Integrating Multi-Omics Data for Plant Stress Response: Current Advances and Future Directions

Amita Kajrolkar
Freelance writer, Mumbai, India
Correspondence to: emmydixit@gmail.com

DOI: https://doi.org/10.70389/PJPB.100012

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Amita Kajrolkar – Conceptualization, Writing – original draft, review and editing
• Guarantor: Amita Kajrolkar
• Provenance and peer-review:
  Commissioned and externally peer-reviewed
• Data availability statement: N/a

Keywords: Multi-omics integration, Plant stress response, Genomics and transcriptomics, Proteomics and metabolomics, Data integration strategies.

Peer Review
Received: 13 October 2024
Revised: 21 January 2025
Accepted: 26 January 2025
Published: 5 February 2025

Introduction

Plants, being immobile organisms, are perpetually exposed to many environmental stresses like drought, salinity, extreme temperatures, heavy metals, and pathogen attacks.¹ These abiotic and biotic stresses significantly impact plant growth, development, and productivity. They are the major threat to global food security, particularly when the world is torn in pieces by climate change and population growth.² Plants are quite adept to arm-wrestle these conditions and make the necessary changes in normal plants via a finely tuned immune system, which includes the operation of an armamentarium of defense weapons against various environmental stress factors and the establishment of microhabitats characterized by an increase in the density of positively selected plant microorganisms at the expense of more pathogenic or avirulent members.³

Plant stress responses need the enlightenment of the entire network system, which consequently leads to the development of stress-tolerant crops and the evolution of sustainable agricultural practices. However, the complexity of these responses, ranging from molecules to networks of regulatory interactions, poses a major challenge to researchers.⁴ Conventional reductionist approaches focusing on individual genes or pathways have been crucial in acquiring significant user insight; however, these methods often fail to see the fuller picture of plant stress responses.⁵ Exploding the use of omics techniques in the research of biological systems has extended the boundaries of the scope and detail of our analysis of the studied substances.⁶ These innovations make it possible to assess molecular components in parallel, from which one can get an overall picture of what happens in cells. In plant stress research, multi-omics tools have become indispensable in identifying the complex interactions among different molecular layers involved in stress perception, singling, and response.⁷

Multi-Omics Technologies in Plant Stress Research

Genomics

Genomics is a crucial method for elucidating the genetic foundations of plant stress tolerance through the comprehensive analysis of its complete gene set and its interactions.⁸ Recently, the advent of next-generation sequencing (NGS) technologies allows plant genomes to be sequenced rapidly and cost-effectively to uncover genes and regulatory elements responsive to stresses.⁹ Genome-wide association studies (GWASs) have developed into an effective strategy to detect specific genetic variants associated with stress tolerance traits.¹⁰ GWAS: A few examples of genetic polymorphisms from QTL studies and candidate stress-response genes by associating the polymorphism with phenotypic differences among a wide range of plant populations, several hundred QTLs have been identified in GWAS;¹¹ these are mainly putative stress-response candidate genes. A GWAS in rice detected multiple drought tolerance loci, many of which were associated with abscisic acid (ABA) signaling and osmotic regulation genes.¹²

The recent advances in long-read sequencing technologies like Oxford Nanopore and PacBio have provided us with more accurate genome assemblies and less error-prone detection of structural variants behind stress tolerance.¹³ Pan genomic analyses integrated with structural and copy number variations contributing to germplasm stress adaptation mechanisms have been revealed across diverse germplasm.¹⁴ However, comparative genomics has also provided valuable insights into the evolution of stress tolerance mechanisms.¹⁵ By comparison of genomes between stress-tolerant species and stress-sensitive species, conserved and species-specific genes and pathways have been highlighted for their role in the stress response.⁹ For example, based on a comparative genomic study of halophytes and glycophytes, some specific features in the genome, including gene families related to salt tolerance, were highlighted to have an important role in their adaptation.¹⁶

