A bypass flow model to study endothelial cell mechanotransduction across diverse flow environments.

Disturbed flow is one of the pathological initiators of endothelial dysfunction in intimal hyperplasia (IH) which is commonly seen in vascular bypass grafts, and arteriovenous fistulas. Various in vitro disease models have been designed to simulate the hemodynamic conditions found in the vasculature. Nonetheless, prior investigations have encountered challenges in establishing a robust disturbed flow model, primarily attributed to the complex bifurcated geometries and distinctive flow dynamics. In the present study, we aim to address this gap by introducing an in vitro bypass flow model capable of inducing disturbed flow and other hemodynamics patterns through a pulsatile flow in the same model. To assess the model's validity, we employed computational fluid dynamics (CFD) to simulate hemodynamics and compared the morphology and functions of human umbilical venous endothelial cells (HUVECs) under disturbed flow conditions to those in physiological flow or stagnant conditions. CFD analysis revealed the generation of disturbed flow within the model, pinpointing the specific location in the channel where the effects of disturbed flow were observed. High-content screening, a single-cell morphological profile assessment, demonstrated that HUVECs in the disturbed flow area exhibited random orientation, and morphological features were significantly distinct compared to cells in the physiological flow or stagnant condition after a two days of flow exposure. Furthermore, HUVECs exposed to disturbed flow underwent extensive remodeling of the adherens junctions and expressed higher levels of endothelial cell activation markers compared to other hemodynamic conditions. In conclusion, our in vitro bypass flow model provides a robust platform for investigating the associations between disturbed flow pattern and vascular diseases.

Identification of chemical composition in Gnetum montanum extract and plasma components after oral administration in cynomolgus monkey by UPLC-Q-TOF-MS and its anti-tumor active components analysis.

Gnetum montanum Markgr. (Gnetaceae) is a commonly used traditional herbal medicine among the Yao ethnic group, with potential effects in preventing and treating tumors. However, the substance basis of its anti-tumor properties remains unclear. This study utilized ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to identify the chemical components of G. montanum extract (GME) and its absorbed prototypes in cynomolgus monkey plasma after oral administration. A total of 57 compounds were detected in the GME, with 14 compounds in positive ion mode and 43 compounds in negative ion mode. In the cynomolgus monkey plasma, 17 compounds were identified, with 3 compounds in positive ion mode and 14 compounds in negative ion mode. Subsequently, we utilized high content screening technology to investigate the anti-tumor effects of GME on colon cancer, lung cancer, breast cancer, gastric cancer, liver cancer, and esophageal cancer. We found that the GME exhibited significant proliferation inhibition on colon cancer cells SW480, with an IC50 value of 50.77 µg/mL. Further research using component separation and pharmacological tracking revealed that the F2 component of the GME demonstrated notable anti-tumor effects. Through UPLC-MS identification, the chemical components in the F2 fraction were identified as pinoresinol diglucoside, (+)-pinoresinol-4-O-beta-D-glucopyranoside, ursolic acid, and gnetol. In conclusion, this study contributes to elucidating the anti-tumor pharmacological basis of GME and provides robust support for future drug design and development.

[Phenotypic drug discovery promotes research and development of innovative drugs based on traditional Chinese medicine].

Traditional Chinese medicine with rich resources in China and definite therapeutic effects on complex diseases demonstrates great development potential. However, the complex composition, the unclear pharmacodynamic substances and mechanisms of action, and the lack of reasonable methods for evaluating clinical safety and efficacy have limited the research and development of innovative drugs based on traditional Chinese medicine. The progress in cutting-edge disciplines such as artificial intelligence and biomimetics, especially the emergence of cell painting and organ-on-a-chip, helps to identify and characterize the active ingredients in traditional Chinese medicine based on the changes in model characteristics, thus providing more accurate guidance for the development and application of traditional Chinese medicine. The application of phenotypic drug discovery in the research and development of innovative drugs based on traditional Chinese medicine is gaining increasing attention. In recent years, the technology for phenotypic drug discovery keeps advancing, which improves the early discovery rate of new drugs and the success rate of drug research and development. Accordingly, phenotypic drug discovery gradually becomes a key tool for the research on new drugs. This paper discusses the enormous potential of traditional Chinese medicine in the discovery and development of innovative drugs and illustrates how the application of phenotypic drug discovery, supported by cutting-edge technologies such as cell painting, deep learning, and organ-on-a-chip, propels traditional Chinese medicine into a new stage of development.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other -omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

High-Throughput Methods for the Discovery of Small Molecule Modulators of Pancreatic Beta-Cell Function and Regeneration.

