CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance. RESULTS: We propose CODEX (COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations) as a general framework for the causal modeling of HTS data, linking perturbations to their downstream consequences. CODEX relies on a stringent causal modeling strategy based on counterfactual reasoning. As such, CODEX predicts drug-specific cellular responses, comprising cell survival and molecular alterations, and facilitates the in silico exploration of drug combinations. This is achieved for both bulk and single-cell HTS. We further show that CODEX provides a rationale to explore complex genetic modifications from CRISPR-interference in silico in single cells. AVAILABILITY AND IMPLEMENTATION: Our implementation of CODEX is publicly available at https://github.com/sschrod/CODEX. All data used in this article are publicly available.

High-throughput assays to assess variant effects on disease.

Interpreting the wealth of rare genetic variants discovered in population-scale sequencing efforts and deciphering their associations with human health and disease present a critical challenge due to the lack of sufficient clinical case reports. One promising avenue to overcome this problem is deep mutational scanning (DMS), a method of introducing and evaluating large-scale genetic variants in model cell lines. DMS allows unbiased investigation of variants, including those that are not found in clinical reports, thus improving rare disease diagnostics. Currently, the main obstacle limiting the full potential of DMS is the availability of functional assays that are specific to disease mechanisms. Thus, we explore high-throughput functional methodologies suitable to examine broad disease mechanisms. We specifically focus on methods that do not require robotics or automation but instead use well-designed molecular tools to transform biological mechanisms into easily detectable signals, such as cell survival rate, fluorescence or drug resistance. Here, we aim to bridge the gap between disease-relevant assays and their integration into the DMS framework.

A high-throughput behavioral screening platform for measuring chemotaxis by C. elegans.

Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these compounds into the rhizosphere and atmosphere where they affect animal and microbe behavior. To survive, nematodes must have evolved the sensory capacity to distinguish plant-made small molecules (SMs) that are harmful and must be avoided from those that are beneficial and should be sought. This ability to classify chemical cues as a function of their value is fundamental to olfaction and represents a capacity shared by many animals, including humans. Here, we present an efficient platform based on multiwell plates, liquid handling instrumentation, inexpensive optical scanners, and bespoke software that can efficiently determine the valence (attraction or repulsion) of single SMs in the model nematode, Caenorhabditis elegans. Using this integrated hardware-wetware-software platform, we screened 90 plant SMs and identified 37 that attracted or repelled wild-type animals but had no effect on mutants defective in chemosensory transduction. Genetic dissection indicates that for at least 10 of these SMs, response valence emerges from the integration of opposing signals, arguing that olfactory valence is often determined by integrating chemosensory signals over multiple lines of information. This study establishes that C. elegans is an effective discovery engine for determining chemotaxis valence and for identifying natural products detected by the chemosensory nervous system.

Construction and application of H1R ligand screening materials based on SMA stabilization and his-tag covalent immobilization of membrane proteins.

The histamine H1 receptor (H1R) plays a pivotal role in allergy initiation and undergoes the necessity of devising a high-throughput screening approach centered on H1R to screen novel ligands effectively. This study suggests a method employing styrene maleic acid (SMA) extraction and His-tag covalent bonding to immobilize H1R membrane proteins, minimizing the interference of nonspecific proteins interference while preserving native protein structure and maximizing target exposure. This approach was utilized to develop a novel material for high-throughput ligand screening and implemented in cell membrane chromatography (CMC). An H1R-His-SMALPs/CMC model was established and its chromatographic performance (selectivity, specificity and lifespan) validated, demonstrating a significant enhancement in lifespan compared to previous CMC models. Subsequently, this model facilitated high-throughput screening of H1R ligands in the compound library and preliminary activity verification of potential H1R antagonists. Identification of a novel H1R antagonist laid the foundation for further development in this area.

'Small volume-big problem': culturing Yarrowia lipolytica in high-throughput micro-formats.

