Buck Institute for Research on Aging’s Post

I'd like to express my gratitude to Stephanie Page, Stephanie Hicks, and Keri Martinowich for providing me with the opportunity to work on this incredible project! Through this experience, I significantly enhanced my computational skills, delved into the fascinating field of spatial transcriptomics, and developed a newfound appreciation for the complexities of the hippocampal structure. #projectexperience #computationalskills #spatialtranscriptomics #hippocampalstructure

The cell type specific expression technology of Split-GAL4 system has been utilized to examine Drosophila melanogaster's brain and CNS connectomes (ref. 1, Figures 2 & 6). Various and very sophisticated Split-GAL4 constructs are explained in Figures 1, 3, and 4 as diagrams (refer to ref. 1). The anatomical Olfactory sensory circuits of fruit flies have been mapped using this cell type specific Split-GAL4 expression systems, and the laser beam dissected fluorescent color-codes are guiding the locations of neurons, features, and fly behavioral patterns in vivo (ref.1, Figure 5). Ref. 1) https://lnkd.in/gNRcZamV

Our U.S. colleagues enjoyed demonstrating our solutions at this year’s Annual Clinical Genetics Meeting held by the American College of Medical Genetics and Genomics. The meeting took place in Toronto, Canada, and brought together leaders and prominent figures in the field of medical genetics and genomics, including clinical research. We hope you had a chance to meet Peter Hartmayer and Jeff Sanford. Selected highlights of karyogram creation with Ikaros include: 🎯 Accommodate a wide range of banding techniques currently in use, 🎯 Utilize diverse specimens, such as amniotic fluid, peripheral blood, chorionic villus, bone marrow, and tissue, without limitations based on specific diseases, for banding analysis, 🎯 Incorporate multiple features to enhance the interpretation of metaphases and streamline the karyotyping process, 🎯 Leverage Deep Neural Networks (DNN) to assist in the separation and assignment of chromosomes. 🎯 Maintain a continuous log of processing steps and enjoy unrestricted access to the original images, 🎯 Facilitate seamless transitions between different capture settings, allowing for easy shifts from brightfield to fluorescence and vice versa, 🎯 Opt for manual image acquisition with one-click capture, automatic contrast enhancement, and the selection of the optimal focus plane. Want to know more about our products and services? Contact us through our website ➡️ https://lnkd.in/d-wZ4VTu #MetaSystems #AutomatedImaging #Ikaros #Metafer #ACGM #ACGM2024

Check out our latest preprint on BioRxiv, introducing organellomics! Our team has developed an AI-driven approach to deep organellar phenotyping of human neurons, providing a systems-level view of cell architecture with 24 organelles. 🧠🔬 With over 1.5 million neuron images encoded, our 𝗡𝗢𝗩𝗔 (𝗡euronal 𝗢rganellomics 𝗩ision 𝗔tlas) enables holistic insights into cellular biology. Shoutout to an amazing team, including Sagy Krispin, Nancy Y., Welmoed van Zuiden Eran Hornstein, and many others!🌟👩💻👨💼 #organellomics #AI #cellbiology #neurons #science

How do neurons respond so quickly to stimuli?

Multiplex imaging can provide important insights into protein distribution in tissue. Yet, annotating individual cells is a slow process when done manually. Muhammad Shaban et al. developed a solution called MAPS (Machine learning for Analysis of Proteomics in Spatial biology). They used Deepcell to segment individual cells using nuclear and membrane features. The expression for each cell was then calculated as the average value of each marker within the cell. Then, they used a neural network to predict the cell class using the average expression values. They evaluated their method on five different MIBI and CODEX datasets, including three different types of cancer. MAPS outperformed other approaches (ASTIR and CellSighter) with respect to accuracy and computational efficiency. MAPS also generalized well from one MIBI dataset to another. paper: https://lnkd.in/eV2H_Uuj code: https://lnkd.in/ea8DdAm2 dataset: https://lnkd.in/ePnZuiZ5 To learn more about the latest research, subscribe to my Computer Vision Insights newsletter: https://lnkd.in/g9bSuQDP #Pathology #CancerResearch #PrecisionMedicine #MedicalImaging #MachineLearning #DeepLearning #ComputerVision

What if we could map every single connection in the brain? Scientists have achieved exactly that with the fruit fly brain, creating the most comprehensive neural wiring diagram ever made. A massive collaboration through the FlyWire consortium has mapped all 140,000 neurons and 55 million synapses in the female fruit fly brain, revealing how vision, movement, and memories are processed at the cellular level. After the sequencing of genome, "mapping the connectome" is the next large-scale biological data achievement. Why this matters: - First complete blueprint showing how an entire brain is wired. - Fruit flies share ~60% of the genes present in humans, including genes for learning and circadian rhythms. 3 in 4 human genetic diseases have a parallel in fruit flies. - Creates foundation for granular understanding how brains process information and control behavior, well beyond the "brain regions" schematics - Identified 8,400+ unique types of neurons with distinct neurotransmitter roles - Provides both detailed circuit-level views, connectivity patterns and broader regional brain connections This breakthrough took over five years and hundreds of scientists (Princeton, Columbia, University of Puerto Rico, Cambridge U and more), combining expert analysis with AI-powered tools to chart this microscopic universe of neural connections.

Behold the power of visualizing cell morphology! Inspiring new work shows AI can (87% successfully) infer the genetic signaling identity of Drosophila neurons -- just from electron micrographs. (Eckstein and Bates et al., 2024) link below This finding seems to highlight the power of imaging cells in their native context. How can we combine our understandings of cell architecture, phenotype and microenvironment to create more useful biological atlases?? I can't wait to see. https://lnkd.in/edXiZpyK

Exciting new research from Massachusetts Institute of Technology and University of Stuttgart, published in American Physical Society PRX Life, reveals how collective cell movement can be predicted from static configurations using graph neural networks (GNN). Leveraging both experimental and synthetic data, the study identified spatial features in cell structures that link to migratory dynamics. The Hermes High Content Screening System from IDEA Bio-Medical was instrumental in this work, capturing time-lapse images of breast cell monolayers to provide the critical data for model training and validation. This breakthrough opens new pathways for predicting cell behavior in complex processes, with promising applications in disease modeling and tissue engineering. https://lnkd.in/dCFmt3WV

The release of Ca2+ ions from intracellular stores plays a crucial role in many cellular processes, acting as a secondary messenger in various cell types, including cardiomyocytes, smooth muscle cells, hepatocytes, and many others. Detecting and classifying associated local Ca2+ release events is particularly important, as these events provide insight into the mechanisms, interplay, and interdependencies of local Ca2+ release events underlying global intracellular Ca2+ signaling. However, time-consuming and labor-intensive procedures often complicate analysis, especially with low signal-to-noise ratio imaging data. Prisca Dotti's PhD thesis, which she successfully defended at the ARTORG Center for Biomedical Engineering Research on 18 July, addresses these challenges by introducing an innovative deep learning-based method for the automatic detection and classification of local Ca2+ release events. The model successfully detects over 75% of events, with performance comparable to expert human annotations. This approach offers a significant, time-saving alternative to traditional labour-intensive analysis methods. 👩🏻🔬🔬 #congrats🎉 #PhDdefense #DeepLearning #CalciumSignaling #CellBiology #ArtificialIntelligence #AI #KI #ConfocalImaging #ResearchInnovation #MedicalResearch #Cardiomyocytes #SignalProcessing #BiomedicalEngineering #ARTORGCenter #PhD #Research #Innovation💡 #MedResearch #Bern #translationalresearch #InselGruppe 🏥#UniversityofBern https://lnkd.in/gWY2ry2w