cellPLATO - an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data.

Advances in imaging, segmentation and tracking have led to the routine generation of large and complex microscopy datasets. New tools are required to process this 'phenomics' type data. Here, we present 'Cell PLasticity Analysis Tool' (cellPLATO), a Python-based analysis software designed for measurement and classification of cell behaviours based on clustering features of cell morphology and motility. Used after segmentation and tracking, the tool extracts features from each cell per timepoint, using them to segregate cells into dimensionally reduced behavioural subtypes. Resultant cell tracks describe a 'behavioural ID' at each timepoint, and similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Here, we use cellPLATO to investigate the role of IL-15 in modulating human natural killer (NK) cell migration on ICAM-1 or VCAM-1. We find eight behavioural subsets of NK cells based on their shape and migration dynamics between single timepoints, and four trajectories based on sequences of these behaviours over time. Therefore, by using cellPLATO, we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.

Multi-omics analyses reveal stone cell distribution pattern in pear fruit.

Stone cells are the brachysclereid cells in pear (Pyrus) fruit, consisting almost entirely of lignified secondary cell walls. They are distributed mainly near the fruit core and spread radially in the whole fruit. However, the development of stone cells has not been comprehensively characterized, and little is known about the regulation of stone cell formation at the transcriptomic, proteomic, and metabolomic levels. In the present study, we performed phenomic analysis on the stone cells and their associated vascular bundles distributed near the fruit cores. Transcriptomic, proteomic, and metabolomic analyses revealed a significant positive regulation of biological processes which contribute to the lignification and lignin deposition in stone cells near the fruit core, including sucrose metabolism and phenylalanine, tyrosine, tryptophan, and phenylalanine biosynthesis. We found many metabolites generated from the phenylpropanoid pathway contributing to the cell wall formation of stone cells near the fruit core. Furthermore, we identified a key transcription factor, PbbZIP48, which was highly expressed near the fruit core and was shown to regulate lignin biosynthesis in stone cells. In conclusion, the present study provides insight into the mechanism of lignified stone cell formation near the pear fruit core at multiple levels.

OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis.

The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.

Enhancing winter wheat prediction with genomics, phenomics and environmental data.

In the realm of multi-environment prediction, when the goal is to predict a complete environment using the others as a training set, the efficiency of genomic selection (GS) falls short of expectations. Genotype by environment interaction poses a challenge in achieving high prediction accuracies. Consequently, current efforts are focused on enhancing efficiency by integrating various types of inputs, such as phenomics data, environmental information, and other omics data. In this study, we sought to evaluate the impact of incorporating environmental information into the modeling process, in addition to genomic and phenomics information. Our evaluation encompassed five data sets of soft white winter wheat, and the results revealed a significant improvement in prediction accuracy, as measured by the normalized root mean square error (NRMSE), through the integration of environmental information. Notably, there was an average gain in prediction accuracy of 49.19% in terms of NRMSE across the data sets. Moreover, the observed prediction accuracy ranged from 5.68% (data set 3) to 60.36% (data set 4), underscoring the substantial effect of integrating environmental information. By including genomic, phenomic, and environmental data in prediction models, plant breeding programs can improve selection efficiency across locations.

Biomarker-based human and animal sperm phenotyping: the good, the bad and the ugly†.

Conventional, brightfield-microscopic semen analysis provides important baseline information about sperm quality of an individual; however, it falls short of identifying subtle subcellular and molecular defects in cohorts of "bad," defective human and animal spermatozoa with seemingly normal phenotypes. To bridge this gap, it is desirable to increase the precision of andrological evaluation in humans and livestock animals by pursuing advanced biomarker-based imaging methods. This review, spiced up with occasional classic movie references but seriously scholastic at the same time, focuses mainly on the biomarkers of altered male germ cell proteostasis resulting in post-testicular carryovers of proteins associated with ubiquitin-proteasome system. Also addressed are sperm redox homeostasis, epididymal sperm maturation, sperm-seminal plasma interactions, and sperm surface glycosylation. Zinc ion homeostasis-associated biomarkers and sperm-borne components, including the elements of neurodegenerative pathways such as Huntington and Alzheimer disease, are discussed. Such spectrum of biomarkers, imaged by highly specific vital fluorescent molecular probes, lectins, and antibodies, reveals both obvious and subtle defects of sperm chromatin, deoxyribonucleic acid, and accessory structures of the sperm head and tail. Introduction of next-generation image-based flow cytometry into research and clinical andrology will soon enable the incorporation of machine and deep learning algorithms with the end point of developing simple, label-free methods for clinical diagnostics and high-throughput phenotyping of spermatozoa in humans and economically important livestock animals.

