Dataset DOI: 10.5061/dryad.3bk3j9m12

Description of the data and file structure

Bulk Transcriptome Analysis of Monocyte-Derived Macrophages (MdMs)

Dataset Overview

This dataset contains bulk RNA sequencing data and associated metadata from monocyte-derived macrophages (MdMs) isolated from bone marrow mixed chimeric mice during experimental autoimmune encephalomyelitis (EAE). The study investigates transcriptional differences between wild-type (wt) and mitochondrial catalase overexpressing (mCAT) MdMs to understand the role of mitochondrial oxidative stress in neuroinflammation.

MdMs were isolated from CD45 congenic mixed bone marrow chimeras, enabling the comparison of wt (CD45.1) and mCAT (CD45.2) cells within the same inflammatory environment.

The dataset supports the analyses described in the associated manuscript investigating phagocyte-mediated oxidative damage in progressive neuroinflammation.

Experimental Design

Bone marrow mixed chimeric mice were generated using:

• Wild-type donors (CD45.1)
• mCAT transgenic donors (CD45.2)

Monocyte-derived macrophages were isolated by flow cytometric sorting from the central nervous system during EAE. RNA was extracted and subjected to bulk RNA sequencing.

Comparative transcriptomic analyses were performed to identify genes and pathways associated with mitochondrial oxidative stress responses in MdMs.

RNA Sequencing Methods

Total RNA from sorted wt (CD45.1) and mCAT (CD45.2) MdMs obtained from bone marrow mixed chimeras was extracted using the Qiagen RNeasy Micro Kit according to the manufacturer’s protocol.

Library preparation was performed using the Smart-seq2 protocol.

Paired-end sequencing (150 bp) was performed on an Illumina NovaSeq X Plus platform, generating approximately 500 million reads per sequencing package.

Bioinformatic Processing Pipeline

Raw sequencing reads were processed using the following pipeline:

Quality Control

Initial quality assessment was performed using:

• FastQC

Adapter sequences and low-quality bases were removed using:

• Fastp

Transcript Quantification

Reads were pseudo-aligned against the mouse reference transcriptome (GRCm39, Ensembl release 111) using:

• Salmon

Selective alignment and decoy-aware indexing were used to improve mapping accuracy and minimize genomic DNA contamination.

Transcript abundance estimates were generated at the transcript level.

These data are deposited in Count_QC-raw-count_file_Villar-Vesga_et_al.csv

File Structure and Column Description

The file Count_QC-raw-count_file_Villar-Vesga_et_al.csv is organized as a gene expression count matrix.

Column Layout

• Column 1: Gene identifier contains the gene name or gene ID (e.g., Ensembl ID or gene symbol). Each row corresponds to one gene
• Column 2: Gene annotation provides additional annotation for each gene. This may include gene symbols, descriptions, or functional labels, depending on the annotation source
• Columns 3 onward: Sample-specific raw counts. Each column represents one biological sample. Values correspond to raw read counts (i.e., unnormalized expression values)

Sample Column Naming (from Column 3 onward)

Each sample column follows the naming structure:

Cohort_Genotype_SampleID

C1 / C2 → Cohort

A / B → Genotype A = wild-type (wt; CD45.1) B = mCAT (CD45.2)

SX → Sample number

 Example: C1_A_S1 → Cohort 1, wt, sample 1

C1_B_S1 → Cohort 1, mCAT, paired sample 1 Paired samples (same S number) originate from the same mixed chimera, enabling controlled comparisons between wt and mCAT MdMs within the same animal.

Code/software

Can be checked in Excel or R.

Access information

Other publicly accessible locations of the data:

• RAW data found in BioProject accession PRJNA1434700

Data was derived from the following sources:

• RAW data found in BioProject accession PRJNA1434700