By integrating spatial transcriptomics with protein markers researchers have substantially improved resolution of differential gene expression analysis across tissue regions. Spatial PrOtein and Transcriptome Sequencing (SPOTS) reveals the activities of specific types of cells in much more detail than unimodal measurements. This method thus allows creation of data-rich maps of organs and tumors.
The study was co-led by researchers at Weill Cornell Medicine, NewYork-Presbyterian, and the New York Genome Center. Their report was published this week in Nature Biotechnology.
“This technology is exciting because it allows us to map the spatial organization of tissues, including cell types, cell activities and cell-to-cell interactions, as never before,” said study co-senior author Dan Landau, an associate professor of medicine in the Division of Hematology and Medical Oncology and a member of the Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine and a core faculty member at the New York Genome Center.
Spatial transcriptomics, which reveals gene expression and its location, is one of the fastest advancing fields in science. But current techniques typically require the dissolution of tissues and the separation of cells from their neighbors, so that information about profiled cells’ original locations within the tissues is lost. SPOTS, the researchers say, captures that spatial information and at high resolution.
SPOTS is based in part on existing technology from 10x Genomics. It uses glass slides suitable for imaging tissue samples with ordinary microscope-based pathology methods, but the slides are coated with thousands of special probe molecules, each of which contains a molecular barcode denoting its two-dimensional position on the slide.
When a thinly sliced tissue sample is placed on the slide and its cells are made permeable, the probe molecules grab adjacent cells’ messenger RNAs—the transcripts of active genes. Designer antibodies bind to proteins of interest in the tissue and to the probe molecules. As a result, researchers can identify the captured mRNAs and selected proteins and map them precisely to their original locations across the tissue sample. Resulting maps can be compared to standard pathology imaging of the sample.
In the current study, the team applied SPOTS on tissue from a normal mouse spleen, revealing clusters of different cell types, their functional states, and how those states varied with the cells’ locations.
The team also used it to map the cellular organization of a mouse breast tumor. The resulting map depicted macrophages in two distinct states as denoted by protein markers—one state active and tumor-fighting, the other immune-suppressive and forming a barrier to protect the tumor.
“We could see that these two macrophage subsets are found in different areas of the tumor and interact with different cells—and that difference in microenvironment is likely driving their distinct activity states,” said Landau, who is also an oncologist.
This first version of SPOTS has a spatial resolution such that each “pixel” of the resulting dataset sums gene activity information for at least several cells. However, the researchers hope soon to narrow this resolution to single cells, while adding other layers of key cellular information, Landau said.