The progression of type II diabetes (T2D) is characterized by a complex and highly variable loss of beta-cell mass, resulting in impaired insulin secretion. Many T2D drug discovery efforts aimed at discovering molecules that can protect or restore beta-cell mass and function have been developed using limited beta-cell lines and primary rodent/human pancreatic islets. Various high-throughput screening methods have been used in the context of drug discovery, including luciferase-based reporter assays, glucose-stimulated insulin secretion, and high-content screening. In this context, a cornerstone of small molecule discovery has been the use of immortalized rodent beta-cell lines. Although insightful, this usage has led to a more comprehensive understanding of rodent beta-cell proliferation pathways rather than their human counterparts. Advantages gained in enhanced physiological relevance are offered by three-dimensional (3D) primary islets and pseudoislets in contrast to monolayer cultures, but these approaches have been limited to use in low-throughput experiments. Emerging methods, such as high-throughput 3D islet imaging coupled with machine learning, aim to increase the feasibility of integrating 3D microtissue structures into high-throughput screening. This review explores the current methods used in high-throughput screening for small molecule modulators of beta-cell mass and function, a potentially pivotal strategy for diabetes drug discovery.

Expanding TheCellVision.org: a central repository for visualizing and mining high-content cell imaging projects.

We previously constructed TheCellVision.org, a central repository for visualizing and mining data from yeast high-content imaging projects. At its inception, TheCellVision.org housed two high-content screening (HCS) projects providing genome-scale protein abundance and localization information for the budding yeast Saccharomyces cerevisiae, as well as a comprehensive analysis of the morphology of its endocytic compartments upon systematic genetic perturbation of each yeast gene. Here, we report on the expansion of TheCellVision.org by the addition of two new HCS projects and the incorporation of new global functionalities. Specifically, TheCellVision.org now hosts images from the Cell Cycle Omics project, which describes genome-scale cell cycle-resolved dynamics in protein localization, protein concentration, gene expression, and translational efficiency in budding yeast. Moreover, it hosts PIFiA, a computational tool for image-based predictions of protein functional annotations. Across all its projects, TheCellVision.org now houses >800,000 microscopy images along with computational tools for exploring both the images and their associated datasets. Together with the newly added global functionalities, which include the ability to query genes in any of the hosted projects using either yeast or human gene names, TheCellVision.org provides an expanding resource for single-cell eukaryotic biology.

Decoding phenotypic screening: A comparative analysis of image representations.

Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop a universal representation model for HCS data, mainly data generated using U2OS cells and CellPainting protocol, using supervised and self-supervised learning approaches. We propose an evaluation protocol that assesses their performance on mode of action and property prediction tasks using a popular phenotypic screening dataset. Results show that the self-supervised approach that uses data from multiple consortium partners provides representation that is more robust to batch effects whilst simultaneously achieving performance on par with standard approaches. Together with other conclusions, it provides recommendations on the training strategy of a representation model for HCS images.

Combined use of multiparametric high-content-screening and in vitro circadian reporter assays in neurotoxicity evaluation.

Accumulating evidence indicates that chronic circadian rhythm disruption is associated with the development of neurodegenerative diseases induced by exposure to neurotoxic chemicals. Herein, we examined the relationship between cellular circadian rhythm disruption and cytotoxicity in neural cells. Moreover, we evaluated the potential application of an in vitro cellular circadian rhythm assay in determining circadian rhythm disruption as a sensitive and early marker of neurotoxicant-induced adverse effects. To explore these objectives, we established an in vitro cellular circadian rhythm assay using human glioblastoma (U87 MG) cells stably transfected with a circadian reporter vector (PER2-dLuc) and determined the lowest-observed-adverse-effect levels (LOAELs) of several common neurotoxicants. Additionally, we determined the LOAEL of each compound on multiple cytotoxicity endpoints (nuclear size [NC], mitochondrial membrane potential [MMP], calcium ions, or lipid peroxidation) using a multiparametric high-content screening (HCS) assay using transfected U87 MG cells treated with the same neurotoxicants for 24 and 72 h. Based on our findings, the LOAEL for cellular circadian rhythm disruption for most chemicals was slightly higher than that for most cytotoxicity indicators detected using HCS, and the LOAEL for MMP in the first 24 h was the closest to that for cellular circadian rhythm disruption. Dietary antioxidants (methylselenocysteine and N-acetyl-l-cysteine) prevented or restored neurotoxicant-induced cellular circadian rhythm disruption. Our results suggest that cellular circadian rhythm disruption is as sensitive as cytotoxicity indicators and occurs early as much as cytotoxic events during disease development. Moreover, the in vitro cellular circadian rhythm assay warrants further evaluation as an early screening tool for neurotoxicants.

