Deep Hierarchical Profiling & Pattern Discovery: Application to Whole Brain Rat Slices After Traumatic Brain Injury - by Jahandar Jahanipour
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Deep learning workshop at Houston machine learning meetup. Video at: https://youtu.be/bAkBGKF2s2I?t=19
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Deep Hierarchical Profiling & Pattern Discovery: Application to Whole Brain Rat Slices After Traumatic Brain Injury - by Jahandar Jahanipour
- 1. Jahandar Jahanipour Department of Electrical and Computer Engineering University of Houston, TX Deep Hierarchical Profiling & Pattern Discovery: Application to Whole Brain Rat Slices After Traumatic Brain Injury Advisor: Prof. Badrinath Roysam
- 2. Introduction Concussion: disruption in the normal function of the brain caused by a bump, blow, … Headache Dizziness Depression Chris Henry John Grimsley 2 1.5 atm Fluid percussion injury multiplexed imaging technique Whole brain rat slice ~ 300,000 cells ~ 3Gb ~32,000 ×47,000 pixels Motivation: Computational data-driven image interpretation on large datasets GOAL: Profile tissue alterations in a manner that is comprehensive , quantitative and sensitive to multiple types of changes.
- 3. Deep Feature Extraction Nuclear morphological features are not able to capture thorough molecular signature. Associative features are dependent on nuclear segmentation of object. 3 NeuN+ NeuN- 40µm NeuN DAPI+Histones Conventional Cytometric Features: Deep Features: Nuclear segmentation of cells using DAPI +Histone channels. Scattering network formed by wavelet-modulus cascading. Deep features capture basal cell morphology and molecular distribution, JOINTLY.
- 4. Can We Perform Computationally Guided Biological Interpretation? Can we profile heterogeneity among cells? 4 S100 GFAP 200 µm 20µm 20µm 20µm Is there any relation between activation level of a cell to the location of the cell from the center of the injury?
- 5. GFAPAPCRECA-1IBA-1S100NeuNDAPI Pipeline 5 DAPI 200 µm Feature Extraction Using Scattering Net Clustering algorithm Highly multiplexed fluorescence image Visualize the clusters on the image Cell Detection Using Faster RCNN S100 GFAP 200 µm
- 6. Profiling Heterogeneity Within Same Structure 6 Astrocyte Classification Biomarkers for astrocyte phenotyping S100 GFAP GLAST Resting Astrocytes All (+) Subset (low) Subset (+) Reactive Astrocytes All (+) All (high) All (+) S100 GFAP 200 µm S100- S100+ S100+ S100- All cells Classifying astrocytes within cortex Astrocyte can be reconstructed using S100 and GFAP biomarkers for further analysis. Li+VPa (treatment)
- 7. Profiling Cell Status Activation 7 S100 GFAP 200 µm 20µm 20µm20µm S100+S100- All cells reactive resting highmoderate Astrocyte Classification Biomarkers for astrocyte phenotyping S100 GFAP GLAST Resting Astrocytes All (+) Subset (low) Subset (+) Reactive Astrocytes All (+) All (high) All (+) Moderately active astrocytes Very active astrocytes Profiling of astrocytes’ activation status reveals the relation of each cell’s location relative to the site of injury. Distance to the injury activation Li+VPa (treatment)
- 8. Profiling Heterogeneity Within Same Structure 8 200 µm group 1 group 2 group 3 group 4 group 5 Oligo-glial markers S100 APC MBP PLP Laminar-like pattern resembling cortical neuronal layers using oligo-glial biomarkers: Identifying 5 different cell subpopulations organized in cortical layer fashion Profiling of oligo-glial biomarkers to discriminate cortical layers. LiVPa