Global Highlights in Neuroengineering 2005-2018


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Global Highlights in Neuroengineering 2005-2018 – Logan Thrasher Collins

Optogenetic stimulation using ChR2

(Boyden, Zhang, Bamberg, Nagel, & Deisseroth, 2005)

  • Ed S. Boyden, Karl Deisseroth, and colleagues developed optogenetics, a revolutionary technique for stimulating neural activity.
  • Optogenetics involves engineering neurons to express light-gated ion channels. The first channel used for this purpose was ChR2 (a protein originally found in bacteria which responds to blue light). In this way, a neuron exposed to an appropriate wavelength of light will be stimulated.
  • Over time, optogenetics has gained a place as an essential experimental tool for neuroscientists across the world. It has been expanded upon and improved in numerous ways and has even allowed control of animal behavior via implanted fiber optics and other light sources. Optogenetics may eventually be used in the development of improved brain-computer interfaces.

Optogenetics

Blue Brain Project cortical column simulation

(Markram, 2006)

  • In the early stages of the Blue Brain Project, ~ 10,000 neurons were mapped in 2-week-old rat somatosensory neocortical columns with sufficient resolution to show rough spatial locations of the dendrites and synapses.
  • After constructing a virtual model, algorithmic adjustments refined the spatial connections between neurons to increase accuracy (over 10 million synapses).
  • The cortical column was emulated using the Blue Gene/L supercomputer and the emulation was highly accurate compared to experimental data.

Cortical Column

Optogenetic silencing using halorhodopsin

(Han & Boyden, 2007)

  • Ed Boyden continued developing optogenetic tools to manipulate neural activity. Along with Xue Han, he expressed a codon-optimized version of a bacterial halorhodopsin (along with the ChR2 protein) in neurons.
  • Upon exposure to yellow light, halorhodopsin pumps chloride ions into the cell, hyperpolarizing the membrane and inhibiting neural activity.
  • Using halorhodopsin and ChR2, neurons could be easily activated and inhibited using yellow and blue light respectively.

Halorhodopsin and ChR2 wavelengths

Brainbow

(Livet et al., 2007)

  • Lichtman and colleagues used Cre/Lox recombination tools to create genes which express a randomized set of three or more differently-colored fluorescent proteins (XFPs) in a given neuron, labeling the neuron with a unique combination of colors. About ninety distinct colors were emitted across a population of genetically modified neurons.
  • The detailed structures within neural tissue equipped with the Brainbow system can be imaged much more easily since neurons can be distinguished via color contrast.
  • As a proof-of-concept, hundreds of synaptic contacts and axonal processes were reconstructed in a selected volume of the cerebellum. Several other neural structures were also imaged using Brainbow.
  • The fluorescent proteins expressed by the Brainbow system are usable in vivo.

Brainbow

High temporal precision optogenetics

(Gunaydin et al., 2010)

  • Karl Deisseroth, Peter Hegemann, and colleagues used protein engineering to improve the temporal resolution of optogenetic stimulation.
  • Glutamic acid at position 123 in ChR2 was mutated to threonine, producing a new ion channel protein (dubbed ChETA).
  • The ChETA protein allows for induction of spike trains with frequencies up to 200 Hz and greatly decreases the incidence of unintended spikes. Furthermore, ChETA eliminates plateau potentials (a phenomenon which interferes with precise control of neural activity).

Ultrafast optogenetics

Hippocampal prosthesis in rats

(Berger et al., 2012)

  • Theodore Berger and his team developed an artificial replacement for neurons which transmit information from the CA3 region to the CA1 region of the hippocampus.
  • This cognitive prosthesis employs recording and stimulation electrodes along with a multi-input multi-output (MIMO) model to encode the information in CA3 and transfer it to CA1.
  • The hippocampal prosthesis was shown to restore and enhance memory in rats as evaluated by behavioral testing and brain imaging.

In vivo superresolution microscopy for neuroimaging

(Berning, Willig, Steffens, Dibaj, & Hell, 2012)

  • Stefan Hell (2014 Nobel laureate in chemistry) developed stimulated emission depletion microscopy (STED), a type of superresolution fluorescence microscopy which allows imaging of synapses and dendritic spines.
  • STED microscopy uses transgenic neurons that express fluorescent proteins. The neurons exhibit fluctuations in their fluorescence over time, providing temporal contrast enhancement to the resolution. Although light’s wavelength would ordinarily limit the resolution (the diffraction limit), STED’s temporal contrast overcomes this limitation.
  • Neurons in transgenic mice (equipped with glass-sealed holes in their skulls) were imaged using STED. Synapses and dendritic spines were observed up to fifteen nanometers below the surface of the brain tissue.

Superresolution microscopy in vivo

Eyewire: crowdsourcing method for retina mapping

(Marx, 2013)

  • The Eyewire project was created by Sebastian Seung’s research group. It is a crowdsourcing initiative for connectomic mapping within the retina towards uncovering neural circuits involved in visual processing.
  • Laboratories first collect data via serial electron microscopy as well as functional data from two-photon microscopy.
  • In the Eyewire game, images of tissue slices are provided to players who then help reconstruct neural morphologies and circuits by “coloring in” the parts of the images which correspond to cells and stacking many images on top of each other to generate 3D maps. Artificial intelligence tools help provide initial “best guesses” and guide the players, but the people ultimately perform the task of reconstruction.
  • By November 2013, around 82,000 participants had played the game. Its popularity continues to grow.

Eyewire

The BRAIN Initiative

(“Fact Sheet: BRAIN Initiative,” 2013)

  • The BRAIN Initiative (Brain Research through Advancing Innovative Technologies) provided neuroscientists with $110 million in governmental funding and $122 million in funding from private sources such as the Howard Hughes Medical Institute and the Allen Institute for Brain Science.
  • The BRAIN Initiative focused on funding research which develops and utilizes new technologies for functional connectomics. It helped to accelerate research on tools for decoding the mechanisms of neural circuits in order to understand and treat mental illness, neurodegenerative diseases, and traumatic brain injury.
  • The BRAIN Initiative emphasized collaboration between neuroscientists and physicists. It also pushed forward nanotechnology-based methods to image neural tissue, record from neurons, and otherwise collect neurobiological data.

