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. 2020 Jun 16;23(7):101276. doi: 10.1016/j.isci.2020.101276

Off-Peak 594-nm Light Surpasses On-Peak 532-nm Light in Silencing Distant ArchT-Expressing Neurons In Vivo

Rieko Setsuie 1,2,3,4,∗, Keita Tamura 1, Kentaro Miyamoto 1, Takamitsu Watanabe 3, Masaki Takeda 1,2, Yasushi Miyashita 1,2,3
PMCID: PMC7326739  PMID: 32599561

Summary

For large brain volume manipulations using optogenetics, both effective opsin excitation and efficient light delivery with minimal light absorption are required to minimize the illuminating light intensity and concomitant off-target effects. ArchT, a widely used potent inhibitory opsin, is commonly activated by 532-nm light, which lies on its in vitro excitation peak. However, 532-nm light also lies on a peak range of the hemoglobin absorption spectrum. Therefore, we predicted that 594-nm light is superior in suppressing distant ArchT-expressing neurons, which is slightly off the ArchT-excitation-plateau and largely off the peak of the hemoglobin absorption spectrum. We quantitatively tested this prediction by the electrophysiological recording of the rat cortex in vivo. At illumination distances greater than 500 μm, 594-nm light was more effective than 532-nm light. Its superiority increased with distance. These results validate our prediction and highlight the significance of excitation-absorption trade-off in selecting illumination wavelength for optogenetics in vivo.

Subject Areas: Optical Imaging, Neuroscience, Technical Aspects of Cell Biology

Graphical Abstract

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Highlights

  • •

    Wavelength-dependency of optogenetic neuronal control was directly measured in vivo

  • •

    Off-peak light silence 1-mm-distant ArchT-neuron twice more than on-peak light in vivo

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    Superiority of off-peak light at distance arose from its less absorption of light

  • •

    Simulation of light propagation supported unexpectedly large effect of hemoglobin

Optical Imaging; Neuroscience; Technical Aspects of Cell Biology

Introduction

Optogenetics has become an essential tool for clarifying the causal roles of genetically defined neuronal circuits in behavior (Tye and Deisseroth, 2012). The spatial volume in which a single optical fiber can manipulate neuronal activity has been reported to be ∼1 mm3 in rodents, with moderate light intensity (Gradinaru et al., 2009; Root et al., 2014). In macaque monkeys, however, considerably large brain volumes have been targeted with pharmacological suppression and lesion formation to induce behavioral changes (Goldman-Rakic, 1996; Squire et al., 2004). Therefore, to assess the brain functions by inducing comparable behavioral impacts in macaque monkeys using optogenetics, illumination of brain volume larger than 1 mm3 would be necessary in most cases.

To efficiently manipulate a large brain volume with optogenetics, the opsin-expressing neurons, distant from the light source, need to be illuminated with a sufficient light intensity at an effective wavelength. However, high-power illumination could induce various off-target effects, primarily because of the heat generated by tissue-absorbed-light in the vicinity of the light source. These off-target effects include alteration of neuronal activity (Owen et al., 2019), vasodilation-related change in blood flow (Rungta et al., 2017), and heat-induced brain damage (Galvan et al., 2017). Therefore, technical modifications are required to enable neuronal activity modulation in a large volume with low-intensity illumination. One feasible strategy for circumventing this difficulty for in vivo opsin stimulation is to choose a light wavelength that satisfies the following two requirements: (1) efficient in vivo propagation in the brain tissue with reduced off-target effects and (2) effective excitation of the target opsin. To do so, the characteristics of each factor and their trade-off should be considered.

With regard to (1), the efficiency of light propagation along a distance is determined by scattering and absorption in the brain (Vo-Dinh T, 2003). In the adult brain with mature myelinated fibers, scattering contributes significantly. Light with longer wavelengths scatters less inside tissue and can propagate over longer distances. Various in vitro and ex vivo measurements and simulations have confirmed this property (Aravanis et al., 2007; Gysbrechts et al., 2016; Huber et al., 2008; Yaroslavsky, 2002; Yizhar et al., 2011). Although it is not taken into consideration in these studies, the chromophores are responsible for light absorption. The absorbed light is converted to heat, which raises the tissue temperature (Arias-Gil et al., 2016; Shin et al., 2016; Stujenske et al., 2015), leading to off-target effects in the brain (Owen et al., 2019; Rungta et al., 2017). Various chromophore-containing molecules had been identified that contribute to the light absorption in the brain tissue. These include hemoglobin (Hb), lipofuscin, cytochrome, and eumelanin. Among them, Hb and lipofuscin are two of the major chromophore-containing molecules that crucially affect light absorption in the brain (Johansson, 2010). Lipofuscin is a complex composed of highly oxidized macromolecules. It cannot be degraded and accumulates mainly in the lysosome (Moreno-Garcia et al., 2018). Its light absorption peaks at around 300 nm and decreases drastically beyond 500 nm (Johansson, 2010). Around 60% of Hb is oxygenated in vivo and light absorption peaks at 415, 540, and 575 nm for oxy-Hb and at 435 and 555 nm for deoxy-Hb. At wavelengths longer than 575 nm, there is a drastic decrease in light absorption by oxy-Hb (Eggert and Blazek, 1987; Robles et al., 2010) (Figure 1A). The extent to which absorption contributes to the propagation of light in the brain, as compared with scattering, under a well-maintained blood flow condition, remains unclear (Aravanis et al., 2007; Azimipour et al., 2015; Johansson, 2010). However, recent in vivo studies (Acker et al., 2016; Azimipour et al., 2015) suggested that even the best reported previous in vivo measurement (Johansson, 2010) likely underestimated the absorption by Hb. Therefore, within 450–600 nm range, where light absorption by lipofuscin and Hb fluctuates drastically, the slightest shift in the illumination wavelength of light may significantly magnify the extent of light propagation, with reduced off-target effect (Figure 1A).

