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Single-Cell B-Cell Sequencing to Generate Natively Paired scFab Yeast Surface Display Libraries

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Genotype Phenotype Coupling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2681))

Abstract

The immune cell profiling capabilities of single-cell RNA sequencing (scRNA-seq) are powerful tools that can be applied to the design of theranostic monoclonal antibodies (mAbs). Using scRNA-seq to determine natively paired B-cell receptor (BCR) sequences of immunized mice as a starting point for design, this method outlines a simplified workflow to express single-chain antibody fragments (scFabs) on the surface of yeast for high-throughput characterization and further refinement with directed evolution experiments. While not extensively detailed in this chapter, this method easily accommodates the implementation of a growing body of in silico tools that improve affinity and stability among a range of other developability criteria (e.g., solubility and immunogenicity).

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References

  1. Tang X, Huang Y, Lei J et al (2019) The single-cell sequencing: new developments and medical applications. Cell Biosci 9:1–9

    Article  Google Scholar 

  2. Cao Y, Qiu Y, Tu G et al (2020) Single-cell RNA sequencing in immunology. Curr Genomics 21:564–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Goldstein LD, Chen YJJ, Wu J et al (2019) Massively parallel single-cell B-cell receptor sequencing enables rapid discovery of diverse antigen-reactive antibodies. Commun Biol 2:1–10

    Article  Google Scholar 

  4. Wen W, Su W, Tang H et al (2020) Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing. Cell Discov 6:1–18

    Google Scholar 

  5. He B, Liu S, Wang Y et al (2021) Rapid isolation and immune profiling of SARS-CoV-2 specific memory B cell in convalescent COVID-19 patients via LIBRA-seq. Signal Transduct Target Ther 6:1–12

    PubMed  PubMed Central  Google Scholar 

  6. Setliff I, Shiakolas AR, Pilewski KA et al (2019) High-throughput mapping of B cell receptor sequences to antigen specificity. Cell 179:1636–1646.e15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Valldorf B, Hinz SC, Russo G et al (2022) Antibody display technologies: selecting the cream of the crop. Biol Chem 403:455–477

    Article  CAS  PubMed  Google Scholar 

  8. Bowley DR, Labrijn AF, Zwick MB et al (2007) Antigen selection from an HIV-1 immune antibody library displayed on yeast yields many novel antibodies compared to selection from the same library displayed on phage. Protein Eng Des Sel 20:81–90

    Article  CAS  PubMed  Google Scholar 

  9. Julian MC, Rabia LA, Desai AA et al (2019) Nature-inspired design and evolution of anti-amyloid antibodies. J Biol Chem 294:8438–8451

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Boder ET, Wittrup KD (1997) Yeast surface display for screening combinatorial polypeptide libraries. Nat Biotechnol 15:553–557

    Article  CAS  PubMed  Google Scholar 

  11. Chao G, Lau WL, Hackel BJ et al (2006) Isolating and engineering human antibodies using yeast surface display. Nat Protoc 1:755–768

    Article  CAS  PubMed  Google Scholar 

  12. Stern LA, Schrack IA, Johnson SM et al (2016) Geometry and expression enhance enrichment of functional yeast-displayed ligands via cell panning. Biotechnol Bioeng 113:2328–2341

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Pellegrini A, Guiñazú N, Aoki MP et al (2007) Spleen B cells from BALB/c are more prone to activation than spleen B cells from C57BL/6 mice during a secondary immune response to cruzipain. Int Immunol 19:1395–1402

    Article  CAS  PubMed  Google Scholar 

  14. Wu X, Ye J, DeLaitsch AT et al (2021) Chemoenzymatic synthesis of 9NHAc-GD2 antigen to overcome the hydrolytic instability of O-acetylated-GD2 for anticancer conjugate vaccine development. Angew Chemie - Int Ed 60:24179–24188

    Article  CAS  Google Scholar 

  15. MojoSortâ„¢ Isolation Kits Protocol - 1, https://www.biolegend.com/en-us/protocols/mojosort-isolation-kits-protocol-1

  16. MojoSortâ„¢ Streptavidin Nanobeads Protocol - Positive Selection, https://www.biolegend.com/protocols/mojosort-streptavidin-nanobeads-protocol-positive-selection/4748/

  17. Analyzing V(D)J, Gene Expression & Feature Barcode with cellranger multi, https://support.10xgenomics.com/single-cell-vdj/software/pipelines/6.1/using/multi

  18. 10X Genomics (2021) Chromium Next GEM Single Cell 5′ Reagent Kits v2 (Dual Index) (CG000331• Rev C),

    Google Scholar 

  19. bcl2fastq2 Conversion Software v2.20 Software Guide (15051736), www.illumina.com/company/legal.html

  20. NEB High Efficiency Transformation Protocol (C2987H/C2987I), https://international.neb.com/protocols/0001/01/01/high-efficiency-transformation-protocol-c2987

  21. Xu J, Tack D, Hughes RA et al (2014) Structure-based non-canonical amino acid design to covalently crosslink an antibody-antigen complex. J Struct Biol 185:215–222

    Article  CAS  PubMed  Google Scholar 

  22. Schoeder CT, Schmitz S, Adolf-Bryfogle J et al (2021) Modeling immunity with Rosetta: methods for antibody and antigen design. Biochemistry 60:825–846

