Single-cell RNA sequencing library preparation relies on PCR amplification steps that are sensitive to variation in cell input and RNA content. Conventional fixed-cycle PCR workflows require prior estimation of these parameters, often involving manual quantification, sample stratification, and multiple thermocycler runs to mitigate under- or over-amplification. These requirements increase technical complexity and may introduce variability when processing heterogeneous samples. We evaluated AutoNorm™, a real-time PCR approach (iconPCR™) incorporating per-well fluorescence monitoring and automated cycle termination to normalize amplification during single-cell library preparation.

Libraries were generated using 10x Genomics GEM-X Universal 3’ Gene Expression v4 chemistry from a single cell population captured across a range of inputs (500–10,000 cells). cDNA amplification and index PCR were performed using either a standard fixed-cycle protocol or real-time, per-sample cycle determination. Automated normalization reduced total PCR cycling by 5-6 cycles and enabled concurrent processing of all samples, reducing the number of thermocycler runs from six to two. Final libraries exhibited reduced variability in concentration across input groups and comparable fragment size distributions between workflows. Downstream sequencing analysis showed consistent gene expression profiles and clustering patterns, with no systematic differences observed between amplification strategies.

These findings indicate that real-time, per-well PCR normalization can reduce technical variability and simplify single-cell RNA sequencing library preparation while maintaining data quality across a broad range of input cell numbers.