Hazel, birch or grass? – Distinguish pollen using microfluidics and neuronal networks
Up to 1000 pollen per second flow by the optical window in a narrow channel on the stamp sized chip. A digital camera captures each of the tiny single grains through a microscope lens.
To receive sharp shots for the following data processing, every analyzed particle has to pass the liquid channel in the focal plane of the lens. The height of the focal plane of the used high-resolution lenses measures less than a hundredth millimetre.
Scientists of Leibniz IPHT met this technological challenge employing a sophisticated design of the components in the microfluidic chip. The patented method enables them to align the pollen grains exactly in the focal plane and therefore to obtain sharp images of all objects.
“Using two liquid streams from the sides, we press the particle stream to a sheet, just like a nozzle. A new arrangement of the micro channels rotates the sheet by 90° into the focal plane,“ explains Andreas Kleiber the technology. in the scope of his PhD thesis, the scientist researches methods for the high-throughput-analysis of bioparticles using microfluidic chips.
The principle of hydrodynamic focusing is already known in the field of flow cytometry for the analysis of cell populations. Here, the cells are focused in a way that they pass by the measurement window along a line. “New to our system is, that we arrange the particles in a thin, two dimensional lamella, and therefore use the whole frame of the camera. This makes the method so rapid“, says Kleiber.
The researchers can actuate the horizontal position and thickness of the particle layer accurately. Therefore, they are able to control the rotation of the pollen in the stream. “Using methods already known from computer-tomography we are able to produce 3D-image data that contain important information e.g. about the three-dimensional morphology of a pollen grain.
The 3D-information improves the reliability of the pollen identification significantly“, elucidated Kleiber. The researcher evaluates images of the different pollen with software tools for particle tracking and feature extraction. A pre-trained convolutional neuronal network classifies the shots to a certain kind of pollen by means of the extracted data. The hit rate is above 98%.
The researchers classified the pollen, which originate from the research group Indoor Climatology at the University Hospital Jena, without any additional label, solely on basis of the image information from microscopy. “We are able to use the method furthermore for the analysis of cells e.g. to distinguish subtypes of white blood cells“, underlines Dr. Thomas Henkel, who leads the relevant research work at Leibniz IPHT.
“In the future, it should be possible to sort bioparticles with our chip“, says Henkel about the planned research, which is funded by the EU in the range of the Era-NET-DLR project “WaterChip“.
Work Group Microfluidics//Leibniz IPHT Jena
+49 (0) 3641 206-357
Dr. Thomas Henkel
Work Group Leader
+49 (0) 3641 206-307
Alle Nachrichten aus der Kategorie: Life Sciences
Articles and reports from the Life Sciences area deal with applied and basic research into modern biology, chemistry and human medicine.
Valuable information can be found on a range of life sciences fields including bacteriology, biochemistry, bionics, bioinformatics, biophysics, biotechnology, genetics, geobotany, human biology, marine biology, microbiology, molecular biology, cellular biology, zoology, bioinorganic chemistry, microchemistry and environmental chemistry.
Safe high-tech batteries for electric cars and laptops
New joint project at the University of Bayreuth Lithium-ion batteries are currently the most important category of electrical energy storage device. Their operational safety depends crucially on separators that ensure…
New study suggests supermassive black holes could form from dark matter
A new theoretical study has proposed a novel mechanism for the creation of supermassive black holes from dark matter. The international team find that rather than the conventional formation scenarios…
Tool that more efficiently analyzes ocean color data will become part of NASA program
Stevens uses machine learning-driven techniques to develop a long-awaited tool that better reveals the health of Earth’s oceans and the impacts of climate change. Researchers at Stevens Institute of Technology…