Fantini Marco, Pandolfini Luca, Lisi Simonetta, Chirichella Michele, Arisi Ivan, Terrigno Marco, Goracci Martina, Cremisi Federico, Cattaneo Antonino
Bio@SNS Laboratory, Scuola Normale Superiore, Pisa, Italy.
European Brain Research Institute, Roma, Italy.
PLoS One. 2017 May 15;12(5):e0177574. doi: 10.1371/journal.pone.0177574. eCollection 2017.
Antibody libraries are important resources to derive antibodies to be used for a wide range of applications, from structural and functional studies to intracellular protein interference studies to developing new diagnostics and therapeutics. Whatever the goal, the key parameter for an antibody library is its complexity (also known as diversity), i.e. the number of distinct elements in the collection, which directly reflects the probability of finding in the library an antibody against a given antigen, of sufficiently high affinity. Quantitative evaluation of antibody library complexity and quality has been for a long time inadequately addressed, due to the high similarity and length of the sequences of the library. Complexity was usually inferred by the transformation efficiency and tested either by fingerprinting and/or sequencing of a few hundred random library elements. Inferring complexity from such a small sampling is, however, very rudimental and gives limited information about the real diversity, because complexity does not scale linearly with sample size. Next-generation sequencing (NGS) has opened new ways to tackle the antibody library complexity quality assessment. However, much remains to be done to fully exploit the potential of NGS for the quantitative analysis of antibody repertoires and to overcome current limitations. To obtain a more reliable antibody library complexity estimate here we show a new, PCR-free, NGS approach to sequence antibody libraries on Illumina platform, coupled to a new bioinformatic analysis and software (Diversity Estimator of Antibody Library, DEAL) that allows to reliably estimate the complexity, taking in consideration the sequencing error.
抗体文库是获取抗体的重要资源，这些抗体可用于广泛的应用，从结构和功能研究到细胞内蛋白质干扰研究，再到开发新的诊断方法和治疗方法。无论目标如何，抗体文库的关键参数是其复杂性（也称为多样性），即文库中不同元件的数量，这直接反映了在文库中找到针对给定抗原的具有足够高亲和力的抗体的概率。由于文库序列的高度相似性和长度，长期以来对抗体文库复杂性和质量的定量评估一直没有得到充分解决。复杂性通常通过转化效率来推断，并通过对几百个随机文库元件进行指纹识别和/或测序来测试。然而，从如此小的样本中推断复杂性是非常粗略的，并且关于实际多样性的信息有限，因为复杂性与样本大小并非线性相关。新一代测序（NGS）为解决抗体文库复杂性质量评估开辟了新途径。然而，要充分发挥NGS在抗体库定量分析方面的潜力并克服当前的局限性，仍有许多工作要做。为了获得更可靠的抗体文库复杂性估计，我们在此展示了一种新的、无需PCR的NGS方法，用于在Illumina平台上对抗体文库进行测序，并结合一种新的生物信息分析和软件（抗体文库多样性估计器，DEAL），该软件能够在考虑测序误差的情况下可靠地估计复杂性。
