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Published March 18, 2023 | Submitted
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spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content

Abstract

Droplet microfluidic methods have massively increased the throughput of single-cell sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples and the overall RNA capture efficiency is lower. These drawbacks stem from the lack of strategies to enrich for high-quality material or specific cell types at the moment of cell encapsulation and the absence of implementable multi-step enzymatic processes that increase capture. Here we alleviate both bottlenecks using fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei, fixed cells or target cell types and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half. We harness these unique properties to deliver a high-quality molecular atlas of mouse brain development, despite starting with highly damaged input material, and provide an atlas of nascent RNA transcription during mouse organogenesis. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost.

Additional Information

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. J.D.J. received scholarship support from the BBSRC, T.S.K. held an EU H2020 Marie Skłodowska-Curie Individual Fellowship (MSCA-IF 750772), A.L.E. was supported by the Cambridge Trusts and the EU H2020 Marie Curie ITN MMBio and T.N.K. by an AstraZeneca studentship. M.T. was supported by the International Centre for Translational Eye Research (project MAB/2019/12, carried out within the International Research Agendas programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund). This work was supported by the EU Horizon 2020 programme (ERC Advanced Investigator Awards to F.H., 69566 and M.Z.G., 669198), the Wellcome Trust (WT108438/C/15/Z to F.H. and 207415/Z/17/Z to M.Z.G.) and the NIH (Pioneer Award to M.Z.G., DP1 HD104575-01). The authors would like to thank the members of the Hollfelder laboratory for their feedback. We thank Dr. Anna Alemany for help and suggestions for the data analysis. Author information. J.D.J., T.S.K. and F.H. conceptualised the study. T.S.K. and J.D.J. developed and optimised the droplet microfluidic workflow. J.D.J. developed and optimised the molecular workflow. J.D.J., A.L.E. and T.N.K. retrieved the cultured cells. G.A., C.H. and J.D.J. retrieved and processed the mouse embryos. J.D.J., T.S.K. and D.B.M. performed the encapsulations. J.D.J. performed library preparation and sequencing. J.D.J. and M.T. performed downstream analysis of sequencing results. J.D.J., T.S.K. and F.H. wrote the manuscript, with input from all authors. F.H., G.M.F., S.T. and M.Z.G. supervised the work. Data availability. The 1:1 3T3 and HEK293T mixture 10x Chromium v3 dataset used for benchmarking HEK293T cells is available on their website in the 'Datasets' category. The sciRNA-seq3 E10 dataset was obtained from http://tome.gs.washington.edu/, and the 10x v1 mouse brain dataset was downloaded from SRA with accession number PRJNA637987. The microfluidic chip designs in Extended Data Figure 1A can be found in our repository DropBase (https://openwetware.org/wiki/DropBase:Devices) Code availability. Code is available at https://github.com/droplet-lab/spinDrop Competing interests. J.D.J., T.S.K. and F.H. are inventors on patent applications submitted on behalf of the University of Cambridge via its technology transfer office, Cambridge Enterprise.

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Additional details

Created:
August 20, 2023
Modified:
December 13, 2023