High-Content Imaging-Based Assay for SARS-CoV-2-Neutralizing Antibodies.

The COVID-19 pandemic and the consequent emergence of new SARS-CoV-2 variants of concern necessitates the determination of populational serum potency against the virus. Here, we standardized and validated an imaging-based method to quantify neutralizing antibodies against lentiviral particles expressing the spike glycoprotein (pseudovirus). This method was found to efficiently quantify viral titers based on ZsGreen-positive cells and detect changes in human serum neutralization capacity induced by vaccination with up to two doses of CoronaVac, Comirnaty, or Covishield vaccines. The imaging-based protocol was also used to quantify serum potency against pseudoviruses expressing spikes from Delta, Omicron BA.1.1.529, and BA.4/5. Our results revealed increases in serum potency after one and two doses of the vaccines evaluated and demonstrated that Delta and Omicron variants escape from antibody neutralization. The method presented herein represents a valuable tool for the screening of antibodies and small molecules capable of blocking viral entry and could be used to evaluate humoral immunity developed by different populations and for vaccine development.

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle.

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.

Identification of fangchinoline as a broad-spectrum enterovirus inhibitor through reporter virus based high-content screening.

Hand, foot, and mouth disease (HFMD) is a common pediatric illness mainly caused by enteroviruses, which are important human pathogens. Currently, there are no available antiviral agents for the therapy of enterovirus infection. In this study, an excellent high-content antiviral screening system utilizing the EV-A71-eGFP reporter virus was developed. Using this screening system, we screened a drug library containing 1042 natural compounds to identify potential EV-A71 inhibitors. Fangchinoline (FAN), a bis-benzylisoquinoline alkaloid, exhibits potential inhibitory effects against various enteroviruses that cause HFMD, such as EV-A71, CV-A10, CV-B3 and CV-A16. Further investigations revealed that FAN targets the early stage of the enterovirus life cycle. Through the selection of FAN-resistant EV-A71 viruses, we demonstrated that the VP1 protein could be a potential target of FAN, as two mutations in VP1 (E145G and V258I) resulted in viral resistance to FAN. Our research suggests that FAN is an efficient inhibitor of EV-A71 and has the potential to be a broad-spectrum antiviral drug against human enteroviruses.

Uncovering the toxicity mechanisms of a series of carboxylic acids in liver cells through computational and experimental approaches.

Hepatotoxicity poses a significant concern in drug design due to the potential liver damage that can be caused by new drugs. Among common manifestations of hepatotoxic damage is lipid accumulation in hepatic tissue, resulting in liver steatosis or phospholipidosis. Carboxylic derivatives are prone to interfere with fatty acid metabolism and cause lipid accumulation in hepatocytes. This study investigates the toxic behaviour of 24 structurally related carboxylic acids in hepatocytes, specifically their ability to cause accumulation of fatty acids and phospholipids. Using high-content screening (HCS) assays, we identified two distinct lipid accumulation patterns. Subsequently, we developed structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) models to determine relevant molecular substructures and descriptors contributing to these adverse effects. Additionally, we calculated physicochemical properties associated with lipid accumulation in hepatocytes and examined their correlation with our chemical structure characteristics. To assess the applicability of our findings to a wide range of chemical compounds, we employed two external datasets to evaluate the distribution of our QSAR descriptors. Our study highlights the significance of subtle molecular structural variations in triggering hepatotoxicity, such as the presence of nitrogen or the specific arrangement of substitutions within the carbon chain. By employing our comprehensive approach, we pinpointed specific molecules and elucidated their mechanisms of toxicity, thus offering valuable insights to guide future toxicology investigations.

High-content image screening to identify chemical modulators for peroxisome and ferroptosis.

