imaging based methods
play

imaging-based methods MARINO ZERIAL Max Planck Institute of - PowerPoint PPT Presentation

Nov 16, 2022 •42 likes •382 views

Structure and dynamics of endosomes analyzed by imaging-based methods MARINO ZERIAL Max Planck Institute of Molecular Cell Biology and Genetics Dresden, Germany QBI 2019 9 - 11 Jan 2019, Rennes Self-Organization Propagated over Scales

  1. Structure and dynamics of endosomes analyzed by imaging-based methods MARINO ZERIAL Max Planck Institute of Molecular Cell Biology and Genetics Dresden, Germany QBI 2019 9 - 11 Jan 2019, Rennes

  2. Self-Organization Propagated over Scales Organism (Mouse, Human) Organ (Liver) Tissue (Liver lobule) Cellular (Hepatocytes) Sub-Cellular (endosomes) Molecular (Endosomal effectors) Understanding biological systems from the molecular to organ scale

  3. Topics Endocytosis and the endosomal network Endosomes structure and function by Multi- Color SMLM CLEM Endosomes dynamics in neurons (Yannis Kalaidzidis) Imaging liver tissue down to sub-cellular resolution

  4. ENDOSOMES AND CARGO SORTING Tfn EGF EGFR TfnR Clathrin-coated vesicle Early endosome Multivesicular body (MVB) or Endocytic carrier vesicle (ECV) Urska Repnik

  5. RAB5 AS MEMBRANE ORGANIZER GDI PRA-1 PI3-K VPS34 Rabankyrin-5 Early EEA1 Endosome Rabenosyn-5 Vps45 Syntaxin13 Vamp4 Syntaxin6 Vti1a

  6. EGF EEA1 APPL1 6

  7. Funnel Model of the Early Endosomal Network CCVS RAB5 Rink et al. 2005 CONVERSION ENDOSOME FUSION → ENDOSOME FISSION → RAB5 RAB7

  8. Funnel Model of the Early Endosomal Network CCVS RAB5 Rink et al. 2005 CONVERSION ENDOSOME FUSION → ENDOSOME FISSION → RAB5 RAB7

  9. confocal multicolor J Cell l Biol. ol. 2000 images scalebar 2μm

  10. MULTI-COLOR SINGLE MOLECULE LOCALIZATION MICROSCOPY (SMLM) OF EARL Y ENDOSOMES • Super- Resolution Imaging • Post-Processing • Quantification CORRELATIVE Sandra Segeletz Christian Franke • Genome-edited cells • High-Resolution • EM Imaging • Post-Processing LLSM & SIM • Quantification Urska Repnik

  11. MULTI-COLOR SINGLE MOLECULE LOCALIZATION MICROSCOPY (SMLM) OF EARL Y ENDOSOMES Confocal EGF (Alexa647) (projection) Tfn (Alexa568) Ra Rab5 (Dronpa) 250 nm 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  12. MULTI-COLOR SINGLE MOLECULE LOCALIZATION MICROSCOPY (SMLM) OF EARL Y ENDOSOMES super-resolved EGF (Alexa647) SMLM Tfn (Alexa568) Ra Rab5 (Dronpa) 250 nm 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  13. MULTI-COLOR SINGLE MOLECULE LOCALIZATION MICROSCOPY (SMLM) OF EARL Y ENDOSOMES EGF (Alexa647) Tfn (Alexa568) Rab5c (Dronpa) merge 250 nm 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  14. CORRELATIVE MULTI-COLOR SMLM & EM-TOMOGRAPHY Z – stack of Tomogram slides 250 nm 250 nm 250 nm z 1 z 2 z 3 Franke C, Repnik U, Segeletz S, Brouilly N

  15. CORRELATIVE MULTI-COLOR SMLM & EM-TOMOGRAPHY 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  16. CORRELATIVE MULTI-COLOR SMLM & EM-TOMOGRAPHY Multi-Color superCLEM - EGF – intraluminar vesicles 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  17. CORRELATIVE MULTI-COLOR SMLM & EM-TOMOGRAPHY Multi-Color superCLEM - EGF – intraluminar vesicles 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  18. CORRELATIVE MULTI-COLOR SMLM & EM-TOMOGRAPHY Multi-Color superCLEM - EGF 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  19. CORRELATIVE MULTI-COLOR SMLM & EM-TOMOGRAPHY Multi-Color superCLEM - EGF – sorting micro-domain 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  20. CORRELATIVE MULTI-COLOR SMLM & EM-TOMOGRAPHY Multi-Color superCLEM - EGF – stages of sorting 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  21. CORRELATIVE MULTI-COLOR SMLM & EM-TOMOGRAPHY Multi-Color superCLEM - Transferrin tubules 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  22. CORRELATIVE MULTI-COLOR SMLM & EM-TOMOGRAPHY Multi-Color superCLEM - Rab5c - nanodomains 250 nm Franke C, Repnik U, Segeletz S, Brouilly N

