells, these assays disregard modulatory effects of the in vivo microenvironment. It is an open question whether and to what extent coculture with bone marrow stromal cells could improve the biological relevance of drug testing assays over monoculture. Here, we established a high-throughput platform to measure ex vivo sensitivity of 108 primary blood cancer samples to 50 drugs in monoculture and coculture with bone marrow stromal cells. Stromal coculture conferred resistance to 52% of compounds in chronic lymphocytic leukemia (CLL) and 36% of compounds in acute myeloid leukemia (AML), including chemotherapeutics, B-cell receptor inhibitors, proteasome inhibitors, and Bromodomain and extraterminal domain inhibitors. Only the JAK inhibitors ruxolitinib and tofacitinib exhibited increased efficacy in AML and CLL stromal coculture. We further confirmed the importance of JAK-STAT signaling for stroma-mediated resistance by showing that stromal cells induce phosphorylation of STAT3 in CLL cells. We genetically characterized the 108 cancer samples and found that drug-gene associations strongly correlated between monoculture and coculture. However, effect sizes were lower in coculture, with more drug-gene associations detected in monoculture than in coculture. Our results justify a 2-step strategy for drug perturbation testing, with large-scale screening performed in monoculture, followed by focused evaluation of potential stroma-mediated resistances in coculture.

An autologous culture model of nodal B-cell lymphoma identifies ex vivo determinants of response to bispecific antibodies.

Bispecific antibodies (BsAbs) can induce long-term responses in patients with refractory and relapsed B-cell lymphoma. Nevertheless, response rates across patients are heterogeneous, and the factors determining quality and duration of responses are poorly understood. To identify key determinants of response to BsAbs, we established a primary, autologous culture model allowing us to mimic treatment with CD3xCD19 and CD3xCD20 BsAbs within the lymph node microenvironment ex vivo. T cell-mediated killing of lymphoma cells and proliferation of T cells varied significantly among patients but highly correlated between BsAbs targeting CD20 or CD19. Ex vivo response to BsAbs was significantly associated with expansion of T cells and secretion of effector molecules (eg, granzyme B, perforin) but not with expression of T-cell exhaustion (eg, PD1, TIM3) or activation markers (eg, CD25, CD69) or formation of intercellular contacts. In addition, we identified a distinct phenotype of regulatory T cells that was linked to ex vivo response independently from T-cell frequency at baseline. High expression levels of Aiolos (IKZF1), ICOS, and CXCR5 were positively associated with ex vivo response, whereas strong expression of Helios (IKZF2) had an unfavorable impact on ex vivo response to BsAbs. We further showed that lenalidomide, nivolumab, and atezolizumab improved ex vivo response to BsAbs by potentiating T-cell effector functions. In summary, our ex vivo study identified a distinct regulatory T-cell phenotype as a potential contributor to treatment failure of BsAbs and suggests drug combinations of high clinical relevance that could improve the efficacy of BsAbs.

Drug-perturbation-based stratification of blood cancer.

As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non-BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care.

RNA-Seq workflow: gene-level exploratory analysis and differential expression.

Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.