How do the various exposures that people face in their day-to-day lives affect their health? That question is at the heart of the rapidly growing field of exposomics, which is the study of how everything we interact with in our lifetimes—from the environment to our jobs to our lifestyle choices—combines with our genetics to impact our health. Exposomics can also enhance research into specific diseases such as cancer, by revealing valuable insights into how different treatments affect healthy and diseased cells. In order to effectively carry out exposomics studies, researchers must first understand what is a healthy cell, and then measure how that picture changes due to variable exposures.
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Single-cell transcriptomics technology is essential for exposomics research, because it provides a snapshot in time, showing how different types of cells are reacting under a given set of conditions, including which genes are upregulated or downregulated. But it’s a labor-intensive process, because exposomics researchers must test a huge number of samples to gain the insights they need to effectively determine how any given variable affects cells. What’s more, they’re often working with rare samples or very small sample sizes, slowing the research process even more.
Automation can enable exposomics researchers to move from question to answer much faster. The combined power of high-fidelity reagents and robust automation helps researchers tackle more complex questions than they could in the past. And by improving the efficiency of single-cell transcriptomics workflows and reducing hands-on time, automation frees researchers up to spend more time analyzing their results and gaining valuable exposomics insights.
Streamlining transcriptomics workflows
Automation can significantly streamline single-cell transcriptomic workflows. For example, an automated method for library preparation supporting the 3’ and 5’ Universal Gene Expression Library Construction can reduce hands-on time from 7.5 hours to 40 minutes, supporting up to 96 samples per run. In a 2024 experiment comparing manually prepared cDNA from 20,000 HEK293T and 500 human peripheral blood mononuclear cells (PBMCs), this automated method generated a total of 216 libraries. By comparison, a hybrid setup generated 80 libraries from three manual library construction runs. The complexity and sensitivity of the results were comparable between the automated and manual workflows, resulting in consistent cell-type profiling of human PBMCs.1
Running just one sample through a manual single-cell transcriptomics process can cost as much as $1,000. Automation has significantly lowered that cost, allowing labs to invest in analyzing more samples than they may have been able to do before. What’s more, the hardware is modular and versatile—and researchers can often advance its capabilities simply by downloading software updates.
Automation is enabling sophisticated exposomic analysis in settings that were traditionally challenging to study. For example, a study published in 2024 demonstrated a method for characterizing changes in the microbiome of four astronauts who had completed a three-day mission. Using single-cell immune cell profiling, they characterized microbiome changes in 750 samples, from 10 different sites on the body, taken before, during, and after the flight. The researchers recorded more than 821,000 statistically significant associations between microbiome shifts and space flight, including more than 314,000 distinct microbial features. By tracking changes in the immune response and linking them to changes in the astronauts’ living environments, they gained insights that they believe could improve future space missions and the design of spacecraft.2
Democratizing exposomics research
Automation is democratizing single-cell transcriptomics by simplifying processes that used to require special training and precise manual techniques. In the study of library preparation for the Universal Gene Expression Library Construction, users who were not familiar with hybrid or automated workflows were able to complete the automated methods and generate comparable results to more experienced researchers who used a manual process.
Another benefit of automation is it reduces the risk of errors, which can be costly for researchers handling rare or small samples. In fact, a researcher using a manual process would likely run no more than 24 samples at a time to avoid cross-contamination and processing errors. Completing 96 samples manually then becomes a multi-day process, instead of the 40-minute, error-free process automation enables.
Exposomics is a boon not just to researchers studying particular diseases, but also to those running longitudinal research projects designed to determine how lifestyle choices and the environment affect human health. One such study is the Human Immunome Project, a global network of research sites that are working together to build a large, diverse dataset of the human immune system, and to make artificial-intelligence models of the immune system publicly available in the hopes of accelerating medical discovery. Single-cell analysis of immune cells will be critical to generating these datasets.3
As transcriptomics tools advance, they’re becoming simpler to use, and the results they generate are becoming easier to interpret. Ultimately, we’ll be able to use these tools to predict potential health risks and intervene before they become a problem—improving the quality of life for future generations.
Crystal Girod is a scientist with an MsC in Bioinformatics. As Senior Commercial Product Manager at Beckman Coulter Life Sciences, she is based at the company’s development and manufacturing center in San Jose, California. She oversees strategic development of the company’s liquid handlers, from market research to new product launches, working closely with research and development teams and customers. Previously, Crystal held the senior applications scientist position with liquid handling in the internal applications team.
References
1. Zhao, A., et al. Accelerating Single Cell Research by Automating Gene Expression Library Construction for 10x Genomics GEM-X Chemistry on the Biomek i7 Hybrid Workstation. 10X Genomics, Beckman Coulter Life Sciences. 2024.
2. Tierney, B., et al. Longitudinal multi-omics analysis of host microbiome architecture and immune responses during short-term spaceflight. Nature Microbiology. 2024. 9;1661–1675.
3. Human Immunome Project. “Scientific Approach”. Available at: https://www.humanimmunomeproject.org/scientific-approach/. Accessed December 10, 2024.