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Multiplexed single-molecule characterization at the library scale

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Abstract

Single-molecule techniques are exceptionally well suited for analyzing the complex dynamic behavior of macromolecules involved in fundamental biological processes. Nevertheless, time and cost usually restrict current single-molecule methods to examining a limited number of different samples. At the same time, a broad sequence or chemical space often needs to be investigated to gain a thorough understanding of complex biological phenomena. To address this urgent need, we have developed multiplexed single-molecule characterization at the library scale (MUSCLE), a method that combines single-molecule fluorescence microscopy with next-generation sequencing to enable highly multiplexed observations of complex dynamics on millions of individual molecules spanning thousands of distinct sequences or barcoded entities. In this protocol, we outline the implementation of MUSCLE and present examples from our recent research, such as the sequence-dependent dynamics of Cas9-induced target DNA unwinding and rewinding. This example demonstrates that MUSCLE can be applied to study protein–nucleic acid interactions, going beyond nucleic-acid-only model systems. We detail the sample and library design, high-throughput single-molecule data acquisition, next-generation sequencing, spatial registration of single-molecule fluorescence and sequencing data and downstream data analysis. The ligation-based surface immobilization approach of MUSCLE ensures high clustering efficiency (>40%), increasing throughput and simplifying registration. In addition, MUSCLE includes a 3D-printed flow cell adapter that enables liquid exchange during single-molecule fluorescence microscopy. The complete procedure typically spans 3–4 days and yields a dataset that comprehensively characterizes the dynamic behavior of a library of constructs.

Key points

  • MUSCLE leverages single-molecule fluorescence microscopy and next-generation sequencing to characterize in parallel the dynamic behavior of millions of DNA molecules immobilized on an Illumina flow cell by matching single-molecule traces to the corresponding sequenced clusters.

  • Beyond probing how sequence affects the dynamics of single DNA molecules, this approach can be used to study protein–nucleic acid interactions, as illustrated by the sequence-dependent dynamics of Cas9-induced target DNA unwinding and rewinding.

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Fig. 1: MUSCLE workflow overview.
Fig. 2: MUSCLE library design and surface immobilization.
Fig. 3: Single-molecule fluorescence imaging.
Fig. 4: Matching sequencing cluster and single-molecule coordinates.
Fig. 5: smFRET data analysis and visualization.

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Data availability

MUSCLE datasets are available in the SciLifeLab Data Repository76. A smaller test MUSCLE dataset and the data for Supplementary Fig. 1 have also been deposited in the SciLifeLab Data Repository77.

Code availability

The latest version of the MUSCLE data analysis codes can be found at https://github.com/deindllab/MUSCLE, while the current version has been archived in the SciLifeLab Data Repository77.

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Acknowledgements

We thank M. Lindell (National Genomics Infrastructure, Scilifelab, Uppsala, Sweden) for Illumina sequencing. The original work that led to the development of this protocol was funded by European Research Council (ERC) Advanced Grant ERC-ADG-101092623 (to S.D.), Knut and Alice Wallenberg Foundation grant KAW/WAF 2019.0306 (to S.D.), Knut and Alice Wallenberg Foundation grant KAW 2024.0012 (to S.D.), Cancerfonden grant 22 2106 Pj (to S.D.) and Swedish Research Council project grants VR 03534 and VR 03255 (to S.D.).

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  1. These authors contributed equally: M. Panfilov, G. Mao.

Authors and Affiliations

  1. Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden

    M. Panfilov, G. Mao, J. Guo, J. Aguirre Rivera, A. Sabantsev & S. Deindl

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  1. M. Panfilov
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Contributions

The comprehensive description of the MUSCLE method is based and extends upon the contributions of the authors listed in the initial publication39. S.D. conceived the project, with input from A.S. J.A.R. designed the 3D-printed adapter, built the optical setup with input from A.S. and implemented automated data acquisition. G.M. and J.A.R. conducted MUSCLE experiments. A.S., M.P. and J.G. implemented the trace-registration pipeline. A.S. and M.P. developed the MATLAB pipeline for trace analysis from MUSCLE data. M.P., G.M., A.S. and S.D. wrote the paper, with input from all authors.

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Correspondence to A. Sabantsev or S. Deindl.

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Key reference

Aguirre Rivera, J. et al. Science 385, 892–898 (2024): https://doi.org/10.1126/science.adn5371

Supplementary information

Supplementary Information

Supplementary Figs. 1–5

Supplementary Data

Blueprint for 3D-printing the flow cell adapter

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Panfilov, M., Mao, G., Guo, J. et al. Multiplexed single-molecule characterization at the library scale. Nat Protoc (2025). https://doi.org/10.1038/s41596-025-01198-w

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