on ‘seq’end thought: advances in scRNAseq

on ‘seq’end thought: advances in scRNAseq

By: Shruthi Kandalai

Single-cell RNA sequencing (scRNAseq) enables researchers to measure transcript levels of various proteins in different cell types. While it is not always possible to measure the RNA expressed in all cells, due to the small amount of RNA, expression patterns can help to group cells together. This clustering process allows for discovery of unique cell subtypes, which may play a role in better understanding disease processes. Current clustering algorithms for scRNAseq tend to focus on mutual or shared nearest neighbors, aggregating information from similar cell types to decrease noise. Various other methods to better cluster cells have been proposed since, yet none of the current methods seem to be applicable to all cell types.

A recent study also analyzed 14 of the current methods of scRNAseq clustering, with the three best methods being those that were superior at clustering over a shorter period of time. A new paper proposed a novel clustering method, known as Batch effects correction with Unknown Subtypes for scRNA-seq data (BUSseq), that is said to be better at clustering a variety of cell types, in contrast to current methods. The paper analyzed hematopoietic cells and pancreatic tissue, in which the BUSseq method was able to better cluster similar cell types. BUSseq was also found to better account for the uncertainties between stages of data analysis.

Many of the current RNAseq papers related to T1D have been focused on analyzing whole blood or pancreatic tissue. Multiple studies of whole blood analyses have been focused on autoimmunity and have found differences in activity of many immune cell types, including increased B cells and decreased neutrophils, increased CD4 T cells specifically reactive against islets, and increased presence of interleukin-32 caused by activated T cells and natural killer cells. Pancreatic tissue analysis has found that rare β cells can remain in the pancreas years after a T1D diagnosis, and yet glucagon secretion of α cells was found to be functionally impaired, suggesting that both cell types may be involved in the disease process. 

The takeaway: By utilizing better methods of clustering, such as BUSseq, new cell types involved in the T1D disease process may be uncovered that would contribute to better understanding of the disease process on a molecular level.

Sources:

  • Haghverdi, L., Lun, A. T., Morgan, M. D., & Marioni, J. C. (2018). Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nature Biotechnology, 36(5), 421-427.
  • Tran, H. T., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y., Goh, M., & Chen, J. (2020). A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biology, 21(1).
  • Song, F., Chan, G. M., & Wei, Y. (2020). Flexible experimental designs for valid single-cell RNA-sequencing experiments allowing batch effects correction. Nature Communications, 11(1).
  • Dufort, M. J., Greenbaum, C. J., Speake, C., & Linsley, P. S. (2019). Cell type–specific immune phenotypes predict loss of insulin secretion in new-onset type 1 diabetes. JCI Insight, 4(4).
  • Cerosaletti, K., Barahmand-Pour-Whitman, F., Yang, J., Deberg, H. A., Dufort, M. J., Murray, S. A., . . . Linsley, P. S. (2017). Single-Cell RNA Sequencing Reveals Expanded Clones of Islet Antigen-Reactive CD4+ T Cells in Peripheral Blood of Subjects with Type 1 Diabetes. The Journal of Immunology, 199(1), 323-335.
  • Kallionpää, H., Somani, J., Tuomela, S., Ullah, U., Albuquerque, R. D., Lönnberg, T., . . . Lahesmaa, R. (2019). Early Detection of Peripheral Blood Cell Signature in Children Developing β-Cell Autoimmunity at a Young Age. Diabetes, 68(10), 2024-2034.
  • Brissova, M., Haliyur, R., Saunders, D., Shrestha, S., Dai, C., Blodgett, D. M., . . . Powers, A. C. (2018). α Cell Function and Gene Expression Are Compromised in Type 1 Diabetes. Cell Reports, 22(10), 2667-2676.

Responses