biostudies-arrayexpressJaehyun LeeMus musculushttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-17136Microglia are increasingly recognized as active drivers of Parkinson's disease (PD), contributing to neuroinflammation, α-synuclein clearance, and dopaminergic neuron loss. Because aging is the strongest risk factor for PD and independently shifts microglia toward a primed, pro-inflammatory state, disentangling age- and disease-related transcriptional programs is essential. Aim and workflow This dataset profiles the bulk transcriptome of microglia from [PD model] and control mice at [young] and [old] ages (2×2 design) to resolve disease, age, and interaction effects. Brains were enzymatically dissociated, microglia isolated by MACS using CD11b MicroBeads, and total RNA extracted for bulk RNA-seq library preparation and sequencing on Novaseq 6000. n = 3-4 per group. Both sexes included. All samples except Sample1 includes two mice. Sequencing was done with a target of 40G per sample PE150.biostudies-arrayexpressSample Collection - Extracted mouse brain tissue was stored overnight at 4 °C in Tissue Storage Solution (Miltenyi Biotec, 130-100-008) and dissociated the next day using the Adult Brain Dissociation Kit (Miltenyi Biotec, 130-107-677) per the manufacturer's instructions. Pellets from two animals were pooled and processed immediately for microglia isolation. All subsequent steps used pre-cooled solutions on ice unless stated otherwise. To improve microglia purity, oligodendrocytes were first depleted. Pellets were resuspended in 97.5 μL PB buffer (D-PBS pH 7.2, 0.5% BSA) with 2.5 μL Anti-O4 MicroBeads (Miltenyi Biotec, 130-096-670) and incubated for 15 min at 2–8 °C in the dark. Cells were washed with 1 mL PB buffer (300 × g, 5 min), resuspended in 500 μL PB buffer, and applied to an MS Column fitted with a 70 μm Pre-Separation Filter in a MACS Separator. The flow-through and three subsequent 500 μL PB washes were combined as the unlabeled fraction; the column with labeled oligodendrocytes was discarded. The combined flow-through was centrifuged (300 × g, 10 min, 4 °C), resuspended in 90 μL PB buffer with 10 μL CD11b MicroBeads (Miltenyi Biotec, 130-093-636), and incubated for 15 min at 2–8 °C in the dark. Cells were washed with 1 mL PB buffer (300 × g, 5 min, 4 °C), resuspended in 500 μL PB buffer, and applied to an equilibrated MS Column in the MACS Separator. After three 500 μL PB washes (allowing complete emptying between washes), the column was removed from the separator and magnetically labeled microglia were eluted with 1 mL PB buffer using the plunger. The positive fraction was centrifuged (300 × g, 5 min, 4 °C), resuspended in 350 μL RLT buffer, and stored at –80 °C, yielding ~2 M microglia per sample.Nucleic Acid Extraction - RNA was extracted using the Quick-RNA Microprep Kit (Zymo Research, R1051) with on-column DNase I treatment, per the manufacturer's instructions, eluted in DNase/RNase-free water, and stored at −80 °C. RNA quality and quantity were assessed using a TapeStation 4150 system (Agilent Technologies) with RNA ScreenTape.Sequencing - Microglia libraries were sequenced on a NovaSeq X (PE150) with a target of 50 Gb per sample (~166 M reads).Library Construction - Strand-specific RNA-seq libraries were prepared using the sparQ RNA-Seq HMR Kit (QuantaBio, 733-2900) with sparQ PureMag Beads (QuantaBio, 733-2626) and sparQ UDI Adapters (QuantaBio, 95211-096) according to the manufacturer's instructions. Amplified libraries were purified by three consecutive clean-up steps using 45 µL sparQ PureMag Beads per purification (0.9× bead-to-sample ratio). Final library quality and concentration were assessed on a TapeStation 4150 system (Agilent Technologies) with D1000 ScreenTape.OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesData Transformation - Lowly expressed genes were removed by requiring ≥10 counts in ≥4 samples. To control for variable non-microglial contamination, the summed DESeq2-normalized expression of marker panels for choroid plexus (Ttr, Enpp2, Clic6, Kcnj13, F5, Prlr, Folr1), astrocytes (Slc1a2, Slc1a3, Atp1a2, Sparcl1, Gjb6, Aqp4, Aldoc, Ttyh1, Bcan), neurons (Snap25, Syt1, Nrxn1, Gpm6a, Pcdh10, Nrcam, Cadm2), and oligodendrocytes (Mbp, Plp1, Mobp, Mag, Mog) was log2(x+1)-transformed and reduced by PCA; the first principal component was included as a covariate in the DESeq2 design. Differential expression was performed with DESeq2 (ref). For pairwise contrasts between age × condition groups, surrogate variables estimated with svaseq (protecting sex, contam_PC1, and condition) were included as covariates when group sizes permitted, followed by Wald testing and apeglm log2 fold-change shrinkage (BH-adjusted padj < 0.05). For continuous age effects, models were fit separately within each condition: a linear effect was tested by Wald (~ SVs + sex + contam_PC1 + age), and non-linearity by likelihood-ratio test against ~ SVs + sex + contam_PC1 + ns(age, df = 2). Significant non-linear genes were classified into shape categories based on relative VST expression changes across young, middle, and old age bins. Gene-expression scoring was done per sample using single-sample GSEA (ssGSEA, GSVA package (Ref)) on VST-transformed counts.Sequence Alignment - Paired-end 150 bp reads were trimmed for adapters and low-quality bases using fastp v0.23.4 with default parameters and --detect_adapter_for_pe. Trimmed reads were aligned to the mm39 mouse reference genome (extended with the Camk2aTTA and V1SSV2 transgene sequences) using STAR v2.7.3a, and gene-level counts were generated with --quantMode GeneCounts.UnknownTranscriptomicsGenomicsProteomics--Illumina NovaSeq XRNA-seq of coding RNAMus musculusKarin DanzerJaehyun LeefalseBulk-RNA of MACS isolated Microglia from a Parkinson Mouse Model brain from young and old mice and Parkinson and ControlMicroglia are increasingly recognized as active drivers of Parkinson's disease (PD), contributing to neuroinflammation, α-synuclein clearance, and dopaminergic neuron loss. Because aging is the strongest risk factor for PD and independently shifts microglia toward a primed, pro-inflammatory state, disentangling age- and disease-related transcriptional programs is essential. Aim and workflow This dataset profiles the bulk transcriptome of microglia from [PD model] and control mice at [young] and [old] ages (2×2 design) to resolve disease, age, and interaction effects. Brains were enzymatically dissociated, microglia isolated by MACS using CD11b MicroBeads, and total RNA extracted for bulk RNA-seq library preparation and sequencing on Novaseq 6000. n = 3-4 per group. Both sexes included. All samples except Sample1 includes two mice. Sequencing was done with a target of 40G per sample PE150.2026-06-17T00:00:00Z2026-06-17T01:00:35.337Z2026-06-09T17:57:26.721ZE-MTAB-17136ERP194440EFO_0002944EFO_0004170EFO_0004917EFO_0005518EFO_0003816EFO_0004184