  23. ENDOSOME DYNAMICS IN NEURONS Heerssen and Segal, 2002

  24. LIVER FUNCTION DEPENDS ON THE BLOOD AND BILE NETWORKS Central vein Portal vein Blood Bile duct Sinusoidal Bile Canalicular Network Network Bile Liver lobule

  25. BILE FLOW BY INTRA VITAL MICROSCOPY Sophie Nehring 6-CF Dextran Merged Two-photon microscopy (Leica SP8 MPI-CBG), 880 nm laser, 40x1.10 NA water

  26. LIVER TISSUE IMAGED WITH CARE Florian Jug

  27. LIVER ARCHITECTURE DEPENDS ON HEPATOCYTE POLARITY ENDOTHELIUM BASAL SURFACE APICAL SURFACE BILE CANALI- BILE BILE CULUS CANALICULUS CANALI- CULUS BASAL SURFACE Apical Surface

  28. APICAL RECYCLING ENDOSOMES IN HEPA TOCYTES Basolateral Endocytosis Lysosomal degradation Apical Apical recycling recycling endosome endosome Bile canaliculus Rab11 Apical Endocytosis Lysosomal degradation

  29. JUNCTION FORMA TION PRECEDES POLARIZED TRAFFICKING IN MAMMALIAN EPITHELIAL CELLS Microtubules Apical Surface Chromatin markers Tight junctions Recycling endosomes Junction Polarized Lumen formation trafficking formation Bryant, D.M. et al. (2010). A molecular network for de novo generation of the apical surface and lumen. Nat. Cell Biol. 12, 1035 – 1045.

  30. APICAL RECYCLINGENDOSOMES CLUSTER AROUND CENTROSOMES CD13 Pericentrin (apical) (Centrosome) Phalloidin (Cell border) 10 µm

  31. ULTRASTRUCTURE OF APICAL RECYCLING ENDOSOMES 5 µm CD13 100 nm 300 nm Urska Repnik

  32. HEPATOCYTE CELL POLARITY ENDOTHELIUM BASAL SURFACE APICAL SURFACE BILE CANALI- BILE BILE CULUS CANALICULUS CANALI- CULUS BASAL SURFACE Apical Surface

  33. CONCLUSIONS Mapping molecules to the ultra-structure of cellular organelles: SMLM-CLEM - SMLM-CLEM applied to reconstituted systems Development of software for cryo-EM tomography (Florian Jug & Gaia Pigino) Multi-scale imaging of cells and tissue: Challenges - Phototoxicity, low SNR (Yannis Kalaidzidis) - Super-resolution in living tissue (IVM)

  34. ACKNOWLEDGEMENTS Christian Urska Sandra Franke Segeletz Repnik Sophie Nehring Yannis Kalaidzidis zerial.mpi-cbg.de

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Imaging Studies Section Imaging Studies Section Studies using imaging are often

Imaging Studies Section Imaging Studies Section Studies using imaging are often

NIHR Statistics Group: Imaging Studies Section Imaging Studies Section Studies using imaging are often interdisciplinary, have complex designs and cut across different clinical areas Methods developed in one area of research are

518 views • 13 slides
Diffraction Methods & Electron Microscopy Lecture 9 Imaging Part I Sandeep Gorantla FYS

Diffraction Methods & Electron Microscopy Lecture 9 Imaging Part I Sandeep Gorantla FYS

FYS 4340/FYS 9340 Diffraction Methods & Electron Microscopy Lecture 9 Imaging Part I Sandeep Gorantla FYS 4340/9340 course Autumn 2016 1 Imaging 2 Abbes principle of imaging Unlike with visible light, due to the small l,

1.1k views • 90 slides
Clustering ! Hierarchical methods ! Model-based methods ! Density-based methods 1 2 What is

Clustering ! Hierarchical methods ! Model-based methods ! Density-based methods 1 2 What is

Preview ! Introduction Lecture 10 ! Partitioning methods Clustering ! Hierarchical methods ! Model-based methods ! Density-based methods 1 2 What is Clustering? Examples of Clustering Applications ! Cluster: a collection of data objects !

