High-throughput Setting Research Articles

Relationship between HETP measurements and breakthrough curves in short chromatography columns.

An analysis of the relationship between the number of plates measured with a small molecule tracer and the breakthrough curve of a strongly bound protein in short laboratory chromatography columns (1-5 cm) considering flow nonuniformity is presented. For practical conditions, while axial dispersion has only a small impact on the breakthrough curve, radial flow nonuniformity has a profound effect. Radial parabolic velocity profiles lead to tailing tracer peaks and broader breakthrough curves. Profiles where the velocity varies radially only in a thin region near the column wall lead to fronting tracer peaks and early breakthrough when the velocity at the wall is higher than the average and to tailing peaks and tailing breakthrough curves when the velocity at the wall is lower than the average. Experiments conducted in laboratory minicolumns (0.5-1 cm diameter, 0.5-1 ml volume) show tracer peaks and protein breakthrough curves that are consistent with higher velocities at the wall. The model presented in this work provides a tool to model experimental breakthrough data and to assess the degree of flow uniformity required to obtain meaningful dynamic binding capacity measurements using minicolumns in a high-throughput lab setting.

From Human Pluripotent Stem Cells to 3D Cardiac Microtissues: Progress, Applications and Challenges.

The knowledge acquired throughout the years concerning the in vivo regulation of cardiac development has promoted the establishment of directed differentiation protocols to obtain cardiomyocytes (CMs) and other cardiac cells from human pluripotent stem cells (hPSCs), which play a crucial role in the function and homeostasis of the heart. Among other developments in the field, the transition from homogeneous cultures of CMs to more complex multicellular cardiac microtissues (MTs) has increased the potential of these models for studying cardiac disorders in vitro and for clinically relevant applications such as drug screening and cardiotoxicity tests. This review addresses the state of the art of the generation of different cardiac cells from hPSCs and the impact of transitioning CM differentiation from 2D culture to a 3D environment. Additionally, current methods that may be employed to generate 3D cardiac MTs are reviewed and, finally, the adoption of these models for in vitro applications and their adaptation to medium- to high-throughput screening settings are also highlighted.

Clinical-Forensic Autopsy Findings to Defeat COVID-19 Disease: A Literature Review.

The severe acute respiratory syndrome (SARS)-CoV-2 was identified for the first time in China, in December 2019. Confirmed cases of COVID-19 have been reported around the world; indeed, this infection has been declared a pandemic. Consequently, the scientific community is working hard to gain useful information about the history of this virus, its transmission, diagnosis, clinical features, radiological findings, research and development of candidate therapeutics as well as vaccines. This review aims to analyze the diagnostic techniques used to ascertain the COVID-19 infection, critically reviewing positive points and criticism for forensic implications, obviously including autopsy. Finally, this review proposes a practical workflow to be applied in the management of corpses during this outbreak of the COVID-19 infection, which could be useful in cases of future infectious disease emergencies. Analyzing the diagnostic methods, to date, virus nucleic acid RT-PCR represents the standard method used to ascertain the COVID-19 infection in living subjects and corpses, even if this technique has several criticisms: mainly, the staff should be highly specialized, working in high-throughput settings, able to handle high workloads and aware of health risks and the importance of the results. Thus, IgG/IgM serological tests have been developed, overcoming RT-qPCR duration, costs, and management, not requiring highly trained personnel. Nevertheless, serological tests present problems; the WHO recommends the use of these new point-of-care immunodiagnostic tests only in research settings. Furthermore, nothing has yet been published regarding the possibility of applying these methods during post-mortem investigations. In light of this scenario, in this review, we suggest a flow chart for the pathologist called on to ascertain the cause of death of a subject with historical and clinical findings of COVID-19 status or without any anamnestic, diagnostic, or exposure information. Indeed, the literature data confirmed the analytical vulnerabilities of the kits used for laboratory diagnosis of COVID-19, particularly during postmortem examinations. For these reasons, autopsy remains the gold standard method to ascertain the exact cause of death (from or with COVID-19 infection, or other causes), to consequently provide real data for statistical evaluations and to take necessary measures to contain the risks of the infection. Moreover, performing autopsies could provide information on the pathogenesis of the COVID-19 infection with obvious therapeutic implications.