Decoding stomatal characteristics regulating water use efficiency at leaf and plant scales in rice genotypes.

MAIN CONCLUSION: Stomatal traits in rice genotypes affect water use efficiency. Low-frequency small-size stomata correlate with whole plant efficiency, while low-frequency large-size stomata show intrinsic efficiency and responsiveness to vapour pressure deficit. Leaf surface and the patterning of the epidermal layer play a vital role in determining plant growth. While the surface helps in determining radiation interception, epidermal pattern of stomatal factors strongly regulate gas exchange and water use efficiency (WUE). This study focuses on identifying distinct stomatal traits among rice genotypes to comprehend their influence on WUE. Stomatal frequency ranged from 353 to 687 per mm2 and the size varied between 128.31 and 339.01 µm2 among 150 rice germplasm with significant variability in abaxial and adaxial surfaces. The cumulative water transpired and WUE determined at the outdoor phenomics platform, over the entire crop growth period as well as during specific hours of a 24 h-day did not correlate with stomatal frequency nor size. However, genotypes with low-frequency and large-size stomata recorded higher intrinsic water use efficiency (67.04 µmol CO2 mol-1 H2O) and showed a quicker response to varying vapour pressure deficit that diurnally ranged between 0.03 and 2.17 kPa. The study demonstrated the role of stomatal factors in determining physiological subcomponents of WUE both at single leaf and whole plant levels. Differential expression patterns of stomatal regulatory genes among the contrasting groups explained variations in the epidermal patterning. Increased expression of ERECTA, TMM and YODA genes appear to contribute to decreased stomatal frequency in low stomatal frequency genotypes. These findings underscore the significance of stomatal traits in breeding programs and strongly support the importance of these genes that govern variability in stomatal architecture in future crop improvement programs.

Regulation of a single inositol 1-phosphate synthase homeologue by HSFA6B contributes to fibre yield maintenance under drought conditions in upland cotton.

Drought stress substantially impacts crop physiology resulting in alteration of growth and productivity. Understanding the genetic and molecular crosstalk between stress responses and agronomically important traits such as fibre yield is particularly complicated in the allopolyploid species, upland cotton (Gossypium hirsutum), due to reduced sequence variability between A and D subgenomes. To better understand how drought stress impacts yield, the transcriptomes of 22 genetically and phenotypically diverse upland cotton accessions grown under well-watered and water-limited conditions in the Arizona low desert were sequenced. Gene co-expression analyses were performed, uncovering a group of stress response genes, in particular transcription factors GhDREB2A-A and GhHSFA6B-D, associated with improved yield under water-limited conditions in an ABA-independent manner. DNA affinity purification sequencing (DAP-seq), as well as public cistrome data from Arabidopsis, were used to identify targets of these two TFs. Among these targets were two lint yield-associated genes previously identified through genome-wide association studies (GWAS)-based approaches, GhABP-D and GhIPS1-A. Biochemical and phylogenetic approaches were used to determine that GhIPS1-A is positively regulated by GhHSFA6B-D, and that this regulatory mechanism is specific to Gossypium spp. containing the A (old world) genome. Finally, an SNP was identified within the GhHSFA6B-D binding site in GhIPS1-A that is positively associated with yield under water-limiting conditions. These data lay out a regulatory connection between abiotic stress and fibre yield in cotton that appears conserved in other systems such as Arabidopsis.

