Capítulo 6
The studies presented in this guide highlight the essential role of multiomics in providing actionable insights to address longstanding and intractable challenges in cancer, neurodegenerative diseases, and microbiome related research endeavors. We hope that the concepts discussed herein inspire your own research.
Metabolon is committed to supporting investigators through this journey by providing the tools, data, and expert guidance needed to harmonize distinct omics datasets and interpret complex data into meaningful discoveries. We would be happy to help you design and execute robust studies, ensuring that your data delivers accurate scientific insights and propels your research forward.
1. Research, P.M., Multiomics Market Size, Share & Analysis Report, 2024-2032. 2024.
2. Gayoso, A., et al., Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nat Methods, 2021. 18(3): p. 272-282.
3. Rohart, F., et al., mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol, 2017. 13(11): p. e1005752.
4. Argelaguet, R., et al., MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol, 2020. 21(1): p. 111.
5. Yoshioka, H., et al., Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions. Environ Microbiome, 2026. 21(1).
6. Reynolds, J. and C. Pan, Benchmarking interpretability of deep learning for predictive genomics: Recall, precision, and variability of feature attribution. PLoS Comput Biol, 2025. 21(12): p. e1013784.
7. Beaude, A., et al., CrossAttOmics: multiomics data integration with cross-attention. Bioinformatics, 2025. 41(6).
8. Wang, B., et al., Similarity network fusion for aggregating data types on a genomic scale. Nat Methods, 2014. 11(3): p. 333-7.
9. Xiao, S., et al., Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis. IEEE J Biomed Health Inform, 2023. 27(9): p. 4591-4600.
10. Agamah, F.E., et al., Computational approaches for network-based integrative multi-omics analysis. Front Mol Biosci, 2022. 9: p. 967205.
11. Kumar, R., J.D. Romano, and M.D. Ritchie, Network-based analyses of multiomics data in biomedicine. BioData Min, 2025. 18(1): p. 37.
12. Korsunsky, I., et al., Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods, 2019. 16(12): p. 1289-1296.
13. Johnson, W.E., C. Li, and A. Rabinovic, Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 2007. 8(1): p. 118-27.
14. Haghverdi, L., et al., Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol, 2018. 36(5): p. 421-427.
15. Ballard, J.L., et al., Deep learning-based approaches for multi-omics data integration and analysis. BioData Min, 2024. 17(1): p. 38.
16. Hao, Y., et al., Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol, 2024. 42(2): p. 293-304.
17. Ashuach, T., Gabitto, M.I., Koodli, R.V., Saldi, G.A., Jordan, M.I., Yosef, N., MultiVI: Deep generative model for the integration of multimodal data. Nature Methods, 2023. 20.
18. Jin, S., et al., Inference and analysis of cell-cell communication using CellChat. Nat Commun, 2021. 12(1): p. 1088.
19. Browaeys, R., W. Saelens, and Y. Saeys, NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods, 2020. 17(2): p. 159-162.
21. Cheng, X., et al., Application of single-cell and spatial omics in deciphering cellular hallmarks of cancer drug response and resistance. J Hematol Oncol, 2025. 18(1): p. 70.
22. Yan, Y., et al., Multi-omic profiling highlights factors associated with resistance to immuno-chemotherapy in non-small-cell lung cancer. Nat Genet, 2025. 57(1): p. 126-139.
23. Chen, J., et al., Spatial landscapes of cancers: insights and opportunities. Nat Rev Clin Oncol, 2024. 21(9): p. 660-674.
20. Sabit, H., et al., Leveraging Single-Cell Multi-Omics to Decode Tumor Microenvironment Diversity and Therapeutic Resistance. Pharmaceuticals (Basel), 2025. 18(1).
