Journal: iScience

Article Title: Comparing the impact of sample multiplexing approaches for single-cell RNA-sequencing on downstream analysis using cerebellar organoids

doi: 10.1016/j.isci.2026.114780

Figure Lengend Snippet: Study design, quality control, and potential biases in the data (A) Three iPSC lines (BIONi010-C, BIONi037-A, and KOLF2.1J) were differentiated to cerebellar organoids until days 35 and 50. The organoids generated from the same cell line were pooled and dissociated into single cells when each single-cell suspension was split into two portions. One set of single-cell suspensions was immediately subjected to sample multiplexing with CellPlex and processed in 10x Genomics 3′GEX+FB pipeline. The second set of single-cell suspensions was frozen until all samples were available. The samples were further processed though Parse Biosciences Evercode v2 pipeline. Libraries were sequenced, and the resulting FASTQ files were processed with technology-specific computational pipelines. Count matrices were further analyzed. Graphic was created with BioRender.com . (B) Quality statistics after quality control. Color represents sample identity with respect to technology (10x or Parse), day of differentiation (D35 or D50), and library (L1 or L2). 10x, n = 29,505, Parse, n = 14,542 cells. Three-way ANOVA, p values represent differences between technologies, ∗∗∗ p < 0.001. (C) Left, density scatterplot showing correlation of average gene expression between the two technologies. Right, scatterplot showing correlation of average gene expression between the two technologies. Color represents gene group. (D) Distributions of gene GC content and gene length for differentially expressed genes between technologies. Two-sided t test, ∗∗∗ p < 0.001. See also – and and .

Article Snippet: The samples were further processed though Parse Biosciences Evercode v2 pipeline.

Techniques: Control, Generated, Single Cell, Suspension, Multiplexing, Gene Expression