Plant stress research has increasingly focused on epigenomics, as this field deals with heritable changes in gene expression without any change in DNA sequence.¹⁷ Bisulfite sequencing and chromatin immunoprecipitation sequencing (ChIP-seq) have demonstrated that DNA methylation, histone modifications, and chromatin structure are closely associated with stress-responsive gene expression.¹⁸ They are the masterminds of stress memory and multigenerational stress tolerance in plants.¹⁹

Transcriptomics

The study of the entire pool or transcribed RNA of an organism under certain conditions is known as transcriptomics. It has been extensively used for years to investigate plant response mechanisms in front of stress.²⁰ A revolutionary conversion in transcriptomics is the advent of RNA sequencing (RNA-seq) technology, which permits the quantitative appraisal of gene expression levels to distinguish novel transcripts and alternative merging events.²¹ Transcriptomic analyses have shown “global transcriptional reprogramming” in response to many abiotic and biotic stresses.²² Studies have shown the potential to define stress-perception, -signaling, and -adaptation related genes, transcription factors (TFs), and regulatory networks essential for abiotic stress responses in plants.²³ Transcriptome profiling of drought-stressed maize plants, for instance, reported up-regulation of genes involved in ABA biosynthesis, osmolyte production, and antioxidant defense.²⁴ The emerging technology of single-cell RNA sequencing (scRNA-seq) provides a powerful tool for studying stress responses at the cell level.²⁵ This approach allows the study of gene expression in individual cells, offering a complementary view of the plant tissue showing different diversity of stress responses.²⁶ For example, scRNA-seq of Arabidopsis roots under salt stress demonstrated cell type-specific transcriptional patterns corresponding to distinct mechanisms in response to stress.²⁵

The ability of spatial transcriptomics to elucidate tissue-specific stress response has emerged as a powerful tool. Recent studies using this technology have mapped spatial patterns of stress-responsive genes across different root zones and leaf tissues, showing detailed insights into how plants couple localized and systemic stress responses.²⁵ Moreover, plenty of studies have revealed that the expression of long non-coding RNAs (lncRNAs) and small RNAs also contribute to the plant stress process.²⁷ Genome-wide transcriptomic analysis revealed the stress-responsive lncRNAs and small RNAs, including microRNAs (miRNAs) and small interfering RNAs (siRNAs), representing important modulators of gene expression and stress-responsive signaling pathways.²⁸ Other important regulators of drought and salt tolerance, such as stress-responsive transcription factors and miRNAs, have also been shown to be the target gene.²⁹

Proteomics

Proteomics provides the functional counterpart to genomics and transcriptomics in large-scale studies of proteins.³⁰ mass spectrometry (MS)-based techniques have allowed for the efficient identification, quantification, and functional properties of proteins that participate in plant stress responses.³¹ Proteomic changes in protein abundance,³² post-translational modifications (PTM),³³ and protein-protein interactions have been reported upon treatment of various stresses.³⁴ These studies have identified important proteins related to stress signaling, osmolyte synthesis, antioxidant defense, and cellular homeostasis in general.³² For example, some proteins assayed by proteomics in wheat exposed to drought stress were related to the processes of photosynthesis, energy metabolism, and stress protection among those differently expressed at various levels under water deficit conditions.³³ Advanced proteomics techniques have enabled more comprehensive quantification of protein dynamics during stress responses such as data-independent acquisition MS.³⁵ “Lastly, integrated thermal proteome profiling has been used to understand how temperature modulates protein stability and interactions under stress.”³⁶

Protein function and signaling pathways are modulated by the PTM of proteins in stress responses.³⁴ Phosphoproteomics, which systematically analyzes the phosphorylation of proteins by stress, has recognized universal stress-incited phosphorylation events.³⁷ These phosphorylation cascades exert fast signal transduction and mediate the activation of stress response mechanisms.³⁸ This mini-review will discuss the advancements and use of plant proteomics techniques to analyze nitrogen stress-related PTM sub-proteomes. The arrow indicates that one type of process can impact other types, like ubiquitination, which is essential for performing sumoylation (as trash cans are needed to clean up parking lots to keep them clear). Stress signaling and response are defined by PPI interaction networks.³⁹ Plant science has used mapping stress-responsive PPI network techniques like yeast two-hybrid screening, bimolecular fluorescence complementation, and Co-immunoprecipitation coupled with MS. These studies have revealed finely woven links between disparate signaling elements and determined pivotal control points within stress response pathways.⁴⁰

