Encoding the Sequence of Specific Autoantibodies Against beta-Amyloid and alpha-Synuclein in Neurodegenerative Diseases

Abstract

There is no effective disease-modifying therapy for Alzheimer's or Parkinson's disease. As pathological hallmarks, the specific peptide amyloid-β and the specific protein α-Synuclein aggregate and deposit in and destabilize neurons, which lead to their degeneration. Within the context of a potential immunization strategy for these diseases, naturally occurring autoantibodies could play a crucial role in treatment due to their ability to inhibit peptide/protein aggregation and mediate their phagocytosis. We developed a procedure to extract the genetic information of such amyloid-β- and α-Synuclein- specific naturally occurring autoantibodies for future passive immunization strategies. We performed FACS-based single-cell sorting on whole blood donated from healthy individuals and performed single-cell RT-PCR analysis to amplify the coding sequences of antigen-binding regions of each antibody-secreting B1 cell. Sequences were further analyzed to determine CDR sequences and germline expression. Therefore, only low percentages of B1 cells obtained were amyloid-β⁺/α-Synuclein⁺. After cell sorting, the variable regions of full IgGs were sequenced, demonstrating preferred usage of IGVH3 and IGKV1. The study we present herein describes an approaching for extracting and amplifying the sequence information of autoantibodies based on single-cell analysis of donated blood and producing a recombinant antibody pool for potential passive immunization against neurodegenerative diseases. We sorted a small pool of CD20⁺ CD27⁺ CD43⁺ CD69^- IgG⁺ and Aβ⁺/α-Syn⁺ B cells.

Keywords: Alzheimer's disease; B1 cell; Parkinson's disease; naturally occurring autoantibodies; passive immunization strategy; single-cell RT-PCR.

Figure 1

nAb-secreting B1 cells are rare and highly specific. (A–F) Gating strategy to sort only CD20⁺ CD27⁺ CD43⁺ CD69⁻ IgG⁺, and Aβ⁺ (E) /α-Syn⁺ (F) B cells. Percentages above each plot indicate the proportion of cells resulting from the previous gate. Arrows indicate single cells in gate R5. CD, cluster of differentiation; FSC, forward scatter; SSC, sideward scatter.

Figure 2

Specific and non-specific gating of Aβ- and α-Syn-FITC signals shows possible error sources. (A,C) Aβ-FITC and (B,D) α-Syn-FITC. Different gates were applied to the same sample. (A,B) Same gating procedure as in Figure 1. (C,D) CD43 and CD69 were omitted for gating. The cells in gate R1 are not necessarily B1 cells, as the gating is not B1 cell specific. Arrows indicate single cells in gate R1 in (A,B).

Figure 3

Clear bands by agarose gel electrophoresis show the composition of antibody chains. PCR products were electrophoresed for 90 min at 110 mV. The 100-bp DNA ladder provides an objective size indicator. A representative sample is shown on the left, and the negative control (water) is shown on the right.

Figure 4

Preferred usage of IGVH3 and IGKV1 of Aβ (A) and α-Syn (B) variable antibody regions. After sequence analysis, the usages of germline genes were compared using the vbase2.org tool (31). Percentages of each variable region family are marked. For Aβ, 13 antibodies were analyzed; 12 were analyzed for α-Syn.

Figure 5

Binding verification of cloned antibodies by ELISA. Monomeric or oligomeric Aβ as well as ADDLs were coated with 5 μg/ml onto the surface of an ELISA plate. Antibodies were applied with 10 or 1.25 μg/ml (nAbs-Aβ and B07 scFv-Fc) and visualized by HRP-conjugated secondary anti-human antibody. The data were normalized to IVIg binding on monomeric Aβ. Mean values ± SD of three independent experiments are shown. B06 IgG, B07 IgG, C06 IgG, and B07 scFv-Fc mentioned different antibody clones.