Skip to main content
Neurophotonics logoLink to Neurophotonics
. 2024 Jan 17;11(3):033402. doi: 10.1117/1.NPh.11.3.033402

Imaging different cell populations in the mouse olfactory bulb using the genetically encoded voltage indicator ArcLight

Lee Min Leong a, Douglas A Storace a,b,c,*
PMCID: PMC10823906  PMID: 38288247

Abstract.

Genetically encoded voltage indicators (GEVIs) are protein-based optical sensors that allow for measurements from genetically defined populations of neurons. Although in vivo imaging in the mammalian brain with early generation GEVIs was difficult due to poor membrane expression and low signal-to-noise ratio, newer and more sensitive GEVIs have begun to make them useful for answering fundamental questions in neuroscience. We discuss principles of imaging using GEVIs and genetically encoded calcium indicators, both useful tools for in vivo imaging of neuronal activity, and review some of the recent mechanistic advances that have led to GEVI improvements. We provide an overview of the mouse olfactory bulb (OB) and discuss recent studies using the GEVI ArcLight to study different cell types within the bulb using both widefield and two-photon microscopy. Specific emphasis is placed on using GEVIs to begin to study the principles of concentration coding in the OB, how to interpret the optical signals from population measurements in the in vivo brain, and future developments that will push the field forward.

Keywords: optical imaging, genetically encoded voltage indicator, ArcLight, fluorescent protein, olfactory bulb


Techniques that allow for monitoring the activity of genetically heterogeneous populations of neurons would facilitate our ability to understand the brain. First reported more than 70 years ago, optical measurements of brain activity offered a potentially powerful answer to this challenging goal.14 The persistent efforts of pioneering neuroscientists and the broader community have yielded remarkable improvements to the hardware and sensors required to perform imaging in the brain. Thanks to these advances, the ability to use points of light to signal the activity of nerve cells, imagined nearly a century ago, is now a commonly used technique in neuroscience.5 Here we discuss some of the techniques used to measure neural activity in neurons and review recent experiments using protein-based sensors of voltage to study the activity of different cell populations in the mouse olfactory bulb (OB).

1. Organic Dyes Versus Genetically Encoded Sensors

Imaging experiments using voltage and calcium dyes have undergone rapid and extensive development and improvements since they were first described in 1968 (8-anilinonaphthalene-1-sulfonic acid) and 1975 (arsenazo III), respectively.1,6,7 Since then, the dye toolkit has undergone extensive development resulting in improvements leading to reduced phototoxicity, improved kinetics, and signal-to-noise ratio.712 For example, the voltage sensitive dyes di-4-ANEPPS and ANNINE-6plus have time constants on the order of 103 to 109  s, and linear relationships with voltage sensitivity that yield fractional changes >8%ΔF/F/100  mV.1318

However, two major challenges of using organic dyes are that they must be physically introduced into the preparation, and they label all cells approximately equally.19,20 The lack of cell-specificity challenged efforts to understand the functional contributions of different cell types, except in exceptional cases where a dye can be anatomically restricted.2129

A class of protein-based optical sensors have been developed as an alternative to organic dyes that allow for neuron-type-specific labeling, which can be used to determine the role of different neuron types within and across brain areas.3032 This review primarily focuses on sensor variants in which the dye has been replaced by an optical reporter (e.g., a fluorescent protein) fused to a sensing element (e.g., a voltage or calcium sensing domain).20,33,34 However, other approaches incorporate a hybrid approach where a genetically encoded protein interfaces with an externally applied organic dye.35,36

The gene encoding the combined protein can be introduced into specific cell types using different promoters, viral vectors, and transgenic mice. Many different kinds of genetically encoded indicators have recently become available that report different cellular signals and binding of different neurotransmitters.3748 Our review discusses sensors of voltage and calcium, with a specific focus on the use of genetically encoded voltage indicators (GEVIs) to study the mammalian OB.

2. Genetically Encoded Calcium and Voltage Indicators

Genetically encoded calcium indicators (GECIs) are protein-based sensors that report changes in intracellular calcium.33 Calcium imaging is often used as a proxy for changes in spiking activity because action potentials cause an increase in intracellular calcium. However, intracellular calcium dynamics are shaped by other sources, including subthreshold depolarization,49,50 through ligand-gated receptors,51,52 intracellular sequestration,5355 and dendritic coincidence detection.16 Intracellular calcium dynamics are also substantially slower than voltage changes and cannot perfectly recapitulate action potential activity except at lower rates of spiking activity.5658 The biophysical properties of genetically encoded indicators can further complicate the interpretation of an optical measurement. The speed in which a GECI transforms changes in intracellular calcium into fluorescence changes can contribute additional temporal filtering,5962 and in some cases, the kinetics have calcium concentration-dependent nonlinearities.63,64 Many GECIs also exhibit nonlinearities in their relationship between calcium binding and fluorescence changes with Hill coefficients (which describes the degree of cooperativity in ligand–receptor binding) as great as 3.59,60,62,64,65 Each GECI has a different calcium affinity, which defines the center of the calcium range they can detect.66 For sensors with linear relationships, this affinity has a straightforward impact on the interpretation of the calcium signals; the same absolute change in calcium will evoke the same magnitude fluorescence change. The interpretation of a nonlinear GECI measurement is more complex because the same change in intracellular calcium could result in different fluorescence changes depending on the calcium concentration. In principle, measurements could be restricted to the linear concentration range of a GECI, although this is difficult or impractical to measure in most preparations. However, ground truth experiments that combined intracellular electrode recordings and calcium imaging have shown that some GECIs have approximately linear relationships between action potential activity and calcium signals across some spike rates.65,67 Since their introduction 25 years ago, engineering efforts have improved GECI sensitivity, brightness, and speed.32,65,66,68,69 Importantly, the growing toolkit of widely available viral vectors and transgenic reporter mice have simplified their use.70,71 These developments have revolutionized neuroscience by facilitating the ability to measure changes in neural signaling in a variety of preparations.

