How to determine the required sequencing read depth for Illumina Single Cell Prep?

Read Depth Recommendations

A minimum of 20,000 reads per captured cell is recommended for Illumina Single Cell 3’ RNA Prep libraries, assuming users are capturing the number of cells specified for each kit size. Performance may vary due to input cell counting accuracy, capture efficiency, and gene expression sensitivity, discussed in greater depth below.

The recommended read depth for kits loaded at the 20,000 reads per captured cell or nuclei for experienced users is as follows (M = million):

• For T2, 2,000 captured cells requires 40 M reads per T2 sample • For T10, 10,000 captured cells requires 200 M reads per T10 sample • For T20, 20,000 captured cells requires 400 M reads per T20 sample • For T100, 100,000 captured cells requires 2 billion reads per T100 sample

Based on the output per flow cell, the following chart provides the numbers of samples that can be run per flow cell, for each Illumina Single Cell 3’ RNA reaction size.

Importantly, capture efficiency may vary between 50% to 85% depending upon cell type, cell size, cell counting accuracy, and purification and counting method. Therefore, new users, or users testing a new cell type or prep method, should consider targeting 20,000 reads per input cell as a starting point to ensure robust gene expression profiling. This will ensure that data will meet or exceed 20,000 reads per captured cell, accounting for variation in input cell counting accuracy, capture efficiency, and gene expression sensitivity. Lab specific desired reads per cell may vary between 10,000 and 40,000 reads per cell depending on the required sensitivity to low expressing genes.

The recommended read depth for kits loaded at the 20,000 reads per input cell or nuclei for new users is as follows (M = million): • For T2, 5,000 cells loaded requires 100 M reads per T2 sample • For T10, 17,000 cells loaded requires 340 M reads per T10 sample • For T20, 40,000 cells loaded requires 800 M reads per T20 sample • For T100, 200,000 cells loaded requires 4 billion reads per T100 sample

Example recommended target of 800 M reads per T20 reaction: 40,000 input cells per T20 reaction x 20,000 reads per cell = 800 M reads total. This provides for >20,000 reads per cell even with 85% capture efficiency.

Input cells, reads/ sample	Capture efficiency	Total Cells Captured	Reads/ captured cell
40,000 input cells, 800 M reads	50%	20,000 cells	40,000 reads
40,000 input cells, 800 M reads	65%	26,000 cells	31,000 reads
40,000 input cells, 800 M reads	75%	30,000 cells	26,000 reads
40,000 input cells, 800 M reads	85%	34,000 cells	23,000 reads

After the first run with a specific sample type and a specific kit size, users can evaluate capture rate and sequencing saturation metrics to determine if sequencing depth should be adjusted in future experiments based on sample type and experimental needs.

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.

⚠️ The 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 start by estimating 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 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 [email protected].