Figure 1.

Figure 1

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Wavelength-Dependent ArchT Excitation and Light Absorption by Hemoglobin, Illumination Properties of the Side-Emitting Optical Fiber, and ArchT-GFP Expression in Rat Cortex

(A) The 532 nm light and 594 nm light relative to the excitation spectra of ArchT in vitro measured by different groups (black) (Chuong et al., 2014; Han et al., 2011; Mattis et al., 2011), and the absorption spectrum of deoxy-hemoglobin (blue) and oxy-hemoglobin (red), which were modified based on the descriptions from (https://omlc.org/spectra/hemoglobin) (Schmitt, 1986; Takatani and Graham, 1979; Zijlstra et al., 1994).

(B) (a) Schematic image of the light illumination area (magenta) emitted from the tip of a blunt-end optical fiber (left) or the tapered surface of a side-emitting optical fiber (right). The electrode is placed in parallel with the optical fiber and the tip of the electrode is located within the light illumination area of the side-emitting optical fiber. EL, microelectrode; OF, optical fiber. (b) Representative image of light propagation by blunt-end and side-emitting optical fibers in fluorescein solution. The dotted lines in the images indicate the position of each fiber’s tip (i.e., axial and lateral zero). Bright-field images of light-off condition are also shown. The border of the optical fibers in the bright-field image is visualized by tracing. (c and d) The mean illuminance of 473 nm light emitted from blunt-end (n = 6; gray) and side-emitting (n = 6; red) optical fibers in the lateral and the axial directions with the maximum illuminance at the fiber-tip normalized to 100. Shades denote the standard deviation (SD).

(C) Coronal image of a rat brain injected with the AAV vector (AAV5-CaMKIIa-ArchT-GFP) in the somatosensory area. Immunofluorescent staining for GFP (green), and a neuron marker NeuN (magenta). The scale bar indicates 1.0 mm.

(D) Confocal images of the ArchT expression area in (C). The white arrows indicate ArchT-GFP-expressing neurons. The level of GFP expression varies among the neurons. Most NeuN-positive neurons (magenta) are positive for ArchT-GFP (green). The scale bar indicates 50 μm.

See also Figure S1.

With regard to (2), we focused on Archaerhodopsin-T (ArchT), a light-driven proton pump, which is widely used to induce potent photoinhibition not only in rodents (Asok et al., 2018; Maier et al., 2015; Stefanik et al., 2013; Trouche et al., 2016; Tsunematsu et al., 2014; Wenker et al., 2017; Xu et al., 2016) but also in non-human primates (Afraz et al., 2015; Cavanaugh et al., 2012; Ohayon et al., 2013). In vitro spectral analyses of ArchT demonstrated its most effective excitation peak to be between 530 and 570 nm (Figure 1A) (Chuong et al., 2014; Han et al., 2011; Mattis et al., 2011). Therefore, most studies using ArchT, including the aforementioned studies, used 532 (Afraz et al., 2015; Asok et al., 2018; Cavanaugh et al., 2012; Maier et al., 2015; Ohayon et al., 2013; Wenker et al., 2017) or 561 nm light (Stefanik et al., 2013; Trouche et al., 2016) (Figure 1A). Unfortunately, the excitation spectrum of ArchT, including that of both the 532 and 561 nm light, largely overlaps with the absorption peak of Hb. However, the relatively wide spectrum of ArchT excitation suggests that ArchT can still be excited to over 80% of its excitation peak, i.e., to about 600 nm, which could avoid large absorbance by Hb (Figure 1A).

Taking factor (1) and (2) into consideration, we predicted that a wavelength of 594 nm would be more effective for ArchT-mediated large-volume optogenetic manipulation than the most conventionally used 532 nm light (Figure 1A). To directly test this prediction, it is essential to quantitatively measure the neural responses to light illuminated from different distances in the cortex where blood-related light-absorption remains stable. The optical responsiveness of individual neurons can vary depending on ArchT expression level, etc. Therefore, to obtain a reliable relationship between neuronal responses and light parameters (wavelength, power, and illumination distances), different wavelengths and illumination distances need to be measured in each recorded neuron. Such in vivo measurements have not been reported for any of the opsins to date.

In the present study, we quantitatively evaluated the effect of the light on the activity of ArchT-expressing neurons in the rat cortex in vivo. To validate our prediction, we selected a 594 nm laser as the light source, with a narrow spectral peak than light-emitting diodes (LEDs), and compared its results with those of a 532 nm laser (Figure 1A). We designed an adaptive electro-fiberoptic array, in which multiple side-emitting optical fibers were placed at different distances around one electrode, which enabled us to quantify the differential effects of light wavelengths, illuminated from multiple distances in the rat cortex.

Results

Fabrication of the Electro-Fiberoptic Array

We designed our experiment so that the recorded neurons receive light from the light sources through the cortical areas with similar optical properties at multiple intracortical distances. White matter shows substantially higher light scattering than does gray matter. Thus, we focused on the light attenuation in the horizontal direction (i.e., parallel to the pial surface) but not in the vertical direction in the cortex, which minimized the confounding effects of white matter. To implement this design, we fabricated an electro-fiberoptic array that arranged a microelectrode and four optical fibers in parallel at defined distances. In this array, the electrode tip is horizontally spaced from the tip of the four optical fibers (see below, Figure 3A, and Transparent Methods “Fabrication of the electro-fiberoptic array” for details).