If the experimental goal is to measure subthreshold events or changes in spiking activity, direct optical measurements of voltage changes would be ideal. GEVIs are protein-based sensors that optically report changes in membrane potential.20,34 GEVI development has been slower in contrast with GECIs, which reflects the unique challenges involved in measuring cellular voltage changes. GEVIs must be sufficiently fast to detect voltage changes that can occur across millisecond timescales, and their expression must be restricted to the membrane because nonmembrane expression will reduce the signal-to-noise ratio by contributing fluorescence that is not voltage-dependent.19,72 Several biophysical properties of a GEVI are important in evaluating its ability to optically report voltage signals in neural tissue and in the interpretation of its optical signals.7375 The voltage-sensitivity range of a GEVI determines the absolute voltage changes that will be translated into optical changes.7577 The sensitivity of a GEVI is defined as the dynamic range of the fluorescence change across its voltage-sensitivity range, which is a key determinant of the signal-to-noise ratio. GEVI brightness is important because relatively few protein molecules can be introduced into the membrane and rapid voltage changes require fast frame rates that limit photon integration time. A sensitive GEVI with very low baseline fluorescence will be difficult to measure due to signal-to-noise issues and may require intense or biologically incompatible illumination levels.78 The speed at which voltage changes are transformed into fluorescence changes defines the kinetics of a GEVI. Onset and offset kinetics are typically measured in response to a voltage step and reported as the time to reach its peak fluorescence change or decay back to its baseline. A highly sensitive GEVI with a slow onset will be unable to reach its peak fluorescence change in response to a fast voltage change.79 The fluorescence signal from a GEVI with a linear voltage-sensitivity slope centered around 20  mV with submillisecond kinetics will include similar proportions of subthreshold and action potential spiking activity. In comparison, the fluorescence from a GEVI with similar voltage sensitivity but slower onset dynamics will likely reflect more subthreshold voltage signals.80 Shifting the voltage sensitivity of a GEVI to a more positive range will yield fluorescence signals that primarily report action potential activity.73,77 In principle, these properties could be optimized to engineer highly specific and targeted probes.74,76,81,82

3. Principles of GEVI Mechanisms of Action

The first generation of GEVIs based on fusions between green fluorescent protein (GFP) and the voltage-sensing domain (VSD) of potassium or sodium channels did not function well in mammalian cells due to their poor membrane expression.31,72,8385 The discovery and integration of the voltage-sensing phosphatase of Ciona intestinalis (sea squirt) with various fluorescent proteins was a milestone in GEVI development.86,87 In this sensor design, optical changes are believed to occur via voltage-dependent movement of the S4 transmembrane domain, which causes movement of the fluorescent protein via its linking element with the VSD.88 The first design strategy using the Ciona intestinalis VSD utilized Förster resonance energy transfer (FRET) fluorescent protein pairs that are attached to the N and C terminus of the VSD, respectively; changes in membrane potential are reported as FRET changes.86,89,90 Another significant milestone in GEVI development was the serendipitous discovery that modifications to the fluorescent protein and its linking position with the VSD can modulate GEVI sensitivity. In a GEVI utilizing the Ciona intestinalis VSD with the eGFP variant super ecliptic pHluorin, a point mutation (A227D) resulted in a large increase in sensitivity.91 Further tuning of this mutation along the linking position between the VSD and fluorescent protein yielded the GEVI ArcLight, which exhibited a 25-fold increase in sensitivity in comparison with its unmutated version.91 ArcLight has a sigmoidal voltage sensitivity relationship with a v1/2 around 26  mV. In response to a 100 mV depolarizing step, ArcLight responds with a sensitivity of 40%ΔF/F, which can be best fit with a double exponential curve with fast onset and offset components of 10 and 50 ms, respectively.34,63,91 In practice, these dynamics are sufficiently fast to solve individual action potentials at upward of 60 Hz.73,91,92 Although ArcLight is based on a variant of eGFP, its brightness will be a complex function of many experimental factors including the preparation type and expression level.91,93

Newer GEVIs have incorporated the voltage-gated phosphatase gene discovered in different species with various combinatorial mutations with alternate fluorescent proteins including a circularly permutated GFP inserted into the S3 and S4 loops of the transmembrane segments.14,92,9499 Another developmental strategy has been to incorporate microbial rhodopsins, which make use of the voltage sensitivity and fluorescence of the retinal residing in the opsin protein at the membrane.78,100,101 A change in membrane potential results in a chemical change that alters the fluorescence of the opsin retinal complex.102 The quantum yield of the opsin retinal complex is low, resulting in weak fluorescence that requires very high illumination intensities.101,103 Some of these limitations have been overcome by fusing opsins with fluorescent proteins to form an FRET pair, resulting in substantially brighter fluorescence than the opsin alone.104107 One hybrid strategy incorporates rhodamine-based voltage dyes with a genetically encoded HaloTag to target the dye to specific neuronal populations. Another strategy based on enzymatic cleavage via photoinduced electron transfer of voltage dyes, where VoltageFluor remains dim until activated porcine liver esterase.108,109 These advances in GEVI engineering have facilitated their use in biological discoveries in different brain regions and preparation types.106,110116

4. Organization and Circuitry of the Olfactory Bulb

The sense of smell is critical for many animals as it plays a key role in locating danger, food, and mates. In mice, olfactory receptor neurons (ORNs) express one out of a large number of olfactory receptor proteins (1000), each with a distinct affinity for an odor.117120 Different odor-concentration pairings evoke varying degrees of activity across the olfactory receptor population, resulting in a combinatorial code, which is transmitted into the OB.121,122 Each ORN type typically maps to one or two regions of OB neuropil called glomeruli.123126 Each glomerulus is innervated by the apical dendrites of a specific population of mitral and tufted cells (MTCs), which receive ORN input and project broadly to the rest of the brain.127,128 This input–output transformation is shaped by a complex synaptic network of anatomically and genetically heterogeneous interneurons.129,130 ORNs drive activity to MTCs through feed-forward and lateral pathways via interneurons that surround glomeruli that innervate their parent or neighboring glomeruli.129,131135 An additional lateral modulatory circuit is mediated via granule cells that make inhibitory connections on the lateral dendrites of MTCs.136,137 These networks are further shaped by presynaptic modulation of the ORN axon terminals134,138,139 and cortical feedback from other brain regions.140145

Defining the functional role(s) of the different populations of neurons within the OB remains an important step in understanding the function of the bulb in olfactory sensory processing.129,146 A recent strategy in addressing this question is to measure and compare the glomerular signals originating from different OB cell types.67,147149 Glomerular imaging is powerful because upward of 100 glomeruli can be simultaneously measured from the dorsal surface of the OB, each of which contains the processes of different cell types involved in the input–output transformation for a single olfactory receptor type.129,150,151 Glomerular measurements to date have reflected the average of different populations of neurons innervating a glomerulus, trading single-cell resolution for a population average. Neural activity measurements of population and single cells can be used to answer different kinds of questions; both have provided critical insight into the logic of olfactory sensory processing.21,148,149,152156

