Concordance of MERFISH Spatial Transcriptomics with Bulk and Single-cell RNA Sequencing

Jonathan Liu, Vanessa Tran, Venkata Naga Pranathi Vemuri, Ashley Byrne, Michael Borja, Snigdha Agarwal, Ruofan Wang, Kyle Awayan, Abhishek Murti, Aris Taychameekiatchai, Bruce Wang, George Emanuel, Jiang He, John Haliburton, Angela Oliveira Pisco, Norma Neff
Life Science Alliance doi:10.26508/lsa.202201701 (2022)


Spatial transcriptomics extends single cell RNA sequencing (scRNA-seq) technologies by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) can achieve single-cell resolution, directly mapping single cell identities to spatial positions. MERFISH produces an intrinsically different data type than scRNA-seq methods and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We used the Vizgen MERSCOPE platform to perform MERFISH on mouse liver and kidney tissues and compared the resulting bulk and single-cell RNA statistics with those from the existing Tabula Muris Senis cell atlas. We found that MERFISH produced measurements that quantitatively reproduced the bulk RNA-seq and scRNA-seq results, with some minor differences in overall gene dropout rates and single-cell transcript count statistics. Finally, we explored MERFISH’s ability to identify cell types, and found that it could independently resolve distinct cell types and spatial structure in both liver and kidney. Computational integration with the Tabula Muris Senis atlas using scVI and scANVI did not noticeably enhance these results. We conclude that compared to scRNA-seq, MERFISH provides a quantitatively comparable method for measuring single-cell gene expression, and that efficient gene panel design allows for robust cell type identification with intact spatial information without the need for computational integration with scRNA-seq reference atlases.