Plus Ultra: Genome-Wide Spatial Transcriptomics with RNA seqFISH+

Citation

Eng, Chee Huat (Linus) (2021) Plus Ultra: Genome-Wide Spatial Transcriptomics with RNA seqFISH+. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/nvfe-5j74. https://resolver.caltech.edu/CaltechTHESIS:05302021-051953086

Abstract

Visualizing single cells and their organization in intact tissue is crucial to understanding their governing biological function. Even though single cell RNA sequencing has provided many insights into the heterogeneity and gene expression profiles across many tissue types, the dissociation process which loses the spatial information is hindering our deeper understanding of how these transcriptional distinct cell types are organized and interacting in their native tissue environment.

The thesis begins by giving a background on how single cell RNA sequencing has transformed biology and the emergence of spatial technology such as sequential fluorescence in situ hybridization (seqFISH). While spatial methods are useful for mapping the cell types identified from single cell RNA sequencing, the need for turning spatial technology such as seqFISH, which has high detection efficiency of the transcriptome with spatial information, into an in situ discovery tool is discussed as the scientific community’s goal heads towards building spatial atlases for every human tissues and organs such as the brain.

While seqFISH has high detection efficiency, it is still limited in the number of genes capable of profiling at once. The major obstacle is the optical crowding problems when more RNA species are targeted and imaged using a fluorescence microscope. In Chapter 2, we first investigated, if the RNA molecules are instead captured on a coverslip and profiled with sequential barcoding strategy, the FISH-based method will reliably characterize the transcriptome when molecular crowding is not an issue.

Finally, in Chapter 3, we demonstrate the barcoding strategy to break through the molecular crowding limit of multiplexed FISH. From being able to profile hundreds to a thousand genes by various multiplexed FISH methods at that time in the field, we succeeded in profiling 10,000 genes by RNA seqFISH+, an evolved version of seqFISH, in various intact tissue sections, turning seqFISH+ into a spatial discovery technology with its genome-wide coverage and high detection efficiency. The work described in this part of the thesis is highlighted in Nature Method’s Method of The Year 2020- Spatially-resolved Transcriptomic article.

Thesis Files