Activation of Distinct Channelrhodopsin Variants Engages Different Patterns of Network Activity

Research Article: New Research | Sensory and Motor Systems Activation of distinct Channelrhodopsin variants engages different patterns of network activity https://doi.org/10.1523/ENEURO.0222-18.2019

Cite as: eNeuro 2019; 10.1523/ENEURO.0222-18.2019 Received: 4 June 2018 Revised: 25 October 2019 Accepted: 1 December 2019

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1 Manuscript Title: Activation of distinct Channelrhodopsin variants engages 2 different patterns of network activity. 3 4 Abbreviated title: Variable network impact of optogenetic tools. 5 6 Authors: Na Young Jun1 and Jessica A. Cardin2,3 7 8 1Department of Ophthalmology, Yale University, New Haven CT 06520 9 10 2Department of Neuroscience, Yale University, New Haven CT 06520 11 12 3Kavli Institute for Neuroscience, Yale University, New Haven CT 06520 13 14 Author contributions: N.Y.J. and J.A.C. designed the study. N.Y.J. performed 15 experiments and analyzed the data. N.Y.J. and J.A.C. wrote the manuscript. 16 17 Correspondence should be addressed to: 18 Jessica A. Cardin 19 333 Cedar St., PO Box 208001 20 New Haven, CT 06511 21 [email protected] 22 23 Figures: 6 Abstract: 152 words 24 Tables: 0 Significance Statement: 121 words 25 Multimedia: 0 Introduction: 713 words 26 Discussion: 1134 words 27 28 Acknowledgments 29 The authors thank Dr. M.J. Higley for valuable discussions. We thank Dr. E.S. Boyden 30 for generously sharing samples of viral vectors for Chronos and Chrimson used in initial 31 pilot experiments. We thank Mr. Jong Wook Kim for assistance with coding and data 32 analysis. 33 34 Funding 35 This work was supported by NIH R01 MH102365, NIH R01 EY022951, NIH R01 36 MH113852, NIH P30 EY026878, a Smith Family Award for Excellence in Biomedical 37 Research, a Klingenstein Fellowship Award, an Alfred P. Sloan Fellowship, a NARSAD 38 Young Investigator Award, a grant from the Ludwig Foundation, and a McKnight 39 Fellowship to JAC. 40 41 Competing interests 42 The authors declare no competing interests 43 44 45

46 Abstract 47 48 Several recently developed Channelrhodopsin (ChR) variants are characterized 49 by rapid kinetics and reduced desensitization in comparison to the widely used 50 Channelrhodopsin-2 (ChR2). However, little is known about how varying opsin 51 properties may regulate their interaction with local network dynamics. We compared 52 evoked cortical activity in mice expressing three ChR variants with distinct temporal 53 profiles under the CamKII promoter: Chronos, Chrimson, and ChR2. We assessed 54 overall neural activation by measuring the amplitude and temporal progression of 55 evoked spiking. Using gamma-range (30-80Hz) LFP power as an assay for local network 56 engagement, we examined the recruitment of cortical network activity by each tool. All 57 variants caused light-evoked increases in firing in vivo, but each demonstrated different 58 temporal patterning of evoked activity. In addition, the three ChRs had distinct effects on 59 cortical gamma-band activity. Our findings suggest the properties of optogenetic tools 60 can substantially affect their efficacy in vivo, as well their engagement of circuit 61 resonance. 62 63 64 Significance statement 65 66 Genetically modified opsins are some of the most widely used experimental tools 67 in modern neuroscience. However, although these tools are well characterized at the 68 single-cell level, little is known about how the varying properties of the opsins affect their 69 interactions with active neural networks in vivo. Here we present data from experiments 70 using three optogenetic tools with distinct activation/inactivation and kinetic profiles. We 71 find that opsin properties regulate the amplitude and temporal pattern of activity evoked 72 in vivo. Despite all evoking elevated spiking, the three opsins also differentially regulate 73 cortical gamma oscillations. These data suggest that the kinetic properties of 74 optogenetic tools interact with active neural circuits on several time scales. Optogenetic 75 tool selection should therefore be a key element of experimental design. 76 77 78 79

