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Single-nucleus RNA-seq of mouse cortex during mid-gestation pregnancy

ABSTRACT: Single-nucleus RNA sequencing (snRNA-seq) was performed on cortical tissue from non-pregnant and pregnant (embryonic day E13.5) female C57BL/6JRj mice (~12 weeks old) to characterize transcriptomic changes in brain cell populations during pregnancy. This dataset accompanies the study "The lipidomic architecture of the mouse brain" (Fusar Bassini et al.), in which MALDI mass spectrometry imaging was used to map 172 lipids across the adult mouse brain. The snRNA-seq data were generated to investigate cell type-specific transcriptomic changes in the cortex of pregnant mice, complementing the spatial lipidomics findings. Nuclei were isolated from microdissected cortex using NP-40 lysis and iodixanol gradient purification, and libraries were constructed using the 10x Genomics Chromium Next GEM Single Cell 3' v4 (GEMX) kit. Sequencing was performed on an Element Biosciences AVITI24 system to a depth of ~50,000 reads per nucleus. Between 6,357 and 6,932 nuclei were recovered per sample, with a median of ~3,900-4,500 genes per nucleus.

ORGANISM(S): Mus musculus

PROVIDER: GSE325586 | GEO | 2026/03/25

REPOSITORIES: GEO

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Project description:We developed cell2location, a principled and versatile Bayesian model that is designed to resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. To validate cell2location in real tissue, we applied the model to data from the mouse brain, which features diverse neural cell types organised in a well characterised spatial architecture across brain areas, thus presenting a canonical use case to test spatial genomics. We generated matched single nucleus (sn, this submission) and Visium spatial RNA-seq (10X Genomics) profiles of adjacent mouse brain sections that contain multiple regions from the telencephalon and diencephalon. To assess the biological and intra-organ technical variation in spatial mapping, we assayed two mouse brains and serial tissue sections from each brain (total of 3 and 2 matched sections from two animals, respectively, and an extra section for snRNA-seq), creating a rich multi-modal and replicated transcriptomic dataset. Tissue processing. Brains of wild-type adult C57BL/6 mice (postnatal day 56, 1 female and 1 male) were dissected, snap frozen, embedded in optimal cutting temperature compound (Tissue-Tek) and stored at -80oC. Brain hemispheres were cryosectioned at -20oC using a cryostat (Leica, CM3050S). To assess tissue quality, RNA was extracted from test tissue sections using the RNeasy Pico Kit (Qiagen) and yielded high RIN values (9.6 and 9.7) on an Agilent Bioanalyser, indicating high RNA quality. For matched single nuclei and Visium RNA-seq experiments, brain hemispheres were cryosectioned to adjacent thick (200 µm) and thin (10 µm) coronal sections, respectively, and processed the same day. In total, four consecutive sets of thick and thin tissue sections were collected from each brain. Five sets of tissue sections yielded both good quality single nuclei and Visium data (three adjacent sections from mouse 1 and two sections from mouse 2) while one additional section from mouse 2 yielded good single nuclei; these were considered for analysis in this study. Single nucleus RNA-sequencing. Thick (200 µm) mouse brain sections were cryosectioned, dissected from OCT and kept in a tube on dry ice until subsequent processing. Nuclei were extracted from each section as described previously. Briefly, nuclei were released from sections via Dounce homogenisation, Hoechst-stained, and isolated via fluorescence-activated cell sorting (FACS). Nuclei were then loaded into the 10X Chromium Single Cell 3′ Kit (v3) to obtain 3,000-7,000 nuclei per well, and library preparation was done per manufacturer’s protocol. Libraries were sequenced on an Illumina NovaSeq S4 system. Sequencing data were processed using 10X CellRanger version 3.0.2, aligned to mouse pre-mRNA genome reference version mm10 and mRNA count matrices were generated by adding intronic and exonic unique molecular identifier (UMI) counts for each gene in each cell. Initially, snRNA-seq counts were processed using standard Seurat V3 workflow without correcting batch effects between 6 individual samples.

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Single-nucleus RNA-seq of mouse cortex during mid-gestation pregnancy

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