Cerebrospinal fluid collection: An informative animation video for patients and caregivers.
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ABSTRACT:
SUBMITTER: Babapour Mofrad R
PROVIDER: S-EPMC6562320 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:There is a long history of research into body fluid biomarkers in neurodegenerative and neuroinflammatory diseases. However, only a few biomarkers in CSF are being used in clinical practice. One of the most critical factors in CSF biomarker research is the inadequate powering of studies because of the lack of sufficient samples that can be obtained in single-center studies. Therefore, collaboration between investigators is needed to establish large biobanks of well-defined samples. Standardized protocols for biobanking are a prerequisite to ensure that the statistical power gained by increasing the numbers of CSF samples is not compromised by preanalytical factors. Here, a consensus report on recommendations for CSF collection and biobanking is presented, formed by the BioMS-eu network for CSF biomarker research in multiple sclerosis. We focus on CSF collection procedures, preanalytical factors, and high-quality clinical and paraclinical information. The biobanking protocols are applicable for CSF biobanks for research targeting any neurologic disease.
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2020-02-25 | GSE125193 | GEO
Project description:RNA was isolated from fresh cerebrospinal fluid samples of multiple sclerosis and control patients and analyzed by hybridization of HG U133 plus 2.0 arrays in order to investigate disease mechanisms of multiple sclerosis and to identify transcriptional biomarker
2014-06-01 | E-MTAB-2374 | biostudies-arrayexpress
Project description:Serum and cerebrospinal fluid (CSF) levels of ?-fetoprotein and ?-subunit of human chorionic gonadotropin are used as biomarkers for the management of central nervous system (CNS) germ cell tumors (GCTs). However, additional discriminating biomarkers are required. Especially, biomarkers to differentiate non-germinomatous germ cell tumors (NGGCTs) from germinomas are critical, as these have a distinct prognosis. We investigated CSF samples from 12 patients with CNS-GCT patients (8 germinomas and 4 NGGCTs). We analyzed circulating tumor DNA (ctDNA) in CSF to detect mutated genes. We also used liquid chromatography-mass spectrometry to characterize metabolites in CSF. We detected KIT and/or NRAS mutation, known as frequently mutated genes in GCTs, in 3/12 (25%) patients. We also found significant differences in the abundance of 15 metabolites between control and GCT, with unsupervised hierarchical clustering analysis. Metabolites related to the TCA cycle were increased in GCTs. Urea, ornithine, and short-chain acylcarnitines were decreased in GCTs. Moreover, we also detected several metabolites (e.g., betaine, guanidine acetic acid, and 2-aminoheptanoic acid) that displayed significant differences in abundance in patients with germinomas and NGGCTs. Our results suggest that ctDNA and metabolites in CSF can serve as novel biomarkers for CNS-GCTs and can be useful to differentiate germinomas from NGGCTs.
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Project description:Purpose: Leptomeningeal metastasis (LM) is a dismal terminal stage disease of solid cancer without definitive treatment. Both the limitation of cerebrospinal fluid (CSF) sample volume and a paucity of floating cancer cells are difficulties to study the genomic profiling of LM. As the profiling of microRNAs reflect the strategy and behavior of cancer cells to survive, and CSF is carrying micro-molecules from central nervous system (CNS), we evaluated the extracellular microRNA profiles of CSF from different CNS tumor status including LM. Materials and Methods: We prospectively collected CSF from 65 patients of five groups of cancer control (CC), healthy control (HC), LM, brain metastasis (BM); and brain tumor (BT). Extracellular RNA was extracted from 2 mL of CSF after proper cell down, and preceded to small RNA microarray with Affymetrix miRNA 4.0 microarray chips. Results: The mean RNA yield of LM patients was significantly higher compared to that of other patients groups (9.28 vs. 6.30 ㎍, p = 0.003). The small RNA (< 70 nucleotides) percentage was the highest in controls than that of other groups (53% vs. 26%, p < 0.001). Among 6,599 small RNA probes, mature microRNAs showed higher expression than that of pre-microRNAs and small nucleolar RNAs. The number of probes with the Present call in all samples is 22 mature microRNAs, 18 pre-mature microRNAs, and 27 small nucleolar RNAs. Expression value of mature microRNAs regarding their direction of 5 prime (5p) and 3 prime (3p), the expression of 3p microRNA is higher than those of 5p microRNA in both LM and BM samples (p < 0.001). Both supervised and unsupervised hierarchical clustering of 263 microRNAs, which showed more than 2 fold change at a significance level of p < 0.05, can differentiate LM from other CNS tumor patient groups. Prediction analysis of microarray (PAM) identified 13 microRNAs differentiate LM group from others including hsa-miR-335-5p and hsa-miR-466. MicroRNA profiling using significance of analysis of microarrays (SAM) analysis did not differentiate HC and CC. Whereas CSF samples from patients with LM showed the most number of 108 differentially expressed (more than 2 fold change) microRNAs compared to control group and also revealed 36 differentially expressed microRNAs between LM and BM including hsa-miR-466, hsa-miR-190a-3p, hsa-miR-98-3p, hsa-miR-34b-3p and so on. Representative discriminative microRNAs from microarray data were confirmed their expression level by digital droplet PCR in available CSF samples among the microarray samples. Gene Set Enrichment Analysis was performed using both 10 highly expressed and 6 suppressed microRNAs between LM and BM after normalization and 13 microRNAs from PAM analysis. The most targeted pathway was ‘positive-/ negative-transcription from RNA polymerase II promoter followed by ‘positive regulation of transcription, DNA-templated’ by discriminative microRNAs. When pathways were denoted by microRNAs from PAM, the most enriched pathway was inflammatory response and transcription relative to immune response. Conclusion: We performed extracellular small RNA microarray successfully with small volume of CSF. Analysis of profiles of microRNA according to patients with various CNS tumor status suggested the unique profiles might responsible for leptomeningeal metastasis.
