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Proteomics Service

Plant Proteomics Service

LC-MS/MS-based plant proteomics for plant protein identification, quantitative proteomics, post-translational modification analysis, and protein interaction analysis across diverse tissues and experimental models.

  • Identify proteins in plant tissues, fractions, complexes, gels, or other research samples.
  • Compare proteome changes across genotype, treatment, stress, developmental stage, tissue, or time point.
  • Characterize regulated modification sites or prioritize proteins associated with plant protein complexes.
Protein IdentificationQuantitative ProteomicsPTM AnalysisProtein InteractionPilot Available
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Research experience

Supporting plant proteomics projects for academic and industry research groups

Researchers from leading academic and industry organizations have worked with MtoZ Biolabs on plant proteomics projects. Project scope, workflow, and controls are tailored to each study.

When Plant Proteomics Is a Good Fit

Plant tissues, organs, and subcellular fractions Good fit for leaves, roots, seeds, fruits, stems, flowers, callus, cultured cells, chloroplasts, mitochondria, membranes, or other plant-derived fractions.
Comparative studies across biological conditions Use this service when the goal is to compare genotype, treatment, stress, developmental stage, tissue, time point, or plant–environment response at the protein level.
Regulatory PTM and signaling studies Suitable for phosphorylation, acetylation, ubiquitination-related remnants, glycosylation, and other supported PTM projects linked to signaling, regulation, and plant response mechanisms.
Protein complexes and interaction-focused projects Appropriate when the research goal is to identify proteins associated with a bait, complex, membrane-associated assembly, or condition-dependent regulatory interaction network.
Non-model species and custom sequence resources Best matched to projects using species with incomplete reference proteomes, where transcriptome-derived, custom FASTA, or homology-supported databases are needed.
Challenging plant samples and complex matrices Useful for pigment-rich, phenolic-rich, polysaccharide-rich, seed, woody, low-protein, or other difficult samples that require matrix-aware extraction and cleanup.

Choose the Right Plant Proteomics Workflow

Workflows can be combined or applied sequentially based on the study objective, enabling different proteomics strategies to complement one another within an integrated project design and support a more comprehensive analysis of the biological question.
Protein Identification

Plant Protein Identification

Choose this workflow when LC-MS/MS protein identification for plant tissues, organelles, purified fractions, enriched samples, and excised gel bands. This workflow is suited to determining sample composition, confirming protein identity, or identifying proteins recovered from a selected fraction or gel band.

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Condition Comparison

Plant Quantitative Proteomics

Choose this workflow when DIA, Label-Free, or TMT proteomics for comparing genotypes, treatments, stress responses, tissues, developmental stages, and time courses. It is designed to reveal condition-associated shifts in protein abundance and compare proteome patterns across experimental groups.

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Site-level Profiling

Plant Protein PTM Analysis

Choose this workflow when Phosphorylation, acetylation, ubiquitination-remnant (diGly), glycosylation, and other supported PTMs using enrichment-based, site-level LC-MS/MS. Regulatory-site profiling can clarify how modification patterns vary across conditions and relate to plant signaling, development, or stress responses.

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Interactor Discovery

Plant Protein Interaction Analysis

Choose this workflow when IP-MS, Co-IP-MS, AP-MS, Pull-Down-MS, proximity labeling, or crosslinking-assisted workflows for bait-centered interactor discovery, membrane-proximal profiling, and condition-dependent comparison. For bait-centered studies, this approach helps characterize associated proteins and assess how interaction patterns vary across biological conditions.

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Workflow preview

Option One

Preview description for the selected option.

Use selected in quote form See sample requirements

Service Workflow

Project Review

Confirm study design and samples.

Extraction

Extract proteins and remove contaminants.

Preparation

Digest proteins and prepare samples.

Acquisition

Acquire data by LC-MS/MS.

Data Processing

Identify, quantify, and analyze proteins.

Report

Deliver results, figures, and interpretation.

Project-specific extraction, database strategy, fractionation, and enrichment steps are selected according to tissue composition and study goals.

What to Send Us

Sample Type Recommended Input Note
Soft plant tissue 2 g Soft tissues such as woody plant leaves or flowers, herbaceous plants, algae, ferns, and macrofungi.
Hard plant tissue 5 g Roots, bark, branches, fruits, seeds, and other tough or fibrous tissues.
Pollen 100 mg Submit clean pollen with minimal foreign material.
Limited sample?
Send the amount you have. Final input is reviewed based on sample type, protein enrichment status, workflow, and required detection depth.
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Results & Deliverables

Protein identification
Protein and peptide identification tables with database annotations, sequence evidence, and project-specific confidence metrics.
Quantitative matrix
Normalized protein or peptide abundance matrix for Label-Free, DIA, TMT, or quantitative PTM workflows as applicable.
QC & statistics
Identification and quantification QC, sample correlation, clustering or PCA, missing-value overview, and differential statistics.
Biological annotation
Gene Ontology, pathway, domain, subcellular localization, functional classification, and enrichment analysis based on available resources.
Visualization package
Publication-ready figures such as volcano plots, heatmaps, enrichment charts, clustering plots, and project-specific summaries.
PTM & interaction outputs
Site-level PTM tables, localization and motif information, or background-filtered interactor/proximity candidate tables when included in the project scope.

Platform & Capabilities

Project design & sample preparation

Built for complex plant matrices

Preparation workflows are selected according to tissue composition, protein abundance, expected contaminants, and the downstream quantitative method.

Matrix-aware extraction Tissue disruption and protein extraction strategies for leaves, roots, seeds, fruits, stems, woody tissues, callus, and cultured plant cells.
Contaminant control Cleanup approaches for pigments, phenolics, polysaccharides, lipids, salts, detergents, and other LC-MS/MS-interfering components.
Database strategy Species-specific, custom FASTA, transcriptome-derived, or homology-supported searching for model and non-model plants.
Design support Guidance on groups, controls, replicates, batch balance, fractionation, PTM enrichment, bait selection, affinity capture, and interaction-background controls.
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LC-MS/MS platform

High-resolution proteomics with integrated quality control

Orbitrap Exploris 480 High-resolution peptide identification and quantitative proteomics.
timsTOF Pro Flexible acquisition for complex proteomes, PTM analysis, and interaction-proteomics characterization.
Orbitrap Astral Robust high-resolution acquisition for comparative and DIA workflows.
Nano-LC Separation Peptide separation optimized for sensitivity, reproducibility, and complex plant digests.
Extraction QCDigestion QCRetention-Time QCMS Performance QCReplicate CorrelationTransparent Data Tables

Applications of Plant Proteomics

Plant Stress and Environmental Response Compare proteome changes under drought, heat, salinity, nutrient limitation, osmotic stress, or combined environmental stresses.
Plant Growth and Development Study proteome and regulatory changes across tissues, developmental stages, and physiological transitions.
Genotype, Trait, and Comparative Proteomics Compare cultivars, genotypes, phenotypes, mutants, or breeding materials with contrasting biological characteristics.
Integrated Proteomics and Multi-Omics Studies Coordinate proteomics with metabolomics or other omics approaches when complementary molecular layers are needed to investigate complex plant phenotypes.

FAQs

Label-Free analysis supports flexible exploratory comparisons, DIA prioritizes consistent quantification across multiple samples, and TMT supports multiplexed group comparison. The final choice depends on sample number, group structure, input consistency, desired coverage, and the downstream analysis plan.
Yes. Projects may use a species-specific protein FASTA, transcriptome-derived database, custom sequence set, or a closely related reference when appropriate. Identification and functional annotation depth depend on the completeness and quality of the available sequence resources.
Collect samples consistently across groups, remove visible soil or medium when appropriate, snap-freeze as soon as practical, and store frozen to limit proteolysis and PTM loss. Avoid repeated freeze-thaw cycles and document collection time, treatment duration, and storage history.
Three or more biological replicates per group are commonly recommended for comparative studies, but the appropriate number depends on biological variability, experimental complexity, expected effect size, and the statistical questions being addressed.
Yes. Global proteome data can help distinguish changes in PTM abundance from changes in total protein abundance. Combined projects require sufficient sample input, coordinated group design, and a workflow matched to the PTM of interest.
Yes. LC-MS/MS-based proteomics can be used to characterize proteins in isolated plant extracellular vesicles or exosome-like nanovesicle preparations and, when the study design supports it, compare protein profiles across conditions. Because results are strongly influenced by vesicle isolation, sample purity, protein input, and background contamination, we recommend a feasibility review of the preparation method and available sample before analysis.
Proteomics can reveal protein-level changes, candidate regulators, PTM patterns, and interaction candidates that support a mechanistic hypothesis. Causal conclusions generally require orthogonal validation, genetic or biochemical experiments, and interpretation within the experimental model.
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