A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptive P-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS association P values play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene-gene coexpression, captured by subnetwork (module) membership. In all, 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefrontal cortex. We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers.

Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture such neighborhood dependency in a graph. However, it is still challenging to apply GCN for SL prediction as SL interactions are extremely sparse, which is more likely to cause overfitting. In this article, we propose a novel dual-dropout GCN (DDGCN) for learning more robust gene representations for SL prediction. We employ both coarse-grained node dropout and fine-grained edge dropout to address the issue that standard dropout in vanilla GCN is often inadequate in reducing overfitting on sparse graphs. In particular, coarse-grained node dropout can efficiently and systematically enforce dropout at the node (gene) level, while fine-grained edge dropout can further fine-tune the dropout at the interaction (edge) level. We further present a theoretical framework to justify our model architecture. Finally, we conduct extensive experiments on human SL datasets and the results demonstrate the superior performance of our model in comparison with state-of-the-art methods. DDGCN is implemented in Python 3.7, open-source and freely available at https://github.com/CXX1113/Dual-DropoutGCN. Supplementary data are available at Bioinformatics online.

Research Techniques Made Simple: CRISPR Genetic Screens

CRISPR and Cas proteins, often referred to as CRISPR/Cas, are the components of a bacterial genome editing system that can be used to perturb genes in cells and tissues. A classic application is to use CRISPR/Cas to generate genetic loss-of-function. When performed at large scale and combined with deep sequencing techniques, CRISPR-based perturbations can be performed in a high throughput setting to screen many candidate genomic elements for their roles in a phenotype of interest. Here, we discuss major considerations in the design, execution, and analysis of CRISPR screens. We focus on CRISPR knockout screens but also review adaptations to the CRISPR/Cas system that highlight the versatility of the system to make other types of experimental genetic changes as well. We also discuss examples of CRISPR genetic screens in investigative dermatology and how they may be used to answer key scientific questions in the field.

Tracy: basecalling, alignment, assembly and deconvolution of sanger chromatogram trace files

BackgroundDNA sequencing is at the core of many molecular biology laboratories. Despite its long history, there is a lack of user-friendly Sanger sequencing data analysis tools that can be run interactively as a web application or at large-scale in batch from the command-line.ResultsWe present Tracy, an efficient and versatile command-line application that enables basecalling, alignment, assembly and deconvolution of sequencing chromatogram files. Its companion web applications make all functionality of Tracy easily accessible using standard web browser technologies and interactive graphical user interfaces. Tracy can be easily integrated in large-scale pipelines and high-throughput settings, and it uses state-of-the-art file formats such as JSON and BCF for reporting chromatogram sequencing results and variant calls. The software is open-source and freely available at https://github.com/gear-genomics/tracy, the companion web applications are hosted at https://www.gear-genomics.com.ConclusionsTracy can be routinely applied in large-scale validation efforts conducted in clinical genomics studies as well as for high-throughput genome editing techniques that require a fast and rapid method to confirm discovered variants or engineered mutations. Molecular biologists benefit from the companion web applications that enable installation-free Sanger chromatogram analyses using intuitive, graphical user interfaces.

A new method for inferring timetrees from temporally sampled molecular sequences.