Metabolomics

Metabolomics, the study of small molecule metabolites, provides a snapshot of the biochemical state of the plant under stress.⁴¹ Metabolites, as the end products of cellular processes, play a crucial role in stress tolerance by being osmolytes, antioxidants, and signaling molecules.⁴² The primary analytical platforms used in plant metabolomics are MS and nuclear magnetic resonance (NMR) spectroscopy.⁴³ These can identify and quantify various metabolites, including primary metabolites (e.g., sugars, amino acids, organic acids) and secondary metabolites (e.g., flavonoids, alkaloids, terpenoids).⁴⁴ More recently, ion mobility MS has been advanced to improve the detection and structural characterization of stress-related metabolites.⁴⁵ Spatial metabolomics has been applied to discover tissue-specific metabolic fingerprints in stress responses that reveal new insight into local adaptation mechanisms.⁴⁶

Metabolomic studies have shown significant changes in metabolite profiles under various abiotic and biotic stresses.⁴⁷ These studies have identified key metabolites and metabolic pathways in stress tolerance.²⁴ For example, metabolomic analysis of drought-stressed rice plants showed accumulation of osmoprotectants like proline, trehalose, and raffinose and changes in signaling molecules like ABA and jasmonic acid.⁴⁸ Flux analysis, which tracks the dynamic flow of metabolites through biochemical pathways, has given us critical insights into the metabolic reprogramming that occurs during stress responses.⁴⁹ Using stable isotope labeling and metabolic flux analysis techniques, researchers have shown the switching of carbon and nitrogen metabolism under stress conditions.⁵⁰

Other Emerging Omics Approaches

Phenomics, the high-throughput study of plant phenotypes, is crucial in stress research.⁵¹ Advanced imaging techniques like hyperspectral, thermal, and 3D scanning allow non-invasive, real-time monitoring of plant physiological and morphological responses to stress.⁵² When combined with other omics data, phenomic methods bridge the gap between genotype and phenotype in stress tolerance studies.⁵³ Ionomics, the study of elemental composition of organisms, has given us a lot of insight into plant mineral nutrition and stress responses.⁵⁴ High-throughput techniques like inductively coupled plasma MS allow us to measure multiple elements in plant tissues at the same time.⁵⁵ Ionomic studies have shown us ion homeostasis and transport mechanisms under salt and heavy metal stress.⁵⁶

This concept, known as harmonics, the analysis of all the plant hormones, is becoming an important key to characterizing stress-signaling networks.⁵⁷ Modern techniques such as ultra-high-performance liquid chromatography-tandem MS enable the measurement of various hormones and their metabolites at one go.⁵⁸ Endocrine analyses have provided complex information about the interactions among hormonal networks in modulating stress responses in plants.⁵⁹ Epigenomics was previously a subtopic of genomics, but it is now considered more than that in plant stress.⁶⁰ Benchtop approaches like ChIP-seq, assay for Transposase-Accessible Chromatin using sequencing, and Hi-C (High-throughput chromosome conformation capture) are giving valuable information on chromatin structure, DNA accessibility, and 3D genome organization in response to stress, respectively (Table 1).⁶¹

