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H.H. received support for the project PID2020-115439GB-I00 funded by MCIN/AEI/10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council under the European Union's Horizon 2020 Research and Innovation Programme (grant agreement numbers 810287 and 874710). This work was supported by a European Research Council Consolidator grant to S.A. (724226_cis-CONTROL), KU Leuven (C14/22/125 to S.A.), Foundation Against Cancer (F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A. and a PhD fellowship to F.D.) and Aligning Science Across Parkinson's (grant number ASAP-000430 to S.A.). K.B.M. and S.A.T. are supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194). Computing was performed at the Vlaams Supercomputer Center and high-throughput sequencing at the Genomics Core Leuven. M.R.C. is supported by the National Institutes on Aging K99/R00AG059918. This work was supported by funding from the Rita Allen Foundation (W.J.G.) and the Human Frontiers Science Program (RGY006S; W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative and National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01-HG00990901 and U19-AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective. B.D. received financial support by Swiss National Science Foundation 310030_197082 and the EPFL. L.S.L. receives support from an Emmy Noether fellowship by the German Research Foundation (LU 2336/2-1), a National Institutes of Health grant UM1HG012076, a Longevity Impetus grant and a Hector Research Career Development Award by the Hector Fellow Academy. A.C.A. is supported by National Institutes of Health grants RF1-MH128842, R35-GM124704 and R01-DA047237 as well as a Silver Family Foundation Innovator Award.

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Systematic benchmarking of single-cell ATAC-sequencing protocols

Publicated to:Nature Biotechnology. - 2023-08-03 (), DOI: 10.1038/s41587-023-01881-x

Authors: De Rop FV; Hulselmans G; Flerin C; Soler-Vila P; Rafels A; Christiaens V; González-Blas CB; Marchese D; Caratù G; Poovathingal S; Rozenblatt-Rosen O; Slyper M; Luo W; Muus C; Duarte F; Shrestha R; Bagdatli ST; Corces MR; Mamanova L; Knights A; Meyer KB; Mulqueen R; Taherinasab A; Maschmeyer P; Pezoldt J; Lambert CLG; Iglesias M; Najle SR; Dossani ZY; Martelotto LG; Burkett Z; Lebofsky R; Martin-Subero JI; Pillai S; Sebé-Pedrós A; Deplancke B; Teichmann SA; Ludwig LS; Braun TP; Adey AC; Greenleaf WJ; Buenrostro JD; Regev A; Aerts S; Heyn H

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Abstract

Benchmarking of scATAC-seq protocols demonstrates the effects of data quality on downstream analyses. Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.

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