Biology subjects

McFarlane, I. R.

Publications and source records attributed to McFarlane, I. R..

3 recordsLinked to original sources

bioRxiv2018

Electrostatic-assemblies of single-walled carbon nanotubes and sequence-tunable peptoid polymers detect a lectin protein and its target sugars

A primary limitation to real-time imaging of metabolites and proteins has been the selective detection of biomolecules that have no naturally-occurring or stable molecular recognition counterparts. We present developments in the design of synthetic near-infrared fluorescent nanosensors based on the fluorescence modulation of single-walled carbon nanotubes (SWNT) with select sequences of surface-adsorbed N-substituted glycine peptoid polymers. We assess the stability of the peptoid-SWNT nanosensor candidates under variable ionic strengths, protease exposure, and cell culture media conditions, and find that the stability of peptoid-SWNTs depends on the composition and length of the peptoid polymer. From our library, we identify a peptoid-SWNT assembly that can selectively detect lectin protein wheat germ agglutinin (WGA) with a sensitivity comparable to the concentration of serum proteins. This WGA protein nanosensor is characterized with near-infrared spectroscopy and microscopy to study protein-nanosensor interaction parameters. To demonstrate the retention of nanosensor-bound protein activity, we show that WGA on the nanosensor produces an additional fluorescent signal modulation upon exposure to the lectins conjugate sugars, suggesting the lectin protein selectively binds its target sugars through ternary molecular recognition interactions relayed to the nanosensor. Our results inform design considerations for developing synthetic molecular recognition elements by assembling peptoid polymers on SWNTs, and also demonstrate these assemblies can serve as optical nanosensors for lectin proteins and their target sugars. Together, these data suggest certain peptoid sequences can be assembled with SWNTs to serve as versatile optical probes to detect proteins and their molecular substrates.

bioengineering↗

bioRxiv2017

Molecular Recognition of Dopamine with Dual Near Infrared Excitation-Emission Two-Photon Microscopy

A key limitation for achieving deep imaging in biological structures lies in photon absorption and scattering leading to attenuation of fluorescence. In particular, neurotransmitter imaging is challenging in the biologically-relevant context of the intact brain, for which photons must traverse the cranium, skin and bone. Thus, fluorescence imaging is limited to the surface cortical layers of the brain, only achievable with craniotomy. Herein, we describe optimal excitation and emission wavelengths for through-cranium imaging, and demonstrate that near-infrared emissive nanosensors can be photoexcited using a two-photon 1560 nm excitation source. Dopamine-sensitive nanosensors can undergo two-photon excitation, and provide chirality-dependent responses selective for dopamine with fluorescent turn-on responses varying between 20% and 350%. We further calculate the two-photon absorption cross-section and quantum yield of dopamine nanosensors, and confirm a two-photon power law relationship for the nanosensor excitation process. Finally, we show improved image quality of the nanosensors embedded 2 mm deep into a brain-mimetic tissue phantom, whereby one-photon excitation yields 42% scattering, in contrast to 4% scattering when the same object is imaged under two-photon excitation. Our approach overcomes traditional limitations in deep-tissue fluorescence microscopy, and can enable neurotransmitter imaging in the biologically-relevant milieu of the intact and living brain.

neuroscience↗

bioRxiv2017

Stochastic Simulation Of Dopamine Neuromodulation For Implementation Of Fluorescent Neurochemical Probes In The Striatal Extracellular Space

Imaging the dynamic behavior of neuromodulatory neurotransmitters in the extracelluar space arising from individual quantal releases would constitute a major advance in neurochemical imaging. Spatial and temporal resolution of these highly stochastic neuromodulatory events requires concurrent advances in the chemical development of optical nanosensors selective for neuromodulators in concert with advances in imaging methodologies to capture millisecond neurotransmitter release. Herein, we develop and implement a stochastic model to describe dopamine dynamics in the extracellular space (ECS) of the brain dorsal striatum. Our model is developed from first principles and simulates release, diffusion, and reuptake of dopamine in a 3D simulation volume of striatal tissue. We find that in vivo imaging of neuromodulation requires simultaneous optimization of dopamine nanosensor reversibility and sensitivity: dopamine imaging in the striatum or nucleus accumbens requires nanosensors with an optimal dopamine dissociation constant (Kd) of 1 M, whereas Kd above 10 M are required for dopamine imaging in the prefrontal cortex. Furthermore, our model reveals that imaging frame rates of 20 Hz are optimal for imaging temporally-resolved dopamine release events based on the probabilistic nature of dopaminergic terminal activity in the striatum. Our work provides a modeling platform to probe how complex neuromodulatory processes can be studied with fluorescent nanosensors and enables direct evaluation of nanosensor chemistry and imaging hardware parameters. Our stochastic model is generic for evaluating fluorescent neurotransmission probes, and is broadly applicable to the design of other neurotransmitter fluorophores and their optimization for implementation in vivo.

neuroscience↗