Image-based Phenotyping Research Articles

Pooled genetic perturbation screens with image-based phenotypes.

Discovery of the genetic components underpinning fundamental and disease-related processes is being rapidly accelerated by combining efficient, programmable genetic engineering with phenotypic readouts of high spatial, temporal and/or molecular resolution. Microscopy is a fundamental tool for studying cell biology, but its lack of high-throughput sequence readouts hinders integration in large-scale genetic screens. Optical pooled screens using in situ sequencing provide massively scalable integration of barcoded lentiviral libraries (e.g., CRISPR perturbation libraries) with high-content imaging assays, including dynamic processes in live cells. The protocol uses standard lentiviral vectors and molecular biology, providing single-cell resolution of phenotype and engineered genotype, scalability to millions of cells and accurate sequence reads sufficient to distinguish >106 perturbations. In situ amplification takes ~2 d, while sequencing can be performed in ~1.5 h per cycle. The image analysis pipeline provided enables fully parallel automated sequencing analysis using a cloud or cluster computing environment.

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients.

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.

Drought Tolerant Near Isogenic Lines of Pusa 44 Pyramided With qDTY2.1 and qDTY3.1, Show Accelerated Recovery Response in a High Throughput Phenomics Based Phenotyping.

Reproductive stage drought stress (RSDS) is a major challenge in rice production worldwide. Cultivar development with drought tolerance has been slow due to the lack of precise high throughput phenotyping tools to quantify drought stress-induced effects. Most of the available techniques are based on destructive sampling and do not assess the progress of the plant’s response to drought. In this study, we have used state-of-the-art image-based phenotyping in a phenomics platform that offers a controlled environment, non-invasive phenotyping, high accuracy, speed, and continuity. In rice, several quantitative trait loci (QTLs) which govern grain yield under drought determine RSDS tolerance. Among these, qDTY2.1 and qDTY3.1 were used for marker-assisted breeding. A set of 35 near-isogenic lines (NILs), introgressed with these QTLs in the popular variety, Pusa 44 were used to assess the efficiency of image-based phenotyping for RSDS tolerance. NILs offered the most reliable contrast since they differed from Pusa 44 only for the QTLs. Four traits, namely, the projected shoot area (PSA), water use (WU), transpiration rate (TR), and red-green-blue (RGB) and near-infrared (NIR) values were used. Differential temporal responses could be seen under drought, but not under unstressed conditions. NILs showed significant level of RSDS tolerance as compared to Pusa 44. Among the traits, PSA showed strong association with yield (80%) as well as with two drought tolerances indices, stress susceptibility index (SSI) and tolerance index (TOL), establishing its ability in identifying the best drought tolerant NILs. The results revealed that the introgression of QTLs helped minimize the mean WU per unit of biomass per day, suggesting the potential role of these QTLs in improving WU-efficiency (WUE). We identified 11 NILs based on phenomics traits as well as performance under imposed drought in the field. The study emphasizes the use of phenomics traits as selection criteria for RSDS tolerance at an early stage, and is the first report of using phenomics parameters in RSDS selection in rice.

Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping

Plant phenotyping refers to a quantitative description of the plant's properties, however in image-based phenotyping analysis, our focus is primarily on the plant's anatomical, ontogenetical and physiological properties. This technique reinforced by the success of Deep Learning in the field of image based analysis is applicable to a wide range of research areas making high-throughput screens of plants possible, reducing the time and effort needed for phenotypic characterization. In this study, we use Deep Learning methods (supervised and unsupervised learning based approaches) to semantically segment grapevine leaves images in order to develop an automated object detection (through segmentation) system for leaf phenotyping which will yield information regarding their structure and function. In these directions we studied several deep learning approaches with promising results as well as we reported some future challenging tasks in the area of precision agriculture. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified, targeted intervention and selective application of agrochemicals and grapevine variety identification which are key prerequisites in sustainable agriculture.

Opportunities to Harness High-Throughput and Novel Sensing Phenotypes to Improve Feed Efficiency in Dairy Cattle.