The CLARITY method for making brains translucent

(Chung & Deisseroth, 2013)

  • Karl Deisseroth and colleagues developed a method called CLARITY to make samples of neural tissue optically translucent without damaging the fine cellular structures in the tissue. Using CLARITY, entire mouse brains have been turned transparent.
  • Mouse brains were infused with hydrogel monomers (acrylamide and bisacrylamide) as well as formaldehyde and some other compounds for facilitating crosslinking. Next, the hydrogel monomers were crosslinked by incubating the brains at 37°C. Lipids in the hydrogel-stabilized mouse brains were extracted using hydrophobic organic solvents and electrophoresis.
  • CLARITY allows antibody labeling, fluorescence microscopy, and other optically-dependent techniques to be used for imaging entire brains. In addition, it renders the tissue permeable to macromolecules, which broadens the types of experimental techniques that these samples can undergo (i.e. macromolecule-based stains, etc.)

CLARITY Imaging TechniqueTelepathic rats engineered using hippocampal prosthesis

(S. Deadwyler et al., 2013)

  • Berger’s hippocampal prosthesis was implanted in pairs of rats. When “donor” rats were trained to perform a task, they developed neural representations (memories) which were recorded by their hippocampal prostheses.
  • The donor rat memories were run through the MIMO model and transmitted to the stimulation electrodes of the hippocampal prostheses implanted in untrained “recipient” rats. After receiving the memories, the recipient rats showed significant improvements on the task that they had not been trained to perform.

Rat Telepathy

Integrated Information Theory 3.0

(Oizumi, Albantakis, & Tononi, 2014)

  • Integrated information theory (IIT) was originally proposed by Giulio Tononi in 2004. IIT is a quantitative theory of consciousness which may help explain the hard problem of consciousness.
  • IIT begins by assuming the following phenomenological axioms; each experience is characterized by how it differs from other experiences, an experience cannot be reduced to interdependent parts, and the boundaries which distinguish individual experiences are describable as having defined “spatiotemporal grains.”
  • From these phenomenological axioms and the assumption of causality, IIT identifies maximally irreducible conceptual structures (MICS) associated with individual experiences. MICS represent particular patterns of qualia that form unified percepts.
  • IIT also outlines a mathematical measure of an experience’s quantity. This measure is called integrated information or ϕ.

Expansion Microscopy

(F. Chen, Tillberg, & Boyden, 2015)

  • The Boyden group developed expansion microscopy, a method which enlarges neural tissue samples (including entire brains) with minimal structural distortions and so facilitates superior optical visualization of the scaled-up neural microanatomy. Furthermore, expansion microscopy greatly increases the optical translucency of treated samples.
  • Expansion microscopy operates by infusing a swellable polymer network into brain tissue samples along with several chemical treatments to facilitate polymerization and crosslinking and then triggering expansion via dialysis in water. With 4.5-fold enlargement, expansion microscopy only distorts the tissue by about 1% (computed using a comparison between control superresolution microscopy of easily-resolvable cellular features and the expanded version).
  • Before expansion, samples can express various fluorescent proteins to facilitate superresolution microscopy of the enlarged tissue once the process is complete. Furthermore, expanded tissue is highly amenable to fluorescent stains and antibody-based labels.

Expansion microscopy

Japan’s Brain/MINDS project

(Okano, Miyawaki, & Kasai, 2015)

  • In 2014, the Brain/MINDS (Brain Mapping by Integrated Neurotechnologies for Disease Studies) project was initiated to further neuroscientific understanding of the brain. This project received nearly $30 million in funding for its first year alone.
  • Brain/MINDS focuses on studying the brain of the common marmoset (a non-human primate abundant in Japan), developing new technologies for brain mapping, and understanding the human brain with the goal of finding new treatments for brain diseases.

Openworm

(Szigeti et al., 2014)

  • The anatomical elegans connectome was originally mapped in 1976 by Albertson and Thomson. More data has since been collected on neurotransmitters, electrophysiology, cell morphology, and other characteristics.
  • Szigeti, Larson, and their colleagues made an online platform for crowdsourcing research on elegans computational neuroscience, with the goal of completing an entire “simulated worm.”
  • The group also released software called Geppetto, a program that allows users to manipulate both multicompartmental Hodgkin-Huxley models and highly efficient soft-body physics simulations (for modeling the worm’s electrophysiology and anatomy).

C. elegans Connectome

The TrueNorth Chip from DARPA and IBM

(Akopyan et al., 2015)

  • The TrueNorth neuromorphic computing chip was constructed and validated by DARPA and IBM. TrueNorth uses circuit modules which mimic neurons. Inputs to these fundamental circuit modules must overcome a threshold in order to trigger “firing.”
  • The chip can emulate up to a million neurons with over 250 million synapses while requiring far less power than traditional computing devices.

Human Brain Project cortical mesocircuit reconstruction and simulation

(Markram et al., 2015)

  • The HBP digitally reconstructed a 0.29 mm3 region of rat cortical tissue (~ 31,000 neurons and 37 million synapses) based on morphological data, “connectivity rules,” and additional datasets. The cortical mesocircuit was emulated using the Blue Gene/Q supercomputer.
  • This emulation was sufficiently accurate to reproduce emergent neurological processes and yield insights on the mechanisms of their computations.

Cortical Mesocircuit

Neural lace

(Liu et al., 2015)

  • Charles Lieber’s group developed a syringe-injectable electronic mesh made of submicrometer-thick wiring for neural interfacing.
  • The meshes were constructed using novel soft electronics for biocompatibility. Upon injection, the neural lace expands to cover and record from centimeter-scale regions of tissue.
  • Neural lace may allow for “invasive” brain-computer interfaces to circumvent the need for surgical implantation. Lieber has continued to develop this technology towards clinical application.

Neural Lace

Expansion FISH

(F. Chen et al., 2016)

  • Boyden, Chen, Marblestone, Church, and colleagues combined fluorescent in situ hybridization (FISH) with expansion microscopy to image the spatial localization of RNA in neural tissue.
  • The group developed a chemical linker to covalently attach intracellular RNA to the infused polymer network used in expansion microscopy. This allowed for RNAs to maintain their relative spatial locations within each cell post-expansion.
  • After the tissue was enlarged, FISH was used to fluorescently label targeted RNA molecules. In this way, RNA localization was more effectively resolved.
  • As a proof-of-concept, expansion FISH was used to reveal the nanoscale distribution of long noncoding RNAs in nuclei as well as the locations of RNAs within dendritic spines.