80 Introduction 81 82 The advent of easily accessible optogenetic tools for manipulating neural activity 83 has substantially altered experimental neuroscience. The current optogenetics toolkit for 84 neuroscience comprises a large number of Channelrhodopsins (ChRs), Halorhodopsins, 85 and Archaerhodopsins that enable activation and suppression of neural activity with 86 millisecond-timescale precision. Within the Channelrhodopsin family, many variants 87 have now been made with altered activation spectra, photocycle kinetics, and ion 88 selectivity. The first tool to be widely used in neuroscientific approaches, 89 Channelrhodopsin-2 (ChR2), is a nonspecific cation channel with sensitivity to blue light. 90 ChR2 conferred the ability to evoke action potentials with high precision and reliability 91 across a wide range of cell types (Boyden et al., 2005; Cardin et al., 2009; Deisseroth, 92 2015). However, the utility of this tool has been somewhat limited by its relatively long 93 offset kinetics and fairly rapid inactivation of photocurrents in response to sustained 94 strong light stimulation (Boyden et al., 2005; Bamann et al., 2008; Ritter et al., 2008; 95 Schoenenberger et al., 2011). In addition, most naturally occurring Channelrhodopsins 96 are sensitive to blue-green light, presenting a challenge to the use of multiple tools for 97 simultaneous optogenetic control of distinct neural populations. A significant effort in the 98 field has therefore been made to develop Channelrhodopsin variants with faster on- and 99 offset temporal kinetics, less desensitization over time, and red-shifted activation spectra. 100 Previous work has suggested that the Channelrhodopsins are highly effective 101 tools for probing the cellular interactions underlying intrinsically generated patterns of 102 brain activity. Stimulation of Parvalbumin (PV)-expressing interneurons in the cortex via 103 ChR2 evokes gamma oscillations, entrains the firing of excitatory pyramidal neurons, 104 and regulates sensory responses (Cardin et al., 2009; Sohal et al., 2009). Similarly, 105 ChR2 stimulation of PV+ GABAergic long-range projection neurons in the basal forebrain 106 generates gamma-range oscillations in frontal cortex circuits (Brown and McKenna, 107 2015). Recent work further suggests that ChR2 activation of Somatostatin-expressing 108 interneurons, which synapse on both PV+ cells and excitatory neurons, evokes cortical 109 oscillations in a low gamma range (Veit et al., 2017). Sustained depolarization of 110 excitatory sensory cortical neurons via ChR2 activation likewise evokes gamma 111 oscillations, likely by engaging reciprocal interactions with local GABAergic interneurons. 112 (Adesnik and Scanziani, 2010). In comparison, activation of pyramidal neurons in 113 mouse motor cortex via ChRGR, another ChR variant, evokes activity in a broad range

114 of lower-band frequencies (Wen et al., 2010). High-fidelity spiking recruited by Chronos, 115 oChiEF, and ReaChR has been used in vitro and in vivo in visual cortex (Chaigneau et 116 al., 2016; Hass and Glickfeld, 2016; Ronzitti et al., 2017) and the auditory midbrain (Guo 117 et al., 2015; Hight et al., 2015), but the impact of such stimulation on the surrounding 118 network remains unclear. 119 Despite the substantial increase in available ChR variants with diverse kinetic 120 and spectral properties, it remains unclear how these properties interact with 121 endogenous temporal patterns of neural circuit activity like gamma oscillations in vivo. 122 Furthermore, the properties of optogenetic tools are typically validated using short 123 pulses of light (1 to 100ms) under quiet network conditions in vitro, but these tools are 124 widely used for sustained neural activation (100s of ms to s) under active network 125 conditions in vivo (Adesnik and Scanziani, 2010; Bortone et al., 2014; Phillips and 126 Hasenstaub, 2016; Burgos-Robles et al., 2017). Here we tested the impact of 127 optogenetic tool properties on evoked activity patterns in the intact brain. We took 128 advantage of the well-characterized gamma oscillation rhythm in mouse primary visual 129 cortex in vivo (Adesnik and Scanziani, 2010; Niell and Stryker, 2010; Vinck et al., 2015) 130 as a metric for optogenetic recruitment of local network activity. Using optogenetic 131 activation of excitatory pyramidal cells as a paradigm to evoke both spiking and cortical 132 gamma oscillations, we compared three Channelrhodopsins with robust photocurrents 133 but distinct kinetic profiles: Chronos, with high-speed on and off kinetics (Klapoetke et al., 134 2014); ChR2, with fast on but relatively slow off kinetics (Boyden et al., 2005); and 135 Chrimson (Klapoetke et al., 2014), with slow on and off kinetics. We found that these 136 tools, although expressed in the same cell types in the same brain region and effective 137 at eliciting action potentials, evoked distinct patterns of activity and had different effects 138 on gamma activity. Together, our data suggest that the kinetic properties of engineered 139 opsin tools affect optogenetic interactions with local circuit activity and should be a key 140 factor in experimental design. 141 142 143 144 145 146 147

