1 Brandon Rumberg West Virginia University [email protected] Analog Filter Banks.
Philip Chu, Josh Peck, Joshua C. Brumberg
Transcript of Philip Chu, Josh Peck, Joshua C. Brumberg
![Page 1: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/1.jpg)
Supplemental Materials
• Philip Chu, Josh Peck, Joshua C. Brumberg
![Page 2: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/2.jpg)
Neuronal morphology
• Neurons have complex physical morphologies (shapes) that help them process information.
• For example, neurons and inhibitory interneurons not only differ in the proteins they express but also their physical structure.
• Neurons from different species have different sizes and specializations
![Page 3: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/3.jpg)
Pyramidal cells
Soma
Dendrites
Axon
~80% of neurons in the neocortex are excitatory pyramidal cells
The rest are interneurons
![Page 4: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/4.jpg)
Morphological differences
• Differences in neuronal morphologies in different brain regions can determine how excitable cells are and specifies how they are connected in a neuronal circuit.
• Neuromorpho.org allows us to make quantitative comparisons of neurons in different species
![Page 5: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/5.jpg)
Using Neuromorpho.org
![Page 6: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/6.jpg)
Using Neuromorpho.orgUse atlas settings to view anatomy through coronal or sagittalcross sections and make the brain translucent
Colored dots are selectable neurons
Select or deselect region options to display or hide neurons
Coronal cross section
3‐D brain
![Page 7: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/7.jpg)
Excitatory pyramidal cell Basket cell (interneuron)
Purple/green=dendriteGray=axon
Qualitative assessmentLeft clicking on orange (Pyramidal) or blue (interneurons) balls in the 3‐D brain loads cell morphology in a separate window
![Page 8: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/8.jpg)
Using Neuromorpho.org Metadata
• We can observe the pyramidal cells in the mouse visual cortex and compare them to monkey visual cortex
Start by clicking search, Then metadata
![Page 9: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/9.jpg)
Mouse Monkey
Neocortex
Visual regions
Principal cells
Pyramidal cells
vs.
Click to show summary
Layer 3
![Page 10: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/10.jpg)
Qualitative assessmentRequires updated Java and security permission
Cell can be rotated in 3‐D
![Page 11: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/11.jpg)
Qualitative assessment
• Pyramidal neurons: two long sets of dendrites that are roughly perpendicular to each other. Dendrites appear to be the major component of the cell. Few axons that infrequently bifurcate.
• Basket interneuron: relatively small, local dendritic arbors. Complex highly bifurcating axonal arbors that densely surround the space around the cell both vertically and horizonally.
![Page 12: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/12.jpg)
Mou
seQuantitative assessment
![Page 13: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/13.jpg)
Mon
key
Quantitative assessment
![Page 14: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/14.jpg)
Parameter definitions• Soma surface area• Total number of trees• Total number of bifurcations• Total number of branches (bifurcations plus terminations)• Neuronal height (95% of first principal component)• Neuronal width (95% of second principal component)• Neuronal depth (95% of third principal component)• Average branch diameter• Total arborization length• Total arborization surface area• Total internal volume of the arborization• Maximum Euclidean (straight) distance from soma to tips• Maximum Path (along the tree) distance from soma to tips• Maximum Branch order (number of bifurcations from soma to tips)• Average Contraction (the ratio between Euclidean and path length calculated on each branch)• Total number of reconstruction points• Topological asymmetry (average over all bifurcations of the absolute value of (n1‐n2)/(n1+n2‐2), where n1 and n2 are the
numbers of tips in the two subtrees)• Rall's Ratio (average over all bifurcations of the sum of the diameters of the two daughters, elevated to 1.5, divided by the
diameter of the parent, elevated to 1.5)• Local Bifurcation angle (average over all bifurcations of the angle between the first two daughter compartments)• Remote Bifurcation angle (average over all bifurcations of the angle between the following bifurcations or tips)
![Page 15: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/15.jpg)
Layer 3 pyramidal neurons in the mouse visual regionshave larger cell bodies than layer 3 pyramidal neurons in the monkey visual cortex.
