Conversion of a classical microbiology laboratory to a total automation laboratory enhanced by the application of lean principles.

Laboratory automation in microbiology improves productivity and reduces sample turnaround times (TATs). However, its full potential can be unlocked through the optimization of workflows by adopting lean principles. This study aimed to explore the relative impact of laboratory automation and continuous improvement events (CIEs) on productivity and TATs. Laboratory automation took place in November 2020 and consisted of the introduction of WASPLab and VITEK MS systems. CIEs were run in May and September 2021. Before the conversion, the laboratory processed about ~492 samples on weekdays and had 10 full-time equivalent (FTE) staff for a productivity of 49 samples/FTE/day. In March 2021, after laboratory automation, the caseload went up to ~621 while the FTEs decreased to 8.5, accounting for productivity improvement to 73 samples/FTE/day. The hypothetical productivity went up to 110 samples/FTE/day following CIEs, meaning that the laboratory could at that point deal with a caseload increase to ~935 with unchanged FTEs. Laboratory conversion also led to an improvement in TATs for all sample types. For vaginal swabs and urine samples, median TATs decreased from 70.3 h [interquartile range (IQR): 63.5-93.1] and 73.7 h (IQR: 35.6-50.7) to 48.2 h (IQR: 44.8-67.7) and 40.0 h (IQR: 35.6-50.7), respectively. Automation alone was responsible for 37.2% and 75.8% of TAT reduction, respectively, while the remaining reduction of 62.8% and 24.2%, respectively, was achieved due to CIEs. The laboratory reached productivity and TAT goals predefined by the management after CIEs. In conclusion, automation substantially improved productivity and TATs, while the subsequent implementation of lean management further unlocked the potential of laboratory automation.IMPORTANCEIn this study, we combined total laboratory automation with lean management to show that appropriate laboratory work organization enhanced the benefit of the automation and substantially contributed to productivity improvements. Globally, the rapid availability of accurate results in the setting of a clinical microbiology laboratory is part of patient-centered approaches to treat infections and helps the implementation of antibiotic stewardship programs backed by the World Health Organization. Locally, from the point of view of laboratory management, it is important to find ways of maximizing the benefits of the use of technology, as total laboratory automation is an expensive investment.

The Laboratory Automation Protocol (LAP) Format and Repository: A Platform for Enhancing Workflow Efficiency in Synthetic Biology.

Laboratory automation deals with eliminating manual tasks in high-throughput protocols. It therefore plays a crucial role in allowing fast and reliable synthetic biology. However, implementing open-source automation solutions often demands experimental scientists to possess scripting skills, and even when they do, there is no standardized toolkit available for their use. To address this, we present the Laboratory Automation Protocol (LAP) Format and Repository. LAPs adhere to a standardized script-based format, enhancing end-user implementation and simplifying further development. With a modular design, LAPs can be seamlessly combined to create customized, target-specific workflows. Furthermore, all LAPs undergo experimental validation, ensuring their reliability. Detailed information is provided within each repository entry, allowing users to validate the LAPs in their own laboratory settings. We advocate for the adoption of the LAP Format and Repository as a community resource, which will continue to expand, improving the reliability and reproducibility of the automation processes.

Automation in the Thrombosis and Hemostasis Laboratory.

Automation continues to advance into hemostasis and thrombosis laboratories. Integration of hemostasis testing into an existing chemistry track systems and adoption of a separate hemostasis track systems are important considerations. Unique issues must be addressed to maintain quality and efficiency when automation is introduced. Among other challenges, this chapter discusses centrifugation protocols, incorporation of specimen-check modules in the workflow, and inclusion of tests amenable to automation.

Dried blood spots for doping controls-Development of a comprehensive initial testing procedure with fully automated sample preparation.