148 Materials and Methods 149 150 Animals 151 All animal procedures were performed in accordance with the Yale University IACUC 152 animal care committee's regulations. We used both female and male C57BL/6J mice 153 ranging from 3-5 months old. 154 155 Surgical procedures 156 To express ChR2, Chronos, and Chrimson in pyramidal neurons, we injected 157 AAV5-CAMKII-ChR2-GFP (Addgene # 26969), AAV5-CAMKII-CHRONOS-GFP 158 (Addgene # 58805), or AAV5-CAMKII-CHRIMSON-GFP (Addgene # 62718), 159 respectively, in the cortex of C57BL/6J mice. For the virus injection surgery, 1μl AAV 160 was injected through a small burrhole craniotomy in the skull over the left visual cortex [- 161 3.2mm posterior, -2.5mm lateral, -500μm deep relative to bregma] using a glass pipette. 162 Injections were made via beveled glass micropipette at a rate of ~100nl/min. After 163 injection, pipettes were left in the brain for ~5 minutes to prevent backflow. Mice were 164 given four weeks for virus expression prior to experiments. 165 166 Electrophysiological recordings 167 Mice were anesthetized with 0.3-0.5% isoflurane in oxygen and head-fixed by 168 cementing a titanium headpost to the skull with Metabond (Butler Schein). All scalp 169 incisions were infused with lidocaine. A craniotomy was made over primary visual cortex 170 and electrodes were lowered through the dura into the cortex. All extracellular multi-unit 171 and LFP recordings were made with an array of independently moveable tetrodes 172 mounted in an Eckhorn Microdrive (Thomas Recording). Signals were digitized and 173 recorded by a Digital Lynx system (Neuralynx). All data were sampled at 40kHz. All LFP 174 recordings were referenced to the surface of the cortex (Buzsaki et al., 2012; Herreras, 175 2016). LFP data were recorded with open filters and MU data were recorded with filters 176 set at 600-9000Hz. 177 Optogenetic stimulation was provided via an optical fiber (200Pm) coupled to a 178 laser (Optoengine) at either 470nm (ChR2 and Chronos stimulation) or 593nm 179 (Chrimson stimulation). In each experiment, the fiber was placed on the surface of the 180 dura over the virus injection site and the tetrodes were placed immediately posterior to 181 the fiber.

182 During each experiment, a total of 150 laser pulses (470 or 593nm) of 1.5s 183 duration were given at varying light intensities (0.5-10mW/mm2) with 10s inter-pulse 184 intervals to allow detection of both transient and sustained spiking and LFP activity in 185 response to light pulses. Bouts of 30 pulses were separated by 5-minute baseline 186 periods. 187 188 Histology 189 Mice were perfused with 0.1M PBS followed by 4% PFA in 0.1M PBS. After 190 perfusion, brains were postfixed for 8 hours in 4% PFA. Brains were sliced at 40Pm on a 191 vibratome (Leica) and mounted on slides with DAPI mounting solution (Vector). Initial 192 images were taken with a 10x objective on an Olympus microscope and the channels 193 were merged using ImageJ (NIH). Laminar distribution of opsin expression was 194 estimated based on DAPI staining. Confocal images for cell counts were taken with a 195 64x oil objective on a Zeiss LSM 800 confocal microscope. Tissue was stained for 196 NeuN (1:500; MAB377; Millipore) using a red secondary antibody (1:1000; Alexa Fluor 197 Plus 594 Goat anti-Mouse; Invitrogen). For each mouse, NeuN+ cells that were positive 198 and negative for the GFP-tagged opsin were counted in three fields of view in each layer 199 (layers 2/3 and 5). 200 201 Data Analysis 202 Data were analyzed using custom scripts written in Matlab (The Mathworks) and 203 Python. Spikes were detected from the MU recordings using a threshold of +3 SD 204 above the mean, where both the mean and SD were calculated from 10 seconds of 205 recording preceding any light stimulation. Detected spikes were then used to calculate 206 peristimulus time histogram (PSTH) and raster plots for visualization of optically evoked 207 spiking. For each tetrode recording site, a two-tailed paired t-test was performed on the 208 firing rates in the pre-stimulus baseline and during light pulses to determine the 209 presence or absence of a light-evoked change in firing rate. Sites with significant evoked 210 firing rate changes were selected for further analysis. For each recording site, firing 211 rates were normalized to spontaneous firing in the initial pre-stimulation period and the 212 latency to peak evoked firing after light pulse onset was obtained by selecting the 10 ms 213 interval with the highest spike counts. 214 215 Inter-spike intervals (ISI) were calculated as the time interval between successive

