Efficient pollen identification
Interdisciplinary team of researchers combines image-based particle
analysis with artificial intelligence

Date:
October 5, 2020
Source:
Helmholtz Centre for Environmental Research - UFZ
Summary:
From pollen forecasting, honey analysis and climate-related
changes in plant-pollinator interactions, analysing pollen plays
an important role in many areas of research. Microscopy is still
the gold standard, but it is very time consuming and requires
considerable expertise. Scientists have now developed a method that
allows them to efficiently automate the process of pollen analysis.



FULL STORY ==========================================================================
From pollen forecasting, honey analysis and climate-related changes
in plant- pollinator interactions, analysing pollen plays an important
role in many areas of research. Microscopy is still the gold standard,
but it is very time consuming and requires considerable expertise. In cooperation with Technische Universita"t (TU) Ilmenau, scientists from
the Helmholtz Centre for Environmental Research (UFZ) and the German
Centre for Integrative Biodiversity Research (iDiv) have now developed
a method that allows them to efficiently automate the process of pollen analysis. Their study has been published in the specialist journal
New Phytologist.


========================================================================== Pollen is produced in a flower's stamens and consists of a multitude of
minute pollen grains, which contain the plant's male genetic material
necessary for its reproduction. The pollen grains get caught in the
tiny hairs of nectar- feeding insects as they brush past and are thus transported from flower to flower. Once there, in the ideal scenario,
a pollen grain will cling to the sticky stigma of the same plant
species, which may then result in fertilisation. "Although pollinating
insects perform this pollen delivery service entirely incidentally, its
value is immeasurably high, both ecologically and economically," says
Dr. Susanne Dunker, head of the working group on imaging flow cytometry
at the Department for Physiological Diversity at UFZ and iDiv. "Against
the background of climate change and the accelerating loss of species,
it is particularly important for us to gain a better understanding of
these interactions between plants and pollinators." Pollen analysis is
a critical tool in this regard.

Each species of plant has pollen grains of a characteristic shape,
surface structure and size. When it comes to identifying and counting
pollen grains - - measuring between 10 and 180 micrometres -- in a
sample, microscopy has long been considered the gold standard. However,
working with a microscope requires a great deal of expertise and is
very time-consuming. "Although various approaches have already been
proposed for the automation of pollen analysis, these methods are
either unable to differentiate between closely related species or do
not deliver quantitative findings about the number of pollen grains
contained in a sample," continues UFZ biologist Dr. Dunker. Yet it is
precisely this information that is critical to many research subjects,
such as the interaction between plants and pollinators.

In their latest study, Susanne Dunker and her team of researchers have developed a novel method for the automation of pollen analysis. To this
end they combined the high throughput of imaging flow cytometry --
a technique used for particle analysis -- with a form of artificial intelligence (AI) known as deep learning to design a highly efficient
analysis tool, which makes it possible to both accurately identify the
species and quantify the pollen grains contained in a sample. Imaging
flow cytometry is a process that is primarily used in the medical field
to analyse blood cells but is now also being repurposed for pollen
analysis. "A pollen sample for examination is first added to a carrier
liquid, which then flows through a channel that becomes increasingly
narrow," says Susanne Dunker, explaining the procedure. "The narrowing of
the channel causes the pollen grains to separate and line up as if they
are on a string of pearls, so that each one passes through the built-in microscope element on its own and images of up to 2,000 individual pollen grains can be captured per second." Two normal microscopic images are
taken plus ten fluorescence microscopic images per grain of pollen. When excited with light radiated at certain wavelengths by a laser, the
pollen grains themselves emit light. "The area of the colour spectrum
in which the pollen fluoresces - - and at which precise location -- is sometimes very specific. This information provides us with additional
traits that can help identify the individual plant species," reports
Susanne Dunker. In the deep learning process, an algorithm works in
successive steps to abstract the original pixels of an image to a greater
and greater degree in order to finally extract the species-specific characteristics. "Microscopic images, fluorescence characteristics and
high throughput have never been used in combination for pollen analysis
before - - this really is an absolute first." Where the analysis of a relatively straightforward sample takes, for example, four hours under
the microscope, the new process takes just 20 minutes. UFZ has therefore applied for a patent for the novel high-throughput analysis method,
with its inventor, Susanne Dunker, receiving the UFZ Technology Transfer
Award in 2019.

The pollen samples examined in the study came from 35 species of meadow
plants, including yarrow, sage, thyme and various species of clover such
as white, mountain and red clover. In total, the researchers prepared
around 430,000 images, which formed the basis for a data set. In
cooperation with TU Ilmenau, this data set was then transferred using
deep learning into a highly efficient tool for pollen identification. In subsequent analyses, the researchers tested the accuracy of their new
method, comparing unknown pollen samples from the 35 plant species against
the data set. "The result was more than satisfactory - - the level of
accuracy was 96 per cent," says Susanne Dunker. Even species that are
difficult to distinguish from one another, and indeed present experts
with a challenge under the microscope, could be reliably identified. The
new method is therefore not only extremely fast but also highly precise.

In the future, the new process for automated pollen analysis will play
a key role in answering critical research questions about interactions
between plants and pollinators. How important are certain pollinators
like bees, flies and bumblebees for particular plant species? What
would be the consequences of losing a species of pollinating insect or
a plant? "We are now able to evaluate pollen samples on a large scale,
both qualitatively and- at the same time - - quantitatively. We are
constantly expanding our pollen data set of insect- pollinated plants for
that purpose," comments Susanne Dunker. She aims to expand the data set
to include at least those 500 plant species whose pollen is significant
as a food source for honeybees.


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


========================================================================== Journal Reference:
1. Susanne Dunker, Elena Motivans, Demetra Rakosy, David Boho, Patrick
Ma"der, Thomas Hornick, Tiffany M. Knight. Pollen analysis using
multispectral imaging flow cytometry and deep learning. New
Phytologist, 2020; DOI: 10.1111/nph.16882 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201005101515.htm

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