Expansion FISH

Neural dust

(Seo et al., 2016)

  • Michel Maharbiz’s group invented implantable, ~ 1 mm biosensors for wireless neural recording and tested them in rats.
  • This neural dust could be miniaturized to less than 0.5 mm or even to microscale dimensions using customized electronic components.
  • Neural dust motes consist of two recording electrodes, a transistor, and a piezoelectric crystal.
  • The neural dust received external power from ultrasound. Neural signals were recorded by measuring disruptions to the piezoelectric crystal’s reflection of the ultrasound waves. Signal processing mathematics allowed precise detection of activity.

Neural Dust

The China Brain Project

(Poo et al., 2016)

  • The China Brain Project was launched to help understand the neural mechanisms of cognition, develop brain research technology platforms, develop preventative and diagnostic interventions for brain disorders, and to improve brain-inspired artificial intelligence technologies.
  • This project will be take place from 2016 until 2030 with the goal of completing mesoscopic brain circuit maps.
  • China’s population of non-human primates and preexisting non-human primate research facilities give the China Brain Project an advantage. The project will focus on studying rhesus macaques.

Somatosensory cortex stimulation for spinal cord injuries

(Flesher et al., 2016)

  • Gaunt, Flesher, and colleagues found that microstimulation of the primary somatosensory cortex (S1) partially restored tactile sensations to a patient with a spinal cord injury.
  • Electrode arrays were implanted into the S1 regions of a patient with a spinal cord injury. The array performed intracortical microstimulation over a period of six months.
  • The patient reported locations and perceptual qualities of the sensations elicited by microstimulation. The patient did not experience pain or “pins and needles” from any of the stimulus trains. Overall, 93% of the stimulus trains were reported as “possibly natural.”
  • Results from this study might be used to engineer upper-limb neuroprostheses which provide somatosensory feedback.

Somatosensory Stimulation

Hippocampal prosthesis in monkeys

(S. A. Deadwyler et al., 2017)

  • Theodore Berger continued developing his cognitive prosthesis and tested it in Rhesus Macaques.
  • As with the rats, monkeys with the implant showed substantially improved performance on memory tasks.

The $100 billion Softbank Vision Fund

(Lomas, 2017)

  • Masayoshi Son, the CEO of Softbank (a Japanese telecommunications corporation), announced a plan to raise $100 billion in venture capital to invest in artificial intelligence. This plan involved partnering with multiple large companies in order to raise this enormous amount of capital.
  • By the end of 2017, the Vision Fund successfully reached its $100 billion goal. Masayoshi Son has since announced further plans to continue raising money with a new goal of over $800 billion.
  • Masayoshi Son’s reason for these massive investments is the Technological Singularity. He agrees with Kurzweil that the Singularity will likely occur at around 2045 and he hopes to help bring the Singularity to fruition. Though Son is aware of the risks posed by artificial superintelligence, he feels that superintelligent AI’s potential to tackle some of humanity’s greatest challenges (such as climate change and the threat of nuclear war) outweighs those risks.

Bryan Johnson launches Kernel

(Regalado, 2017)

  • Entrepreneur Bryan Johnson invested $100 million to start Kernel, a neurotechnology company.
  • Kernel plans to develop implants that allow for recording and stimulation of large numbers of neurons at once. The company’s initial goal is to develop treatments for mental illnesses and neurodegenerative diseases. Its long-term goal is to enhance human intelligence.
  • Kernel originally partnered with Theodore Berger and intended to utilize his hippocampal prosthesis. Unfortunately, Berger and Kernel parted ways after about six months because Berger’s vision was reportedly too long-range to support a financially viable company (at least for now).
  • Kernel was originally a company called Kendall Research Systems. This company was started by a former member of the Boyden lab. In total, four members of Kernel’s team are former Boyden lab members.

Elon Musk launches NeuraLink

(Etherington, 2017)

  • Elon Musk (CEO of Tesla, SpaceX, and a number of other successful companies) initiated a neuroengineering venture called NeuraLink.
  • NeuraLink will begin by developing brain-computer interfaces (BCIs) for clinical applications, but the ultimate goal of the company is to enhance human cognitive abilities in order to keep up with artificial intelligence.
  • Though many of the details around NeuraLink’s research are not yet open to the public, it has been rumored that injectable electronics similar to Lieber’s neural lace might be involved.

Facebook announces effort to build brain-computer interfaces

(Constine, 2017)

  • Facebook revealed research on constructing non-invasive brain-computer interfaces (BCIs) at a company-run conference in 2017. The initiative is run by Regina Dugan, Facebook’s head of R&D at division building 8.
  • Facebook’s researchers are working on a non-invasive BCI which may eventually enable users to type one hundred words per minute with their thoughts alone. This effort builds on past investigations which have been used to help paralyzed patients.
  • The building 8 group is also developing a wearable device for “skin hearing.” Using just a series of vibrating actuators which mimic the cochlea, test subjects have so far been able to recognize up to nine words. Facebook intends to vastly expand this device’s capabilities.

DARPA funds research to develop improved brain-computer interfaces

(Hatmaker, 2017)

  • The U.S. government agency DARPA awarded $65 million in total funding to six research groups.
  • The recipients of this grant included five academic laboratories (headed by Arto Nurmikko, Ken Shepard, Jose-Alain Sahel and Serge Picaud, Vicent Pieribone, and Ehud Isacoff) and one small company called Paradromics Inc.
  • DARPA’s goal for this initiative is to develop a nickel-sized bidirectional brain-computer interface (BCI) which can record from and stimulate up to one million individual neurons at once.

Human Brain Project analyzes brain computations using algebraic topology

(Reimann et al., 2017)

  • Investigators at the Human Brain Project utilized algebraic topology to analyze the reconstructed ~ 31,000 neuron cortical microcircuit from their earlier work.
  • The analysis involved representing the cortical network as a digraph, finding directed cliques (complete directed subgraphs belonging to a digraph), and determining the net directionality of information flow (by computing the sum of the squares of the differences between in-degree and out-degree for all the neurons in a clique). In algebraic topology, directed cliques of n neurons are called directed simplices of dimension n-1.
  • Vast numbers of high-dimensional directed cliques were found in the cortical microcircuit (as compared to null models and other controls). Spike correlations between pairs of neurons within a clique were found to increase with the clique’s dimension and with the proximity of the neurons to the clique’s sink. Furthermore, topological metrics allowed insights into the flow of neural information among multiple cliques.
  • Experimental patch-clamp data supported the significance of the findings. In addition, similar patterns were found within the C. elegans connectome, suggesting that the results may generalize to nervous systems across species.