Pathogen timetrees are phylogenies scaled to time. They reveal the temporal history of a pathogen spread through the populations as captured in the evolutionary history of strains. These timetrees are inferred by using molecular sequences of pathogenic strains sampled at different times. That is, temporally sampled sequences enable the inference of sequence divergence times. Here, we present a new approach (RelTime with Dated Tips [RTDT]) to estimating pathogen timetrees based on a relative rate framework underlying the RelTime approach that is algebraic in nature and distinct from all other current methods. RTDT does not require many of the priors demanded by Bayesian approaches, and it has light computing requirements. In analyses of an extensive collection of computer-simulated datasets, we found the accuracy of RTDT time estimates and the coverage probabilities of their confidence intervals (CIs) to be excellent. In analyses of empirical datasets, RTDT produced dates that were similar to those reported in the literature. In comparative benchmarking with Bayesian and non-Bayesian methods (LSD, TreeTime, and treedater), we found that no method performed the best in every scenario. So, we provide a brief guideline for users to select the most appropriate method in empirical data analysis. RTDT is implemented for use via a graphical user interface and in high-throughput settings in the newest release of cross-platform MEGA X software, freely available from http://www.megasoftware.net.

Residence Time Distribution and Specific Mechanical Energy in Solid‐State Shear Pulverization: Processing‐Structure‐Property Relationships in a Chilled Extruder

Solid‐state shear pulverization (SSSP) is a continuous polymer processing methodology based on a modified twin‐screw extruder. The unique application of low barrel temperatures and mechanochemistry has contributed to the development of an extensive range of polymer‐based materials, from environmentally responsible polymer blends to nanocomposites, for more than 30 years. The complex processing‐structure‐processing relationships in SSSP can be elucidated by way of integrated, measured covariants that capture the interplay between numerous processing parameters. Residence time distribution and specific mechanical energy are evaluated in a base case polypropylene (PP) study under a full factorial experiment involving screw design, screw speed, and throughput parameters. These factors are in turn correlated to dispersion morphology and thermal property results from a parallel study based on a model PP/carbon black composite. This investigation highlights the tunability of SSSP processing parameters for tailored output with desired purposes and applications. In particular, enhanced residence distribution can be achieved with low screw speed and high throughput settings, leading to high levels of material mixing and shear. POLYM. ENG. SCI., 60:503–511, 2020. © 2019 Society of Plastics Engineers

High-Throughput Implementation of the NanoBRET Target Engagement Intracellular Kinase Assay to Reveal Differential Compound Engagement by SIK2/3 Isoforms.

The real-time quantification of target engagement (TE) by small-molecule ligands in living cells remains technically challenging. Systematic quantification of such interactions in a high-throughput setting holds promise for identification of target-specific, potent small molecules within a pathophysiological and biologically relevant cellular context. The salt-inducible kinases (SIKs) belong to a subfamily of the AMP-activated protein kinase (AMPK) family and are composed of three isoforms in humans (SIK1, SIK2, and SIK3). They modulate the production of pro- and anti-inflammatory cytokines in immune cells. Although pan-SIK inhibitors are sufficient to reverse SIK-dependent inflammatory responses, the apparent toxicity associated with SIK3 inhibition suggests that isoform-specific inhibition is required to realize therapeutic benefit with acceptable safety margins. Here, we used the NanoBRET TE intracellular kinase assay, a sensitive energy transfer technique, to directly measure molecular proximity and quantify TE in HEK293T cells overexpressing SIK2 or SIK3. Our 384-well high-throughput screening of 530 compounds demonstrates that the NanoBRET TE intracellular kinase assay was sensitive and robust enough to reveal differential engagement of candidate compounds with the two SIK isoforms and further highlights the feasibility of high-throughput implementation of NanoBRET TE intracellular kinase assays for target-driven small-molecule screening.

A Method for High-Throughput Measurements of Viscosity in Sub-micrometer-Sized Membrane Systems.

To unravel the underlying principles of membrane adaptation in small systems like bacterial cells, robust approaches to characterize membrane fluidity are needed. Currently available relevant methods require advanced instrumentation and are not suitable for high‐throughput settings needed to elucidate the biochemical pathways involved in adaptation. We developed a fast, robust, and financially accessible quantitative method to measure the microviscosity of lipid membranes in bulk suspension using a commercially available plate reader. Our approach, which is suitable for high‐throughput screening, is based on the simultaneous measurements of absorbance and fluorescence emission of a viscosity‐sensitive fluorescent dye, 9‐(2,2‐dicyanovinyl)julolidine (DCVJ), incorporated into a lipid membrane. We validated our method using artificial membranes with various lipid compositions over a range of temperatures and observed values that were in good agreement with previously published results. Using our approach, we were able to detect a lipid phase transition in the ruminant pathogen Mycoplasma mycoides.

