A systematic multi-factorial benchmarking reveals critical technical biases in single-nucleus RNA sequencing of the brain

Single-nucleus RNA sequencing (snRNA-seq) is pivotal for dissecting cellular heterogeneity in the brain, yet the effects of technical variables, namely tissue harvest, nuclear fixation, and sequencing platform, on cell composition and gene expression remain poorly quantified. Here, we generated snRNA-seq data from adult mouse brains under distinct experimental designs to present a systematic multi-factorial benchmarking study. This work dissects the individual and interactive effects of harvest methods (post-mortem vs. in vivo), fixation treatments (methanol vs. fresh), and sequencing platforms (10x Genomics vs. SeekOne Digital Droplet). The standard post-mortem harvest induces rapid, selective loss of metabolically active glutamatergic neuron subpopulations, preserving global transcriptome but obscuring transcripts related to advanced cognitive functions. Methanol fixation minimally altered cell-type compositions but introduced cell-type-specific gene expression shifts, preferentially stabilizing structural and core functional transcripts in neurons and glia. Crucially, the sequencing platform shaped biological interpretation. The 10x and SeekOne platforms captured largely non-overlapping differential expression signatures in response to harvest stress, with 10x showing a bias toward detecting synaptic transcript degradation and SeekOne toward preserved metabolic and glial programs. Our study provides a systematic map of technical confounders in snRNA-seq of mouse brain, identifying glutamatergic subpopulations as particularly vulnerable and platform choice as a critical, non-neutral variable. It also provides an experimental and analytical framework to separate technical artifacts from true biological signal, which is important for building reproducible and integrative brain cell atlases. These findings suggest that precise reporting and control of workflow variables are prerequisites for accurate biological discovery in single-cell transcriptomics studies.