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Data · dataset · 2021

Small non-coding RNA profiling across cellular and biofluid compartments and their implications for multiple sclerosis immunopathology

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To improve our understanding of MS pathology and heterogeneity and provide new strategies for biomarkers and treatments development we carried out a genome-wide small non-coding RNA analysis.

Description

The analysis was performed in paired peripheral blood mononuclear cells, plasma, cerebrospinal fluid (CSF) cells and cell-free CSF from 29 MS patients and 16 controls using next-generation sequencing. We aimed to detect differentially expressed small non-coding RNAs between MS and controls.

The data contains unique molecular identifier (UMI) count information for each transcript. The small non-coding RNA analysis was performed in paired peripheral blood mononuclear cells, plasma, cerebrospinal fluid (CSF) cells and cell-free CSF from 29 MS patients and 16 controls using next-generation sequencing. One INDC control was missing a CSF cell and cell-free CSF sample and one NINDC control was missing a PBMC and plasma sample.

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Small non-coding RNAs were isolated from 300 ul of plasma or CSF using the miRCURY RNA isolation kit for biofluids (Exiqon, Denmark) or from CSF cells and peripheral blood mononuclear cells precipitate using the miRNAeasy micro kit (Qiagen, Germany). Small non-coding RNA libraries were prepared as previously described at PMID: 27798564. The libraries were sequenced on eight lanes of HiSeq2500.

Preprocessing and alignment were done according to PMID: 30250291. The "Unique molecular identifier_sncRNAs_analysis_MS" file contains unique molecular identifier count information for each small non-coding RNA transcript as well as other types of transcripts identified in the sequencing libraries from 44 individuals and 4 compartments (PBMCs, CSF cells, plasma, cell-free CSF). Altogether 176 samples.

The metafile contains information about the disease status, sex, age for all MS patients and controls. The "readme" explains the contents of the data files and contains the variables list.

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Where it is published

Catalogue records · 1

Topics

Inferred from text
Disease 75% · Genomics and transcriptomics 76% · Sequencing 75%
Provenance · 1 source records, 12 field assertions
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