216 spikes, and cumulative distributions of ISIs during the light pulses and spontaneous 217 activity prior to the light pulses were calculated for each data set (Fig. 3a-c). Paired ratio 218 measurements were taken for each light intensity. For measurements of the effect of 219 optogenetic stimulation on mean firing rate, we measured the ratio between the pre- 220 stimulus baseline (1-second period before each light pulse) and evoked firing (1-second 221 period during each light pulse). 222 Spectrograms of LFP activity were obtained using 400ms-long Hann windows 223 sliding by 10ms. Prior to short-time Fourier transform (STFT), the mean was subtracted 224 to remove DC bias. Each trial was normalized by dividing by the RMS amplitude of the 225 1s window preceding onset of the light pulse. Spectrograms were averaged across the 226 LFP responses to 30 pulses of 10mW/mm2 of 1.5s duration. Relative power in the 227 frequency band of interest was then calculated per frequency bin, setting the average 228 power in the first 0.5 s in each bin to be 1. To determine whether optogenetic 229 stimulation changed power in the gamma range (30-80Hz), we compared the average 230 gamma power during stimulation at 10mW/mm2 with the average gamma power during 231 spontaneous activity prior to the light pulses. To further evaluate the changes in gamma 232 range activity evoked by optogenetic stimulation, we calculated the ratio of the power 233 spectral density in this frequency band during baseline and all stimulation conditions. 234 The LFP signals included low-amplitude, additive line noise at 60Hz. The method 235 used by Burns et. al. (2010) was not applicable because (1) the amplitude 236 difference caused by the line noise at 60Hz in the full spectrum was not significant 237 enough, and (2) the 60Hz line noise was wide-band and leaked to the neighboring 238 frequency bins of the spectrogram as well. Instead, we noticed that the line noise caused 239 a constant phase shift at the 60Hz line on the spectrogram. The amplitude and phase of 240 the line noise was estimated from the mean value of the complex spectrogram at 60Hz 241 over all time bins during the 4-second interval, assuming that the true 60Hz signal 242 coming from LFP would not have a significant phase bias over the period. When 243 subtracted the estimated line noise from the 60Hz frequency bin as well as the two 244 neighboring bins, this method effectively eliminated the artifact on the spectrogram 245 coming from the 60Hz line noise. 246

247 248

249 Statistics 250 All analyses were performed on an animal-wise basis throughout, and all data 251 are denoted as mean r S.E.M. For some comparisons, a one-way ANOVA or Kruskal- 252 Wallis test was used, followed by a Dunn’s post-test where appropriate. In cases where 253 nonparametric statistics were appropriate due to non-normal data distributions, a two- 254 tailed Wilcoxon signed rank test or the Kruskal-Wallis test were used.

255 Results 256 257 Cell type-specific expression of Channelrhodopsins in mouse visual cortex 258 259 To understand the efficacy and utility of recently developed Channelrhodopsin 260 variants with differing kinetic properties, we compared three tools: Channelrhodopsin-2, 261 Chronos, and Chrimson (Fig. 1a). We expressed each tool using an AAV construct, 262 under the control of the excitatory neuron-specific CaMKII promoter, into the visual 263 cortex of wild-type mice. Four weeks after virus injection, each of the three 264 Channelrhodopsins was robustly expressed in a characteristic distribution of excitatory 265 pyramidal neurons in cortical layers 2/3, 5, and 6 (Fig. 1c) (Longson et al., 1997; Cardin 266 et al., 2009). In each case, opsin expression was widespread in visual cortex, covering 267 up to a distance of up to 410 μm from the initial injection site (Fig. 1b). To quantify the 268 expression of each opsin, we performed confocal microscopy of tissue co-stained for the 269 neuronal marker NeuN (Fig. 1d). We observed similar numbers of opsin-expressing 270 neurons in layers 2/3 and 5 across all three groups of animals (Layer 2/3: ChR2 74.3 r 271 3.5 %, Chronos 70.7 r 4.5%, Chrimson 72.3 r 4.9 %; Kruskal-Wallis test p = 0.87, dF = 272 2, H = 0.36; Layer 5: ChR2 63 r 6.1%, Chronos 64.7 r 5.5%, Chrimson 66.3 r 3.3%; p 273 =0.92, dF = 2, H =0.2; Fig. 1e). 274 275 Different Channelrhodopsins evoke distinct cortical activity profiles in vivo 276 277 The temporal profile of circuit activity evoked by different opsins may differentially 278 engage network dynamics. To assess the initial and sustained levels of spiking evoked 279 by each opsin, we recorded population multiunit (MU) and local field potential (LFP) 280 activity at multiple cortical sites around each viral injection (ChR2: 18 sites in 6 animals, 281 Chronos: 11 sites in 5 animals, Chrimson: 15 sites in 6 animals). Spontaneous firing

