A proteomics approach for the identification and cloning of monoclonal antibodies from serum

Journal name:
Nature Biotechnology
Year published:
Published online
Corrected online


We describe a proteomics approach that identifies antigen-specific antibody sequences directly from circulating polyclonal antibodies in the serum of an immunized animal. The approach involves affinity purification of antibodies with high specific activity and then analyzing digested antibody fractions by nano-flow liquid chromatography coupled to tandem mass spectrometry. High-confidence peptide spectral matches of antibody variable regions are obtained by searching a reference database created by next-generation DNA sequencing of the B-cell immunoglobulin repertoire of the immunized animal. Finally, heavy and light chain sequences are paired and expressed as recombinant monoclonal antibodies. Using this technology, we isolated monoclonal antibodies for five antigens from the sera of immunized rabbits and mice. The antigen-specific activities of the monoclonal antibodies recapitulate or surpass those of the original affinity-purified polyclonal antibodies. This technology may aid the discovery and development of vaccines and antibody therapeutics, and help us gain a deeper understanding of the humoral response.

At a glance


  1. Overview of proteomics approach for identifying functionally relevant monoclonal antibodies from an immunized animal.
    Figure 1: Overview of proteomics approach for identifying functionally relevant monoclonal antibodies from an immunized animal.

    Serum or plasma from an immunized animal is first purified by protein A or G and subsequently subjected to antigen affinity purification. Purified polyclonal antibodies are then functionally characterized to ensure specific activity enrichment. Validated purified antibodies are digested with various proteases to prepare peptide fragments to be analyzed by high mass accuracy LC-MS/MS. To identify peptide sequences corresponding to antibody fragments by SEQUEST, we generated a reference database of immunoglobulin V-region sequences by next generation sequencing (NGS) of the immunized animal's B-cell repertoire. V-region sequences identified with high confidence that correspond to antibodies purified from the serum are identified using in-house software. These heavy and light chain sequences are then synthesized and cloned into a single-open-reading-frame antibody expression platform. Recombinant monoclonal antibodies are expressed combinatorially in a matrix of heavy and light chains and screened for precise function and compared to the specificity and activity of the original polyclonal antibody mixture.

  2. Affinity purification of progesterone receptor-specific polyclonal rabbit IgG.
    Figure 2: Affinity purification of progesterone receptor–specific polyclonal rabbit IgG.

    (a) Total IgG from the serum of the immunized rabbit was isolated with protein A and further affinity purified on immobilized antigen peptides by gravity flow. After extensive washing to reduce nonspecific IgG, a sequential elution with progressively acidic pH was used to fractionate the antigen-specific polyclonal IgG. Each fraction was tested for specific activity by western blot analysis at matched antibody concentration (21.5 ng/ml) to detect PR A/B in lysates from T47D cells (+). Negative control lysates from HT1080 (−) were also tested. (b) The fraction with the highest specific activity, pH1.8, was processed with four proteases for LC-MS/MS analysis. (c) An MS/MS spectrum matched by SEQUEST to the V-region full tryptic peptide GFALWGPGTLVTVSSGQPK containing CDR3 (underlined) with an XCorr of 5.560 and a ΔM (observed m/z – expected m/z) of 0.39 p.p.m. (d) Rabbit heavy and light chain sequence identification coverage of clone F9 (heavy chain, 77% coverage of 50 peptides total; light chain, 65% coverage of 24 peptides total). The depicted V-region sequences, when paired, specifically bind human PR A/B (Fig. 3). Amino acids mapped by one or more peptides are shown in bold. To maximize V-region coverage and account for highly variable amino acid composition, complementary proteases were used.

  3. Identification and characterization of functional monoclonal antibodies against progesterone receptor A/B.
    Figure 3: Identification and characterization of functional monoclonal antibodies against progesterone receptor A/B.

    (a) Combinatorial pairing of heavy and light chains yielded 12 antigen-specific, ELISA-reactive clones (in yellow). CDR3 sequence is used as an identifier. √, western blot–positive clones (see b). (b) Six clones (F1, F9, H1, C1, F7 and H9) were specific for progesterone receptor A/B detection by western blot analysis. Clones E6 (negative by ELISA and western blot) and H7 (positive by ELISA, negative by western blot) are shown as controls. +, T47D cell lysate (PR A/B positive); −, MDA-MB-231 cell lysate (PR A/B negative). All antibodies tested at 21.5 ng/ml. Ab, antibody. (c) Comparison of specific activity of clone F9 to the affinity-purified polyclonal mixture by immunohistochemistry. F9 (0.4 μg/ml) specifically stained PR A/B-positive tissue or cell lines (T47D and MCF-7), but not MDA-MB-231. Polyclonal (0.2 μg/ml) antibody was used as a positive control. 20× magnification, (d) Flow cytometry analysis. Polyclonal antibody signal/noise ratio, 1.69; concentration, 3.7 μg/ml. Monoclonal antibody F9 signal/noise ratio, 36.4; concentration, 0.5 μg/ml. (e) Confocal immunofluorescence microscopy analysis showed specific nuclear staining pattern on MCF-7 but not on MDA-MB-231 cells at 0.46 μg/ml. No primary antibody was included as background staining control. Polyclonal antibodies were also used as comparison at a concentration of 1.85 μg/ml. 20× magnification.

Change history

Corrected online 28 March 2012
In the version of this supplementary file originally posted online, the Figures and Tables were corrupted. The error has been corrected in this file as of 28 March 2012.


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Author information

  1. These authors contributed equally to this work.

    • Wan Cheung Cheung &
    • Sean A Beausoleil


  1. Cell Signaling Technology, Inc., Danvers, Massachusetts, USA.

    • Wan Cheung Cheung,
    • Sean A Beausoleil,
    • Xiaowu Zhang,
    • Shuji Sato,
    • Sandra M Schieferl,
    • James S Wieler,
    • Jason G Beaudet,
    • Ravi K Ramenani,
    • Lana Popova,
    • Michael J Comb,
    • John Rush &
    • Roberto D Polakiewicz


W.C.C., S.A.B. and R.D.P. developed the methodology, designed experiments, analyzed the data and wrote the manuscript. W.C.C. and S.A.B. performed experiments and did the bioinformatic analysis. S.S. designed experiments, analyzed data and wrote the manuscript. X.Z., S.M.S., J.S.W., J.G.B., R.K.R. and L.P. performed experiments. M.J.C. and J.R. helped analyze the data and write the manuscript.

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All authors are employees of Cell Signaling Technology.

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