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.

Spatial biology studies look at which cells are present, where they are located in tissue, their biomarker co-expression patterns, and how they organize and interact to influence the tissue microenvironment. By studying cells in context, you will gain novel biological insights.

True spatial biology studies combine whole-slide imaging at single cell resolution to visualize and quantitate biomarker expression and reveal how cells interact and organize across the entire tissue landscape. We call this approach spatial phenotyping. Watch Video ➜

The Evolution of Single-cell Spatial Analysis

From bulk measurements to single-cell to single-cell, spatial analysis.

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

Bulk   

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 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

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 Transcriptomics
Cell states

Spatial-Transcriptomics-217x286

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.

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.

Answer Questions Across the Research Continuum

Studies in human biology span a continuum from discovery to clinical and translational research. Different ends of this continuum require unique capabilities, warranting different workflows at different stages of research

I want to discover new cell types
I want to determine the functions of my cell types
I want to learn how cells are impacted by their neighborhoods
I want to discover how cells organize and interact to influence disease progression
I want to find patterns in the cellular landscape that correlate with treatment response or patient outcomes
I want to determine the efficacy of my prognostic or predictive biomarker