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High-throughput genome-wide phenotypic screening via immunomagnetic cell sorting

Nature Biomedical Engineering volume 3, pages 796–805 (2019)Cite this article

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Abstract

Genome-scale functional genetic screens are used to identify key genetic regulators of a phenotype of interest. However, the identification of genetic modifications that lead to a phenotypic change requires sorting large numbers of cells, which increases operational times and costs and limits cell viability. Here, we introduce immunomagnetic cell sorting facilitated by a microfluidic chip as a rapid and scalable high-throughput method for loss-of-function phenotypic screening using CRISPR–Cas9. We used the method to process an entire genome-wide screen containing more than 108 cells in less than 1 h—considerably surpassing the throughput achieved by fluorescence-activated cell sorting, the gold-standard technique for phenotypic cell sorting—while maintaining high levels of cell viability. We identified modulators of the display of CD47, which is a negative regulator of phagocytosis and an important cell-surface target for immuno-oncology drugs. The top hit of the screen, the glutaminyl cyclase QPCTL, was validated and shown to modify the N-terminal glutamine of CD47. The method presented could bridge the gap between fluorescence-activated cell sorting and less flexible yet higher-throughput systems such as magnetic-activated cell sorting.

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Fig. 1: A microfluidic chip for high-throughput cell sorting.
Fig. 2: FACS-free MICS–CRISPR screen identifies QPCTL as a modifier of CD47.
Fig. 3: QPCTL regulates CD47pyro-Glu formation.
Fig. 4: Direct detection of CD47pyro-Glu by MS.

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Data availability

The main data supporting the results in this study are available within the paper and the Supplementary Information. Supplementary Tables 2–4 contain raw read counts, normalized read counts and normalized Z scores for all of the screens. Unprocessed sequencing files are available from the corresponding authors on reasonable request.

Code availability

The ImageJ custom macro used for automated image segmentation is provided in the Supplementary Information.

References

  1. Sharma, S. & Petsalaki, E. Application of CRISPR-Cas9 based genome-wide screening approaches to study cellular signalling mechanisms. Int. J. Mol. Sci. 19, 933 (2018).

    Article  Google Scholar 

  2. Burr, M. L. et al. CMTM6 maintains the expression of PD-L1 and regulates anti-tumour immunity. Nature 549, 101–105 (2017).

    Article  CAS  Google Scholar 

  3. Mezzadra, R. et al. Identification of CMTM6 and CMTM4 as PD-L1 protein regulators. Nature 549, 106–110 (2017).

    Article  CAS  Google Scholar 

  4. Binek, A. et al. Flow cytometry has a significant impact on the cellular metabolome. J. Proteome Res. 18, 169–181 (2019).

    CAS  PubMed  Google Scholar 

  5. Llufrio, E. M., Wang, L., Naser, F. J. & Patti, G. J. Sorting cells alters their redox state and cellular metabolome. Redox Biol. 16, 381–387 (2018).

    Article  CAS  Google Scholar 

  6. Brockmann, M. et al. Genetic wiring maps of single-cell protein states reveal an off-switch for GPCR signalling. Nature 546, 307–311 (2017).

    Article  CAS  Google Scholar 

  7. Wroblewska, A. et al. Protein barcodes enable high-dimensional single-cell CRISPR screens. Cell 175, 1141–1155 (2018).

    Article  CAS  Google Scholar 

  8. de Groot, R., Lüthi, J., Lindsay, H., Holtackers, R. & Pelkmans, L. Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens. Mol. Syst. Biol. 14, e8064 (2018).

    Article  Google Scholar 

  9. Haney, M. S. et al. Identification of phagocytosis regulators using magnetic genome-wide CRISPR screens. Nat. Genet. 50, 1716–1727 (2018).

    Article  CAS  Google Scholar 

  10. Parnas, O. et al. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell 162, 675–686 (2015).

    Article  CAS  Google Scholar 

  11. Han, X. et al. CRISPR-Cas9 delivery to hard-to-transfect cells via membrane deformation. Sci. Adv. 1, e1500454 (2015).

    Article  Google Scholar 

  12. Han, X. et al. Microfluidic cell deformability assay for rapid and efficient kinase screening with the CRISPR-Cas9 system. Angew. Chem. Int. Edn 55, 8561–8565 (2016).

