Glucose monitoring in living cells with single fluorescent protein-based sensors

Hu, Hanyang; Wei, Yufeng; Wang, Daocheng; Su, Ni; Chen, Xianjun; Zhao, Yuzheng; Liu, Guixia; Yang, Yi

doi:10.1039/c7ra11347a

Cited by 35 publications

(25 citation statements)

References 30 publications

(64 reference statements)

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“…Occasionally, individual peptide linkers are not used for biosensor design; instead, the N- and C-terminal amino acid residues of cpFP are modified. This approach was used in designing FGBP—biosensor for glucose monitoring in living cells [93]—or FlicR1 for voltage detection [94]. Using special peptide linkers is not always necessary, for example, in sensors such as FHisJ [95] for histidine detection and in the sensor for phosphonates [96] they are absent.…”

Section: General Principles For Developing Cpfp-based Gefismentioning

confidence: 99%

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Circularly Permuted Fluorescent Protein-Based Indicators: History, Principles, and Classification

Kostyuk

Demidovich

Kotova

et al. 2019

IJMS

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Genetically encoded biosensors based on fluorescent proteins (FPs) are a reliable tool for studying the various biological processes in living systems. The circular permutation of single FPs led to the development of an extensive class of biosensors that allow the monitoring of many intracellular events. In circularly permuted FPs (cpFPs), the original N- and C-termini are fused using a peptide linker, while new termini are formed near the chromophore. Such a structure imparts greater mobility to the FP than that of the native variant, allowing greater lability of the spectral characteristics. One of the common principles of creating genetically encoded biosensors is based on the integration of a cpFP into a flexible region of a sensory domain or between two interacting domains, which are selected according to certain characteristics. Conformational rearrangements of the sensory domain associated with ligand interaction or changes in the cellular parameter are transferred to the cpFP, changing the chromophore environment. In this review, we highlight the basic principles of such sensors, the history of their creation, and a complete classification of the available biosensors.

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Section: General Principles For Developing Cpfp-based Gefismentioning

confidence: 99%

“…The series of FGBP probes with differences in affinity to glucose was created by inserting cpYFP into the glucose/galactose-binding protein of E. coli [93]. Subsequently, the probe with physiologically relevant value of Kd, FGBP 1mM , was selected for further characterization.…”

Section: Classification Of Cpfp-based Gefis By Analyte Measured Anmentioning

confidence: 99%

Circularly Permuted Fluorescent Protein-Based Indicators: History, Principles, and Classification

Kostyuk

Demidovich

Kotova

et al. 2019

IJMS

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“…With high accuracy, any suspension culture of mixed populations could be maintained in log phase growth for days in order to study transient invaders into microbial communities 33 or even microbiome system dynamics 34 . The advent of small molecule fluorescent reporters for metabolic fitness 35 , pH 36,37 , and CO2 38 , in addition to the hundreds of fluorescent protein sensors available to the synthetic biology community at large 39,40 , also impresses the seemingly unlimited potential of being able to multiplex and quantify changes in growth, gene expression, and the environment in real-time.…”

