Creative Proteomics offers longitudinal cohort multi-omics solutions that reveal molecular changes over time. Our platform integrates genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling into a cohesive workflow. With advanced bioinformatics support, we deliver dynamic, multi-layered molecular insights to help researchers uncover disease mechanisms, identify predictive biomarkers, and accelerate data-driven decisions in complex biological studies.
What is Longitudinal Cohort Multi-Omics
Longitudinal multi-omics unlocks dynamic biological signals that cross-sectional studies miss. It creates a time-resolved molecular atlas for tracking changes over months or years. Cohort multi-omics combines repeated sampling with broad 'omics' profiling across individuals.
Integrating genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomes can provide more insight. This multi-layered approach reveals how networks shift with health, environment, and time. It shows how genetic risk interacts with exposures to alter molecular pathways. This approach provides insights into how molecular networks shift across biological states, how genetic predispositions interact with environmental exposures, and how individual heterogeneity influences molecular pathways.
Fig 1. Overview of longitudinal cohort multi-omics approach (Bodein A, et al., 2022).
Technical Workflow for Longitudinal Cohort Multi-Omics Solutions
Technical Advantages of Our Longitudinal Cohort Multi-Omics Solutions
- Comprehensive Multi-Omics Coverage: Provides a full molecular spectrum, capturing genetic, transcriptional, protein, metabolic, and microbial dynamics simultaneously.
- Temporal Resolution: Unlike static datasets, longitudinal profiling captures molecular changes across time, enabling trajectory inference and dynamic network modeling.
- Advanced Bioinformatics and Data Mining: Creative Proteomics offers mature, modular bioinformatics solutions for longitudinal data, including time-series clustering, predictive modeling, and customized deep analysis tailored to project needs.
- Scalability and Flexibility: Adaptable to projects of varying sizes, from small cohorts to large-scale, multi-center studies.
Multi-Omics Analysis Service We Provide
- Proteomics: Label and label-free proteomic quantification for deep coverage of proteins in body fluids and tissues.
- Metabolomics: Advanced full-spectrum metabolomics platforms for comprehensive metabolic profiling.
- Transcriptomics: Bulk and single-cell RNA sequencing for gene expression and cell-type-specific dynamics.
- Microbiome: 16S rRNA and metagenomic sequencing to capture microbial composition and functional shifts.
Data Analysis and Interpretation
- Data Preparation and Quality Assurance: Apply strict filtering, normalize datasets, and correct for batch effects to ensure reliable results.
- Temporal Trajectory Mapping: Track molecular changes over time across multi-omics layers to reveal dynamic patterns.
- Molecular Clustering and Subgrouping: Segment samples based on molecular profiles, highlighting subpopulations with unique signatures.
- Interaction Network Analysis: Build gene, protein, and metabolite networks to identify key regulatory hubs.
- Predictive Analytics: Leverage machine learning models to forecast outcomes from time-series multi-omics data.
Application for Longitudinal Cohort Multi-Omics
- Disease Progression Analysis: tracking how biological changes unfold across different stages.
- Biomarker Identification: finding early molecular signs that indicate outcomes or group differences.
- Drug Response Investigation: monitoring how molecules change over time in response to compounds.
- Population-Level Insights: uncovering differences between individuals in large cohorts.
- Targeted Research Design: using multi-omics data to guide interventions for specific molecular subgroups.
Sample Requirements (Recommended)
| Sample Type | Recommended Volume/Quantity | Collection Timepoints | Storage Conditions |
|---|---|---|---|
| Blood / Plasma / Serum | 1-2 mL plasma or serum; 2-5 mL whole blood | Multiple defined intervals (e.g., baseline, follow-up, endpoint) | Store at -80 °C; avoid repeated freeze-thaw cycles |
| Fecal Samples | ≥200 mg per collection | Longitudinal sampling across timepoints | Store at -80 °C; stabilize immediately after collection |
| Tissue Samples | ≥50 mg (depending on analysis) | Defined collection intervals if available | Snap-freeze in liquid nitrogen or RNAlater |
Why Choose Creative Proteomics
- Complete Workflow: From experimental design to sample processing, data analysis, and result interpretation.
- Advanced Bioinformatics: Tailored pipelines for integrating time-series and multi-omics data.
- Flexible Scalability: Supports both small exploratory studies and large multi-center cohorts.
- Client-Focused Solutions: Customized approaches designed to meet specific project needs..
References
- Bodein A, et al. Interpretation of network-based integration from multi-omics longitudinal data. Nucleic acids research, 2022, 50(5): e27-e27.
- Mardinoglu A, et al. Longitudinal big biological data in the AI era. Molecular Systems Biology, 2025: 1-19.
- Mohammadi-Shemirani P, Sood T, Paré G. From 'omics to multi-omics technologies: the discovery of novel causal mediators. Current atherosclerosis reports, 2023, 25(2): 55-65.