    Article  CAS  PubMed  Google Scholar 

  23. Warszawski S, Katz AB, Lipsh R et al (2019) Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces. PLoS Comput Biol 15:e1007207

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Marks C, Hummer AM, Chin M et al (2021) Humanization of antibodies using a machine learning approach on large-scale repertoire data. Bioinforma 37:4041–4047

    Article  CAS  Google Scholar 

  25. Prihoda D, Maamary J, Waight A et al (2022) BioPhi: a platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning. MAbs 14:2020203

    Article  PubMed  PubMed Central  Google Scholar 

  26. Khetan R, Curtis R, Deane CM et al (2022) Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics. MAbs 14:2020082

    Article  PubMed  PubMed Central  Google Scholar 

  27. Yamawaki TM, Lu DR, Ellwanger DC et al (2021) Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling. BMC Genomics 22:1–18

    Article  Google Scholar 

  28. Gao C, Zhang M, Chen L (2020) The comparison of two single-cell sequencing platforms: BD rhapsody and 10x genomics chromium. Curr Genomics 21:602–609

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Picelli S, Björklund ÅK, Faridani OR et al (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10:1096–1100

    Article  CAS  PubMed  Google Scholar 

  30. Wang X, He Y, Zhang Q et al (2021) Direct comparative analyses of 10X genomics chromium and smart-seq2. Genom Proteom Bioinforma 19:253–266

    Article  CAS  Google Scholar 

  31. See P, Lum J, Chen J et al (2018) A single-cell sequencing guide for immunologists. Front Immunol 9:2425

    Article  PubMed  PubMed Central  Google Scholar 

  32. Safdari Y, Farajnia S, Asgharzadeh M et al (2013) Antibody humanization methods - a review and update. Biotechnol Genet Eng Rev 29:175–186

    Article  CAS  PubMed  Google Scholar 

  33. Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Nivón LG, Moretti R, Baker D (2013) A Pareto-optimal refinement method for protein design scaffolds. PLoS One 8:e59004

    Article  PubMed  PubMed Central  Google Scholar 

  35. Mendenhall J, Brown BP, Kothiwale S et al (2021) BCL::Conf: improved open-source knowledge-based conformation sampling using the crystallography open database. J Chem Inf Model 61:189–201

    Article  CAS  PubMed  Google Scholar 

  36. Rabia LA, Desai AA, Jhajj HS et al (2018) Understanding and overcoming trade-offs between antibody affinity, specificity, stability and solubility. Biochem Eng J 137:365–374

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Raybould MIJ, Marks C, Krawczyk K et al (2019) Five computational developability guidelines for therapeutic antibody profiling. Proc Natl Acad Sci U S A 116:4025–4030

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Benatuil L, Perez JM, Belk J et al (2010) An improved yeast transformation method for the generation of very large human antibody libraries. Protein Eng Des Sel 23:155–159

    Article  CAS  PubMed  Google Scholar 

  39. Horns F, Quake SR (2020) Cloning antibodies from single cells in pooled sequence libraries by selective PCRF. PLoS One 15:e0236477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Thean RKR, Ong DXY, Heng ZSL et al (2021) To plate or to simply unfreeze, that is the question for optimal plasmid extraction. J Biomol Tech 32:57–62

    PubMed  PubMed Central  Google Scholar 

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Acknowledgments

We would like to thank the Genomics Core at Michigan State University for assisting us with the documentation for the sequencing workflow. We would also like to thank Dr. Kevin Childs and Emily Crisovan of Michigan State University for the informative discussion on the 10× Genomics sequencing process and Michael Cartwright of 10× Genomics for providing advice on the cloning of antibodies from cDNA. Some of the figures produced above were created with BioRender.com.

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Authors and Affiliations

  1. Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA

    Nathaniel Pascual, Theodore Belecciu, Sam Schmidt & Daniel Woldring

  2. Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA

    Nathaniel Pascual, Theodore Belecciu, Sam Schmidt, Athar Nakisa, Xuefei Huang & Daniel Woldring

  3. Department of Chemistry, Michigan State University, East Lansing, MI, USA

    Athar Nakisa & Xuefei Huang

Authors
  1. Nathaniel Pascual
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  2. Theodore Belecciu
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  3. Sam Schmidt
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  4. Athar Nakisa
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  5. Xuefei Huang
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  6. Daniel Woldring
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Corresponding author

Correspondence to Daniel Woldring .

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Editors and Affiliations

  1. Protein Engineering and Antibody Technologies (PEAT), Merck Healthcare KGaA, Darmstadt, Germany

    Stefan Zielonka

  2. Protein Engineering and Antibody Technologies (PEAT), Merck Healthcare KGaA, Darmstadt, Germany

    Simon Krah

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Pascual, N., Belecciu, T., Schmidt, S., Nakisa, A., Huang, X., Woldring, D. (2023). Single-Cell B-Cell Sequencing to Generate Natively Paired scFab Yeast Surface Display Libraries. In: Zielonka, S., Krah, S. (eds) Genotype Phenotype Coupling. Methods in Molecular Biology, vol 2681. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3279-6_11

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  • DOI: https://doi.org/10.1007/978-1-0716-3279-6_11

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3278-9

  • Online ISBN: 978-1-0716-3279-6

  • eBook Packages: Springer Protocols

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