Multi-Omics Integration Service — Integrated Metabolomics, Proteomics & Transcriptomics Analysis

What is Multi-Omics Integration and Why It Matters

Biological systems operate through complex, interconnected molecular networks where genes, transcripts, proteins, metabolites, and lipids dynamically interact to regulate cellular function and organismal health. Traditional single-omics approaches, while powerful, capture only a limited view of these intricate networks, often missing the causal relationships and regulatory feedback loops that drive phenotypes. Multi-omics integration addresses this fundamental limitation by combining data from multiple molecular layers — metabolomics, proteomics, transcriptomics, lipidomics, and microbiome profiling — enabling researchers to construct a holistic, systems-level understanding of biology.

Our multi-omics integration service provides end-to-end support from study design and sample processing through integrated bioinformatics analysis and biological interpretation. We employ advanced multi-block data integration algorithms — including DIABLO, MOFA+, and O2PLS — to identify correlated features across omics layers, uncover hidden regulatory mechanisms, and prioritize multi-dimensional biomarker panels. For studies requiring absolute quantitative readouts, our targeted metabolomics service delivers precise, QC-validated concentration data that integrates seamlessly with other omics layers.

A key strength of our multi-omics platform is the ability to combine metabolomics with complementary molecular phenotyping. Untargeted lipidomics captures hundreds of lipid species across major classes, while free fatty acids analysis provides focused fatty acid profiles — both integrable with transcriptomic and proteomic datasets for a complete picture of lipid metabolism.

What Problem Do We Solve?

• Uncover System-Level Mechanisms — Move beyond single-pathway analysis to reveal how metabolic, signaling, and regulatory networks converge in disease. Integrate metabolomics + proteomics + transcriptomics to pinpoint reaction-level dysregulation and resolve conflicting single-omics results through cross-omics correlation across multiple molecular layers simultaneously.
• Accelerate Drug Target Discovery & MoA Validation — Combine target proteomics with metabolomics and transcriptomics to identify on-target vs. off-target effects early. Our dedicated integrative metabolome and proteome analysis provides comprehensive protein-metabolite correlation profiling.
• Enable Biomarker Stratification & Precision Medicine — Use multi-omics integration to define patient subgroups based on metabolic phenotype and transcriptional state. Our untargeted metabolomics service provides broad metabolite coverage for initial biomarker screening.
• Enable Longitudinal Studies & Microbiome Integration — Track host omics changes over time alongside microbiome composition. Our Longitudinal Multi-Omics Analysis service handles multi-timepoint study designs with batch-harmonized workflows.
• Reduce Batch Effects & Ensure Data Quality — Single-sample submission eliminates inter-lab batch effects. All omics layers acquired in parallel with synchronized QC, harmonized normalization, and comprehensive cross-omics concordance checking.

Multi-Omics Integration Workflow — A Step-by-Step Guide

1

Study Design Consultation

Our scientists define omics selection (metabolomics foundational; proteomics adds enzyme function; transcriptomics offers mechanism), sample strategy, integration approach (DIABLO, MOFA+, O2PLS, or network-based), and statistical power requirements for your specific biological question.

2

Standardized Sample Preparation

All samples prepared using harmonized extraction protocols optimized for concurrent multi-omics analysis. Metabolomics: cold organic quenching with internal standards. Proteomics and transcriptomics: respective matrix-specific protocols with rigorous QC at every step.

3

Parallel Analytical Acquisition

Metabolomics (LC-MS/MS, targeted + untargeted), proteomics (DIA/TMT, 3,000–5,000 proteins), transcriptomics (RNA-seq ≥30M reads/sample), and microbiome profiling (16S or shotgun metagenomics) run simultaneously under synchronized QC protocols.

4

Quality Control & Cross-Omics Harmonization

Batch-effect correction (ComBat), cross-omics alignment, outlier detection (PCA), and precision validation (metabolite RSD <15%, protein %CV ≤10%). Harmonized normalization ensures data comparability across all omics layers before integration.

5

Multi-Block Data Integration

DIABLO for supervised multi-class comparison, MOFA+ for unsupervised factor discovery, O2PLS for two-block predictive modeling, or sPLS-DA for variable selection. Method selection tailored to your study design. Explore our specialized mGWAS service for integrating metabolomics with genomic data.

6

Biological Annotation & Pathway Analysis

Integrated feature lists mapped to KEGG, Reactome, and HMDB pathways with enrichment analysis across all omics layers simultaneously. Central carbon metabolism analysis is frequently included as a core pathway component in multi-omics studies.

