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bioRxiv · 10.64898/2025.12.29.696950

MusTer: A Generalizable Microfluidic Platform Combining Multi-Parametric Droplet Sorting and Multi-Droplet Merging in Single-Cell Sequencing

Li, L.·Liu, W.·Cheng, G.·Qu, F.·zhong, s.·Ho, Y.-P.
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Abstract

Droplet microfluidics is a core technology that powers high-throughput single cell sequencing. However, the current generation of single-cell microfluidics faces notable limitations, including cell aggregation, suboptimal on-chip reactions that compromise experimental outcomes and elevate background noise, as well as a dependence on costly commercial barcode beads. To address these challenges, we present MusTer, an integrated next-generation platform with multi-parametric singlet droplet sorting and triple-droplet merging capability. MusTers multi-parametric singlet sorting module enables in-line droplet analysis of intrinsic fluorescence peak amplitude, width and interval from single-nucleus- (singlet) or multiple-nuclei (multiplet)-encapsulating droplets, subsequently allowing an effective separation of the singlet droplets from multiplet droplets and empty droplets. MusTers triple-droplet merging module enables precise multi-step reactions, with each step performed under its own optimal conditions, thereby significantly enhancing experimental flexibility and efficiency. We validated MusTers performance by performing single-cell ATAC-seq on maize leaves. The results demonstrate that MusTer significantly reduces the doublet rate, enhances the signal-to-noise ratio, and yields improved cell clustering compared with traditional methods. These results validate MusTers capability to overcome key limitations in droplet-based single-cell analysis, effectively enhancing data quality and reliability, and also paves the way for its use in other challenging sample types and multi-step single-cell assays.

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Li, L., Liu, W., Cheng, G., Qu, F., zhong, s., Ho, Y.-P.. 2025-12-30. MusTer: A Generalizable Microfluidic Platform Combining Multi-Parametric Droplet Sorting and Multi-Droplet Merging in Single-Cell Sequencing. https://doi.org/10.64898/2025.12.29.696950

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