Figure 3.

Figure 3

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Comparison of the Suppression Efficacy between 532 nm Light and 594 nm Light at Four Different Distances

(A) The electro-fiberoptic array is configured so that the side-emitting optical fibers are placed at a distance of 250, 500, 700, and 1,000 μm from the microelectrode. The same single/multi-unit was maintained throughout a single experiment to test the responses to different distances and wavelengths (see Transparent Methods “Electrophysiological recording and optogenetic stimulation” for details on how we defined the unit online). The height of the magenta line depicts the light power (see Transparent Methods “Electrophysiological recording and optogenetic stimulation” for how we determined the light power for each distance).

(B) Photostimulation by 532 and 594 nm light. Left, raster plots and PSTHs of a representative unit by 532 or 594 nm photostimulation at four different distances. Stimulated and non-stimulated trials were interleaved. The shaded area demarcates the stimulation period with 1-s continuous light illumination. Bin size is 100 ms. Right, the graphs represent the suppression efficacy for 532 and 594 nm light at different distances.

(C) Population suppression efficacy of 532 and 594 nm light at different distances. The black lines indicate individual units and the red lines indicate the mean of the units [two-way ANOVA: distance, F(3,82) = 4.42, p = 0.0062; wavelength, F(1, 46) = 40.50, p < 0.0001; distance ˣ wavelength, F(3, 82) = 6.89, p = 0.0003; followed by Bonferroni's multiple comparisons, p = 0.56, p = 0.0007, p < 0.0001, p < 0.0001 for 250 μm (n = 16; 4 single- and 12 multi-units), 500 μm (n = 21; 5 single- and 16 multi-units), 700 μm (n = 15; 3 single- and 12 multi-units), and 1,000 μm (n = 16; 4 single- and 12 multi-units) respectively, ∗∗p < 0.005, ∗∗∗∗p < 0.0001].

(D) Relative suppression efficacy between 532 and 594 nm light at different distances. The relative suppression efficacy for each unit is the ratio of the suppression efficacy between 532 and 594 nm light at each distance. The circles indicate individual units, and the red lines indicate the average [one-way ANOVA; distance, F(3, 63) = 11.59, p < 0.0001; followed by Tukey-Kramer multiple comparisons, ∗p < 0.05, ∗∗∗p < 0.0005, and ∗∗∗∗p < 0.0001].

See also Figures S2 and S3.

With blunt-end optical fibers, which are most commonly used in optogenetic experiments, the area of light emission is restricted to the facet of the optical fibers (see Transparent Methods “Analysis of the light emission properties of the side-emitting optical fibers” for the details of analysis). Unless several orders of magnitude higher input was applied, blunt-end optical fibers could not illuminate areas where electrode tip of our electro-fiberoptic array was placed (Figure 1Ba). Thus, we employed side-emitting optical fibers, which substantially enlarge the area of light illumination in the axial and lateral directions by extending the light-emitting surface toward the basal part of the optical fiber (Figure 1B) (Acker et al., 2016; Pisanello et al., 2017).

A representative image of light emission shows that the side-emitting optical fiber emitted light from its lateral surface along the axial direction of the optical fiber over 1.5 mm from the tip, with an increased angle of light emission (Figure 1Bb). The analysis of spatial illumination pattern demonstrated that the side-emitting optical fibers illuminated a significantly wider area compared with the conventional blunt-end optical fibers, in the axial and lateral direction (Figures 1Bc,d) (t test with Bonferroni corrections, n = 6 optical fibers, p < 0.05). This quantitative estimation is consistent with that of the previous reports (Acker et al., 2016; Pisanello et al., 2017).

Expression of ArchT-GFP in the Rat Cortex

We injected an adeno-associated virus vector encoding ArchT-GFP under the CaMKIIα promoter into the somatosensory area of rat cortex (Tsubota et al., 2015). Immunofluorescent double staining for GFP and a neuron-specific marker NeuN confirmed the selective expression with approximately 70% of ArchT-GFP in neurons (Figures 1C, D, and S1; see Transparent Methods “Immunofluorescent analysis of ArchT-GFP expression in the rat cortex” for details). To record unit activities during photostimulation, the electro-fiberoptic array was inserted in the cortical region, where ArchT-GFP expression was observed under a fluorescent stereomicroscope.

Relationship between Light Power and Suppression Efficacy of ArchT-Expressing Neurons In Vivo

The effects of photostimulation by two wavelengths of light need to be compared within the dynamic range of neuronal activity where the neuronal activity follows the modulation of the light power without saturation or insufficiency. Thus, we first explored such a dynamic range at a fixed distance (500 μm) for both wavelengths (Figure 2A). The activity of the same single/multi-unit was continuously recorded throughout a session in which we tested different light power. Suppression efficacy (supEff) is defined as [1 – (mean firing rate 1-s during the photostimulation)/(mean firing rate 1-s before the photostimulation)]. If photostimulation induces no (or complete) suppression of the unit firing, the suppression efficacy value is 0 (or 1).

Figure 2.

Figure 2

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Relationships between the Suppression Efficacy of ArchT-Expressing Neurons and the Stimulation Light Power for 594 and 532 nm Light

(A) One microelectrode and one side-emitting optical fiber are placed at the distance of 500 μm from each other. The same single/multi-units were held throughout a single experiment to test the responses to different light powers emitted in a random order (single- and multi-units were recorded online with a time-voltage window discriminator; see Transparent Methods “Electrophysiological recording and optogenetic stimulation” for details). The height of the magenta line depicts the light power.

