Plant Omics Panorama: Workflow Frameworks and Multi-Omics Integration from Genomics to Transcriptomics, Proteomics, and Metabolomics

Omics systematically characterizes, in a high-throughput manner, the molecular composition and dynamics of biological systems across DNA, RNA, proteins, and metabolites, giving rise to core branches including genomics, transcriptomics, proteomics, and metabolomics. In plant research, omics is not only used to decode genetic information and regulatory programs, but also directly supports trait dissection, elucidation of stress-resistance mechanisms, discovery of quality formation rules, and breeding improvement. With advances in high-throughput sequencing and mass spectrometry platforms, multi-omics strategies further enable cross-layer association and systems-level modeling, allowing the causal chain of “gene–transcript–protein–metabolite–phenotype” to be constructed and validated with finer resolution.

Keywords: plant omics; genomics; transcriptomics; proteomics; metabolomics; multi-omics integration; phenomics; epigenomics

I. Overview: Hierarchical Framework and Technical Features of Plant Omics Research

1.1 Research objects and hierarchical relationships in omics

(1) The core goal of omics is to obtain, on a global scale, the composition, structure, and dynamic information of molecular layers, and to establish testable associations with biological functions and trait outcomes.

(2) The classical biological information flow can be summarized as DNA → RNA → protein → metabolite, corresponding to genomics, transcriptomics, proteomics, and metabolomics. In plant systems, this chain is strongly shaped by organellar genomes, tissue differentiation, and environmental responses.

(3) In study design, single-omics is suitable for defining differences and candidate molecules within one layer; multi-omics is suitable for cross-layer explanation, network reconstruction, and mechanistic attribution, especially for complex traits, metabolic pathways, and stress-regulatory problems.

1.2 Shared challenges in plant systems

(1) Complex genome architecture

① Genome size varies dramatically across plant species.

② High repeat content, active transposons, and abundant structural variation are common.

③ Polyploidy and gene duplication are widespread, driving expansion and functional divergence of homologous gene families.

(2) Strong sample heterogeneity

① Diverse tissues and cell types, with substantial differences across developmental stages.

② Environmental factors (light, temperature, water status, salinity, pathogens) strongly affect expression and metabolism, increasing batch-effect risks.

(3) Uneven annotation and database coverage

① Non-model plants often lack adequate functional annotation, limiting cross-species comparison and pathway interpretation.

② Secondary metabolites are numerous and structurally diverse; limited standards and incomplete spectral libraries make metabolite identification difficult.

II. Genomics: Organization of Genetic Information, Annotation, and Breeding Applications

2.1 Definition and major tasks of genomics

(1) Genomics focuses on the organization of genes and regulatory elements in the genome, the spectrum of sequence and structural variation, and duplication events, and evaluates their impacts on traits and adaptation.

(2) Plant genome studies typically target two objectives:

① Build high-quality reference genomes and annotation systems for mechanism studies and comparative genomics.

② Establish genetic mapping and molecular marker systems for molecular breeding and gene cloning.

2.2 Standard workflow and key checkpoints

(1) Genome sequencing and assembly

① Data generation: improve continuity using short-read/long-read complementarity or long-read–dominant strategies, and combine chromosome conformation capture–type data to achieve chromosome-level assemblies.

② Assembly evaluation: emphasize continuity metrics, completeness, error rates, and contamination assessment.

(2) Repeat identification and masking

① Because repeats are abundant in plant genomes, repeat library construction, annotation, and masking are required to reduce false positives in downstream structural annotation.

② Masking strategies must balance reduced repeat interference against the risk of masking true genic sequences.

(3) Structural and functional annotation

① Structural annotation: integrate ab initio prediction, homology evidence, and transcript evidence to define exon–intron structures, start/stop codons, and alternative splicing events.

② Functional annotation: compare predicted proteins against curated databases; infer functions using domains, Gene Ontology, and pathway databases; perform focused annotation for transcription factors, resistance genes, and metabolic enzyme families.

(4) Genetic maps and physical maps

① Genetic maps use molecular markers to represent linkage relationships and recombination distances, supporting QTL mapping and breeding selection.

② Physical maps use base-pair coordinates to represent genomic positions, supporting fine mapping and gene cloning.

2.3 Typical applications

(1) Molecular breeding and genetic improvement

① Marker-assisted selection and genomic selection.

② Mapping loci related to disease resistance, stress tolerance, and quality traits.

(2) Comparative genomics and evolutionary analysis

① Reconstruct gene family expansion/contraction, synteny, and polyploidy events.

② Identify signals of species divergence and adaptive evolution.

