Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

High-throughput phenotyping: Breaking through the bottleneck in future crop breeding

With the rapid development of genetic analysis techniques and crop population size,phenotyping has become the bottleneck restricting crop breeding.Breaking through this bottleneck will require phenomics,defined as the accurate,high-throughput acquisition and analysis of multi-dimensional phenotypes during crop growth at organism-wide levels,ranging from cells to organs,individual plants,plots,and fields.Here we offer an overview of crop phenomics research from technological and platform viewpoints at various scales,including microscopic,ground-based,and aerial phenotyping and phenotypic data analysis.We describe recent applications of high-throughput phenotyping platforms for abiotic/biotic stress and yield assessment.Finally,we discuss current challenges and offer perspectives on future phenomics research.

High-throughput screening of mouse gene knockouts identifies established and novel skeletal phenotypes

Screening gene function in vivo is a powerful approach to discover novel drug targets. We present high-throughput screening （HTS） data for 3 762 distinct global gene knockout （KO） mouse lines with viable adult homozygous mice generated using either gene-trap or homologous recombination technologies. Bone mass was determined from DEXA scans of male and female mice at 14 weeks of age and by microCT analyses of bones from male mice at 16 weeks of age. Wild-type （WT） cagemates/littermates were examined for each gene KO. Lethality was observed in an additional 850 KO lines. Since primary HTS are susceptible to false positive findings, additional cohorts of mice from KO lines with intriguing HTS bone data were examined. Aging, ovariectomy, histomorphometry and bone strength studies were performed and possible non-skeletal phenotypes were explored. Together, these screens identified multiple genes affecting bone mass： 23 previously reported genes （Calcr, Cebpb, Crtap, Dcstamp, Dkkl, Duoxa2, Enppl, Fgf23, Kissl/Kisslr, Kl （Klotho）, Lrp5, Mstn, Neol, Npr2, Ostml, Postn, Sfrp4, S1c30a5, Sic39a13, Sost, Sumf1, Src, Wnt10b）, five novel genes extensively characterized （Cldn18, Fam20c, Lrrkl, Sgpll, Wnt16）, five novel genes with preliminary characterization （Agpat2, RassfS, Slc10a7, Stc26a7, Slc30a10） and three novel undisclosed genes coding for potential osteoporosis drug targets.

High-throughput phenotyping in cotton:a review

Recent technological advances in cotton(Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis.High-throughput phenotyping(HTP) is a non-destructive and rapid approach of monitoring and measuring multiple phenotypic traits related to the growth,yield,and adaptation to biotic or abiotic stress.Researchers have conducted extensive experiments on HTP and developed techniques including spectral,fluorescence,thermal,and three-dimensional imaging to measure the morphological,physiological,and pathological resistance traits of cotton.In addition,ground-based and aerial-based platforms were also developed to aid in the implementation of these HTP systems.This review paper highlights the techniques and recent developments for HTP in cotton,reviews the potential applications according to morphological and physiological traits of cotton,and compares the advantages and limitations of these HTP systems when used in cotton cropping systems.Overall,the use of HTP has generated many opportunities to accurately and efficiently measure and analyze diverse traits of cotton.However,because of its relative novelty,HTP has some limitations that constrains the ability to take full advantage of what it can offer.These challenges need to be addressed to increase the accuracy and utility of HTP,which can be done by integrating analytical techniques for big data and continuous advances in imaging.

High-throughput phenotyping identifies plant growth differences under well-watered and drought treatments

The ability to screen larger populations with fewer replicates and non-destructive measurements is one advantage of high-throughput phenotyping(HTP)over traditinal phenotyping techniques.In this study,two wheat accessions were grown in a controlled-environment with a moderate drought imposed from stem elongation to post-anthesis.Red-green-blue(RGB)imaging was performed on 17 of the 22 d following the start of drought imposition.Destructive measurements from all plants were performed at the conclusion of the experiment.The effect of line was signifcant for shoot dry matter,spike dry matter,root dry matter,and tller number,while the water treatment was significant on shoot dry matter and root dry matter.The temporal,non-destructive nature of HTP allowed the drought treatment to be significantly differentiated from the well-watered treatment after 6 d in a line from Argentina and 9 d in a line from Chile.This difference of 3 d indicated an increased degree of drought tolerance in the line from Chile.Furthermore,HTP from the final day of imaging accurately predicted reference plant height(r=1),shoot dry matter(r=0.95)and tller number(r=0.91).This experiment ilustrates the potential of HTP and its use in modeling plant growth and development.