Currently, primarily urine, whole blood and serum samples are analyzed for doping-relevant substances in professional sports, but recently dried blood spots (DBS) have been introduced as complementary matrix, offering advantageous features, e.g. a minimally invasive sampling procedure. In order to cope with the increased application of DBS, a comprehensive initial testing procedure (ITP) was developed, optimized and validated, comprising a total of 233 substances representing all groups on the World Anti-Doping Agency's (WADA's) Prohibited List. The sample preparation was conducted by employing a fully automated system using an efficient flow-through extraction of a 4 mm diameter spot followed by LC-HRMS/MS analysis. The procedure was successfully validated in terms of selectivity, limit of detection, reproducibility, carryover and robustness with respect to an alternative manual sample preparation, an alternative dried blood collection device and the sample extract stability, and was thus found to meet the required criteria of the relevant guidelines published by WADA for routine application. As a proof-of-concept, DBS samples were analyzed after the administration of the glucocorticoids prednisone and dexamethasone, as well as the stimulant pseudoephedrine and the beta-blocker propranolol. All substances were detected in post-administration samples for at least 4 h and up to 24 h after intake, depending on the collection time period, using the developed testing procedure. In particular, for substances that are only banned in-competition, data obtained from DBS samples can be useful for the interpretation of adverse analytical findings. In conclusion, the developed ITP accounts for the anticipated increasing relevance of DBS in anti-doping analysis in the future and provides a foundation for optimized approaches for specific substance classes.

Establishment of low-cost laboratory automation processes using AutoIt and 4-axis robots.

In most small laboratories, many processes are not yet automated because existing laboratory automation solutions are usually expensive and inflexible to use. Examples of this are autosamplers that are only compatible with one specific laboratory instrument or larger liquid handling stations that are expensive and usually self-contained. A flexible and inexpensive way to automate laboratory processes would be to automate existing laboratory equipment with the help of suitable robotic arms. In this study, we investigate the feasibility of such a strategy based on a low-cost 4-axis robot and freely available software. We used the scripting language AutoIt that automates any Windows-based instrument control software. Using these tools, we automated three fundamentally different laboratory processes: a pipetting process, a use as an autosampler for an atomic absorption spectroscopy instrument, and a more complex process involving the inoculation of bacterial cultures. We also integrated a conventional webcam for 2D barcode recognition. Compared to a trained professional who performed all experiments manually, all setups showed no significant differences in accuracy and precision. In summary, the tested system consisting of a 4-axis robot and freely available software is suitable for flexible automation and has potential for even more complex laboratory processes. Limitations such as a lack of collaboration and speed will be addressed in follow-up studies. The system thus represents a well-suited flexible laboratory automation system for both research and teaching purposes.

Total Laboratory Automation for Rapid Detection and Identification of Microorganisms and Their Antimicrobial Resistance Profiles.

At a time when diagnostic bacteriological testing procedures have become more complex and their associated costs are steadily increasing, the expected benefits of Total laboratory automation (TLA) cannot just be a simple transposition of the traditional manual procedures used to process clinical specimens. In contrast, automation should drive a fundamental change in the laboratory workflow and prompt users to reconsider all the approaches currently used in the diagnostic work-up including the accurate identification of pathogens and the antimicrobial susceptibility testing methods. This review describes the impact of TLA in the laboratory efficiency improvement, as well as a new fully automated solution for AST by disk diffusion testing, and summarizes the evidence that implementing these methods can impact clinical outcomes.

ViralFlow: A Versatile Automated Workflow for SARS-CoV-2 Genome Assembly, Lineage Assignment, Mutations and Intrahost Variant Detection.

The COVID-19 pandemic is driven by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) that emerged in 2019 and quickly spread worldwide. Genomic surveillance has become the gold standard methodology used to monitor and study this fast-spreading virus and its constantly emerging lineages. The current deluge of SARS-CoV-2 genomic data generated worldwide has put additional pressure on the urgent need for streamlined bioinformatics workflows. Here, we describe a workflow developed by our group to process and analyze large-scale SARS-CoV-2 Illumina amplicon sequencing data. This workflow automates all steps of SARS-CoV-2 reference-based genomic analysis: data processing, genome assembly, PANGO lineage assignment, mutation analysis and the screening of intrahost variants. The pipeline is capable of processing a batch of around 100 samples in less than half an hour on a personal laptop or in less than five minutes on a server with 50 threads. The workflow presented here is available through Docker or Singularity images, allowing for implementation on laptops for small-scale analyses or on high processing capacity servers or clusters. Moreover, the low requirements for memory and CPU cores and the standardized results provided by ViralFlow highlight it as a versatile tool for SARS-CoV-2 genomic analysis.

Experience of quantity and quality of DNA and RNA extraction from limited pediatric blood samples: A comparative analysis of automated and manual kit-based method.