III. Transcriptomics: Expression Profiles, Splicing, and Regulatory Network Inference

3.1 Definition and data forms in transcriptomics

(1) Transcriptomics uses RNA as the target to characterize transcript composition and abundance changes under specific tissues, cell types, or treatments. Key readouts include gene expression levels, transcript structures, and alternative splicing.

(2) In plant research, transcriptomics is commonly used for:

① Building spatiotemporal expression atlases across development.

② Dissecting stress responses and immune signaling pathways.

③ Screening candidate enzymes and regulatory factors for secondary metabolic pathways.

3.2 Standard workflow and key quality control

(1) Experimental design

① Define biological replication and batch-control strategies; prioritize interpretable comparison frameworks such as treatment–control–time gradients or tissue gradients.

② Standardize sampling location, developmental stage, and treatment timing to reduce expression noise.

(2) Sequencing and assembly strategies

① Reference-based analysis: when a high-quality reference genome is available, alignment and quantification improve mapping accuracy and cross-sample comparability.

② De novo assembly: used when no reference exists or reference quality is inadequate; pay attention to redundancy, chimeras, and quantification biases.

(3) Functional annotation and differential analysis

① Differential expression should include multiple-testing correction, and filtering should consider effect size, baseline expression, and tissue specificity.

② At network and pathway levels, enrichment analysis, co-expression modules, and regulatory network inference can build intermediate evidence chains linking candidate genes, modules, and phenotypes.

(4) Common extended analyses

① Alternative splicing and isoform analysis.

② Expression quantitative trait loci (eQTL) and associations with regulatory elements.

③ Transcript-level evidence for SNPs and editing events.

IV. Metabolomics: Chemical Phenotypes, Pathway Reconstruction, and Physiological Interpretation

4.1 Targets and methodological features of metabolomics

(1) Metabolomics studies the composition and abundance of small-molecule metabolites, directly reflecting pathway states and physiological phenotypes and serving as a key bridge between genotype and phenotype.

(2) Unlike nucleic-acid omics, metabolomics lacks a unified reference sequence; identification depends on spectral matching, standards, and structural inference. Therefore, qualitative accuracy and cross-batch comparability are major methodological constraints.

4.2 Standard workflow and key control points

(1) Sample preparation and extraction

① Define target metabolite classes (polar/non-polar; volatile/non-volatile) and select appropriate solvent systems and quenching strategies.

② Strictly control sampling time, tissue location, and storage conditions to reduce bias from rapid metabolite turnover.

(2) Data acquisition and interpretation

① NMR is suitable for structure elucidation and scenarios requiring high quantitative stability, but has relatively limited sensitivity.

② MS-based platforms offer high sensitivity and broad coverage for discovering novel metabolites and differential biomarkers, but require careful handling of ion suppression, retention time drift, and batch correction.

(3) Data processing and identification

① After peak extraction, alignment, normalization, and batch-effect correction, screen differential metabolites and perform pathway enrichment.

② Clearly distinguish identification levels: library-based putative annotation versus authentic standard confirmation; keep evidence levels consistent in conclusions.

4.3 Typical applications

(1) Quality trait dissection: flavor, color, nutrition, and medicinal compound profiles.

(2) Stress resistance and immunity: osmolytes, antioxidant metabolism, and defense-related secondary metabolic pathways.

(3) Metabolic pathway engineering: identify rate-limiting steps and branch points to guide metabolic modification and breeding selection.

V. Proteomics: Quantification, Modification, and Interactions at the Functional Execution Layer

5.1 Research targets and advantages of proteomics

(1) Proteomics directly profiles the functional execution layer, compensating for interpretability gaps caused by discordance between RNA abundance and protein abundance.

(2) Plant proteomics is commonly used to analyze:

① Key nodes in signaling pathways and protein complexes.

② Organelle- or cellular compartment–specific protein profiles.

③ Rapid regulation mediated by post-translational modifications.

5.2 Standard workflow and key checkpoints

(1) Sample preparation and fractionation

① Choose total protein, subcellular fractionation, or membrane protein enrichment strategies according to the research goal.

② Reduce complexity via gel-based or liquid-phase separation to provide controlled inputs for MS identification and quantification.

(2) Digestion and MS identification

① Proteins are digested into peptides and analyzed by MS; database searching enables protein identification.

② Quantification can be label-based or label-free; focus on peptide detectability differences and batch drift.

(3) Post-translational modifications and functional interpretation

① PTM omics typically requires modification-specific enrichment and site-level statistics linked to pathways.

② Protein interaction and complex studies can be combined with interactomics strategies to strengthen mechanistic attribution.