With the current progress in the 'design' and 'build' stages of the 'design-build-test-learn' cycle, many synthetic biology projects become 'test-limited'. Advances in the parallelization of microbes cultivations are of great aid, however, for many species down-scaling leaves a metabolic footprint. Yarrowia lipolytica is one such demanding yeast species, for which scaling-down inevitably leads to perturbations in phenotype development. Strictly aerobic metabolism, propensity for filamentation and adhesion to hydrophobic surfaces, spontaneous flocculation, and high acidification of media are just several characteristics that make the transfer of the micro-scale protocols developed for the other microbial species very challenging in this case. It is well recognized that without additional 'personalized' optimization, either MTP-based or single-cell-based protocols are useless for accurate studies of Y. lipolytica phenotypes. This review summarizes the progress in the scaling-down and parallelization of Y. lipolytica cultures, highlighting the challenges that occur most frequently and strategies for their overcoming. The problem of Y. lipolytica cultures down-scaling is illustrated by calculating the costs of micro-cultivations, and determining the unintentionally introduced, thus uncontrolled, variables. The key research into culturing Y. lipolytica in various MTP formats and micro- and pico-bioreactors is discussed. Own recently developed and carefully pre-optimized high-throughput cultivation protocol is presented, alongside the details from the optimization stage. We hope that this work will serve as a practical guide for those working with Y. lipolytica high-throughput screens.

Biomarkers for aging in Caenorhabditis elegans high throughput screening.

Aging is characterized by a functional decline in organism fitness over time due to a complex combination of genetic and environmental factors [ 1-4]. With an increasing elderly population at risk of age-associated diseases, there is a pressing need for research dedicated to promoting health and longevity through anti-aging interventions. The roundworm Caenorhabditis elegans is an established model organism for aging studies due to its short life cycle, ease of culture, and conserved aging pathways. These benefits also make the worm well-suited for high-throughput screening (HTS) methods to study biomarkers of the molecular changes, cellular dysfunction, and physiological decline associated with aging. Within this review, we offer a summary of recent advances in HTS techniques to study biomarkers of aging in C. elegans.

An Extremely Sensitive Ultra-High Throughput Growth Selection Assay for the Identification of Amidase Activity.

In the last decades, biocatalysis has offered new perspectives for the synthesis of (chiral) amines, which are essential building blocks for pharmaceuticals, fine and bulk chemicals. In this regard, amidases have been employed due to their broad substrate scope and their independence from expensive cofactors. To expand the repertoire of amidases, tools for their rapid identification and characterization are greatly demanded. In this work an ultra-high throughput growth selection assay based on the production of the folate precursor p-aminobenzoic acid (PABA) is introduced to identify amidase activity. PABA-derived amides structurally mimic the broad class of commonly used chromogenic substrates derived from p-nitroaniline. This suggests that the assay should be broadly applicable for the identification of amidases. Unlike conventional growth selection assays that rely on substrates as nitrogen or carbon source, our approach requires PABA in sub-nanomolar concentrations, making it exceptionally sensitive and ideal for engineering campaigns that aim at enhancing amidase activities from minimally active starting points, for example. The presented assay offers flexibility in the adjustment of sensitivity to suit project-specific needs using different expression systems and fine-tuning with the antimetabolite sulfathiazole. Application of this PABA-based assay facilitates the screening of millions of enzyme variants on a single agar plate within two days, without the need for laborious sample preparation or expensive instruments, with transformation efficiency being the only limiting factor. KEY POINTS: • Ultra-high throughput assay (tens of millions on one agar plate) for amidase screening • High sensitivity by coupling selection to folate instead of carbon or nitrogen source • Highly adjustable in terms of sensitivity and expression of the engineering target.

A homogeneous time-resolved fluorescence screen to identify SIRT2 deacetylase and defatty-acylase inhibitors.