Glomerular signals have been measured from nonspecific neuronal populations by measuring changes in light reflectance, autofluorescence, and fluorescence from bath applied dyes.151,157165 Early efforts to perform cell-specific glomerular imaging were carried out by infusing calcium sensitive organic dyes into the nasal cavity of rodents, which results in the dye being transported to the ORN axon terminals in the OB where they can be imaged.21,23,26,166 ORNs have also been imaged using genetically encoded indicators of synaptic vesicle release and calcium.148,149,167169 Glomerular measurements have been carried out from MTCs and two interneuron populations using transgenic mice expressing a GECI under control of a cell-specific promoter, or that express Cre recombinase combined with Cre-dependent adeno associated virus (AAV) transduction or reporter transgenic mice.67,148,149,152,170174 Cell-specific glomerular measurements have been important in defining basic coding principles of how odor information is encoded across the OB input–output transformation,118,148,149,152,172,174,175 the plasticity of different cell types to perceptual experiences,168,176178 and testing models of olfactory sensory processing.152,170

Surprisingly, few of the studies examining sensory coding in the mouse OB have been carried out using voltage imaging.63,148,150,165,179 In addition to providing a direct readout of changes in membrane potential from genetically defined cell populations, voltage imaging can measure fast changes in neural activity that would be otherwise obscured when measured using slower imaging methods, and in principle, should be able to measure depolarizing and hyperpolarizing signals. Together, these advances may eventually provide clearer insight into fast temporal coding, and the nature of the inhibitory circuits within the OB. Here we review recent studies that used the GEVI ArcLight to perform glomerular imaging in the mouse OB.

5. ArcLight Measurements from Nonspecific Cell Populations in the Olfactory Bulb

The first GEVI recordings from the OB were conducted using ArcLight constitutively expressed using an adeno associated virus serotype 1 (AAV1).63 The AAV1 was designed to co-express a nuclear localized mCherry fluorescent protein to facilitate visualization of the labeled neurons. Injecting the AAV1 into the mouse OB resulted in membrane localized expression of ArcLight and intracellular expression of mCherry [Fig. 1(a), left]. ArcLight was expressed in a population of OB neurons that included MTCs, consistent with prior reports of the tropism of AAV1 [Fig. 1(a), right].180 In vivo measurements were made from the dorsal surface of the OB in anesthetized mice using widefield fluorescence microscopy. Odors were presented using a flow-dilution olfactometer and respiration was monitored using a piezosensor placed against the chest of the animal.181 Individual glomeruli were not evident in the in vivo baseline fluorescence likely because of the presence of diffuse fluorescence originating from ArcLight expressed in neuropil in deeper layers of the OB [Fig. 1(b), left]. Using a frame subtraction analysis in which the imaging frames prior to the odor were subtracted from those acquired during the odor revealed odor-evoked fluorescence changes in glomerular sized regions of interest across the dorsal bulb [Fig. 1(b), right panel]. The fluorescence signals from the glomerular sized regions of interest had a high signal-to-noise ratio that was similar from trial to trial and were often coupled to respiration [Fig. 1(c)]. The resting fluorescence was similarly stable across different trials within a recording session, showing an average decline of 1% across individual imaging trials that lasted for 8  s (0.6%±0.02, measurements of baseline fluorescence in 3 to 8 consecutive trials in six different preparations; illumination provided by a 150-W Opti Quip Xenon arc lamp). The fluorescence from the ArcLight expression was substantially brighter than the endogenous autofluorescence present in the uninjected hemibulb, allowing it to overshadow the intrinsic autofluorescence signal that can be measured using the same filter set [Fig. 1(d)].163,164 Furthermore, intrinsic optical signals measured as changes in light reflectance at 705 nm were not statistically different in the ArcLight injected and uninjected hemibulb [Fig. 1(e)]. Therefore, AAV1 transduction of ArcLight yielded sufficiently bright fluorescence for in vivo imaging that exhibited minimal photobleaching and phototoxicity.

Fig. 1.

Fig. 1

(a) ArcLight expression in the OB from histological images at high (left) and low (right) magnification. (b) ArcLight fluorescence in vivo from the OB (left) and a frame subtraction analysis illustrating peaks of activity following odor presentation (right). (c) Single-trial measurements aligned to the onset of respiration from the region of interest in panel (b). (d) Fluorescence measurements from ArcLight injected and uninjected hemibulbs. The inset illustrates the baseline fluorescence and regions of interest. (e) Intrinsic light reflectance measurements from injected and uninjected hemibulbs. The inset illustrates that the intrinsic glomerular signals were not significantly different in the ArcLight injected and uninjected hemibulbs. onl, olfactory nerve layer; gl, glomerular layer; epl, external plexiform layer; and mcl, mitral cell layer.

In a subset of the ArcLight injected preparations, an AAV1 expressing the GECI GCaMP3 or GCaMP6f was also injected into the opposite hemibulb of the same animal [Figs. 2(a) and 2(b), insets]. Single inspirations of odor resulted in ArcLight and GECI fluorescence changes that could be detected in single trials [Figs. 2(a) and 2(b)]. In trials where the mouse took multiple sniffs of the odor, the ArcLight signal returned most of the way to the baseline between each inspiration; respiratory coupled modulations were less prominent when measured with GECIs [Figs. 2(c) and 2(d), compare red, green, and blue traces]. ArcLight had sufficient temporal resolution to resolve temporal heterogeneity across the glomerular population.182 Caudal-lateral glomeruli tended to respond earlier following a single sniff, whereas rostral-medial glomeruli responded more slowly [Fig. 2(e)]. ArcLight signals were smaller than either GECI but had significantly faster onset kinetics, rise times, and decay times [Fig. 2(f)]. To compare the ability of ArcLight and both GECIs to track respiratory coupling, the respiratory frequency was measured using a piezosensor pressed against the mouse’s chest. The signals from ArcLight and both GECIs were normalized to control for differences in signal-to-noise ratio, and their power at the respiratory frequency was measured before and after odor presentation. Odor presentation resulted in a significant increase in power at the respiratory frequency for all three sensors, although ArcLight had a significantly larger change than either GECI [Fig. 2(g)]. The time course of the ArcLight signal is similar to the timing of action potential activity of mitral cells in anesthetized rodents, which spike during inspiration.183185 The higher temporal resolution of ArcLight may more faithfully represent the time course of respiratory coupled activity in the OB.

Fig. 2.