301 views • 8 slides
Mathematical Methods for Photoacoustical Imaging Otmar Scherzer Computational Science Center,

Mathematical Methods for Photoacoustical Imaging Otmar Scherzer Computational Science Center,

Mathematical Methods for Photoacoustical Imaging Otmar Scherzer Computational Science Center, University Vienna, Austria & Radon Institute of Computational and Applied Mathematics (RICAM), Linz, Austria Coupled Physics Imaging: Sunlights

538 views • 34 slides
Laplacian Pyramid-based Complex Neural Network Learning for Fast MR Imaging Haoyun Liang Paul C.

Laplacian Pyramid-based Complex Neural Network Learning for Fast MR Imaging Haoyun Liang Paul C.

Laplacian Pyramid-based Complex Neural Network Learning for Fast MR Imaging Haoyun Liang Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS Accelerating MRI Fast Imaging Sequence Parallel Imaging Compressed Sensing Deep

836 views • 8 slides
Generalized Row-Action Methods for Tomographic Imaging Sparse Tomo Days, Technical University of

Generalized Row-Action Methods for Tomographic Imaging Sparse Tomo Days, Technical University of

Generalized Row-Action Methods for Tomographic Imaging Sparse Tomo Days, Technical University of Denmark, March 27, 2014 Martin S. Andersen joint work with Per Christian Hansen Scientific Computing Section Outline Algebraic methods for X-ray

739 views • 37 slides
Nuclear Imaging Medical Imaging Medical Imaging Nuclear Imaging Nuclear Imaging Nuclear

Nuclear Imaging Medical Imaging Medical Imaging Nuclear Imaging Nuclear Imaging Nuclear

Nuclear Imaging Medical Imaging Medical Imaging Nuclear Imaging Nuclear Imaging Nuclear imaging is the use of radioactive materials for imaging structure and function inside body. ED X Wu Overview Overview Radioactive Decay and

542 views • 18 slides
Tree-based Methods Here we describe tree-based methods for regression and classification.

Tree-based Methods Here we describe tree-based methods for regression and classification.

Tree-based Methods Here we describe tree-based methods for regression and classification. These involve stratifying or segmenting the predictor space into a number of simple regions. Since the set of splitting rules used to segment the

785 views • 51 slides
Tree-based Methods Here we describe tree-based methods for regression and classification.

Tree-based Methods Here we describe tree-based methods for regression and classification.

Tree-based Methods Here we describe tree-based methods for regression and classification. These involve stratifying or segmenting the predictor space into a number of simple regions. Since the set of splitting rules used to segment the

1.08k views • 55 slides
Objectives : Lower Extremity Imaging Lower Extremity Imaging What to Different Imaging

Objectives : Lower Extremity Imaging Lower Extremity Imaging What to Different Imaging

12/12/2015 Objectives : Lower Extremity Imaging Lower Extremity Imaging What to Different Imaging Modalities Order and How to Interpret the Report Imaging orders that make you look awesome C. Benjamin Ma, MD Interpretation

117 views • 11 slides
Learning based MR Imaging Systems - Deployment Challenges Magnetic Resonance (MR) imaging - image

Learning based MR Imaging Systems - Deployment Challenges Magnetic Resonance (MR) imaging - image

MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction Sriprabha Ramanarayanan , Balamurali Murugesan, Keerthi Ram, Mohanasankar Sivaprakasam Healthcare

491 views • 18 slides
Small, Medium, and Big Data: Application of Machine Learning Methods to the Solution of

Small, Medium, and Big Data: Application of Machine Learning Methods to the Solution of

Small, Medium, and Big Data: Application of Machine Learning Methods to the Solution of Real-World Imaging and Printing Problems A Personal Journey Jan Allebach Electronic Imaging Systems Laboratory (EISL) Purdue University 1 May 2018 SCV

904 views • 73 slides
DRLs and exposure monitoring in CT: quantities, procedures, methods, international experience Sue

DRLs and exposure monitoring in CT: quantities, procedures, methods, international experience Sue

DRLs and exposure monitoring in CT: quantities, procedures, methods, international experience Sue Edyvean ICTP-IAEA Workshop on Establishment and Utilization of Diagnostic Reference Levels in Medical Imaging Imaging (smr3333): 18-22 November

1.59k views • 124 slides
Today Image segmentation methods: Boundaries, Hough transform, Intelligent Scissors Slides based

Today Image segmentation methods: Boundaries, Hough transform, Intelligent Scissors Slides based

Today Image segmentation methods: Boundaries, Hough transform, Intelligent Scissors Slides based on Steve Seitz, Kristen Grauman, and others Boundaries in Medical Imaging Machine Edge Detection Image Human Boundary Marking Boundaries in

