Dataset Information

Blood contamination in CSF and its impact on quantitative analysis of alpha-synuclein

ABSTRACT: Analysis of cerebrospinal fluid (CSF) is important for diagnosis of neurological diseases. Specific proteins, which are released from the CNS can be quantitatively monitored. Especially, in the field of neurodegenerative diseases abnormal protein abundance is an important biomarker. However, the quality of the CSF sample is a key factor for the analytic outcome. In this study we evaluated the effect of blood contamination in CSF with respect to protein biomarker identification. We compared three distinct measures: semiquantitative Combur10-Test® strips for urinary hemoglobin, a specific hemoglobin ELISA and bottom-up mass spectrometry (MS) based proteomics for the determination of the general blood contamination level. In parallel, we studied the impact of blood contamination on the detectability of alpha-synuclein (aSyn), a highly abundant protein in blood/erythrocytes and potential biomarker for Parkinson’s disease. Based on our results we identified other markers for blood contamination beyond hemoglobin and defined a grading system for blood levels in CSF samples including a lower limit of tolerable blood contamination for mass spectrometry-based biomarker studies and suggest the Combur10-Test® as a cost-effective test for simple and fast determination of blood contaminations in CSF.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cerebrospinal Fluid

SUBMITTER: Katalin Barkovits

LAB HEAD: Katrin Marcus

PROVIDER: PXD014515 | Pride | 2020-02-17

REPOSITORIES: Pride

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Publications

Blood Contamination in CSF and Its Impact on Quantitative Analysis of Alpha-Synuclein.

Barkovits Katalin K Kruse Niels N Linden Andreas A Tönges Lars L Pfeiffer Kathy K Mollenhauer Brit B Marcus Katrin K

Cells 20200205 2

Analysis of cerebrospinal fluid (CSF) is important for diagnosis of neurological diseases. Especially for neurodegenerative diseases, abnormal protein abundance in CSF is an important biomarker. However, the quality of CSF is a key factor for the analytic outcome. Any external contamination has tremendous impact on the analysis and the reliability of the results. In this study, we evaluated the effect of blood contamination in CSF with respect to protein biomarker identification. We compared thr ...[more]

PMID: 32033488

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Project description:Granulocyte-colony stimulating factor (G-CSF) is used to boost granulocyte counts in immunocompromised patients, but its effects on the immune system may be counter productive. We tested the hypothesis that G-CSF mobilized peripheral blood stem cell (PBSC) products are immunologically down regulated based on gene microarray analysis. Ten peripheral blood samples from normal donors for allogeneic PBSC transplantation were obtained before and after administration of G-CSF and tested on Affymetrix Human U133 Plus 2.0 GeneChipÂ® microarrays. Significant changes in gene expression after G-CSF mobilization were reported by controlling the false discovery rate at 5%. Immune-related genes were isolated from the data set and categorized according to probe set annotations and a thorough, independent literature search. We found that G-CSF up-regulated inflammatory and neutrophil activation pathway gene expression; however, adaptive immune-related gene expression, such as antigen presentation, co-stimulation, T cell activation and cytolytic effector pathways, were generally down-regulated. Thus, despite significant increases in stem cells, lymphocytes and antigen presenting cells, G-CSF mobilized PBSC allografts exhibit a suppressive adaptive immune-related gene expression profile. Our data provides an explanation for the potentially immunosuppressive effects observed after G-CSF administration. Experiment Overall Design: This study was approved by the University of Florida Institutional Review Board. Five healthy donors (A-E) for allogeneic PBSC transplantation consented and were mobilized with rHu G-CSF at 10Âµ/kg/day for five days. Approximately 4mL of venous blood was collected before (Pre-G-CSF) and after (Post-G-CSF) mobilization. Whole blood leukocyte RNA was purified from each sample and used to generate cRNAs which were subsequently hybridized onto Affymetrix Human U133 Plus 2.0 GeneChipÂ® microarrays. Representative genes were successfully validated with quantitative RT-PCR.