282 rates did not differ among the three groups of animals (ChR2: 37.7 ± 5.4, Chronos: 72.8 283 ± 21.32, Chrimson: 42.9 ± 7.4; One-way AVOVA, p = 0.14, dFn = 2, dFd = 14, F = 2.28). 284 285 When stimulated with 1.5 seconds of continuous light in an appropriate wavelength 286 (10mW/mm2, Wavelength: 470nm for ChR2 and Chronos, 593nm for Chrimson), all 287 three ChRs evoked sustained firing (Fig. 2a-c). However, each opsin was associated 288 with a distinct temporal profile of spiking. Whereas stimulation of ChR2- (Fig. 2a,d) or 289 Chrimson- (Fig. 2c,f) expressing neurons evoked sustained firing over ~1-2s, stimulation 290 of Chronos-expressing neurons generated strong initial spiking followed by a decrease 291 towards baseline firing levels (Fig. 2b,e). The peak firing evoked by ChR2 and Chronos 292 was rapid and reliable, whereas the peak firing achieved by Chrimson stimulation was 293 delayed and highly variable (Fig. 2d-f inset panels). 294 295 To quantify these differences in temporal kinetics induced by the three ChR 296 variants, we compared the time between the light pulse onset and the center of the 10 297 ms interval with the most frequent spikes, averaged over all recording sites and mice for 298 each ChR variant. Chronos showed the shortest peak latency of 0.005 ± 0.001s, 299 whereas Chrimson had a peak latency of 0.42 ± 0.13s, compared to ChR2 at 0.01 ± 300 0.01s. (Fig. 2d-f; Kruskal-Wallis test; p = 0.0015, dF = 2, H = 10.74). The latency to peak 301 was longer for Chrimson, but not Chronos, compared to ChR2 (Dunn’s post-test; p = 302 0.04, p = 0.75). 303 304 To assay the efficacy of each optogenetic tool in engaging local cortical neurons, 305 we compared the recruitment of spikes in response to a range of low illumination 306 intensities. We examined the difference in spike timing between spontaneous firing and 307 optogenetic stimulation periods by comparing the distributions of inter-spike intervals 308 (ISIs) evoked by activation of ChR2, Chronos, and Chrimson (Fig. 3). ChR2 (Fig. 3a) 309 and Chrimson (Fig. 3c) both evoked a robust decrease in ISI, consistent with the 310 sustained increase in firing rate, whereas activation of Chronos (Fig. 3b) had only a 311 modest effect on the overall ISI distribution. ChR2- (Fig. 3d) and Chrimson- (Fig. 3f) 312 expressing mice showed increasing evoked spiking as the light stimulation intensity 313 increased (linear regression slopes: ChR2= 0.16 ± 0.04, r2 = 0.82, p = 0.04, dFn = 1, dFd 314 = 3, F = 13.56; Chrimson= 0.18 ± 0.01, r2 = 0.9, p<0.0001, dFn = 1, dFd = 3, F = 969.1). 315 However, mean firing rates evoked by Chronos activation did not increase with light

316 intensity (linear regression slope: 0.04 ± 0.01, r2 = 0.99, p = 0.01, dFn = 1, dFd = 3, F = 317 28.1). Restricting analysis to the first 100ms of the stimulation period revealed the 318 decreased ISIs initially evoked by all three opsins (Fig. 4a-c). However, the profiles of 319 evoked firing responses across increasing light intensities were similar to those 320 observed in the full analysis shown in Figure 3d-f (linear regression slope: ChR2 = 0.26 321 ± 0.1, r2 = 0.67, p = 0.09, dFn = 1, dFd = 3, F = 6.02; Chrimson= 0.16 ± 0.07, r2 = 0.91, p 322 = 0.09, dFn = 1, dFd = 3, F = 29.2; Chronos = 0.036 ± 0.007, r2 = 0.66, p = 0.01, dFn = 1, 323 dFd = 3, F = 5.91; Fig. 4d-f). 324 325 Opsin-specific recruitment of cortical gamma rhythms 326 327 Previous work has found that ChR2 stimulation of pyramidal neurons engages 328 the cortical gamma rhythm (30-80Hz), an outcome of resonant excitatory-inhibitory 329 circuit interactions (Cardin, 2016), in vitro and in vivo (Adesnik and Scanziani, 2010). 330 Using evoked gamma power as a measure of network activation, we assayed the 331 efficacy of each optogenetic tool in driving recurrent circuit interactions. Activation of 332 ChR2-expressing excitatory neurons evoked a response in the local field potential (LFP) 333 and a broadband increase in high-frequency activity centered around the gamma band 334 (Fig. 5a). Chronos and Chrimson likewise evoked an initial deflection of the LFP signal 335 (Fig. 5b-c). However, neither Chronos nor Chrimson activation of excitatory neurons 336 evoked the characteristic sustained high-frequency LFP activity observed following 337 ChR2 stimulation of the same population of neurons. 338 339 In agreement with previous work (Adesnik and Scanziani, 2010), we found that 340 the light-activation of ChR2-expressing excitatory neurons amplified local field potential 341 (LFP) power in the gamma range (p = 0.03, W = 19; Wilcoxon signed rank test; Fig. 342 6a,d). In contrast, neither Chrimson nor Chronos demonstrated similar engagement of 343 endogenous patterns of cortical network activity during stimulation. Stimulation of 344 excitatory neurons via activation of Chronos had little effect on gamma power (p = 0.23, 345 W = -8; Fig. 6b,e). Surprisingly, excitatory neuron stimulation via Chrimson significantly 346 suppressed cortical power across a range of high frequencies, including the gamma 347 band, in a light-intensity dependent manner (p = 0.023, W = -15; Fig 5c, 6c,f). Together, 348 these data suggest that the pattern of activity recruited by stimulation with these three 349 different tools engages distinct modes of endogenous circuit interactions.