Mouse Monkey
Given that monkeys rely on vision more than rodents do, this may imply that soma sizeis not the controlling factor in computational complexity. Just as modern circuits are smaller thantransistors, the internal mechanisms or packing density may be more telling of the computational power than the size.
![Page 16: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/16.jpg)
Inhibitory interneurons vs. Pyramidal cells
• Inhibitory interneurons (e.g. basket interneuron) in the neocortex use GABA for inhibitory neurotransmission whereas excitatory neurons (pyramidal) use glutamate.
• They also differ in morphology– Different patterns of dendritic fanning– Different patterns of axonal projections
– Different morphologies imply different connections and functions in neuronal circuits
![Page 17: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/17.jpg)
Digital histology: Mouse
• Research is often conducted “offline” using data that has already been collected and saved for later analysis.
• Like Neuromorpho.org, the Allen Brain Atlas (ABA) allows us to do this without physically conducting the histological procedures ourselves
![Page 18: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/18.jpg)
Digital histology
![Page 19: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/19.jpg)
Series of brain slices that you can navigate organized from rostral to caudal
Browse brain regions
Brain region search
Specimen information (postnatal day 56, ie. 56 days old)
Brain region:Primary somatosensory cortex
![Page 20: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/20.jpg)
Digital histology
• The menu for serial sections are shown as well (the brain was sliced up from anterior to posterior in 100 µm sections, and labeled with nissl and labeled in detail by neuroanatomistsfor your convenience).
• Nissl/DAPI are basic dyes that binds to negatively charged nucleic acids (DNA,RNA).– All cells with nuclei are labeled
• Red blood cells do not have nuclei and are not labeled
![Page 21: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/21.jpg)
Digital histology• Go to http://www.brain‐map.org/• Select Data and Tools, mouse connectivity, then reference
data from the menu.– select the NeuN and NF160 box
• Shown next is the example using NeuN which stains for all neurons, and Neurofilament‐M a type of cytoskeletal protein, which labels cell bodies, dendrites and axons of neurons. – All the slices are counterstained with DAPI
• Counterstains are usually stains that label all cell types to enhance anatomical delineation and are done in conjunction with more specific stains. – DAPI is a basic molecule that binds to acids like DNA and RNA (similar to a
Nissl stain).
![Page 22: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/22.jpg)
Digital histology: Mouse
![Page 23: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/23.jpg)
Press this button to expand, then full screen
![Page 24: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/24.jpg)
Filters for the different fluorescent molecules can be adjusted or turned off
High resolution image viewer
Scroll through the images to match with your brain region from the reference atlas
*
Scale bar (can be moved)
Color code
![Page 25: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/25.jpg)
Reference atlas can be pulled up right besides the image to verify
![Page 26: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/26.jpg)
Reference atlas is searchable!Syncs histology image to atlas
Hippocampus highlighted purple
![Page 27: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/27.jpg)
Filters for the different fluorescent molecules can be adjusted or turned off
High resolution image viewer
Scroll through the images to match with your brain region from the reference atlas
*
Scale bar (can be moved)
Color code
![Page 28: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/28.jpg)
Use this scale bar to measure the thickness of the neocortex
High resolution image viewer
![Page 29: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/29.jpg)
Brain size
• The thickness of a human neocortex is 2‐4 mm– How does this compare to your measurements of the mouse neocortex?
![Page 30: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/30.jpg)
Cellular distributions
• Neurons are the computational units of the brain
• The brain has a diverse set of units that allow it to function the way it does
• Using the ABA reference atlas we can get an idea of the complexity of neuronal units using markers for different cell types
![Page 31: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/31.jpg)
Corpus callosum(myelin) MoreNeurofilament
Anterior commisure(myelin) More Neurofilament
SomatosensoryCortexMore NeUN
Motor cortex
StriatumMore Neurofilament
Differential distribution of neurofilament, NeUN and DAPI
![Page 32: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/32.jpg)
Observations
• There are more neurons in the neocortex than in the striatum
• The distribution of neurons in the neocortex is not uniform, it is striated (bands).
