Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the single-cell level. Recently, spatial technology has taken transcriptomics to the next level by adding spatial information. But, it lacks single-cell resolution. Now, a group from the University of Texas MD Anderson Cancer Center has developed a computational method called CellTrek that combines these two datasets to achieve single-cell spatial mapping.

The new approach successfully combines data from parallel gene-expression profiling methods to create spatial maps of a given tissue at single-cell resolution. The resulting maps can provide unique biological insights into the cancer microenvironment and many other tissue types. The researchers presented findings from analysis of kidney and brain tissues as well as samples of ductal carcincoma in situ breast cancer.

The study was published in Nature Biotechnology in the article, “Spatial charting of single-cell transcriptomes in tissues.

“Single-cell RNA sequencing provides tremendous information about the cells within a tissue, but, ultimately, you want to know where these cells are distributed, particularly in tumor samples,” said Nicholas Navin, PhD, professor of genetics and bioinformatics and computational biology at MD Anderson Cancer Center. “This tool allows us to answer that question with an unbiased approach that improves upon currently available spatial mapping techniques.”

Single-cell RNA sequencing is an established method to analyze the gene expression of many individual cells from a sample, but it cannot provide information on the location of cells within a tissue. On the other hand, spatial transcriptomics assays can measure spatial gene expression by analyzing many small groups of cells across a tissue but are not capable of providing single-cell resolution.

Current computational approaches, known as deconvolution techniques, can identify different cell types present from spatial data, but they are not capable of providing detailed information at the single-cell level, Navin explained. The team noted that they “benchmarked CellTrek using simulation and in situ hybridization datasets, which demonstrated its accuracy and robustness.”

Indeed, using publicly available scRNA-seq and spatial data from brain and kidney tissues, the researchers demonstrated that CellTrek achieved the most accurate and detailed spatial resolution of the methods evaluated. The CellTrek approach also was able to distinguish subtle gene expression differences within the same cell type to gain information on their heterogeneity within a sample.

They then applied CellTrek to existing mouse brain and kidney datasets and showed that CellTrek can detect topological patterns of different cell types and cell states.

The researchers collaborated with Savitri Krishnamurthy, MD, professor of pathology at MD Anderson Cancer Center, to apply CellTrek to study ductal carcincoma in situ breast cancer tissues. In an analysis of 6,800 single cells and 1,500 spatial transcriptomic regions from a single ductal carcincoma in situ sample, the team learned that different subgroups of tumor cells were evolving in unique patterns within specific regions of the tumor. Analysis of a second ductal carcincoma in situ sample demonstrated the ability of CellTrek to reconstruct the spatial tumor-immune microenvironment within tumor tissue.

“While this approach is not restricted to analyzing tumor tissues, there are obvious applications for better understanding cancer,” Navin said. “Pathology really drives cancer diagnoses and, with this tool, we’re able to map molecular data on top of pathological data to allow even deeper classifications of tumors and to better guide treatment approaches.”

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