Table 1: Omics technologies comparison.
Omics Technology	Focus & Data Types	Key Applications	Integration Strategies	Technical Challenges	Data Integration Challenges
Genomics	• DNA sequence analysis • SNPs, CNVs • Structural variants • Whole genome sequences	• Genetic variation studies • Disease risk assessment • Evolutionary studies	• Network analysis • Machine learning • Multi-omics correlation	• Large data storage needs • Complex variant calling • Repetitive sequences • Quality control issues	• Platform differences • Data normalization • Missing data handling • Computational resources
Transcriptomics	• RNA expression analysis • Gene expression levels • Alternative splicing • Non-coding RNAs	• Gene regulation studies • Cellular response analysis • Disease mechanisms	• Expression correlation • Pathway integration • Time-series analysis	• RNA sample degradation • Splice variant detection • Batch effects • Normalization issues	• Temporal resolution • RNA stability differences • Cross-platform integration • Biological variance
Proteomics	• Protein abundance • Post-translational modifications • Protein interactions	• Protein function analysis • Pathway mapping • Biomarker discovery	• Protein-protein networks • Pathway mapping • Multi-omics integration	• Limited dynamic range • Identification accuracy • Sample complexity • Quantification issues	• Different molecular timescales • Missing proteins • Data completeness • Technical variability
Metabolomics	• Small molecule analysis • Metabolite concentrations • Metabolic flux	• Metabolic pathways • Disease biomarkers • Drug response studies	• Pathway integration • Correlation networks • Flux analysis	• Metabolite identification • Sample stability • Chemical diversity • Method standardization	• Rapid turnover rates • Sample matching • Data normalization • Technical reproducibility

Data Integration and Analysis

Thus, the true potential for multi-omics approaches is found in combining data generated at different molecular levels, thus providing a systems biology perspective of plant stress responses. Nevertheless, integrating these various omics types of data presents certain difficulties because they are heterogenic, vary in size, and interact in complex ways (figure 1).⁶²

Data Integration Strategies

Several strategies have been developed to integrate multi-omics data in plant stress research:

1. Sequential integration: This approach treats each omic separately, and the result is combined to generate biological conclusions.⁶³ However, it can hide important effects associated with the superposition of molecular layers.
2. Pathway-based integration: Here, omics data is aligned on known biological pathways, and network analysis is used to find coherent changes observed at multiple molecular levels.⁶⁴ As stated by the author, MapMan and PathVisio are the frequent software used for pathway-based integration of plant omics data.⁶⁵
3. Network-based integration: This approach builds connected modules based on the interactions of different omics platforms such as gene regulatory, PPI, and metabolic networks.⁶⁶ It is thus possible to use the approach to recognize special regulatory nodes and clusters specific for stress responses.
4. Machine learning and statistical approaches: Multiple algorithms and statistical techniques integrate multi-omics data sources.⁶⁷ Those include partial least squares regression, canonical correlation, and multi-omics factor analysis.⁶⁸
5. Bayesian approaches: It has a high potential of integrating such heterogeneous omics data since it assumes probabilistic relations between molecular layers using Bayesian methods.⁶⁹ These approaches manage well with uncertainty and noise characteristic of biological data.

Indeed, the emergence of deep learning approaches, particularly graph neural networks, has proved to be powerful ways to integrate multi-omics data to predict stress responses.⁷⁰ They can capture the complex non-linear relationships between different molecular layers spanning years of data and significantly enhance prediction accuracies of stress tolerance traits (Table 2).

Table 2: Common integration strategies across all omics.
Integration Type	Methods	Applications	Challenges
Vertical Integration	• Network analysis • Statistical correlation • Machine learning • Pathway mapping	• Multi-omics signatures • Systems biology • Drug response prediction	• Data scaling differences • Missing data • Temporal mismatches • Computational complexity
Horizontal Integration	• Meta-analysis • Batch correction • Cross-platform normalization	• Population studies • Disease classification • Biomarker validation	• Batch effects • Study heterogeneity • Platform differences • Data standardization

Computational Tools and Resources

Numerous computational tools and resources have been established to facilitate multi-omics data integration and analysis in plant research:

1. Databases: Using combined databases like PlantGDB, Phytozome, and TAIR, abundant information regarding plant genomics and transcriptomics is available, in addition to functional annotations.⁷¹ Some of these stress-specific databases include PlantstressDB and StressDB, which contain comprehensive information about stress-responsive genes as well as pathways.⁷²
2. Visualization tools: Indeed, Cytoscape, Gephi, and ggplot2 are essential to represent extensive multi-omics data and networks.⁷³ These tools are useful in facilitating the interpretation of assessment findings and enhancing the communication of integrated analysis results.
3. Workflow management systems: Some interfaces that are easy to use when programming for multi-omics data analysis procedures include the Galaxy, Taverna, and Nextflow. These systems encourage reproducibility and also facilitate the sharing of analysis pipelines.
4. Machine learning frameworks: Different machine learning algorithms are available from scikit-learn, TensorFlow, and Keras for multi-omics data integration and predictive modeling.⁷⁴
5. Web-based platforms: There are several available web-based tools for omics data analysis, which allow working within one integrated environment: XCMS Online, MetaboAnalyst, and KBase come with a clear interface intended for users with low coding experience.⁷⁵

Challenges in Data Integration

Despite advancements in data integration strategies and tools, several challenges persist in the analysis of multi-omics data for plant stress research:

1. Data heterogeneity: Despite the vast differences in scale, distribution, and noise characteristics, direct integration of different types of omics datasets is challenging.⁷⁶
2. Temporal and spatial resolution: The integration of data acquired at different temporal and spatial scales is challenging due to the differences in times and spatial scales introduced by omics technologies .⁷⁷
3. Biological complexity: Due to the complexity and nonlinearity of biological systems, their interactions and dependencies are hard to model in integrated ways.⁷⁸
4. Computational complexity: Integrating and analyzing large-scale omics datasets demands much compute capacity and is beyond the scope of many researchers.⁷⁹
5. Lack of standardization: Nine of them mentioned that formats of data vary or are of differing types; eight said that protocols are experimental, and seven stated that the reporting standard interferes with data integration attempts.⁸⁰
6. Biological interpretation: Another major issue for integrated analysis is the ease of contextualizing findings into biological frameworks, which often involves substantial manual curation and understanding of context-specific biological processes.⁶⁴

Biological Insights from Multi-Omics Integration

The use of multi-omics data has provided novel Scheme 5 insights into the regulation of plant stress response. Below, we highlight some key biological discoveries and their implications for crop improvement and stress tolerance:

Stress Signaling Networks

Efforts utilizing various omics methodologies have elucidated the integrated and composite stress signaling processes in plants.⁸¹ The combination of transcriptomics, proteomics, and metabolomics datasets has provided evidence about the hierarchical structure of signaling pathways, from stress recognition to subsequent events.⁸² For example, a systematic study of Arabidopsis salt stress response showed effective transporters, osmoprotectant biosynthesis enzymes, and antioxidant systems of different omics levels. This study defined a group of principal regulators containing transcriptional factors and protein kinases involved in the salt stress response network (figure 2). In another multi-omics study on drought stress in rice, genomics, transcriptomics, and metabolomics data showed that ABA played a significant role in controlling the drought stress response through ABA-dependent and ABA-independent feedback regulatory mechanisms.⁸³ Such an integrated approach revealed new drought-responsive genes and metabolites that the single-omics analysis had not detected. More recently, phase separation has been shown to play a role in organizing stress granules and stress response.⁸⁴ Metabolite Protein Interactions were integrated with proteomics and metabolomics data to relate how these metabolite protein interactions contribute to stabilizing and destabilizing these biomolecular condensates under stress conditions.

Metabolic Reprogramming

The integration of transcriptomics, proteomics, and metabolomics data has provided a systematic view of the reprogramming of metabolism in plant stress responses.⁸⁵ Such studies have demonstrated how plants reprogram their metabolism to correct energy and metabolic imbalance, maintain cellular homeostasis, and produce stress protectants. For instance, the study on cold stress in Arabidopsis using integrative multi-omics analysis of the transcriptome, proteome, and metabolome was useful in delineating coordinated regulation of carbohydrate and lipid metabolism.⁸⁶ This study revealed a genetic module, post-translational regulation, and metabolic control, all playing a role in response to freezing injuries. In another study, comparing proteomics and metabolomics data from drought-stressed maize plants and identifying changes in plants’ metabolic processes showed that plants change at a metabolic level to meet energy demands under drought-stress conditions, specifically towards the catabolism of amino acids and organic acids.⁸⁷ This metabolic rewiring was associated with changes in the levels of some enzymes and metabolites implicated in these processes.