Integration of expression profiles and endo-phenotypes in genetic association studies: A Bayesian approach to determine the path from gene to disease

In genetic association studies of complex diseases, endo-phenotypes such as expression profiles, epigenetic data, or clinical intermediate-phenotypes provide insight to understand the underlying biological path of the disease. In such situations, in order to establish the path from the gene to the disease, we have to decide whether the gene acts on the disease phenotype primarily through a specific endo-phenotype or whether the gene influences the disease through an unidentified path which is characterized by different intermediate phenotypes. Here, we address the question that a genetic locus, given its effect on an endo-phenotype, influences the trait of interest primarily through the path of the endo-phenotype. We propose a Bayesian approach that can evaluate the genetic association between the genetic locus and the phenotype of interest in the presence of the genetic effect on the endo-phenotype. Using simulation studies, we verify that our approach has the desired properties and compare this approach with a mediation approach. The proposed Bayesian approach is illustrated by an application to genome-wide association study for childhood asthma (CAMP) that contains expression profiles.

Metabolic profiles and morphological characteristics of leaf tips among different sweet potato(Ipomoea batatas Lam.)varieties

Sweet potato leaf tips have high nutritional value,and exploring the differences in the metabolic profiles of leaf tips among different sweet potato varieties can provide information to improve their qualities.In this study,a UPLC-Q-Exactive Orbitrap/MS-based untargeted metabolomics method was used to evaluate the metabolites in leaf tips of 32 sweet potato varieties.Three varieties with distinct overall metabolic profiles(A01,A02,and A03),two varieties with distinct profiles of phenolic acids(A20 and A18),and three varieties with distinct profiles of flavonoids(A05,A12,and A16)were identified.In addition,a total of 163 and 29 differentially expressed metabolites correlated with the color and leaf shape of sweet potato leaf tips,respectively,were identified through morphological characterization.Group comparison analysis of the phenotypic traits and a metabolite-phenotypic trait correlation analysis indicated that the color differences of sweet potato leaf tips were markedly associated with flavonoids.Also,the level of polyphenols was correlated with the leaf shape of sweet potato leaf tips,with lobed leaf types having higher levels of polyphenols than the entire leaf types.The findings on the metabolic profiles and differentially expressed metabolites associated with the morphology of sweet potato leaf tips can provide useful information for breeding sweet potato varieties with higher nutritional value.

Image-based root phenotyping for field-grown crops:An example under maize/soybean intercropping

Root architecture,which determines the water and nutrient uptake ability of crops,is highly plastic in response to soil environmental changes and different cultivation patterns.Root phenotyping for field-grown crops,especially topological trait extraction,is rarely performed.In this study,an image-based semi-automatic root phenotyping method for field-grown crops was developed.The method consisted of image acquisition,image denoising and segmentation,trait extraction and data analysis.Five global traits and 40 local traits were extracted with this method.A good consistency in 1st-order lateral root branching was observed between the visually counted values and the values extracted using the developed method,with R^(2)=0.97.Using the method,we found that the interspecific advantages for maize mainly occurred within 5 cm from the root base in the nodal roots of the 5th-7th nodes,and that the obvious inhibition of soybean was mostly reflected within 20 cm from the root base.Our study provides a novel approach with high-throughput and high-accuracy for field research on root morphology and branching features.It could be applied to the 3D reconstruction of field-grown root system architecture to improve the inputs to data-driven models(e.g.,OpenSimRoot)that simulate root growth,solute transport and water uptake.

Analysis of phenotype array data from Biolog MicroPlates TM.