VI. Multi-Omics Integration: System-Level Interpretation from Correlation to Causality

6.1 Core logic of multi-omics integration

(1) The central value of multi-omics lies in cross-layer linkage: genetic variation affects transcriptional regulation; transcription affects protein abundance and modification states; proteins and pathways jointly shape metabolite profiles; the result maps to phenotypes.

(2) Integration goals typically include:

① Prioritizing candidate genes.

② Reconstructing pathways and networks.

③ Identifying key regulatory nodes for mechanistic validation.

6.2 Typical integration levels and strategies

(1) Element-level integration

① Map gene–transcript–protein–metabolite relationships as one-to-one or one-to-many links, suitable for vertical validation of candidate pathways.

(2) Pathway-level integration

① Use metabolic or signaling pathways as scaffolds, overlay expression/protein/metabolite changes, and localize rate-limiting steps, feedback regulation, and branch points.

(3) Model-level integration

① Apply statistical learning or mechanistic models to include multi-omics variables in a unified framework for phenotype prediction, key-factor screening, and intervention strategy inference.

VII. Other Omics Branches: Completing the Regulatory-to-Phenotype Continuum

7.1 Epigenomics

(1) Studies non-sequence regulatory information such as DNA methylation, histone modifications, and chromatin accessibility, explaining why the same genotype can exhibit different expression states across tissues or environments.

(2) In plants, epigenetic regulation is closely associated with developmental plasticity, stress memory, and transposon silencing.

7.2 Phenomics

(1) Uses high-throughput imaging and sensors to systematically measure traits from morphology to physiological parameters, providing high-resolution phenotype inputs for genotype–phenotype association.

(2) When combined with genomic selection, QTL mapping, and multi-omics integration, phenomics can markedly improve trait-dissection efficiency.

7.3 Interactomics

(1) Focuses on interaction networks such as protein–protein and protein–DNA interactions, enabling a shift from molecule lists to mechanistic networks.

(2) Provides direct value for interpreting transcriptional regulation, signal transduction, and complex functions.

VIII. Study Design and Methodological Essentials: Improving Reproducibility and Interpretability

8.1 Design principles

(1) Choose omics layers driven by the biological question: candidate gene localization prioritizes genomics and transcriptomics; mechanistic attribution prioritizes proteomics and PTM omics; phenotype interpretation prioritizes metabolomics and phenomics.

(2) Use a tiered validation strategy: discovery via high-throughput screening; validation via targeted quantification and functional experiments to close the loop.

8.2 Key quality-control points

(1) Batch-effect management: standardize sampling, library preparation, and measurement workflows; correct via randomization and internal reference systems.

(2) Consistent evidence levels: explicitly distinguish inference, support, and confirmation in conclusions, especially for metabolite identification and functional annotation.

(3) Reproducibility safeguards: retain raw data, parameters, and software versioning; establish traceable analysis pipelines.

8.3 Cross-omics sample chemical environment and MS-compatibility control essentials

(1) Solvent and salt system choices directly determine extraction efficiency, ionization efficiency, and background noise, and are foundational variables for multi-omics comparability and transferability.

① For metabolomics/lipidomics, prioritize high-purity, low-background LC–MS–grade solvents; evaluate matrix effects with solvent blanks, process blanks, and spike-in recovery.

② Proteomics samples often require strong denaturation/detergent systems for complete lysis, but most detergents and non-volatile salts severely suppress MS and digestion; matched removal strategies are required (precipitation, solid-phase cleanup, filtration-based strategies).

③ Genomics/transcriptomics benefit from strong denaturing salts and phenolic systems for nucleic-acid protection, but residues inhibit downstream enzymatic reactions and library preparation; thorough purification and washing are mandatory.

(2) Antioxidants and anti-browning components can be valuable in plant sample handling, but may alter true abundances of redox-related metabolites or introduce characteristic background peaks; define their boundaries during method validation.

(3) It is recommended to standardize method records with fixed fields of “reagent–purpose–risk–removal/monitoring measures” to facilitate cross-batch bridging and cross-platform reproducibility.

IX. Application Scenarios: From Mechanism Studies to Molecular Breeding

9.1 Complex trait dissection and candidate gene prioritization

(1) Use genomic variation and QTL/association analysis to locate candidate intervals.

(2) Overlay transcriptomics to identify tissue- and treatment-specific expression and reduce the candidate list.

(3) Validate pathway responses and key-node consistency using proteomics and metabolomics to form high-confidence targets.

9.2 Secondary metabolic pathway discovery and metabolic engineering

(1) Identify differential metabolites and key branch changes via metabolomics.