    Article  CAS  Google Scholar 

  13. Aldridge, P. M. et al. Prismatic deflection of live tumor cells and cell clusters. ACS Nano 12, 12692–12700 (2018).

    Article  CAS  Google Scholar 

  14. Matlung, H. L., Szilagyi, K., Barclay, N. A. & van den Berg, T. K. The CD47-SIRPα signaling axis as an innate immune checkpoint in cancer. Immunol. Rev. 276, 145–164 (2017).

    Article  CAS  Google Scholar 

  15. Weiskopf, K. Cancer immunotherapy targeting the CD47/SIRPα axis. Eur. J. Cancer 76, 100–109 (2017).

    Article  CAS  Google Scholar 

  16. Advani, R. et al. CD47 blockade by Hu5F9-G4 and rituximab in non-Hodgkin’s lymphoma. N. Engl. J. Med. 379, 1711–1721 (2018).

    Article  CAS  Google Scholar 

  17. Kong, F. et al. CD47: a potential immunotherapy target for eliminating cancer cells. Clin. Transl. Oncol. 18, 1051–1055 (2016).

    Article  CAS  Google Scholar 

  18. Seiffert, M. et al. Human signal-regulatory protein is expressed on normal, but not on subsets of leukemic myeloid cells and mediates cellular adhesion involving its counterreceptor CD47. Blood 94, 3633–3643 (1999).

    CAS  PubMed  Google Scholar 

  19. Leclair, P. et al. CD47-ligation induced cell death in T-acute lymphoblastic leukemia. Cell Death Dis. 9, 544 (2018).

    Article  Google Scholar 

  20. Carette, J. E. et al. Ebola virus entry requires the cholesterol transporter Niemann–Pick C1. Nature 477, 340–343 (2011).

    Article  CAS  Google Scholar 

  21. Bürckstümmer, T. et al. A reversible gene trap collection empowers haploid genetics in human cells. Nat. Methods 10, 965–971 (2013).

    Article  Google Scholar 

  22. Lee, S.-E. et al. Proteogenomic analysis to identify missing proteins from haploid cell lines. Proteomics 18, e1700386 (2018).

    Article  Google Scholar 

  23. Paulo, J. A. & Gygi, S. P. Isobaric tag-based protein profiling of a nicotine-treated alpha7 nicotinic receptor-null human haploid cell line. Proteomics 18, e1700475 (2018).

    Article  Google Scholar 

  24. Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).

    Article  CAS  Google Scholar 

  25. Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 (Bethesda) 7, 2719–2727 (2017).

    Article  CAS  Google Scholar 

  26. Mair, B. et al. Essential gene profiles for human pluripotent stem cells identify uncharacterized genes and substrate dependencies. Cell Rep. 27, 599–615 (2019).

    Article  CAS  Google Scholar 

  27. Colic, M. et al. Identifying chemogenetic interactions from CRISPR knockout screens with drugZ. Genome Med. 11, 52 (2019).

    Article  Google Scholar 

  28. Logtenberg, M. E. W. et al. Glutaminyl cyclase is an enzymatic modifier of the CD47–SIRPα axis and a target for cancer immunotherapy. Nat. Med. 25, 612–619 (2019).

    Article  CAS  Google Scholar 

  29. Wu, Z. et al. Identification of glutaminyl cyclase isoenzyme isoQC as a regulator of SIRPα-CD47 axis. Cell Res. 29, 502–505 (2019).

    Article  Google Scholar 

  30. Cynis, H. et al. Isolation of an isoenzyme of human glutaminyl cyclase: retention in the Golgi complex suggests involvement in the protein maturation machinery. J. Mol. Biol. 379, 966–980 (2008).

    Article  CAS  Google Scholar 

  31. Stephan, A. et al. Mammalian glutaminyl cyclases and their isoenzymes have identical enzymatic characteristics. FEBS J. 276, 6522–6536 (2009).

    Article  CAS  Google Scholar 

  32. Hatherley, D. et al. Paired receptor specificity explained by structures of signal regulatory proteins alone and complexed with CD47. Mol. Cell 31, 266–277 (2008).

    Article  CAS  Google Scholar 

  33. Ho, C. C. M. et al. “Velcro” engineering of high affinity CD47 ectodomain as signal regulatory protein α (SIRPα) antagonists that enhance antibody-dependent cellular phagocytosis. J. Biol. Chem. 290, 12650–12663 (2015).