Human sirtuin-2 (SIRT2) has emerged as an attractive drug target for a variety of diseases. The enzyme is a deacylase that can remove chemically different acyl modifications from protein lysine residues. Here, we developed a high-throughput screen based on a homogeneous time-resolved fluorescence (HTRF) binding assay to identify inhibitors of SIRT2's demyristoylase activity, which is uncommon among many ligands that only affect its deacetylase activity. From a test screen of 9600 compounds, we identified a small molecule that inhibited SIRT2's deacetylase activity (IC50 = 7 µM) as well as its demyristoylase activity (IC50 = 37 µM). The inhibitor was composed of two small fragments that independently inhibited SIRT2: a halogenated phenol fragment inhibited its deacetylase activity, and a tricyclic thiazolobenzimidazole fragment inhibited its demyristoylase activity. The high-throughput screen also detected multiple deacetylase-specific SIRT2 inhibitors.

High-Throughput Protein Expression Screening of Cell-Surface Protein Ectodomains.

Cell-surface receptors can be difficult to express and purify for structural and biochemical studies due to low expression levels, misfolding, aggregation, and instability. Cell-surface receptor ectodomains are more amenable to large-scale production, but this requires designing and testing various truncation constructs. However, since each protein is unique, testing these constructs individually for many targets is a time-consuming process. In this context, we present a high-throughput ELISA fluorescence approach that allows the rapid assessment of numerous recombinant constructs simultaneously. Cell-surface ectodomains are expressed in small scale, enzymatically biotinylated, and detected using a C-terminal His-tag. As an example, we tested the expression of truncation constructs for the neurexin, neuroligin, and latrophilin families and show that the small-scale ELISA allowed us to prioritize well-expressing construct for large-scale production. By employing this method, one can efficiently detect clones with low expression levels, streamlining the process and saving valuable time in identifying optimal candidates for further study.

Genome-Wide High-Throughput RNAi Screening for Identification of Genes Involved in Protein Production.

With an increasing number of blockbuster drugs being recombinant mammalian proteins, protein production platforms that focus on mammalian proteins have had a profound impact in many areas of basic and applied research. Many groups, both academic and industrial, have been focusing on developing cost-effective methods to improve the production of mammalian proteins that would support potential therapeutic applications. As it stands, while a wide range of platforms have been successfully developed for laboratory use, the majority of biologicals are still produced in mammalian cell lines due to the requirement for posttranslational modification and the biosynthetic complexity of target proteins. An unbiased high-throughput RNAi screening approach can be an efficient tool to identify target genes involved in recombinant protein production. Here, we describe the process of optimizing the transfection conditions, performing the genome-wide siRNA screen, the activity and cell viability assays, and the validation transfection to identify genes involved with protein expression.

Assessment and process optimization of high throughput biofabrication of immunocompetent breast cancer model for drug screening applications.

Recent advancements in 3D cancer modeling have significantly enhanced our ability to delve into the intricacies of carcinogenesis. Despite the pharmaceutical industry's substantial investment of both capital and time in the drug screening and development pipeline, a concerning trend persists: drug candidates screened on conventional cancer models exhibit a dismal success rate in clinical trials. One pivotal factor contributing to this discrepancy is the absence of drug testing on pathophysiologically biomimetic 3D cancer models during pre-clinical stages. Unfortunately, current manual methods of 3D cancer modeling, such as spheroids and organoids, suffer from limitations in reproducibility and scalability. In our study, we have meticulously developed 3D bioprinted breast cancer model utilizing decellularized adipose tissue-based hydrogel obtained via a detergent-free decellularization method. Our innovative printing techniques allows for rapid, high-throughput fabrication of 3D cancer models in a 96-well plate format, demonstrating unmatched scalability and reproducibility. Moreover, we have conducted extensive validation, showcasing the efficacy of our platform through drug screening assays involving two potent anti-cancer drugs, 5-Fluorouracil and PRIMA-1Met. Notably, our platform facilitates effortless imaging and gene expression analysis, streamlining the evaluation process. In a bid to enhance the relevance of our cancer model, we have introduced a heterogeneous cell population into the DAT-based bioink. Through meticulous optimization and characterization, we have successfully developed a biomimetic immunocompetent breast cancer model, complete with microenvironmental cues and diverse cell populations. This breakthrough paves the way for rapid multiplex drug screening and the development of personalized cancer models, marking a paradigm shift in cancer research and pharmaceutical development.