(B) Raster plots and peri-stimulus time histograms (PSTH) of a representative multi-unit tested for 594 nm light stimulation at eight different powers. The shaded area demarcates the stimulation period with 1-s continuous light illumination. The bin size is 100 ms.

(C) The relationship between light power and neuronal suppression efficacy (power-suppression curve) for the representative unit in (B). Suppression efficacy is defined as [1 – (mean firing rate 1-s during the photostimulation)/(mean firing rate 1-s before the photostimulation)]. If photostimulation induces no (or complete) suppression of the unit firing, the suppression efficacy value is 0 (or 1). The magenta trace represents a fitted curve estimated by logistic regression for the data from the unit (βsupEffmax = 1, βinflection = 3.23, βsteepness = 2.87, R2 = 0.98). (see Transparent Methods “Optogenetic data analysis” for an explanation of logistic regression coefficients).

(D) Power-suppression curves of different units for 594 nm photostimulation (n = 9 units; 3 single units and 6 multi-units). Light power is normalized to the half-maximum effective light power (EP50) for each unit. The magenta trace represents the power-suppression curve estimated by logistic regression for the data from all units (βsupEffmax = 0.98, βinflection = 0.91, βsteepness = 1.73, R2 = 0.94).

(E) Power-suppression curves of different units for 532 nm photostimulation (n = 6 units; 2 single units and 4 multi-units). The green trace represents the power-suppression curve estimated by logistic regression for the data from all units (βsupEffmax = 0.95, βinflection = 0.96, βsteepness = 2.00, R2 = 0.94).

(F) The slope of normalized EP50 from the power-suppression curves of each unit for 532 (n = 6) and 594 nm (n = 9). Values were obtained from individually fitted curves. Each dot represents the data from the fitted curve to each unit, and lines represent mean.

(G) Suppression efficacy of upper limit within the dynamic range of the power-suppression curves of each unit for 532 (n = 6) and 594 nm (n = 9). The values were obtained from individually fitted curves. Each dot represents the unit, and each line represents mean.

In a representative unit (Figure 2B), the firing rate decreased as the light power increased from 0 to 3.4 mW and remained nearly zero at 4.1 and 4.8 mW. The change in the activity of this unit as a function of the light power was fitted by logistic regression (the power-suppression curve; Figure 2C) (see Transparent Methods “Optogenetic data analysis” for details). To compare the power-suppression curves between two wavelength lights (6 and 9 units for 532 and 594 nm light, respectively), the mean βsupEffmax, βsteepness, and the statistical values for the goodness of fit to the logistic regression curve (R2) were determined; for 594 nm light, βsupEffmax 0.99 ± 0.0096 (mean ± SEM), βsteepness 1.87 ± 0.21, R2 0.96 ± 0.0100, and for 532 nm light, βsupEffmax 0.95 ± 0.0220, βsteepness 2.27 ± 0.37, R2 0.97 ± 0.0085. The comparison of βsupEffmax, βsteepness, and R2 values between 532 and 594 nm light did not show significant difference [βsupEffmax: t(13) = 1.52, p = 0.15, βsteepness: t(13) = 0.98, p = 0.34, R2: t(13) = 0.32, p = 0.75]. Since the βinflection values naturally differ between the individual unit (2.58 ± 1.00 for 594 nm and 6.43 ± 1.86 for 532 nm), we normalized the different light powers in each unit by setting the light power to achieve the half-maximum-suppression (EP50) to 1 and estimated the power-suppression curves of the population data (magenta trace for 594 nm light, R2 = 0.94, Figure 2D; green trace for 532 nm light, R2 = 0.94, Figure 2E). The comparison of the slope of EP50 between 532 and 594 nm light also showed no significant difference [Figure 2F; t(13) = 1.52, p = 0.15 for supEff and t(13) = 0.86, p = 0.41 for the slope; two-tailed unpaired t test]. These results indicated that 532 and 594 nm light elicit indistinguishable power-suppression curves.

Then, we defined the effective dynamic range of supEff from 0 (lower limit) to the values of the point at which the slope of the power-suppression curve decreased to less than 0.1 (upper limit). The supEff values at the point were 0.81 ± 0.03 for the 532 nm light and 0.81 ± 0.02 for the 594 nm light [t(13) = 0.015, p = 0.99, two-tailed unpaired t test] (Figure 2G). These results ensured that we could reliably compare supEff by two light wavelengths with the same light power within this range of the suppression efficacy.

Suppression of ArchT-Expressing Neurons at Different Light Transmission Distances

We next compared the supEff between 532 and 594 nm light at four different transmission distances in vivo. Since the photosensitivity of individual neurons varies (indicated by the SEM for βinflection values as shown above; Mahn et al., 2018), the effect of different photoillumination conditions should be evaluated on the same neuron. To directly examine the effect of light transmission distance on the suppression of the same ArchT-expressing neurons, we placed one microelectrode and four side-emitting optical fibers at the horizontal distances of 250, 500, 700, and 1,000 μm in our electro-fiberoptic array (Figure 3A). This arrangement enabled us to measure the response of the same unit while changing the combination of distance from the light source and the illumination wavelengths (Figure 3A).

