Computational approach enables spatial mapping of single-cell data
within tissues
Tool pinpoints location of individual cell types to drive biological
insights

Date:
March 21, 2022
Source:
University of Texas M. D. Anderson Cancer Center
Summary:
A new computational 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.



FULL STORY ==========================================================================
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 insights into the cancer microenvironment and many
other tissue types.


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

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

"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 senior
author Nicholas Navin, Ph.D., professor of Genetics and Bioinformatics & Computational Biology. "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, 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 cell types 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 Laboratory led the 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
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.

The researchers also collaborated with Savitri Krishnamurthy, M.D.,
professor of Pathology, to apply CellTrek to study DCIS breast cancer
tissues. In an analysis of 6,800 single cells and 1,500 ST regions
from a single DCIS 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 DCIS sample demonstrated the ability
of CellTrek to reconstruct the spatial tumor- immune microenvironment
within a 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." 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 Challenges Grant.

Navin is supported by the American Association for the Advancement of
Science (AAAS) Martin and Rose Wachtel Cancer Research Award, the Damon Runyon-Rachleff 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.

Collaborating MD Anderson authors include Shanshan Bai, Emi
Sei, Ph.D., and Min Hu, all of Genetics; and Ken Chen, Ph.D., of Bioinformatics. Additional authors include Alastair Thompson, M.D.,
of Baylor College of Medicine, Houston. The authors have no conflicts
of interest.


========================================================================== Story Source: Materials provided by University_of_Texas_M._D._Anderson_Cancer_Center. Note: Content may be
edited for style and length.


========================================================================== Journal Reference:
1. Runmin Wei, Siyuan He, Shanshan Bai, Emi Sei, Min Hu, Alastair
Thompson,
Ken Chen, Savitri Krishnamurthy, Nicholas E. Navin. Spatial charting
of single-cell transcriptomes in tissues. Nature Biotechnology,
2022; DOI: 10.1038/s41587-022-01233-1 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220321132143.htm

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