Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

ABSTRACT Multiple methods have recently been developed to reconstruct full-length B-cell receptors (BCRs) from single-cell RNA-seq (scRNA-seq) data. This need emerged from the expansion of scRNA-seq techniques, the increasing interest in antibody-based drug development and the importance of BCR repertoire changes in cancer and autoimmune disease progression. However, a comprehensive assessment of performance-influencing factors like the sequencing depth, read length or the number of somatic hypermutations (SHMs) as well as guidance regarding the choice of methodology are still lacking. In this work, we evaluated the ability of six available methods to reconstruct full-length BCRs using one simulated and three experimental SMART-seq datasets. In addition, we validated that the BCRs assembled in silico recognize their intended targets when expressed as monoclonal antibodies. We observed that methods like BALDR, BASIC and BRACER showed the best overall performance across the tested datasets and conditions whereas only BASIC demonstrated acceptable results on very short read libraries. Furthermore, the de novo assembly-based methods BRACER and BALDR were the most accurate in reconstructing BCRs harboring different degrees of SHMs in the variable domain, while TRUST4, MiXCR and BASIC were the fastest. Finally, we propose guidelines to select the best method based on the given data characteristics.

Authors 12

1. Tommaso Andreani corresponding

   Sanofi (Germany)

   Affiliation as printed

   AI & Deep Analytics – Omics Data Science, Sanofi, Frankfurt am Main, 65926, Germany

2. Linda M. Slot Aachen Department of Rheumatology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Rheumatology, Leiden University Medical Center, 2333 RC Leiden, The Netherlands

3. Samuel Gabillard
   Affiliation as printed

   Life & Soft, Le Plessis-Robinson, Paris, 92260, France

4. Carsten Struebing

   Sanofi (Germany)

   Affiliation as printed

   Immunology & Inflammation Research, Sanofi GmbH, Frankfurt am Main, 65926, Germany

   Immunology & Inflammation Research, Sanofi GmbH, Frankfurt am Main, 6596, Germany

5. Claus Reimertz

   Sanofi (Germany)

   Affiliation as printed

   Immunology & Inflammation Research, Sanofi GmbH, Frankfurt am Main, 65926, Germany

   Immunology & Inflammation Research, Sanofi GmbH, Frankfurt am Main, 6596, Germany

6. Yaligara Veeranagouda

   Sanofi (France)

   Affiliation as printed

   Molecular Biology & Genomics, Translational science unit, Sanofi, Chilly-Mazarin, 91385, France

   Molecular Biology & Genomics, Translational science unit, Sanofi, Chilly-Mazarin, 9138, France

7. Aleida M. Bakker Aachen Department of Rheumatology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Rheumatology, Leiden University Medical Center, 2333 RC Leiden, The Netherlands

8. Reza Olfati-Saber
   Affiliation as printed

   AI & Deep Analytics, Sanofi, Cambridge, MA, 02142, United States

9. René E. M. Toes Aachen Department of Rheumatology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Rheumatology, Leiden University Medical Center, 2333 RC Leiden, The Netherlands

10. Hans Ulrich Scherer Aachen Department of Rheumatology, Leiden University Medical Center

    Leiden University Medical Center

    Affiliation as printed

    Department of Rheumatology, Leiden University Medical Center, 2333 RC Leiden, The Netherlands

11. Franck Augé

    Sanofi (France)

    Affiliation as printed

    AI & Deep Analytics – Omics Data Science, Sanofi, Paris, 91385, France

12. Deimantė Šimaitė

    Sanofi (Germany)

    Affiliation as printed

    AI & Deep Analytics – Omics Data Science, Sanofi, Frankfurt am Main, 65926, Germany