PT - JOURNAL ARTICLE AU - Liu, Jonathan AU - Tran, Vanessa AU - Vemuri, Venkata Naga Pranathi AU - Byrne, Ashley AU - Borja, Michael AU - Kim, Yang Joon AU - Agarwal, Snigdha AU - Wang, Ruofan AU - Awayan, Kyle AU - Murti, Abhishek AU - Taychameekiatchai, Aris AU - Wang, Bruce AU - Emanuel, George AU - He, Jiang AU - Haliburton, John AU - Oliveira Pisco, Angela AU - Neff, Norma F TI - Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing AID - 10.26508/lsa.202201701 DP - 2023 Jan 01 TA - Life Science Alliance PG - e202201701 VI - 6 IP - 1 4099 - http://www.life-science-alliance.org/content/6/1/e202201701.short 4100 - http://www.life-science-alliance.org/content/6/1/e202201701.full SO - Life Sci. Alliance2023 Jan 01; 6 AB - Spatial transcriptomics extends single-cell RNA sequencing (scRNA-seq) 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 a different data type than scRNA-seq, and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on the mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas and from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both the liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that MERFISH provides a quantitatively comparable method for single-cell gene expression and can identify cell types without the need for computational integration with scRNA-seq atlases.