Fig. 2

Response to one (a, b) and two (c, d) inhalations of an odor from opposite hemibulbs in two preparations injected with (a, c) ArcLight and GCaMP3 and (b, d) ArcLight and GCaMP6f. The mean fluorescence and regions of interest for the traces for the two preparations are illustrated in the insets. (e) Frame subtraction from the ArcLight injected hemibulb at the timepoints illustrated by the gray bars in panel (a). (f) Temporal properties of the three sensors to one odor inhalation. (g) Power at the respiration frequency for the three sensors during multiple odor inhalations.

6. ArcLight in Specific OB Cell Types

Glomerular measurements from cell-specific populations have been carried out with ArcLight expressed using Cre-dependent AAV transduction and using the Ai86 ArcLight reporter transgenic line, which requires an intersectional breeding strategy to drive co-expression of ArcLight, tTA, and Cre recombinase.70,71,148,179 ORN targeting was achieved by first mating Ai86 to the Camk2a-tTA transgenic line.70,186 The resulting offspring that expressed both ArcLight and tTA were subsequently mated to the OMP-Cre transgenic line.187 The offspring that expressed all three genes exhibited ArcLight fluorescence in the olfactory nerve layer and glomeruli [Fig. 3(a)].179 Selective expression of ArcLight in MTCs was achieved by injecting a Cre-dependent AAV into the OB of protocadherin21-Cre transgenic mice, which resulted in ArcLight fluorescence present in MTC bodies, and their lateral and apical dendrites [Fig. 3(b)].148,188 A Cre-dependent AAV was injected into the OB of TH-Cre transgenic mice to express ArcLight in a population of interneurons. ArcLight fluorescence was present in neurons that surrounded glomeruli along with their associated glomerular processes [Fig. 3(c)].135,150,170 ArcLight was also expressed in a population of OB interneurons driven by the intersection of the Camk2a and EMX promoters by mating the offspring from pairings between Ai86 and Camk2a-tTA to the EMX-Cre transgenic line. The offspring that expressed all three genes exhibited ArcLight fluorescence in a subset of periglomerular and granule cells189 [Fig. 3(d)].

Fig. 3.

Fig. 3

ArcLight expression in (a) ORNs, (b) MTCs, (c) TH interneurons, and (d) Camk2a-EMX interneurons. onl, olfactory nerve layer; gl, glomerular layer; epl, external plexiform layer; mcl, mitral cell layer; and gcl, granule cell layer.

7. ArcLight Measurements From Olfactory Receptor Neuron Glomeruli

Olfactory perception must be determined by the pattern of olfactory receptors that are activated by an odor stimulus. Because each ORN glomerulus reflects the input from one receptor type, glomerular measurements provide a readout of how odors are encoded across the receptor population. Widefield fluorescence imaging was used to measure odor-evoked activity from ORNs in anesthetized mice. Individual glomeruli were more clearly visible in the mean fluorescence than in the constitutive AAV experiment, consistent with the expression of ArcLight being restricted to glomeruli [Fig. 4(a)]. Odors evoked fluorescence changes with a high signal-to-noise ratio that were often coupled to each inspiration of the stimulus [Fig. 4(b)]. A frame subtraction analysis revealed that different odors evoked distinct patterns of activity across the glomerular population [Fig. 4(c), columns]. The same odors presented at different concentrations also changed the glomerular patterns; increasing the concentration increased the amplitude of active glomeruli and recruited glomeruli that were not responsive at lower concentrations [Fig. 4(c), rows]. The combination of activated ORNs changes in response to different odors as well as different concentrations of the same odor; representations of odor identity and concentration are confounded at the input to the mouse OB.21 The effect of concentration on different ORNs is evident in the fluorescence versus time course signal measured from different glomeruli [Fig. 4(d)]. The steep concentration dependence is quantified by plotting the peak normalized amplitude versus concentration for each glomerulus [Fig. 4(e), rois from panel (c)] and a population of preparations [Fig. 4(f)]. The Hill coefficients of ORN measurements across many glomerulus-odor pairings in a population of preparations ranged between 1 and 2.2, which are consistent with previous ORN concentration response relationships in a variety of different preparations.21,118,190195 These results are consistent with a model in which ORNs are broadly tuned with a range of affinities for different odors.117 The steep concentration-response relationships of individual ORNs combined with their narrow dynamic range is the basis of changes in the ORN glomerular maps in response to an odor or concentration change. A threshold concentration of an odor will activate a subset of high affinity ORNs; increasing the concentration saturates high-affinity receptors and recruits inactive low affinity receptors. Importantly, the glomerular ORN voltage imaging results are consistent with similar measurements using other kinds of optical sensors.21,23,118,148,167

Fig. 4.

Fig. 4

Odor-evoked measurements in OMP-ArcLight transgenic mice. (a) Mean fluorescence in vivo. (b) Single-trial odor response from one glomerulus. (c) Frame subtraction maps in response to different odor-concentration pairings. The numbers at the top left of the bottom panels indicate the maximum ΔF/F. (d) Methyl valerate responses at different concentrations in two different glomeruli. (e, f) Normalized response amplitude versus odor concentration for the glomeruli in panel (c) and for a population of preparations.

8. ArcLight Measurements From MTC Glomeruli

Humans and other animals exhibit concentration invariant olfactory perception, which refers to the ability to recognize an odor as the same across concentration changes.196200 The observation that the patterns of activated ORN glomeruli change in response to different odors, and concentration changes of the same odor has raised the question of where and how concentration invariance is generated in the brain.117 Experimental and modeling work supported the possibility that circuitry within the OB could be important for generating more stable olfactory representations.117,201,202 This possibility was tested using a two-color imaging strategy, in which the signals from the ORNs innervating OB glomeruli were compared with the signals from the MTCs innervating the same glomeruli. In these experiments, the calcium sensitive dye fura dextran was infused into the nasal cavity of Pcdh21-Cre transgenic mice that received a Cre-dependent ArcLight-expressing AAV injection. The resulting mice had their ORN glomeruli selectively labeled with fura dextran and MTCs with ArcLight.26,188 Because fura dextran has an absorption spectra that is distinct from ArcLight, the signals originating from the ORNs and the MTCs innervating the same glomeruli could be differentiated by changing the excitation wavelength.55,63,91,148

The ORN and MTC glomerular signals were imaged from anesthetized mice using widefield fluorescence microscopy in response to odors presented across 2 log units of concentration. Increasing the concentration of the same odor increased the number of activated ORN glomeruli and altered the combinatorial pattern [Fig. 5(a), input]. In comparison, the spatial pattern of activation measured from the MTC glomeruli changed less across the same concentration changes [Fig. 5(a), output]. The change in concentration dependence can be visualized by comparing the time versus fluorescence traces for ORN and MTC signals innervating different glomeruli [Fig. 5(b)]. The peak ORN and MTC responses were quantified across different concentrations to illustrate the transformation from a highly concentration-dependent ORN input to a MTC response with a less steep concentration-response relationship [Fig. 5(c)]. The different effects of odor concentration on ORN and MTC glomerular maps are qualitatively similar to previous glomerular ORN measurements and single-cell MTC measurements.21,148,167,179,203

Fig. 5.