    Article  CAS  Google Scholar 

  34. Pozzi, C., Di Pisa, F., Benvenuti, M. & Mangani, S. The structure of the human glutaminyl cyclase-SEN177 complex indicates routes for developing new potent inhibitors as possible agents for the treatment of neurological disorders. J. Biol. Inorg. Chem. 23, 1219–1226 (2018).

    Article  CAS  Google Scholar 

  35. Ramsbeck, D. et al. Structure-activity relationships of benzimidazole-based glutaminyl cyclase inhibitors featuring a heteroaryl scaffold. J. Med. Chem. 56, 6613–6625 (2013).

    Article  CAS  Google Scholar 

  36. Lues, I. et al. A phase 1 study to evaluate the safety and pharmacokinetics of PQ912, a glutaminyl cyclase inhibitor, in healthy subjects. Alzheimers Dement. 1, 182–195 (2015).

    Google Scholar 

  37. Hoffmann, T. et al. Glutaminyl cyclase inhibitor PQ912 improves cognition in mouse models of Alzheimer’s disease—studies on relation to effective target occupancy. J. Pharmacol. Exp. Ther. 362, 119–130 (2017).

    Article  CAS  Google Scholar 

  38. Kumar, A. & Bachhawat, A. K. Pyroglutamic acid: throwing light on a lightly studied metabolite. Curr. Sci. 102, 288–297 (2012).

    CAS  Google Scholar 

  39. Kehlen, A. et al. N-terminal pyroglutamate formation in CX3CL1 is essential for its full biologic activity. Biosci. Rep. 37, BSR20170712 (2017).

  40. Cynis, H. et al. The isoenzyme of glutaminyl cyclase is an important regulator of monocyte infiltration under inflammatory conditions. EMBO Mol. Med. 3, 545–558 (2011).

    Article  CAS  Google Scholar 

  41. Leonidas, D. D. et al. Refined crystal structures of native human angiogenin and two active site variants: implications for the unique functional properties of an enzyme involved in neovascularisation during tumour growth. J. Mol. Biol. 285, 1209–1233 (1999).

    Article  CAS  Google Scholar 

  42. Deuse, T. et al. Hypoimmunogenic derivatives of induced pluripotent stem cells evade immune rejection in fully immunocompetent allogeneic recipients. Nat. Biotechnol. 37, 252–258 (2019).

    Article  CAS  Google Scholar 

  43. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  Google Scholar 

  44. Adams, J. D., Kim, U. & Soh, H. T. Multitarget magnetic activated cell sorter. Proc. Natl Acad. Sci. USA 105, 18165–18170 (2008).

    Article  CAS  Google Scholar 

  45. Labib, M. et al. Aptamer and antisense-mediated two-dimensional isolation of specific cancer cell subpopulations. J. Am. Chem. Soc. 138, 2476–2479 (2016).

    Article  CAS  Google Scholar 

  46. Philpott, D. et al. High-throughput microfluidic cell sorting platform (MICS). Prot. Exch. https://doi.org/10.21203/rs.2.10282/v1 (2019).

  47. Uhlen, M. et al. A pathology atlas of the human cancer transcriptome. Science 357, eaan2507 (2017).

    Article  Google Scholar 

  48. Sasaki, S., Futagi, Y., Kobayashi, M., Ogura, J. & Iseki, K. Functional characterization of 5-oxoproline transport via SLC16A1/MCT1. J. Biol. Chem. 290, 2303–2311 (2015).

    Article  CAS  Google Scholar 

  49. Boix, E. et al. Role of the N terminus in RNase A homologues: differences in catalytic activity, ribonuclease inhibitor interaction and cytotoxicity. J. Mol. Biol. 257, 992–1007 (1996).

    Article  CAS  Google Scholar 

  50. Liao, Y.-D. et al. The structural integrity exerted by N-terminal pyroglutamate is crucial for the cytotoxicity of frog ribonuclease from Rana pipiens. Nucleic Acids Res. 31, 5247–5255 (2003).

    Article  CAS  Google Scholar 

  51. La Mendola, D. et al. Copper binding to naturally occurring, lactam form of angiogenin differs from that to recombinant protein, affecting their activity. Metallomics 8, 118–124 (2016).

    Article  Google Scholar 

  52. Ren, Y. et al. A simple and reliable PDMS and SU-8 irreversible bonding method and its application on a microfluidic-MEA device for neuroscience research. Micromachines 6, 1923–1934 (2015).

