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New alternately colored FRET sensors for simultaneous monitoring of Zn²? in multiple cellular locations.

ABSTRACT: Genetically encoded sensors based on fluorescence resonance energy transfer (FRET) are powerful tools for reporting on ions, molecules and biochemical reactions in living cells. Here we describe the development of new sensors for Zn²?based on alternate FRET-pairs that do not involve the traditional CFP and YFP. Zn²? is an essential micronutrient and plays fundamental roles in cell biology. Consequently there is a pressing need for robust sensors to monitor Zn²? levels and dynamics in cells with high spatial and temporal resolution. Here we develop a suite of sensors using alternate FRET pairs, including tSapphire/TagRFP, tSapphire/mKO, Clover/mRuby2, mOrange2/mCherry, and mOrange2/mKATE. These sensors were targeted to both the nucleus and cytosol and characterized and validated in living cells. Sensors based on the new FRET pair Clover/mRuby2 displayed a higher dynamic range and better signal-to-noise ratio than the remaining sensors tested and were optimal for monitoring changes in cytosolic and nuclear Zn²?. Using a green-red sensor targeted to the nucleus and cyan-yellow sensor targeted to either the ER, Golgi, or mitochondria, we were able to monitor Zn²? uptake simultaneously in two compartments, revealing that nuclear Zn²? rises quickly, whereas the ER, Golgi, and mitochondria all sequester Zn²? more slowly and with a delay of 600-700 sec. Lastly, these studies provide the first glimpse of nuclear Zn²? and reveal that nuclear Zn²? is buffered at a higher level than cytosolic Zn²?.

SUBMITTER: Miranda JG

PROVIDER: S-EPMC3500285 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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New alternately colored FRET sensors for simultaneous monitoring of Zn²⁺ in multiple cellular locations.

Miranda Jose G JG Weaver Amanda L AL Qin Yan Y Park J Genevieve JG Stoddard Caitlin I CI Lin Michael Z MZ Palmer Amy E AE

PloS one 20121116 11

Genetically encoded sensors based on fluorescence resonance energy transfer (FRET) are powerful tools for reporting on ions, molecules and biochemical reactions in living cells. Here we describe the development of new sensors for Zn²⁺based on alternate FRET-pairs that do not involve the traditional CFP and YFP. Zn²⁺ is an essential micronutrient and plays fundamental roles in cell biology. Consequently there is a pressing need for robust sensors to monitor Zn²⁺ levels and dynamics in cells with ...[more]

PMID: 23173058

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Project description:Real-time monitoring of the brain is potentially valuable for performance monitoring, communication, training or rehabilitation. In natural situations, the brain performs a complex mix of various sensory, motor or cognitive functions. Thus, real-time brain monitoring would be most valuable if (a) it could decode information from multiple brain systems simultaneously, and (b) this decoding of each brain system were robust to variations in the activity of other (unrelated) brain systems. Previous studies showed that it is possible to decode some information from different brain systems in retrospect and/or in isolation. In our study, we set out to determine whether it is possible to simultaneously decode important information about a user from different brain systems in real time, and to evaluate the impact of concurrent activity in different brain systems on decoding performance.We study these questions using electrocorticographic signals recorded in humans. We first document procedures for generating stable decoding models given little training data, and then report their use for offline and for real-time decoding from 12 subjects (six for offline parameter optimization, six for online experimentation). The subjects engage in tasks that involve movement intention, movement execution and auditory functions, separately, and then simultaneously. Main Results: Our real-time results demonstrate that our system can identify intention and movement periods in single trials with an accuracy of 80.4% and 86.8%, respectively (where 50% would be expected by chance). Simultaneously, the decoding of the power envelope of an auditory stimulus resulted in an average correlation coefficient of 0.37 between the actual and decoded power envelopes. These decoders were trained separately and executed simultaneously in real time.This study yielded the first demonstration that it is possible to decode simultaneously the functional activity of multiple independent brain systems. Our comparison of univariate and multivariate decoding strategies, and our analysis of the influence of their decoding parameters, provides benchmarks and guidelines for future research on this topic.

Project description:BackgroundMobility impairment is common in people with multiple sclerosis (PwMS) and there is a need to assess mobility in remote settings. Here, we apply a novel wireless, skin-mounted, and conformal inertial sensor (BioStampRC, MC10 Inc.) to examine gait characteristics of PwMS under controlled conditions. We determine the accuracy and precision of BioStampRC in measuring gait kinematics by comparing to contemporary research-grade measurement devices.MethodsA total of 45 PwMS, who presented with diverse walking impairment (Mild MS = 15, Moderate MS = 15, Severe MS = 15), and 15 healthy control subjects participated in the study. Participants completed a series of clinical walking tests. During the tests participants were instrumented with BioStampRC and MTx (Xsens, Inc.) sensors on their shanks, as well as an activity monitor GT3X (Actigraph, Inc.) on their non-dominant hip. Shank angular velocity was simultaneously measured with the inertial sensors. Step number and temporal gait parameters were calculated from the data recorded by each sensor. Visual inspection and the MTx served as the reference standards for computing the step number and temporal parameters, respectively. Accuracy (error) and precision (variance of error) was assessed based on absolute and relative metrics. Temporal parameters were compared across groups using ANOVA.ResultsMean accuracy±precision for the BioStampRC was 2±2 steps error for step number, 6±9ms error for stride time and 6±7ms error for step time (0.6-2.6% relative error). Swing time had the least accuracy±precision (25±19ms error, 5±4% relative error) among the parameters. GT3X had the least accuracy±precision (8±14% relative error) in step number estimate among the devices. Both MTx and BioStampRC detected significantly distinct gait characteristics between PwMS with different disability levels (p<0.01).ConclusionBioStampRC sensors accurately and precisely measure gait parameters in PwMS across diverse walking impairment levels and detected differences in gait characteristics by disability level in PwMS. This technology has the potential to provide granular monitoring of gait both inside and outside the clinic.

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New alternately colored FRET sensors for simultaneous monitoring of Zn²? in multiple cellular locations.

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New alternately colored FRET sensors for simultaneous monitoring of Zn²⁺ in multiple cellular locations.

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