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Rapid metabolic profiling of one microliter crude CSF by MALDI MS can differentiate de novo Parkinson’s disease

Vallianatou, Theodosia (author)
Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab
Nilsson, Anna (author)
Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab
Bjärterot, Patrik (author)
Uppsala universitet,Science for Life Laboratory, SciLifeLab,Institutionen för farmaceutisk biovetenskap
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Shariatgorji, Reza (author)
Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab
Slijkhuis, Nuria (author)
Aerts, Jordan (author)
Uppsala universitet,Institutionen för farmaceutisk biovetenskap
Jansson, Erik T., Docent, tekn. dr. 1984- (author)
Uppsala universitet,Analytisk kemi,Institutionen för farmaceutisk biovetenskap
Svenningsson, Per (author)
Andrén, Per E., Professor, 1957- (author)
Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Institutionen för läkemedelskemi,Science for Life Laboratory, SciLifeLab
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 (creator_code:org_t)
English.
  • Other publication (other academic/artistic)
Abstract Subject headings
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  • Parkinson’s disease (PD) is a highly prevalent neurodegenerative disorder affecting the motor system. However, the correct diagnosis of PD and atypical parkinsonism may be difficult, with high clinical uncertainty. The disease is diagnosed solely based on the presence of clinical symptoms, and there is an urgent need to identify reliable biomarkers using high-throughput, molecular-specific methods to improve current diagnostics. Here, we present a matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) method that requires minimal sample preparation and only one µl of crude cerebrospinal fluid (CSF). The method enables analysis of hundreds of samples in a single experiment while simultaneously detecting numerous metabolites with sub-ppm mass accuracy. To test the method, we analyzed CSF samples from 12 de novo PD patients (that is, newly diagnosed and previously untreated) and 12 age-matched controls. Within the identified molecules, we found neurotransmitters and their metabolites, such as γ-aminobutyric acid, 3-methoxytyramine, homovanillic acid, serotonin, histamine, amino acids, and metabolic intermediates. Limits of detection were estimated for multiple neurotransmitters with high linearity (R2 > 0.99) and sensitivity (as low as 16 pg/µl). Application of multivariate classification led to a highly significant (P <0.001) model of PD prediction with 100% classification rate, which was further thoroughly validated with permutation test and univariate analysis. Molecules related to the neuromelanin pathway were found to be increased in the PD group. Our method enables rapid detection of PD-related biomarkers in low sample volumes and could serve as a valuable tool in the development of robust PD diagnostics.

Keyword

Klinisk kemi
Clinical Chemistry

Publication and Content Type

vet (subject category)
ovr (subject category)

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