Single-Cell, Spatial Analysis
Single-Cell, Spatial Analysis for Discovery Biology
Spatial phenotyping (via multiplex immunofluorescence) enables researchers to visualize and quantitate protein and biomarker expression to reveal how cells interact and organize across the entire tissue landscape. The CODEX® technology combines the advantages of single-cell biology with histology at single-cell spatial resolution. Capable of imaging 40+ biomarkers on a single tissue, CODEX enables analysis with both high dimensionality and spatial context to drive unbiased, single-cell biological discovery.
Find rare cell populations within your tissue sample
Detection of rare cells with CODEX. Left: T-SNE population map of 63’056 cells clustered from image shown above. A single cell population of 44 cells (0.07% of total) is indicated in cyan. Right: anatomical data from the same experiment confirming the Ker14/Ker8 phenotype of rare cell population.
Using unbiased cell phenotyping with CODEX to study breast cancer in human FFPE tissue, we independently recapitulated the discovery of a rare epithelial cell type, demonstrating the ability of the CODEX technology to resolve rare cell populations via unrestricted imaging of large tissue samples.
Learn how you can discover rare cell types with whole tissue, single-cell imaging.
Discover how cells organize and interact across the tissue landscape
Representative Voronoi diagrams of cellular neighborhoods (CNs) selected to show the nine different CNs (left) and corresponding seven-color image (right).
Dr. Garry Nolan and his team at Stanford University collaborated with the University of Bern to conduct deep single-cell phenotyping and spatial analysis on a cohort of colorectal cancer FFPE samples using CODEX. As a result, the team discovered nine distinct cellular neighborhoods, each uniquely composed of certain immune and cancer cell types. These cellular neighborhoods were found to interact with one another in a manner that correlated with disease progression and prognosis.
Additional Case Studies:
Human Cell Atlas: A Spatially Resolved Map of Human Breast Tissue
The mission of the Human Cell Atlas is to create comprehensive reference maps of all human cells to describe and define the cellular basis of health and disease. An upcoming Nature Research webinar will illustrate how spatial phenotyping with CODEX can enhance the biological insights from Human Cell Atlas initiatives. In an on-demand webinar, Dr. Kai Kessenbrock — using the Human Breast Cell Atlas project as an example — discusses how his team discovered unique cellular niches within the breast tissue microenvironment and how their spatial proximity gives us a more comprehensive view of the biology underlying each sample.
A Single-Cell GPS to Discover Novel Spatial Biomarkers in FFPE Tissues
As part of a study on graft-versus-host disease (GvHD) in the gut mucosa, Kohta Miyawaki, PhD, Physician-Scientist at Kyushu University partnered with Enable Medicine to perform multiplex imaging of gut GvHD samples using CODEX. Dr. Miyawaki combined CODEX imaging data with gene expression data, revealing spatial associations and interactions between macrophages and lymphatic endothelial cells that may govern inflammation in gut GvHD.
Analysis Strategies for Spatial Multi-Omics
Several algorithms have emerged for combining CODEX mIF images with other omics techniques. These approaches represent the growing interest in more comprehensive investigation biological pathways at multiple levels. Integrating single-cell, spatial proteomics data generated by CODEX with other omics data has the potential to uncover novel associations, rare cell populations, and complex markers of disease.
StVEA (Spatially-resolved Transcriptomics via Epitope Anchoring) is an approach which combines ultra-high multiplex IF data obtained with CODEX with CITE-seq data, a high throughput single-cell RNA sequencing method that captures cell phenotypic information encoded by cell surface proteins. STvEA integrates the ‘protein space’ that is measured in both CODEX and CITE-seq experiments, resulting in a true multi-omic dataset with single cell resolution in intact tissue samples.
GLUER (inteGrative anaLysis of mUlti-omics at single-cEll Resolution) is a flexible tool for integrating single-cell multi-omics data with imaging data via joint nonnegative matrix factorization (NMF), mutual nearest neighbor algorithm, and deep neural network. GLUER has been applied to integrate single-cell RNA sequencing data with CODEX data from murine spleen cells.