ClusterMap for multi-scale clustering analysis of spatial gene expression

Yichun He,Emanuelle I Grody,Albert Liu,Kevin Chen,Jingyi Ren,Jian Shu,Qiang Li,Johain Ounadjela,Jiahao Huang,Jia Liu,Xiao Wang,Zuwan Lin,Haowen Zhou,Abhishek Aditham,Hailing Shi,Xin Tang

doi:10.1038/s41467-021-26044-x

Abstract

Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we introduce an unsupervised and annotation-free framework, termed ClusterMap, which incorporates the physical location and gene identity of RNAs, formulates the task as a point pattern analysis problem, and identifies biologically meaningful structures by density peak clustering (DPC). Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and performs consistently on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell niche, and tissue organization principles from images with high-dimensional transcriptomic profiles.

Highlights

Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues
The density of RNA molecules is higher inside cells than outside cells; second, cellular RNAs encoded by different genes are enriched at different subcellular locations, cell types, and tissue regions[16, 17]
ClusterMap started with pre-processed imaging-based in situ transcriptomic data (Methods), where raw fluorescent images were converted into discrete RNA spots with a physical 3D location and a gene identity

Summary

Introduction

Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. The most common cell segmentation strategy is labeling cell nuclei or cell bodies by fluorescent staining[9,10,11] (e.g., DAPI, Nissl, WGA, etc.) and segmenting the continuous fluorescent signals by conventional or machine learning (ML)-based methods[12]. Conventional methods, such as distance-transformed watershed[13], require manual curation to achieve optimal but still unsatisfactory segmentation results. Instead of using fluorescent staining, we directly utilized the patterns of spatially resolved RNAs that intrinsically encode high-dimensional gene expression information for subcellular and cellular segmentation, followed by cell-type spatial mapping. We demonstrated that this computational framework (termed ClusterMap) can identify subcellular structures, cells, and tissue regions (Fig. 1)

Objectives

Methods

Results

Conclusion