Fig. 5

(a) Frame subtraction analysis from ORN (left) and MTC (right) glomeruli at five concentrations of the odor methyl valerate. (b) Fluorescence traces from the regions of interest illustrated in panel (a). (c) Normalized amplitude versus concentration for all the identified glomeruli from the preparation in panel (a).

The mechanism(s) underlying this transformation remain unclear. Presynaptic inhibition onto the ORNs could dynamically compress the ORN concentration dependence into a narrower range.134 However, presynaptic inhibition seems unlikely to be the dominant mechanism since the ORN maps were still concentration-dependent and comparisons of Hill coefficients measured from ORN somata and ORN glomeruli were not significantly different.118 A second proposed mechanism involves a global normalization across the glomerular population, a process that can rescale the overall output while maintaining activity across glomeruli at relatively similar levels.117,201 There exist multiple mechanisms that could support a normalization process within the OB. One candidate is a population of interneurons that have long-range lateral connections within the OB and are important in generating stable concentration response relationships in individual mitral cells.117,135,170,202

9. ArcLight Measurements From TH-Expressing Glomeruli

Measurements from dopaminergic interneuron glomeruli support their role in the broad transmission of concentration-dependent ORN signals.170 We performed voltage imaging from the TH interneuron glomeruli to test this model of concentration sensitivity. A Cre-dependent ArcLight-expressing AAV was injected into the OB of TH-Cre transgenic mice [Fig. 3(c)]. Imaging was performed in anesthetized mice in response to different odor-concentration pairings using a widefield fluorescence microscope. A frame subtraction analysis demonstrated that different odors evoked distinct glomerular peaks of activity across the dorsal bulb [Fig. 6(a), columns]. Increasing the concentration of the same odors strongly influenced the amplitude and pattern of the glomerular activity maps [Fig. 6(a), rows]. The impact of concentration on individual glomeruli is illustrated by comparing the fluorescence time course measurements of four TH glomeruli at different concentrations [Fig. 6(b)]. The effect of concentration on TH expressing glomeruli was quantified by measuring their peak response at different concentrations. The TH-ArcLight concentration response relationships were overlaid with the measurements from ORNs and MTCs also carried out using ArcLight [Fig. 6(c), red, blue, and black].148,150,179 The signals from ORNs and TH glomeruli were similarly concentration-dependent, and the MTC glomeruli changed much less across the same concentration range. These results indicate that the concentration-dependence of TH glomeruli are more similar to ORNs, a result consistent with a previous calcium imaging study from dopamine-expressing glomeruli.170

Fig. 6.

Fig. 6

(a) Frame subtraction analysis of different odor-concentration pairings. The odor concentration and maximum ΔF/F for each panel is listed in the top left and bottom right of each panel, respectively. (b) Responses from four glomeruli in response to methyl valerate from panel (a). (c) Normalized response amplitude versus odor concentration for three TH-ArcLight preparations (blue) overlaid with concentration response data from ORN (red) and MTC glomeruli (black).

10. Two-Photon Imaging with ArcLight in the OB

Brain tissue is highly scattering that poses interpretive challenges for experiments using GEVIs with widefield fluorescence imaging. The degree of scattering is influenced by multiple factors that include the optical properties of the tissue, the imaging optics, and wavelength. In experiments carried out using a photodiode array, light focused on a slice of brain tissue resulted in photons striking multiple detectors separated up to 100  μm.204 This results in blurring of signals that will reflect an average of the signals emitted from all the labeled processes.30,204,205 Therefore, scattering imposes a fundamental limitation on the spatial resolution that can be measured in densely labeled tissue. These issues are exacerbated when imaging from voltage sensors because the signals originate from all parts of a cell. Sparser labeling can facilitate the interpretation of widefield fluorescence measurements by trading off the ability to record from many cells. The value of sparse labeling in voltage imaging is highlighted by experiments in which a voltage sensitive dye is restricted to a single cell, allowing high spatial and temporal resolution measurements from different processes within the same neuron.30,206208 Sparser expression has been achieved with GEVIs using low titer AAV transduction and incorporating targeting sequences to restrict expression to parts of a cell.70,81,98,110,209,210 Another strategy has been to use two-photon microscopy, a laser scanning technique that reduces light scattering by localizing the fluorescence emission to a small spatial volume, reducing the fluorescence that originates from out of focus areas, which substantially improves the lateral and axial resolution that can be achieved in scattering tissue.211,212 Early efforts illustrated the feasibility of incorporating GEVIs with two-photon imaging, although the measurements were limited by signal-to-noise considerations.213,214 Because not all GEVIs work similarly well under widefield fluorescence and two-photon microscopy, it is important to validate each GEVI using both imaging techniques.96,215

We tested the ability of ArcLight in reporting odor-evoked activity in the in vivo mouse OB using two-photon microscopy. Imaging was carried out following an injection of a constitutive AAV1 that expressed ArcLight in the OB. In comparison with widefield imaging experiments that used the same AAV1, individual glomeruli and neighboring processes could be clearly resolved in the mean fluorescence using two-photon imaging [Fig. 7(a), red polygon]. In experiments using slow frame rates (3  Hz), odor responses could be detected across a range of concentrations, although no respiratory coupling was observed [Fig. 7(b)]. Similar measurements done at a higher sampling rate (100 Hz) from a smaller imaging area [Fig. 7(a), red rectangle] were able to capture respiratory coupled oscillations [Fig. 7(c), taken from red rectangle in panel (a)].

Fig. 7.

Fig. 7

(a) In vivo two-photon mean fluorescence following constitutive AAV1 transduction of ArcLight. Odor responses from the regions of interest in panel (a) measured at (b) 3  Hz and (c) 100 Hz. Baseline fluorescence from the EMX-ArcLight transgenic mouse measured with (d) widefield fluorescence and (e) two-photon microscopy. The right panel was imaged at a higher magnification zoom from the box in the middle panel. (f) Odor evoked signals from the regions of interest in panels (d) and (e).

