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Sci Adv. 2016 Mar 11;2(3):e1501371. doi: 10.1126/sciadv.1501371. eCollection 2016 Mar.

Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting.

Author information

1
Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
2
Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.; Scientific IT Services, ETH Zurich, 4058 Basel, Switzerland.
3
Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.; Scientific IT Services, ETH Zurich, 4058 Basel, Switzerland.; SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland.

Abstract

High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion-the intraclonal diversity index-which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology.

KEYWORDS:

B cell; barcode; bias correction; error correction; immunoglobulin; monoclonal antibody; multiplex-PCR; next-generation sequencing; systems immunology

PMID:
26998518
PMCID:
PMC4795664
DOI:
10.1126/sciadv.1501371
[Indexed for MEDLINE]
Free PMC Article

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