Multi-scale characterisation of cold response reveals immediate and long-term impacts on cell physiology up to seed composition in sunflower.

Early sowing can help summer crops escape drought and can mitigate the impacts of climate change on them. However, it exposes them to cold stress during initial developmental stages, which has both immediate and long-term effects on development and physiology. To understand how early night-chilling stress impacts plant development and yield, we studied the reference sunflower line XRQ under controlled, semi-controlled and field conditions. We performed high-throughput imaging of the whole plant parts and obtained physiological and transcriptomic data from leaves, hypocotyls and roots. We observed morphological reductions in early stages under field and controlled conditions, with a decrease in root development, an increase in reactive oxygen species content in leaves and changes in lipid composition in hypocotyls. A long-term increase in leaf chlorophyll suggests a stress memory mechanism that was supported by transcriptomic induction of histone coding genes. We highlighted DEGs related to cold acclimation such as chaperone, heat shock and late embryogenesis abundant proteins. We identified genes in hypocotyls involved in lipid, cutin, suberin and phenylalanine ammonia lyase biosynthesis and ROS scavenging. This comprehensive study describes new phenotyping methods and candidate genes to understand phenotypic plasticity better in response to chilling and study stress memory in sunflower.

Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars.

Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.

Maternal asthma symptoms during pregnancy on child behaviour and executive function: A Bayesian phenomics approach.

OBJECTIVE: Maternal history of inflammatory conditions has been linked to offspring developmental and behavioural outcomes. This phenomenon may be explained by the maternal immune activation (MIA) hypothesis, which posits that dysregulation of the gestational immune environment affects foetal neurodevelopment. The timing of inflammation is critical. We aimed to understand maternal asthma symptoms during pregnancy, in contrast with paternal asthma symptoms during the same period, on child behaviour problems and executive function in a population-based cohort. METHODS: Data were obtained from 844 families from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) birth cohort. Parent asthma symptoms during the prenatal period were reported. Asthma symptoms in children were reported longitudinally from two to five years old, while behavioural problems and executive functioning were obtained at seven years old. Parent and child measures were compared between mothers with and without prenatal asthma symptoms. Generalized linear and Bayesian phenomics models were used to determine the relation between parent or child asthma symptoms and child outcomes. RESULTS: Children of mothers with prenatal asthma symptoms had greater behavioural and executive problems than controls (Cohen's d: 0.43-0.75; all p < 0.05). This association remained after adjustments for emerging asthma symptoms during the preschool years and fathers' asthma symptoms during the prenatal period. After adjusting for dependence between child outcomes, the Bayesian phenomics model showed that maternal prenatal asthma symptoms were associated with child internalising symptoms and higher-order executive function, while child asthma symptoms were associated with executive function skills. Paternal asthma symptoms during the prenatal period were not associated with child outcomes. CONCLUSIONS: Associations between child outcomes and maternal but not paternal asthma symptoms during the prenatal period suggests a role for MIA. These findings need to be validated in larger samples, and further research may identify behavioural and cognitive profiles of children with exposure to MIA.

Craniofacial anomalies in a murine model of heterozygous fibroblast growth factor 10 gene mutation.