HBP algebraic topology

Early testing of hippocampal prosthesis algorithm in humans

(Song, She, Hampson, Deadwyler, & Berger, 2017)

  • Dong Song (who was working alongside Berger) tested the MIMO algorithm on human epilepsy patients using implanted recording and stimulation electrodes. The full hippocampal prosthesis was not implanted, but the electrodes acted similarly, though in a temporary capacity. Although only two patients were tested in this study, many trials were performed to compensate for the small sample size.
  • Hippocampal spike trains from individual cells in CA1 and CA3 were recorded from the patients during a delayed match-to-sample task. The patients were shown various images while neural activity data were recorded by the electrodes and processed by the MIMO model. The patients were then asked to recall which image they had been shown previously by picking it from a group of “distractor” images. Memories encoded by the MIMO model were used to stimulate hippocampal cells during the recall phase.
  • In comparison to controls in which the same two epilepsy patients were not assisted by the algorithm and stimulation, the experimental trials demonstrated a significant increase in successful pattern matching.

Brain imaging factory in China

(Cyranoski, 2017)

  • Qingming Luo started the HUST-Suzhou Institute for Brainsmatics, a brain imaging “factory.” Each of the numerous machines in Luo’s facility performs automated processing and imaging of tissue samples. The devices make ultrathin slices of brain tissue using diamond blades, treat the samples with fluorescent stains or other contrast-enhancing chemicals, and image then using fluorescence microscopy.
  • The institute has already demonstrated its potential by mapping the morphology of a previously unknown neuron which “wraps around” the entire mouse brain.

China Brain Mapping Image

Automated patch-clamp robot for in vivo neural recording

(Suk et al., 2017)

  • Ed S. Boyden and colleagues developed a robotic system to automate patch-clamp recordings from individual neurons. The robot was tested in vivo using mice and achieved a data collection yield similar to that of skilled human experimenters.
  • By continuously imaging neural tissue using two-photon microscopy, the robot can adapt to a target cell’s movement and shift the pipette to compensate. This adaptation is facilitated by a novel algorithm called an imagepatching algorithm. As the pipette approaches its target, the algorithm adjusts the pipette’s trajectory based on the real-time two-photon microscopy.
  • The robot can be used in vivo so long as the target cells express a fluorescent marker or otherwise fluoresce corresponding to their size and position.

Automated Patch Clamp System

Genome editing in the mammalian brain

(Nishiyama, Mikuni, & Yasuda, 2017)

  • Precise genome editing in the brain has historically been challenging because most neurons are postmitotic (non-dividing) and the postmitotic state prevents homology-directed repair (HDR) from occurring. HDR is a mechanism of DNA repair which allows for targeted insertions of DNA fragments with overhangs homologous to the region of interest (by contrast, non-homologous end-joining is highly unpredictable).
  • Nishiyama, Mikuni, and Yasuda developed a technique which allows genome editing in postmitotic mammalian neurons using adeno-associated viruses (AAVs) and CRISPR-Cas9.
  • The AAVs delivered ssDNA sequences encoding a single guide RNA (sgRNA) and an insert. Inserts encoding a hemagglutinin tag (HA) and inserts encoding EGFP were both tested. Cas9 was encoded endogenously by transgenic host cells and in transgenic host animals.
  • The technique achieved precise genome editing in vitro and in vivo with a low rate of off-target effects. Inserts did not cause deletion of nearby endogenous sequences for 98.1% of infected neurons.

Genome Editing Neurons

Near-infrared light and upconversion nanoparticles for optogenetic stimulation

(S. Chen et al., 2018)

  • Upconversion nanoparticles absorb two or more low-energy photons and emit a higher energy photon. For instance, multiple near-infrared photons can be converted into a single visible spectrum photon.
  • Shuo Chen and colleagues injected upconversion nanoparticles into the brains of mice and used them to convert externally applied near-infrared (NIR) light into visible light within the brain tissue. In this way, optogenetic stimulation was performed without the need for surgical implantation of fiber optics or similarly invasive procedures.
  • The authors demonstrated stimulation via upconversion of NIR to blue light (to activate ChR2) and inhibition via upconversion of NIR to green light (to activate a rhodopsin called Arch).
  • As a proof-of-concept, this technology was used to alter the behavior of the mice by activating hippocampally-encoded fear memories.

Upconversion nanoparticles and NIR

Map of all neuronal cell bodies within mouse brain

(Murakami et al., 2018)

  • Ueda, Murakami, and colleagues combined methods from expansion microscopy and CLARITY to develop a protocol called CUBIC-X which both expands and clears entire brains. Light-sheet fluorescence microscopy was used to image the treated brains and a novel algorithm was developed to detect individual nuclei.
  • Although expansion microscopy causes some increased tissue transparency on its own, CUBIC-X greatly improved this property in the enlarged tissues, facilitating more detailed whole-brain imaging.
  • Using CUBIC-X, the spatial locations of all the cell bodies (but not dendrites, axons, or synapses) within the mouse brain were mapped. This process was performed upon several adult mouse brains as well as several developing mouse brains to allow for comparative analysis.
  • The authors made the spatial atlas publicly available in order to facilitate global cooperation towards annotating connectivity among the neural cell bodies within the atlas.

CUBIC-X

Clinical testing of hippocampal prosthesis algorithm in humans

(Hampson et al., 2018)

  • Further clinical tests of Berger’s hippocampal prosthesis were performed. Twenty-one patients took part in the experiments. Seventeen patients underwent CA3 recording so as to facilitate training and optimization of the MIMO model. Eight patients received CA1 stimulation so as to improve their memories.
  • Electrodes with the ability to record from single neurons (10-24 single-neuron recording sites) and via EEG (4-6 EEG recording sites) were implanted such that recording and stimulation could occur at CA3 and CA1 respectively.
  • Patients performed behavioral memory tasks. Both short-term and long-term memory showed an average improvement of 35% across the patients who underwent stimulation.