Purifying Low-Volume Combinatorial Polymer Libraries with Gel Filtration Columns.

Recent advances in oxygen-tolerant controlled/living radical polymer chemistry now enable efficient synthesis of diverse and combinatorial polymer libraries. While library synthesis has been dramatically simplified, equally efficient purification strategies for removal of small-molecule impurities are not yet established in high throughput settings. It is shown that gel filtration columns for chromatography frequently used in the protein science community are well suited for high throughput polymer purification. Using either single-use columns or gel filtration plates, the purification of 32 diverse polymers is demonstrated in a library with >95% removal of small molecule impurities and >85% polymer retention in a single purification step. Doing so replaces the typical procedure of polymer precipitation, which requires solvent optimization for each polymer in a complex library. Overall, this work raises awareness in the polymer science community that gel filtration is amenable to purification of large polymer libraries and can speed up the progress of combinatorial polymer chemistry.

Quantitative analysis of underivatized 17 β-estradiol using a high-throughput LC–MS/MS assay – Application to support a pharmacokinetic study in ovariectomized guinea pigs

Difference in female sex hormone, β-estradiol (E2), levels can contribute to sex differences in biological processes that underlie target tissue functions (QT interval), vulnerability to diseases (hepatitis or HIV), and response toward therapies. Accurate quantification of plasma E2 level is thus an important aspect in both basic science research examining hormone-regulated physiological mechanisms and in clinical settings to support patient care associated with altered E2 levels. Due to lack of a high-throughput high-sensitivity analytical method, we developed and validated a LC–MS/MS assay for accurate low-level quantification of E2 and demonstrated its application to a guinea pig pharmacokinetic study in which guinea pigs were treated with 10 or 40 μg/kg E2 subcutaneously and blood samples collected at 0 (pre-dose), 0.25, 0.5, 1, 2, 4, 8, 12 and 24 h post-dosing. E2 was extracted using 90 μL ovariectomized guinea pig plasma by liquid-liquid extraction. The method was robust, sensitive with linear range from 3.9 to 1000 pg/mL, and the assay met acceptance criteria for validation parameters listed in the current FDA Guidance on Bioanalytical Method Validation. Compared to the 10 μg/kg dose, more than dose proportional increase in maximum E2 plasma concentration (Cmax) and AUC0-∞ and correspondingly longer half-life were observed after 40 μg/kg dose. This assay is a significant improvement over existing E2 quantification methods in bioanalytical field, with high precision and accuracy, low sample and injection volumes, no derivatization, and short assay run time of 3 min. This assay is amenable in high-throughput settings requiring low-level E2 quantitation in basic science research and clinical settings.

FRET biosensor-based kinase inhibitor screen for ERK and AKT activity reveals differential kinase dependencies for proliferation in TNBC cells

Enhanced expression and activity of protein kinases are critical in tumor cell proliferation and cancer progression. These various cancer-related kinases form intricate interdependent signaling networks. Evaluation of the effect of various kinase inhibitors on these networks is critical to understand kinase inhibitor efficacy in cancer therapy. The dynamic activation of some kinases can be monitored by fluorescence resonance energy transfer (FRET) biosensors with high temporal resolution. Here, we established a FRET biosensor-based high throughput imaging approach to determine ERK and AKT activity in two triple negative breast cancer (TNBC) cell lines HCC1806 and Hs578T. FRET functionality was systematically evaluated using EGF stimulation and different MEK and AKT inhibitors, respectively. Next, we assessed the effect of a kinase inhibitor library containing >350 different kinase inhibitors (KIs) on ERK and AKT kinase activity using a FRET high-throughput screening setting. Suppression of FRET-ERK activity was generally positively correlated with the proliferation phenotype against inhibitors targeting MAPK signaling in both cell lines containing FRET-ERK reporter. AKT inhibitor (AKTi) resistant HCC1806 showed decreased proliferation associated with downregulated dynamics of FRET-ERK when treated with KIs targeting protein receptor tyrosine kinase (RTK). Yet, MEK inhibitor (MEKi) resistant Hs578T showed positively correlated FRET-AKT and proliferative responses against different PI3K and AKT inhibitors. Altogether, our data demonstrate the feasibility to integrate high throughput imaging-based screening of intracellular kinase activity using FRET-based biosensors in assessing kinase specificity and possible signaling crosstalk in direct relation to therapeutic outcome.