Biolog MicroPlates TM. are employed to characterize Trichoderma isolates based on differential assimilation of test substrates and redox reactions in a 96-well test plate. The Biolog method is potentially advantageous in being relatively simple, fast and economical, and data acquisition can be automated using a microplate reader and applicable software. Several research applications of the Biolog system are presented: i) “monophenetic groups” from cluster analyses of phenotype array data are investigated for previously undetected new species in Trichoderma, ii) metabolic characters differentiating species are identified, and multivariate analyses performed to complement molecular data in validating new species and significant variants, and iii) phenotype array data for more than 1200 Trichoderma strains are analysed to select strains that might be exploited for bioconversions and commercial production of enzymes. Phenotype arrays are much more sensitive to strain level variation than molecular techniques, however, phenotype array data do not consistently reflect phylogenies constructed from molecular data. Nevertheless, the Biolog phenotype array is an economical alternative method for surveying biological diversity, and provides data that complements molecular data in phylogenetic studies.

Autoantigen Microarray for High-throughput Autoantibody Profiling in Systemic Lupus Erythematosus

Systemic lupus erythematosus （SLE） is a complex autoimmune disease characterized by the production of autoantibodies to a broad range of self-antigens. Profiling the autoantibody repertoire using array-based technology has emerged as a powerful tool for the identification of biomarkers in SLE and other autoimmune diseases. Proteomic microarray has the capacity to hold large number of self-antigens on a solid surface and serve as a high-throughput screening method for the determination of autoantibody specificities. The autoantigen arrays carrying a wide variety of self-antigens, such as cell nuclear components （nucleic acids and associated proteins）, cytoplas- mic proteins, phospholipid proteins, cell matrix proteins, mucosal/secreted proteins, glomeruli, and other tissue-specific proteins, have been used for screening of autoantibody specificities associated with different manifestations of SLE. Arrays containing synthetic peptides and molecular modified proteins are also being utilized for identification of autoantibodies targeting to special antigenic epi- topes. Different isotypes of autoantibodies, including IgG, IgM, IgA, and IgE, as well as other Ig subtypes, can be detected simultaneously with multi-color labeled secondary antibodies. Serum and plasma are the most common biologic materials for autoantibody detection, but other body fluids such as cerebrospinal fluid, synovial fluid, and saliva can also be a source of autoantibody detection.

Identification and Profiling of Known and Novel Fiber MicroRNAs during the Secondary Wall Thickening Stage in Cotton(Gossypium hirsutum) via High-Throughput Sequencing

Upland cotton （Gossypium hirsutum L.） is an allotetraploid species originated from interspecific hybridization between AA-genome diploid （G. arboretum） and DD-genome diploid （G. raimondii） （Wendel et al., 1992）. Cotton fibers are single-celled trichomes that emerge from the ovule epidermal cells. Indexed by the number of days post-anthesis （dpa）, fiber morphogenesis includes four distinct but overlapping steps： initiation （0-3 dpa）, elongation （3-20 dpa）, secondary cell wall thickening （15-45 dpa） and maturation （40-60 dpa） （Yang et al., 2008, Du et al., 2013）. The efficiency and duration of each morphogenesis stage is important to the quality attributes of the mature fiber. Cell elongation is critical for fiber length, whereas secondary cell wall thickening is important for fiber fineness and strength （Meinert and Delmer, 1977）.

