At first glance, biology and computer science are fundamentally different fields: one studies the nuances of life while the other examines more rigid, mathematical concepts. However, the intersections of these two resoundingly different fields have expanded upon what is currently possible. A groundbreaking study published in Cell has utilized computer science to advance single-cell genomics, a subfield of biology (Hao et al., 2021). With abundant data, this new approach enables more robust analysis, revealing biological insights beyond our current knowledge.
For a quick moment, let's pretend our existing data are the countless grains and impurities on a beach. Hidden beneath lie precious shells. Previously, we could only find shells manually, catching their fleeting glimmer. This new technique acts like sieves and cranes, rapidly sifting through more data to uncover buried insights - like crystals we didn't know existed. Applying computer science to single-cell genomics can profoundly transform our comprehension of human health and disease (Hao et al., 2021).
Traditionally, researchers have studied genetic material—namely DNA and RNA—by evaluating cells in bulk. Single-cell genomics takes a more granular approach by studying single cells separately. This technique helps us see the incredible diversity of cells within our bodies as well as study more subtle aspects of their behavior and responses to various stimuli.
Using this approach on a large dataset of human peripheral blood mononuclear cells (PBMCs) with an extensive panel of 228 antibodies, the researchers created a multimodal reference atlas of the circulating immune system (Hao et al., 2021). In other words, they built a detailed map of different immune cells and their characteristics. This atlas provided valuable insights into cell states, identifying immune cells not discovered previously (Newell & Cheng, 2016).
The potential impact of this research is immense, as it offers unprecedented precision in studying cellular heterogeneity, leading to new frontiers in human health research (Tanay & Regev, 2017). By overcoming the limitations of traditional transcriptomics and embracing multimodal approaches (Stuart et al., 2019), we gain a deeper understanding of disease mechanisms, paving the way for more targeted treatments for conditions like cancer and autoimmune disorders.
However, while exploring integrated single-cell analysis, it is crucial to consider data quality and reproducibility (Stuart et al., 2019; Hao et al., 2021). Collaborative efforts between scientists, computational experts, and policymakers are essential to ensure the responsible and transparent use of these cutting-edge technologies. With this balanced approach, we can harness the power of single-cell genomics to unlock the secrets of the immune system and drive transformative advancements in medicine.
The fusion of biology and computer science in single-cell genomics holds tremendous promise for advancing our knowledge of the immune system and revolutionizing human health research. By promoting the implementation of newer and more advanced computational models, we can effectively analyze complex data and drive scientific progress.
Ultimately, as the world becomes increasingly digital, it would appear that the same approach would come towards medicine. A historic single step in combining the study of computer science and biology further has allowed for the exponential exploration of our body.
References
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M., Wilk, A. J., Darby, C. A., Zager, M., Hoffman, P., Stoeckius, M., Efthymia Papalexi, Mimitou, E. P., Jain, J., Srivastava, A., Stuart, T., Fleming, L., Yeung, B. Z., & Rogers, A. J. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573-3587.e29. https://doi.org/10.1016/j.cell.2021.04.048
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019). Comprehensive integration of single-cell data. Cell, 177(7), 1888-1902. https://doi.org/10.1016/j.cell.2019.05.031
Tanay, A., & Regev, A. (2017). Scaling single-cell genomics from phenomenology to mechanism. Nature, 541(7637), 331–338. https://doi.org/10.1038/nature21350
Newell, E. W., & Cheng, Y. (2016). Mass cytometry: blessed with the curse of dimensionality. Nature immunology, 17(8), 890–895. https://doi.org/10.1038/ni.3485