10x Genomics RNA-seq

Single-Nucleus and Single-Cell RNA Sequencing: gene expression profiling methods at single-nucleus or single-cell resolution. These methods enable characterisation of the heterogeneity of complex tissues, identification of cell types and subpopulations, and determination of their characteristic gene expression profiles. Unlike conventional RNA-seq, which produces an averaged expression profile for the entire sample, snRNA-seq separates signals from individual cell populations. This also enables identification of rare subpopulations that may be masked when analysing the whole tissue. The method uses 10x Genomics Chromium to partition individual nuclei and label their RNA molecules with unique barcodes. It is particularly useful for frozen material, tissues that are difficult to dissociate, and samples from which a high-quality whole-cell suspension cannot be obtained

10x Genomics RNA-seq workflow

Applications of 10x Genomics RNA-seq

Single-Nucleus RNA-seq or Single-Cell RNA-seq?

The choice between single-nucleus and whole-cell analysis depends primarily on the type of biological material and how it has been preserved.

Single-Nucleus RNA Sequencing:

• enables analysis of frozen material,
• can be used for tissues that are difficult to dissociate into individual cells,
• is less limited by whole-cell size than Single Cell and Spatial Omics,
• may limit expression changes induced by prolonged enzymatic digestion,
• enables analysis of large, fragile or multinucleated cells,
• primarily covers RNA present in the cell nucleus.

Single-Cell RNA Sequencing:

• usually requires fresh material,
• requires a high-quality suspension of individual viable cells,
• covers RNA present in both the nucleus and cytoplasm,
• may be limited by cell size and susceptibility to damage,
• enzymatic tissue dissociation may induce a stress response and alter the gene expression profile.

In snRNA-seq the nuclear transcriptome is analysed, so the resulting profile is not identical to that of the whole cell. The method should be selected with consideration for tissue characteristics, sample storage and the biological objective of the project.

Technical specifications of 10x Genomics RNA-seq

Sequencing parameters and the target number of nuclei/cells should be selected individually according to the material type and experimental objective. 

Sample Requirements

Approximate material requirements:

• animal tissue: at least 200 mg,
• plant tissue: at least 400 mg.

These values are approximate. The actual amount required depends, among other factors, on tissue type, the size and number of cell nuclei/cells, connective-tissue content, secondary metabolites, storage method and the target number of nuclei/cells to be analysed.

Before starting the project, we recommend consulting us about the material type and its collection, preservation and transport.

10x Genomics RNA-seq bioinformatics analysis

Bioinformatics analysis may include:

• Data quality control:
  • assessment of raw-read quality,
  • library quality control,
  • assessment of sequencing saturation,
  • statistics on the number of nuclei/cells analysed,
  • assessment of reads per nucleus/cell,
  • assessment of the number of detected genes and UMI molecules,
  • identification of nuclei that do not meet quality criteria.
• Gene-expression analysis:
  • mapping reads to the reference genome,
  • assigning reads to genes and individual nuclei/cells,
  • creation of a gene expression matrix,
  • quantification and analysis of gene expression distributions,
  • data normalisation and filtering.
• Clustering and identification of cell populations:
  • dimensionality reduction,
  • grouping nuclei based on expression profiles,
  • visualisation of results using UMAP or t-SNE,
  • identification of marker genes for individual clusters,
  • identification of known cell types,
  • characterisation of subpopulations and cell states.
• Clustering and identification of cell populations:
  • dimensionality reduction,
  • grouping nuclei based on expression profiles,
  • visualisation of results using UMAP or t-SNE,
  • identification of marker genes for individual clusters,
  • identification of known cell types,
  • characterisation of subpopulations and cell states.
• Differential-expression analysis:
  • identification of genes differentially expressed between clusters,
  • comparison of expression between experimental groups,
  • hierarchical clustering of differentially expressed genes,
  • analysis of marker genes for individual cell populations.
• Functional analysis:
  • functional annotation of genes,
  • enrichment analysis of biological processes and metabolic pathways,
  • prediction of transcription factors associated with differentially expressed genes,
  • protein-interaction analysis,
  • functional analysis of marker genes for individual clusters,
  • annotation of genes using appropriate databases.

The exact scope of bioinformatics analysis is determined individually and depends on the organism, availability of a high-quality reference genome, number of samples and project objective.