OBJECTIVE: Dysregulation of Fibroblast Growth Factor 10 (FGF10), a member of the family of Fibroblast Growth Factor (FGF) proteins, has been implicated in craniofacial and dental anomalies, including craniosynostosis, cleft palate, and Lacrimo-Auriculo-Dento-Digital Syndrome. The aim of this murine study was to assess the craniofacial and dental phenotypes associated with a heterozygous FGF10 gene (FGF10+/- ) mutation at skeletal maturity. METHODS: Skulls of 40 skeletally mature mice, comprising two genotypes (heterozygous FGF10+/- mutation, n = 22; wildtype, n = 18) and two sexes (male, n = 23; female, n = 17), were subjected to micro-computed tomography. Landmark-based linear dimensions were measured for the cranial vault, maxilla, mandible, and first molar teeth. Multivariate analysis of variance was performed to assess whether there were significant differences in the craniofacial and dental structures between genotypes and sexes. RESULTS: The craniomaxillary skeleton and the first molar teeth were smaller in the FGF10+/- mice (P < .05), but the mandible was unaffected. Sex did not have a significant effect on these structures (P > .05). Cranial sutural defects were noted in 5/22 (22.7%) mutant versus 2/18 (11.1%) wildtype mice, and cleft palate in only one (4.5%) mutant mouse. None of the mice displayed craniosynostosis, expansive bony lesions, bifid condyles, or impacted teeth. CONCLUSION: The FGF10+/- mutation was associated with craniomaxillary skeletal hypoplasia that probably arose from deficient (delayed) intramembranous ossification of the sutured bones. Overall, the skeletal and dental data suggest that the FGF10 gene plays an important role in the aetiology of craniofacial dysmorphology and malocclusion.

Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of MIR-spectra traits for animal breeding.

With the rapid development of animal phenomics and deep phenotyping, we can get thousands of traditional but also molecular phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the Mega-scale linear mixed model (MegaLMM), a factor model-based approach, able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding values (u) predictions for focal traits (i.e., traits that are targeted for prediction, compared with secondary traits that are helping to evaluate), from single-trait (ST) and TT models, respectively; (3) propose a new approximate method of estimated genomic breeding values (U) prediction with TT models and MegaLMM. 3,421 milk mid-infrared (MIR) spectra wavepoints (called secondary traits) and 3 focal traits [average fat percent (Fat), average methane (CH4), and average somatic cell score (SCS)] collected on 3,302 first-parity Holstein cows were used. The 3,421 milk MIR wavepoints traits were composed of 311 wavepoints in 11 classes (months in lactation). Genotyping information of 564,439 SNP was available for all animals and was used to calculate the genomic relationship matrix. The MegaLMM was implemented in the framework of the Bayesian sparse factor model and solved through Gibbs sampling (Markov chain Monte Carlo). The heritabilities of the studied 3,421 milk MIR wavepoints gradually increased and then decreased in units of 311 wavepoints throughout the lactation. The genetic and phenotypic correlations between the first 311 wavepoints and the other 3,110 wavepoints were low. The accuracies of phenotype predictions from the ST model were lower than those from the TT model for Fat (0.51 vs. 0.93), CH4 (0.30 vs. 0.86), and SCS (0.14 vs. 0.33). The same trend was observed for the accuracies of u predictions: Fat (0.59 vs. 0.86), CH4 (0.47 vs. 0.78), and SCS (0.39 vs. 0.59). The average correlation between U predicted from the TT model and the new approximate method was 0.90. The new approximate method used for estimating U in MegaLMM will enhance the suitability of MegaLMM for applications in animal breeding. This study conducted an initial investigation into the application of thousands of traits in animal breeding and showed that the TT model is beneficial for the prediction of focal traits (phenotype and breeding values), especially for difficult-to-measure traits (e.g., CH4).

Genetic Analysis of Soybean Flower Size Phenotypes Based on Computer Vision and Genome-Wide Association Studies.

The dimensions of organs such as flowers, leaves, and seeds are governed by processes of cellular proliferation and expansion. In soybeans, the dimensions of these organs exhibit a strong correlation with crop yield, quality, and other phenotypic traits. Nevertheless, there exists a scarcity of research concerning the regulatory genes influencing flower size, particularly within the soybean species. In this study, 309 samples of 3 soybean types (123 cultivar, 90 landrace, and 96 wild) were re-sequenced. The microscopic phenotype of soybean flower organs was photographed using a three-eye microscope, and the phenotypic data were extracted by means of computer vision. Pearson correlation analysis was employed to assess the relationship between petal and seed phenotypes, revealing a strong correlation between the sizes of these two organs. Through GWASs, SNP loci significantly associated with flower organ size were identified. Subsequently, haplotype analysis was conducted to screen for upstream and downstream genes of these loci, thereby identifying potential candidate genes. In total, 77 significant SNPs associated with vexil petals, 562 significant SNPs associated with wing petals, and 34 significant SNPs associated with keel petals were found. Candidate genes were screened by candidate sites, and haplotype analysis was performed on the candidate genes. Finally, the present investigation yielded 25 and 10 genes of notable significance through haplotype analysis in the vexil and wing regions, respectively. Notably, Glyma.07G234200, previously documented for its high expression across various plant organs, including flowers, pods, leaves, roots, and seeds, was among these identified genes. The research contributes novel insights to soybean breeding endeavors, particularly in the exploration of genes governing organ development, the selection of field materials, and the enhancement of crop yield. It played a role in the process of material selection during the growth period and further accelerated the process of soybean breeding material selection.

