AI4Life Open Calls Special Edition

February 2024

Welcome to the special edition of the AI4Life newsletter featuring the second Open Call and the first use cases following the initial Open Call.

Enjoy exploring the various use cases and the scientific challenges they address, along with interviews with the experts who assisted in providing solutions. Thank you for reading!

The Second AI4Life Open Call offers Deep Learning support to life scientists tackling image analysis challenges.

In this Open Call, we will have a 2-stage process: first, a consultation phase in which experts will assess the availability and format of the data, as well as the suitability of deep learning methods for each pre-selected project. A subset of these projects will then be selected for a second stage, in which AI4Life experts will provide technical support for 6 months.

Researchers at the University of Trieste are investigating the metabolic regulation of murine skeletal muscles through the analysis of multiple sections from a single muscle. These sections are stained with various markers and imaged using light microscopy. The primary objective is to segment and identify all cells in each section and across different acquired images. However, the varying morphology of cells poses a significant challenge to this identification process.

🎥 Meet the expert: Watch the video in which the AI4Life expert Vera Galinova explains the project.

Researchers from CNRS and Grenoble University face a challenge when segmenting various organelles of microalgae in large 3D electron microscopy images for this project. The segmentation is crucial for reconstructing and quantifying the morphometrics of key organelles, including the chloroplast, in both free-living cell and symbiotic forms. However, the complexity of the cells and the size of the stacks make manual annotation extremely time-consuming and costly. 

🎥 Meet the expert: Watch the video in which the AI4Life expert Mehdi Seifi explains the project.

Researchers at Illinois State University are studying how the expression level of motor transport proteins affects their function in mediating the assembly and length of cilia.

🎥 Meet the expert: Watch the video in which the AI4Life expert Damian Dalle Nogare explains the project.

At the University of Toledo (USA), researchers are examining the migration behavior of epithelial cells in a confluent 2D monolayer. By creating a scratch using a pipette tip, they observe the cells as they migrate to close the wound. The goal was to automate the comparison of various cell lineages, including analyzing cell morphology, which requires segmentation and tracking of individual cells and nuclei over time.

🎥 Meet the expert: Watch the video in which the AI4Life expert Vera Galinova explains the project.

Researchers from RD Néphrologie in Montpellier (France) are investigating the impact of Chronic Kidney Disease (CKD) on collagen density in tissues like the heart and kidney. Employing mouse and rat models, they extract tissue slices and detect collagen using a biochemical marker, aiming to understand the disease's effects on tissue composition.

🎥 Meet the expert: Watch the video in which the AI4Life expert Joran Deschamps explains the project.

In this project, a researcher from the European Molecular Biology Laboratory (EMBL) in Heidelberg is using a cutting-edge commercial flow cytometer to sort phytoplankton from lab-grown cultures or field samples. Sorting is traditionally done by selecting features measured by the instrument on the sample and manually drawing a gate defining the range of values in these features that correspond to the cells being selected. However, this new instrument is image-enabled and allows exporting not only traditional features but features derived from fluorescent images as well. In addition, it supports the import of gating strategies to the control software. This opens the door to automated analysis of the features and consequently, the generation of a gating strategy that can be uploaded directly to the flow cytometer.

🎥 Meet the expert: Watch the video in which the AI4Life expert Mehdi Seifi explains the project. 

Researchers at Wageningen University utilize the NPEC growing facility to monitor plant growth systematically. By capturing images of each plant over several weeks, they aim to develop an AI model that can analyze individual leaves' developmental stages. This project enhances insights into leaf physiology and development under varying light conditions, offering a deeper understanding of plant growth dynamics.

🎥 Meet the expert: Watch the video in which the AI4Life expert Vera Galinova explains the project.

Interested in learning more? Stay tuned for additional use cases as they're published on the AI4Life website: https://ai4life.eurobioimaging.eu/use-cases/

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