In EMX-ArcLight transgenic mice, widefield fluorescence signals were diffuse, consistent with the presence of ArcLight in deeper layers that contribute out of focus fluorescence [Fig. 7(d), histology in Fig. 3(d)]. In comparison, individual glomeruli and neighboring processes could be clearly visualized using two-photon microscopy [Fig. 7(d) versus Fig. 7(e)]. Odor responses were detected using widefield fluorescence and two-photon imaging [Fig. 7(f), left versus middle panel]. Respiratory coupled oscillations were detected in the widefield fluorescence data that were sampled at 125 Hz and in the two-photon imaging data that were performed at 31  Hz [Fig. 7(f), compare left and middle panels]. Using two-photon imaging, odor responses could be detected in small regions of interest that were consistent with the morphology of individual neurons [Fig. 7(e)]. The odor responses were not present everywhere in the imaging field of view, highlighting the higher spatial resolution afforded by the use of two-photon microscopy [Fig. 7(f), compare ROI1 and ROI2 in the middle and right panels]. Therefore, ArcLight reports odor-evoked activity in the mouse OB using both widefield fluorescence and two-photon imaging. These results also highlight that slower two-photon imaging scanning speeds can limit the ability to capture fast events [compare Fig. 7(b) to Figs. 7(c) and 7(f)]. Resonant-based scanners have increased the full frame imaging speed over earlier systems, although additional increases in speed come at a cost of reduced spatial resolution and signal-to-noise since faster scanning shortens the laser dwell time, which shortens the time during which emitted photons can be measured. Recent advances in the use of acousto-optic modulators that allow for arbitrary imaging patterns at high temporal resolution offer a promising alternative to current scanners to capture higher frequency neuronal signaling.98,110,216

11. Interpretation of the Population Measurements

The interpretation of the optical signals measured using ArcLight is complicated and depends on the targeting strategy and imaging modality. Although ArcLight was targeted to specific OB cell populations in these experiments, the expression was present in many neurons. Consider OMP-ArcLight, which is less impacted by the complications of light scattering because the fluorescence was restricted to ORN glomeruli on the dorsal surface [Fig. 3(a)]. Ignoring the impact of scattered fluorescence from lateral glomeruli, the signal from each glomerulus reflects the input from many individual ORNs and therefore the measurement from a glomerulus is an average of all the active and inactive labeled neurons. If 1000 asynchronously active neurons in a volume of tissue express a GEVI, the ΔF from any single-active neuron will be overshadowed by the fluorescence of the inactive cells and reduced proportionally by the number of inactive neurons. Moreover, any hyperpolarized neurons would fluoresce in the opposite direction of the depolarized neurons, which would further diminish the averaged fluorescence change. Therefore, the signal from a single-action potential would depend on the number of synchronously active and inactive cells, and therefore measurements of action potential activity in population measurements likely requires precise temporal synchronization of spiking activity across many of the labeled neurons. Indeed, OB spiking activity is highly coupled to respiration in anesthetized mice, which provides a mechanism to tightly synchronize action potentials.154,183 However, the maximum ΔF/F response of ArcLight is only measured with a constant depolarization of 10 ms or longer.63,91 Since the voltage-dependence range of ArcLight includes subthreshold potentials, the population signals measured from ArcLight likely reflect some complex mixture of subthreshold and action potential activity. The use of GEVIs tuned to different voltage-ranges would allow for a clearer interpretation as to whether a population signal primarily reflects hyperpolarizing, subthreshold, or voltage changes.

Recent efforts to shape the voltage-response relationships of some GEVIs illustrate that this property can be modified with appropriate mutations.74,82,92 Ultimately there may not be a perfect GEVI for all applications, but a series of differently tuned GEVIs optimized to measure different kinds of voltage signals (e.g., subthreshold versus action potential events), analogous to the current strategies used for GECI development.65,66,69,82,217 For example, a GEVI with high sensitivity and instantaneous onset kinetics but with a voltage-dependence tuned to action potentials and a slow decay rate would be ideal for two-photon imaging on commonly available systems. A slower decay rate would extend the integration time to measure an action potential at a cost of losing the ability to resolve individual action potentials from exceptionally fast spiking neurons. The parallel development of sensitive GEVIs available in a range of fluorophores will facilitate the ability to perform simultaneous imaging from multiple sensors in a single imaging plane allowing for measurements of subthreshold and action potential activity, from different cell types or different neuronal signaling molecules.82,107

12. Conclusions

Here we reviewed recent work demonstrating that the GEVI ArcLight can be used to measure glomerular signals from odor-evoked activity in the mouse OB from different cell types using widefield fluorescence and two-photon imaging. The faster temporal dynamics of ArcLight more precisely captured the respiratory coupled dynamics of glomerular activity when compared to GCaMP3 and GCaMP6f (Fig. 2). The ArcLight measurements illustrated that concentration coding is transformed across the OB circuit. These voltage imaging experiments validate work using GECIs demonstrating that a highly concentration-dependent ORN input signal is transformed into a more concentration invariant output response.148 Moreover, the glomerular output still maintained some sensitivity to concentration changes, indicating that concentration differences are still represented. In principle, such a coding scheme would allow downstream targets to recognize odors in a concentration invariant manner while still maintaining the ability to distinguish between concentration differences based on the output pattern of the bulb.

The growth of the number of laboratories developing and improving GEVIs over the last decade suggests an optimistic trajectory for making GEVIs easier to use in vivo, although the increasing number of those that are available complicates the choice of any particular one without clear comparisons of how they work in the in vivo brain. Moreover, the optimal GEVI choice likely depends on region-specific physiological parameters (e.g., varying spike widths and the overall balance of depolarization and hyperpolarization). Given that caveat, we propose that one potentially useful future goal is to have a model testing system for in vivo measurements by which all GEVIs could be screened for their ability to report population and single-cell activity using different microscopy techniques. We propose that the OB may be such a useful model brain area due to it containing dense neuropil within glomeruli as well as single cells that surround glomeruli that are easily accessible on the dorsal surface. Together, such developments and improvements will facilitate the uptake of GEVIs by the broader community to advance our understanding of the brain.

Acknowledgments

This work was supported by the National Institutes of Health (NIH), through a grant (no. DC020519) from the National Institute of Deafness and Communication Disorders.

Biographies

Lee Min Leong received her PhD in biomedical science and technology from the Korea Institute of Science and Technology in Seoul, South Korea, where she worked on GEVI development and understanding the mechanism of GEVIs in the laboratory of Bradley J. Baker. She is currently a postdoctoral fellow in the laboratory of Douglas A. Storace at Florida State University using voltage and calcium sensors in the olfactory system.