7

Integrative Visualization & Network Analysis

Cluster heatmaps, correlation circos plots, network diagrams, factor loading plots, and pathway maps with fold-change heat coloring per omics layer generated to communicate integrated results effectively and support publication.

8

Final Report & Expert Consultation

Comprehensive report including methods, quantification matrices, integrated analysis results, visualization suite, and biological interpretation. Delivered with follow-up expert consultation to ensure your team can act on the findings.

Multi-Omics Data Integration Methods at a Glance

Selecting the right integration algorithm is critical for extracting meaningful biological insights. Our platform supports multiple complementary methods, each suited to different study designs and data structures:

Service Scope — Creative Proteomics Multi-Omics Modules

Choose the omics combination that answers your research question from our seven major integration panels:

Why Choose Our Multi-Omics Integration Service?

• Single-Sample Submission, No Vendor Coordination
  All omics from one sample; no need to distribute across labs or reconcile inter-lab batch effects. Our unified platform covers the full spectrum from metabolomics to proteomics and transcriptomics under one project management framework.
• Advanced Integration Algorithms
  Access to DIABLO, MOFA+, O2PLS, sPLS-DA, correlation network analysis, and pathway-level enrichment integration — selected and tuned for your specific data structure and biological question.
• Rigorous Quality Management
  QC-validated workflows with pooled QC samples, internal standards, cross-platform normalization, and comprehensive batch-effect correction across all omics layers for reproducible, publication-ready results.
• Expert Bioinformatics Support In-House
  Dedicated team providing full-service data processing, statistical modeling, network analysis, pathway enrichment, and customized visualization for integrated omics datasets.
• Track Record
  50+ publications citing our multi-omics integration services; established in oncology, cardiology, drug discovery, immunology, and agricultural biotechnology. Our peer-reviewed case studies demonstrate real-world impact across multiple disease areas and biological systems.

Applications of Multi-Omics Integration

Sample Preparation Guide

Notes

• Avoid repeated freeze-thaw cycles
• Clearly label each tube with sample ID, matrix type, and collection date
• Submit sample list (Excel preferred) with matching metadata
• Contact us before sending rare, low-volume, or highly variable matrices

Deliverables: What You Receive from Our Multi-Omics Service

• Executive Summary: Top dysregulated pathways, key metabolite–protein–transcript correlations, mechanistic insights, actionable next steps
• Quantification Tables: Metabolite concentrations (absolute, per analyte), protein abundance (normalized intensity), gene expression (FPKM, log₂FC). Excel + CSV formats with metadata
• Quality Control Report: Calibration curves (R² ≥0.99), intra- and inter-batch precision (%CV ≤15%), batch-effect correction plots, outlier detection results
• Integrated Analysis Report: Pathway enrichment (KEGG, Reactome; FDR-adjusted p-values), network diagrams, co-dysregulation heatmaps, multi-block integration performance metrics
• Visualization Suite: PCA/UMAP projections, correlation Circos plots, factor loading plots, network diagrams, pathway maps with fold-change heat coloring per omics layer
• Methodology & Experimental Details: Full instrument parameters, extraction protocols, data processing pipelines, software versions, and parameter settings for complete experimental transparency and reproducibility
• Raw & Processed Data: Vendor instrument files (.raw, .d), processed peak lists (.mzML, .mgf), open-format matrices for independent analysis

Cross-omics correlation Circos plot displaying significant correlations (|r| > 0.7, FDR p < 0.05) between metabolites, proteins, and transcripts across matched sample sets.

DIABLO multi-block integration results showing sample clustering across omics layers with component 1 vs component 2 score plots colored by experimental groups.

QC reproducibility assessment across all omics layers: PCA scores plot showing tight clustering of pooled QCs with RSD distribution histogram demonstrating >90% features within 15% RSD.

Integrated multi-omics pathway map with KEGG pathway enrichment across omics layers: node color indicates omics origin, node size reflects enrichment significance (FDR-adjusted p-value).

Multi-Omics Case Study: Lipid Droplet-Associated LncRNA LIPTER Preserves Cardiac Lipid Metabolism

Background & Challenge

Obesity and type 2 diabetes impair cardiac fatty acid oxidation, leading to lipid accumulation and cardiomyopathy. The underlying mechanism — how long non-coding RNAs (lncRNAs) regulate lipid droplet trafficking and metabolism in human cardiomyocytes — was unknown. Single-layer transcriptomics would reveal LINC00881 downregulation in diabetic hearts but not the metabolic consequence. A multi-omics integration approach was required to bridge the gap between gene dysregulation and metabolic phenotype.

