Abstract
Pairs of membrane-associated molecules exhibiting fluorescence resonance energy transfer (FRET) provide a sensitive technique to measure changes in a cell’s membrane potential. One of the FRET pair binds to one surface of the membrane and the other is a mobile ion that dissolves in the lipid bilayer. The voltage-related signal can be measured as a change in the fluorescence of either the donor or acceptor molecules, but measuring their ratio provides the largest and most noise-free signal. This technology has been used in a variety of ways; three are documented in this chapter: (1) high throughput drug screening, (2) monitoring the activity of many neurons simultaneously during a behavior, and (3) finding synaptic targets of a stimulated neuron. In addition, we provide protocols for using the dyes on both cultured neurons and leech ganglia. We also give an updated description of the mathematical basis for measuring the coherence between electrical and optical signals. Future improvements of this technique include faster and more sensitive dyes that bleach more slowly, and the expression of one of the FRET pair genetically.
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Adkins CE, Pillai GV et al (2001) alpha4beta3delta GABA(A) receptors characterized by fluorescence resonance energy transfer-derived measurements of membrane potential. J Biol Chem 276:38934–38939
Ataka K, Pieribone VA (2002) A genetically targetable fluorescent probe of channel gating with rapid kinetics. Biophys J 82:509–516
Baca SM, Marin-Burgin A, Wagenaar DA, Kristan WB Jr (2008) Widespread inhibition proportional to excitation controls the gain of a leech behavioral circuit. Neuron 57:276–289
Baker BJ, Kosmidis EK et al (2005) Imaging brain activity with voltage- and calcium-sensitive dyes. Cell Mol Neurobiol 25:245–282
Blunck R, Cordero-Morales JF, Cuello LG, Perozo E, Bezanilla F (2006) Detection of the opening of the bundle crossing in KcsA with fluorescence lifetime spectroscopy reveals the existence of two gates for ion conduction. J Gen Physiol 128:569–581
Briggman KL, Kristan WB Jr (2006) Imaging dedicated and multifunctional neural circuits generating distinct behaviors. J Neurosci 26:10925–10933
Briggman KL, Abarbanel HD, Kristan WB Jr (2005) Optical imaging of neuronal populations during decision-making. Science 307:896–901
Bugianesi RM, Augustine PR et al (2006) A cell-sparing electric field stimulation technique for high-throughput screening of voltage-gated ion channels. Assay Drug Dev Technol 4:21–35
Burgstahler R, Koegel H et al (2003) Confocal ratiometric voltage imaging of cultured human keratinocytes reveals layer-specific responses to ATP. Am J Physiol Cell Physiol 284:C944–C952
Cacciatore TW, Brodfuehrer PD et al (1999) Identification of neural circuits by imaging coherent electrical activity with FRET-based dyes. Neuron 23:449–459
Chanda B, Blunck R et al (2005) A hybrid approach to measuring electrical activity in genetically specified neurons. Nat Neurosci 8:1619–1626
Clegg RM (1995) Fluorescence resonance energy transfer. Curr Opin Biotechnol 6:103–110
DiFranco M, Capote J, Quinonez M, Vergara JL (2007) Voltage-dependent dynamic FRET signals from the transverse tubules in mammalian skeletal muscle fibers. J Gen Physiol 130:581–600
Dimitrov D, He Y et al (2007) Engineering and characterization of an enhanced fluorescent protein voltage sensor. PLoS One 2:e440
Dumas D, Stoltz JF (2005) New tool to monitor membrane potential by FRET voltage sensitive dye (FRET-VSD) using spectral and fluorescence lifetime imaging microscopy (FLIM). Interest in cell engineering. Clin Hemorheol Microcirc 33:293–302
Ebner TJ, Chen G (1995) Use of voltage-sensitive dyes and optical recordings in the central nervous system. Prog Neurobiol 46:463–506
Flewelling RF, Hubbell WL (1986) The membrane dipole potential in a total membrane potential model. Applications to hydrophobic ion interactions with membranes. Biophys J 49:541–552
Förster VT (1948) Zwischenmolekulare energiewanderung und fluoreszenz. Ann Phys 6:54–75
Gonzalez JE, Tsien RY (1995) Voltage sensing by fluorescence resonance energy transfer in single cells. Biophys J 69:1272–1280
Gonzalez JE, Tsien RY (1997) Improved indicators of cell membrane potential that use fluorescence resonance energy transfer. Chem Biol 4:269–277
Gonzalez JE, Maher MP (2002) Cellular fluorescent indicators and voltage/ion probe reader (VIPR) tools for ion channel and receptor drug discovery. Receptors Channels 8:283–295
