Within tissue tissue, there is a spatial mapping of single-cell data

Within tissue tissue, there is a spatial mapping of single-cell data ...

A new computational approach developed by researchers at the University of Texas'' Anderson Cancer Center successfully combines data from parallel gene expression profiling methods to create spatial maps of a given tissue at a single-cell resolution. The resulting maps can provide unique biological insights into the cancer microenvironment and many other tissue types.

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

The scientific research has shown that data from scRNA-seq and the spatial transcriptomics (ST) assays that measure spatial gene expression in many small groups of cells to accurately identify the location of individual cell types within a tissue.

RNA sequencing in single cell provides significant information about the cells inside a tissue, but ultimately, you want to know where these cells are distributed, particularly in tumor samples, according to senior author Nicholas Navin, Ph.D., an expert in geneticsandBioinformatics and computational biology. This tool allows us to answer this question with a subjective approach that is enhanced by currently available spatial mapping techniques.

RNA sequencing is an established method to investigate 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, ST experiments can measure spatial gene expression by analyzing large groups of cells across a tissue, although they aren''t capable of providing single-cell resolution.

Current computational methods, commonly known as deconvolution techniques, are capable of identifying different cell types present from ST data, but they aren''t capable of providing detailed information at the single-cell level, according to Navin.

Runmin Wei, Ph.D., and Siyuan He of theNavin Laboratory launched a series of experiments to develop CellTrek as a tool to combine the unique benefits of scRNA-seq and ST assays and create precise spatial maps of tissue samples.

The researchers analyzed publicly available scRNA-seq and ST data from the brain and kidney tissues and found that CellTrek achieved the most accurate and detailed spatial resolution of the methods evaluated. The CellTrek approach also able to distinguish subtle gene expression differences within the same cell type in order to gain insights about their heterogeneity within a sample.

CellTrek developed a specific spatial tumor-immune microenvironment within a tumor tissue, according to a second DCIS study.

Although this approach is not restricted to studying tumor tissue, there are many benefits to better understanding cancer. With this tool, we analyzed molecular information on top of pathological data to help identify tumors more easily and help with treatment procedures.

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