Precise optogenetic manipulation of fifty neurons

(Mardinly et al., 2018)

  • Mardinly and colleagues engineered a novel excitatory optogenetic ion channel called ST-ChroME and a novel inhibitory optogenetic ion channel called IRES-ST-eGtACR1. The channels were localized to the somas of host neurons and generated stronger photocurrents over shorter timescales than previously existing opsins, allowing for powerful and precise optogenetic stimulation and inhibition.
  • 3D-SHOT is an optical technique in which light is tuned by a device called a spatial light modulator along with several other optical components. Using 3D-SHOT, light was precisely projected upon targeted neurons within a volume of 550×550×100 μm3.
  • By combining novel optogenetic ion channels and the 3D-SHOT technique, complex patterns of neural activity were created in vivo with high spatial and temporal precision.
  • Simultaneously, calcium imaging allowed measurement of the induced neural activity. More custom optoelectronic components helped avoid optical crosstalk of the fluorescent calcium markers with the photostimulating laser.

Optogenetic control of fifty neurons

Whole-brain Drosophila connectome data acquired via serial electron microscopy

(Zheng et al., 2018)

  • Zheng, Bock, and colleagues collected serial electron microscopy data on the entire adult Drosophila connectome, providing the data necessary to reconstruct a complete structural map of the fly’s brain at the resolution of individual synapses, dendritic spines, and axonal processes.
  • The data are in the form of 7050 transmission electron microscopy images (187500 x 87500 pixels and 16 GB per image), each representing a 40nm-thin slice of the fly’s brain. In total the dataset requires 106 TB of storage.
  • Although much of the the data still must be processed to reconstruct a 3-dimensional map of the Drosophila brain, the authors did create 3-dimensional reconstructions of selected areas in the olfactory pathway of the fly. In doing so, they discovered a new cell type as well as several other previously unrealized insights about the organization of Drosophila’s olfactory biology.

Drosophila connectome with SEM

 

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Notes on fiber biomechanics


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PDF version: Notes on fiber biomechanics – Logan Thrasher Collins

Elastic fiber models

  • For an elastic fiber in which a linear relationship between force and change in length is assumed, the force is given by F = k(L – L0).
  • To normalize for other elastic fibers with different starting lengths, this equation is divided by L0 to give F = k(L/L0 – 1). It is common practice to represent L/L0 as a parameter λ (called the stretch ratio).
  • As such, F is found using the formula below. Note that the quantity λ – 1 is referred to as the strain.

Eq1

  • While linear models are often useful, many real fibers exhibit finite extensibility (a nonlinear phenomenon) after exceeding a certain critical strain value λc. That is, the force necessary to extend the fiber farther after exceeding λc increases rapidly. Finite extensibility can be modeled using the following equation which divides k by a term dependent on λ and λc.

Eq2

  • To model a muscle, let L0 represent the muscle’s length in its inactive state and Lcontracted represent the muscle’s length in its contracted state. Unlike the spring, the contracted state is used as the reference length. The contraction stretch is described by the ratio λcontracted = Lcontracted/L0 while the stretch ratio remains as λ = L/L0.

Eq3

  • If this muscle is contracted without carrying a load such that F = 0, then λ = λcontracted. If the muscle acquires a load and so must maintain a constant length equal to its original length L0 (to “hold the load steady”), then the force in the muscle is F = k(1/λcontracted – 1).
  • To generalize this model for 3-dimensional space, the locations of the fiber’s endpoints A and B are used. The fiber’s length and orientation are given below.

Eq4

  • The following force vectors can act on point B and on point A. The stretch ratio is still λ = L/L0.

Eq5

Viscous fiber models

  • Purely viscous behavior (as with liquids) can be described 1-dimensionally using the equation below where cη is a damping coefficient.

Eq6

  • The normalized rate of deformation is equivalent to the above formula without the damping coefficient. In addition, the rate of deformation can be written in terms of the stretch ratio λ = L/L0.

Eq7

  • If one endpoint of a filament of fluid is moved with constant velocity, its position is given by xB = L0 + vt. This means that the rate of deformation is v/xB.

Eq8

  • Solving the above equation gives the following result. For a constant rate of elongation, the point xB must be displaced exponentially over time.

Eq9

  • Given endpoint displacements uA and uB, the total displacement is ΔL = uB – uA. Using this quantity, the stretch λ and the strain ε can be written using the equations below.

Eq10

  • For the small strains (i.e. |ε| is much less than 1) that result from small stretches, some approximations can be made which reduce the force equation to the following form.

Eq11

Viscoelastic fiber models

  • Many biological materials exhibit viscoelastic behavior rather than elastic behavior. In viscoelastic systems, the force on a fiber with a constant length decreases over time and applying constant force causes the length to increase.
  • The strain response of a fiber to force is given as ε(t). The creep function J(t) describes the fiber’s tendency to permanently deform as strain is applied. Many possible creep functions can be devised depending on the system. The creep function is related to the strain by a factor of force increase F0.

Eq12

  • Strain responses follow the principle of superposition. That is, if a force is applied at τ1 and then another force is applied at τ2, a total strain response can be expressed as a sum of the two individual strain responses.

Eq14

  • For an arbitrary history of applied forces, an integral formulation of strain response is used. In this equation, the change in force at each infinitesimal time interval is multiplied by the creep function.

Eq15

  • Similarly, the force resulting from an imposed strain history can be expressed as an integral where G is a function that describes the relaxation of the fiber with time (analogous to the creep function, but the opposite concept).

 

Reference: Oomens, C., Brekelmans, M., & Baaijens, F. (2009). Biomechanics: Concepts and Computation. Cambridge University Press.

