BEST PRACTICES FOR scRNAseq

REDUCING YOUR scRNAseq SEQUENCING COSTS

Key Takeaways

  • Using the Cellometer® K2 Cell Counter to count cells optimizes costs, and helps labs avoid issues arising from poor sample quality, such as sample bias, low cell recovery, low gene counts, and high mitochondrial reads.
Best Practices for scRNA-seq

Check out our application note:
Best Practices for scRNAseq – Reducing your scRNAseq Sequencing Costs

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There is a fine line between liberating single cells from their sample matrix and compromising cell integrity. Single cell RNAseq is more sensitive to sample preparation and storage than traditional sequencing, so even seasoned researchers may underestimate the importance of QC for cell viability and sample purity.

The Cellometer® K2 Fluorescent Cell Counter measures viability and purity more reliably than trypan blue1,2 and brightfield,3 so you can quickly optimize your sample prep without wasting your sequencing budget. Using the Cellometer® K2 Cell Counter to count cells optimizes costs, and helps labs avoid issues arising from poor sample quality, such as sample bias, low cell recovery, low gene counts, and high mitochondrial reads. This rapid QC technology can help you gather reliable, scRNAseq data with the HIVE™ scRNAseq Solution.

QUALITY IN = QUALITY OUT

Poor sample quality costs money at the sequencer

  • Harsh prep or storage kills cells
  • Trypan blue can overestimate viability
  • You may waste reads on dying cells
  • Brightfield microscopy can misidentify contaminating red blood cells (RBCs)
  • Overestimating cell counts wastes reads

Sample QC with the Cellometer® K2 Fluorescent Cell Counter

  • Fluorescent staining for live/dead (AO/PI)
  • Stain excludes RBCs from total cell count
  • Automated counting, reproducible QC step

Use QC to save sequencing time and money

  • Rapidly optimize sample prep and storage
  • Use counts to adjust sequencing for optimal reads/cell
Use QC to save scRNA seq sequencing time and money

Study Parameters

Sample Processing

  • Healthy human bone marrow
    • 3×HIVEs™ devices per condition
  • Sample Prep
    • High viability = Fresh sample
    • Low viability = Cryopreservation
    • High RBCs = 2×RBC removal4
    • Low RBCs = 3×RBC removal4
  • Cellometer® K2 Cell Counter for Cell Counting
    • Fluorescence: live (AO) vs dead (PI)

Harsh Sample Prep or Storage Can Alter Sample Viability & Makeup

Harsh scRNAseq Sample Prep or Storage Can Alter Sample Viability & Makeup

QC SAMPLES FOR VIABILITY

High vs Low Viability

  • 96% (high) vs 62% (low)
  • 15,000 total cells (live+dead) loaded for each case
  • Acquired same reads/HIVE™ scRNAseq solution

Low Sample Quality, Poor Data

  • Fewer cells recovered
    • Reads lost to dead cells
    • Fewer genes identified
    • Higher mito reads

Low Cell Viability Yields Poor Data Quality from Fewer Cells

scRNAseq Low Cell Viability Yields Poor Data Quality from Fewer Cells

QC SAMPLES FOR PURITY

High vs Low RBCs

  • 15,000 total cells (brightfield) loaded for each case
  • Acquired same reads/HIVE™ scRNAseq solution

Use the Nexcelom™ Cellometer® K2 Fluorescent Cell Counter

  • Low-cost, rapid sample QC
    • Distinguishes live/dead
    • More reliable than trypan
    • No RBC miscounting

Cellometer® Device Automatically Measures Cell Counts, Viability, & Purity

K2 Cellometer Automatically Measures Cell Counts, Viability, and Purity for scRNAseq
For High RBCs, cell recovery was taken after 2× passes of RBC removal;4 Cellometer® images were taken after 1× pass. Viability images were captured during High vs Low Viability experiments; dead cells are marked with white circles.

References

  1. Chan, L., Rice, W., Qiu, J. Observation and quantification of the morphological effect of trypan blue rupturing dead or dying cells. PLoS One 15, 1 (2020). https://doi.org/10.1371/journal.pone.0227950
  2. Chan, L., Kuksin, D., Laverty, D., Saldi, S., Qiu, J. Morphological observation and analysis using automated image cytometry for the comparison of trypan blue and fluorescence-based viability detection method. Cytotechnol 67, 3 (2015). https://doi.org/10.1007/s10616-014-9704-5
  3. Chan, L., Laverty, D., Smith, T., Nejad, P., Hei, H., Gandhi, R., Kuksin, D., Qiu, J. Accurate measurement of peripheral blood mononuclear cell concentration using image cytometry to eliminate RBC-induced counting error. J Immunol Methods 388, 1-2 (2013). https://doi.org/10.1016/j.jim.2012.11.010
  4. EasySep™ RBC Depletion Reagent (Catalog # 18170). STEMCELL Technologies Inc. https://www.stemcell.com/products/easysep-rbc-depletionreagent.
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For research use only. Not for use in diagnostic procedures.