scholarly journals High-throughput evolution of near-infrared serotonin nanosensors

2019 ◽  
Vol 5 (12) ◽  
pp. eaay3771 ◽  
Author(s):  
Sanghwa Jeong ◽  
Darwin Yang ◽  
Abraham G. Beyene ◽  
Jackson Travis Del Bonis-O’Donnell ◽  
Anneliese M. M. Gest ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Molecular Recognition ◽  
High Throughput ◽  
Near Infrared ◽  
Fluorescence Enhancement ◽  
Brain Slices ◽  
Emerging Technology ◽  
Signal Transducers ◽  
Fluorescent Signal ◽  
Repeat Exposure

Imaging neuromodulation with synthetic probes is an emerging technology for studying neurotransmission. However, most synthetic probes are developed through conjugation of fluorescent signal transducers to preexisting recognition moieties such as antibodies or receptors. We introduce a generic platform to evolve synthetic molecular recognition on the surface of near-infrared fluorescent single-wall carbon nanotube (SWCNT) signal transducers. We demonstrate evolution of molecular recognition toward neuromodulator serotonin generated from large libraries of ~6.9 × 1010 unique ssDNA sequences conjugated to SWCNTs. This probe is reversible and produces a ~200% fluorescence enhancement upon exposure to serotonin with a Kd = 6.3 μM, and shows selective responsivity over serotonin analogs, metabolites, and receptor-targeting drugs. Furthermore, this probe remains responsive and reversible upon repeat exposure to exogenous serotonin in the extracellular space of acute brain slices. Our results suggest that evolution of nanosensors could be generically implemented to develop other neuromodulator probes with synthetic molecular recognition.

scholarly journals High Throughput Evolution of Near Infrared Serotonin Nanosensors

2019 ◽  
Author(s):  
Sanghwa Jeong ◽  
Darwin Yang ◽  
Abraham G. Beyene ◽  
Anneliese M.M. Gest ◽  
Markita P. Landry
Keyword(s):  
Carbon Nanotube ◽  
High Throughput ◽  
Extracellular Space ◽  
Near Infrared ◽  
Fluorescence Enhancement ◽  
Signaling System ◽  
Serotonin Signaling ◽  
Spatiotemporal Scales ◽  
The Brain ◽  
Brain Extracellular Space

ABSTRACTRelease and reuptake of neuromodulator serotonin, 5-HT, is central to mood regulation and neuropsychiatric disorders, whereby imaging serotonin is of fundamental importance to study the brain’s serotonin signaling system. We introduce a reversible near-infrared nanosensor for serotonin (nIRHT), for which synthetic molecular recognition toward serotonin is systematically evolved from ssDNA-carbon nanotube constructs generated from large libraries of 6.9 × 1010unique ssDNA sequences. nIRHT produces a ∼200% fluorescence enhancement upon exposure to serotonin with a Kd= 6.3 µM affinity. nIRHT shows selective responsivity towards serotonin over serotonin analogs, metabolites, and receptor-targeting drugs, and a 5-fold increased affinity for serotonin over dopamine. Further, nIRHT can be introduced into the brain extracellular space in acute slice, and can be used to image exogenous serotonin reversibly. Our results suggest evolution of nanosensors could be generically implemented to rapidly develop other neuromodulator probes, and that these probes can image neuromodulator dynamics at spatiotemporal scales compatible with endogenous neuromodulation.

scholarly journals Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  
Keyword(s):  
High Throughput ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Texture Attributes

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

scholarly journals A Near‐Infrared Organic Fluorescent Probe for Broad Applications for Blood Vessels Imaging by High‐Throughput Screening via 3D‐Blood Vessel Models (Small Methods 8/2021)

Small Methods ◽  
2021 ◽  
Vol 5 (8) ◽  
pp. 2170036
Author(s):  
Muhammad Asri Abdul Sisak ◽  
Fiona Louis ◽  
Ichio Aoki ◽  
Sun Hyeok Lee ◽  
Young‐Tae Chang ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Fluorescent Probe ◽  
High Throughput ◽  
High Throughput Screening ◽  
Near Infrared

scholarly journals Spatiotemporal Intracellular Nitric Oxide Signaling Captured Using Internalized, Near-Infrared Fluorescent Carbon Nanotube Nanosensors

Nano Letters ◽  
2014 ◽  
Vol 14 (8) ◽  
pp. 4887-4894 ◽  
Author(s):  
Zachary W. Ulissi ◽  
Fatih Sen ◽  
Xun Gong ◽  
Selda Sen ◽  
Nicole Iverson ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Carbon Nanotube ◽  
Near Infrared ◽  
Nitric Oxide Signaling ◽  
Fluorescent Carbon

scholarly journals Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Taher Hajilounezhad ◽  
Rina Bao ◽  
Kannappan Palaniappan ◽  
Filiz Bunyak ◽  
Prasad Calyam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Carbon Nanotube ◽  
High Throughput ◽  
Self Assembly ◽  
Mechanical Performance ◽  
Physical Parameters ◽  
Structure Property ◽  
Processing Parameter ◽  
Physics Based Simulation

AbstractUnderstanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

Single-Chirality Near-Infrared Carbon Nanotube Sub-Cellular Imaging and FRET Probes

Nano Letters ◽  
2021 ◽  
Author(s):  
Rachel Langenbacher ◽  
Januka Budhathoki-Uprety ◽  
Prakrit V. Jena ◽  
Daniel Roxbury ◽  
Jason Streit ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Near Infrared ◽  
Cellular Imaging

scholarly journals High Throughput Screening of Elite Loblolly Pine Families for Chemical and Bioenergy Traits with Near Infrared Spectroscopy

Forests ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 418
Author(s):  
Gifty Acquah ◽  
Brian Via ◽  
Tom Gallagher ◽  
Nedret Billor ◽  
Oladiran Fasina ◽  
...  
Keyword(s):  
High Throughput ◽  
Loblolly Pine ◽  
Environmental Sustainability ◽  
High Throughput Screening ◽  
Near Infrared ◽  
The United States ◽  
P Value ◽  
Cellulose Content ◽  
Forest Sites ◽  
Chemical Traits

Pinus taeda L. (loblolly pine) dominates 13.4 million ha of US southeastern forests and contributes over $30 billion to the economy of the region. The species will also form an important component of the renewable energy portfolio as the United States seeks national and energy security as well as environmental sustainability. This study employed NIR-based chemometric models as a high throughput screening tool to estimate the chemical traits and bioenergy potential of 351 standing loblolly pine trees representing 14 elite genetic families planted on two forest sites. The genotype of loblolly pine families affected the chemical, proximate and energy traits studied. With a range of 36.7% to 42.0%, the largest genetic variation (p-value < 0.0001) was detected in the cellulose content. Furthermore, although family by site interactions were significant for all traits, cellulose was the most stable across the two sites. Considering that cellulose content has strong correlations with other properties, selecting and breeding for cellulose could generate some gains.

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