INTRODUCTION: Optimal DNA and RNA quantity and purity is essential for downstream molecular biology experimentation and to avoid re-processing of sample. Despite availability of different kits and automated systems for nucleic acid isolation there is limited data on their performance evaluation, more so with pediatric blood samples, that are usually compromised in quantity. Hence, we evaluated the performance of automated QIAcube platform using pediatric blood samples in parallel with manual Qiagen extraction kits. MATERIALS AND METHODS: : A total of 500 samples were analyzed based on groups of PBMC and direct blood input. The isolated DNA and RNA were surveyed for quantity and quality tests by spectrophotometric and downstream analysis. RESULTS: : There was no significant difference in the DNA quantity (ng/ul) between manual and automated method based on similar sample input but quality (260/280) was significantly better with the QIAcube platform when direct blood and or PBMCs were used for extraction respectively (1.82 ± 004 Vs. 1.84.002; P-0.000008 and 1.859 ± 005 Vs. 1.843 ± 0.003; P-0.02). Moreover, the standard error mean was low for both quantity and quality in the QIAcube method suggesting uniformity. Comparison of quality assessment by spectrophotometer and qubit fluorimeter showed that QIAcube sheared DNA less (P- 0.038) as compared to manual method (P-0.013). Also, time taken to process the samples in QIAcube was 23% less than the kit-based method. CONCLUSION: Overall analysis of QIAcube platform suggests that it yields more better, uniform, and less-sheared quality of nucleic acid in a relatively less time as compared to manual extraction kits.

Deep learning model for fully automated breast cancer detection system from thermograms.

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

Modular development enables rapid design of media for alternative hosts.

Developing media to sustain cell growth and production is an essential and ongoing activity in bioprocess development. Modifications to media can often address host or product-specific challenges, such as low productivity or poor product quality. For other applications, systematic design of new media can facilitate the adoption of new industrially relevant alternative hosts. Despite manifold existing methods, common approaches for optimization often remain time and labor-intensive. We present here a novel approach to conventional media blending that leverages stable, simple, concentrated stock solutions to enable rapid improvement of measurable phenotypes of interest. We applied this modular methodology to generate high-performing media for two phenotypes of interest: biomass accumulation and heterologous protein production, using high-throughput, milliliter-scale batch fermentations of Pichia pastoris as a model system. In addition to these examples, we also created a flexible open-source package for modular blending automation on a low-cost liquid handling system to facilitate wide use of this method. Our modular blending method enables rapid, flexible media development, requiring minimal labor investment and prior knowledge of the host organism, and should enable developing improved media for other hosts and phenotypes of interest.

Post-Alignment Adjustment and Its Automation.

Multiple sequence alignment (MSA) is the basis for almost all sequence comparison and molecular phylogenetic inferences. Large-scale genomic analyses are typically associated with automated progressive MSA without subsequent manual adjustment, which itself is often error-prone because of the lack of a consistent and explicit criterion. Here, I outlined several commonly encountered alignment errors that cannot be avoided by progressive MSA for nucleotide, amino acid, and codon sequences. Methods that could be automated to fix such alignment errors were then presented. I emphasized the utility of position weight matrix as a new tool for MSA refinement and illustrated its usage by refining the MSA of nucleotide and amino acid sequences. The main advantages of the position weight matrix approach include (1) its use of information from all sequences, in contrast to other commonly used methods based on pairwise alignment scores and inconsistency measures, and (2) its speedy computation, making it suitable for a large number of long viral genomic sequences.

Sequential Injection Analysis for Automation and Evaluation of Drug Liberation Profiles: Clotrimazole Liberation Monitoring.

A fully automated sequential injection system was tested in terms of its application in liberation testing, and capabilities and limitations were discussed for clotrimazole liberation from three semisolid formulations. An evaluation based on kinetic profiles obtained in short and longer sampling intervals and steady-state flux values were applied as traditional methods. The obtained clotrimazole liberation profile was faster in the case of Delcore and slower for Clotrimazol AL and Canesten cream commercial formulations. The steady-state flux values for the tested formulations were 52 µg cm-2 h-1 for Canesten, 35 µg cm-2 h-1 for Clotrimazol AL, and 7.2 µg cm-2 h-1 for Delcore measured in 4 min sampling intervals. A simplified approach for the evaluation of the initial rate based on the gradient between the second and third sampling points was used for the first time and was found to correspond well with the results of the conventional methods. A comparison based on the ratio of the steady-state flux and the initial rate values for Canesten and Clotrimazol AL proved the similarity of the obtained results. The proposed alternative was successfully implemented for the comparison of short-term kinetic profiles. Consequently, a faster and simpler approach for dissolution/liberation testing can be used.