The units were recorded only when the neuronal activity of the unit could be completely suppressed by a brief preliminary qualitative testing. To compare the supEff between the two wavelengths, the light power was determined for each distance so that the firing of the unit is suppressed within its dynamic range. In a representative unit, we used the light power of 0.46, 1.3, 3.0, and 7.6 mW for the optical fibers at the distance of 250, 500, 700, and 1,000 μm, for both 532 and 594 nm light (Figure 3B). No-stimulation trials were interleaved between stimulation trials to ensure the stability of the firing rate and recovery from the previous stimuli. In this unit, the results of two recording blocks were averaged into one peri-stimulus time histograms (PSTHs) (Figure 3B). This representative unit showed stronger suppression with 594 nm light than with 532 nm light as the distance increased (Figure 3B, right graph). Population data showed a consistent result (Figure 3C) [two-way ANOVA; main effect of distance, F(3, 82) = 4.42, p = 0.0062; main effect of wavelength, F(1, 46) = 40.50, p < 0.0001; interaction between distance and wavelength, F(3, 82) = 6.89, p = 0.0003]. At 250 μm, the suppression efficacy did not show a difference between the two wavelengths. However, at 500 μm, the 594 nm light began to show a larger suppression efficacy than did the 532 nm light, and the difference is also observed at 700 and 1,000 μm [Bonferroni's multiple comparisons after two-way ANOVA, p = 0.56 (n = 16, 250 μm), p = 0.0007 (n = 21, 500 μm), p < 0.0001 (n = 15, 700 μm), p < 0.0001 (n = 16, 1,000 μm)].

To compare the distance-dependent changes of the suppression efficacy between the two wavelengths, we plotted the relative suppression efficacy between two wavelengths against the distance (Figure 3D). The relative suppression efficacy showed a significant decrease with distance [F(3, 63) = 11.59, p < 0.0001, one-way ANOVA]. The relative suppression efficacy was significantly smaller at 500, 700, and 1,000 μm compared with that at 250 μm (p = 0.044, p = 0.0006, p < 0.0001 respectively, Tukey-Kramer post hoc test after one-way ANOVA) and was significantly smaller at 1,000 μm than that at 500 μm (p = 0.011). The relative suppression efficacy was 0.91 ± 0.05 (mean ± SEM) at 250 μm but decreased to 0.53 ± 0.05 at 1,000 μm, indicating that the 594 nm light is about twice more effective than the 532 nm light at 1,000 μm (Figure 3D).

Although the quantitative analyses can be best performed at the light intensity within the dynamic range, as shown above, the analysis of data including those outside the dynamic range replicated the results of Figures 3C and 3D (Figures S2A and S2B). We also examined single-unit data sorted offline (Figures S3A–S3E; see Transparent Methods “Sorting and analysis of single-unit” for the offline-sorting procedure). The results with single-unit data (Figures S3F and S3G) replicated those with single/multi-unit data (Figures 3C and 3D).

To confirm that light stimulus itself does not affect the activity of non-ArchT-expressing neurons, we conducted a separate experiment and recorded neuronal activities at the cortex of naive rats. The results confirmed that, within the light intensity range we used in the experiments with the ArchT-expressing rats, neither 532 nor 594 nm light changed the neuronal activity of non-ArchT-expressing neurons [two-way ANOVA: main effect of light, F(1, 7) = 2.47, P = 0.16; main effect of wavelength, F(1, 7) = 0.188, p = 0.68; interaction between light and wavelength, F(1, 7) = 1.31, p = 0.29] (Figure S2C).

Next, we compared the relative input light power to equally suppress the neural firing of ArchT-expressing neurons among different wavelengths and distances by estimating the relative EP50 values for each condition (Figure 4A; see Transparent Methods “Optogenetic data analysis” for details). The required relative input light power [Exp-data (firing)] increased with distance; this was significantly different between the two wavelengths, and the difference increased with distance [two-way ANOVA; main effect of distance, F (3, 48) = 49.61, p < 0.0001; main effect of wavelengths, F (1, 16) = 5.08, p = 0039; interaction between distance and wavelength, F (3, 48) = 5.04, p = 0.0041]. At a distance of 1,000 μm, the estimated EP50 of 594 nm light was significantly smaller than that of 532 nm light [p = 0.0003 (n = 9), Bonferroni's multiple comparisons after two-way ANOVA] (Figure 4A). When the distance of illumination expanded 4-fold (from 250 to 1,000 μm), the estimated light power needed to suppress the same unit was increased 21.0-fold with 532 nm light, but the increase remained at only 11.4-fold with 594 nm light. Convergingly, photostimulation by the 594 nm light is estimated to require only 54% of the light power, compared with photostimulation by the 532 nm light at the distance of 1,000 μm.

Figure 4.

Figure 4

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Input Light Power Required for Equivalent Neural Suppression between 532 and 594 nm In Vivo and Comparison between the Simulation Results and the Experimental Data

(A) Estimated relative input light power (EP50) required to induce half-maximum suppression for 532 nm light [Exp-data 532 (firing), green] and 594 nm light [Exp-data 594 (firing), magenta], at the distances of 250, 500, 700, and 1,000 μm. The estimated EP50 was normalized to the value of 532 nm at 250 μm. The circles indicate the mean of 9 units. The error bar denotes the SEM [two-way ANOVA: distance, F(3, 48) = 49.61, p < 0.0001; wavelength, F(1, 16) = 5.08, p = 0.039; distance ˣ wavelength, F(3, 48) = 5.04, p = 0.0041; ∗∗∗p < 0.0005, post hoc Bonferroni's multiple comparisons].