Root system architecture in cereals: progress, challenges and perspective.

Roots are essential multifunctional plant organs involved in water and nutrient uptake, metabolite storage, anchorage, mechanical support, and interaction with the soil environment. Understanding of this 'hidden half' provides potential for manipulation of root system architecture (RSA) traits to optimize resource use efficiency and grain yield in cereal crops. Unfortunately, root traits are highly neglected in breeding due to the challenges of phenotyping, but could have large rewards if the variability in RSA traits can be fully exploited. Until now, a plethora of genes have been characterized in detail for their potential role in improving RSA. The use of forward genetics approaches to find sequence variations in genes underpinning desirable RSA would be highly beneficial. Advances in computer vision applications have allowed image-based approaches for high-throughput phenotyping of RSA traits that can be used by any laboratory worldwide to make progress in understanding root function and dissection of the genetics. At the same time, the frontiers of root measurement include non-invasive methods like X-ray computer tomography and magnetic resonance imaging that facilitate new types of temporal studies. Root physiology and ecology are further supported by spatiotemporal root simulation modeling. The discovery of component traits providing improved resilience and yield advantage in target environments is a key necessity for mainstreaming root-based cereal breeding. The integrated use of pan-genome resources, now available in most cereals, coupled with new in-field phenotyping platforms has the potential for precise selection of superior genotypes with improved RSA.

From data to knowledge - big data needs stewardship, a plant phenomics perspective.

The research data life cycle from project planning to data publishing is an integral part of current research. Until the last decade, researchers were responsible for all associated phases in addition to the actual research and were assisted only at certain points by IT or bioinformaticians. Starting with advances in sequencing, the automation of analytical methods in all life science fields, including in plant phenotyping, has led to ever-increasing amounts of ever more complex data. The tasks associated with these challenges now often exceed the expertise of and infrastructure available to scientists, leading to an increased risk of data loss over time. The IPK Gatersleben has one of the world's largest germplasm collections and two decades of experience in crop plant research data management. In this article we show how challenges in modern, data-driven research can be addressed by data stewards. Based on concrete use cases, data management processes and best practices from plant phenotyping, we describe which expertise and skills are required and how data stewards as an integral actor can enhance the quality of a necessary digital transformation in progressive research.

Ectopic Expression of Distinct PLC Genes Identifies 'Compactness' as a Possible Architectural Shoot Strategy to Cope with Drought Stress.

Phospholipase C (PLC) has been implicated in several stress responses, including drought. Overexpression (OE) of PLC has been shown to improve drought tolerance in various plant species. Arabidopsis contains nine PLC genes, subdivided into four clades. Earlier, OE of PLC3, -5 or -7 were found to increase Arabidopsis' drought tolerance. Here, we confirm this for three other PLCs: PLC2, the only constitutively expressed AtPLC; PLC4, reported to have reduced salt tolerance; and PLC9, of which the encoded enzyme was presumed to be catalytically inactive. To compare each PLC and to discover any other potential phenotype, two independent OE lines of six AtPLC genes, representing all four clades, were simultaneously monitored with the GROWSCREEN FLUORO phenotyping platform, under both control- and mild drought conditions. To investigate which tissues were most relevant to achieve drought survival, we additionally expressed AtPLC5 using 13 different cell- or tissue-specific promoters. While no significant differences in plant size, biomass or photosynthesis were found between PLC lines and wild-type (WT) plants, all PLC-OE lines, as well as those tissue-specific lines that promoted drought survival, exhibited a stronger decrease in convex hull perimeter (= increase in compactness) under water deprivation compared to WT. Increased compactness has not been associated with drought or decreased water loss before, though a hyponastic decrease in compactness in response to increased temperatures has been associated with water loss. We pose that increased compactness could lead to decreased water loss and potentially provides a new breeding trait to select for drought tolerance.