A Guide to Biodetection in Droplets.

Droplet-based methods for optical biodetection enable unprecedented high-throughput experimental parameters. The methods, however, remain underused due to the accompanying multidisciplinary and complicated experimental workflows. Here, we provide a tutorial for droplet-based optical biodetection workflows with a focus on the key aspect of label selection. By discussing and guiding readers through recent state-of-the-art studies, we aim to make droplet-based approaches more accessible to the general scientific public.

A single-step high-throughput bioassay for quantifying Fc-containing recombinant proteins based on non-classical calculation of fluorescence polarization.

The titer of recombinant proteins is one of the key parameters in biopharmaceutical manufacturing processes. The fluorescence polarization (FP)-based assay, a homogeneous, high-throughput and real-time analytical method, had emerged as a powerful tool for biochemical analysis and environmental monitoring. In this study, an FP-based bioassay was utilized to quantify antibody fragment crystallizable (Fc)-containing proteins, such as recombinant monoclonal antibodies (mAbs) and mAb derivatives, in the cell culture supernatant, and the impacts of tracer molecular weight and FITC-coupling conditions on fluorescence polarization were methodically examined. Distinct from the fluorescence polarization potency calculated by classical formula, we for the first time proposed a new concept and calculation of fluorescence polarization intensity, based on which an analytical method with broader detection range and analysis window was established for quantifying Fc-containing proteins. This provided new ideas for the practical application of fluorescence polarization theory. The established method could detect 96 samples within 30 minutes, with dynamic titer range of 2.5-400 mg L-1, and a linear fitting R2 between the measured and actual concentration reaching 0.99. The method had great application prospects in determining the titer of recombinant proteins with Fc fragments, especially when applied to large-scale screening of high-yield and stable expression CHO cell lines commonly used in biopharmaceutical industry.

Novel Coumarins Derivatives for A. baumannii Lung Infection Developed by High-Throughput Screening and Reinforcement Learning.

With the increasing resistance of Acinetobacter baumannii (A. baumannii) to antibiotics, researchers have turned their attention to the development of new antimicrobial agents. Among them, coumarin-based heterocycles have attracted much attention due to their unique biological activities, especially in the field of antibacterial infection. In this study, a series of coumarin derivatives were synthesized and screened for their bactericidal activities (Ren et al. 2018; Salehian et al. 2021). The inhibitory activities of these compounds on bacterial strains were evaluated, and the related mechanism of the new compounds was explored. Firstly, the MIC values and bacterial growth curves were measured after compound treatment to evaluate the antibacterial activity in vitro. Then, the in vivo antibacterial activities of the new compounds were assessed on A. baumannii-infected mice by determining the mice survival rates, counting bacterial CFU numbers, measuring inflammatory cytokine levels, and histopathology analysis. In addition, the ROS levels in the bacterial cells were measured with DCFH-DA detection kit. Furthermore, the potential target and detailed mechanism of the new compounds during infection disease therapy were predicted and evidenced with molecular docking. After that, ADMET characteristic prediction was completed, and novel, synthesizable, drug-effective molecules were optimized with reinforcement learning study based on the probed compound as a training template. The interaction between the selected structures and target proteins was further evidenced with molecular docking. This series of innovative studies provides important theoretical and experimental data for the development of new anti-A. baumannii infection drugs.

Rapid Determination of Kinetic Constants for Slow-Binding Inhibitors and Inactivators of Human Histone Deacetylase 8.

