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Murphy_PSLID_BOSC2009
- 1. PSLID, the Protein Subcellular Loca4on Image Database: Subcellular loca4on assignments, annotated image collec4ons, image analysis tools, and genera4ve models of protein distribu4ons Estelle Glory, Jus.n Newberg, Tao Peng, Ivan Cao‐Berg, and Robert F. Murphy Departments of Biological Sciences, Biomedical Engineering and Machine Learning and 1
- 2. Contributors • Michael Boland • Mia Markey • David Casasent • Gregory Porreca • Simon Watkins • Meel Velliste • Jon Jarvik, Peter Berget • Kai Huang • Jack Rohrer • Xiang Chen • Tom Mitchell • Yanhua Hu • Christos Faloutsos • Juchang Hua • Jelena Kovacevic • Ting Zhao • Geoff Gordon • Shann‐Ching Chen • B. S. Manjunath, Ambuj Singh • Elvira Osuna Highley • Les Loew, Ion Moraru, Jim Schaff • Jus4n Newberg • Gustavo Rohde • Estelle Glory • Ghislain Bonamy, Sumit Chanda, • Tao Peng Dan Rines • Luis Coelho • Ivan Cao‐Berg
- 3. Overview • SLIC – Subcellular Loca.on Image Classifica.on, Clustering, Comparison • PUnMix – Subcellular PaVern Unmixing • SLML Tools – Genera.ve Models of Cells and Subcellular Organelles • PSLID – Protein Subcellular Loca.on Image Database
- 4. The Challenge Comparison of cell images pixel‐by‐pixel or region‐by‐region matching does not work for cell paVerns because different cells have different shapes, sizes, orienta4ons Organelles/structures within cells are not found in fixed loca4ons Instead, describe each image numerically and operate on the descriptors (“SLF” ‐ Subcellular Loca=on Features)
- 5. SLIC tool categories • Segmenta.on • Feature calcula.on • Classifica.on • Clustering • Comparison
- 6. Feature levels and granularity Single Single Single Object Cell Field Object Cell Field features features features Aggregate/average operator Granularity: 2D, 3D, 2Dt, 3Dt
- 7. ER gian.n gpp130 2D LAMP Mito Nucleolin Images of HeLa cells Ac.n TfR Tubulin DNA 100 Subcellular PaVern 90 Human Accuracy 80 Classifica.on: 70 Computer vs. Human 60 50 40 Even beVer results using mul.resolu.on methods 40 50 60 70 80 90 100 Computer Accuracy Even beVer results for 3D images
- 8. SLIC versions – Source code • Matlab • Python • C++/ITK (subset; from Badri Roysam’s group)
- 9. Decomposing mixture paVerns • Proteins can be in more than one structure • Clustering or classifying whole cell paVerns will consider each combina.on of two or more “basic” paVerns as a unique new paVern • Desirable to have a way to decompose mixtures instead • Our approach: assume that each basic paVern has a recognizable combina.on of different types of objects
- 12. 0.5 0.4 Amt fluor. 0.3 Pure Lysosomal Pattern 0.2 0.1 0 Golgi class 1 2 Lysosomal class 3 4 5 Nuclear class 6 7 Object type 8 0.5 0.4 Pure Golgi PaRern 0.3 Amt fluor. 0.2 0.1 0 Golgi class 1 2 Lysosomal class 3 4 5 Nuclear class 6 7 Object type 8 0.25 0.2 0.15 Amt fluor. 0.1 0.05 All 0 Golgi class 1 2 Lysosomal class 3 4 5 Nuclear class 6 7 Object type 8
- 13. Test samples • How do we test a subcellular paVern unmixing algorithm? • Need images of known mixtures of pure paVerns – difficult to obtain “naturally” • Created test set by mixing different propor.ons of two probes that localize to different cell parts (lysosomes and mitochondria)
- 14. Tao Peng, Ghislain Bonamy, Estelle Glory, Sumit Chanda, Dan Rines (Genome Research Institute of Novartis Foundation) • Lysotracker
- 15. • Mitotracker
- 17. PaVern unmixing results 17
- 18. PUnMix versions • Open source – Matlab including C++ • Compiled versions (not requiring Matlab license) for MacOS, Windows, Linux
- 19. SLML Tools ‐ Genera.ve models of subcellular paVerns • Build model instance from image collec.on • Generate images from model instance • View mul.‐paVern images
- 20. LAMP2 paVern Cell membrane Nucleus Protein
- 21. Nuclear Shape ‐ Medial Axis Model width Rotate Represented by two curves Medial axis width along the the medial axis medial axis
- 24. Cell Shape Descrip.on: Distance Ra.o d1 + d 2 d1 r = d2 d2
- 25. Genera.on
- 26. Models for protein‐containing objects • Mixture of Gaussian objects • Learn distribu.ons for number of objects and r: normalized distance, a: angle to major axis object size • Learn probability density func.on for objects rela.ve to nucleus and cell
- 27. Synthesized Images Lysosomes Endosomes Have XML design for capturing model parameters SLML toolbox ‐ Ivan Cao‐Berg, Tao Peng, Ting Zhao Have portable tool for genera.ng images from model 27
- 28. Model Distribu.on • Genera.ve models provide beVer way of distribu.ng what is known about “subcellular loca.on families” (or other imaging results, such as illustra.ng change due to drug addi.on) • Have XML design for capturing the models for distribu.on • Have portable tool for genera.ng images from the model
- 29. Combining Models for Cell Simula.ons Protein 1 Cell Shape Nuclear Model Protein 2 Cell Shape Simulation Nuclear Model Protein 3 Cell Shape Shared Nuclear Model Nuclear and Cell XML Shape
- 30. Example combina.on Red = nuclear membrane, plasma membrane Blue = Golgi Green = Lysosomes Cyan = Endosomes
- 31. SLML Tools versions • Open source – Matlab including C++ • Compiled versions (not requiring Matlab license) for MacOS, Windows, Linux
- 32. PSLID • Loading pipeline driven by script – Calculates thumbnail images, features, segmenta.on – Creates database records and links – Creates predefined sets • Web applica.on – Create sets by searching on context or content – Analyze sets with any SLIC tool – Full display or summary – SOAP/XML interface
- 34. Annotated Datasets • 2D and 3D images of 9 major subcellular paVerns in HeLa cells • 3D images of ~300 proteins in 3T3 cells • 2D images of ~3000 proteins in 3T3 cells • 2D and 3D images for paVern unmixing • Datasets from other inves.gators
- 35. • hVp://murphylab.web.cmu.edu/sooware • hVp://murphylab.web.cmu.edu/data • Past major support from NSF • Current support from NIH NIGMS and NCRR – Na.onal Center for Networks and Pathways: Molecular Biosensors and Imaging Center (Alan Waggoner)