肝豆状核变性儿童临床表型与ATP7B基因突变谱变化研究

目的总结肝豆状核变性(WD)儿童临床表型特征及其ATP7B基因突变谱变化。方法2020年1月~2023年1月我院收治的WD患儿62例,其中肝病组41例(临床型11例和亚临床型30例)和神经病组21例(临床型6例和亚临床型15例),采用直接测序法检测外周静脉血ATP7B基因突变谱,登录ATP7B突变数据库,比对突变结果。采用火焰原子吸收光谱法检测24 h尿铜和血清铜蓝蛋白。结果肝病组起病年龄、角膜K-F环发生率、血清谷丙转氨酶和24 h尿铜水平分别为(6.1±2.5)岁、17.0%、(149.6±51.3)U/L和(157.0±25.7)μg,与神经病组【分别为(9.6±2.9)岁、76.1%、(67.1±11.0)U/L和(272.2±30.8)μg】比,差异均具有统计学意义(P<0.05);临床型肝病组起病年龄、角膜K-F环、血清胆汁酸、谷丙转氨酶和24 h尿铜水平分别为(6.8±1.9)岁、54.5%、(158.5±23.6)μmol/L、(279.6±17.5)U/L和(196.6±62.8)μg,与亚临床型肝病组【分别为(5.1±2.3)岁、3.3%、(16.1±4.1)μmol/L、(90.5±12.1)U/L和(118.1±41.0)μg】比,差异均具有统计学意义(P<0.05),临床型神经病组角膜K-F环、血清胆汁酸、谷丙转氨酶和24 h尿铜水平分别为100.0%、(26.8±5.8)μmol/L、(96.7±10.1)U/L和(376.5±48.9)μg,与亚临床型神经病组【分别为66.7%、(14.4±3.2)μmol/L、(48.5±5.2)U/L和(214.7±55.4)μg】比,差异均具有统计学意义(P<0.05);在ATP7B基因突变谱中,c.2333G>T(Arg778Leu)见于41个等位基因,等位频率占比为33.1%,其次为c.2975G>T(Pro992Leu),见于10个等位基因,等位频率占比为8.1%,再次为c.2621C>T(Ala874Val)、c.1708-5t>g(Ala874Val)和c.994G>T(Glu332stop),等位频率占比分别为4.8%、3.2%和3.2%;检测到错义突变34种,插入突变15种,剪切突变8种,无义突变5种;复合杂合突变45例,杂合突变7例,纯合突变10例;肝病组与神经病组前5致病变异基因(p.Arg778Le、p.Pro992Leu、p.Ala874Val、IVS4-5:t>g和p.Glu332stop)突变频率比较无统计学差异(P>0.05)。结论WD儿童以肝脏受累多见,排名前5位的致病变异有Arg778Leu、Pro992Leu、Ala874Val、Ala874Val和Glu332stop,因此为诊断可以优先选择检测这些热点基因,但基因检测可能无法预测临床表型。

2D profiling of tumor chemotactic and molecular phenotype at single cell resolution using a SERS-microfluidic chip

Emerging single-cell technologies create new opportunities for unraveling tumor heterogeneity.However,the development of high-content phenotyping platform is still at its infancy.Here,we develop a microfluidic chip for two-dimensional(2D)profiling of tumor chemotactic and molecular features at single cell resolution.Individual cells were captured by the triangular micropillar arrays in the cell-loading channel,facilitating downstream single-cell analysis.For 2D phenotyping,the chemotactic properties of tumor cells were visualized through cellular migratory behavior in microchannels,while their protein expression was profiled with multiplex surface enhanced Raman scattering(SERS)nanovectors,in which Raman reporter-embedded gold@silver core-shell nanoparticles(Au@Ag REPs)were modified with DNA aptamers targeting cellular surface proteins.As a proof of concept,breast cancer cells with diverse phenotypes were tested on the chip,demonstrating the capability of this platform for simultaneous chemotactic and molecular analysis.The chip is expected to provide a powerful tool for investigating tumor heterogeneity and promoting clinical precision medicine.

病毒性心肌炎中髓源树突状细胞的数量和表型功能变化

目的观察病毒性心肌炎(VMC)中髓源树突状细胞(DCs)的数量和表型功能变化。方法从小鼠骨髓中分离、诱导、培养DCs,应用流式细胞仪(FCM)检测细胞中DCs的标志CD11c-PE和共刺激分子CD40-FITC、CD80-FITC的表达变化;应用免疫组化鉴定心肌组织中DCs的标志S-100和CD40、CD80的表达变化。结果和正常组相比,从VMC组小鼠的骨髓中分离培养的DCs在形态学上更显成熟;FCM检测发现表达CD11c的细胞明显增多(P＜0．01),同时表达CD40或CD80的双阳性细胞的平均荧光强度增强(P＜0．05);VMC组心肌组织中S-100的表达量增加(P＜0．01)并且出现CD40和CD80的阳性表达。结论VMC中骨髓动员生成DCs增加,心肌组织中DCs数量增多, DCs的成熟度提高。