Simple SummarySensors, routinely collected on-farm tests, and other repeatable, high-throughput measurements can provide novel phenotype information on a frequent basis. Information from these sensors and high-throughput measurements could be harnessed to monitor or predict individual dairy cow feed intake. Predictive algorithms would allow for genetic selection of animals that consume less feed while producing the same amount of milk. Improved monitoring of feed intake could reduce the cost of milk production, improve animal health, and reduce the environmental impact of the dairy industry. Moreover, data from these information sources could aid in animal management (e.g., precision feeding and health detection). In order to implement tools, the relationship of measurements with feed intake needs to be established and prediction equations developed. Lastly, consideration should be given to the frequency of data collection, the need for standardization of data and other potential limitations of tools in the prediction of feed intake. This review summarizes measurements of feed efficiency, factors that may impact the efficiency and feed consumption of an animal, tools that have been researched and new traits that could be utilized for the prediction of feed intake and efficiency, and prediction equations for feed intake and efficiency presented in the literature to date.Feed for dairy cattle has a major impact on profitability and the environmental impact of farms. Sustainable dairy production relies on continued improvement in feed efficiency as a way to reduce costs and nutrient loss from feed. Advances in breeding, feeding and management have led to the dilution of maintenance energy and thus more efficient dairy cattle. Still, many additional opportunities are available to improve individual animal feed efficiency. Sensing technologies such as wearable sensors, image-based and high-throughput phenotyping technologies (e.g., milk testing) are becoming more available on commercial farm. The application of these technologies as indicator traits for feed intake and efficiency related traits would be advantageous to provide additional information to predict and manage feed efficiency. This review focuses on precision livestock technologies and high-throughput phenotyping in use today as well as those that could be developed in the future as possible indicators of feed intake. Several technologies such as milk spectral data, activity, rumen measures, and image-based phenotypes have been associated with feed intake. Future applications will depend on the ability to repeatably measure and calibrate these data across locations, so that they can be integrated for use in predicting and managing feed intake and efficiency on farm.

A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian study

MotivationMachine learning (ML) algorithms can provide better cardiovascular event (CVE) prediction. However, ML algorithms are mostly explored for predicting a single CVE at a time. The objective of this study is to design and develop an ML-based system to predict multi-label CVEs, such as (i) coronary artery disease, (ii) acute coronary syndrome, and (iii) a composite CVE—a class of AtheroEdge 3.0 (ML) system. MethodsFocused carotid B-mode ultrasound and coronary angiography are performed on a group of 459 participants consisting of three cardiovascular labels. Initially, 23 risk predictors comprising (i) patients’ demographics, (ii) clinical blood-biomarkers, and (iii) carotid ultrasound image-based phenotypes are collected. Six types of classification techniques comprising (a) four problem transformation methods (PTM) and (b) two algorithm adaptation methods (AAM) are used for multi-label CVE prediction. The performance of the proposed system is evaluated for accuracy, sensitivity, specificity, F1-score, and area-under-the-curve (AUC) using 10-fold cross-validation. The proposed system is also verified using another database of 522 participants. ResultsFor the primary database, PTM demonstrated a better multi-label CVE prediction than AAM (mean accuracy: 80.89% vs. 62.83%, mean AUC: 0.89 vs. 0.63), validating our hypothesis. The PTM-based binary relevance (BR) technique provided optimal performance in multi-label CVE prediction. The overall multi-label classification accuracy, sensitivity, specificity, F1-score, and AUC using BR are 81.2 ± 3.01%, 76.5 ± 8.8%, 83.8 ± 3.8%, 75.37 ± 5.8%, and 0.89 ± 0.02 (p < 0.0001), respectively. When used on the second Canadian database with seven cardiovascular events (acute coronary syndrome, myocardial infarction, angina, stroke, transient ischemic attack, heart failure, and death), the proposed system showed an accuracy of 96.36 ± 0.87% (AUC: 0.61 ± 0.06, p < 0.0001). ConclusionML-based multi-label classification algorithms, such as binary relevance, yielded the best predictions for three cardiovascular endpoints.

A view from above: the use of drones and image-based phenotyping in breeding and production of woody ornamentals

A view from above: the use of drones and image-based phenotyping in breeding and production of woody ornamentals

Image Based-Phenotyping and Selection Index Based on Multivariate Analysis for Rice Hydroponic Screening under Drought Stress

Image Based-Phenotyping and Selection Index Based on Multivariate Analysis for Rice Hydroponic Screening under Drought Stress

Gene-corrected p.A30P SNCA patient-derived isogenic neurons rescue neuronal branching and function

Parkinson’s disease (PD) is characterised by the degeneration of A9 dopaminergic neurons and the pathological accumulation of alpha-synuclein. The p.A30P SNCA mutation generates the pathogenic form of the alpha-synuclein protein causing an autosomal-dominant form of PD. There are limited studies assessing pathogenic SNCA mutations in patient-derived isogenic cell models. Here we provide a functional assessment of dopaminergic neurons derived from a patient harbouring the p.A30P SNCA mutation. Using two clonal gene-corrected isogenic cell lines we identified image-based phenotypes showing impaired neuritic processes. The pathological neurons displayed impaired neuronal activity, reduced mitochondrial respiration, an energy deficit, vulnerability to rotenone, and transcriptional alterations in lipid metabolism. Our data describes for the first time the mutation-only effect of the p.A30P SNCA mutation on neuronal function, supporting the use of isogenic cell lines in identifying image-based pathological phenotypes that can serve as an entry point for future disease-modifying compound screenings and drug discovery strategies.