2020-09-29 | GSE138092 | GEO
Project description:We reported that the microRNA profiling of exosome isolated from 58 cerebrospinal fluid (CSF) of 38 medulloblastoma (MBL) patient with or without letomeningeal metastases (LM). CSF exosome was precipitated using Exo2DTM for RNA assay (EXOSOME plus, Suwon, South Korea) according to the manufacture’s protocol (Kim, et al. 2017). 2 ml of CSF were added to Exo2DTM reagent B following mixing by inverting, and incubated at room temperature for 30 min. The supernatant was discarded and the exosome containing pellet was resuspended in 100 ul of PBS to further RNA isolation. Total RNA was extracted from the CSF exosome fraction using a microRNA purification kit (Norgen Biotek corp., Thorold, Canada) according to the manufacture’s protocol. Briefly, samples were mixed with lysis buffer, and the lysate was added to absolute ethanol. The lysate was applied up to the column and centrifuged at 8,000 rpm for 1 min. The column was washed with wash solution and centrifuged at 14,000 rpm for 1 min. Finally, the column was applied to 20 ul of elution solution and centrifuged at 14,000 rpm for 2 min, and the flow-through was stored at –80 ℃ for storage. RNA precipitates were quantified and qualified with Bioanalyzer Pico 6000 Chip (Agilent Technologies, Santa Clara, CA). The 10 ng of total RNA was provided to the cDNA library generation using SMARTer smRNA-Seq kit (Illumina Inc., San Diego, CA), according to the user manual. The cDNA library was subjected to sequencing using Truseq SBS Kit and HiSeq 2500 System (Illumina Inc., San Diego, CA), according to user guide.
2020-09-15 | GSE157916 | GEO
Project description:Analysis of microRNA (miRNA) expression in CSF from prodromal Huntington disease patients.
2020-05-10 | GSE150174 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE37664: Human cerebrospinal fluid autoantibody lipid microarray profiling (Fig. 1A) GSE37670: Human cerebrospinal fluid autoantibody lipid microarray profiling (Fig. 2A) GSE37826: Human cerebrospinal fluid autoantibody lipid microarray profiling (Fig. 2C) Refer to individual Series
2012-05-08 | E-GEOD-37830 | biostudies-arrayexpress
Project description:During acute Lyme disease, bacteria can disseminate to the central nervous system (CNS), leading to the development of meningitis and other neurologic symptoms. Here we have analyzed pooled cerebrospinal fluid (CSF) allowing a deep view into the proteome for patients diagnosed with early disseminated Lyme disease and CSF inflammation. Additionally, we analyzed individual patient samples and quantified differences in protein abundance employing label-free quantitative mass spectrometry-based methods. We identified 108 proteins that differ significantly in abundance in patients with acute Lyme disease from controls. Comparison between infected patients and control subjects revealed differences in proteins in the CSF associated with cell death localized to brain synapses and others that likely originate from brain parenchyma.
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Project description:Central nervous system (CNS) infections by human polyomaviruses (HPyVs), with the exception of JC (JCPyV), have been poorly studied. In total, 234 cerebrospinal fluid (CSF) samples were collected from patients affected with neurological disorders. DNA was isolated and subjected to quantitative real-time PCR (Q-PCR) for the detection of six HPyVs: JCPyV, BKPyV, Merkel cell PyV (MCPyV), HPyV6, HPyV7, and HPyV9. Where possible, the molecular characterization of the viral strains was carried out by nested PCR and automated sequencing. JCPyV was detected in 3/234 (1.3%), BKPyV in 15/234 (6.4%), MCPyV in 22/234 (9.4%), and HPyV6 in 1/234 (0.4%) CSF samples. JCPyV was detected at the highest (p < 0.05) mean load (3.7 × 107 copies/mL), followed by BKPyV (1.9 × 106 copies/mL), MCPyV (1.9 × 105 copies/mL), and HPyV6 (3.3 × 104 copies/mL). The noncoding control regions (NCCRs) of the sequenced viral strains were rearranged. HPyVs other than JCPyV were found in the CSF of patients affected with different neurological diseases, probably as bystanders, rather than etiological agents of the disease. However, the fact that they can be latent in the CNS should be considered, especially in immunosuppressed patients.
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