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Single-cell RNA-seq (scRNA-Seq) and ATAC-seq are versatile tools to study complex biological systems at single-cell resolution. They are widely utilized to define cell subtypes and states, gene networks and to assess cellular heterogeneity. A prerequisite for performing scRNA-seq is the preparation of a single cell suspension by tissue dissociation. Due to the variation in the extracellular matrix composition and mechanical properties, individual tissues require optimization of the dissociation protocol for preparation of single cell suspension.
Dissociation of tissues into single cells typically involves treatment with digestive enzymes, including collagenase, dispase or trypsin, at 37 °C1,2,3,4. As transcriptional machinery remains active at 37 °C, enzymatic dissociation can introduce mRNA expression artifacts and noise5,6. Notably, prolonged incubation can induce stress responsive genes and heat-shock response in a non-uniform manner – leading to technical variability in the experiment7.
Another drawback of generating a single cell suspension is the difficulty in obtaining viable and intact cell-types with complex morphologies. In particular, neurons, adipocytes and podocytes are challenging to isolate8,9,10,11. For instance, Wu and colleagues demonstrated the absence of glomerular podocytes in scRNA profiles from an adult mouse kidney12. Similar nonoptimal observations have been made regarding the recovery of interconnected neurons from brain tissue8,13,14. In sum, dissociation protocols can introduce detection bias towards easier to dissociate cell-types, leading to a misrepresentation of the cellular architecture of the organ.
To overcome the technical noise and bias introduced during sample preparation in scRNA-Seq., isolation and profiling the nucleus provides an attractive alternative. As nuclear morphology is similar between different cell-types, isolation of the nuclei circumvents the issue of isolating intact and viable cells with complex morphologies. For instance, Wu and colleagues demonstrated successful profiling of glomerular podocytes with the single-nucleus RNA-Seq. (snRNA-Seq.) of an adult mouse kidney, which was missing from scRNA-Seq12. Intriguingly, comparative studies between single-cell and single-nucleus RNA-seq have suggested a decrease in induction of stress and heat-shock response genes with snRNA-Seq12. The studies further suggest a high correlation between the genes detected by the two methods. However, a recent study on human microglia failed to detect genetic activation in Alzheimer’s disease15. Thus in certain contexts, snRNA-Seq is a suitable alternative for scRNA-Seq16,17. Additionally, the nuclear isolation can be utilized for single-cell ATAC-Seq., providing information about the regions of open-chromatin within individual cells.
The protocol for nuclei isolation involves three major steps: i) detergent-based lysis of cell membrane to release the nucleus; ii) tissue homogenization using a Dounce homogenizer; and iii) enrichment of nuclei and removal of cell debris using gradient centrifugation or flow cytometry18,19,20,21,22. Among this, the first two steps depend on the tissue type and need to be empirically optimized. Mild detergent leads to partial rupture of cell membrane and inefficient retrieval of nuclei from the tissue23. On the other hand, high level of detergent and harsh homogenization leads to rupture of the nuclear membrane and their loss24,25. Ruptured nuclei further tend to clump together and form aggregates, which if not removed can lead to artifacts in the downstream profiling experiment.
To circumvent the issues related to detergent optimization for nuclei isolation, we introduce a protocol to isolate intact nuclei from fresh samples using a detergent-free and spin-column-based method. The protocol yields nuclei from whole organ within 20 minutes, limiting the induction of artifactual transcription. The isolated nuclei can be enriched with FACS for single-nuclei RNA-Seq. and ATAC-seq, providing a simple and universal method that enables robust and reproducible high-throughput profiling.