# Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

## TL;DR

This study compares MERFISH, a spatial transcriptomics method, with single-cell RNA sequencing and finds that MERFISH can accurately capture gene expression and cell types while preserving spatial information.

## Contribution

The study demonstrates that MERFISH is quantitatively comparable to scRNA-seq and provides spatial resolution without needing integration with existing atlases.

## Key findings

- MERFISH reproduced bulk RNA-seq and scRNA-seq results with lower dropout rates and higher sensitivity.
- MERFISH independently resolved distinct cell types and spatial structures in mouse liver and kidney tissues.
- Computational integration with the Tabula Muris Senis atlas did not improve MERFISH results.

## Abstract

Single-cell RNA-seq is an important technology for capturing gene expression in individual cells. It has limitations as spatial context and accurate representation of cell types in the tissue are lost. Here, we show that MERFISH is comparable to scRNA-seq.

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.