    Article  Google Scholar 

  53. Luk, V. N., Mo, G. C. & Wheeler, A. R. Pluronic additives: a solution to sticky problems in digital microfluidics. Langmuir 24, 6382–6389 (2008).

    Article  CAS  Google Scholar 

  54. Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).

    Article  Google Scholar 

  55. Hsiau, T. et al. Inference of CRISPR edits from Sanger trace data. Preprint at bioRxiv https://doi.org/10.1101/251082 (2019).

  56. Nielsen, H. in Protein Function Prediction: Methods in Molecular Biology Vol. 1611 (ed. Kihara, D.) 59–73 (Springer, 2017).

  57. Gogleva, A., Drost, H.-G. & Schornack, S. SecretSanta: flexible pipelines for functional secretome prediction. Bioinformatics 34, 2295–2296 (2018).

    Article  CAS  Google Scholar 

  58. Burdukiewicz, M., Sobczyk, P., Chilimoniuk, J., Gagat, P. & Mackiewicz, P. Prediction of signal peptides in proteins from malaria parasites. Int. J. Mol. Sci. 19, 3709 (2018).

    Article  Google Scholar 

  59. Käll, L., Krogh, A. & Sonnhammer, E. L. L. A combined transmembrane topology and signal peptide prediction method. J. Mol. Biol. 338, 1027–1036 (2004).

    Article  Google Scholar 

  60. Fortelny, N., Yang, S., Pavlidis, P., Lange, P. F. & Overall, C. M. Proteome TopFIND 3.0 with TopFINDer and PathFINDer: database and analysis tools for the association of protein termini to pre- and post-translational events. Nucleic Acids Res. 43, D290–D297 (2015).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank members of the Kelley, Moffat, Angers and C. Boone and B. Andrews laboratories for helpful discussions; K. Chan for TKOv3 library virus preparation; M. Usaj for help with data analysis; P. Mero for administrative assistance; J. Tomic for help with tissue culture; E. Cohen, M. Soste and F. Soares for technical assistance; D. White and J. Warzyszynska for flow cytometry assistance; and staff at the Centre for Applied Genomics (TCAG) at the Hospital for Sick Children (SickKids) for sequencing. This work was supported by grants from the Canadian Institutes for Health Research (to J.M., S.O.K. and S.A.) and the University of Toronto’s Medicine by Design initiative, which receives funding from the Canada First Research Excellence Fund (to S.O.K., J.M. and S.A). J.M. is a Canada Research Chair in Functional Genomics.

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Author notes

  1. These authors contributed equally: Barbara Mair, Peter M. Aldridge.

Authors and Affiliations

  1. Donnelly Centre, University of Toronto, Toronto, Ontario, Canada

    Barbara Mair, Sanna N. Masud, Amy H. Y. Tong & Jason Moffat

  2. Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada

    Peter M. Aldridge, Randy S. Atwal, Meng Zhang, Mahmoud Labib, Stéphane Angers & Shana O. Kelley

  3. Department of Electrical & Computer Engineering, University of Toronto, Toronto, Ontario, Canada

    David Philpott & Edward H. Sargent

  4. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Sanna N. Masud & Jason Moffat

  5. Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada

    Stéphane Angers & Shana O. Kelley

  6. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada

    Jason Moffat

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Contributions

B.M. and P.M.A. performed most of the experiments and analysed data with help from R.S.A., D.P., M.L. and S.N.M. M.Z. and R.S.A. developed the MS assay. A.H.Y.T. helped with screen sequencing and data analysis. B.M., P.M.A., R.S.A., J.M. and S.O.K. wrote the manuscript. B.M., P.M.A., E.H.S., S.A., J.M. and S.O.K. designed the study. S.A., J.M. and S.O.K. supervised the study.

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Correspondence to Jason Moffat or Shana O. Kelley.

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Supplementary information

Supplementary Information

Supplementary figures, tables, protocols and codes.

Reporting Summary

Supplementary Dataset 1

Screen data for MACS.

Supplementary Dataset 2

Screen data for immunomagnetic cell sorting.

Supplementary Dataset 3

Screen data for FACS.

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Mair, B., Aldridge, P.M., Atwal, R.S. et al. High-throughput genome-wide phenotypic screening via immunomagnetic cell sorting. Nat Biomed Eng 3, 796–805 (2019). https://doi.org/10.1038/s41551-019-0454-8

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