High-throughput and automation advances for accelerating single-cell cloning, monoclonality and early phase clone screening steps in mammalian cell line development for biologics production.

Mammalian cell line development is a multistep process wherein timelines for developing clonal cells to be used as manufacturing cell lines for biologics production can commonly extend to 9 months when no automation or modern molecular technologies are involved in the workflow. Steps in the cell line development workflow involving single-cell cloning, monoclonality assurance, productivity and stability screening are labor, time and resource intensive when performed manually. Introduction of automation and miniaturization in these steps has reduced the required manual labor, shortened timelines from months to weeks, and decreased the resources needed to develop manufacturing cell lines. This review summarizes the advances, benefits, comparisons and shortcomings of different automation platforms available in the market for rapid isolation of desired clonal cell lines for biologics production.

High-Throughput Selection and Characterisation of Aptamers on Optical Next-Generation Sequencers.

Aptamers feature a number of advantages, compared to antibodies. However, their application has been limited so far, mainly because of the complex selection process. 'High-throughput sequencing fluorescent ligand interaction profiling' (HiTS-FLIP) significantly increases the selection efficiency and is consequently a very powerful and versatile technology for the selection of high-performance aptamers. It is the first experiment to allow the direct and quantitative measurement of the affinity and specificity of millions of aptamers simultaneously by harnessing the potential of optical next-generation sequencing platforms to perform fluorescence-based binding assays on the clusters displayed on the flow cells and determining their sequence and position in regular high-throughput sequencing. Many variants of the experiment have been developed that allow automation and in situ conversion of DNA clusters into base-modified DNA, RNA, peptides, and even proteins. In addition, the information from mutational assays, performed with HiTS-FLIP, provides deep insights into the relationship between the sequence, structure, and function of aptamers. This enables a detailed understanding of the sequence-specific rules that determine affinity, and thus, supports the evolution of aptamers. Current variants of the HiTS-FLIP experiment and its application in the field of aptamer selection, characterisation, and optimisation are presented in this review.

Automation assisted anaerobic phenotyping for metabolic engineering.

BACKGROUND: Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. RESULTS: In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies-an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale. CONCLUSIONS: Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.

Cytomegalovirus Viral Load in Transplanted Patients Using the NeuMoDx™ (Qiagen) Automated System: A 1-Month Experience Feedback.

Cytomegalovirus (CMV) reactivations represent a significant morbidity and mortality problem in transplant patients. Reliable and rapid measurement of CMV viral load is a key issue for optimal patient management. We report here the evaluation of NeuMoDx™ (Qiagen) in a routine hospital setting (University Hospitals of Marseille, France) in comparison with our classical reference technique R-GENE. During one month, 719 CMV viral loads from 507 patients were measured in parallel in both techniques. Using the ROC (receiver operating characteristic) curve and our biological experience we suggest that values <52 IU/mL (geometric mean) correspond to negative samples, values >140 IU/mL (Fowlkes-Mallows index) correspond to quantifiable positive results and values ranging from 52 to 140 IU/mL represent non-quantifiable positive results. Follow-up of 15 transplant patients who developed CMV reactivation during the study showed that NeuMoDx™ provided higher viral load measurement during the first two weeks of follow-up for three patients. These important intra-individual variations resulted in a significant median increase considering the whole data set (6.7 points of difference expressed as a percentage of the initial viral load). However, no difference between the two techniques was noticeable after two weeks of treatment. Subsequent to this first study we conclude that NeuMoDx™, used with optimized logistics and an adapted threshold, allows a rapid CMV viral load measurement and that its use does not lead to any difference in patient management compared to the reference technique R-GENE®.

An automated real-time microfluidic platform to probe single NK cell heterogeneity and cytotoxicity on-chip.