Fantastic genes: where and how to find them? Exploiting rice genetic resources for the improvement of yield, tolerance, and resistance to a wide array of stresses in rice.

Rice production is a critical component of global food security. To date, rice is grown in over 100 countries and is the primary source of food for more than 3 billion people. Despite its importance, rice production is facing numerous challenges that threaten its future viability. One of the primary problems is the advent of climate change. The changing climatic conditions greatly affect the growth and productivity of rice crop and the quality of rice yield. Similarly, biotic stresses brought about by pathogen and pest infestations are greatly affecting the productivity of rice. To address these issues, the utilization of rice genetic resources is necessary to map, identify, and understand the genetics of important agronomic traits. This review paper highlights the role of rice genetic resources for developing high-yielding and stress-tolerant rice varieties. The integration of genetic, genomic, and phenomic tools in rice breeding programs has led to the development of high-yielding and stress-tolerant rice varieties. The collaboration of multidisciplinary teams of experts, sustainable farming practices, and extension services for farmers is essential for accelerating the development of high-yielding and stress-tolerant rice varieties.

Professor Andreas Graner: driven by the quest to unlock crop plant genomes for conservation and utilization of germplasm for breeding.

Professor Andreas Graner stands as a towering figure in international crop plant genomics research, leaving an indelible imprint on the field over the past four decades. As we commemorate the 80th anniversary of Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany and Professor Graner's retirement in September 2023, here we celebrate and acknowledge his profound impact on crop genome analyses and genebank genomics. His trailblazing work extends from developing the first integrated RFLP map of barley, establishing the foundation of barley genome sequencing, and advancing functional genomics of malting quality, to pioneering the use of high-throughput phenomics. As the dedicated custodian of Germany's largest ex situ genebank at IPK Gatersleben, Professor Graner has fortified the institution's collection management and crop research, thereby contributing significantly to global efforts on conservation and utilization of plant genetic resources through genomics approaches. Alongside his impressive array of scientific achievements, Professor Graner's inspiring mentorship has nurtured a new generation of scientists, including us, leaving a lasting legacy in the field. This tribute underscores his enduring influence and celebrates his unwavering dedication to the scientific community.

The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas.

A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.

High-throughput phenotyping-based quantitative trait loci mapping reveals the genetic architecture of the salt stress tolerance of Brassica napus.

Salt stress is a major limiting factor that severely affects the survival and growth of crops. It is important to understand the salt stress tolerance ability of Brassica napus and explore the underlying related genetic resources. We used a high-throughput phenotyping platform to quantify 2111 image-based traits (i-traits) of a natural population under three different salt stress conditions and an intervarietal substitution line (ISL) population under nine different stress conditions to monitor and evaluate the salt stress tolerance of B. napus over time. We finally identified 928 high-quality i-traits associated with the salt stress tolerance of B. napus. Moreover, we mapped the salt stress-related loci in the natural population via a genome-wide association study and performed a linkage analysis associated with the ISL population, respectively. These results revealed 234 candidate genes associated with salt stress response, and two novel candidate genes, BnCKX5 and BnERF3, were experimentally verified to regulate the salt stress tolerance of B. napus. This study demonstrates the feasibility of using high-throughput phenotyping-based quantitative trait loci mapping to accurately and comprehensively quantify i-traits associated with B. napus. The mapped loci could be used for genomics-assisted breeding to genetically improve the salt stress tolerance of B. napus.