Genetic techniques for plant breeding

Genetic techniques for plant breeding

Improvement of Phosphorus Use Efficiency in Rice by Adopting Image-Based Phenotyping and Tolerant Indices.

Phosphorus is one of the second most important nutrients for plant growth and development, and its importance has been realised from its role in various chains of reactions leading to better crop dynamics accompanied by optimum yield. However, the injudicious use of phosphorus (P) and non-renewability across the globe severely limit the agricultural production of crops, such as rice. The development of P-efficient cultivar can be achieved by screening genotypes either by destructive or non-destructive approaches. Exploring image-based phenotyping (shoot and root) and tolerant indices in conjunction under low P conditions was the first report, the epicentre of this study. Eighteen genotypes were selected for hydroponic study from the soil-based screening of 68 genotypes to identify the traits through non-destructive (geometric traits by imaging) and destructive (morphology and physiology) techniques. Geometric traits such as minimum enclosing circle, convex hull, and calliper length show promising responses, in addition to morphological and physiological traits. In 28-day-old seedlings, leaves positioned from third to fifth played a crucial role in P mobilisation to different plant parts and maintained plant architecture under P deficient conditions. Besides, a reduction in leaf angle adjustment due to a decline in leaf biomass was observed. Concomitantly, these geometric traits facilitate the evaluation of low P-tolerant rice cultivars at an earlier stage, accompanying several stress indices. Out of which, Mean Productivity Index, Mean Relative Performance, and Relative Efficiency index utilising image-based traits displayed better responses in identifying tolerant genotypes under low P conditions. This study signifies the importance of image-based phenotyping techniques to identify potential donors and improve P use efficiency in modern rice breeding programs.

A digital sensor to measure real-time leaf movements and detect abiotic stress in plants.

Plant and plant organ movements are the result of a complex integration of endogenous growth and developmental responses, partially controlled by the circadian clock, and external environmental cues. Monitoring of plant motion is typically done by image-based phenotyping techniques with the aid of computer vision algorithms. Here we present a method to measure leaf movements using a digital inertial measurement unit (IMU) sensor. The lightweight sensor is easily attachable to a leaf or plant organ and records angular traits in real-time for two dimensions (pitch and roll) with high resolution (measured sensor oscillations of 0.36 ± 0.53° for pitch and 0.50 ± 0.65° for roll). We were able to record simple movements such as petiole bending, as well as complex lamina motions, in several crops, ranging from tomato to banana. We also assessed growth responses in terms of lettuce rosette expansion and maize seedling stem movements. The IMU sensors are capable of detecting small changes of nutations (i.e. bending movements) in leaves of different ages and in different plant species. In addition, the sensor system can also monitor stress-induced leaf movements. We observed that unfavorable environmental conditions evoke certain leaf movements, such as drastic epinastic responses, as well as subtle fading of the amplitude of nutations. In summary, the presented digital sensor system enables continuous detection of a variety of leaf motions with high precision, and is a low-cost tool in the field of plant phenotyping, with potential applications in early stress detection.

Computer vision and weigh scale-based prediction of milk yield and udder traits for individual cows

