ArticleCell reports methods2026
Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing.
Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A who's who of cell types in skeletal muscle.Communications biology · 2026Review
- Cholesterol dysregulation in APOE4 astrocytes promotes α-synuclein pathology in miBrains.Cell stem cell · 2026Article
- Unique transcriptomic alterations in 5XFAD;PS19 mouse model identify glial lipid dysregulation and coordinated microglial-oligodendrocyte responses.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Tau Ablation Rewires Brain Cell Programs in Health and Restores Function in Disease.bioRxiv : the preprint server for biology · 2026Article
- Nuclei isolation from rat and cow white adipose tissues for single-nucleus RNA sequencing; rat WAT remains a challenge.Frontiers in physiology · 2026Article
- Deletion of neuronal Idol ameliorates Alzheimer's disease-related pathologies via APOE receptors.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
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7 authors.
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Abstract
Single-nucleus RNA sequencing (snRNA-seq) enables resolving cellular heterogeneity in complex tissues by using nuclei instead of cells, overcoming limitations of single-cell RNA sequencing and enabling analysis of frozen and hard-to-isolate tissues. Despite advances in isolation techniques, systematic evaluations of their effects on nuclear integrity and subsequent data quality remain lacking, a critical gap with profound implications for rigor and reproducibility. To address this, we compared three mechanistically distinct nuclei isolation strategies with brain tissue: a sucrose gradient centrifugation-based method, a spin column-based method, and a machine-assisted platform. All methods captured diverse cell types but revealed considerable protocol-dependent differences in cell type proportions, transcriptional homogeneity, and the preservation of cell-state-specific markers. Moreover, workflows differentially influenced contamination levels from ambient, mitochondrial, and ribosomal RNAs, with the machine-assisted method exhibiting the highest overall data quality. Our findings establish nuclei isolation methodology as a critical experimental variable shaping snRNA-seq data quality and biological interpretation.
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