Section: Discussionmentioning

confidence: 99%

Flexible open-source automation for robotic bioengineering

Chory

Gretton

DeBenedictis

et al. 2020

Preprint

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INTRODUCTIONLiquid handling robots have become a biotechnology staple 1,2 , allowing laborious or repetitive protocols to be executed in highthroughput. However, software narrowly designed to automate traditional hand-pipetting protocols often struggles to harness the full capabilities of robotic manipulation. Here we present Pyhamilton, an open-source Python package that eliminates these constraints, enabling experiments that could never be done by hand. We used Pyhamilton to double the speed of automated bacterial assays over current software and execute complex pipetting patterns to simulate population dynamics. Next, we incorporated feedback-control to maintain hundreds of remotely monitored bacterial cultures in log-phase growth without user intervention. Finally, we applied these capabilities to comprehensively optimize bioreactor protein production by maintaining and monitoring fluorescent protein expression of nearly 500 different continuous cultures to explore the carbon, nitrogen, and phosphorus fitness landscape. Our results demonstrate Pyhamilton's empowerment of existing hardware to new applications ranging from biomanufacturing to fundamental biology. MAIN TEXTAutomation has been widely implemented in biotechnology 3 to facilitate routine tasks involved in DNA sequencing 4 , chemical synthesis 5 , drug discovery 6 , and molecular biology 7 . In principle, flexibly programmable robots could enable diverse experiments beyond the capabilities of human researchers, across a range of disciples within the sciences. Existing robotic software easily automates protocols designed for hand pipettes, but struggles to enable more specialized or sophisticated methods. As such, truly custom robot manipulation remains out of reach for most laboratories 2 , even those with well-established automation infrastructures.Bioautomation lags behind the rapidly advancing field of manufacturing, where robots are expected to be task-flexible, responsive to new situations, and interactive with humans or remote management systems when ambiguous situations or errors arise 2 . A key limitation is the lack of a comprehensive, suitably abstract, and accessible software ecosystem 8-10 . Though bioinformatics is becoming increasingly opensourced 11,12 , bioautomation has been slow to adopt key practices such as modularity, version control, and asynchronous programming.To address these issues, we developed Pyhamilton, a Python package that not only facilitates high-throughput operations within the laboratory, but also allows liquid-handling robots to execute previously unimaginable and increasingly impressive methods. With this package, users can use process scheduling, run simulations for experimental planning, implement error handling for straightforward troubleshooting, and easily integrate robots with external laboratory equipment. Design of Pyhamilton SoftwarePyhamilton enables Hamilton STAR and STARlet liquid handling robots to be programmed using standard Python. This allows for robotic method development to benefit from standa...

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“…These glucose sensors have been applied to the realtime monitoring of glucose dynamics in yeast and mammalian cells [43,56]. Very recently, we developed a highly-responsive, cpYFP-based glucose sensor, FGBP 1mM , which displays an * 700% fluorescence change in vitro, almost 10-fold greater than that of FRET-based glucose sensors [57] (Table 1).…”

Section: Glucose Sensor Flii 12 Pglumentioning

confidence: 99%

Spatiotemporal Imaging of Cellular Energy Metabolism with Genetically-Encoded Fluorescent Sensors in Brain

et al. 2018

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The brain has very high energy requirements and consumes 20% of the oxygen and 25% of the glucose in the human body. Therefore, the molecular mechanism underlying how the brain metabolizes substances to support neural activity is a fundamental issue for neuroscience studies. A well-known model in the brain, the astrocyte-neuron lactate shuttle, postulates that glucose uptake and glycolytic activity are enhanced in astrocytes upon neuronal activation and that astrocytes transport lactate into neurons to fulfill their energy requirements. Current evidence for this hypothesis has yet to reach a clear consensus, and new concepts beyond the shuttle hypothesis are emerging. The discrepancy is largely attributed to the lack of a critical method for real-time monitoring of metabolic dynamics at cellular resolution. Recent advances in fluorescent protein-based sensors allow the generation of a sensitive, specific, real-time readout of subcellular metabolites and fill the current technological gap. Here, we summarize the development of genetically encoded metabolite sensors and their applications in assessing cell metabolism in living cells and in vivo, and we believe that these tools will help to address the issue of elucidating neural energy metabolism.

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Glucose monitoring in living cells with single fluorescent protein-based sensors

Abstract: Glucose is the main source of energy and carbon in organisms and plays a central role in metabolism and cellular homeostasis.

Cited by 35 publications

References 30 publications

Circularly Permuted Fluorescent Protein-Based Indicators: History, Principles, and Classification

Circularly Permuted Fluorescent Protein-Based Indicators: History, Principles, and Classification

Flexible open-source automation for robotic bioengineering

Spatiotemporal Imaging of Cellular Energy Metabolism with Genetically-Encoded Fluorescent Sensors in Brain

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