The number of publications in the field of spatial biology has been growing exponentially, demonstrating the rising interest in these approaches. As the year comes to a close, we’re reflecting on some of the most impactful findings in spatial biology from the last few years. Check out our top picks.
1. Tissues are organized into “cellular neighborhoods”
Dr. Garry Nolan’s team at Stanford University developed a novel analysis framework which can be used to study tissue biology at two levels – the distinct regions of the tissue and the cell types present in these regions. Their findings were published in Cell.
Representative tissue microarray cores from colorectal cancer patients depicted as 7-color images (top). Voronoi diagrams of clustered cell types, merged to reduce complexity (bottom). Source: Schürch et al. / CC BY
Using the CODEX® system, Schürch et al. generated high-dimensional spatial maps of colorectal cancer (CRC) tissue and identified distinct cell types and “cellular neighborhoods” to understand how interactions between cells and neighborhoods can influence CRC patient outcomes. The framework for cellular neighborhood analysis presented in this study can be used to interpret spatial biology in a dynamic tissue, such as the tumor microenvironment, and has the potential to yield clinical biomarkers, therapeutic strategies, and insights into the mechanisms behind antitumoral immunity.
2. Spatial multiomics combines high-resolution imaging with single-cell genomics
STvEA (Spatially-resolved Transcriptomics via Epitope Anchoring) was developed by Dr. Pablo Camara’s lab at the University of Pennsylvania to overcome the limitations of CITE-seq, which enables simultaneous analysis of RNA and protein expression, but lacks spatial information. In a paper published in Science, Govek et al. demonstrated how STvEA can map the CITE-seq transcriptome to spatially resolved CODEX imaging data and mass cytometry data.
Another method for integrative spatial multiomic analysis is GLUER (inteGrative anaLysis of mUlti-omics at single-cEll Resolution). A preprint from Dr. Kai Tan’s lab at the Children’s Hospital of Philadelphia details how GLUER merges imaging-based spatial proteomic data from CODEX with single-cell RNA sequencing data to study the spatial distribution of transcript and protein expression in murine spleen tissue. Dr. Camara and Dr. Tan joined us for a webinar series on spatial multiomics along with Dr. Will Wang of Stanford University.
3. Spatial phenotyping outperforms other biomarker approaches
A comprehensive meta-analysis by Lu et al. in JAMA Oncology sought to understand which biomarker approaches are most effective.
Summary receiver operating characteristic (sROC) curves for each biomarker approach. The relative area under the curves (AUCs) measure each approach’s ability to distinguish responders from non-responders.
They showed that spatial phenotyping, enabled by multiplex immunofluorescence (mIF), more accurately predicts patient response to anti-PD-1/PD-L1 therapy compared to other biomarker assays, including PD-L1 IHC, tumor mutational burden (TMB), and gene expression profiling.
4. AstroPath identifies novel spatial phenotypic signatures
In an incredible example of interdisciplinary collaboration, researchers from the astrophysics and pathology departments at Johns Hopkins University (JHU) collaborated to develop AstroPath™. The platform enables deep whole-slide imaging and spatial profiling of microscopic tumor sections by combining the Phenoptics™ mIF platform with sky-mapping algorithms derived from the Sloan Digital Sky Survey.
Berry et al. published findings from AstroPath in Science, demonstrating how they used AstroPath to build two-dimensional maps of the tumor microenvironment in metastatic melanoma, identifying a composite spatial phenotypic signature that is highly predictive of response to anti-PD-1 immune checkpoint inhibitor therapy and patient outcomes.
5. Standardizing a spatial biology workflow for clinical research
For spatial phenotypic signatures to have an impact on immunotherapy outcomes, the scientific community must come to a consensus and standardize a workflow to measure this biomarker class. The Phenoptics mIF workflow was validated across multiple institutions in a study published in the Journal for ImmunoTherapy of Cancer (JITC).
The Multi-Institutional TSA-amplified Multiplexed Immunofluorescence Reproducibility Evaluation, or MITRE study, demonstrated high inter- and and intra-site reproducibility of an automated 6-plex, 7-color assay focused on the PD-1/PD-L1 axis. Their findings suggest that mIF methods are robust enough to support translation into clinical trials and eventually, clinical practice. The approach described by this study may serve as a template for assessing reproducibility of emerging mIF panels.
Spatial context has the power to revolutionize biological research, from discovery all the way to the clinic. At Spatial Day, we shared some exciting new updates to our product portfolio, including the launch of the PhenoCycler™-Fusion system. We can’t wait to support more groundbreaking research in this field with our integrated suite of spatial biology solutions.
