Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring

Klaus Eyer (1) , Raphaël C L Doineau (2) , Carlos E Castrillon (3) , Luis Briseño-Roa (4) , Vera Menrath (4) , Guillaume Mottet (3) , Patrick England (5) , Alexei Godina (2) , Elodie Brient-Litzler (2) , Clément Nizak (2) , Allan Jensen (4) , Andrew D Griffiths (2) , Jérôme Bibette (1) , Pierre Bruhns (3) , Jean Baudry (1)

1 LCMD - Laboratoire Colloïdes et Matériaux Divisés
2 ESPCI Paris - Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris
3 Anticorps en thérapie et pathologie - Antibodies in Therapy and Pathology
4 HiFiBio SAS
5 CITECH - Centre de Recherche et Innovation Technologique

Studies of the dynamics of the antibody-mediated immune response have been hampered by the absence of quantitative, high-throughput systems to analyze individual antibody-secreting cells. Here we describe a simple microfluidic system, DropMap, in which single cells are compartmentalized in tens of thousands of 40-pL droplets and analyzed in two-dimensional droplet arrays using a fluorescence relocation-based immunoassay. Using DropMap, we characterized antibody-secreting cells in mice immunized with tetanus toxoid (TT) over a 7-week protocol, simultaneously analyzing the secretion rate and affinity of IgG from over 0.5 million individual cells enriched from spleen and bone marrow. Immunization resulted in dramatic increases in the range of both single-cell secretion rates and affinities, which spanned at maximum 3 and 4 logs, respectively. We observed differences over time in dynamics of secretion rate and affinity within and between anatomical compartments. This system will not only enable immune monitoring and optimization of immunization and vaccination protocols but also potentiate antibody screening.

Domaines: Matière Molle [cond-mat.soft]

Klaus Eyer, Raphaël C L Doineau, Carlos E Castrillon, Luis Briseño-Roa, Vera Menrath, et al.. Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring. Nature Biotechnology, 2017, 35 (10), pp.977 - 982. ⟨10.1038/nbt.3964⟩. ⟨hal-01639647⟩