Scientists develop a novel approach, enabling spatial mapping of single-cell data within tissue

Scientists at The University of Texas MD Anderson Cancer Center develop a new approach that efficiently integrates information from parallel gene-expression profiling methods at single-cell resolution to form spatial maps of a given tissue. The unique biological observations into the cancer microenvironment and many such tissue types can be offered by the resulting maps.

Scientists develop a novel approach, enabling spatial mapping of single-cell data within tissue
Nicholas Navin, PhD. Image Credit: The University of Texas MD Anderson Cancer Center

The research was reported in Nature Biotechnology and will be presented at the upcoming Annual Meeting 2022 of the American Association for Cancer Research (AACR).

To precisely highlight the position of individual cell types inside a tissue, the CellTrek tool uses information from single-cell RNA sequencing (scRNA-seq) along with that of spatial transcriptomics (ST) assays, measuring spatial gene expression in several tiny groups of cells. The research team presented results from the analysis of kidney and brain tissues, together with the ductal carcinoma in situ (DCIS) breast cancer samples.

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.”

Nicholas Navin PhD, Study Senior Author and Professor, Genetics and Bioinformatics & Computational Biology, MD Anderson Cancer Center

This tool allows us to answer that question with an unbiased approach that improves upon currently available spatial mapping techniques,” Navin added.

Single-cell RNA sequencing is a well-known approach to examine the gene expression of several individual cells from a sample. However, it cannot offer data on the cell location inside a tissue. ST assays, on the other hand, can quantify spatial gene expression by examining several tiny groups of cells throughout a tissue but are incapable of offering single-cell resolution.

Deconvolution techniques, also considered the contemporary computational approaches, can find various cell types present from ST data, but they are incapable of offering elaborate data at the single-cell level, Navin clarified.

Hence, co-first authors Runmin Wei PhD, and Siyuan He of the Navin Laboratory headed the initiatives to create CellTrek as a tool to integrate the special advantages of scRNA-seq and ST assays and develop precise tissue samples’ spatial maps.

The researchers established that CellTrek attained the most precise and elaborate spatial resolution of the methods assessed using freely available scRNA-seq and ST data from brain and kidney tissues. The CellTrek method also was able to differentiate differences in subtle gene expression inside the same cell type to achieve data on their heterogeneity within a sample.

The scientists also co-worked with Savitri Krishnamurthy, MD, professor of Pathology, to employ CellTrek to investigate DCIS breast cancer tissues. In an examination of 6,800 single cells and 1,500 ST regions from a single DCIS sample, the researchers got to know that different subgroups of tumor cells were evolving in unique patterns within specific regions of the tumor.

The capacity of CellTrek to rebuild the spatial tumor-immune microenvironment inside a tumor tissue was demonstrated by the analysis of a second DCIS sample.

While this approach is not restricted to analyzing tumor tissues, there are obvious applications for better understanding cancer. 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.”

Nicholas Navin PhD, Study Senior Author and Professor, Genetics and Bioinformatics & Computational Biology, MD Anderson Cancer Center

Source:
Journal reference:

Wei, R., et al. (2022) Spatial charting of single-cell transcriptomes in tissues. Nature Biotechnology. doi.org/10.1038/s41587-022-01233-1.

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