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64: How To Overcome Challenges In Image Analysis For Spatial Biology w/ Lorenz Rognoni, Ultivue
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Treść dostarczona przez Aleksandra Zuraw, DVM, PhD, Aleksandra Zuraw, and DVM. Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Aleksandra Zuraw, DVM, PhD, Aleksandra Zuraw, and DVM lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.
Exploring Spatial Biology and Image Analysis with Lorenz Rognoni
Get ready for a deep dive into spatial biology and image analysis with Lorenz Rognoni, the Director of Image Data Science at Ultivue. Ultivue is a company specializing in spatial biology and Lorenz brings his wealth of knowledge in multiplex immunofluorescence (mIF) and image data science to this great conversation.
Multiplex IF: Challenges and Complexities
We kick off our discussion by addressing the inherent challenges in multiplex IF. The conversation spans a range of issues including tissue preparation artifacts, unique tissue morphology, and antibody-specific staining. The vast variability of tissues, differing across body regions, species, and health conditions, is a recurring theme. We also delve into the effectiveness of expert visual evaluation for traditional stains and the need for new strategies to interpret high-dimensional data.
Brightfield Imaging in Spatial Biology: Does it Still Play a Role?
Shifting gears, we discuss the role of brightfield imaging in spatial biology. Is there still space for brightfield if we want to learn the spatial interactions of cells in the tissue? Is this method not too limiting?
Lorenz underscores its continued relevance, particularly when robustness and scalability are prerequisites. He suggests transitioning to simpler methods like singleplex IF or even brightfield imaging, once research zeroes in on specific biomarkers of relevance with multiplex IF.
Transitioning from Image Analysis to Data Interpretation: Navigating the Pitfalls
Our conversation culminates in a look at the challenges and potential missteps in moving from image analysis to interpreting the data generated. Lorenz points out the crucial process of extracting meaningful insights from millions of cells, defining appropriate phenotypes, and considering the intricacies of downstream data mining.
Key Takeaways
- mIF is an exploratory method and the insights gained can later be transitioned ti simpler methods such as single market IF or IHC
- The spatial biology research relies on accurate cell segmentation and identifying the correct phenotypes of cells.
- Correct segmentation is the first step to explore the insights and this exploration is being done through informed data mining that takes into consideration all the information about the study. This is best done by an image data science team where image analysis scientists, data mining experts and pathologists work together.
Join us for this insightful conversation and gain a deeper understanding of the complexities and nuances of spatial biology and image data science with Lorenz Rognoni.
Keywords: Lorenz Rognoni, Ultivue, spatial biology, image analysis, multiplex immunofluorescence, tissue morphology, brightfield imaging, data mining
THIS EPISODE'S RESOURCES:
- Lorenz Rognoni on LinkedIn
- Ultivue Official Website
DIGITAL PATHOLOGY PLACE RESOURCES:
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