The ability to respond quickly and effectively to emerging pathogens relies largely on rapid vaccine development. However, developing vaccines is a notoriously complicated endeavor because the human immune system is complex and influenced by factors such as age, genetics and prior exposures. Measuring the effectiveness of a novel vaccine requires the use of sophisticated and rapidly evolving technologies that can capture a comprehensive picture of the immune response.

One of the most powerful tools for improving our understanding of immunity and vaccine development is single-cell analysis. Researchers can use technologies such as high-dimensional flow cytometry, single-cell RNA sequencing and three-dimensional assays to unravel the complexities of immune responses to vaccination with significant depth and complexity. As this technology continues to improve, artificial intelligence will enable predictive analysis, offering insights that will accelerate vaccine development and enhance public health outcomes.

The challenge facing biopharmaceutical developers today is that single-cell research requires specialized skills and advanced equipment, making it far from easy for many labs to implement. It’s essential that industry leaders embrace cross-disciplinary cooperation to overcome these hurdles. By expanding the use of single-cell analysis, they can drive progress that will lead to more effective and personalized vaccines, bolstering preparedness against future infectious disease threats.

Single-cell advances

The beauty of single-cell analysis is that it provides the most comprehensive picture of the heterogeneity and the complexity of the immune system. While several assays (e.g., serological, hemagglutination) can measure the antibody response after vaccination, a more detailed analysis of the complex immune response is necessary for identifying how and why people do or do not respond to vaccination. Every cell is different, even those that are members of the same subgroup. The single-cell approach allows researchers to gain an understanding of proteomic and genomic features of individual cells, and to determine how cells interact with each other.

Flow cytometry is helping drive advances in single-cell analysis. Flow cytometry uses hydrodynamic focusing to reveal cell size and complexity. Spectral flow cytometry is even more advanced, offering a degree of resolution that facilitates the simultaneous study of up to 50 distinct molecules (1).

Single-cell RNA sequencing allows researchers to dive even deeper into the immune response to vaccines by providing data on gene expression (and that can be combined with surface marker expression). The ability to measure transcriptomes of individual cells enables researchers to better characterize their functions, in addition to promoting the discovery of novel cell types and providing insights into immune activation, memory formation and more. These details can help pinpoint biomarkers of response and identify the responsive cells, thus improving vaccine design and strategies. One way to think about these technologies is this: flow cytometry reveals how a cell looks physically, while single-cell RNA sequencing tells you how the cells can be modulated.

Embracing systems vaccinology

Several recent studies illustrate the value of single-cell analysis in characterizing response to pathogens and developing future vaccines. For example, a study published during the COVID-19 pandemic demonstrated how researchers used single-cell RNA sequencing to characterize B-cell responses against SARS-CoV-2, the virus that causes the disease. They identified transcriptionally distinct B-cell populations, including some populations that produced potent neutralizing antibodies (2). Another example involved studying the immune response to influenza and yellow fever. Using the method CITE-seq (cellular indexing of transcriptomes and epitopes), the researchers profiled surface proteins and transcriptomes for over 50,000 single cells, identifying transcriptional signatures that predicted antibody responses to influenza and yellow fever vaccinations (3).

Imagine if researchers could instantly compare their data on vaccine response to all the data generated from studies of the disease in question over the past 10 years. By combining data on proteomics, lipidomics, genomics and more, researchers could further use AI tools to build digital models of immune cells. This may help them predict the immune response to the predicted antigenic epitopes and immunogenicity which single-cell analysis promises to greatly improve.

An integrative approach, often referred to as “systems vaccinology,” combines high-throughput technologies, computational biology and immunology to fully characterize vaccine responses. The ability to analyze multiple data sets within a single cell could allow vaccine developers to model immune responses based on a variety of factors by building “virtual twins” – highly flexible in silico cellular models built and analyzed by AI. Let’s say, for example, a vaccine developer wants to model the likely response to an experimental vaccine in elderly Asian women. Virtual twins may make that possible.

Initiatives exist to converge the single cell data, such as the Human Cell Atlas project (4). If the systems vaccinology vision could become a reality, we could lessen the reliance on animal studies and large clinical trials, lower costs and speed up the timeline for novel vaccine development – a process that can take up to 15 years today. By pushing the boundaries of cellular analysis with technology, we will be better prepared to combat emerging disease threats with unprecedented accuracy and efficiency.

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