 

Notes on nanoparticle self-assembly


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Preparation of nanoparticle superlattices

  • Nanoparticle superlattices can be prepared using solvent evaporation, solvent destabilization, or gravitational sedimentation methods.
  • Solvent evaporation involves evaporating a nanoparticle-containing solvent to induce ordered aggregation of the particles. Note that many inorganic nanoparticles are insoluble in polar solvents and soluble in nonpolar solvents, though the presence of polar surface ligands can alter this behavior. Fig. 1
  • Solvent evaporation techniques include (i) placing a small droplet of nanoparticle-containing solvent on a solid substrate and allowing for evaporation to occur, (ii) evaporating a nanoparticle-containing solvent from a tilted vial so as to control the orientation of the meniscus, (iii) placing a small droplet of polar solvent on a solid substrate and then adding a nonpolar nanoparticle-containing solvent over the top to facilitate aggregation in the thin layer of nonpolar solvent, and (iv) filling a tray with a polar solvent and adding nonpolar nanoparticle-containing solvent over the top to facilitate aggregation in the thin layer of nonpolar solvent.
  • Solvent destabilization promotes gradual clustering of nanoparticle in solution via slowly changing the solvent conditions. Solvent destabilization techniques include (i) allowing for a polar and a nonpolar solvent to gradually intermingle and so facilitate a steady increase in the favorability of nanoparticle-nanoparticle interactions and (ii) heating a premixed solvent mixture that includes both polar and nonpolar components and so facilitating a controlled enrichment of the solvent with a higher boiling point. This increases the favorability of nanoparticle-nanoparticle interactions in a controlled fashion. Because many nonpolar liquids possess lower boiling points, the nanoparticle lattices prepared in this way may require nanoparticles equipped with polar surface ligands.
  • Gravitational sedimentation is less common than the other techniques since many nanoparticles are small enough to remain dissolved in spite of gravitational forces. But very large nanoparticles (100-1,000 nm) often do sediment, facilitating close-packing and the assembly of superlattices.

Characterization of nanoparticle superlattices

  • Transmission electron microscopy (TEM) is used to visualize nanoparticle superlattices directly. As TEM requires very thin slices, it makes 2-dimensional images of nanoparticle superlattices.
  • TEM operates best when there is a high contrast between the atomic number of the nanoparticles and the atomic number of the background support structure. For instance, PbS is easily imaged on a carbon support.
  • To circumvent issues that arise with atomic number contrast, ultrathin (i.e. graphene) supports or supports that possess numerous holes can be used. Ultrathin supports absorb less electrons while “holey” supports allow some nanoparticles to be positioned over the holes during imaging, preventing background absorption.
  • TEM often requires a vacuum chamber and so necessitates dry samples, meaning  that superlattice structure can be visualized after removal of the solvent, but snapshots of the self-assembly process cannot be taken. However, recent investigations into designing liquid-cell TEM may circumvent this problem.
  • Scanning electron microscopy (SEM) generates 3-dimensional images via a scanning electron beam and so is useful for imaging nanoparticles and Fig. 2nanoparticle superlattices that exhibit some kinds of notable 3-dimensional geometric characteristics.
  • Atomic force microscopy, a technique in which a nanoscale probe is moved across a sample to reconstruct its shape via a “sense of touch,” has also been used for nanoparticle superlattice characterization.
  • Images of repetitive superlattices are amenable to processing with two-dimensional fast Fourier transforms (FFTs) that can reveal insights about the lattice’s characteristics. Performing this form of FFT upon an image of a repetitive crystal structure creates a plot of spatial frequencies. This plot is said to display reciprocal space (or Fourier space).
  • Distinct points on the reciprocal space plot correspond to certain properties of the lattice that are sometimes not apparent from the image prior to the FFT. In this way, very similar lattices can be clearly distinguished.

Kinetics of nanoparticle superlattice formation

  • Homogenous nucleation occurs in solution and requires overcoming a nucleation barrier while heterogenous nucleation occurs as nanoparticles are added to a preexisting seed crystal. Homogenous nucleation typically leads to disordered solids and is typically much slower than heterogenous nucleation. Fig. 3
  • In heterogenous nucleation, crystal growth occurs at differing rates depending on how many new contacts are formed (assuming attractive interparticle interactions). If more new contacts occur upon the addition of a nanoparticle, the process will exhibit grater energetic favorability and occur at a faster rate.
  • This means that adding nanoparticles to kinks and vacancies happens more rapidly than the adsorption of nanoparticles to steps, terraces, and “adatoms” (see figure at right). As such, large scale structures that minimize surface energy tend to form.

Thermodynamics of nanoparticle superlattices

  • As mentioned, if superlattice assembly occurs rapidly, disordered aggregates can form. Allowing the process to occur more slowly facilitates sampling of many states as assembly proceeds. As such, the most thermodynamically stable structures can form when gradual assembly is performed.
  • Van der Waals interactions between nanoparticles are often approximated using the following pair potential equation. U is the potential energy for the interparticle interaction, C represents a proportionality constant for the interparticle interaction, ρ1 and ρ2 are the number of atoms per unit volume in two interacting bodies, and r is the distance between the bodies.

Eq.1

  • For nanoparticles with volumes V1 and V2, the total van der Waals energy of attraction is obtained by the following integral that performs a pairwise summation of all the atomic van der Waals interactions.

Eq.2

  • If two nanoparticles are spherical with radii R1 and R2, the integral can be solved analytically to give their interparticle potential energy.

Eq.3

  • When the distance d between two nanoparticles is much less than the radius of either nanoparticle, the above equation can be approximated using the following formula.

Eq.4

  • For many nanoparticles without chemical ligands, these van der Waals interactions would cause rapid aggregation in solution. However, the presence of certain surface ligands gives repulsion that maintains colloidal solutions of nanoparticles.
  • In order to convey repulsive effects between nanoparticles, the surface ligands require a proper solvent. Such solvents exhibit negative free energy upon ligand-solvent mixing. That is, interactions between the surface ligand and the solvent are energetically favorable.
  • Surface ligand repulsion includes an osmotic component and an elastic component. As the solvent molecules are sterically blocked by surface ligands, when the surface ligands of two nanoparticles start to interact, the volume that the solvent cannot enter increases. This situation is osmotically unfavorable, so osmotic repulsion occurs. When surface ligands are compressed because two nanoparticles are close to each other, elastic repulsion occurs.
  • When a solution of nanoparticles with surface ligands that are attracted to each other (i.e. hydrophobic chains) is dried, the ligands begin to freeze together rather than experiencing repulsion.
  • Equilibrium superlattice structures minimize the free energy G in terms of enthalpy U and entropy S according to Gibb’s equation ΔG = ΔU – TΔS.
  • The contributions of cores and ligands can be decomposed into the terms ΔUcores, ΔUligands, ΔScores, and ΔSligands. The energy terms can be further broken down into the components of van der Waals interactions (London dispersion forces, dipole-induced dipole interactions, and dipole-dipole interactions). The entropy terms can be further broken down into configurational, rotational, and translational components.

