Single Cell Genomics

Droplet technology (10x Genomics)

Combinatorial barcoding (Parse)

Visium HD technology

*Patch-Seq

Patch-seq experiments profile the electrophysiological properties and transcriptome of the same individual neurons, with the goal of identifying the underlying relationships between gene expression and neuronal function.

Additionally, recorded neurons can be backfilled with appropriate dies to evaluate cell morphology.

You

• perform electrophysiology part
• aspirate neuronal content and snap freeze
• send to our facility

We

• provide you protocol including special conditions of electrophysiology part, neuronal collection, and shipment
• perform RNA Isolation, cDNA Synthesis, and Library Preparation
• sequence cDNA libraries

Links to more detailed description of 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
• 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) : example
• Patch-seq: original paper 1 and paper 2

Spatial omics

• 10x Genomics Visium Spatial Gene Expression
• 10x Genomics Visium HD Spatial Gene Expression

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.

Proper preparation of cells/nuclei from tissue is a crucial step for all single-cell methods. We are not able to perform this step on your behalf, as different tissue and cell types require different isolation protocols. Users are expected to provide a cell/nuclei suspension, which we will process further for library preparation.

To ensure optimal results, the submitted cell/nuclei suspension must meet the following criteria:

• ≥70% viable cells (however, viability is not relevant in case of nuclei preparation when you have frozen or fixated tissue)
• Free of visible debris and aggregates

Excess debris or clumped cells can negatively affect data quality and may clog the microfluidic chip during cell loading (10x Genomics platform), which can prevent successful cell recovery and result in experiment failure.

Please consult our recommended protocols on the Resources subpage.

Cell sizes limitation for 10x Genomics droplet technology

Cells can have a diameter of up to 30 µm. Cells larger than 30 µm increase the risk of chip clogging and should not be processed using 10x droplet-based methods. For samples containing large cells, Parse Biosciences workflow, which has no cell size restrictions, is recommended.

FACS and 10x Genomics droplet technology (without fixation)

FACS-sorted samples are compatible with 10x Genomics workflow. FACS is an effective method to enrich for specific cell populations based on cell surface markers, and to remove dead cells and debris, resulting in a cleaner cell suspension.

Note: Cell counts obtained by FACS are often overestimated. We strongly recommend re-counting cells after sorting f.e. using a microscope.

We offer computational analysis and consultation for single cell data from multiple platforms, such as 10x and Parse Biosciences.

We strongly recommend contacting us as early as possible in your experimental design.

Below, in the section “Standard analysis”, there is a suggestion of the most common steps in the analysis of single cell data and the expected number of hours in a simple type of dataset which would involve one condition or two conditions to be compared. For more complex datasets, please contact us and we can make a better estimate for your particular case.

Further types of analyses can be done on single cell data depending on your needs. In section “Further analyses” you can find suggestions of analyses that we could help you with. Please, don’t hesitate to contact us if you are interested in something else as we may not include all the possibilities in this list or to discuss the estimation of number of hours needed for each analysis. Also note that the time dedicated to meetings or communications is not included and will need to be taken into account.

Standard analysis

Raw data processing [5 hrs]

• Set up the computational environment
• Produce count matrices
• Initial quality control of sequencing and cell calling

Quality control of cells

• Remove low-quality cells, typically based on UMI counts and mitochondrial read fraction [3 hrs]
• Remove potential doublets [3 hrs]

Initial data analysis

• Normalization, pre-processing, integration of different samples/conditions and dimensionality reduction [3 hr]
• Overview of sample differences [1 hr]

Cells clustering and annotation

* Note: this part of the analysis can be done multiple times until the user is satisfied with the results. The number of hours shown for each step is the expected maximum for all the expected iterations. If less hours are required, the user will be billed just for the hours that have been used. Alternatively, if the number of hours is going to be surpassed for the analysis the user will be informed beforehand.

