Cell preparation is arguably one of the most important stages in the single-cell RNA-seq workflow. From sample prep to cell counting, there are several factors that can influence cell viability and data quality in your single cell project. To obtain optimal results with PIPseq, below are valuable tips for proper sample preparation.
Common variables that can negatively impact your results:
- low cell viability (< 85%)
- pipetting technique
- improper size gating on automated cell counters
- large amounts of debris in cell suspension leading to over or underestimating cell count
- counting overly concentrated or overly dilute cell suspensions
- cell aggregation
1. Low Cell Viability
Low viability can lead to higher mitochondrial RNA expression and lower gene expression overall. In complex samples this can lead to worse resolution between different cell types.
2. Pipetting Technique
Pipetting technique can lead to cell aggregation for adherent cell lines if the cell biologist is too gentle in their prep of cells, leading to large clumps of cells that are difficult to count, emulsify, and isolate into PIPs.
Alternatively, pipetting too aggressively can lead to cell shearing and reduced viability of your cell population overall, leading to data that may resemble a bulk RNAseq experiment. The use of standard bore pipette tips may introduce more mechanical damage to the cell during mixing than wide bore pipette tips. However, it remains critical to use standard bore tips to declump cell aggregates. Insufficient mixing of cells will introduce variation in the result. Cells do not remain distributed homogeneously in the media and will settle to the bottom of the tube if mixing is not done before each step which will introduce capture issues and aggregation problems.
It is best practice to conduct practice cell preparations for every novel cell type to ensure that a high viability monodisperse suspension can be reliably obtained prior to the start of a PIPseq study.
3. Improper Size Gating on Automated Cell Counters:
Automated counters require proper size gating for your cell type, a sample concentration within the dynamic range of your instrument, and low debris for accurate and repeatable cell counts. Ensure your sample meets the specifications of your preferred automated cell counter.
4. Large Amounts of Debris
Some dyes such as trypan blue can form precipitate debris that can sometimes be counted as cells and therefore lead to an overestimated cell input in your assay, which will lead to less real cells loaded than desired in your assay. Conversely, if the count is an underestimation due to improper dye usage or size-gating, this will lead to an overloaded cell input, which is a problem because it can lead to higher multiplet rates and errors when estimating sequencing depth which leads to loss of power in downstream analyses.
5. Counting Overly Concentrated or Dilute Samples
Manual counts on a hemocytometer are preferable in many labs, as experienced cell biologists can usually define and exclude debris better than automated counters. There can be variability in manual counting when changing users however. In some cases, cell aggregates and/or debris may be too overwhelming for an automated or manual counter to resolve. If dye debris is overwhelming, filtering and spinning down your dye may help. Always refer to your cell counter manual’s suggested cell concentration limits for accurate counting.
6. Cell Aggregation
If cell aggregates are overwhelming, returning your sample to the hood for additional dissociation using a narrow bore pipette tip and recounting can help. Having high cell aggregation will result in overloading and increased debris. So it is crucial to keep the aggregation to minimum specially while working with a cell line that is known for aggregation issues (e.g. HEK293).
Simplify Your Single-Cell Sequencing:
PIPseq was developed to streamline the scRNA-seq process, delivering high quality data. Assuring a successful cell prep is a key step in the PIPseq workflow. For any questions regarding our simplified workflow or to learn more about our novel solutions for single-cell RNA sequencing contact our team at email@example.com.