Methods for GRC scRNAseq Poster - Wilmot Symposium 2026 v1

Introduction:Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of complex tissues and has advanced our understanding of cellular heterogeneity. Many platforms are available, each with trade-offs in lab methodology, sensitivity, accuracy, throughput, and cost. Methods:We evaluated five scRNA-seq platforms - 10x Genomics GEM-X, 10x Genomics Flex, 10x Genomics OCM, Parse Biosciences, and Illumina Single Cell - using manufacturer protocols and a uniform bioinformatics pipeline. Unlike typical benchmarking with peripheral blood mononuclear cells, we used patient-derived acute myeloid leukemia (AML) apheresis samples to evaluate performance on clinically relevant, heterogeneous material. We compared platform differences in standard QC metrics and cell typing and measured the ability of each platform to detect expected cell type markers. We additionally explore the value of increased sequencing depth compared to vendor recommendations. Results:While most identified cell types were represented across all platforms, there were differences in standard QC metrics among the platforms and several platform-specific cell types identified. However, these platform-specific types were almost always attributed to poor QC metrics and therefore likely represent technical artifacts. Specifically, several cell types unique to the Illumina platform and with large cell numbers appear to be a technical artifact, not unique biology. Generally, GEM-X and OCM platforms were most similar in the recovered cell types and differentially expressed genes. While Flex did not align with cell type marker detection profiles of GEM-X and OCM, this can potentially be attributed to it being a probe-based assay. Additionally, in agreement with a previously published benchmarking study, the Parse platform did not detect several non-malignant cell types that were commonly detected in other platforms. Surprisingly, although the number of genes detected increased significantly, higher sequencing depth did not dramatically affect clustering, independent of platform. Conclusions:Platform performance differed by metric, underscoring that no single technology is universally optimal. Ultimately, selection of an optimal scRNA-seq platform depends on budget, biological system, and specific experimental goals.