The measurement of the performance of dairy cow milk production contributes to improve the productivity and profitability of dairy farms. Milk yield is used on-farm for decisions around culling, drying off, heat detection and off-farm for estimation of sire breeding values. Milk yield is measured by herd testing at multiple times through a lactation and by individual milk meters, although the rate of herd testing is decreasing and only a minority of milking systems have milk meters. The goal of this study was to develop a non-contact computer vision and/or walk-over weigh scale technology based on cow 3D images and/or weights before and after milking to determine individual cow milk yield and the lactation milk yield for individual cows. This technology could provide some of the benefits of individual milk meters, although only 1–2 sensors would be required per milking apparatus as opposed to a milk meter per bale on the milking apparatus. An algorithm was developed to estimate the udder volume before milking, udder volume after milking and the difference in udder volume before and after milking. A calibration model based on in-line milk meter measured milk yield from 20 cows was developed to predict milk yield per milking. The model based on udder volume before milking and the difference in udder volume before and after milking had a calibration R2 = 0.92 and RMSE = 0.84 L. The algorithm RMSE was inferior to the in-line milk meter (RMSE = 0.5 L for this trial). This imaging technology provides an inexpensive method to determine the milk yield of individual cows, which can be used for better on-farm management of cows in the herd. The feasibility of determining lactation yield from walk-over weigh scale measurements of each cow before and after each milking was also demonstrated, with algorithm prediction (based on a 2.5% weigh scale error) of lactation milk yield estimated to be similar to that based on two herd tests per lactation. Furthermore, the potential of image-based phenotyping of udder traits such as front/rear teat placement, teat length and teat orientation were demonstrated, with an estimated model accuracy of 94% for front/rear teat placement on a 9-point scale. Average rear teat length was 39 ± 1.5 mm and average front teat length was 49 ± 1.3 mm. This highlights the potential for using computer vision for on-farm prediction of milk performance and udder traits from dairy cows.

BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. Here, we propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSeg achieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes, whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.

Nanovaccine for Plants from Organic Waste: d-Limonene-Loaded Chitosan Nanocarriers Protect Plants against Botrytis cinerea

Chitosan and d-limonene are bioactive molecules derived from organic waste. We repurposed them in the form of a drug delivery system for agricultural applications in crop protection to combatBotrytis cinerea, a broad host-range necrotrophic (BHN) pathogen that causes economic losses worldwide. Synthetic fungicide application remains the most common method to control this pathogen, but this comes with a significant environmental cost. Drug delivery systems from naturally occurring biomaterials can offer efficient treatment options to combat this pathogen. We hypothesized that engineered chitosan nanoparticles (CSNPs) loaded with d-limonene can trigger an on-demand systemic defense response in plants that enhances its quantitative disease resistance against BHN. Chitosan nanocarriers encapsulated with d-limonene as cargo were fabricated through a rational formulation via ionic gelation and the spontaneous emulsification method, respectively, to produce chitosan d-limonene nanoparticles (CdlNPs). The therapeutic effect of the d-limonene nanoemulsion, CSNPs, and CdlNPs on the plants’ defense response against necrotrophic fungal pathogens was evaluated by monitoring the dynamic morpho-physiological changes via multispectral image-based phenotyping in an Arabidopsis thaliana (Col-0)–B. cinerea model system. Functional analysis of the differentially expressed genes revealed that at the concentration of significant disease resistance (0.5%), CSNPs downregulated the biological process involved in plant growth and development, but upregulated the main process controlling response to stress.

Development of image-based phenotyping for selection characters of rice adaptability on the seedling salinity screening

Development of adaptability rice under salinity stress needs effective and selective methods in the screening process. The seedling screening method is a general method used in salinity screening. However, this screening method often uses conventional observation in its screening process. This observation is rated that has a high error level. Therefore, the development of a digital approach through image-based phenotyping expected could minimize the error in the adaptability screening. This study was designed with a nested randomized complete group design, where replications were nested in a stressful environment. The environment in this study was normal (0 mM NaCl) and salinity stress (120 mM NaCl). The genotype used consisted of 8 genotypes which were repeated three times. The number of characters observed was nine image-based phenotyping. The results of this study showed that green percentage, the 3rd leaf length, and total area were the selection characters of image-based phenotyping under seedling salinity screening. Besides that, the used adaptability index in salinity screening became a good approach in considered and distinguished tolerance responses among varieties, especially to Pokkali (tolerant control variety) and IR 29 (sensitive control variety). Based on this study, the application of image-based phenotyping recommended in the screening process of line adaptability under salinity stress.

Wild emmer introgression alters root-to-shoot growth dynamics in durum wheat in response to water stress

Water deficit during the early vegetative growth stages of wheat (Triticum) can limit shoot growth and ultimately impact grain productivity. Introducing diversity in wheat cultivars to enhance the range of phenotypic responses to water limitations during vegetative growth can provide potential avenues for mitigating subsequent yield losses. We tested this hypothesis in an elite durum wheat background by introducing a series of introgressions from a wild emmer (Triticum turgidum ssp. dicoccoides) wheat. Wild emmer populations harbor rich phenotypic diversity for drought-adaptive traits. To determine the effect of these introgressions on vegetative growth under water-limited conditions, we used image-based phenotyping to catalog divergent growth responses to water stress ranging from high plasticity to high stability. One of the introgression lines exhibited a significant shift in root-to-shoot ratio in response to water stress. We characterized this shift by combining genetic analysis and root transcriptome profiling to identify candidate genes (including a root-specific kinase) that may be linked to the root-to-shoot carbon reallocation under water stress. Our results highlight the potential of introducing functional diversity into elite durum wheat for enhancing the range of water stress adaptation.