1.14k views • 89 slides
INDAM intensive period Computational Methods for Inverse Problems in Imaging Conclusive

INDAM intensive period Computational Methods for Inverse Problems in Imaging Conclusive

INDAM intensive period Computational Methods for Inverse Problems in Imaging Conclusive Workshop, Como, Italy, August 16-18, 2018 (Numerical linear algebra, regularization in Banach spaces and) variable exponent Lebesgue spaces for

1.16k views • 68 slides
A seismic reflection imaging workflow based on the Common-Reflection-Surface (CRS) stack:

A seismic reflection imaging workflow based on the Common-Reflection-Surface (CRS) stack:

74 th Annual Meeting SEG, Denver 2004 Hertweck et al. A seismic reflection imaging workflow based on the Common-Reflection-Surface (CRS) stack: Introduction Seismic imaging CRS stack theoretical background and case study Applications

1.11k views • 58 slides
Pathology Pointers A supplement to Kirk Jones talk Biopsy Adequacy Attempt to biopsy two to

Pathology Pointers A supplement to Kirk Jones talk Biopsy Adequacy Attempt to biopsy two to

11/10/2014 Pathology Pointers A supplement to Kirk Jones talk Biopsy Adequacy Attempt to biopsy two to three lobes Biopsy of superior and basilar segment of one lobe can count as two. Lingula and right middle lobe often behave

197 views • 8 slides
Material representation, Reflectance, BRDFs Local illumination models A single point light

Material representation, Reflectance, BRDFs Local illumination models A single point light

Material representation, Reflectance, BRDFs Local illumination models A single point light source Linear combination for several light sources I(a+b) = I(a)+I(b) I(s . a) = s . I(a) No interactions between objects No

508 views • 39 slides
Calibration Modeling Errors in the 21cm Power Spectrum Aaron Ewall-Wice (MIT) Joshua S. Dillon,

Calibration Modeling Errors in the 21cm Power Spectrum Aaron Ewall-Wice (MIT) Joshua S. Dillon,

arXiv:1610.02689 Calibration Modeling Errors in the 21cm Power Spectrum Aaron Ewall-Wice (MIT) Joshua S. Dillon, Adrian Liu, Jacqueline Hewitt Foreground Isolation Requires Smooth Gains Fourier Space Brightness Real Space F(g) g F(s) s

516 views • 16 slides
Cherenkov & Jansky: Our Understanding of AGN at the Highest Energies Jeremy S. Perkins

Cherenkov & Jansky: Our Understanding of AGN at the Highest Energies Jeremy S. Perkins

Cherenkov & Jansky: Our Understanding of AGN at the Highest Energies Jeremy S. Perkins (CRESST/UMBC/GSFC) on behalf of the VERITAS and Fermi LAT Collaborations Wednesday, November 9, 2011 VHE: E > 100 GeV Necessarily ground based -

510 views • 29 slides
Emergency Telecommunications Emergency Telecommunications & & The Tampere Tampere

Emergency Telecommunications Emergency Telecommunications & & The Tampere Tampere

I nternational Telecom m unication Union ITU-T Emergency Telecommunications Emergency Telecommunications & & The Tampere Tampere Convention Convention The Tor Bothner bothner@ un.org UN Office for the Coordination of

286 views • 16 slides
SBIR IRT Screening, Brief Intervention & Referral to Treatment Welcome & In

SBIR IRT Screening, Brief Intervention & Referral to Treatment Welcome & In

SBIR IRT Screening, Brief Intervention & Referral to Treatment Welcome & In Introductions This Photo by Unknown Author is licensed under CC BY-SA Goals Present the ideas and concepts for the Screening and Brief Intervention and

571 views • 40 slides
Gov 2002 - Causal Inference III: Regression Discontinuity Designs Matthew Blackwell Arthur

Gov 2002 - Causal Inference III: Regression Discontinuity Designs Matthew Blackwell Arthur

Gov 2002 - Causal Inference III: Regression Discontinuity Designs Matthew Blackwell Arthur Spirling October 16th, 2014 Introduction Causal for us so far: selection of observables, instrumental variables for when this doesnt hold

2.07k views • 179 slides
Reimbursement: How do we get there? Bruce Boardman Senior Director of Treatment Services Social

Reimbursement: How do we get there? Bruce Boardman Senior Director of Treatment Services Social

Treatment Reimbursement: How do we get there? Bruce Boardman Senior Director of Treatment Services Social Model Recovery Systems Socialmodel.com Utilization Management Utilization Management (UM) is defined as "a set of techniques used

141 views • 13 slides

More recommend