Epigenetic Regulation

Based on multi-omics analysis, it was evident that epigenetic regulation plays a significant role in stress responses in plants.⁸⁸ Recently, combining epigenomic data with RNA-Seq and MS protein profiles, the interconnections between chromatin variations and DNA methylome, gene expression, and protein levels during stress acclimatization have been identified. A single systematic study in rice that integrated ChIP-seq, RNA-seq, and proteomics data did show how histone modifications regulate the drought response of genes.⁸⁹ This combined approach highlighted specific histone marks characteristic of stress gene activation or repression and offered an important epigenetic understanding of drought tolerance. nAnother multi-omics study, by integrating DNA methylomes, transcriptomes, and metabolomes in salt-stressed Arabidopsis plants, showed that epigenetic regulation networks interact with metabolic remodeling.⁹⁰ This study also identified methylation-sensitive factors implicated in controlling enzymes related to osmolyte synthesis and cellular ion balance.

Stress Memory and Priming

Research using multiple omics techniques has yielded significant insights into the mechanisms underlying SM and P, which are processes through which plants exhibit increased stress resistance after experiencing prior stress.¹⁹ These adaptive responses have been explained by the merging of epigenomic, transcriptomic, and metabolomic data. Even in species such as Arabidopsis, ChIP-seq, RNA-seq, and metabolomic analysis were performed in a recent study to investigate the molecular reprogramming that occurs during heat stress memory.⁹¹ This multi-omics approach used the following molecular markers as memory-related genes that displayed long-lasting histone modifications, changes in the levels of histone expression during thermotolerance, and metabolic changes for improved thermotolerance. The second integrated study in this review compared the transcriptome, proteome, and metabolome data of primed and non-primed maize plants under drought stress conditions. It highlighted the importance of metabolic priming in tolerance to drought.⁹² From this analysis, diet-derived metabolites and enzymes support primed states and serve as targets for crop improvement.

Genotype-Environment Interactions

Understanding how genotype/environment interactions operate in plant stress responses has been enhanced by integrating genomic, transcriptomics, and phenomic data about plants. These multi-omics approaches show that genetic variation controls stress adaptation in different environments with great benefits that can be applied to improving crop plants. To study large datasets on rice, the scientists used GWAS together with transcriptomics and high-throughput phenotyping aimed at mechanisms of drought tolerance.⁹³ In addition, by performing this multi-omics integration approach, this study successfully newly mapped QTLs and candidate genes associated with drought-adaptive traits, demonstrating the potential of this strategic approach for the analysis of complex traits. Yet another multi-omics study combined genomic, transcriptomic, and metabolomic data, but in maize-inbred lines, researchers found that the response of this crop to nitrogen stress is rather genotype-dependent.94 Breeding activities were enhanced by this analysis, as they exposed the genetic factors and metabolic pathways involved in nitrogen use efficiency.

Applications in Crop Improvement

The insights obtained from multi-omics integration hold substantial implications for crop improvement and the creation of stress-tolerant varieties. Here, we outline several key applications and their potential impact on sustainable agriculture:

Marker-Assisted Selection and Genomic Selection

Multiple omics have contributed hugely to identifying biomarkers associated with stress tolerance traits.95 Indeed, through a combination of genomic, transcriptomic, and metabolomic data, the scientific community has identified robust signifiers that capture the terse nature of stress responses. For example, a study using multiple omics approach included genome, transcriptome, and metabolome data to identify heat and drought tolerance in wheat.⁹⁶ This comprehensive approach made it possible to develop SNP markers for the marker-assisted selection of climate-resilient wheat varieties. Furthermore, different GS models have been improved by incorporating multi-omic data.⁹⁷ Combined with data for other omics layers, such as transcriptomes, metabolomes, and genomes, the latter has augmented the precision of genomic prediction equations for multiple stress tolerance traits.⁹⁸

