Why is Illumina Single Cell sequencing read depth based on input cells?

Illumina Single Cell 3’ RNA Prep kits (eg, T2, T10, T20, T100) are named based on the expected number of captured cells or nuclei. However, recommended sequencing read depth is calculated using reads per input cell (RPIC) rather than the expected number of captured cells, due to variability in capture efficiency across sample types.

ILMN SC 3’ RNA Prep Kit	Input Cells/Nuclei	Sequencing Depth (RPIC)	Total Reads per Sample
T2	5,000 X	20,000 =	100 million
T10	17,000 X	20,000 =	340 million
T20	40,000 X	20,000 =	800 million
T100	200,000 X	20,000 =	4 billion

Table 1. Recommend sequencing read depth for Illumina Single Cell 3’ RNA Prep libraries.

Capture Efficiency Factors

Capture efficiency in Illumina Single Cell sequencing varies depending on sample type (eg, tissue characteristics, viability, transcript diversity, cell size/shape) and quality.

To achieve expected capture rates:

• Accurately count and dilute samples to the target concentration. Illumina recommends quantifying samples with a fluorescent nucleic acid stain (eg, AO/PI) using a fluorescence-capable automated counter with proper size gating. Conduct replicate counts to ensure accuracy.

⚠️ Use of trypan blue is not recommended.

• Meet sample, cDNA, and library quality criteria specified in the reference guide and training packet.

• Handle PIPs properly. PIP loss prior to the Synthesize cDNA and Amplify cDNA steps can reduce capture rates by physically removing sample from the workflow before amplification of the soluble cDNA used in library preparation.

Why Base Sequencing Depth on Input Cells?

Sequencing depth is calculated based on input cell count because capture rates vary across sample types. Input count provides a consistent and controllable metric for planning sequencing depth. This standardization helps ensure that the desired sequencing depth is achieved, regardless of the effective cell capture and identification rate.

Illumina has optimized each kit for specific input ranges, making input-based planning a validated and reliable approach that supports reproducible, high-quality results.

Sequencing Depth Considerations

• The optimal sequencing depth depends on the researcher’s experimental goals and the biological questions being addressed.

• Higher read depth may be necessary to detect low-abundance transcripts or rare cell populations.

• After initial sequencing, review key metrics such as capture rate and sequencing saturation to evaluate whether read depth adjustments are needed for future runs.

For any feedback or questions regarding this article (Illumina Knowledge Article #9561), contact Illumina Technical Support [e-mail].