• There are more neurons in the somatosensorycortex than in the motor cortex
• There is more neurofilament expression in myelin fiber pathways than in gray matter regions
![Page 33: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/33.jpg)
Interpretation• The computational units of the brain (neurons) are more
greatly distributed in the neocortex. Thus the neocortexmay have a greater role in processing more complex sets of information.
• The distribution of neurons is nonuniform, suggesting a specific pattern of connectivity between different types of cells.
• The somatosensory cortex in particular has a large expression of neurons, suggesting an importance of this sensory region for the mouse.
• Myelin pathways express most of the neurofilament, suggesting that this marker labels a cytoskeletal protein involved in neuronal communication through axons.
![Page 34: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/34.jpg)
Digital histology: Mouse. Methods showcase (Figure 1.)
What are we looking at here??
A. B.
C. D.
![Page 35: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/35.jpg)
Types of brain cells
• Differential neuron distribution across brain regions and different patterns of distribution provides information about how those cell types contribute to normal brain function and how that may change following brain disease states.
![Page 36: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/36.jpg)
Digital histology: MouseData collection
• 1. Choose one other stain
*
![Page 37: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/37.jpg)
Inhibitory interneurons
A. B.
Parvalbumin (PV+) cells are specifically implicated in normal cognitive processing and are dysregulated in diseases like schizophreniaFewer neurons in the brain are Parvalbumin expressing inhibitory interneurons (Compare theDensity of green in these images with the previous NeuN staining). Note the differential distribution of PV+ cells in the neocortex compared to other parts of the brain
Green = parvalbumin+ interneuronBlue= ALL cells
Coronal brain slice
Low magnification Higher magnification
![Page 38: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/38.jpg)
• How can we interpret cell distributions based on fluorescent labels for different proteins (different types of cells express different proteins)?
![Page 39: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/39.jpg)
Blue (DAPI)= 10Red (NeuN)=4
10 DAPI CELLS (neurons,glia, ependymalcells, etc)5 NeuN NEURONS
Logical Interpretation:1. 50% of all cells are neurons2. 50% are not neurons
Speculative interpretation1. Non‐neuronal cells are As important as neuronsIn supporting brain function
NeuNDAPI
![Page 40: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/40.jpg)
Blue (DAPI)= 20Red (NeuN)=9Green (Parvalbumin, typeof interneuron)=1
Observations: 50% of all brain cells are neurons5% of all brain cells are PV+ Inhibitory interneurons10% of all neurons are PV+Inhibitory Interneurons
Fewer inhibitory interneuronsthan excitatory neurons raise thequestion of how inhibition can balance excitation to preventSeizure‐like activity in the brain(runaway excitation).
NeuNDAPIParvalbumin
![Page 41: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/41.jpg)
Quantification of cell type distribution can also be done using ABA
![Page 42: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/42.jpg)
Change to expression to view heat map
Relative expression ofParvalbumin RNAIn different brain regions(hover to see names)
Sequence of probeFor Parvalbumin RNA
Quantification of cell type distribution can also be done using ABA
![Page 43: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/43.jpg)
Quantification of cell type distribution can also be done using ABA
Heat map expression ofParvalbumin RNA Redder = more expression
3‐D rotatable brain with Heat map expression ofParvalbumin RNA
![Page 44: Philip Chu, Josh Peck, Joshua C. Brumberg](https://reader031.fdocuments.us/reader031/viewer/2022012505/6180c4fd4010c7674e693d07/html5/thumbnails/44.jpg)
Summary
• Neuromorpho can be used to search for cells based on various parameters (e.g. species, brain region, published research article) – Qualitative and quantitative assessments can be made (3‐D neuron viewer and measurement parameters respectively)
• The ABA allows the visualization and analysis of gene expression. – Can be used to identify cell type distributions across different regions of the brain.
– Quantitative analysis conducted by the ABA can be compared with qualitative observations.