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lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements

Nature Protocols volume 15, pages 2387–2412 (2020)Cite this article

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An Author Correction to this article was published on 30 October 2020

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Abstract

Massively parallel reporter assays (MPRAs) can simultaneously measure the function of thousands of candidate regulatory sequences (CRSs) in a quantitative manner. In this method, CRSs are cloned upstream of a minimal promoter and reporter gene, alongside a unique barcode, and introduced into cells. If the CRS is a functional regulatory element, it will lead to the transcription of the barcode sequence, which is measured via RNA sequencing and normalized for cellular integration via DNA sequencing of the barcode. This technology has been used to test thousands of sequences and their variants for regulatory activity, to decipher the regulatory code and its evolution, and to develop genetic switches. Lentivirus-based MPRA (lentiMPRA) produces ‘in-genome’ readouts and enables the use of this technique in hard-to-transfect cells. Here, we provide a detailed protocol for lentiMPRA, along with a user-friendly Nextflow-based computational pipeline—MPRAflow—for quantifying CRS activity from different MPRA designs. The lentiMPRA protocol takes ~2 months, which includes sequencing turnaround time and data processing with MPRAflow.

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Fig. 1: Schematics of lentiMPRA.
Fig. 2: Overview of MPRAflow association utility.
Fig. 3: Overview of count utility.
Fig. 4: Overview of saturation mutagenesis utility.

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Data availability

A 5′ lentiMPRA dataset conducted in HepG2 cells15 has been deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession no. GSE142696.

Code availability

The source code is freely available at https://github.com/shendurelab/MPRAflow.

Change history

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Acknowledgements

This work was supported by National Human Genome Research Institute grants 1UM1HG009408 (N.A. and J.S.) and 1R21HG010065 and 1R21HG010683 (N.A.), as well as a Ruth L. Kirschstein Predoctoral Individual National Research Service Award 1F31HG011007 (M.G.G.), an NRSA NIH fellowship 5T32HL007093 (V.A.), National Institute of Mental Health grants 1R01MH109907 and 1U01MH116438 (N.A. and K.S.P.), and the Uehara Memorial Foundation (F.I.). J.S. is an investigator of the Howard Hughes Medical Institute.

Author information

Author notes

  1. These authors contributed equally: M. Grace Gordon, Fumitaka Inoue, Beth Martin and Max Schubach.

Authors and Affiliations

  1. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA

    M. Grace Gordon, Fumitaka Inoue, Shiyun Feng, Jingjing Zhao, Ryan Ziffra, Anat Kreimer, Ilias Georgakopoulos-Soares, Chun Jimmie Ye & Nadav Ahituv

  2. Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

    M. Grace Gordon, Fumitaka Inoue, Shiyun Feng, Jingjing Zhao, Ryan Ziffra, Anat Kreimer, Ilias Georgakopoulos-Soares, Chun Jimmie Ye, Katherine S. Pollard & Nadav Ahituv

  3. Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA

    M. Grace Gordon

  4. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Beth Martin, Vikram Agarwal & Jay Shendure

  5. Berlin Institute of Health (BIH), Berlin, Germany

    Max Schubach & Martin Kircher

  6. Charité–Universitätsmedizin Berlin, Berlin, Germany

    Max Schubach & Martin Kircher

  7. Calico Life Sciences LLC, South San Francisco, CA, USA

    Vikram Agarwal

  8. Gladstone Institutes, San Francisco, CA, USA

    Sean Whalen & Katherine S. Pollard

  9. Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA

    Tal Ashuach, Anat Kreimer & Nir Yosef

  10. Chan-Zuckerberg Biohub, San Francisco, CA, USA

    Nir Yosef, Chun Jimmie Ye & Katherine S. Pollard

  11. Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Chun Jimmie Ye

  12. Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, California, USA

    Chun Jimmie Ye

  13. Department of Epidemiology and Biostatistics and Institute of Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA

    Katherine S. Pollard

  14. Howard Hughes Medical Institute, Seattle, WA, USA

    Jay Shendure

  15. Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA

    Jay Shendure

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  1. M. Grace Gordon
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Contributions

F.I. and B.M. developed lentiMPRA; R.Z. assisted in developing lentiMPRA; M.G.G., M.S., V.A., S.W., S.F., J.Z., T.A., A.K., I.G.-S., N.Y., C.J.Y., K.S.P., M.K., J.S. and N.A. assisted in developing MPRAflow; and all authors contributed to writing the manuscript.

Corresponding authors

Correspondence to Fumitaka Inoue, Jay Shendure, Martin Kircher or Nadav Ahituv.

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Competing interests

The authors declare no competing interests.

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Related links

Key references using this protocol

Inoue, F. et al. Genome Res. 27, 38–52 (2017): https://doi.org/10.1101/gr.212092.116

Klein, J. et al. Preprint at bioRxiv 576405 (2019): https://doi.org/10.1101/576405

Kircher, M. et al. Nat. Commun. 10, 3583 (2019): https://doi.org/10.1038/s41467-019-11526-w

Key data used in this protocol

Klein, J. et al. Preprint at bioRxiv 576405 (2019): https://doi.org/10.1101/576405

Extended data

Extended Data Fig. 1 Sequence scheme of lentiMPRA.

a, Synthesized CRS oligo sequence. b, Primers and their binding in 1st and 2nd round PCR for library amplification. c, Recombination and plasmid library sequence. d, Primers and their binding in library amplification and sequencing for CRS–barcode association. e, Primers and their binding in reverse transcription, library amplification and sequencing for barcode counting.

Extended Data Fig. 2 Time complexity study of MPRAflow.

a, The Association Utility run time scales with number of reads when holding the number of FASTQ chunks at 2M reads. As this is an alignment the memory requirements are not trivial, requiring approximately 1GB of memory per 3M reads. b, The Count Utility run time scales with number of reads divided by the number of experiments running in parallel. This step does not require much memory, where 500M reads can be processed in <0.5GB.

Supplementary information

Reporting Summary

Supplementary Table 1

Calculation for each step of the experimental procedures.

Supplementary Table 2

Lentivirus titration by qPCR.

Supplementary Table 3

Primer sequences.

Supplementary Table 4

Sample pooling.

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Gordon, M.G., Inoue, F., Martin, B. et al. lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements. Nat Protoc 15, 2387–2412 (2020). https://doi.org/10.1038/s41596-020-0333-5

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