P329G-CAR-J: a novel Jurkat-NFAT-based CAR-T reporter system recognizing the P329G Fc mutation.

Monoclonal antibody-based therapeutics are an integral part of treatment of different human diseases, and the selection of suitable antibody candidates during the discovery phase is essential. Here, we describe a novel, cellular screening approach for the identification and characterization of therapeutic antibodies suitable for conversion into T cell bispecific antibodies using chimeric antigen receptor (CAR) transduced Jurkat-NFAT-luciferase reporter cells (CAR-J). For that purpose, we equipped a Jurkat-NFAT reporter cell line with a universal CAR, based on a monoclonal antibody recognizing the P329G mutation in the Fc-part of effector-silenced human IgG1-antibodies. In addition to scFv-based second generation CARs, Fab-based CARs employing the P329G-binder were generated. Using these anti-P329G-CAR-J cells together with the respective P329G-mutated IgG1-antibodies, we established a system, which facilitates the rapid testing of therapeutic antibody candidates in a flexible, high throughput setting during early stage discovery. We show that both, scFv- and Fab-based anti-P329G-CAR-J cells elicit a robust and dose-dependent luciferase signal if the respective antibody acts as an adaptor between tumor target and P329G-CAR-J cells. Importantly, we could demonstrate that functional characteristics of the antibody candidates, derived from the anti-P329G-CAR-J screening assay, are predictive for the functionality of these antibodies in the T cell bispecific antibody format.

Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging

BackgroundMultiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind.ResultsCytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset.ConclusionCytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit.

Detecting wMel Wolbachia in field-collected Aedes aegypti mosquitoes using loop-mediated isothermal amplification (LAMP)

BackgroundThe World Mosquito Program uses Wolbachia pipientis for the biocontrol of arboviruses transmitted by Aedes aegypti mosquitoes. Diagnostic testing for Wolbachia in laboratory colonies and in field-caught mosquito populations has typically employed PCR. New, simpler methods to diagnose Wolbachia infection in mosquitoes are required for large-scale operational use.MethodsField-collected Ae. aegypti mosquitoes from North Queensland were tested using primers designed to detect the Wolbachia wsp gene, specific to the strain wMel. The results were analysed by colour change in the reaction mix. Furthermore, to confirm the efficiency of the LAMP assay, the results were compared to the gold-standard qPCR test.ResultsA novel loop-mediated isothermal amplification (LAMP) colorimetric test for the wMel strain of Wolbachia was designed, developed and validated for use in a high-throughput setting. Against the standard qPCR test, the analytical sensitivity, specificity and diagnostic metrics were: sensitivity (99.6%), specificity (92.2%), positive predictive value (97.08%) and negative predictive value (99.30%).ConclusionsWe describe an alternative, novel and high-throughput method for diagnosing wMel Wolbachia infections in mosquitoes. This assay should support Wolbachia surveillance in both laboratory and field populations of Ae. aegypti.