350 351 Discussion 352 353 Although recent work has resulted in the development of new Channelrhodopsin 354 variants to meet experimental needs, the in vivo effects of opsins with distinct properties 355 have not been fully explored. In particular, varying temporal profiles of optogenetically 356 evoked neural activation may substantially affect the manner in which the surrounding 357 neural circuit is engaged. Here we expressed three opsins (ChR2, Chronos, and 358 Chrimson) with different kinetics in excitatory pyramidal neurons in the primary visual 359 cortex. Using a previously validated paradigm for optogenetic recruitment of gamma- 360 range resonance in the local cortical circuit, we compared the temporal envelope of 361 evoked spiking and gamma-range activity across the three opsins. Although all three 362 tools were effective in driving enhanced spiking, the temporal profile of the evoked 363 activity was distinct. In addition, only ChR2 stimulation generated increased cortical 364 gamma activity. 365 Recently developed Channelrhodopsins vary extensively in their kinetic profiles.

366 ChR2 exhibits a relatively fast onset (Won) but a long offset time (Woff), leading to

367 diminished temporal fidelity in spike responses (Gunaydin et al., 2010). The Woff of 368 several ChRs also slows further upon membrane depolarization (Mattis et al., 2011). The

369 cumulative effect of this long W off is to cause a prolonged depolarization after the evoked 370 action potential, preventing rapid re-hyperpolarization of the membrane and contributing 371 to artificial spike doublets. Prolonged depolarization may also inactivate voltage-gated 372 channels needed for high-frequency spiking. In comparison, Chronos exhibits large 373 photocurrents, rapid deactivation, and improved efficacy in eliciting high-fidelity fast 374 spiking (Lin et al., 2009; Klapoetke et al., 2014). 375 Another recent series of tools were developed with absorption spectra shifted 376 towards longer wavelengths compatible with 2-photon imaging (Packer et al., 2012; 377 Prakash et al., 2012; Agetsuma et al., 2017) and dual-channel optogenetic circuit 378 interrogation. Chrimson exhibits a red-shifted absorption peak and very large 379 photocurrents, making it highly effective for driving robust neural activity (Klapoetke et al.,

380 2014). Chrimson has substantially slower Won and Woff properties than ChR2, Chronos, 381 ReaChR, or bReaCHES (Yizhar et al., 2011; Lin et al., 2013; Klapoetke et al., 2014; 382 Rajasethupathy et al., 2015). Based on their low toxicity, robust expression levels, large

383 peak photocurrents, and distinct kinetic profiles, we selected Chronos and Chrimson for 384 in vivo comparison with ChR2. 385 We found a strong relationship between the properties of the individual opsins 386 and the temporal profile of the spiking they evoked. The two tools with relatively rapid 387 onset kinetics, ChR2 and Chronos, each evoked a precisely timed initial spike event 388 across the neuronal population, followed by sustained spiking at lower firing rates. In 389 contrast, Chrimson, with slow onset kinetics, did not evoke reliable spiking at stimulation 390 onset and gave rise to a much broader temporal distribution of spike frequencies. These 391 results, along with previous findings (Mattis et al., 2011; Schoenenberger et al., 2011), 392 suggest that the kinetics of the opsins interact meaningfully with intrinsic neuronal 393 membrane properties to affect the temporal pattern of evoked spiking. Rapid membrane 394 depolarization, like that caused by ChR2 or Chronos activation, contributes to 395 recruitment of voltage-gated channels and enhances the reliability and precision of the 396 initial evoked action potentials in cortical neurons (Wilent and Contreras, 2005; Cardin et 397 al., 2010). In comparison, a slow rate of depolarization, like that caused by Chrimson, 398 leads to temporally dispersed spiking. We further found that the kinetics of the opsins 399 shaped the overall temporal envelope of the sustained spiking evoked by long 400 stimulation. Whereas the initial efficacy of ChR2 and Chronos resulted in an early peak 401 in evoked firing rates within the first 50ms, the spike response to Chrimson stimulation 402 peaked several hundred milliseconds later. However, the sustained firing rates evoked 403 by ChR2 and Chrimson were higher than that evoked by Chronos, suggesting that rapid 404 deactivation of this opsin may reduce overall spike rates. 405 Gamma oscillations are generated by reciprocal, rhythmic interactions between 406 excitatory and inhibitory neurons (Cardin, 2016). Gamma activity in cortical circuits can 407 be evoked by optogenetically stimulating either the inhibitory interneurons (Cardin et al., 408 2009; Sohal et al., 2009; Veit et al., 2017) or the excitatory neurons (Adesnik and 409 Scanziani, 2010; Lu et al., 2015). Generation of gamma oscillations by excitatory neuron 410 stimulation likely results from the highly synchronous activation of a large volley of 411 spikes from excitatory neurons, which are highly effective in activating the inhibitory 412 neuron spiking that sets the temporal pattern for resonance in the network (Cardin et al., 413 2009; Sohal et al., 2009; Buzsaki and Wang, 2012; Cardin, 2016). Several cycles of 414 gamma can be produced by even a single brief stimulation of excitatory pyramidal 415 neurons(Sohal et al., 2009), but sustained gamma oscillations in active cortical networks 416 in vivo may require consistently elevated excitatory spiking (Brunel and Wang, 2003;