Project description:For the blood contamination studies a CSF pool was made with 1mL CSF free of blood from n=4 patients. The pool was divided into four aliquots. One aliquot was kept as reference CSF without added blood (named “neat” in the raw files), one was spiked with 20 µL blood/mL CSF (2%) (“20S”) and two were spiked with 5 µL blood/mL CSF (0.5%) (named “5S” and “5U”, S=centrifuged, U=not centrifuged). The sample spiked with 2% blood and one of the samples spiked with 0.5% blood were centrifuged at 4C at 400 x g for 10 minutes. In one experiment (BloodContamination_GeLC-MS_comb1-10) the reference CSF (neat), and 0.5% centrifuged (5S) and 2% centrifuged (20S) were protein depleted using the MARS Hu-14 column, separated by SDS-PAGE into ten fractions and in-gel digested. The samples (30 in total) were analysed by LC-MS on an OrbiTrap Velos Pro online coupled to a Dionex Ultimate 3000 nano RSLC system. The data was analysed by the Progenesis LC MS software 2.7 (Nonlinear Dynamics), and the MS/MS spectra were searched against UniProt/SwissProt using the open-source graphical user interface SearchGUI (version 1.7.3), with search engines OMSSA and X!Tandem. PeptideShaker (version 0.14.7) was used to assemble the peptides into proteins. The raw files were named according to sample and fraction, e.g. the first fraction of the reference CSF was called “BK_GeLC_neat_F1”, and the second fraction was called “BK_GeLC_neat_F2”. In the second blood contamination study the reference CSF (neat), and the 0.5% blood spiked samples centrifuged (5S) and not centrifuged (5U) were trypsin digested by in solution protocol and analysed using the same instruments as in the first study. (In the search output file are also the results for 2% blood spiked with and without centrifugation, 20S and 20U, but since the data was not used, the raw files are not distributed). The raw files were named “BK_Insol_FD_X” (X = neat, 5S or 5U). In the third experiment we examined the rostro-caudal gradient (RCG) on CSF in the spinal cord by sampling the 1st, 10th, 16th, 24th, 31st, 38th and 44th mL CSF in volumes of approximately 1 mL of a PSP patient during lumbar puncture. The CSF was centrifuged at 2000 x g for 10 min. We did an iTRAQ discovery study, and to be able to compare all seven RCG points, three related iTRAQ experiments (RCG exp 1, 2 and 3) were done. In each related experiment we included an identical reference which we labeled with the iTRAQ 114 reagent. The reference sample contained equal volumes of the seven RCG points, and was used as the reference in the data analysis. In the experiment we had twelve samples (equal volume) that were digested and labeled with iTRAQ reagents according to the vendor’s manual. The samples were combined into three related experiments as follows: Exp. 1 (common reference, 44th mL, 24th mL and 1st mL), Exp. 2 (common reference, 1st mL, 38th mL and 16th mL), and Exp. 3 (common reference, 10th mL, 44th mL and the 31st mL). The 1st and 44th mL were included twice, since they were expected to be the most different samples. The three combined samples (RCG exp 1, 2 and 3) were fractionated into 21 fractions using mixed mode reversed phase-anion chromatography (MM (RP-AX)). Fractions 1-4 were excluded from LC-MS analysis and the two latest fractions were combined before analysis on an Orbitrap Velos Pro, resulting in 16 fractions per combined sample. The raw files were named according to experiment (RCG 1, 2 or 3) and number of fraction (F4-F19), e.g. the raw file by the name EA_RCG3_F15 is fraction 15 from RCG experiment 3.

Dataset Information

Blood contamination in CSF and its impact on quantitative analysis of alpha-synuclein

Publications

Blood Contamination in CSF and Its Impact on Quantitative Analysis of Alpha-Synuclein.

Similar Datasets

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