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Neuroprotective role of sialic-acid-binding immunoglobulin-like lectin-11 in humanized transgenic mice

GSE282391 Mus musculus (mouse) RNA-seq (gene expression) 17 samples Submitted 2024/12/20 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 17 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
17 samples: 17 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Neuroprotective role of sialic-acid-binding immunoglobulin-like lectin-11 in humanized transgenic miceFrontiers in neuroscience · 2024
Summary (written by the submitters)
Brain aging is a chronic process linked to inflammation, microglial activation, and oxidative damage, which can ultimately lead to neuronal loss. Sialic acid-binding immunoglobulin-like lectin-11 (SIGLEC-11) is a human lineage-specific microglial cell surface receptor that recognizes α-2-8-linked oligo-/polysialylated glycomolecules with inhibitory effects on the microglial inflammatory pathways.
Read the full summary ↓
Recently, the SIGLEC11 gene locus was prioritized as a top tier microglial gene with potential causality to Alzheimer’s disease, although its role in inflammation and neurodegeneration remains poorly understood. In this study, aged Siglec-11 transgenic (tg) mice, which expressed the human SIGLEC-11 receptor on microglia and tissue macrophages, were investigated. The brains of the Siglec-11 tg mice were analyzed in 6-month-old mature mice and 24-month-old aged mice using immunohistochemistry and transcriptomics. Results showed decreased density and fewer clusters of ionized calcium binding adaptor molecule 1 (Iba1)-positive microglial cells in the hippocampus and substantia nigra, as well as less lipid-laden microglia in the Siglec-11 tg in comparison to wildtype (WT) controls. Additionally, Siglec-11 tg mice exhibited less age-related neuronal loss in the substantia nigra pars compacta in comparison to WT mice. Transcriptome analysis revealed suppression of oxidative phosphorylation and inflammatory pathways in Siglec-11 tg brains at 6 months, with further suppression of complement and coagulation cascades at 24 months of age in comparison to WT mice. Gene transcript levels of the pro-inflammatory cytokines tumor necrosis factor alpha (Tnf) and interleukin 1 beta (Il-1b), as well as the oxidative stress markers cytochrome b-245 alpha and beta (Cyba and Cybb) and the nitric oxide synthase 2 (Nos2) were reduced in the brains of 24-month-old Siglec-11 tg mice relative to WT controls. Brains of 24-month-old Siglec-11 tg mice also exhibited lower gene transcription of complement component 4 and integrin alpha M (C4 and Itgam), along with the complement C1q subcomponents a and c (C1qa and C1qc). In summary, aged Siglec-11 tg mice displayed reduced brain inflammation and oxidative stress, as well as protection against age-related neuronal loss in the substantia nigra.
Published in
Neuroprotective role of sialic-acid-binding immunoglobulin-like lectin-11 in humanized transgenic mice
Abou Assale T, Afrang N, Wissfeld J et al. · Frontiers in neuroscience 2024 · PMID 39764391 · doi:10.3389/fnins.2024.1504765
This dataset
Open GSE282391 in finder → Find more mouse RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE282391_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1188487 and SRA study SRP546370. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39764391) ↗
Study design
17 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 17 sample titles: 17 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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