(B) Computationally estimated input light power required to deliver equal light power at the distance of 250, 500, 700, and 1,000 μm using a Monte Carlo simulation (see Figure S4; Transparent Methods “Monte Carlo simulation” for details). The model in the Monte Carlo simulation is defined by a set of three input parameters: scattering coefficients [μs(λ)], absorption coefficients [μa(λ)], and anisotropy factors [μaf(λ)]. In the first simulation model [J-model: (5), (6)], we applied a set of input parameters obtained in vivo by Johansson (2010). The Johansson's μa(λ) are known to lack the contribution from absorption by oxy-hemoglobin and, partially, by deoxy-hemoglobin (Johansson, 2010). In the second simulation model [woJ-model: (7), (8)), μa(λ] were set to 0, along with the same μs(λ) and μaf(λ) as those in J-model. With woJ-model, we simulated the light propagation by considering only the spatially non-homogeneous light scattering in the brain tissue. In the third simulation model [Exp-model: (3), (4)], μa(λ) was estimated to properly capture the trajectory of light propagation patterns of the Exp-data (light) (see Transparent Methods “Monte Carlo simulation” for details) along with the same μs(λ) and μaf(λ) as those in J-model [note that the plots for the Exp-model almost overlapped with those for the Exp-data (light) at both 532 and 594 nm light, suggesting that the estimation of Expμa(λ) values is valid]. The estimated input light power was normalized to the value of 532 nm at 250 μm in each model. The experimental data [Exp-data (light): (1), (2)] was also plotted, whose values were linearly converted from the [Exp-data (firing)] values in (A) by using the ArchT excitation efficiency values at 532 and 594 nm light that were obtained from ArchT excitation spectrum in Figure 1A (see Transparent Methods “Optogenetic data analysis” for details). The inset figure in (B) represents the differences in relative input light power (delta values in relative input light power) between 532 and 594 nm light at each distance within the dataset.

See also Figure S4.

Finally, we simulated the light propagation from a side-emitting optical fiber in the brain and compared the simulated results with the experimental data. Specifically, we conducted a random-walk Monte Carlo simulation of photon packets (see Transparent Methods “Monte Carlo simulation” for details) (Pisanello et al., 2017; Sileo et al., 2018; Stujenske et al., 2015) to estimate the light propagation in the brain. The simulation requires a set of three input parameters: scattering coefficients [μs(λ)] (λ = wavelength), absorption coefficients [μa(λ)], and anisotropy factors [μaf(λ)]. We simulated light propagation by using the following three sets of parameters. In the first simulation (J-model), we applied a set of parameters obtained experimentally in vivo by Johansson (2010). The Johansson's absorption coefficients are known to lack the contribution of absorption by oxy-Hb and to represent the absorption by a partial amount of deoxy-Hb (Johansson, 2010). In the second simulation (woJ-model), μa(λ) was set to 0, along with the same μs(λ) and μaf(λ) as those in J-model. Thus, in this woJ-model, we simulated the light propagation by considering only the scattering in the brain tissue. In the third simulation (Exp-model), μa(λ) was estimated to fit the light propagation patterns obtained from our experimental data [the Exp-data (light); see Transparent Methods “Monte Carlo Simulation” for details] along with the same μs(λ) and μaf(λ) as those in J-model. Based on these simulations (Figure S4), we calculated the relative input light power required to deliver equal light power at the distances of 250, 500, 700, and 1,000 μm from the tip of the optical fiber in the lateral direction with 532 and 594 nm (Figure 4B). The differences in relative input light power between 532 and 594 nm light within each dataset were also plotted (inset Figure 4B). The comparison between the experimental data [Exp-data (light)] and the simulation results of woJ-model and J-model provided evidence that the light absorption by Hb is responsible for the superiority of 594 nm light to 532 nm light in suppressing the activity of ArchT-expressing neurons (for further details, see Figure 4 Legend and Discussion). The comparison also demonstrated that the contribution of scattering is smaller compared with that of absorption, in the lateral direction of light propagation.

Discussion

In the present study, we directly compared the suppression efficacy of 532 and 594 nm light at multiple illuminating distances by developing an electro-fiberoptic array with multiple side-emitting optical fibers. The 594 nm light was found to be 2-fold more effective than 532 nm light to silence ArchT-expressing neurons through an intracortical distance of 1,000 μm. The 594 nm light was slightly off the excitation plateau and largely off the peak of the Hb absorption spectrum, whereas the 532 nm light falls within the excitation peak and on the peak of the Hb absorption spectrum. This result provided direct positive evidence for our prediction that 594 nm light is more effective than the conventionally used 532 nm light for silencing ArchT-expressing neurons in vivo. In addition, a comparison between the results obtained from the simulation of light propagation in the brain and our experimental data demonstrated that light absorption by Hb is responsible for the difference observed between 532 and 594 nm light. This result also supports our premise that light absorption by Hb should have a greater influence on light propagation than previously estimated, particularly in the lateral direction of light propagation. Our results highlighted the general importance of considering both the wavelength dependence of the opsin's excitation spectrum in vitro and the absorption of light in vivo. We call it an excitation-absorption trade-off.

Previous measurements of in vivo light propagation (Acker et al., 2016; Johansson, 2010) suggested that the absorption of light by the chromophore-harboring molecules, especially the Hb in the blood (Mahn et al., 2018), had been greatly underestimated. In the present study, we directly compared the responses of ArchT-expressing neurons with 532 and 594 nm light illumination using DPSS lasers, whose spectral widths are narrower than LEDs, under precise control of input light power. Based on our neurophysiological measurements, we demonstrated that 594 nm light required only 50% of the power required by 532 nm light to achieve an equivalent suppression at a distance of 1,000 μm (Figure 4A). The present in vivo observation cannot be directly predicted from the excitation spectrum of ArchT measured in vitro, where the largest photocurrents are observed at 530–560 nm (Chuong et al., 2014; Han et al., 2011; Mattis et al., 2011) (Figure 1A).