Styles of Valuation: Algorithms and Agency in High-throughput Bioscience

In science and technology studies today, there is a troubling tendency to portray actors in the biosciences as “cultural dopes” and technology as having monolithic qualities with predetermined outcomes. To remedy this analytical impasse, this article introduces the concept styles of valuation to analyze how actors struggle with valuing technology in practice. Empirically, this article examines how actors in a bioscientific laboratory struggle with valuing the properties and qualities of algorithms in a high-throughput setting and identifies the copresence of several different styles. The question that the actors struggle with is what different configurations of algorithms, devices, and humans are “good bioscience,” that is, what do the actors perform as a good distribution of agency between algorithms and humans? A key finding is that algorithms, robots, and humans are valued in multiple ways in the same setting. For the actors, it is not apparent which configuration of agency and devices is more authoritative nor is it obvious which skills and functions should be redistributed to the algorithms. Thus, rather than tying algorithms to one set of values, such as “speed,” “precision,” or “automation,” this article demonstrates the broad utility of attending to the multivalence of algorithms and technology in practice.

Improving laboratory workflow through automated pre-processing of SurePath specimens for human papillomavirus testing with the Abbott RealTime assay.

Cervical specimens collected in liquid-based cytology (LBC) are used for cervical screening in the UK. The Abbott RealTime high-risk human papillomavirus (hrHPV) assay for the detection of 14 hrHPV types is approved by the English NHS Cervical Screening Programme (NHSCSP). As per manufacturer's instructions, pre-analytic processing of LBC involves a manual vortex and liquid transfer into a secondary tube. In high-throughput settings, hands-on time for pre-processing is considerable and poses the potential for human error. Implementation of hrHPV primary screening planned for 2019 accompanied by centralisation of services is a major change for the NHSCSP, increasing the demand for availability of automated pre-analytics. This study evaluated a custom-configured work-table setup of the Tecan Freedom EVO designed to automate pre-processing of BD SurePath LBC prior to HPV testing on the Abbott m2000. Automatically and manual pre-processed specimen results were compared (primary screening population n=307; triage population n=169). Excellent agreement of overall hrHPV results (98.1%; k: 0.95) was observed. On average, it takes approximately 1.5minutes hands-on-time per sample to process manually compared to 45minutes to aliquot 48 samples using the Freedom EVO 150. The Tecan worktable configuration designed to automate and control pre-analytics required for preparing SurePath LBC samples for HPV testing using Abbott RealTime resulted in assay performance comparable to that following the manufacturer validated manual process. It significantly reduces hands-on-time and allows for complete specimen identification tracking and documentation of process control.

Differential Dynamic Changes of Reduced Trait Model for Analyzing the Plastic Response to Drought Phases: A Case Study in Spring Wheat.

Current limited water availability due to climate changes results in severe drought stress and desiccation in plants. Phenotyping drought tolerance remains challenging. In particular, our knowledge about the discriminating power of traits for capturing a plastic phenotype in high-throughput settings is scant. The study is designed to investigate the differential performance and broad-sense heritability of a battery set of morphological, physiological, and cellular traits to understand the adaptive phenotypic response to drought in spring wheat during the tillering stage. The potential of peroxisome abundance to predict the adaptive response under severe drought was assessed using a high-throughput technique for peroxisome quantification in plants. The research dissected the dynamic changes of some phenological traits during three successive phases of drought using two contrasting genotypes of adaptability to drought. The research demonstrates 5 main findings: (1) a reduction of the overall dimension of the phenological traits for robust phenotyping of the adaptive performance under drought; (2) the abundance of peroxisomes in response to drought correlate negatively with grain yield; (3) the efficiency of ROS homeostasis through peroxisome proliferation which seems to be genetically programmed; and (4) the dynamics of ROS homeostasis seems to be timing dependent mechanism, the tolerant genotype response is earlier than the susceptible genotype. This work will contribute to the identification of robust plastic phenotypic tools and the understanding of the mechanisms for adaptive behavior under drought conditions.Summary statementThis study presents the estimated broad-sense heritability of 24 phenological traits under drought compared with non-stressed conditions. The results demonstrated a reduced model of the overall dimension of the phenological traits for phenotyping drought tolerant response including a novel trait (peroxisome abundance). Also, it displays that the adaptive mechanism through peroxisomes proliferation that is a genetic-dependent manner and related to the stress phase, since tolerant plants can sense the stress and maintain the cellular balance earlier than the sensitive plants.