Why Spatial Biology
Analyze cell phenotypes with full spatial context in the tissue microenvironment
What is Spatial Biology?
Spatial biology is the study of the diverse cellular landscape across multiple dimensions.
The Evolution of Single-cell Spatial Analysis
From bulk measurements to single-cell to single-cell, spatial analysis.
- Measure analyte abundance
- Study population-level diversity
Bulk
- Measure analyte abundance
- Study population-level diversity
- Distinguish cell types and functional states
Single-cell
- Measure analyte abundance
- Study population-level diversity
- Distinguish cell types and functional states
- Characterize cellular organization and interactions
Single-cell, Spatial
Bulk
Bulk measurement approaches like RNA sequencing and mass spectrometry measure average RNA and protein abundance in a substrate. These approaches are useful for studying population-level diversity but do not inform us about individual cell phenotypes.
- Measure analyte abundance
- Study population-level diversity
Single-cell
Single-cell analysis techniques, such as flow cytometry and single-cell sequencing, offer a higher resolution picture of the sample. By capturing RNA and/or protein expression in single cells, we can catalogue all the different cell types and functional states present to study cell-level diversity. However, this approach cannot characterize the spatial organization and interactions between cells.
- Measure analyte abundance
- Study population-level diversity
- Distinguish cell types and functional states
Single-cell, Spatial
Spatial biology adds another dimension to single-cell analysis. Spatial context is essential to understanding how cells organize and interact across the tissue landscape to drive disease progression and response to therapy. Spatial phenotyping, enabled by multiplex imaging, reveals these spatial relationships and interactions by imaging whole tissue sections with single-cell resolution.
- Measure analyte abundance
- Study population-level diversity
- Distinguish cell types and functional states
- Characterize cellular organization and interactions
Complementary Approaches to Spatial Phenotyping
Spatial Proteomics
Cell phenotypes
Proteins are the functional molecules of all cells, and ultimately the effectors of almost all biological processes. Accordingly, the vast majority of drug targets are proteins, which makes protein biomarkers especially useful for developing therapies and diagnostic tools.
While high-plex transcriptomic methods have proven highly effective for discovery research, proteomics is catching up. Spatial proteomics is the large-scale analysis of proteins and their localization and dynamics within tissue. Imaging-based spatial proteomics methods enable quantitative and spatial analysis of over 40 protein markers across a whole tissue section at single-cell resolution.
A spatial multiomics approach, which integrates single-cell spatial proteomic data with single-cell spatial transcriptomic data, provides more comprehensive insights into tissue biology and the discovery of novel biomarker signatures.
Spatial Transcriptomics
Cell states
Transcriptomic analysis, the survey of RNA transcripts in a sample, is a powerful tool for discovery research. With the advent of next-generation sequencing, we can now analyze whole transcriptomes with RNA-seq, and single-cell RNA sequencing methods have made it possible to profile gene expression in individual cells.
Spatial transcriptomics is an emerging method which quantifies transcriptomes from tissue sections to enable cell state analysis while retaining spatial context. However, current spatial transcriptomics approaches provide high levels of multiplexing at the cost of single-cell resolution, relying on region-of-interest or spot-based capture methods. Future imaging-based approaches may be able to bridge this gap.
The ability to profile hundreds to thousands of transcripts makes single-cell, spatial transcriptomics a powerful tool for unbiased discovery and highly complementary to spatial proteomics.
Answer Questions Across the Research Continuum
