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Applications | Blood Function and Disease

Blood Function and Disease

Unravel the complexities of blood diseases and disorders with metabolomics.

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Featured Blood Function & Disease Resources

Infectious Disease and Metabolomics

Metabolomics in Blood Function & Disease Research

Blood is a complex and dynamic tissue comprising various components including red blood cells, white blood cells, platelets, and plasma, each with distinct functions and roles in different diseases. Both genetic and environmental factors contribute to blood disorders, necessitating the study of both to unravel the complexities. Rare diseases are prevalent in this field, posing difficulties due to limited patient populations and resources. Moreover, disease heterogeneity, non-specific symptoms, and overlapping conditions complicate accurate diagnosis. While significant progress has been made, limited treatment options persist.

Metabolomics is emerging as a powerful tool for unraveling the complexities of these conditions. By analyzing the unique metabolic fingerprint of blood cells and plasma, researchers can gain insights into abnormal cellular processes associated with various blood disorders. This approach can help identify new biomarkers for early disease detection, differentiate between different types of blood cancers, and monitor a patient’s response to treatment. For example, metabolomic profiles might reveal disruptions in energy metabolism or altered levels of specific metabolites linked to abnormal cell growth – valuable clues that can guide the development of more targeted therapies for blood diseases.

Infectious Disease

Uncover Functional, Actionable Insights with Metabolomics

More research is needed to develop effective treatments for blood diseases. Metabolon can enable researchers to uncover the insights needed to understand disease pathogenesis and to identify promising therapeutic targets. Global metabolomics can be used to identify biomarkers for blood cancers, parasitic infections, and other blood disorders. Targeted metabolomics panels can translate early findings to actionable biomarkers.

Identify Metabolites with a Role in Disease Etiology
Patient Stratification
Biomarker Discovery

​Identify Metabolites with a Role in Disease Etiology

The most common autosomal recessive disorder affecting humans is sickle cell disease (SCD), which is a disorder of red blood cells that leaves them sickle shaped and causes them to break down. Researchers have been studying the role of sphingosine-1-phosphate (S1P), a metabolite stored in red blood cells, in blood cell sickling and SCD progression. Using metabolomics analysis in humans and mice followed by gene knockdown in mice, researchers confirmed that S1P elevation contributes to sickling and disease progression through activation of sphingosine kinase 1 (SPHK1) in erythrocytes, supporting SPHK1 inhibition as a promising therapeutic approach in SCD.

Zhang Y, Berka V, Song A, et al. Elevated sphingosine-1-phosphate promotes sickling and sickle cell disease progression. J Clin Invest. 2014;124(6):2750-2761. doi: 10.1172/JCI74604

Patient Stratification

Acute myeloid leukemia (AML) is a serious type of cancer that causes the bone marrow to make large numbers of abnormal blood cells. This cancer is often treated with allogeneic hematopoietic stem cell transplantation (allo-HSCT), which has a high risk of severe transplantation-related mortality (TRM). Using metabolomics analysis of plasma from patients with AML and myelodysplastic syndrome (MDS; another disease commonly treated with allo-HSCT), researchers identified significantly altered amino acid metabolism associated with three TRM risk factors (fluid retention, pre-transplantation inflammation, and development of systemic steroid-requiring acute graft-vs-host disease (aGVHD)). Metabolite-based clustering analysis based on these three risk factors identified a patient subset with a significant association with TRM. The data suggest that metabolic profiling may be a promising approach for identifying high-risk individuals who may benefit from alternative treatment strategies in AML and MDS.

Reikvam H, Bruserud O, and Hatfield KJ. Pretransplant systemic metabolic profiles in allogeneic hematopoietic stem cell transplant recipients – identification of patient subsets with increased transplant-related mortality. Transplant Cell Ther. 2023;29(6):375.e1-375.e14. doi: 10.1016/j.jtct.2023.03.020

Biomarker Discovery

Sepsis is a serious and often fatal condition systemic response to infection. Metabolomics can help identify diagnostic and prognostic biomarkers and potential therapeutic targets by shedding more light on the molecular mechanisms behind this complex condition. Researchers leveraged machine learning to deeply analyze plasma metabolomes from patients suffering from sepsis and identified several metabolites, including lactate, bilirubin, bile salt metabolites, and several amino acids, associated with clinical outcomes. This work lays the foundation for future studies leveraging metabolomics to identify critical biomarkers in blood diseases and disorders.

Kosyakovsky LB, Somerset E, Rogers AJ, et al. Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival. Intensive Care Med Exp. 2022;10(1):24. doi: 10.1186/s40635-022-00445-8

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Metabolomics Applications for Blood Disease Research

  • EBiomarker discovery
  • EPersonalized medicine research
  • EPrecision medicine research
  • EDrug discovery and development
  • EUnderstanding disease etiology
  • EEarly detection of altered pathways
  • EPatient stratification
  • ETreatment risk prediction
  • EDisease prognosis/survival prediction
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“Metabolic alterations have represented an underexplored pathophysiologic axis in sepsis. Unbiased, high-dimensional molecular platforms for profiling hundreds of circulating metabolites in concert (metabolomics) have opened opportunities for data-driven, systems biology approaches to clinical risk prediction and the search for potential novel therapeutic targets in humans.”

Kosyakovsky LB, Somerset E, Rogers AJ, et al.
Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival. Intensive Care Med Exp. 2022;10(1):24. doi: 10.1186/s40635-022-00445-8

Metabolomics Insights into Blood Parasites

Human babesiosis is caused by the red blood cell parasite Babesia divergens and is associated with transfusion-transmitted illness and relapsing disease among the immunosuppressed. The ability of this parasite to alter the metabolic environment of its host red blood cells is well recognized; therefore, plasma metabolomics may be a powerful tool for identifying key metabolic pathways that may be targeted for therapeutic intervention.  Because blood parasites are a significant cause of morbidity and mortality globally, establishing metabolomics-based analyses of the parasite-host interaction has the potential to impact a wide range of the global population. Using Metabolon’s Global Discovery Platform, researchers performed the first global metabolomics study of host red blood cells infected with Babesia divergens, identifying key metabolic changes associated with infection.

blood metabolomics

Figure 1. Metabolic map of human red blood cells infected with Babesia diversigens.
Click here to view full-size image

The researchers performed untargeted metabolomics on human red blood cell cultures experiencing both low and high parasitemia, detecting changes in 150 metabolites in low parasitemia and in 350 metabolites in high parasitemia. Lipids were the major class of metabolites impacted by parasite growth, and treatment with a lipase inhibitor stalled parasite egress from the host and disrupted its membrane. As growth continued, the parasites demonstrated an increased reliance on energy pathways such as glycolysis and the TCA cycle. Growth inhibition assays and high-resolution STED microscopy confirmed these changes and revealed that Babesia divergens scavenges all of the cholesterol it needs for growth from the host cell. Disrupting this scavenging proved lethal for the parasite. The experimental procedures described in this work support the use of metabolomics to unravel the complex relationships between parasites and host cells and to potentially identify novel therapeutic targets to improve treatment.

Beri D, Singh, M, Rodriguez M, et al. Global Metabolomic Profiling of Host Red Blood Cells Infected with Babesia divergens Reveals Novel Antiparasitic Target Pathways. Microbiology Spectrum Volume 11 Issue 2. doi:

Blood Disease Publications and Citations

Metabolon has contributed extensively to publications ranging from basic research to clinical trials.

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