(2) Screen candidate enzymes and transcription factors via transcriptomics and build co-expression modules.

(3) Localize activity-regulation layers via proteomics and PTM omics to guide targeted engineering.

X. Common Reagent List (for Multi-Omics Sample Preparation and Analysis)

Scenario	Name	CAS No.	Typical use	Key notes
Metabolomics/Lipidomics (quenching/polar extraction)	Methanol	67-56-1	Metabolic quenching; polar metabolite extraction; LC–MS solvent	Use LC–MS grade; hygroscopic—monitor blanks and batch background
Metabolomics (protein precipitation/extraction)	Acetonitrile	75-05-8	Protein precipitation; metabolite extraction; organic phase of LC–MS mobile phase	Highly volatile; assess salt suppression and matrix effects via blanks/spike-in recovery
Transcriptomics/Genomics (nucleic-acid precipitation)	Isopropanol	67-63-0	DNA/RNA precipitation and washing	Co-precipitates salts/polysaccharides more readily; wash and dry adequately to avoid enzyme inhibition
Metabolomics (solubilization of poorly soluble metabolites/standards)	Dimethyl sulfoxide (DMSO)	67-68-5	Solubilize difficult standards/extracts	Keep LC–MS fraction as low as possible; control impurity background and carryover
Proteomics (buffer system)	Tris (Tris Base)	77-86-1	Lysis/resuspension/reaction buffer	Non-volatile salt system not MS-direct; desalting/cleanup required before injection
Proteomics/Transcriptomics (inhibit metal-dependent nucleases/proteases)	Disodium EDTA dihydrate	6381-92-6	Chelate metal ions; inhibit metalloproteases/nucleases	Inhibits metal-dependent enzymes (some digestions/processing steps); remove prior to MS
Metabolomics/Proteomics (volatile buffer, MS-friendly)	Ammonium formate	540-69-2	Volatile LC–MS buffer/ionic strength adjustment	Hygroscopic; high concentrations still suppress ionization—optimize method
Metabolomics (volatile buffer; common in negative ion mode)	Ammonium acetate	631-61-8	Buffer/ionic strength for negative ion mode	Use MS grade; high concentrations reduce ionization efficiency
Lipidomics (MTBE-based extraction)	Methyl tert-butyl ether (MTBE)	1634-04-4	Lipid extraction (often as alternative to chloroform)	Volatile; may form peroxides on storage—monitor storage and blanks
Lipidomics (non-polar elution/enrichment)	n-Hexane	110-54-3	Elution of non-polar lipids; SPE elution solvent	Flammable; background contamination risk—use high-purity/chromatography grade and solvent blanks
Lipidomics (antioxidant protection)	BHT (butylated hydroxytoluene)	128-37-0	Antioxidant in extraction to reduce lipid oxidation	Can introduce characteristic background peaks; include blanks and controls
Plant metabolomics/transcriptomics (anti-browning/oxidation inhibition)	Ascorbic acid (vitamin C)	50-81-7	Suppress polyphenol oxidation; reduce browning/interference	Oxidizes readily—prepare fresh; may affect redox-related metabolite readouts
Plant metabolomics/transcriptomics (anti-browning)	Sodium metabisulfite	7681-57-4	Reducing antioxidant; suppress pigment/polyphenol oxidation	May affect quantification/ionization of some metabolites; validate methodically
Transcriptomics/Genomics (remove polyphenol/polysaccharide interference)	PVP (polyvinylpyrrolidone)	9003-39-8	Adsorb polyphenols; reduce inhibitors	Polymer residues raise MS background/viscosity; remove by centrifugation/filtration
Transcriptomics (phenol/chloroform lysis)	Phenol	108-95-2	Strong denaturing lysis; deproteinization	Corrosive; oxidizes; RNA quality is sensitive—require RNase-free conditions
Transcriptomics (strong denaturant to inhibit RNases)	Guanidinium thiocyanate	593-84-0	Denaturing lysis; RNase inhibition	Strong denaturant inhibits downstream enzymes—must be thoroughly removed
Transcriptomics/Genomics (precipitation salt)	Sodium acetate (anhydrous)	127-09-3	Salt for DNA/RNA precipitation	Non-volatile; wash pellets thoroughly with ethanol to avoid PCR/library inhibition
Transcriptomics (selective precipitation to remove polysaccharides)	Lithium chloride	7447-41-8	Selective RNA precipitation; remove polysaccharide/polyphenol interference	High-salt residues inhibit reactions—wash and optimize re-solubilization
Transcriptomics (remove DNA contamination)	DNase I	9003-98-9	Remove genomic DNA contamination	Inhibited by EDTA; inactivate/cleanup after treatment to avoid library interference
Proteomics (lysis/denaturation solubilization)	Urea	57-13-6	Protein denaturation and solubilization	Carbamylation at high temperature/high pH—avoid long incubations
Proteomics (membrane protein solubilization)	Thiourea	62-56-6	Improve membrane protein solubility with urea	Verify compatibility with digestion/labeling; remove residues appropriately
Proteomics (mild detergent)	CHAPS	75621-03-3	Solubilize membrane proteins; mild lysis	Detergents are generally MS-unfriendly—remove (precipitation/SPE, etc.)
Proteomics (strong detergent lysis)	SDS	151-21-3	Strong lysis; extract membrane proteins	Strongly suppresses MS and digestion—must be removed (FASP/SP3, etc.)
Proteomics (disulfide reduction)	DTT	3483-12-3	Reduce disulfide bonds	Oxidizes readily—prepare fresh; follow with alkylation and control excess
Proteomics (disulfide reduction)	TCEP hydrochloride	51805-45-9	Stable reducing agent; alternative to DTT	Control excess and clean up; check compatibility with labeling workflows
Proteomics (alkylation)	Iodoacetamide (IAA)	144-48-9	Alkylate thiols	Light-sensitive; excess causes side reactions—define quenching/termination
Proteomics (protein precipitation/desalting/detergent removal)	Trichloroacetic acid (TCA)	76-03-9	Protein precipitation; remove salts/detergents	Corrosive; pellets may be hard to resolubilize—optimize resolubilization
Proteomics (MS-friendly digestion buffer)	Ammonium bicarbonate	1066-33-7	Trypsin digestion buffer; volatile MS-friendly salt	Hygroscopic; use fresh to avoid concentration drift
Proteomics (enzymatic digestion)	Trypsin	9002-07-7	Digest proteins into peptides	Sensitive to salt/detergent/pH; control enzyme:substrate ratio and time
Metabolomics/GC–MS (carbonyl oximation)	Methoxyamine hydrochloride	593-56-6	Stabilize carbonyls via oximation	Moisture-sensitive; require anhydrous conditions and derivatization blanks
Metabolomics/GC–MS (silylation derivatization)	MSTFA	24589-78-4	Silylation to increase volatility	Extremely moisture-sensitive; dry samples completely to avoid failure
Metabolomics/GC–MS (silylation derivatization)	BSTFA	25561-30-2	Silylation derivatization	Also moisture-sensitive; monitor background peaks with reagent/solvent blanks
Metabolomics/GC–MS (catalyst/solvent)	Pyridine	110-86-1	Solvent/alkaline catalyst for derivatization	Toxic and strong odor; introduces background—ensure ventilation and blanks
Transcriptomics (cleanup/size selection prior to library construction)	PEG 8000	25322-68-3	PEG/salt-based nucleic-acid size selection/enrichment (common with beads)	PEG residues affect quantification and reactions—wash thoroughly and set controls