Grinvald A, Ross WN, Farber I (1981) Simultaneous optical measurements of electrical activity from multiple sites on processes of cultured neurons. Proc Natl Acad Sci U S A 78:3245–3249
Guerrero G, Siegel MS, Roska B, Loots E, Isacoff EY (2002) Tuning FlaSh: redesign of the dynamics, voltage range, and color of the genetically encoded optical sensor of membrane potential. Biophys J 83:3607–3618
Hannan EJ (1970) Multiple time series. Wiley, New York, NY
Huang CJ, Harootunian A et al (2006) Characterization of voltage-gated sodium-channel blockers by electrical stimulation and fluorescence detection of membrane potential. Nat Biotechnol 24:439–446
Jarvis MR, Mitra PP (2001) Sampling properties of the spectrum and coherency of sequences of action potentials. Neural Comput 13:717–749
Kleinfeld D (2008) Application of spectral methods to representative data sets in electrophysiology and functional neuroimaging. In: Syllabus for Society for Neuroscience Short Course III on “Neural Signal Processing: Quantitative Analysis of Neural Activity”, Society for Neuroscience, vol 3, p. 21–34
Knopfel T, Tomita K, Shimazaki R, Sakai R (2003) Optical recordings of membrane potential using genetically targeted voltage-sensitive fluorescent proteins. Methods 30:42–48
Kristan WB Jr, Calabrese RL, Friesen WO (2005) Neuronal control of leech behavior. Prog Neurobiol 76:279–327
Kuznetsov A, Bindokas VP, Marks JD, Philipson LH (2005) FRET-based voltage probes for confocal imaging: membrane potential oscillations throughout pancreatic islets. Am J Physiol Cell Physiol 289:C224–C229
Lakowicz JR (2006) Principles of fluorescence spectroscopy. Springer, New York, NY
Maher MP, Wu NT, Ao H (2007) pH-insensitive FRET voltage dyes. J Biomol Screen 12:656–667
Marin-Burgin A, Eisenhart FJ, Baca SM, Kristan WB Jr, French KA (2005) Sequential development of electrical and chemical synaptic connections generates a specific behavioral circuit in the leech. J Neurosci 25:2478–2489
Momose-Sato Y, Sato K et al (1999) Evaluation of voltage-sensitive dyes for long-term recording of neural activity in the hippocampus. J Membr Biol 172:145–157
Mutoh H, Perron A et al (2009) Spectrally-resolved response properties of the three most advanced FRET based fluorescent protein voltage probes. PLoS One 4:e4555
Palozza P, Krinsky NI (1992) Astaxanthin and canthaxanthin are potent antioxidants in a membrane model. Arch Biochem Biophys 297:291–295
Percival DB, Walden AT (1993) Spectral analysis for physical applications: multitaper and conventional univariate techniques. Cambridge University Press, New York, NY
Piston DW, Kremers GJ (2007) Fluorescent protein FRET: the good, the bad and the ugly. Trends Biochem Sci 32:407–414
Rink TJ, Montecucco C, Hesketh TR, Tsien RY (1980) Lymphocyte membrane potential assessed with fluorescent probes. Biochim Biophys Acta 595:15–30
Sacconi L, Dombeck DA, Webb WW (2006) Overcoming photodamage in second-harmonic generation microscopy: real-time optical recording of neuronal action potentials. Proc Natl Acad Sci U S A 103:3124–3129
Sakai R, Repunte-Canonigo V, Raj CD, Knopfel T (2001) Design and characterization of a DNA-encoded, voltage-sensitive fluorescent protein. Eur J Neurosci 13:2314–2318
Selvin PR (2000) The renaissance of fluorescence resonance energy transfer. Nat Struct Biol 7:730–734
Siegel MS, Isacoff EY (1997) A genetically encoded optical probe of membrane voltage. Neuron 19:735–741
Sjulson L, Miesenbock G (2008) Rational optimization and imaging in vivo of a genetically encoded optical voltage reporter. J Neurosci 28:5582–5593
Solly K, Cassaday J et al (2008) Miniaturization and HTS of a FRET-based membrane potential assay for K(ir) channel inhibitors. Assay Drug Dev Technol 6:225–234
Stryer L (1978) Fluorescence energy transfer as a spectroscopic ruler. Annu Rev Biochem 47:819–846
Taylor AL, Cottrell GW, Kleinfeld D, Kristan WB Jr (2003) Imaging reveals synaptic targets of a swim-terminating neuron in the leech CNS. J Neurosci 23:11402–11410
Thomson DJ (1982) Spectrum estimation and harmonic-analysis. Proc IEEE 70:1055–1096
Thomson DJ, Chave AD (1991) Jackknifed error estimates for spectra, coherences, and transfer functions. In: Haykin S (ed) Advances in spectrum analysis and array processing. Upper Saddle River, NJ, Prentice Hall
Tsau Y, Wu JY et al (1994) Distributed aspects of the response to siphon touch in Aplysia: spread of stimulus information and cross-correlation analysis. J Neurosci 14:4167–4184