 

Reference: Boles, M. A., Engel, M., & Talapin, D. V. (2016). Self-Assembly of Colloidal Nanocrystals: From Intricate Structures to Functional Materials. Chemical Reviews, 116(18), 11220–11289. https://doi.org/10.1021/acs.chemrev.6b00196

 

 

 

Notes on Honeybee Sensory Neurobiology


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PDF version: Notes on Honeybee Sensory Neurobiology – Logan Thrasher Collins

Olfaction

Antennal lobes

  • Honeybee antennal lobes (ALs) are composed of about 160 regions called glomeruli in which olfactory receptor neurons from the antennae make synapses on projection neuron cell bodies as well as inhibitory local neurons.
  • The projection neurons send cholinergic axons to the mushroom bodies and to the lateral horn (LH) while the GABAergic local neurons facilitate olfactory computations within the antennal lobes.

Mushroom bodies

  • The mushroom bodies are paired structures located on either side of the central brain (CB). They are known to facilitate higher sensory integration as well as associative learning processes.Fig. 1
  • In honeybees, the mushroom bodies use cup-shaped medial calyces (MCAs) and lateral calyces (LCAs) as their major sensory input regions while using the pedunculi (PEDs) as their major sensory output regions.
  • The calyces contain Kenyon cells which receive cholinergic axons from the projection neurons of the antennal lobes and the pedunculi contain the efferent axons of the Kenyon cells.

Associative olfactory learning

  • Honeybee associative olfactory learning can occur where the olfactory pathway converges with other pathways.Fig. 2
  • Specific odors can serve as conditioned stimuli when they are associated with unconditioned stimuli of appetitive or aversive character.
  • Experimental evidence shows that the VUMmx1 neuron is sufficient for olfactory reward learning in bees. Its cell body is located within a region called the subesophageal ganglion and it synapses upon cells in the calyces, the lateral horn, and the antennal lobe.

Vision

Honeybee eyes

  • Honeybees possess two frontal compound eyes and three ocelli (simple eyes) located on the top of the head.
  • The retinas of honeybee compound eyes are composed of ommatidia, each with nine photoreceptor cells. The types of bee photoreceptor cells include S, M, and L photoreceptors corresponding to UV, blue, and green wavelengths respectively.
  • Ocellar retinas are composed of rod cells (note that they do not have ommatidia) and are covered by a lens. However, the focal plane of this lens is behind the actual retina, leading to much lower resolving power than that of the compound eyes. Although the function of ocelli is not entirely understood, they may operate as widefield detectors of illumination changes. In addition, ocellar retinas can be subdivided into dorsal and ventral regions which view the horizon and the sky respectively. Distinct neuronal pathways are associated with these subdivisions.

Optic lobe

  • Honeybee vision (associated with the compound eyes) starts with the optic lobe’s three regions; the lamina (La), medulla (Me), and lobula (Lo).
  • The lamina is positioned directly under the compound eye’s photoreceptors. It receives inputs mainly from the L photoreceptors, which are involved in the achromatic pathway and exhibit fast response times. However, some very rough color processing may still occur in the lamina. Fig. 3
  • In the medulla, neurons are organized in a columnar retinotopic fashion with eight layers. The columns also possess horizontal connections (unlike the lamina) which likely facilitate color opponency. The medulla’s outer layers contain neurons that respond to specific wavelengths and neurons that respond to a broad range of wavelengths while the medulla’s inner layers contain color-opponent neurons that compare colors at center and surround regions of receptive fields.
  • The lobula consists of six layers. Its outer layers (1-4) are part of the achromatic pathway and exhibit motion sensitivity. Its inner layers (5-6) continue the color processing pathway. Some projections from the inner layers go to the mushroom bodies, possibly facilitating sensory crosstalk and learning.
  • Beyond the optic lobe, further visual processing of the achromatic and color pathways occurs in the protocerebrum and central brain.

Audition and antennal somatosensation

Johnston’s organ

  • Honeybees use Johnston’s organ as their sensory organ for audition. In bees, audition also acts as a form of somatosensation. Johnston’s organ is located on the antennae. It detects vibrations during the waggle dance and air currents during flight. Fig. 4
  • Johnston’s organ contains about 240 scolopidia, mechanosensory complexes which include bristles that deform and trigger action potentials along efferent axons.
  • The soma of neurons within Johnston’s organ are divided into dorsal (dJO), ventral (vJO), and anterior groups (aJO).

Projections from Johnston’s organ

  • The main axons from the soma within Johnston’s organ trifurcate into the fascicles called T6I, T6II, and T6III. The T6I axons terminate at the ventro-medial superior posterior slope (vmSPS), the T6II axons terminate at the antennal mechanosensory and motor center (AMMC), and the T6III axons terminate at the ventro-central superior posterior slope (vcSPS). Fig. 5
  • In the vmSPS, the axons show some degree of somatotopy arising from the dorsal, ventral, and anterior Johnston’s organ regions. Somatotopy is not observed in the AMMC or vcSPS.
  • All the sensory axons from Johnston’s organ also send small collateral branches to the bee’s dorsal lobe (DL).

The AMMC

  • The AMMC contains two classes of interneuron, AMMC-Int-1 and AMMC-Int-2. AMMC-Int-1 neurons have somas located in the honeybee’s primary auditory center, which is near the central brain. They densely arborize at the AMMC and thinly arborize in the ventral protocerebrum (the protocerebrum is a region of the insect brain that includes the mushroom bodies and central brain as well as several other structures). Their dense arborization in the AMMC runs close to the T6 collaterals at the dorsal lobe.
  • AMMC-Int-1 neurons demonstrate spontaneous spiking without sensory input.Fig. 6 During exposure to a vibratory stimulus, the spike rate slows slightly. After the stimulus is removed, the spike rate increases to a higher rate than that of the spontaneous spiking, but eventually returns to the basal rate. However, it should be noted that olfactory stimuli and other modulating factors can drastically alter the response properties of AMMC-Int-1 neurons.
  • AMMC-Int-2 neurons have somas located in the dorsal lobe. Their dendrites split into three main branches called x, y, and z. Branch y is the axon while branches x and z are dendritic. It sends a long process to the lateral protocerebrum (LP) and makes synapses. The x arborization represents the densest of the three branches and is located in the AMMC. Branch z passes through the dorsal lobe and into the lateral superior posterior slope (lateral SPS). Fig. 7
  • AMMC-Int-2 neurons respond to relatively high vibratory amplitudes, especially those which cause 30 μm (or greater) shifts in antennal position. Their sensitivity reaches a maximum at 265 Hz (a frequency that occurs during the waggle dance), though they also respond to other frequencies.