A Digital Image-Based Phenotyping Platform for Analyzing Root Shape Attributes in Carrot

Root shape in carrot (Daucus carota subsp. sativus), which ranges from long and tapered to short and blunt, has been used for at least several centuries to classify carrot cultivars. The subjectivity involved in determining market class hinders the establishment of metric-based standards and is ill-suited to dissecting the genetic basis of such quantitative phenotypes. Advances in digital image acquisition and analysis has enabled new methods for quantifying sizes of plant structures and shapes, but in order to dissect the genetic control of the shape features that define market class in carrot, a tool is required that quantifies the specific shape features used by humans in distinguishing between classes. This study reports the construction and demonstration of the first such platform, which facilitates rapid phenotyping of traits that are measurable by hand, such as length and width, as well as principal component analysis (PCA) of the root contour and its curvature. This latter approach is of particular interest, as it enabled the detection of a novel and significant quantitative trait, defined here as root fill, which accounts for 85% of the variation in root shape. Curvature analysis was demonstrated to be an effective method for precise measurement of the broadness of the carrot shoulder, and degree of tip fill; the first principal component of the respective curvature profiles captured 87% and 84% of the total variance. This platform’s performance was validated in two experimental panels. First, a diverse, global collection of germplasm was used to assess its capacity to identify market classes through clustering analysis. Second, a diallel mating design between inbred breeding lines of differing market classes was used to estimate the heritability of the key phenotypes that define market class, which revealed significant variation in the narrow-sense heritability of size and shape traits, ranging from 0.14 for total root size, to 0.84 for aspect ratio. These results demonstrate the value of high-throughput digital phenotyping in characterizing the genetic control of complex quantitative phenotypes.

Biophysical analysis of drug efficacy on C. elegans models for neurodegenerative and neuromuscular diseases.

Caenorhabditis elegans has emerged as a powerful model organism for drug screening due to its cellular simplicity, genetic amenability and homology to humans combined with its small size and low cost. Currently, high-throughput drug screening assays are mostly based on image-based phenotyping with the focus on morphological-descriptive traits not exploiting key locomotory parameters of this multicellular model with muscles such as its thrashing force, a critical biophysical parameter when screening drugs for muscle-related diseases. In this study, we demonstrated the use of a micropillar-based force assay chip in combination with a fluorescence assay to evaluate the efficacy of various drugs currently used in treatment of neurodegenerative and neuromuscular diseases. Using this two-dimensional approach, we showed that the force assay was generally more sensitive in measuring efficacy of drug treatment in Duchenne Muscular Dystrophy and Parkinson’s Disease mutant worms as well as partly in Amyotrophic Lateral Sclerosis model. These results underline the potential of our force assay chip in screening of potential drug candidates for the treatment of neurodegenerative and neuromuscular diseases when combined with a fluorescence assay in a two-dimensional analysis approach.

Impact of Moderate Cold and Salt Stress on the Accumulation of Antioxidant Flavonoids in the Leaves of Two Capsicum Cultivars.

The horticultural production of bell peppers generates large quantities of residual biomass. Abiotic stress stimulates the production of protective flavonoids, so the deliberate application of stress to the plants after fruit harvest could provide a strategy to valorize horticultural residuals by increasing flavonoid concentrations, facilitating their industrial extraction. Here we exposed two Capsicum cultivars, a chilli and a bell pepper, to cold and salt stress and combinations thereof to determine their valorization potential. Noninvasive image-based phenotyping and multiparametric fluorescence measurements indicated that all stress treatments inhibited plant growth and reduced the leaf chlorophyll fluorescence index, with the chilli cultivar showing greater sensitivity. The fluorescence-based FLAV index allowed the noninvasive assessment of foliar luteolin glycosides. High-performance liquid chromatography-mass spectrometry (HPLC-MS) analysis showed that moderate cold increased the levels of two foliar antioxidant luteolin glycosides in both cultivars, with bell pepper containing the highest amounts (induced to maximum 5.5 mg g-1 DW cynaroside and 37.0 mg g-1 DW graveobioside A) after combined stress treatment. These data confirm the potential of abiotic stress for the valorization of residual leaf biomass to enhance the industrial extraction of antioxidant and bioactive flavonoids.