Experimental Design: Multi-Omics Integration

Han et al. (Nature Cell Biology 2023) integrated four complementary omics layers to investigate LIPTER function:

• Transcriptomics (RNA-seq): Identified cardiomyocyte-enriched lncRNAs; prioritized LINC00881 (renamed LIPTER) as most downregulated in type 2 diabetes hearts
• Untargeted Metabolomics (LC-MS/MS): Profiled lipid droplet composition, free fatty acids, acylcarnitines (C2–C24), phospholipids, and energy metabolites. Detected dysregulated acylcarnitine profiles upon LIPTER knockout
• Targeted Proteomics: Quantified myosin motors (MYH10), lipid droplet-associated proteins (PLIN5, ATGL), and mitochondrial respiratory chain proteins
• Functional Assays: Measured lipid droplet transport by live-cell imaging, fatty acid oxidation capacity, oxygen consumption rate, and ATP production

Key Findings from Multi-Omics Integration

• Transcriptomics: LIPTER downregulated ~5-fold in type 2 diabetes vs. controls
• Metabolomics: LIPTER knockout led to accumulation of long-chain acylcarnitines (C14, C16, C18), reduced acetyl-CoA production, and impaired phospholipid remodeling
• Proteomics: LIPTER directly binds MYH10 (myosin motor protein); knockout reduces myosin-LD association
• Phenotype: Impaired lipid droplet transport from ER to mitochondria, reduced fatty acid oxidation, mitochondrial dysfunction, cardiomyocyte death under metabolic stress

Why This Case Study Matters for Your Multi-Omics Research

This study exemplifies how integrated multi-omics analysis can bridge the gap between transcriptomic discovery and metabolic mechanism — a challenge faced by many research projects across diverse biological systems. Four key takeaways for researchers considering multi-omics integration:

• Transcriptomics identified the target: RNA-seq screening of patient samples pinpointed LINC00881 (LIPTER) as the most dysregulated lncRNA, but could not reveal its functional role — only multi-omics integration could bridge discovery to mechanism.
• Metabolomics provided the functional readout: Untargeted LC-MS/MS profiling detected ~1,000 features across lipid classes, acylcarnitines (C2–C24), and energy metabolites. The accumulation of long-chain acylcarnitines (C14, C16, C18) upon LIPTER knockout directly demonstrated impaired fatty acid transport into mitochondria — a functional phenotype invisible to transcriptomics alone.
• Proteomics identified the molecular mechanism: Targeted proteomics revealed that LIPTER directly binds MYH10 (myosin motor protein), establishing the physical link between lncRNA dysregulation and impaired lipid droplet transport.
• Cross-omics correlation validated the pathway: The concordance between metabolomic (acylcarnitine accumulation), proteomic (MYH10 binding), and phenotypic (impaired FAO, reduced ATP) data provided multi-layered evidence for the LIPTER/MYH10/LD axis — a level of confidence unattainable from any single omics approach.

This integrated strategy can be applied to any biological question where mechanism discovery is the goal — cancer metabolism, immunology, plant stress responses, or microbial host interactions. The same multi-omics workflow used here is accessible through our service, tailored to your specific model system and research question.

Creative Proteomics' Contribution to This Study

Creative Proteomics provided the untargeted metabolomics (LC-MS/MS on Q Exactive MS systems) and targeted acylcarnitine profiling that established the metabolic phenotype. Specifically:

• Untargeted metabolomics: Detected ~1,000 molecular features across lipid classes, free fatty acids, acylcarnitines, phospholipids, and polar metabolites using Ultimate 3000 UHPLC coupled to Q Exactive MS with ESI ionization
• Targeted acylcarnitine panel: Absolute quantification of short-chain (C2–C6), medium-chain (C8–C12), and long-chain (C14–C24) acylcarnitines with QC-validated calibration (R² ≥0.99, RSD ≤15%)
• Data integration support: Provided processed metabolite feature tables and normalized abundance matrices compatible with downstream multi-omics integration (transcriptomics + proteomics correlation analysis)

The metabolomics data was essential for demonstrating that LIPTER loss impairs fatty acid β-oxidation — a finding that connected the transcriptomic discovery to the functional phenotype and validated the proposed mechanism.

Reference

1. Han, L., Huang, D., Wu, S., et al. Lipid droplet-associated lncRNA LIPTER preserves cardiac lipid metabolism. Nature Cell Biology 25, 1033–1046 (2023).