BACKGROUND: The peroxisome is a dynamic organelle with variety in number, size, shape, and activity in different cell types and physiological states. Recent studies have implicated peroxisomal homeostasis in ferroptosis susceptibility. Here, we developed a U-2OS cell line with a fluorescent peroxisomal tag and screened a target-selective chemical library through high-content imaging analysis. METHODS: U-2OS cells stably expressing the mOrange2-Peroxisomes2 tag were generated to screen a target-selective inhibitor library. The nuclear DNA was counterstained with Hoechst 33342 for cell cycle analysis. Cellular images were recorded and quantitatively analyzed through a high-content imaging platform. The effect of selected compounds on ferroptosis induction was analyzed in combination with ferroptosis inducers (RSL3 and erastin). Flow cytometry analysis was conducted to assess the level of reactive oxygen species (ROS) and cell death events. RESULTS: Through the quantification of DNA content and peroxisomal signals in single cells, we demonstrated that peroxisomal abundance was closely linked with cell cycle progression and that peroxisomal biogenesis mainly occurred in the G1/S phase. We further identified compounds that positively and negatively regulated peroxisomal abundance without significantly affecting the cell cycle distribution. Some compounds promoted peroxisomal signals by inducing oxidative stress, while others regulated peroxisomal abundance independent of redox status. Importantly, compounds with peroxisome-enhancing activity potentiated ferroptosis induction. CONCLUSIONS: Our findings pinpoint novel cellular targets that might be involved in peroxisome homeostasis and indicate that compounds promoting peroxisomal abundance could be jointly applied with ferroptosis inducers to potentiate anticancer effect.

5ß-reduced neuroactive steroids as modulators of growth and viability of postnatal neurons and glia.

Endogenous neurosteroids (NS) and their synthetic analogs, neuroactive steroids (NAS), are potentially useful drug-like compounds affecting the pathophysiology of miscellaneous central nervous system disorders (e.g. Alzheimer´s disease, epilepsy, depression, etc.). Additionally, NS have been shown to promote neuron viability and neurite outgrowth upon injury. The molecular, structural and physicochemical basis of the NS effect on neurons is so far not fully understood, and the development of new, biologically relevant assays is essential for their comparative analysis and for assessment of their mechanism of action. Here, we report the development of a novel, plate-based, high-content in vitro assay for screening of NS and newly synthesized, 5ß-reduced NAS for the promotion of postnatal neuron survival and neurite growth using fluorescent, postnatal mixed cortical neuron cultures isolated from thy1-YFP transgenic mice. The screen allows a detailed time course analysis of different parameters, such as the number of neurons or neurite lengths of 7-day, in vitro neuron cultures. Using the screen, we identify a new NAS, compound 42, that promotes the survival and growth of postnatal neurons significantly better than several endogenous NS (dehydroepiandrosterone, progesterone, and allopregnanolone). Interestingly, we demonstrate that compound 42 also promotes the proliferation of glia (in particular oligodendrocytes) and that the glial function is critical for its neuron growth support. Computational analysis of the biological data and calculated physicochemical properties of tested NS and NAS demonstrated that their biological activity is proportional to their lipophilicity. Together, the screen proves useful for the selection of neuron-active NAS and the comparative evaluation of their biologically relevant structural and physicochemical features.

High content screening strategies for large-scale compound libraries with a focus on high-containment viruses.

A majority of viral diseases do not have FDA-approved drugs. The recent outbreaks caused by SARS-CoV-2, monkeypox, and Sudan ebolavirus have exposed the critical need for rapid screening and identification of antiviral compounds against emerging/re-emerging viral pathogens. A high-content screening (HCS) platform is becoming an essential part of the drug discovery process, thanks to developments in image acquisition and analysis. While HCS has several advantages, its full potential has not been realized in antiviral drug discovery compared to conventional drug screening approaches, such as fluorescence or luminescence-based microplate assays. Therefore, this review aims to summarize HCS workflow, strategies, and developments in image-based drug screening, focusing on high-containment viruses.

Establishment of a screening platform based on human coronavirus OC43 for the identification of microbial natural products with antiviral activity.

IMPORTANCE: The COVID-19 pandemic has revealed the lack of effective treatments against betacoronaviruses and the urgent need for new broad-spectrum antivirals. Natural products are a valuable source of bioactive compounds with pharmaceutical potential that may lead to the discovery of new antiviral agents. Specifically, compared to conventional synthetic molecules, microbial natural extracts possess a unique and vast chemical diversity and are amenable to large-scale production. The implementation of a high-throughput screening platform using the betacoronavirus OC43 in a human cell line infection model has provided proof of concept of the approach and has allowed for the rapid and efficient evaluation of 1,280 microbial extracts. The identification of several active compounds validates the potential of the platform for the search for new compounds with antiviral capacity.

Virtual plates: Getting the best out of high content screens.