Cytotoxicity is a vital effector mechanism used by immune cells to combat pathogens and cancer cells. While conventional cytotoxicity assays rely on averaged end-point measures, crucial insights on the dynamics and heterogeneity of effector and target cell interactions cannot be extracted, emphasizing the need for dynamic single-cell analysis. Here, we present a fully automated droplet-based microfluidic platform that allowed the real-time monitoring of effector-target cell interactions and killing, allowing the screening of over 60,000 droplets identifying 2000 individual cellular interactions monitored over 10 h. During the course of incubation, we observed that the dynamics of cytotoxicity within the Natural Killer (NK) cell population varies significantly over the time. Around 20% of the total NK cells in droplets showed positive cytotoxicity against paired K562 cells, most of which was exhibited within first 4 h of cellular interaction. Using our single cell analysis platform, we demonstrated that the population of NK cells is composed of individual cells with different strength in their effector functions, a behavior masked in conventional studies. Moreover, the versatility of our platform will allow the dynamic and resolved study of interactions between immune cell types and the finding and characterization of functional sub-populations, opening novel ways towards both fundamental and translational research.

Automated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinoma.

Programmed cell death ligend-1 (PD-L1) expression by immunohistochemistry (IHC) assays is a predictive marker of anti-PD-1/PD-L1 therapy response. With the popularity of anti-PD-1/PD-L1 inhibitor drugs, quantitative assessment of PD-L1 expression becomes a new labor for pathologists. Manually counting the PD-L1 positive stained tumor cells is an obviously subjective and time-consuming process. In this paper, we developed a new computer aided Automated Tumor Proportion Scoring System (ATPSS) to determine the comparability of image analysis with pathologist scores. A three-stage process was performed using both image processing and deep learning techniques to mimic the actual diagnostic flow of the pathologists. We conducted a multi-reader multi-case study to evaluate the agreement between pathologists and ATPSS. Fifty-one surgically resected lung squamous cell carcinoma were prepared and stained using the Dako PD-L1 (22C3) assay, and six pathologists with different experience levels were involved in this study. The TPS predicted by the proposed model had high and statistically significant correlation with sub-specialty pathologists' scores with Mean Absolute Error (MAE) of 8.65 (95% confidence interval (CI): 6.42-10.90) and Pearson Correlation Coefficient (PCC) of 0.9436 ([Formula: see text]), and the performance on PD-L1 positive cases achieved by our method surpassed that of non-subspecialty and trainee pathologists. Those experimental results indicate that the proposed automated system can be a powerful tool to improve the PD-L1 TPS assessment of pathologists.

A mass spectrometry-based targeted assay for detection of SARS-CoV-2 antigen from clinical specimens.

BACKGROUND: The COVID-19 pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has overwhelmed health systems worldwide and highlighted limitations of diagnostic testing. Several types of diagnostic tests including RT-PCR-based assays and antigen detection by lateral flow assays, each with their own strengths and weaknesses, have been developed and deployed in a short time. METHODS: Here, we describe an immunoaffinity purification approach followed a by high resolution mass spectrometry-based targeted qualitative assay capable of detecting SARS-CoV-2 viral antigen from nasopharyngeal swab samples. Based on our discovery experiments using purified virus, recombinant viral protein and nasopharyngeal swab samples from COVID-19 positive patients, nucleocapsid protein was selected as a target antigen. We then developed an automated antibody capture-based workflow coupled to targeted high-field asymmetric waveform ion mobility spectrometry (FAIMS) - parallel reaction monitoring (PRM) assay on an Orbitrap Exploris 480 mass spectrometer. An ensemble machine learning-based model for determining COVID-19 positive samples was developed using fragment ion intensities from the PRM data. FINDINGS: The optimized targeted assay, which was used to analyze 88 positive and 88 negative nasopharyngeal swab samples for validation, resulted in 98% (95% CI = 0.922-0.997) (86/88) sensitivity and 100% (95% CI = 0.958-1.000) (88/88) specificity using RT-PCR-based molecular testing as the reference method. INTERPRETATION: Our results demonstrate that direct detection of infectious agents from clinical samples by tandem mass spectrometry-based assays have potential to be deployed as diagnostic assays in clinical laboratories, which has hitherto been limited to analysis of pure microbial cultures. FUNDING: This study was supported by DBT/Wellcome Trust India Alliance Margdarshi Fellowship grant IA/M/15/1/502023 awarded to AP and the generosity of Eric and Wendy Schmidt.

High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance.

Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria's high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes.