Exosome Multi-Omics Cargo Profiling
OverviewServicesSamplesAdvantagesApplicationsCase StudyFAQs
Overview
At Creative BioMart Microbe, we provide comprehensive multi-omics cargo profiling services purpose-built for microbial extracellular vesicles (mEVs), including bacterial outer membrane vesicles (OMVs), membrane vesicles (MVs) from Gram-positive bacteria, fungal EVs, and phage-induced membrane vesicles. Our platform integrates small RNA sequencing (sRNA-seq), quantitative proteomics, untargeted metabolomics, and lipidomics into a single, standardized analytical workflow that delivers deep, systems-level characterization of vesicle cargo composition and functional potential.
Unlike generic multi-omics CROs that apply mammalian exosome protocols to microbial samples, we have optimized every library preparation, mass spectrometry parameter, and bioinformatics pipeline for the unique RNA, protein, metabolite, and lipid landscapes of mEVs. From purified vesicle suspension to publication-ready report, clients receive comprehensive cargo atlases, pathway enrichment maps, and functional annotation datasets that support mechanism-of-action studies, intellectual property claims, and regulatory submissions for therapeutic, vaccine, and cosmetic applications. Contact us to discuss your specific omics platform and cargo profiling objectives.
Figure 1. Schematic overview of the integrated multi-omics cargo profiling platform for microbial extracellular vesicles, spanning sRNA-seq transcriptomics, quantitative proteomics, untargeted metabolomics, lipidomics, and systems-level data integration.
Services
Service Workflow
Service Details
Small RNA Sequencing (sRNA-seq) & Bioinformatics
We profile the small RNA cargo of mEVs using next-generation sequencing (NGS) with libraries prepared from total RNA extracted from purified vesicle suspensions. Our sRNA-seq pipeline captures small non-coding RNAs (sncRNAs), including small regulatory RNAs (sRNAs), tRNA fragments, rRNA fragments, and other regulatory RNAs carried within bacterial, fungal, and phage-induced membrane vesicles. Bioinformatics analysis includes adapter trimming, quality filtering, read mapping against reference genomes, differential expression analysis, and functional prediction of cargo RNAs. This service is essential for understanding how mEVs mediate intercellular and inter-species communication through RNA transfer.
Proteomic Profiling (Label-free & TMT)
We characterize the complete protein cargo of mEVs using label-free quantitative proteomics and tandem mass tag (TMT) multiplexing by nanoLC-MS/MS. Our proteomics pipeline identifies and quantifies membrane proteins, secreted virulence factors, metabolic enzymes, and stress-response proteins encapsulated within vesicles. For Gram-negative OMVs, we specifically profile outer membrane proteins, periplasmic cargo, and LPS-associated proteins. For fungal EVs, we identify cell-wall remodeling enzymes and virulence-associated secreted proteins. Deliverables include protein identification lists, quantitative abundance tables, Gene Ontology (GO) enrichment, and KEGG pathway analysis.
Metabolomic Profiling (Untargeted & Targeted)
We profile the metabolite cargo of mEVs using untargeted LC-MS/MS and GC-MS to detect and identify hundreds of polar and non-polar small molecules, including amino acids, organic acids, nucleotides, cofactors, and secondary metabolites. For targeted analysis, we offer absolute quantification of specific metabolite classes (e.g., short-chain fatty acids, quorum-sensing molecules, siderophores) using LC-MS/MS with reference standards and CAS assay for total siderophore activity, as needed. Metabolomic cargo profiling reveals the metabolic state of the parent microorganism and identifies bioactive molecules that mediate host-pathogen or host-probiotic interactions. This service is particularly valuable for functional mechanism studies and bioactive compound discovery.
Lipidomic Profiling (LC-MS/MS)
We characterize the lipid composition of mEV membranes and cargo using shotgun lipidomics and LC-MS/MS with high-resolution mass spectrometry. Our lipidomic platform identifies phospholipids (phosphatidylethanolamine, phosphatidylglycerol, cardiolipin), sphingolipids, sterols, and fatty acid species across bacterial, fungal, and phage-induced membrane vesicles. For Gram-negative OMVs, we specifically profile lipid-A structural variants including acylation, phosphorylation, and glycosylation patterns, which directly influence endotoxicity and immunogenicity. For fungal EVs, we quantify representative lipid classes including ergosterol, glycosphingolipids, phosphatidylinositol, and ceramides. Results include lipid class abundance, fatty acid chain length and saturation profiles, and membrane biogenesis pathway annotations.
Multi-Omics Data Integration & Functional Annotation
Individual omics datasets are consolidated into an integrated systems biology analysis using proprietary bioinformatics pipelines. Cross-omics correlation analysis links protein cargo to metabolic pathways, lipid composition to membrane biogenesis, and RNA content to regulatory networks. We perform pathway enrichment analysis (KEGG, Reactome), protein-metabolite interaction mapping, and multi-omics factor analysis (MOFA) to identify latent patterns across omics layers, which can be correlated with functional phenotypes through follow-up validation assays. Deliverables include interactive pathway visualizations, network graphs, correlation matrices, and a comprehensive cargo atlas report that connects molecular composition to predicted biological function.
Service Specifications & QC Standards
Instrumentation & Capability
- sRNA-seq: Illumina NextSeq 2000 or equivalent, small RNA library prep with gel-free protocols, minimum 1 ng total RNA input.
- Proteomics: nanoLC-MS/MS (Orbitrap Exploris 480 or Fusion Lumos) with DDA, DIA, or TMT10/TMT16 multiplexing.
- Metabolomics: UHPLC-QTOF (Agilent 6546 or equivalent) for untargeted; triple quadrupole (SCIEX 7500) for targeted quantification.
- Lipidomics: LC-MS (Q Exactive Focus or equivalent) with HILIC and reversed-phase separation; LipidMaps database annotation.
- Bioinformatics: MaxQuant/Perseus (proteomics), XCMS/METLIN (metabolomics), LipidSearch (lipidomics), in-house RNA-seq pipelines.
- Sample input per omics platform: 10–100 µg total protein equivalent (vesicle lysate) for MS-based assays; 1–100 ng total RNA for sRNA-seq.
Typical Data Range
- sRNA-seq: 1–20 million reads per sample; detection of 100–5,000+ unique small RNA species.
- Label-free proteomics: 500–3,000+ protein identifications per sample; quantification CV ≤ 20%.
- TMT multiplexing: up to 16-plex; 3,000–5,000+ protein identifications per multiplex.
- Untargeted metabolomics: 200–1,000+ metabolite features detected; 100–400 putatively identified.
- Targeted metabolomics: absolute quantification of 50–200 metabolites with calibration curves.
- Lipidomics: 100–500+ lipid species identified; 10–20 lipid classes annotated.
Turnaround Time
| Project Type |
Timeline |
| sRNA-seq only |
14–21 business days |
| Proteomics only (label-free) |
14–21 business days |
| Metabolomics only (untargeted) |
10–14 business days |
| Lipidomics only |
10–14 business days |
| Dual omics (any two platforms) |
21–28 business days |
| Complete multi-omics (all four platforms) |
35–45 business days |
| Expedited analysis |
+50% fee, 50% time reduction |
Timeline may vary based on sample type, volume, and bioinformatics complexity.
Deliverables
- sRNA-seq: Raw FASTQ files, QC report, read alignment BAM files, differential expression tables, volcano plots, GO enrichment.
- Proteomics: Protein identification list, quantitative abundance matrix, GO/KEGG enrichment, pathway maps, MS raw files (mzML).
- Metabolomics: Feature table, metabolite identification report, pathway enrichment, heatmaps, calibration curves (targeted).
- Lipidomics: Lipid species list, class abundance table, fatty acid profile, lipid-A variant report (for OMVs).
- Integrated analysis: Cross-omics correlation matrix, MOFA results, network visualization, functional annotation report.
- Comprehensive cargo atlas: PDF report with all omics summaries, interactive HTML visualizations, raw data archives.
Quality Control
- sRNA-seq: small RNA quality score ≥ 7.0 (Fragment Analyzer); library concentration ≥ 2 nM; Q30 ≥ 85%.
- Proteomics: FDR ≤ 1% at peptide and protein level; minimum 2 unique peptides per protein; BSA spike-in QC for sample recovery assessment, supplemented with iRT peptides for retention time stability monitoring.
- Metabolomics: Pooled QC samples analyzed every 10 injections; RSD ≤ 20% for retention time; internal standard recovery 80–120%.
- Lipidomics: Deuterated internal standards for each lipid class; matrix effect evaluation < 20%.
- Cross-platform QC: Correlation of housekeeping protein/metabolite levels across replicates ≥ 0.9 (Pearson).
- Compliance checklist for minimal characterization requirements aligned with industry guidelines for extracellular vesicle studies.
- Optional GxP-aligned assay validation and CQA trending analysis for lot-release documentation.
Sample Requirements
| Required Information |
Optional Information |
Not Accepted |
- Sample type (OMVs, CMVs, fungal EVs, phage vesicles)
- Purified vesicle suspension
- Approximate total protein or particle concentration
- Sample volume (minimum 200 µL for dual omics)
- Buffer composition and pH
- Species/strain identification
- Storage conditions and shipping temperature
|
- Prior purification method and yield data
- Target application (research, IP, regulatory)
- Reference genome or proteome availability
- Specific pathway or metabolite class of interest
- Reference batch for comparative analysis
|
- Samples in organic solvents or strong detergents (>0.1% SDS)
- Samples with visible precipitation or aggregation
- Samples without proper cold-chain documentation
- Intact bacterial/fungal cell cultures (must be clarified)
- Contaminated or mixed samples
- Samples shipped at room temperature
|
Recommended Sample Quantity by Omics Platform:
| Omics Platform |
Minimum Input |
Recommended Input |
| sRNA-seq |
1 ng total RNA |
10–100 ng total RNA |
| Proteomics (label-free) |
10 µg protein |
50–100 µg protein |
| Proteomics (TMT) |
20 µg protein per channel |
50–100 µg protein per channel |
| Metabolomics (untargeted) |
10 µg protein equivalent |
50–100 µg protein equivalent |
| Lipidomics |
10 µg protein equivalent |
50–100 µg protein equivalent |
| Complete multi-omics |
200 µL vesicle suspension |
500 µL–1 mL vesicle suspension |
Storage & Shipping: Ship purified vesicle suspensions on dry ice (–80°C) with cold-chain documentation. Avoid repeated freeze-thaw cycles. Recommended buffer: sterile PBS or ammonium bicarbonate buffer (for MS-based assays). For sRNA-seq, ship in RNA stabilization buffer or TRIzol. Provide sample manifest with estimated protein concentration and particle count if available.
Our Advantages
- Microbial EV-Optimized Omics Pipelines: Our library prep protocols, MS parameters, and bioinformatics databases are optimized for microbial vesicles. We use bacterial, fungal, and archaeal reference genomes; account for cell-wall debris interference; and apply lipid-A-specific fragmentation rules that mammalian exosome workflows ignore.
- True Multi-Omics Integration: Unlike CROs that outsource individual omics to different labs, we perform all analyses under one roof with harmonized sample preparation, cross-platform QC standards, and unified data normalization. This eliminates batch effects and ensures biological consistency across RNA, protein, metabolite, and lipid datasets.
- Function-Linked Cargo Annotation: Every cargo atlas includes functional prediction through KEGG/Reactome pathway mapping, GO enrichment, and protein-metabolite interaction networks. We connect molecular composition to biological function, enabling mechanism-of-action hypotheses and target identification.
- Flexible Platform Configuration: Clients can order individual omics platforms, dual-omics combinations, or complete four-ome profiling. Custom bioinformatics analysis (differential cargo analysis, time-course profiling, strain comparison) is available to match specific research objectives.
- Regulatory-Ready Data Packages: Raw data (FASTQ, mzML), processed outputs, bioinformatics code, and method validation summaries are delivered in formats compatible with CMC documentation, patent filings, and peer-reviewed publication requirements.
Applications
OMV Vaccine Cargo Atlas & CQA Documentation
Comprehensive cargo profiling establishes molecular identity and consistency of OMV vaccine lots, supporting CMC and lot-release documentation.
Therapeutic mEV Cargo Optimization
Multi-omics cargo data identifies optimal drug loading candidates and predicts functional outcomes for engineered mEV nanocarriers.
Probiotic mEV Bioactive Discovery
Metabolomic and proteomic cargo profiling identifies immunomodulatory and barrier-enhancing molecules from GRAS probiotic-derived vesicles.
Cosmetic mEV Active Ingredient Discovery
Lipidomic and metabolomic profiling reveals antioxidant, anti-inflammatory, and barrier-repair cargo for cosmetic vesicle actives.
Case Study
Case Study: Proteomic and Metabolomic Profiling of Extracellular Vesicles Produced by Human Gut Archaea
Human gut methanogenic archaea, including Methanobrevibacter smithii and Methanosphaera stadtmanae, produce extracellular vesicles (EVs) that mediate host-microbe interactions, yet their molecular cargo remained uncharacterized. Researchers isolated EVs from four strains and applied parallel proteomic and metabolomic profiling. Proteomic analysis identified 1,475 vesicular proteins, with adhesin-like proteins (ALPs) strikingly enriched in EVs (20%) versus membrane fractions (4%). Metabolomic profiling revealed significantly elevated glutamic acid, aspartic acid, choline glycerophosphate, arginine, salicylic acid, and oxalic acid. Functional assays demonstrated species-specific uptake by macrophages and induction of CXCL9, CXCL11, CX3CL1, and IL-8, establishing the first comprehensive cargo atlas for human gut archaeal EVs.
Figure 2. Heatmap showing enrichment of 46 adhesin-like proteins in archaeal extracellular vesicles compared to whole-cell lysates. (Weinberger, et al. 2025).
FAQs
Q: What is the difference between single-omics and multi-omics cargo profiling?
A: Single-omics profiling characterizes one molecular class (e.g., proteins only). Multi-omics integrates data from multiple platforms (RNA, protein, metabolite, lipid) to generate a systems-level view of vesicle cargo. Multi-omics reveals cross-molecular relationships (e.g., which metabolic pathways are reflected in both protein enzyme and metabolite levels) that single-omics cannot detect.
Q: How much vesicle sample do I need for complete multi-omics profiling?
A: For the complete four-ome package (sRNA-seq + proteomics + metabolomics + lipidomics), we recommend 500 µL to 1 mL of purified vesicle suspension at a concentration of ≥0.5 mg/mL total protein equivalent. This allows for technical replicates, method optimization, and reserve material for confirmation assays. Contact us for sample-specific recommendations.
Q: Can you analyze vesicles from non-model microorganisms?
A: Yes. For non-model species without complete genome annotation, we perform de novo transcriptome assembly and protein identification using homology-based searches against related species. Metabolomics and lipidomics are species-agnostic and do not require reference genomes. We recommend providing taxonomic information to optimize database selection. Note that Gene Ontology (GO) and KEGG pathway annotations may have limited coverage for non-model microorganisms; in such cases, we supplement with COG, eggNOG, and InterPro domain annotations to ensure comprehensive functional characterization.
Q: What bioinformatics outputs will I receive?
A: You will receive raw data files (FASTQ, mzML), processed data tables (differential expression, protein abundance, metabolite features), quality control reports, pathway enrichment results (KEGG, GO, Reactome), correlation matrices, and interactive visualization files. For integrated multi-omics, we also provide MOFA results, network graphs, and a comprehensive cargo atlas PDF.
Q: How does multi-omics cargo profiling support patent applications?
A: Comprehensive cargo atlases provide molecular evidence of vesicle uniqueness, identifying novel protein, RNA, metabolite, or lipid signatures that can be claimed as inventions. Quantitative abundance data and comparative analysis against competitor products strengthen patent claims for therapeutic compositions, diagnostic markers, and manufacturing methods.
Q: Can I compare cargo profiles between different strains or growth conditions?
A: Yes. We offer comparative multi-omics analysis that profiles vesicle cargo across multiple strains, fermentation batches, or growth conditions (planktonic vs. biofilm, stress-induced vs. normal). Statistical analysis includes principal component analysis (PCA), differential cargo analysis, and pathway enrichment comparison.
Q: What is MOFA, and how is it used in multi-omics integration?
A: Multi-Omics Factor Analysis (MOFA) is a computational framework that decomposes heterogeneous omics datasets into shared latent factors, identifying molecular drivers that co-vary across RNA, protein, metabolite, and lipid layers. MOFA reveals hidden biological patterns (e.g., stress response, virulence activation) that are not apparent in individual omics datasets.
Q: Is your multi-omics service compatible with GxP requirements?
A: Our standard service is research-grade (R&D) with validated methods and full QC documentation. GxP-aligned analysis (IQ/OQ/PQ instrument qualification, method validation per ICH Q2(R1), audit trails, electronic signatures, and 21 CFR Part 11-compliant data management) is available as a custom service. Contact us to discuss your regulatory pathway requirements.
References:
- Weinberger, V., et al. (2025). Proteomic and metabolomic profiling of extracellular vesicles produced by human gut archaea. Nature Communications, 16, 4024.