Tsien RY, Gonzalez JE (2002) Detection of transmembrane potentials by optical methods. US patent # 6,342,379 B1
Tsutsui H, Karasawa S, Okamura Y, Miyawaki A (2008) Improving membrane voltage measurements using FRET with new fluorescent proteins. Nat Methods 5:683–685
Weinglass AB, Swensen AM et al (2008) A high-capacity membrane potential FRET-based assay for the sodium-coupled glucose co-transporter SGLT1. Assay Drug Dev Technol 6:255–262
Wu P, Brand L (1994) Resonance energy transfer: methods and applications. Anal Biochem 218:1–13
Wu JY, Cohen LB, Falk CX (1994a) Neuronal activity during different behaviors in Aplysia: a distributed organization? Science 263:820–823
Wu JY, Tsau Y et al (1994b) Consistency in nervous systems: trial-to-trial and animal-to-animal variations in the responses to repeated applications of a sensory stimulus in Aplysia. J Neurosci 14:1366–1384
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Appendix
Appendix
We consider the statistical analysis for determining follower cells (Cacciatore et al. 1999; Taylor et al. 2003; Kleinfeld 2008). The significance of the spectral coherence between the response of any cell, labeled “i”, and the driven cell is used to determine if two cells are functionally related and thus are a candidate for a synaptically driven pair. The coherence is a complex function, denoted Ci(f), that it is calculated over the time period of the stimulus, denoted T. We further denote the time series of the optical signals as Vi(t) and the electrical reference drive signal as U(t). The mean value is removed to form:
with a similar expression for δU(t). The Fourier transform of δVi(t) with respect to the k-th Slepian window (Thomson 1982; Percival and Walden 1993), denoted w(k)(t), is:
with a similar expression for δŨ(k) (f). The use of multiple tapers allow averaging over a bandwidth that is set by the number of tapers, K, with the half-bandwidth at half-maximal response given by Δf = (1/T)(K + 1)/2. Our interest lies in the values of Ci(f) for f = fDrive and the confidence limits for these values. We chose the bandwidth so that the estimate of |Ci(fDrive)| is kept separate from that of the harmonic |Ci(2fDrive)|. The choice Δf = 0.4 fDrive works well, so that for fDrive = 1 Hz and T = 9 s the integer part of 2 • 0.4 • 1 Hz • 9 s—1 yields K = 6 tapers (Fig. 6.3). The spectral coherence between the optical signal and the reference is give by:
To calculate the standard errors for the coherence estimates, we use the jackknife (Thomson and Chave 1991) and compute delete-one averages of coherence, denoted Ci (n) (f), where n is the index of the deleted taper:
Estimating the standard error of the magnitude of Ci(f) requires an extra step since |Ci(f)| is defined on the interval [0, 1] while Gaussian variables exist on (−∞, ∞). Thus the delete-one estimates, |Ci (n)(f)|, were replaced with the transformed values:
The mean of the transformed variable is:
and the standard error of the transformed variable is:
The 95 % confidence interval for the coherence is thus:
We now turn to an estimate of the standard deviation of the phase of C(f). Conceptually, the idea is to compute the variation in the relative directions of the delete-one unit vectors Ci(f)/|Ci(f)|. The standard error is computed as:
We graph the magnitude and phase of Ci(fDrive) for all neurons, along with the confidence interval, on a polar plot (Fig. 6.4e). Finally, we consider whether the coherence of a given cell at fDrive is significantly greater than zero, that is, larger than one would expect to occur by chance from a signal with no coherence. We compared the estimate for each value of |Ci(fDrive)| to the null distribution for the magnitude of the coherence, which exceeds
only in α of the trials (Hannan 1970; Jarvis and Mitra 2001). We use α = 0.001 to avoid false-positives. We also calculate the multiple comparisons of α level for each trial, given by αmulti = 1 − (1 − α)N, where N is the number of cells in the functional image, and verified that it did not exceed αmulti = 0.05 on any trial.
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Briggman, K.L., Kristan, W.B., González, J.E., Kleinfeld, D., Tsien, R.Y. (2015). Monitoring Integrated Activity of Individual Neurons Using FRET-Based Voltage-Sensitive Dyes. In: Canepari, M., Zecevic, D., Bernus, O. (eds) Membrane Potential Imaging in the Nervous System and Heart. Advances in Experimental Medicine and Biology, vol 859. Springer, Cham. https://doi.org/10.1007/978-3-319-17641-3_6
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