Gene Discovery and Functional Characterization

Recently, multi-omics integration has greatly enhanced the identification and functional dissection of stress-related genes in crop plants.⁹⁹ New gene functions and stress tolerance factors have been revealed by integrating genomic, transcriptomic, and proteomic data. For example, a recent, large-scale investigation of tomato integrated genomic, transcriptomic, and metabolomic profiles to identify candidate genes underlying drought and salt tolerance.¹⁰⁰ Unlike previous studies, this multi-omics approach identified other novel stress-protectant transcription factors and metabolic enzymes. Furthermore, the functional characterization of candidate genes is improved using omics approaches, including multi-omics. One study on rice used transcriptomics, proteomics, and metabolomics to understand the function of a specific transcription factor involved in stress response.²³ This analysis pointed out the targets and metabolic pathways regulated by the transcription factor and shed considerable light on its role in stress tolerance.

Metabolic Engineering

Information obtained from multi-omics has considerably impacted the metabolic engineering paradigms to improve stress tolerance in crop plants.¹ Researchers have identified relevant metabolic pathways and enzymes for genetic editing through transcriptomics, proteomics, and metabolomics. For example, in soybean plants exposed to drought stress using the multi-omics approach, the particular metabolic enzymes that play a role in osmoprotectant synthesis were identified.¹⁰¹ Based on these studies, further research was carried out to over-express these enzymes in soybean plants, and the performance showed increased drought tolerance and yield stability under water deficit conditions. In another study, pls-SEC analysis of salt stress response in rice plants revealed that the polyamine metabolism pathway plays a pivotal role in stress tolerance.¹⁰² Transgenic rice lines with altered polyamine biosynthesis ability were produced due to this discovery, which led to high salt tolerance and increased grain yield under saline conditions.

Precision Agriculture and Phenotyping

Multi-omics techniques have significantly enhanced the progress of precision agriculture methods and fast phenotyping technologies.¹⁰³ They have combined Genomic, Transcriptomic, and Phenom data to develop an index that evaluates crop function under different environments. In a large-scale study on maize, the information derived from the genomics, transcriptomics, and phenotyping at a high throughput were used to establish the models for yield under water deficit.⁹³ These models use genetics and physiological traits, making predicting crops under water-stressed environments easier. Moreover, multi-omics data integration has enhanced the analysis of phenotyping data, allowing the mapping of observable traits to molecular mechanisms to be enhanced.⁵² For instance, wheat research involved using transcriptomics, metabolomics, and high-throughput imaging where molecular markers for heat tolerance were established.¹⁰⁴ This approach enabled the creation of high-throughput phenotyping techniques for heat-tolerant wheat varieties and molecular markers to reach the same end (figure 3).

Future Directions and Challenges

As multi-omics approaches continue to advance our understanding of plant stress responses, several key areas emerge as important directions for future research:

Single-Cell and Spatial Omics

Single-cell and spatial omics advances bring new possibilities to elucidate plant stress tolerance mechanisms in May/June.¹⁰⁵ Such strategies allow for describing the cell type-specific response and visualizing the spatial patterns of molecular events in plant tissues. Stress response mechanisms in cells other than hepatocytes and stress response heterogeneity should be the research focus in the future based on single-cell transcriptome, proteome, and metabolome studies.¹⁰⁶ Furthermore, the effort made using spatial transcriptomics and imaging-based proteomics methods will provide crucial information about the stress-responsive genes and protein distribution in various parts of the plant.¹⁰⁷ This field is poised for a revolution: multi-modal single-cell technologies, capable of profiling transcriptomes, proteomes, and metabolomes at the cellular scale, have recently emerged to fundamentally improve our understanding of cell type-specific responses to stress.¹⁰⁸

Temporal Dynamics and Stress Memory

Understanding the time-dependent nature of stress reactions and the molecular mechanisms of stress signaling is difficult.¹⁹ Subsequent multi-omics study designs should incorporate time-course examination to dissect the changes occurring at different molecular system layers during stress, rest, and other exposures. Furthermore, future studies combining epigenomic profiles with other omics data will play a crucial role in defining the processes governing stress memory and/or stress/cardiovascular disease transgenerational adaptation.⁸⁸ Large-scale research projects that integrate omics platforms with multi-generation experiments will provide the basis for understanding the inter-generational continuity and resilience of the stress adaptive phenotypes.