417 Buzsaki and Wang, 2012). Given these temporal constraints, the different temporal 418 profiles of evoked excitatory neuron spiking evoked by the three ChRs could potentially 419 engage varying network responses. 420 In good agreement with previous work (Adesnik and Scanziani, 2010), we found 421 that ChR2 stimulation of excitatory pyramidal neurons evokes robust cortical gamma 422 activity. In contrast, stimulation of the same neuronal population via Chronos evoked 423 little to no gamma activity, presumably because the initial, highly precise spiking from 424 stimulated cells is not followed by sufficiently elevated excitatory spiking to sustain 425 network oscillations. In comparison, Chrimson might be expected to evoke little gamma 426 because the slow increase in activity precludes an initial burst of spikes. Surprisingly, 427 we found that stimulation via Chrimson also significantly suppressed endogenous power 428 in a broad range of high frequencies, including the gamma band. These results suggest 429 that Chrimson’s slow temporal kinetics and late firing peak destabilize the highly precise 430 interplay between E and I cells, increasing the firing rates of excitatory neurons but 431 broadly disrupting high-frequency activity and precluding rhythmic entrainment by 432 inhibition. Because the impact of optogenetic drive to interneurons on oscillatory activity 433 may vary with behavioral state (Veit et al., 2017), further work may be necessary to 434 determine whether these findings, obtained under a low level of anesthesia, extend to 435 other states such as wakefulness. 436 437 Overall, we found that differences in the properties of three Channelrhodopsins 438 were associated with distinct profiles of evoked cortical activity. Although this does not 439 represent an exhaustive evaluation of all available opsins, our data suggest that the 440 temporal properties of the opsins affect the temporal profile of evoked activity on multiple 441 time scales. Rapid onset kinetics may facilitate the recruitment of highly precise initial 442 spike responses, whereas slow onset kinetics preclude synchronous spiking and result 443 in delayed peak responses. In addition, opsins with distinct kinetics interact differently 444 with endogenous circuit resonance, affecting the sustained patterns of activity evoked in 445 cortical networks over longer time periods. Our findings suggest complex interactions 446 between optogenetic tools and active neuronal networks in the intact brain. The 447 optogenetics toolkit for neuroscience includes an ever-increasing variety of tools with 448 varying properties, and individual tools may be appropriate for different experimental 449 goals. 450

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565 Figure Legends 566 567 Figure 1. Expression of three Channelrhodopsin variants in excitatory neurons of the 568 mouse visual cortex. (A) AAVs carrying three opsins were injected into primary visual 569 cortex of wild-type mice. ChR2, Channelrhodopsin-2; ITR, inverted terminal repeat; 570 WRPE, woodchunk hepatitis B virus post-transcriptional element. (B) Spread of viral 571 infection in the cortex. Example image showing GFP expression (green) around the 572 area of a cortical injection of AAV5 carrying the Chronos construct. Magnification: 4x. 573 (C) ChR2-GFP, Chronos-GFP, and Chrimson-GFP were robustly expressed in excitatory 574 neurons in cortical layers 2,3, and 5, as confirmed by DAPI staining (blue). 575 Magnification: 10x. (D) Example confocal images showing expression of the three 576 Channelrhodopsin variants (green) in layer 5 pyramidal neurons stained for the neuronal 577 marker NeuN (red). Scale bar: 10Pm Magnification: 64x. E. Quantification of expression 578 in layers 2/3 (left) and 5 (right) for each opsin. 579 580 Figure 2. Different Channelrhodopsins evoke cortical activity with distinct temporal 581 profiles in vivo. (A) Raster plots (upper) and histograms (lower) of example multi-unit 582 spike activity during stimulation of excitatory pyramidal neurons with ChR2. The 1.5s- 583 long interval of light stimulation (10mW/mm2) is indicated as shaded box. Star (*) 584 indicates the peak firing evoked by the light pulse. (B) Same as in (A), for Chronos. (C) 585 Same as in (A), for Chrimson. (D) Average PSTH for all recorded sites in ChR2- 586 expressing mice. Red symbols and lines indicate the mean peak time and s.e.m. of the 587 peak time, respectively. Inset shows the initial period if evoked firing in the first 600ms 588 of light stimulation. (E) Same as in (D), for Chronos. (F) Same as in (D), for Chrimson. 589 590 Figure 3. Amplitude and frequency distribution of evoked spike response varies with 591 optogenetic tool. (A) ISIs of spontaneous (black) and evoked (red) MU activity during 592 optogenetic stimulation (10mW/mm2) in ChR2-expressing cortex. Inset shows an 593 enlarged plot of the initial 200ms of the evoked spike response. (B) Same as in (A), for 594 Chronos. (C) Same as in (A), for Chrimson. Error bars denote s.e.m. (D) Firing rates 595 evoked by ChR2 stimulation over a range of intensities, divided by baseline spontaneous 596 firing immediately prior to the light pulses. (E) Same as in (D), for Chronos. (F) Same 597 as in (D), for Chrimson. Dashed lines indicate linear regression of the data. Error bars 598 denote s.e.m.