Douglas A. Storace received his PhD in behavioral neuroscience from the University of Connecticut in Storrs, Connecticut, before completing a postdoctoral fellowship in the laboratory of Lawrence B. Cohen. He is currently an assistant professor at Florida State University in the Department of Biological Science. His research group uses in vivo widefield and 2-photon microscopy to understand sensory coding in the olfactory system.

Contributor Information

Lee Min Leong, Email: ll23j@fsu.edu.

Douglas A. Storace, Email: dstorace@fsu.edu.

Disclosures

The authors declare no financial or scientific conflicts of interest.

Code and Data Availability

All data described in this manuscript are available from the authors upon request.

References

  • 1.Tasaki I., et al. , “Changes in fluorescence, turbidity, and birefringence associated with nerve excitation,” Proc. Natl. Acad. Sci. U. S. A. 61, 883–888 (1968). 10.1073/pnas.61.3.883 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Cohen L. B., Keynes R. D., Hille B., “Light scattering and birefringence changes during nerve activity,” Nature 218, 438–441 (1968). 10.1038/218438a0 [DOI] [PubMed] [Google Scholar]
  • 3.Hill D. K., Keynes R. D., “Opacity changes in stimulated nerve,” J. Physiol. 108, 278–281 (1949). 10.1113/jphysiol.1949.sp004331 [DOI] [PubMed] [Google Scholar]
  • 4.Hill D. K., “The effect of stimulation on the opacity of a crustacean nerve trunk and its relation to fibre diameter,” J. Physiol. 111, 283–303 (1950). 10.1113/jphysiol.1950.sp004480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Sherrington C. S., Man on His Nature, Cambridge University Press, Cambridge, UK: (1937). [Google Scholar]
  • 6.Brown J. E., De Weer P., Salzberg B. M., “Optical measurement of changes in intracellular calcium,” Biophys. J. 118, 788–789 (2020). 10.1016/j.bpj.2019.06.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Brown J. E., et al. , “Rapid changes in intracellular free calcium concentration. Detection by metallochromic indicator dyes in squid giant axon,” Biophys. J. 15, 1155–1160 (1975). 10.1016/S0006-3495(75)85891-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Davila H. V., et al. , “A large change in axon fluorescence that provides a promising method for measuring membrane potential,” Nat. New Biol. 241, 159–160 (1973). 10.1038/newbio241159a0 [DOI] [PubMed] [Google Scholar]
  • 9.Grinvald A., Salzberg B. M., Cohen L. B., “Simultaneous recording from several neurones in an invertebrate central nervous system,” Nature 268, 140–142 (1977). 10.1038/268140a0 [DOI] [PubMed] [Google Scholar]
  • 10.Salzberg B. M., Davila H. V., Cohen L. B., “Optical recording of impulses in individual neurones of an invertebrate central nervous system,” Nature 246, 508–509 (1973). 10.1038/246508a0 [DOI] [PubMed] [Google Scholar]
  • 11.Canepari M., Zecevic D., Membrane Potential Imaging in the Nervous System: Methods and Applications, Springer; (2010). [Google Scholar]
  • 12.Braubach O., Cohen L. B., Choi Y., “Historical overview and general methods of membrane potential imaging,” Adv. Exp. Med. Biol. 859, 3–26 (2015). 10.1007/978-3-319-17641-3_1 [DOI] [PubMed] [Google Scholar]
  • 13.Wachowiak M., et al. , Brain Mapping: The Methods, pp. 77–95, Elsevier; (2002). [Google Scholar]
  • 14.Baker B. J., et al. , “Genetically encoded fluorescent voltage sensors using the voltage-sensing domain of Nematostella and Danio phosphatases exhibit fast kinetics,” J. Neurosci. Methods 208, 190–196 (2012). 10.1016/j.jneumeth.2012.05.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Frey W., et al. , “Plasma membrane voltage changes during nanosecond pulsed electric field exposure,” Biophys. J. 90, 3608–3615 (2006). 10.1529/biophysj.105.072777 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Roome C. J., Kuhn B., “Simultaneous dendritic voltage and calcium imaging and somatic recording from Purkinje neurons in awake mice,” Nat. Commun. 9, 3388 (2018). 10.1038/s41467-018-05900-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Pagès S., Côté D., De Koninck P., “Optophysiological approach to resolve neuronal action potentials with high spatial and temporal resolution in cultured neurons,” Front. Cell. Neurosci. 5, 20 (2011). 10.3389/fncel.2011.00020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Fromherz P., et al. , “ANNINE-6plus, a voltage-sensitive dye with good solubility, strong membrane binding and high sensitivity,” Eur. Biophys. J. 37, 509–514 (2008). 10.1007/s00249-007-0210-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Baker B. J., et al. , “Genetically encoded fluorescent sensors of membrane potential,” Brain Cell Biol. 36, 53–67 (2008). 10.1007/s11068-008-9026-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Storace D., et al. , “Genetically encoded protein sensors of membrane potential,” Adv. Exp. Med. Biol. 859, 493–509 (2015). 10.1007/978-3-319-17641-3_20 [DOI] [PubMed] [Google Scholar]
  • 21.Wachowiak M., Cohen L. B., “Representation of odorants by receptor neuron input to the mouse olfactory bulb,” Neuron 32, 723–735 (2001). 10.1016/S0896-6273(01)00506-2 [DOI] [PubMed] [Google Scholar]
  • 22.Verhagen J. V., et al. , “Sniffing controls an adaptive filter of sensory input to the olfactory bulb,” Nat. Neurosci. 10, 631–639 (2007). 10.1038/nn1892 [DOI] [PubMed] [Google Scholar]
  • 23.Fried H. U., Fuss S. H., Korsching S. I., “Selective imaging of presynaptic activity in the mouse olfactory bulb shows concentration and structure dependence of odor responses in identified glomeruli,” Proc. Natl. Acad. Sci. U. S. A. 99, 3222–3227 (2002). 10.1073/pnas.052658399 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Friedrich R. W., Korsching S. I., “Combinatorial and chemotopic odorant coding in the zebrafish olfactory bulb visualized by optical imaging,” Neuron 18, 737–752 (1997). 10.1016/S0896-6273(00)80314-1 [DOI] [PubMed] [Google Scholar]