Multi-Stress Interactions

As discussed earlier, it is common that plants in both natural and agricultural environments are exposed to a multiplicity of stresses, either individual stresses acting all at once or individually yet cyclical.¹⁰⁹ Next-generation multi-omics should capture the complex interplay between stress signaling and the iso- and heterotypic trade-offs that occur during multiple stress transitions. The assessment of data collected from experiments involving combinatorial stress treatments will be essential to developing plants with broad stress tolerance.¹¹⁰ This is, however, possible through the development of higher-order computations to describe the non-linear crosstalk among the different stress signaling pathways and physiological processes in plants.

Microbiome Interactions

Microbiota at the plant roots also play a critical role in trials to adapt plant stress responses and their health.111 Subsequent multi-omics investigations of stress conditions of plants and their microbiomes should consider analyses of metagenomic and meta-transcriptomic data and metaproteomic data. Researchers can unravel the interactions between plants and beneficial microorganisms using omics data from plants and microbiomes. Such information could be used to construct microbial-informed approaches to improve crop stress resistance.¹¹²

Data Integration and Interpretation Challenges

As the volume and complexity of multi-omics data grow, developing robust methods for data integration and biological interpretation remains a significant challenge.⁷⁸ Future research should focus on:

1. Advanced machine learning techniques: Innovating new machine learning algorithms, including deep learning and graph neural networks, to integrate heterogeneous omics data and extract valuable biological insights.¹¹³
2. Causal inference methodologies: Utilizing causal inference methods to go beyond mere correlations and uncover causal relationships among various molecular layers within stress response networks.¹¹⁴
3. Knowledge-driven integration: Leveraging existing biological knowledge and curated databases in multi-omics analysis workflows to improve the interpretability and biological significance of the findings.⁶²
4. Standardization and data sharing initiatives: Creating community-wide standards for collecting, analyzing, and reporting multi-omics data to enhance integration and reproducibility across different studies.⁸⁰
5. User-friendly software solutions: Developing accessible software tools and platforms that empower plant biologists with limited bioinformatics experience to conduct integrative multi-omics analyses (figure 4).¹¹⁵

Conclusion

Overall, combinatorial multi-omics data analysis has qualitatively shifted the knowledge of how plants accommodate stresses by fine-tuning all aspects of stress perception and signaling. Integrating data from other platforms, including genomics, transcriptomics, proteomics, metabolomics, and other emerging omics methods, has helped researchers discover new genes, stress tolerance pathways, and regulatory networks. The combined analysis of these multi-omics data provides significant potential for optimizing crop traits and improving climate-smart agriculture, especially under changing climate conditions. Due to improved omics approaches, enhanced breeding and management principles are seen, including marker- assisted selection, metabolic engineering, precision agricultural practices, and high-throughput phenotyping (Table 3).

Table 3: Best practices for integration.
Aspect	Key Considerations
Data Preparation	• Quality control • Standardized preprocessing • Batch correction • Proper normalization
Analysis Methods	• Multi-step integration • Biological knowledge incorporation • Statistical validation • Interpretable modeling
Documentation	• Detailed metadata • Standard formats • Analysis documentation • Data availability

However, several limitations remain on exploiting the multi-omics approach to its full potential for studying plant stress. Single-cell and spatial omics technologies, temporal and multi-stress responses, challenging data integration, and interpretation techniques will be vital areas for future development in this field. Considering the continuously progressing climate change and the problem of food security, the further development and implementation of an integrated multi-omics approach will become essential for improving the ability to create stress-tolerant crops and sustain agriculture. Thus, by implementing these extensive approaches to investigating plant stress responses, we can contribute to constructing a more sustainable and efficient world of agriculture.

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Cite this article as:
Kajrolkar A. Integrating Multi-Omics Data for Plant Stress Response: Current Advances and Future Directions. Premier Journal of Plant Biology 2025;3:100012

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