Tool identifies location of individual cell types to generate biological insights – ScienceDaily

A new computational approach developed by researchers at the University of Texas MD Anderson Cancer Center 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 information about the microenvironment of cancer and many other tissue types.

The study was published today in Natural biotechnology and will be presented at the upcoming 2022 American Association for Cancer Research (AACR) Annual Meeting (abstract 2129).

The tool, called CellTrek, uses data from single-cell RNA sequencing (scRNA-seq) together with data from spatial transcriptomics (ST) assays – which measure the spatial expression of genes in many small groups of cells – to pinpoint the location of individual cell types within a tissue. The researchers presented the results of analysis of kidney and brain tissue as well as ductal carcinoma in situ (DCIS) breast cancer samples.

“Single-cell RNA sequencing provides tremendous information about cells in a tissue, but ultimately you want to know where those cells are distributed, especially in tumor samples,” said the lead author. Nicholas Navin, Ph.D., professor of genetics. and bioinformatics and computational biology. “This tool allows us to answer this question with an unbiased approach that improves on currently available spatial mapping techniques.”

Single-cell RNA sequencing is an established method for analyzing the gene expression of many individual cells from a sample, but it cannot provide information about the location of cells in a tissue. On the other hand, ST 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 types of cells present from ST data, but they are not capable of providing detailed information at the single cell level, Navin explained.

Therefore, co-first authors Runmin Wei, Ph.D., and Siyuan He of the Navin lab led efforts to develop CellTrek as a tool to combine the unique advantages of scRNA-seq and ST assays and create accurate spatial maps of tissue samples. .

Using publicly available scRNA-seq and ST data from brain and kidney tissue, the researchers demonstrated that CellTrek achieved the most precise and detailed spatial resolution of the methods evaluated. The CellTrek approach was also able to distinguish subtle differences in gene expression within the same cell type to gain insight into their heterogeneity within a sample.

The researchers also collaborated with Savitri Krishnamurthy, MD, professor of pathology, to apply CellTrek to study DCIS breast cancer tissue. In an analysis of 6,800 individual cells and 1,500 ST regions from a single DCIS sample, the team learned that different tumor cell subgroups evolved in unique patterns in specific regions of the tumor. Analysis of a second sample of DCIS demonstrated the ability of CellTrek to reconstruct the tumor-immune spatial microenvironment in tumor tissue.

“Although this approach is not limited to the analysis of tumor tissue, there are obvious applications for better understanding cancer,” Navin said. “Pathology really determines cancer diagnoses and with this tool we are able to map molecular data in addition to pathological data to enable even deeper classifications of tumors and better guide treatment approaches. »

This research was supported by the National Institutes of Health/National Cancer Institute (RO1CA240526, RO1CA236864, CA016672), the Cancer Prevention and Research Institute of Texas (CPRIT) (RP180684), the Chan Zuckerberg Initiative SEED Network grant and the PRECISION Cancer Grand Grant Challenges. Navin is supported by the American Association for the Advancement of Science (AAAS) Martin and Rose Wachtel Cancer Research Award, the Damon Runyon-Rahleff Innovation Award, the Andrew Sabin Family Fellowship, and the Jack and Beverly Randall Prize for Excellence in Cancer Research. Wei is supported by a Damon Runyon Quantitative Biology Fellowship Award.

Contributing authors to MD Anderson include Shanshan Bai, Emi Sei, Ph.D., and Min Hu, all of genetics; and Ken Chen, Ph.D., of Bioinformatics. Other authors include Alastair Thompson, MD, of Baylor College of Medicine, Houston. The authors have no conflict of interest.

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