Identifying low-abundance monoclonal antibodies within a population of polyclonal antibodies provides insights for various applications, including understanding disease mechanisms, vaccine development, disease diagnostics, serological surveillance, and therapeutic antibody discovery.
Despite their utility, current methodologies have substantial drawbacks, such as complex processes, high costs, and time-intensive workflows. These limitations frequently restrict their widespread application.
This article outlines a method that leverages Quantum-Si’s next-generation protein sequencing™ technology on the Platinum® instrument to successfully identify two low-abundance monoclonal antibodies within a population of polyclonal antibodies.
Introduction
Identifying low-abundance monoclonal antibodies within a diverse population of polyclonal antibodies holds significant importance in several clinical and research settings. In disease diagnostics, especially in cancers and autoimmune diseases, detecting monoclonal antibodies can serve as valuable biomarkers for early disease progression detection or monitoring.1–3
Monoclonal antibodies also play a significant role in immunology research by providing insights into the diversity and specificity of an immune response to an antigen. This contributes to our understanding of disease mechanisms.
In vaccine development and therapeutic antibody discovery, researchers must isolate specific monoclonal antibodies with high affinity for target antigens, even if present in small amounts.
Serological surveillance also depends on the detection of antibodies to monitor infectious disease exposure in populations over time.4 Similarly, identifying low-abundance monoclonal antibodies can contribute to developing novel antibody-drug conjugates (ADCs), where an antibody selectively targets and delivers a drug to tumor cells, sparing normal cells.5
Various methods are available to detect low-abundance monoclonal antibodies within a population of polyclonal antibodies. Western blots and ELISA are frequently employed as an easy method of screening for antibodies. However, these assays may lack the sensitivity to identify very low-abundance antibodies and often necessitate larger sample volumes.
Fluorescence-activated cell sorting (FACS) and flow cytometry also have the potential for high sensitivity, but they require expensive and sophisticated equipment and involve a technically challenging complex of deriving monoclonal antibodies from sorted B cells.
Mass spectrometry (MS) provides a detailed view of the antibody repertoire. However, it is time-consuming, complex, and demands high levels of expertise to interpret data, leading to additional time and resource requirements.
Lastly, phage display, though beneficial for high-throughput screening, is labor-intensive and demands specialized molecular biology methods.
Quantum-Si’s Platinum next-generation protein sequencing platform is an affordable, compact benchtop instrument, adept at overcoming the many challenges associated with traditional antibody identification techniques.
Its user-friendly interface removes the requirement for specialized expertise to operate or interpret data, rendering it a practical choice for a diverse range of laboratories.
It also provides a rapid and efficient method of identifying low-abundance mono-clonal antibodies, expediting the pace of research and clinical diagnostics without the time-consuming procedure associated with many traditional methods.
To showcase the capabilities of Quantum-Si’s technology to detect low-abundance monoclonal antibodies, the Fab fragments of two monoclonal antibodies were individually sequenced and successfully identified within a mixed population of polyclonal antibodies.
References and further reading
- M. J. Monroy-Iglesias, S. Crescioli, K. Beckmann, N. Le, S. N. Karagiannis, M. van Hemelrijck, A. Santaolalla. Antibodies as biomarkers for cancer risk: a systematic review. Clin. Exp. Immunol. 209 (1), 46–63 (2022).
- M. A. M. van Delft, T. W. J. Huizinga. An overview of autoantibodies in rheumatoid arthritis. J. Autoimmun. 110, 102392 (2020).
- P. D. Burbelo , S. M. Gordon, M. Waldman, J. D. Edison, D. J. Little, R. S. Stitt, W. T. Bailey, J. B. Hughes, S. W. Olson. Autoantibodies are present before the clinical diagnosis of systemic sclerosis. PloS One 14 (3), e0214202 (2019).
- C. Y.-P. Lee, R. T. P. Lin, L. Renia, L. F. P. Ng. Serological Approaches for COVID-19: Epidemiologic Perspective on Surveillance and Control. Front. Immunol. 11, 879 (2020).
- M. S. Castelli, P. McGonigle, P. J. Hornby. The pharmacology and therapeutic applications of monoclonal antibodies. Pharmacol. Res. Perspect.
About Quantum-SI
Inspired by Ion Torrent’s success at shrinking next-generation sequencing technology into a benchtop instrument, Jonathan Rothberg founded Quantum-Si™ to bring the same semiconductor technology to protein sequencing with the launch of the Platinum® Next-Generation Protein Sequencer™.
That was in Guilford, CT, back in 2013. Fast forward to today and we now have over 1,000 patents issued and applications pending, plus a groundbreaking single-molecule protein sequencing technology platform, the Platinum.
Along the way, we solved critical challenges around sensitive and unambiguous amino acid detection, blending biology, chemistry, and semiconductor technology to help biologists see what other approaches cannot deliver. We also set the stage for a revolution in how scientists understand biology and build new treatments for disease by making single molecule protein sequencing accessible to every lab everywhere.
We are now entering a new phase of our development as a company. Starting with an initial public offering in June 2021 (QSI on the NASDAQ) and continuing with a new product development and operations facility in San Diego, CA, in 2022, we have entered a period of rapid growth. Through this expansion, we will be able to fuel a new era of biology, the post-genomic era, where biologists accelerate basic scientific insight and biomedical advances through the power of next-generation protein sequencing.
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