The efficiency of light propagation along a distance is determined by scattering and absorption in the brain. Light with longer wavelengths scatters less inside tissue and can propagate over longer distances. Various chromophore-harboring molecules are responsible for light absorption in the brain, such as Hb and lipofuscin. To determine which of these factors are mainly responsible for the observed differences between 532 and 594 nm light, we compared the experimental results [Exp-data (light)] with the results obtained from the Monte Carlo simulations (woJ-model and J-model; Figure 4B). The woJ-model reflected only the scattering, and J-model reflected both the scattering and the absorption, although the absorption by the Hb might only partially be reflected (Johansson, 2010). In contrast to J-model, Exp-data (light) fully reflected the light absorption, including that by Hb. Therefore, the comparisons in Figure 4B suggested the following two conclusions: (1) The scattering is responsible for the difference between 532 and 594 nm light; however, its contribution is predicted to be relatively small in the lateral direction of light propagation. (2) The absorption by Hb would be mainly responsible for the difference between 532 and 594 nm light observed in this study.

To quantitatively support the above conclusion, we further estimated the absorption coefficients for Exp-data (light) [Expμa(λ)] [note that Exp-model is the result of applying Expμa(λ) for simulation] [see Transparent Methods “Estimation of the absorption coefficients for Exp-data (light) ” for details], as well as the partial absorption coefficients of Hb for Exp-data (light) [Expμa,Hb(λ)] at both 532 and 594 nm [see Transparent Methods “Estimation of the partial absorption coefficients of Hb for Exp-data (light) ” for details]: Expμa(532) = 0.392 mm−1, Expμa(594) = 0.234 mm−1; Expμa,Hb(532) = 0.248 mm−1, Expμa,Hb(594) = 0.159 mm−1. The ratio of the Hb contribution to the total light absorption was larger than 0.6 for both wavelengths. Moreover, the contribution of Hb was larger than the contribution of the rest of the chromophore-harboring molecules to the difference in light absorption between 532 and 594 nm. Although Expμa,Hb(λ) was smaller than theoretically predicted at both 532 and 594 nm (see Transparent Methods “Estimation of the partial absorption coefficients of Hb for Exp-data” for details), the results suggested that, among all of the chromophore-harboring molecules present, Hb is the primary component contributing to light absorption at both wavelengths and Hb would be the main factor responsible for the differences observed between 532 and 594 nm light in our experimental data. Altogether, our results underscored the general importance of considering both the wavelength dependence of the opsin's excitation spectrum in vitro and the absorption of light primarily by Hb in vivo.

In this study, we evaluated the effects of distance changes in the lateral direction, but not in the vertical direction, to avoid the interference from the white matter. The light propagation patterns in the vertical direction of Exp-model in Figure S4, as well as the ratio (532 nm/594 nm = 1.1) of the estimated relative light input power to deliver equal light power at 1,000 μm in the vertical direction from the tip of the optical fiber in this model, suggest that the superiority of 594 nm light over 532 nm light in light propagation in the vertical direction is not as prominent as in the lateral direction (the ratio is 2.1; Figure 4B).

In addition to the decrease of light propagation, light absorption has been considered to be responsible for various off-target effects. Owen et al. (2019) demonstrated that, in wild-type mice without opsin expression, continuous 1-s illumination of 532 nm light at 15 mW could induce significant suppression of neuronal activity resulting in a behavioral change, presumably due to the heat generated from the absorbed light. Rungta et al. (2017) demonstrated that light with wavelengths between blue and red with illumination parameters of 20-ms pulse width, 20 Hz, 5 mW, 2-s duration could cause an off-target effect on vasodilation and the magnitude of such vasodilation decreased with increasing wavelength. This photo-vasodilation could affect the blood-oxygen-level-dependent (BOLD) signals of functional MRI. Hb being the primary molecule responsible for light absorption in vivo, it is likely that these phenomena are induced by the heat generated from the light absorbed by Hb. Although we did not directly measure temperature changes in this study, Shin et al. (2016) measured brain temperature in vivo and reported that illumination with 532 nm light causes a prominent rise in temperature compared with that by 594 nm light. Since we observed comparable suppression between the illumination of 532 and 594 nm light at the closest distance of 250 μm, 594 nm light might be preferable over 532 nm light to suppress a small brain area as well as and to minimize the off-target effects due to the heat generated by the absorbed light.

To expand the spatial volume of optogenetic manipulation, efforts have been made along several lines of strategy such as (1) increasing the light-emitting surface area and/or sites of the light-emitting optical fibers, (2) selecting a wavelength with superior propagation and reduced off-target effects in the brain, (3) modifying opsin proteins to improve its sensitivity to light, and (4) enhancing the amount of opsins to be expressed without side effects. The findings of the present study contribute to strategy (2). For strategy (1), various novel implementations have been applied (for the review see Galvan et al., 2017). For example, Tamura et al. (2012) developed a glass-coated optrode that can linearly arrange four sharpened optical fibers and activate a sufficient number of ChR2-expressing neurons in the perirhinal cortex of macaque monkeys, which drastically shifted their recognition judgment of visual objects (Tamura et al., 2017). In the present study, we used side-emitting optical fibers to assemble the electro-fiberoptic array. This side-emitting optical fiber substantially expanded the photoillumination area, both laterally and axially [strategy (1), Figure 2A], as reported previously (Acker et al., 2016; Pisanello et al., 2017). Our glass-etching protocol to render side emission of light does not use the toxic chemical, hydrofluoric acid, and is applicable in all laboratory settings. For strategy (3), some opsins have been modified to enhance light sensitivity by accumulating the opsin proteins in the open state (step function variants; Bamann et al., 2010; Berndt et al., 2009; Yizhar et al., 2011) and other opsins have been modified to enable excitation by longer wavelength light, including red-shifted opsins such as Chrimson and Jaws (Chuong et al., 2014; Inoue et al., 2019; Klapoetke et al., 2014). Jaws was used in macaques, which significantly disrupted the memory-guided saccade of macaques (Acker et al., 2016). Strategy (4) has been reviewed elsewhere (Kim et al., 2017; Mattis et al., 2011).

