AI methods of analyzing social networks find new cell types in tissue


Date:
October 19, 2020
Source:
Uppsala University
Summary:
In situ sequencing enables gene activity inside body tissues to
be depicted in microscope images. To facilitate interpretation of
the vast quantities of information generated. Researchers have
now developed an entirely new method of image analysis. Based
on algorithms used in artificial intelligence, the method was
originally devised to enhance understanding of social networks.



FULL STORY ==========================================================================
In situ sequencing enables gene activity inside body tissues to be
depicted in microscope images. To facilitate interpretation of the vast quantities of information generated, Uppsala University researchers
have now developed an entirely new method of image analysis. Based on algorithms used in artificial intelligence, the method was originally
devised to enhance understanding of social networks. The researchers'
study is published in The FEBS Journal.


==========================================================================
The tissue composing our organs consists of trillions of cells with
various functions. All the cells in an individual contain the same genes
(DNA) in their nuclei. Gene expression occurs by means of "messenger RNA" (mRNA) -- molecules that carry messages from the nucleus to the rest of
the cell, to direct its activities. The mRNA combination thus defines
the function and identity of every cell.

RNA transcripts are obtainable through in situ sequencing. The researchers behind the new study had previously been involved in developing this
method, which shows millions of detected mRNA sequences as dots in
microscope images of the tissue. The problem is that distinguishing all
the important details may be difficult. This is where the new AI-based
method may come in useful, since it allows unsupervised detection of
cell types as well as detection of functions within an individual cell
and of interactions among cells.

"We're using the latest AI methods -- specifically, graph neural networks, developed to analyse social networks; and adapting them to understand biological patterns and successive variation in tissue samples. The cells
are comparable to social groupings that can be defined according to the activities they share in their social networks like Twitter, sharing their Google search results or TV recommendations," says Carolina Wa"hlby,
professor of quantitative microscopy at the Department of Information Technology, Uppsala University.

Earlier analytical methods of this type of data depend on knowing which
cell types the tissue contains, and identifying the cell nuclei in it,
in advance.

The method conventionally used, known as "single-cell analysis," may
lose some mRNA and miss certain cell types. Even with advanced automated
image analysis, it is often difficult to find the various cell nuclei if,
for example, the cells are packed densely together.

"With our analysis, which we call 'spage2vec', we can now get
corresponding results without any previous knowledge of expected
cell types. And what's more, we can find new cell types and intra-
or intercellular functions in tissue," Wa"hlby says.

The research group are now working further on its analytical method
by investigating differentiation and organisation of various types
of cells during the early development of the heart. This is pure basic research, intended to provide more knowledge of the mechanisms that govern development, both when everything is functioning as it should and when
a disease is present. In another project, a collaboration with cancer researchers, the Uppsala group are hoping to be able to apply the new
methods to gain a better understanding of how tumour tissue interacts,
at molecular level, with surrounding healthy tissue. The aim is that,
in the long term, this will culminate in better treatments that can be
adapted to individual patients.


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


========================================================================== Journal Reference:
1. Gabriele Partel, Carolina Wa"hlby. Spage2vec: Unsupervised
representation
of localized spatial gene expression signatures. The FEBS Journal,
2020; DOI: 10.1111/febs.15572 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201019111918.htm

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