The Development of a Novel Fusion Protein to Facilitate Connectomic Analysis of Brain Networks

1. The Development of a Novel Fusion
Protein to Facilitate Connectomic
Analysis of Brain Networks
Gurion Marks
Bronx High School of Science, Bronx, NY

2. The Problem: Neurodegenerative Disease
• According to the World Health Organization, Alzheimer’s
disease and other forms of dementia represent the fourth
highest disease burden in high-income countries .
– The US has the 10th highest GDP per capita and ranks 34th in the world in life
expectancy, showing its increased risk of population suffering from
Alzheimer’s or memory related disorders.
• 35% of Europe’s health problems stem from brain disorders.
• Economically, the effects are huge.
– The 2010 World Alzheimer Report by Alzheimer’s Disease International
reported that the cost of caring for the disease exceeded US$600 billion,
globally – consuming approximately 1% of the entire world’s GDP [3].
– Worldwide, Alzheimer’s is projected to cost US$1.1 trillion and affect 114.5
million people by 2050.

3. Curing Neurodegenerative Diseases
• Many neurodegenerative disorders are circuit
based
• To understand and cure these ailments, one
must discern how neurons are affected at
cellular and intercellular levels – how neurons
work in circuits
– Understand how the change or decrease in neural
“wiring” creates diseases

4. Circuitry of the Brain – the
“Connectome”
• The ‘Connectome’ is a “comprehensive
structural description of the network of
elements and connections forming the brain.”
– In short, it’s a “wiring diagram” of the brain
– They show which neurons connect to which other
neurons

5. A Simple Neural Circuit
A Simple Model of a Neural Circuit - A simple neural system in which there is a connection from neuron 1 to neuron 2,
and neuron 2 to neuron 1. Neuron 3 connects to both neurons 1 and 2; neither neuron 1 nor neuron 2 connects to neuron 3.
This may only be a tiny fraction of a real neural circuit which encompasses a vastly greater number of neurons.
Neuron 1
Neuron 2
Neuron 3
Photo Credit: Lichtman et al. 2008

6. Finding Connectomes
• Many types of data must be utilized.
– Light or fluorescence microscopy may be used to
functionally identify neurons within a circuit.
– Electron microscopy is used to make a high
resolution stack of images.
• These images are then analyzed by tracing identified
neurons to form three-dimensional reconstructions.

7. The Scale of Connectomics
The Scale of Connectomics – the isolation of one neuron in the larval zebrafish hindbrain. Massive sets of data are needed to
utilize the resolution necessary for connectomics research. The scope of data is so large that humans will never analyze an
entire brain by hand, expressing the need for machine learning strategies to reconstruct neural matrices.
Endoplasmic
Reticulum Mitochondrion
Soma

8. Problems in Finding Connectomes
• Massive amounts of data and an inability to
use computer software to trace neurons
effectively
– Massive amounts of data have to be analyzed by
hand, as machine learning algorithms do not have
the pattern recognizing abilities for interpolating
stacks of electron microscopy images
• Neural constructs look similar near the soma
– Algorithms cannot differentiate between axons and
dendrites

9. Issue with Electron Microscopy
Branching Dendrite
The Uncertainty of Electron Microscopy – errors in electron microscopy leading to the ineffectiveness of machine learning techniques. (a)
Shows issues regarding too little contrast. Without distinct boundaries between Soma 1 and Soma 2, the branch of Soma 1 may be incorrectly
linked to Soma 2. There are great numbers of instances like these in any set of data. Computer algorithms cannot distinguish these errors as
humans can, thus connectomes created by machine learning strategies with current imaging technology are largely erroneous. (b) Shows another
instance of error – overexposure of a region or group of cells makes it nearly impossible for computer vision to trace branches of neurons.
Soma 2
Soma 1
Lack of Contrast between Somae
Overexposure creates
uncertainty in distinguishing
neural constructs
(a) (b)

10. Axon and Dendrite Near the Soma
(a)
(b)
Branching Axon
Branching
Dendrite
Differentiating Axons and Dendrites – (a) shows an axon branching
off the soma, while (b) shows a branching dendrite. Both constructs
appear very similar and are not easily differentiated close to the soma.
This spurs a need to create a tag to mark only one of the two structures.
Both constructs look
essentially the same,
showing why computers
are not able to tell the
difference

11. How Can We Fix Those Issues?
• Using genetically encoded tags to increase EM
contrast at only the axon, close to the soma.
– This region is called the axon initial segment
– Engineered Ascorbate Peroxidase (APEX) is a
recently created tag that increases EM contrast
– The protein AnkyrinG is localized to the axon
initial segment