- Lancaster S M, et al. A customizable analysis flow in integrative multi-omics. Biomolecules, 2020, 10(12): 1606.
Longitudinal multi-omics of host–microbe dynamics in prediabetes
Journal: Nature
Impact factor: 48.5
Published: 2019
DOI: 10.1038/s41586-019-1236-x
Backgrounds
The study explores the earliest molecular changes that occur as people progress from health to prediabetes and then to type 2 diabetes (T2D). Since single-timepoint studies cannot capture these dynamic processes, the researchers conducted a prospective longitudinal cohort study, repeatedly profiling host and microbial molecular layers. This approach allowed them to track individual and population-level responses to events such as viral infections and vaccinations. They aimed to identify early, time-resolved molecular signatures that differentiate insulin-sensitive from insulin-resistant states and provide an open multi-omics resource for future research.
Materials & Methods
106 adult participants (aged 25-75) followed for up to ~4 years (median ~1.6 years), with routine quarterly "healthy" visits and dense sampling during stress events (respiratory viral infection, immunization, antibiotics, etc.).
| Whole-Exome Sequencing (WES) | Somatic mutations, copy number alterations | Identified recurrent mutations in KRAS (43%), TP53 (60%), and PIK3CA (17%) |
| Transcriptomics (RNA-seq) | Gene expression profiling | Detected overexpression of VEGFA, ERBB2, and FGFR1 in specific patient subsets |
| Proteomics (Mass Spectrometry) | Protein abundance, signaling pathway activation | Quantified >11,000 proteins; high phosphorylation of MAPK pathway proteins observed |
| Phosphoproteomics | Post-translational modifications (PTMs), kinase activity | Identified ~20,000 phosphosites; hyperactivation of AKT and mTOR pathways in resistant tumors |
| Metabolomics (LC-MS) | Metabolic pathway alterations | Elevated lactate and glutamine metabolism in drug-resistant models |
Results
- Personal stability vs. perturbation-driven dynamics: Many host and microbial features are relatively stable within healthy individuals over time, yet show marked, coordinated changes during perturbations (e.g., respiratory viral infections, immunization).
- Insulin-resistant vs insulin-sensitive differences: People with insulin resistance showed distinct baseline profiles and weaker or altered responses to respiratory viral infection compared to insulin-sensitive ones. Links between the host and the microbiome also varied depending on insulin resistance status.
- Early personal molecular signatures preceding T2D: In one participant, rising levels of the inflammatory markers interleukin-1 receptor antagonist (IL-1RA) and high-sensitivity C-reactive protein (hs-CRP), along with signals linked to exposure to foreign chemicals, were detected before a clinical diagnosis of type 2 diabetes (T2D). This shows that longitudinal multi-omics can uncover early, individual-specific warning signs.
- Host-microbiome co-associations: Global co-association analyses revealed specific host–microbial interactions and pathway linkages that differ between phenotypic subgroups and across perturbation states.
Fig 2. Summary of study design, cohort details and data.
Fig 3. Correlational networks capture multi-omics association structures that differ between insulin-resistant and insulin-sensitive groups.
Conclusions
The study shows that dense, time-series multi-omics profiling creates a powerful resource for understanding human biology. It captures each person's molecular baseline and changes over time, reveals how both the host and microbiome respond to real-life challenges, and detects early molecular shifts that can signal transitions toward conditions like type 2 diabetes. By making most of the dataset publicly available, the work supports future discoveries and method development. Overall, it demonstrates that longitudinal multi-omics can track disease processes at both the population and individual level.
How is longitudinal cohort multi-omics different from cross-sectional studies?
Cross-sectional studies provide a snapshot of molecular data at one point in time, offering only a static view. In contrast, longitudinal cohort multi-omics tracks the same individuals over multiple time points, revealing how molecular patterns shift over time and helping identify potential cause-and-effect relationships in biology.
What role does machine learning play in longitudinal multi-omics?
Machine learning enables feature selection, dimensionality reduction, trajectory prediction, and classification of molecular patterns across time. Deep learning models are increasingly applied for capturing non-linear interactions across omics layers.
Machine learning helps pick informative molecular features, simplify high-dimensional data, forecast temporal trajectories, and classify time-dependent molecular patterns. Modern deep-learning methods further model complex, non-linear relationships across different omics layers that traditional approaches often miss.
How is data harmonization handled across different omics platforms?
Data harmonization involves normalization of batch effects, feature alignment, integration of different measurement scales, and the use of multi-omics data integration algorithms such as MOFA (Multi-Omics Factor Analysis), DIABLO, and network-based approaches.
Demo: Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19
Fig 4. Clinical significance of co-expression modules in a longitudinal cohort of severe COVID-19 patients (Bernardes J P, et al., 2020).