Danish Neuro Single Cell (NeuSiC) platform

The service offered includes all work from library preparation to data analysis, and consists of the following blocks:

1. Preparation of single cell/spatial libraries
2. Sequencing (Illumina NovaSeq6000)
3. Data analysis

User can choose either block 1 only, block 1 and 2, or block 1, 2 and 3.

Users are responsible for nuclei/cell isolation and fixation. Fixation allows for storage of nuclei suspensions and provides a more practical experimental workflow. This option is available only when using the 10x Genomics Flex and Parse kits, but not with the 10x Genomics Universal 3' or 5' kit. For the latter nuclei/cells must be isolated and delivered to our facility on the same day, limiting it to 2–4 samples per day.

Additional information about selecting the appropriate technology is available on the subpage TECHNOLOGIES.

Offered technologies

Single cell omics

• 10x Genomics Universal 3’ Gene Expression (no sample fixation)
• 10x Genomics Universal 5’ Gene Expression (no sample fixation)
• 10x Genomics Flex Gene Expression with sample fixation and multiplexing of samples (only human or mouse samples)
• 10x Genomics Epi Multiome ATAC + Gene Expression (tentatively available in fall 2025)
• Parse Evercode^TM WT Mini (up to 12 samples), Evercode^TM WT (up to 48 samples), and Evercode^TM WT Mega (up to 96 samples) with sample fixation
• Smart-seq2 (full-length transcript coverage) (tentatively available in fall 2025): example
• Patch-seq (tentatively available fall 2025): original paper 1 and paper 2

Spatial omics

• 10x Genomics Visium Spatial Gene Expression (tentatively available in fall 2025)
• 10x Genomics Visium HD Spatial Gene Expression (tentatively available in fall 2025)

Data analysis

There will be two rounds of data analysis. In the initial round, all data from standard pipelines is delivered (specific requests can be discussed in an initial meeting), including an interactive application for the user to assist with further data analysis (example on youtube).

In the final round user-specific adjustments are made and publication-ready figures are delivered.

Standard analysis pipeline for small-scale single cell omics

See also our github repository for example code and plots: https://github.com/NeuSiC/scRNAseq_R_code

1) Raw data processing

• generate fastq files
• produce count matrices
• initial quality control of sequencing and cell calling

2) Quality control of cells (CRMetrics)

• remove low-quality cells based on UMI counts (example) and mitochondrial read fraction (example)
• remove potential doublets (example)
• optional: CellBender to remove ambient RNA (example)

3) Initial data analysis (conos)

• normalization, pre-processing (pagoda2)
• integration of different samples/conditions
• dimensionality reduction
• clustering

4) Cell type annotation

• automated cell type annotation
• or optional performed by user

5) Differential analysis (cacoa):

• Compositional changes (example)
• Transcriptomic expression shifts (example)
• Inspection of sample differences (example)
• Differentially expressed gene analysis (example)
• Genes set enrichment analysis (example)

Additional analysis (user-specific)

• Cluster-free differential analysis (cacoa)
• Cell-cell interaction (CellChat)
• Cellular trajectory analysis: pseudotime (slingshot) or RNA velocity (scVelo)
• Integration with genetic data (GWAS) (CELLECT)
• Gene regulatory networks (CollecTRI + decoupleR)

More analysis tools are available upon request.

Users are only responsible for covering the expenses for reagents used in library preparation and sequencing, while the working hours are fully subsidized.

*Price of reagents for fixation of cells or nuclei depends on number of samples to be loaded on Parse kit, should be estimated separately (fixation of 1 sample: 300 DKK).

In single-cell transcriptomics, careful experimental design is crucial to minimize batch effects and confounding factors that could lead to spurious clustering and mask the true biological signal. Ensuring an adequate number of samples and incorporating appropriate controls tailored to your research question is equally important to generate robust and interpretable data.

This guide provides key considerations for designing your single-cell experiment. We do not take responsibility for the completeness of the information provided. Always adapt your experimental design to the specifics of your study.

What is a batch effect?

A batch effect refers to systematic differences in your data caused by technical factors rather than biological variation. These effects can arise due to:

• Differences in sample preparation protocols

• Optimize your sample preparation protocol and maintain consistency! Always use the same reagents and equipment.

• The person performing the sample preparation

• Ideally, a single person should handle all samples to minimize variability.

• The day of sample preparation

• Process as many samples as possible per day to reduce variability across batches.

Some batch effects are unavoidable, such as those caused by processing samples on different days or involving multiple individuals in sample preparation. To mitigate their impact, it is essential to:

• Record all batch-related variables (e.g. date, operator, reagent lot numbers).
• Ensure a balanced experimental design, distributing conditions (e.g. treatment, genotype) and biological covariates (e.g. sex, age, post-mortem interval) evenly across batches.
• Address batch effects during data analysis if necessary.

How to design your single-cell experiment:

1. Define sample groups and controls
• Determine the number of samples per group based on your research question.
• Ensure appropriate controls (e.g., age-matched samples).
2. Identify key biological covariates
• Common covariates include sex, age, treatment, genotype, and post-mortem interval (for human samples).
3. Plan sample preparation to minimize batch effects
• Distribute covariates evenly across batches.
• Avoid processing only control samples on one day or all younger samples first.
• Create a sample processing schedule and document it for downstream analysis.

By carefully planning and recording your experiment, you can reduce batch effects and improve the reliability of your single-cell data.

Our in-house protocols for nuclei preparation

• Nuclei isolation with gradient
  • Protocol for nuclei isolation without fixation: NO_FIXATION_Nuclei_isolation_with_gradient.pdf
  • Protocol for nuclei isolation for subsequent fixation for 10x Genomics Flex or Parse Biosciences: FOR_FIXATION_Nuclei_isolation_with_gradient.pdf

Protocols for nuclei/cell fixation are available on the companies webpages

• 10x Genomics FLEX
• Parse Biosciences:
  
  • Nuclei fixation v3
  • Cell fixation v3
  • Low input cell and nuclei fixation v3