Intelligent software tackles plant cell jigsaw puzzle

Date:
August 31, 2020
Source:
European Molecular Biology Laboratory
Summary:
Scientists have developed a machine learning-based algorithm to
study the morphogenesis of plants at a cellular level. So far it
was impossible to solve this evolving and changing puzzle.



FULL STORY ========================================================================== Imagine working on a jigsaw puzzle with so many pieces that even the
edges seem indistinguishable from others at the puzzle's centre. The
solution seems nearly impossible. And, to make matters worse, this puzzle
is in a futuristic setting where the pieces are not only numerous, but ever-changing. In fact, you not only must solve the puzzle, but "un-solve"
it to parse out how each piece brings the picture wholly into focus.


========================================================================== That's the challenge molecular and cellular biologists face in sorting
through cells to study an organism's structural origin and the way it
develops, known as morphogenesis. If only there was a tool that could
help. An eLife paper out this week shows there now is.

An EMBL research group led by Anna Kreshuk, a computer scientist and
expert in machine learning, joined the DFG-funded FOR2581 consortium of
plant biologists and computer scientists to develop a tool that could
solve this cellular jigsaw puzzle. Starting with computer code and
moving on to a more user-friendly graphical interface called PlantSeg,
the team built a simple open-access method to provide the most accurate
and versatile analysis of plant tissue development to date. The group
included expertise from EMBL, Heidelberg University, the Technical
University of Munich, and the Max Planck Institute for Plant Breeding
Research in Cologne.

"Building something like PlantSeg that can take a 3D perspective of cells
and actually separate them all is surprisingly hard to do, considering
how easy it is for humans," Kreshuk says. "Computers aren't as good as
humans when it comes to most vision-related tasks, as a rule. With all
the recent development in deep learning and artificial intelligence at
large, we are closer to solving this now, but it's still not solved --
not for all conditions. This paper is the presentation of our current
approach, which took some years to build." If researchers want to look
at morphogenesis of tissues at the cellular level, they need to image individual cells. Lots of cells means they also have to separate or
"segment" them to see each cell individually and analyse the changes
over time.

"In plants, you have cells that look extremely regular that in a
cross-section looks like rectangles or cylinders," Kreshuk says. "But you
also have cells with so-called 'high lobeness' that have protrusions,
making them look more like puzzle pieces. These are more difficult to
segment because of their irregularity." Kreshuk's team trained PlantSeg
on 3D microscope images of reproductive organs and developing lateral
roots of a common plant model, Arabidopsis thaliana, also known as thale
cress. The algorithm needed to factor in the inconsistencies in cell
size and shape. Sometimes cells were more regular, sometimes less. As
Kreshuk points out, this is the nature of tissue.



==========================================================================
A beautiful side of this research came from the microscopy and images it provided to the algorithm. The results manifested themselves in colourful renderings that delineated the cellular structures, making it easier to
truly "see" segmentation.

"We have giant puzzle boards with thousands of cells and then we're
essentially colouring each one of these puzzle pieces with a different
colour," Kreshuk says.

Plant biologists have long needed this kind of tool, as morphogenesis
is at the crux of many developmental biology questions. This kind
of algorithm allows for all kinds of shape-related analysis, for
example, analysis of shape changes through development or under a
change in environmental conditions, or between species. The paper gives
some examples, such as characterising developmental changes in ovules,
studying the first asymmetric cell division which initiates the formation
of the lateral root, and comparing and contrasting the shape of leaf
cells between two different plant species.

While this tool currently targets plants specifically, Kreshuk points out
that it could be tweaked to be used for other living organisms as well.

Machine learning-based algorithms, like the ones used at the core of
PlantSeg, are trained from correct segmentation examples. The group
has trained PlantSeg on many plant tissue volumes, so that now it
generalises quite well to unseen plant data. The underlying method is,
however, applicable to any tissue with cell boundary staining and one
could easily retrain it for animal tissue.

"If you have tissue where you have a boundary staining, like cell walls in plants or cell membranes in animals, this tool can be used," Kreshuk says.

"With this staining and at high enough resolution, plant cells look very similar to our cells, but they are not quite the same. The tool right
now is really optimised for plants. For animals, we would probably have
to retrain parts of it, but it would work." Currently, PlantSeg is an independent tool but one that Kreshuk's team will eventually merge into
another tool her lab is working on, ilastik Multicut workflow.


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


========================================================================== Journal Reference:
1. Adrian Wolny, Lorenzo Cerrone, Athul Vijayan, Rachele Tofanelli,
Amaya
Vilches Barro, Marion Louveaux, Christian Wenzl, So"ren Strauss,
David Wilson-Sa'nchez, Rena Lymbouridou, Susanne S Steigleder,
Constantin Pape, Alberto Bailoni, Salva Duran-Nebreda, George
W Bassel, Jan U Lohmann, Miltos Tsiantis, Fred A Hamprecht, Kay
Schneitz, Alexis Maizel, Anna Kreshuk. Accurate and versatile 3D
segmentation of plant tissues at cellular resolution. eLife, 2020;
9 DOI: 10.7554/eLife.57613 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200831090127.htm

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