食管鳞癌恶性表型相关蛋白的蛋白质组学研究

目的:利用蛋白质组学技术探讨与人食管鳞癌细胞(esophageal squamous cell carcinoma,ESCC)恶性表型转化相关的差异表达的蛋白质谱。方法:采用二维双向电泳(two-dimensional electrophoresis,2-DE)和基质辅助激光解吸电离飞行时间质谱(matrix assisted laser desorption ionisation time-of-flight mass spectrometry,MALDI-TOF-MS)法鉴定人食管上皮永生化细胞株NECA-E6E7-hTERT和ESCC细胞株EC1、EC18、EC109差异表达的蛋白质分子,采用Western印迹法和免疫细胞化学法验证annexinA2在人食管上皮永生化细胞和ESCC细胞中的差异表达,实时荧光定量PCR(real-time fluorogentic quantitative PCR,RFQ-PCR)分析annexin A2 mRNA的表达水平。结果:鉴定出5倍以上差异表达的蛋白质分子15个,其中3个蛋白质在ESCC细胞中表达下调,12个蛋白质表达上调;Western印迹法和免疫细胞化学法验证了ESCC细胞中annexin A2蛋白的表达低于人食管上皮永生化细胞;而annexin A2 mRNA表达与其蛋白表达模式不一致。结论:本研究鉴定出的差异表达蛋白质谱为建立食管癌高发区高危人群筛查和早期诊断的分子指标和生物预防提供了重要线索。annexin A2翻译后调控可能是导致ESCC中annexin A2蛋白表达下调的主要原因。

汉朝以前百岁老人的表型谱及其长寿相关因素分析

目的分析百岁老人表型谱的类型及其影响寿限的环境。方法对220 A.D.以前2 000+a的在3本史书上记载的百岁以上老人的相关资料进行归类列表分析。结果百岁老人的长寿表型谱,分别具有1或3类不等;长寿与职业(含无论修道与否)无关;适度的并与其表型谱相适应的长寿相关环境因素有:营养、适量地运动、音乐、天人相应适应自然节律、气功、心理修养、药物、针灸、性卫生、吸入外源长寿蛋白等。结论根据遗传学表型的表达式可导出长寿线性数学模型:P’寿限(长寿)表型=G’(长寿遗传背景)+E’(环境经历)+G’E’(与遗传背景相或不相适应的措施的相互作用)+ε’(失误及误差)。

癌基因组学研究进展

癌是一种基因组疾病,其主要的特征是基因组不稳定性。癌基因组不稳定性表现为单核苷酸突变、微卫星不稳定性、基因结构和拷贝数改变、染色体杂合性和纯合性丢失以及表基因组效应等。癌基因组不稳定性来源于胚系突变和体细胞突变。搞清基因组不稳定性与癌发生发展的关系对于癌的诊断、治疗、预防和药物研制具有重要意义。本文介绍癌基因组学的几个主要研究领域,包括癌基因组不稳定性、癌易感基因的筛查与鉴定以及基因表达谱与临床表型的关系等。

Crop Phenomics and High-Throughput Phenotyping:Past Decades,Current Challenges,and Future Perspectives

Since whole-genome sequencing of many crops has been achieved,crop functional genomics studies have stepped into the big-data and high-throughput era.However,acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies.Nevertheless,recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years.In this article,we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades.We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies.Finally,we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap.It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

High-throughput Phenotyping and Genomic Selection:The Frontiers of Crop Breeding Converge

Genomic selection （GS） and high-throughput phenotyping have recently been captivating the interest of the crop breeding com- munity from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and compa- rable to genomic selection. Despite the fact that the two method- ological approaches represent the extremes of what is understood as the breeding process （phenotype versus genome）, they both consider the targeted traits （e.g. grain yield, growth, phenology, plant adaptation to stress） as a black box instead of dissectingthem as a set of secondary traits （i.e. physiological） putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy （NIRS） and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.

Crop 3D a LiDAR based platform for 3D high-throughput crop phenotyping

With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis.As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging(LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional(3 D) data accurately,and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China,we developed a high-throughput crop phenotyping platform, named Crop 3 D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3 D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs,functions and testing results of the Crop 3 D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.