The SPS

  • The SPS contains an interneuron known as SPS-D-1 which projects to the ipsilateral and contralateral SPS.
  • SPS-D-1 does not respond to 265 Hz alone. However, it responds to long-lasting 265 Hz vibratory stimulation with simultaneous olfactory stimulation at the contralateral antenna.

Gustation

Gustatory sensilla

  • Gustatory receptor cells are found in sensilla, structures which resemble hairs or pegs. Sensilla are located on the glossa, antennae, labial palps, and several other parts of the bee’s body. Fig. 8
  • Each sensillum contains 3-5 gustatory receptor neurons that send dendrites up the shaft towards a pore at the sensillum’s tip. The somas of the receptor cells (along with a mechanoreceptor cell) are encapsulated by auxiliary cells and bathed in a receptor hemolymph. The gustatory receptor neurons likely use GPCRs to detect various food molecules while the mechanoreceptor facilitates evaluation of the food’s position and density.
  • Antennal sensilla respond in a dose-dependent and highly sensitive manner to sucrose solutions. In addition, antennal sensilla respond to aqueous NaCl. As the antennal sensilla do not respond to very low concentrations of KCl, they probably do not contain a receptor that responds to water alone (unlike in many other insects). Sensilla on the mouthparts respond to aqueous sucrose, glucose, fructose, LiCl, KCl, and NaCl. They do not respond to CaCl2 or MgCl2. Foreleg sensilla respond to sucrose as well as very low concentrations of KCl, suggesting that these sensilla may contain a receptor that responds to water alone (unlike the bee’s other sensilla).

Honeybee central gustatory processing

  • Honeybee central gustatory processing takes place primarily in their subesophageal ganglion (SEG). Axons of gustatory neurons and the mechanosensory neurons found in the sensilla project to the SEG’s mandibular, maxillary, and labial neuromeres via the mandibular, maxillary, and labial nerves respectively.
  • As mentioned, the SEG contains the VUMmx1 neuron, which facilitates pairing of olfactory and gustatory stimuli for reward learning. Other VUM neurons have been identified in the SEG, but their function remains unclear.
  • Beyond the SEG, other neurons might be involved in the honeybee’s gustatory processing. In the mushroom bodies, the PE1 neuron exhibits increased spiking in response to sucrose gustation. However, PE1 also responds to mechanical and olfactory inputs. Also located in the mushroom bodies are cells dubbed as “feedback neurons” which respond to odors and sucrose as well. In these cases, multisensory integration likely occurs.

With the exception of image created for the section “projections from Johnston’s organ,” images were modified from: (Steijven, Spaethe, Steffan-Dewenter, & Härtel, 2017), (R. Menzel, 2012), (Kiya & Kubo, 2011),  and (Galizia, Eisenhardt, & Giurfa, 2011).

References

Dyer, A. G., Paulk, A. C., & Reser, D. H. (2011). Colour processing in complex environments: insights from the visual system of bees. Proceedings of the Royal Society B: Biological Sciences, 278(1707), 952 LP-959. Retrieved from http://rspb.royalsocietypublishing.org/content/278/1707/952.abstract

Galizia, C. G., Eisenhardt, D., & Giurfa, M. (2011). Honeybee Neurobiology and Behavior: A Tribute to Randolf Menzel. Springer Netherlands.

Heisenberg, M. (2003). Mushroom body memoir: from maps to models. Nature Reviews Neuroscience, 4, 266. Retrieved from https://doi.org/10.1038/nrn1074

Hung, Y.-S., & Ibbotson, M. (2014). Ocellar structure and neural innervation in the honeybee. Frontiers in Neuroanatomy. Retrieved from https://www.frontiersin.org/article/10.3389/fnana.2014.00006

Kiya, T., & Kubo, T. (2011). Dance Type and Flight Parameters Are Associated with Different Mushroom Body Neural Activities in Worker Honeybee Brains. PLOS ONE, 6(4), e19301. Retrieved from https://doi.org/10.1371/journal.pone.0019301

Menzel, R. (2012). The honeybee as a model for understanding the basis of cognition. Nature Reviews Neuroscience, 13, 758. Retrieved from http://dx.doi.org/10.1038/nrn3357

Mota, T., Yamagata, N., Giurfa, M., Gronenberg, W., & Sandoz, J.-C. (2011). Neural Organization and Visual Processing in the Anterior Optic Tubercle of the Honeybee Brain. The Journal of Neuroscience, 31(32), 11443 LP-11456. Retrieved from http://www.jneurosci.org/content/31/32/11443.abstract

Sandoz, J.-C. (2013). Chapter 30 – Neural Correlates of Olfactory Learning in the Primary Olfactory Center of the Honeybee Brain: The Antennal Lobe. In R. Menzel & P. R. B. T.-H. of B. N. Benjamin (Eds.), Invertebrate Learning and Memory (Vol. 22, pp. 416–432). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-12-415823-8.00030-7

Steijven, K., Spaethe, J., Steffan-Dewenter, I., & Härtel, S. (2017). Learning performance and brain structure of artificially-reared honey bees fed with different quantities of food. PeerJ, 5, e3858. https://doi.org/10.7717/peerj.3858

Design of a De Novo Aggregating Antimicrobial Peptide and a Bacterial Conjugation-Based Delivery System (journal article)


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My first scientific journal article (I am the lead author)! I came up with the idea for this project during middle school. In high school, I started working at a university laboratory. Over the course of this research, I competed in the International Science and Engineering Fair three times, gave a TEDx presentation, fought through countless obstacles, ignored the naysayers, witnessed the Nobel Prize ceremonies firsthand, and brought my idea to fruition.

ACS Biochemistry: Design of a De Novo Aggregating Antimicrobial Peptide and a Bacterial Conjugation-Based Delivery System

Local copy (full text): Design of a De Novo Aggregating Antimicrobial Peptide and a Bacterial Conjugation-Based Delivery System