Single cell RNA sequencing has become a powerful way to study how individual cells behave within complex tissues. One challenge, however, is throughput, running many samples and many cells can quickly become expensive and time consuming. To address this, researchers often use multiplexing, a strategy that labels cells from different samples so they can be processed together in a single experiment.

Researchers from the University of Tübingen, Germany compared two widely used multiplexing approaches for single cell RNA sequencing. These were combinatorial barcoding from Parse Biosciences and microfluidic capture using 10x Genomics CellPlex. The goal was to understand how these methods perform when applied to a complex and biologically relevant system, cerebellar organoids.

Cerebellar organoids are three dimensional tissue models grown from stem cells that mimic aspects of human cerebellum development. They are especially useful for studying brain development and neurological disease, but their cellular complexity makes them a demanding test case for sequencing technologies.

The team applied both multiplexing strategies to the same organoid system and examined how well each method captured cellular diversity and gene expression patterns. Overall, both approaches performed reliably and identified similar types of cells, showing that either workflow can provide meaningful biological insight.

However, important differences emerged. Cells processed with the 10x Genomics workflow showed higher levels of stress related signals, reflected by increased mitochondrial and ribosomal RNA fractions. In contrast, the Parse approach captured a broader range of gene biotypes and showed lower indicators of cellular stress. Parse also performed better at capturing longer RNA transcripts, which can be important for studying gene regulation and isoform usage.

These findings suggest that while both methods are suitable for profiling cerebellar organoids, the choice of multiplexing strategy can influence subtle but important aspects of the data. Factors such as experimental flexibility, transcript coverage, and cell stress should be considered when designing single cell RNA sequencing experiments, especially for delicate or complex tissues like brain organoids.

By directly comparing these two popular workflows, the authors provide practical guidance for researchers aiming to balance throughput, data quality, and biological fidelity in future single cell studies.