24. Addala, V., et al., Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat Rev Clin Oncol, 2024. 21(1): p. 28-46.
25. Porcu, E., et al., Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat Commun, 2019. 10(1): p. 3300.
26. Howey, R., et al., Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data. PLoS Genet, 2020. 16(3): p. e1008198.
27. Cai, X., J.A. Bazerque, and G.B. Giannakis, Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations. PLoS Comput Biol, 2013. 9(5): p. e1003068.
28. Patel-Murray, N.L., et al., A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules. Sci Rep, 2020. 10(1): p. 954.
29. Replogle, J.M., et al., Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell, 2022. 185(14): p. 2559-2575 e28.
30. Budhu, A., et al., Integrated metabolite and gene expression profiles identify lipid biomarkers associated with progression of hepatocellular carcinoma and patient outcomes. Gastroenterology, 2013. 144(5): p. 1066-1075 e1.
31. Lucarelli, G., et al., Integrated multi-omics characterization reveals a distinctive metabolic signature and the role of NDUFA4L2 in promoting angiogenesis, chemoresistance, and mitochondrial dysfunction in clear cell renal cell carcinoma. Aging (Albany NY), 2018. 10(12): p. 3957-3985.
32. Frankel, A.E., et al., Metagenomic Shotgun Sequencing and Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and Metabolites Associated with Immune Checkpoint Therapy Efficacy in Melanoma Patients. Neoplasia, 2017. 19(10): p. 848-855.
33. Francois, M., et al., Multi-Omics, an Integrated Approach to Identify Novel Blood Biomarkers of Alzheimer’s Disease. Metabolites, 2022. 12(10).
34. Clark, C., et al., An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer’s disease. Alzheimers Res Ther, 2021. 13(1): p. 71.
35. Yulug, B., et al., Multi-omics characterization of improved cognitive functions in Parkinson’s disease patients after the combined metabolic activator treatment: a randomized, double-blinded, placebo-controlled phase II trial. Brain Commun, 2025. 7(1): p. fcae478.
36. Paramsothy, S., et al., Specific Bacteria and Metabolites Associated With Response to Fecal Microbiota Transplantation in Patients With Ulcerative Colitis. Gastroenterology, 2019. 156(5): p. 1440-1454 e2.
37. Alexeev, E.E., et al., Microbiota-Derived Indole Metabolites Promote Human and Murine Intestinal Homeostasis through Regulation of Interleukin-10 Receptor. Am J Pathol, 2018. 188(5): p. 1183-1194.
38. Shoer, S., et al., Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines. Nat Commun, 2023. 14(1): p. 5384.
Una vez que se comprende todo el valor de la metabolómica, la única pregunta que queda es: ¿quién lo hace mejor? Aunque muchos laboratorios cuentan con capacidades para la elaboración de perfiles de metabolitos o de química analítica, las tecnologías de metabolómica integrales son extremadamente escasas.
La identificación precisa e imparcial de los metabolitos en todo el metaboloma plantea retos en cuanto a la relación señal-ruido que muy pocos laboratorios están preparados para afrontar. Además, convertir cantidades ingentes de datos en información útil resulta lento, cuando no imposible, para la mayoría, ya que una interpretación adecuada requiere dos elementos de los que se carece: experiencia y una base de datos exhaustiva.
Gracias a nuestra sólida plataforma y a nuestras herramientas de visualización, nuestros expertos son los únicos capaces de ofrecerle más información sobre su molécula y desarrollar paneles de ensayos que le ayuden a concentrarse en los resultados que necesita.
Cobertura
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Comparabilidad
Capacidad para integrar los datos de diferentes estudios en el mismo conjunto de datos, en diferentes geografías, entre diferentes pacientes a lo largo del tiempo.
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Capacidad para informar sobre el diseño adecuado de los estudios, generar datos de alta calidad, obtener conocimientos biológicos y formular recomendaciones prácticas.
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Solicite un presupuesto para nuestros servicios, obtenga más información sobre tipos de muestras y procedimientos de manipulación, solicite una carta de apoyo o envíe una pregunta sobre cómo la metabolómica puede hacer avanzar su investigación.
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