599 600 Figure 4. Distinct changes in evoked spike patterns during the first 100ms after light 601 onset. (A) ISIs of spontaneous (black) and evoked (red) MU activity during the initial 602 100ms of optogenetic stimulation (10mW/mm2) in ChR2-expressing cortex. (B) Same as 603 in (A), for Chronos. (C) Same as in (A), for Chrimson. Error bars denote s.e.m. (D) 604 Firing rates evoked by the initial 100ms of ChR2 stimulation over a range of intensities, 605 divided by baseline spontaneous firing rates immediately prior to the light pulses. (E) 606 Same as in (D), for Chronos. (F) Same as in (D), for Chrimson. Dashed lines indicate 607 linear regression of the data. Error bars denote s.e.m. 608 609 Figure 5. Different Channelrhodopsins evoke distinct cortical activity profiles in vivo. 610 (A) Example traces of cortical LFP activity in response to 1.5s of 10mW/mm2 light 611 stimulation of ChR2-expressing pyramidal neurons (upper). Average changes in spectral 612 power density at this site across stimulation trials (lower). (B) Same as in (A), for 613 Chronos. (C) Same as in (A), for Chrimson. 614 615 Figure 6. Distinct recruitment of gamma-band activity by different Channelrhodopsins. 616 (A) Normalized power spectra (upper) for spontaneous (blue) and evoked (red) cortical 617 LFP activity in response to ChR2 stimulation, averaged across all stimulation levels, and 618 the ratio between evoked and spontaneous spectra (lower). (B) Same as in (A), for 619 Chronos. (C) Same as in (A), for Chrimson. Shaded areas denote ± s.e.m. (D) Change 620 in the relative power in the gamma band (30-80Hz) in response to varying light 621 intensities in cortex expressing ChR2. (E) Same as in (D), for Chronos. (F) Same as in 622 (D), for Chrimson. Error bars denote s.e.m. 623 624

DEF A Norm. Firing Rate (Hz) 10 15 20 25 30

0 5 Spikes/bin 0 2 4 6 -1 -1 13-10123 -101230123 -10123 ih Light Light Time (s) 30 0 0 0.6 BC 13-10123 Time (s) 30 0 0 0.6 Light 0123 Time (s) 30 0 0 0.6 ABC1 0.8 1 1 1 0.6 0.4 Spont. 0.2 0.02 0.02 0.02 Evoked Cumulative Frequency 00.20.05 0.1 0.15 0.2 00.20.05 0.1 0.15 0 0.05 0.1 0.15 DEFISI (s) ISI (s) ISI (s) 6 6 6

4 4 4

2 2 2

0 0 0 Evoked FR / Baseline 012345678910 012345678910 012345678910 Light Intensity(mW/mm2) Light Intensity(mW/mm2) Light Intensity(mW/mm2) A DEF

Evoked FR/ Baseline FR Cumulative Frequency 0.2 0.4 0.6 0.8 1 0 2 4 6 0.2 0 0123456789 Light Intensity(mW/mm 0.05 1 0.02 ISI (s) 0.1 0.15 2 ) Light Intensity(mW/mm Light ) 0.2 10 BC 4 0 2 6 00.2 0123456789 0.05 1 0.02 0.1 ISI (s) 0.15 2 LightIntensity(mW/mm ) 10 4 6 0 2 0 0123456789 0.05 1 0.02 0.1 ISI (s) 0.15 Spont. Evoked 2 ) 10 Power ratio

1 mV 10 1 Time (s) 0 -1 1 2 3 C Time (s) 0 -1 1 2 3 B Time (s) 0 -1 1 2 3 0 80 60 40 20

140 120 100 A (Hz) Frequency A 1 BC1 1

Spont. Evoked

Relative Power 0 0 0

Base 3 3 3 2 2 2

/Power 1 1 1 0.8 0.8 0.8 0.6 0.6 0.6 Evoked 0.4 0.4 0.4 0.2 0.2 0.2

Power 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140 Frequency (Hz) Frequency (Hz) Frequency (Hz) DEF 0.02 0.02 0.02

0.00 0.00 0.00

-0.02 -0.02 -0.02

-0.04 -0.04 -0.04 PSratio (30-80 Hz) Δ -0.06 -0.06 -0.06 0468102 0468102 0468102 Light IntensityIntensity(mW/mm m 2 ) Light Intensity(mW/mm2) Light Intensity(mW/mm2)