The kinetics and mechanism of drug binding to its target are critical to pharmacological efficacy. A high throughput (HTS) screen often results in hundreds of hits, of which usually only simple IC50 values are determined during reconfirmation. However, kinetic parameters such as residence time for reversible inhibitors and the kinact/KI ratio, which is the critical measure for evaluating covalent inactivators, are early predictive measures to assess the chances of success of the hits in the clinic. Using the promising cancer target human histone deacetylase 8 as an example, we present a robust method that calculates concentration-dependent apparent rate constants for the inhibition or inactivation of HDAC8 from dose-response curves recorded after different pre-incubation times. With these data, hit compounds can be classified according to their mechanism of action, and the relevant kinetic parameters can be calculated in a highly parallel fashion. HDAC8 inhibitors with known modes of action were correctly assigned to their mechanism, and the binding mechanisms of some hits from an internal HDAC8 screening campaign were newly determined. The oxonitriles SVE04 and SVE27 were classified as fast reversible HDAC8 inhibitors with moderate time-constant IC50 values of 4.2 and 2.6 µM, respectively. The hit compound TJ-19-24 and SAH03 behave like slow two-step inactivators or reversible inhibitors, with a very low reverse isomerization rate.

Monitoring and quantifying replication fork dynamics with high-throughput methods.

Before each cell division, eukaryotic cells must replicate their chromosomes to ensure the accurate transmission of genetic information. Chromosome replication involves more than just DNA duplication; it also includes chromatin assembly, inheritance of epigenetic marks, and faithful resumption of all genomic functions after replication. Recent progress in quantitative technologies has revolutionized our understanding of the complexity and dynamics of DNA replication forks at both molecular and genomic scales. Here, we highlight the pivotal role of these novel methods in uncovering the principles and mechanisms of chromosome replication. These technologies have illuminated the regulation of genome replication programs, quantified the impact of DNA replication on genomic mutations and evolutionary processes, and elucidated the mechanisms of replication-coupled chromatin assembly and epigenome maintenance.

Arabidopsis transcriptome responses to low water potential using high-throughput plate assays.

Soil-free assays that induce water stress are routinely used to investigate drought responses in the plant Arabidopsis thaliana. Due to their ease of use, the research community often relies on polyethylene glycol (PEG), mannitol, and salt (NaCl) treatments to reduce the water potential of agar media, and thus induce drought conditions in the laboratory. However, while these types of stress can create phenotypes that resemble those of water deficit experienced by soil-grown plants, it remains unclear how these treatments compare at the transcriptional level. Here, we demonstrate that these different methods of lowering water potential elicit both shared and distinct transcriptional responses in Arabidopsis shoot and root tissue. When we compared these transcriptional responses to those found in Arabidopsis roots subject to vermiculite drying, we discovered many genes induced by vermiculite drying were repressed by low water potential treatments on agar plates (and vice versa). Additionally, we also tested another method for lowering water potential of agar media. By increasing the nutrient content and tensile strength of agar, we show the 'hard agar' (HA) treatment can be leveraged as a high-throughput assay to investigate natural variation in Arabidopsis growth responses to low water potential.

A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation.

The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.

High-throughput screening of non-conventional yeasts for conversion of organic waste to microbial oils via carboxylate platform.

Converting waste into high-value products promotes sustainability by reducing waste and creating new revenue streams. This study investigates the potential of diverse yeasts for microbial oil production by utilizing short-chain fatty acids (SCFAs) that can be produced from organic waste and focuses on identifying strains with the best SCFA utilisation, tolerance and lipid production. A collection of 1434 yeast strains was cultivated with SCFAs as the sole carbon source. Eleven strains emerged as candidates with promising growth rates and high lipid accumulation. Subsequent fermentation experiments in liquid SCFA-rich media, which focused on optimizing lipid accumulation by adjusting the carbon to nitrogen (C/N) ratio, showed an increase in lipid content at a C/N ratio of 200:1, but with a concurrent reduction in biomass. Two strains were characterized by their superior ability to produce lipids compared to the reference strain Yarrowia lipolytica CECT124: Y. lipolytica EXF-17398 and Pichia manshurica EXF-7849. Characterization of these two strains indicated that they exhibit a biotechnologically relevant balance between maximizing lipid yield and maintaining growth at high SCFA concentrations. These results emphasize the potential of using SCFAs as a sustainable feedstock for oleochemical production, offering a dual benefit of waste valorisation and microbial oil production.