The combination of our side-emitting optical fibers, ArchT, and the use of 594 nm light, which avoids absorption by Hb, provides a promising strategy for large-scale optogenetic silencing in non-human primates. It can enable milli-second-level manipulation of the neural circuits for high-level assessment of cognitive/metacognitive behaviors, previously verified by a pharmacological intervention (Miyamoto et al., 2017, 2018).

Limitation of the Study

There are several opsins, besides ArchT, whose excitation peak overlaps fully or partially with the absorption spectrum of Hb, such as the red-shifted channelrhodopsins (VChR1, V1C1, ChrimsonR, ReaChR, etc.) (Kim et al., 2017) and chloride channelrhodopsins (GtACR1, mdGtACR2, PsChR1, PaACR1, etc.) (Wiegert et al., 2017). The implications of our study on the strategy for manipulating neuronal activities over a wide brain area with minimal off-target effects may also be applicable to these opsins.

For example, the optimal wavelength for eNpHR3 is reported at around 570 nm (Tye, 2011). Even though the effect of light absorption by deoxy-Hb is relatively small at 570 nm, it disappears at wavelengths longer than 600 nm. The effect of scattering is also smaller at wavelengths longer than 600 nm. Thus, we suppose that illumination of light at around 600 nm may be optimal for suppressing neuronal activities of a wider brain area when using eNpHR3, according to the “excitation-absorption trade-off. ” Note that Jaws, a red-shifted halorhodopsin, is also suited for 600-nm excitation.

The optimal wavelength for GtACR1 is reported at around 520 nm (Govorunova et al., 2015). At the proximity of 520 nm, light absorption both by lipofuscin and Hb could limit light propagation within the brain in vivo because their absorption peaks are located at 300–500 and 530–570 nm, respectively. The excitation-absorption trade-off suggests that we could experimentally determine the optimal wavelength of light to illuminate GtACR1 in the brain in vivo by comparing the suppression efficacies between 520 nm and around 600 nm, as we have demonstrated in the present study with ArchT. We also note that, as GtACR1 is an inhibitory anion-conducting channelrhodopsin, pulsed illumination is sufficient to induce its activation, which is different from pump-type inhibitory opsins, including ArchT and eNpHR3, which require continuous illumination. Pulsed illumination allows us to use a relatively high intensity of light (Owen et al., 2019) as compared with continuous illumination that we used in our experiments with ArchT. In fact, 635 nm light was used to suppress the activity of distant GtACR1-expressing neurons in vivo with the relatively high light intensity illumination (Li et al., 2019).

As shown above, because different opsins harbor different excitation peaks, our result with ArchT cannot be applied directly to other opsins. Therefore, a preferable stimulation wavelength for in vivo application should be examined based on the excitation-absorption trade-off and experimentally determined for each opsin by direct measurement of neuronal activity, as reported in this study.

Resource Availability

Lead Contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Rieko Setsuie (rieko.setsuie@riken.jp).

Materials Availability

This study did not generate new materials. Detailed information on how we fabricated the electro-fiberoptic array can be found in the accompanying Transparent Methods supplemental file.

Data and Code Availability

This study did not generate a new code. All relevant data are available from the Lead Contact upon reasonable request.

Methods

All methods can be found in the accompanying Transparent Methods supplemental file.

Acknowledgments

We thank F. Pisanello (Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies) for sharing the OpticStudio script and the MATLAB script. We thank T. Osada and T. Watanabe-Fukui for their technical assistance. This work was supported in part by MEXT/JSPS KAKENHI grants 17H06161 to Y.M., 17K07062, 18H04953, 18H05140, to M.T., and 19K06949 to R.S., The Uehara Memorial Foundation to M.T., JSPS Fellowships for Research Abroad and for Young Scientists to K.M. (265926) and K.T. (280123), Brain Science Foundation, The Ichiro Kanehara Foundation, and Research Foundation for Opto-Science and Technology to K.T.

Author Contributions

R.S. and K.T. conducted the experiments; R.S., K.T., and Y.M. designed the experiments; T.W. conducted the simulations; R.S., K.T., and K.M. analyzed the data; and R.S., K.T., K.M., M.T., T.W., and Y.M. wrote the paper.

Declaration of Interests

The authors declare no competing financial interests.

Published: July 24, 2020

Footnotes

Supplemental Information can be found online at https://doi.org/10.1016/j.isci.2020.101276.

Supplemental Information

Document S1. Transparent Methods and Figures S1–S4
mmc1.pdf (1.3MB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Transparent Methods and Figures S1–S4
mmc1.pdf (1.3MB, pdf)

Data Availability Statement

This study did not generate a new code. All relevant data are available from the Lead Contact upon reasonable request.

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