Spatial molecular profiling: platforms, applications and analysis tools

Abstract

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

Keywords: FISH; cell morphology; mass spectrometry; scRNA-seq; spatial molecular profiling; spatial organization; spatial transcriptomic data; super-resolution microscopy.

Figure 1

Overview of the workflows of imaging- and sequencing-oriented spatial molecular profiling technologies. Because each method differs in technical detail, the figure is intended to give only a demonstrative idea. (A) Prepared tissue slides. (B) Hybridization phase. Proteins or RNAs are hybridized with metal conjugated antibodies or fluorescent probes. (C) Quantification phase. Metal isotopes or fluorescent sequences are quantified as readout by MS or microscopy. (D) Barcoding phase. RNA molecules are captured by barcoded surface probes. (E) Sequencing phase. cDNA library is synthesized and sequenced. (F) Visualization of spatial transcriptomic data as a heatmap.

Figure 2

Summary of the applications of spatial transcriptomic data. (A) Identify SV genes. (B) Cluster SV genes into patterns. (C) Spatial cell–cell interaction analysis. (D) Integrate image data with spatial transcriptomic data for downstream functional analysis.