The value of plant omics derives not only from high-throughput measurement at a single layer, but also from controllable sample preparation, chemical systems, and quality-control strategies, and from the degree to which evidence chains are closed in cross-layer integration. Incorporating MS compatibility, residue risks, and blank monitoring for key reagent systems into standardized workflows can substantially improve comparability, interpretability, and reproducibility in multi-omics studies.

References

[1] Argueso CT, et al. Directions for research and training in plant omics: Big Questions and Big Data. Plant Direct, 2019.

[2] Crandall SG, et al. A multi-omics approach to solving problems in plant disease ecology. PLoS ONE, 2020.

[3] Fukushima A, et al. Integrated omics approaches in plant systems biology. Current Opinion in Chemical Biology, 2009.

[4] Guo J, et al. Research Progress and Future Development Trends in Medicinal Plant Transcriptomics. Frontiers in Plant Science, 2021.

[5] Hakeem KR, et al. Plant omics: Trends and applications. In Plant Omics: Trends and Applications, 2016.

[6] Houle D, Govindaraju DR, Omholt S. Phenomics: The next challenge. Nature Reviews Genetics, 2010.

[7] Jamil IN, et al. Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology. Frontiers in Plant Science, 2020.

[8] Kalavacharla V, et al. Chapter 16 – Plant Epigenomics. Handbook of Epigenetics, 2017.

[9] Kumar R, et al. Metabolomics for plant improvement: Status and prospects. Frontiers in Plant Science, 2017.

[10] Turumtay H, et al. Plant metabolomics and strategies. In Plant Omics: Trends and Applications, 2016.

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