• Cells clustering [5 hr]
• Cell type annotation in collaboration with the user [20 hrs]

Cell type specific differential analysis

* Note 1: this part of the analysis can be done multiple times as a continuation of cell cluster and annotation. The number of expected hours indicated in this section is only for 1 iteration.

* Note 2: The number of expected hours indicated in this section is only for 1 comparison between 2 conditions.

• Transcriptomic expression shifts [1 hr]
• Differentially expressed gene analysis [1 hr]
• Gene set enrichment analysis [2.5 hr]

Further analyses

• Gene network analysis (with WGCNA)

* Note 1: this part of the analysis can be done multiple times as a continuation of cell cluster and annotation. The number of expected hours indicated in this section is only for 1 iteration by the user.

* Note 2: The identification of gene modules may involve an iterative process by the bioinformatician in which different parameters will be tested to obtain the best results. The number of hours indicated shows the maximum expected number of hours for the first iteration. Further iterations after discussions with the user may be shorter. Variations on this number will be notified to the user in due time.

• Identification of gene modules [20 hr]
• Cluster-free differential analysis (using cacoa)
• Cell-cell interactions (using CellChat)
• Cellular trajectory analysis (using scVelo)

Prices

BRIC users: 400 DKK/hour

UCPH users: 500 DKK/hour

External users: 980 DKK/hour

Project data storage fee: 82 DKK per Tb per month.

BRIC autonomous users

Or contact us by email: Irina.korshunova@bric.ku.dk 

Please schedule any single cell experiment at least 2 weeks in advance.

If you have no experience, consultation is necessary before planning an experiment.

Info for non-BRIC users

Contact us by email to discuss your project Irina.korshunova@bric.ku.dk 

We will also need this info from you:

• Your name
• Your email
• Affiliation
• Adress
• Leader group name
• Leader group email
• CVR/VAT no.
• EAN
• Optional: Contact information for the accountant-manager

Prices for BRIC-users

Last update: January 2026

The provided here examples are for informational purposes. The exact cost is calculated individually for each specific experiment.

*Price of reagents for fixation of cells or nuclei depends on amount of samples to be loaded on Parse kit, it should be estimated additionally.

Fixation of 1 sample - 300 DKK

At! Note that Single Cell Genomics facility (SCG) cannot cover the cost in the case of failure. If the failure is due to manufacturing flaws of the 10X components or the Illumina system, the companies will compensate.

Protocols for nuclei/cell fixation are available on the company's webpages

• Cell Preparation Guide – general recommendations
• 10x Genomics FLEX
• Parse Biosciences:
  • Nuclei fixation v3
  • Cell fixation v3
  • Low input cell and nuclei fixation v3

 Our in-house protocols for nuclei preparation

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

Other helpful links

• 10x Genomics support – protocols, Q&A, video etc
• Parse user guides
• Illumina Flowcell Capacity Calculator
• Illumina NovaSeq 6000 Sequencing kits

Contact to Parse and 10x Genomics representatives in Denmark

Parse

Sales Manager: 
Anne Helness, anne@parsebiosciences.com 

Field application scientist:
Michael Palmer, mpalmer@parsebiosciences.com

10x Genomics

Sales Manager:
John Wahlgren, john.wahlgren@10xgenomics.com

Field application scientist:
Aydan Derdiyok, aydan.derdiyok@10xgenomics.com 

Please recognize and acknowledge Single Cell Genomics facility (SCG) contributions to the scientific improvements in your project. Being acknowledged in publications is very important as it serves as documentation for SCG  value and performance.

Remember to notify us when a SCG acknowledgement is published. 

Acknowledgment section

“We acknowledge [X], [Y], [Z] and the Single Cell Genomics facility   for technical and computational expertise and support.”  (Please [insert] the names of SCG members that have helped you.)

Material and Method section

Contact SCG if you need a method description of the provided NGS service(s) and please add the following acknowledgment:

 “_____________ was performed by Single Cell Genomics facility (BRIC, University of Copenhagen)”