Quantification of metabolites in dried blood spots by direct infusion high resolution mass spectrometry

Velden, Monique G.M. de Sain–van der; Ham, Maria van der; Gerrits, Johan; Prinsen, Hubertus C.M.T.; Willemsen, Marcel; Pras‐Raves, Mia L.; Jans, Judith; Verhoeven‐Duif, Nanda M.

doi:10.1016/j.aca.2017.04.038

Cited by 34 publications

(24 citation statements)

References 13 publications

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“…High-resolution mass spectrometry (HRMS) has the potential to detect hundreds to thousands of metabolites in a single analysis and is readily used for a broad overview of the metabolome [58]. For this purpose, Velden M.G.M et al [59] developed a chip based on nanospray ionization (nanoESI), which provides direct input of a biological sample. The primary advantages of this system include high sample throughput, no sample transfer, and the use of small volumes of biological material.…”

Section: Metabolite Detectionmentioning

confidence: 99%

Dried Blood Spot in Laboratory: Directions and Prospects

et al. 2020

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Over the past few years, dried blood spot (DBS) technology has become a convenient tool in both qualitative and quantitative biological analysis. DBS technology consists of a membrane carrier (MC) on the surface of which a biomaterial sample becomes absorbed. Modern analytical, immunological or genomic methods can be employed for analysis after drying the sample. DBS has been described as the most appropriate method for biomaterial sampling due to specific associated inherent advantages, including the small volumes of biomaterials required, the absence of a need for special conditions for samples’ storage and transportation, improved stability of analytes and reduced risk of infection resulting from contaminated samples. This review illustrates information on the current state of DBS technology, which can be useful and helpful for biomedical researchers. The prospects of using this technology to assess the metabolomic profile, assessment, diagnosis of communicable diseases are demonstrated.

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Section: Metabolite Detectionmentioning

confidence: 99%

Dried Blood Spot in Laboratory: Directions and Prospects

et al. 2020

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“…Although enabling ultra-sensitive, untargeted analysis of many metabolic pathways and processes all at once (amino acids and peptides, carbohydrates, cofactors and vitamins, purines and pyrimidines, fatty acids and ketones, sterols, porphyrin and heme, lysosomal, peroxisomal, lipoprotein, neurotransmission, trace elements and metals), this high mass accuracy tandem MS method is not without challenges, and it is not yet possible to analyze the complete metabolome. To start with, essential information on the effect of clinically relevant metabolite/feature information on IEM disease pathogenesis is lacking, especially as not all 10,000 features detected by the semi-automated data-processing pipelines (signals with a specific mass to charge ratio, intensity, and retention time) can be correctly annotated and reference ranges have yet to be established (Ramos et al 2017 ; De Sain-van der Velden et al 2017 ). This method, therefore, requires control samples, which are often hard to come by in pediatric populations.…”

Section: Further Acceleration Of Imd Discoverymentioning

confidence: 99%

The role of the clinician in the multi‐omics era: are you ready?

Karnebeek

Wortmann

Tarailo‐Graovac

et al. 2018

J of Inher Metab Disea

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Since Garrod’s first description of alkaptonuria in 1902, and newborn screening for phenylketonuria introduced in the 1960s, P4 medicine (preventive, predictive, personalized, and participatory) has been a reality for the clinician serving patients with inherited metabolic diseases. The era of high-throughput technologies promises to accelerate its scale dramatically. Genomics, transcriptomics, epigenomics, proteomics, glycomics, metabolomics, and lipidomics offer an amazing opportunity for holistic investigation and contextual pathophysiologic understanding of inherited metabolic diseases for precise diagnosis and tailored treatment. While each of the -omics technologies is important to systems biology, some are more mature than others. Exome sequencing is emerging as a reimbursed test in clinics around the world, and untargeted metabolomics has the potential to serve as a single biochemical testing platform. The challenge lies in the integration and cautious interpretation of these big data, with translation into clinically meaningful information and/or action for our patients. A daunting but exciting task for the clinician; we provide clinical cases to illustrate the importance of his/her role as the connector between physicians, laboratory experts and researchers in the basic, computer, and clinical sciences. Open collaborations, data sharing, functional assays, and model organisms play a key role in the validation of -omics discoveries. Having all the right expertise at the table when discussing the diagnostic approach and individualized management plan according to the information yielded by -omics investigations (e.g., actionable mutations, novel therapeutic interventions), is the stepping stone of P4 medicine. Patient participation and the adjustment of the medical team’s plan to his/her and the family’s wishes most certainly is the capstone. Are you ready?

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“…A metabolic fingerprint was identified using a machine learning algorithm, and subsequently a binary classification model was designed. The model showed high performance characteristics (AUC 0.990, 95%CI 0.981-0.999) and an accurate class assignment was achieved for all newly added control (13) and patient samples (6), with the exception of one patient (accuracy 94%). Important metabolites in the metabolic fingerprint included glycolytic intermediates, polyamines and several acyl carnitines.…”

Section: Introductionmentioning

confidence: 98%

Untargeted metabolic profiling in dried blood spots identifies disease fingerprint for pyruvate kinase deficiency

et al. 2020

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The diagnostic evaluation and clinical characterization of rare hereditary anemia (RHA) is to date still challenging. In particular, there is little knowledge on the broad metabolic impact of many of the molecular defects underlying RHA. In this study we explored the potential of untargeted metabolomics to diagnose a relatively common type of RHA: Pyruvate Kinase Deficiency (PKD). In total, 1903 unique metabolite features were identified in dried blood spot samples from 16 PKD patients and 32 healthy controls. A metabolic fingerprint was identified using a machine learning algorithm, and subsequently a binary classification model was designed. The model showed high performance characteristics (AUC 0.990, 95%CI 0.981-0.999) and an accurate class assignment was achieved for all newly added control (13) and patient samples (6), with the exception of one patient (accuracy 94%). Important metabolites in the metabolic fingerprint included glycolytic intermediates, polyamines and several acyl carnitines. In general, the application of untargeted metabolomics in dried blood spots is a novel functional tool that holds promise for diagnostic stratification and studies on disease pathophysiology in RHA.

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Quantification of metabolites in dried blood spots by direct infusion high resolution mass spectrometry

Cited by 34 publications

References 13 publications

Dried Blood Spot in Laboratory: Directions and Prospects

Dried Blood Spot in Laboratory: Directions and Prospects

The role of the clinician in the multi‐omics era: are you ready?

Untargeted metabolic profiling in dried blood spots identifies disease fingerprint for pyruvate kinase deficiency

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