High content screening (HCS) is becoming widely adopted as a high throughput screening modality, using hundred-of-thousands compounds library. The use of machine learning and artificial intelligence in image analysis is amplifying this trend. Another factor is the recognition that diverse cell phenotypes can be associated with changes in biological pathways relevant to disease processes. There are numerous challenges in HCS campaigns. These include limited ability to support replicates, low availability of precious and unique cells or reagents, high number of experimental batches, lengthy preparation of cells for imaging, image acquisition time (45-60 min per plate) and image processing time, deterioration of image quality with time post cell fixation and variability within wells and batches. To take advantage of the data in HCS, cell population based rather than well-based analyses are required. Historically, statistical analysis and hypothesis testing played only a limited role in non-high content high throughput campaigns. Thus, only a limited number of standard statistical criteria for hit selection in HCS have been developed so far. In addition to complex biological content in HCS campaigns, additional variability is impacted by cell and reagent handling and by instruments which may malfunction or perform unevenly. Together these can cause a significant number of wells or plates to fail. Here we describe an automated approach for hit analysis and detection in HCS. Our approach automates HCS hit detection using a methodology that is based on a documented statistical framework. We introduce the Virtual Plate concept in which selected wells from different plates are collated into a new, virtual plate. This allows the rescue and analysis of compound wells which have failed due to technical issues as well as to collect hit wells into one plate, allowing the user easier access to the hit data.

Bromocriptine protects perilesional spinal cord neurons from lipotoxicity after spinal cord injury.

Recent studies have revealed that lipid droplets accumulate in neurons after brain injury and evoke lipotoxicity, damaging the neurons. However, how lipids are metabolized by spinal cord neurons after spinal cord injury remains unclear. Herein, we investigated lipid metabolism by spinal cord neurons after spinal cord injury and identified lipid-lowering compounds to treat spinal cord injury. We found that lipid droplets accumulated in perilesional spinal cord neurons after spinal cord injury in mice. Lipid droplet accumulation could be induced by myelin debris in HT22 cells. Myelin debris degradation by phospholipase led to massive free fatty acid production, which increased lipid droplet synthesis, ß-oxidation, and oxidative phosphorylation. Excessive oxidative phosphorylation increased reactive oxygen species generation, which led to increased lipid peroxidation and HT22 cell apoptosis. Bromocriptine was identified as a lipid-lowering compound that inhibited phosphorylation of cytosolic phospholipase A2 by reducing the phosphorylation of extracellular signal-regulated kinases 1/2 in the mitogen-activated protein kinase pathway, thereby inhibiting myelin debris degradation by cytosolic phospholipase A2 and alleviating lipid droplet accumulation in myelin debris-treated HT22 cells. Motor function, lipid droplet accumulation in spinal cord neurons and neuronal survival were all improved in bromocriptine-treated mice after spinal cord injury. The results suggest that bromocriptine can protect neurons from lipotoxic damage after spinal cord injury via the extracellular signal-regulated kinases 1/2-cytosolic phospholipase A2 pathway.

Establishment of a high-content imaging assay for tau aggregation in hiPSC-derived neurons differentiated from two protocols to routinely evaluate compounds and genetic perturbations.

Aberrant protein aggregation is a pathological cellular hallmark of many neurodegenerative diseases, such as Alzheimer's disease (AD) and frontotemporal dementia (FTD), where the tau protein is aggregating, forming neurofibrillary tangles (NFTs), and propagating from neuron to neuron. These processes have been linked to disease progression and a decline in cognitive function. Various therapeutic approaches aim at the prevention or reduction of tau aggregates in neurons. Human induced pluripotent stem cells (hiPSCs) are a very valuable tool in neuroscience discovery, as they offer access to potentially unlimited amounts of cell types that are affected in disease, including cortical neurons, for in vitro studies. We have generated an in vitro model for tau aggregation that uses hiPSC - derived neurons expressing an aggregation prone, fluorescently tagged version of the human tau protein after lentiviral transduction. Upon addition of tau seeds in the form of recombinant sonicated paired helical filaments (sPHFs), the neurons show robust, disease-like aggregation of the tau protein. The model was developed as a plate-based high content screening assay coupled with an image analysis algorithm to evaluate the impact of small molecules or genetic perturbations on tau. We show that the assay can be used to evaluate small molecules or screen targeted compound libraries. Using siRNA-based gene knockdown, genes of interest can be evaluated, and we could show that a targeted gene library can be screened, by screening nearly 100 deubiquitinating enzymes (DUBs) in that assay. The assay uses an imaging-based readout, a relatively short timeline, quantifies the extent of tau aggregation, and also allows the assessment of cell viability. Furthermore, it can be easily adapted to different hiPSC lines or neuronal subtypes. Taken together, this complex and highly relevant approach can be routinely applied on a weekly basis in the screening funnels of several projects and generates data with a turnaround time of approximately five weeks.