12. Components of the Plasmid
• APEX is a 28 kDa monomeric peroxidase. The
peroxidase, upon the addition of H2O2, catalyzes the
oxidation of diaminobenzidine to create a local
precipitate, which when treated with OsO4 gives local
electron microscopy contrast.
• Fluorescent proteins – ‘X’FP for checking expression
• The protein AnkyrinG is localized to the axon initial
segment
• The Tol1 transposon system for incorporating the
plasmid DNA into the zebrafish genome

13. Methods
pDon122 Vector BackbonepEGFP-N1 Vector
Backbone
AnkG-XFP
XbaI
HindIII
AnkG-XFP
XbaIHindIII
pDon122-mCherry-GCaMP6f
Connexin43-GFP-
APEX2
XbaI
Connexin43-EGFP
APEX2
AscI
Starting Plasmids, AnkG-XFP, pDon122-mCherry-GCaMP6f, Connexin43-GFP-APEX2 – plasmids were amplified in
DH5 and extracted via Qiagen Maxi-prep, then digested at appropriate restriction sites for subsequent gel extraction
pcDNA3 Vector Backbone
Starting Plasmids

14. Methods Continued
HindIII
AnkG-XFP
XbaIHindIII XbaI
pDon122 Vector
Backbone
AscI
APEX2
XbaI
Cut and Purified DNA Fragments – AnkG-XFP and pDon122-mCherry-GCaMP6f were cut at
HindIII and XbaI. Connexin43-GFP-APEX2 was cut at AscI and XbaI
10 kb
8.5 kb
3.024 kb
2 kb
.4 kb
AnkG-mCherry AnkG-GFP pDon122 2-Log DNA Ladder (NEB)
10 kb
7.2 kb
3 kb
1 kb
.793 kb
1 kb DNA Ladder (NEB) APEX2 APEX2
Gel Purification of (a) AnkG-XFP and pDon122 Vector Backbone; (b) APEX2 – The 8.5 kb AnkG-XFP constructs
were extracted via a QiaQUICK gel extraction kit after running a 0.8% agarose gel at 100 V for 1 hr. The 0.793 kb
APEX2 was extracted via the same methods, after a 1.0% agarose gel at 100 V for 45 min.
(a) (b)

15. Methods Continued
APEX2
XbaI
AnkG-XFP
AnkG-XFP
XbaI
AscI
APEX2
HindIII
HindIII
pDon122 Vector Backbone
AscI
APEX2
XbaI
Ligation Steps to Obtain pDon122-AnkG-XFP-APEX2 – APEX2 was ligated into the pDon122 vector
backbone. Then AnkG-XFP ws ligated into pDon122-APEX2. Finally, the plasmid pDon122-AnkG-XFP-
APEX2 was created by blunt ligating the construct to fuse the site formerly XbaI on AnkG-XFP with the
site formerly AscI on APEX2.
pDon122 Vector Backbone
XbaI
pDon122 Vector Backbone
HindIII
Ligation of Plasmid

16. Results
>10 kb ≈ 12.5 kb
10 kb
5 kb
1 kb Supercoiled DNA Ladder (NEB) pDon122-AnkG-GFP-APEX2 pDon122-AnkG-mCherry-APEX2
Ligation Product pDon122-AnkG-XFP-APEX2 – Creation of pDon122-AnkG-
XFP-APEX2 was confirmed after running the ligation product on a 0.8% agarose
gel for 1 hr.
Confirmation of Ligation

17. Significance
• This construct will allow for greater contrast to be
created in specific areas of neurons, allowing for
machine learning algorithms to be used in
connectomics, greatly expediting the process of
finding connectomes.
• Determining axons, rather than dentrites, will
allow for macroscale connectomes that show the
linkage of brain regions
• Faster creation of connectomes will allow for a
more comprehensive picture of neural circuitry,
and a more informed view into neurodegenerative
disease

18. Significance Continued
• Significance of AnkyrinG
– In abnormal animals, AnkG has been seen to
migrate out of the axon initial segment, and
accumulate in beta-amyloid plaque
• A main cause of Alzheimer’s Disease
– This construct will allow for pinpointing of AnkG
and the tracing of AnkG beta-amyloid plaque
– Opens the possibility of AnkG mediated
therapeutics

19. Further Research
• Testing in zebrafish for expression
• Testing of algorithms on tagged EM data
• Functional Connectome – Tag synaptic
vesicles to determine the strength of synapses
between neurons (step after finding the
structural “wiring diagram” connectome)
• Studies testing link of AnkG and beta-amyloid
plaque

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