  • 25.Friedrich R. W., Korsching S. I., “Chemotopic, combinatorial, and noncombinatorial odorant representations in the olfactory bulb revealed using a voltage-sensitive axon tracer,” J. Neurosci. 18, 9977–9988 (1998). 10.1523/JNEUROSCI.18-23-09977.1998 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Korsching S., “Selective imaging of the receptor neuron population in the olfactory bulb of zebrafish and mice,” Chem. Sens. 30(Suppl. 1), i101–i102 (2005). 10.1093/chemse/bjh134 [DOI] [PubMed] [Google Scholar]
  • 27.Ackman J. B., Burbridge T. J., Crair M. C., “Retinal waves coordinate patterned activity throughout the developing visual system,” Nature 490, 219–225 (2012). 10.1038/nature11529 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Tsau Y., et al. , “Dye screening and signal-to-noise ratio for retrogradely transported voltage-sensitive dyes,” J. Neurosci. Methods 70, 121–129 (1996). 10.1016/S0165-0270(96)00109-4 [DOI] [PubMed] [Google Scholar]
  • 29.Wenner P., et al. , “Voltage-sensitive dye recording using retrogradely transported dye in the chicken spinal cord: staining and signal characteristics,” J. Neurosci. Methods 70, 111–120 (1996). 10.1016/S0165-0270(96)00108-2 [DOI] [PubMed] [Google Scholar]
  • 30.Zochowski M., et al. , “Imaging membrane potential with voltage-sensitive dyes,” Biol. Bull. 198, 1–21 (2000). 10.2307/1542798 [DOI] [PubMed] [Google Scholar]
  • 31.Siegel M. S., Isacoff E. Y., “A genetically encoded optical probe of membrane voltage,” Neuron 19, 735–741 (1997). 10.1016/S0896-6273(00)80955-1 [DOI] [PubMed] [Google Scholar]
  • 32.Miyawaki A., et al. , “Fluorescent indicators for Ca2+ based on green fluorescent proteins and calmodulin,” Nature 388, 882–887 (1997). 10.1038/42264 [DOI] [PubMed] [Google Scholar]
  • 33.Hires S. A., Tian L., Looger L. L., “Reporting neural activity with genetically encoded calcium indicators,” Brain Cell Biol. 36, 69–86 (2008). 10.1007/s11068-008-9029-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Storace D., et al. , “Toward better genetically encoded sensors of membrane potential,” Trends Neurosci. 39, 277–289 (2016). 10.1016/j.tins.2016.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Deo C., et al. , “The HaloTag as a general scaffold for far-red tunable chemigenetic indicators,” Nat. Chem. Biol. 17, 718–723 (2021). 10.1038/s41589-021-00775-w [DOI] [PubMed] [Google Scholar]
  • 36.Deo C., Lavis L. D., “Synthetic and genetically encoded fluorescent neural activity indicators,” Curr. Opin. Neurobiol. 50, 101–108 (2018). 10.1016/j.conb.2018.01.003 [DOI] [PubMed] [Google Scholar]
  • 37.Duffet L., et al. , “A genetically encoded sensor for in vivo imaging of orexin neuropeptides,” Nat. Methods 19, 231–241 (2022). 10.1038/s41592-021-01390-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Patriarchi T., et al. , “Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors,” Science 360, eaat4422 (2018). 10.1126/science.aat4422 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Dong H., et al. , “Genetically encoded sensors for measuring histamine release both in vitro and in vivo,” Neuron 111, 1564–1576.e6 (2023). 10.1016/j.neuron.2023.02.024 [DOI] [PubMed] [Google Scholar]
  • 40.Qian T., et al. , “A genetically encoded sensor measures temporal oxytocin release from different neuronal compartments,” Nat. Biotechnol. 41, 944–957 (2023). 10.1038/s41587-022-01561-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Dong A., et al. , “A fluorescent sensor for spatiotemporally resolved imaging of endocannabinoid dynamics in vivo,” Nat. Biotechnol. 40, 787–798 (2022). 10.1038/s41587-021-01074-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Wu Z., et al. , “A sensitive GRAB sensor for detecting extracellular ATP in vitro and in vivo,” Neuron 110, 770–782.e5 (2022). 10.1016/j.neuron.2021.11.027 [DOI] [PubMed] [Google Scholar]
  • 43.Wan J., et al. , “A genetically encoded sensor for measuring serotonin dynamics,” Nat. Neurosci. 24, 746–752 (2021). 10.1038/s41593-021-00823-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Jing M., et al. , “An optimized acetylcholine sensor for monitoring in vivo cholinergic activity,” Nat. Methods 17, 1139–1146 (2020). 10.1038/s41592-020-0953-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Aggarwal A., et al. , “Glutamate indicators with improved activation kinetics and localization for imaging synaptic transmission,” Nat. Methods 20, 925–934 (2023). 10.1038/s41592-023-01863-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Marvin J. S., et al. , “Stability, affinity, and chromatic variants of the glutamate sensor iGluSnFR,” Nat. Methods 15, 936–939 (2018). 10.1038/s41592-018-0171-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Wu J., et al. , “Genetically encoded glutamate indicators with altered color and topology,” ACS Chem. Biol. 13, 1832–1837 (2018). 10.1021/acschembio.7b01085 [DOI] [PubMed] [Google Scholar]
  • 48.Marvin J. S., et al. , “An optimized fluorescent probe for visualizing glutamate neurotransmission,” Nat. Methods 10, 162–170 (2013). 10.1038/nmeth.2333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Charpak S., et al. , “Odor-evoked calcium signals in dendrites of rat mitral cells,” Proc. Natl. Acad. Sci. U. S. A. 98, 1230–1234 (2001). 10.1073/pnas.98.3.1230 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Helmchen F., et al. , “In vivo dendritic calcium dynamics in deep-layer cortical pyramidal neurons,” Nat. Neurosci. 2, 989–996 (1999). 10.1038/14788 [DOI] [PubMed] [Google Scholar]
  • 51.Schiller J., et al. , “NMDA spikes in basal dendrites of cortical pyramidal neurons,” Nature 404, 285–289 (2000). 10.1038/35005094 [DOI] [PubMed] [Google Scholar]
  • 52.Ross W. N., “Understanding calcium waves and sparks in central neurons,” Nat. Rev. Neurosci. 13, 157–168 (2012). 10.1038/nrn3168 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Garaschuk O., Yaari Y., Konnerth A., “Release and sequestration of calcium by ryanodine-sensitive stores in rat hippocampal neurones,” J. Physiol. 502(1), 13–30 (1997). 10.1111/j.1469-7793.1997.013bl.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Emptage N., Bliss T. V., Fine A., “Single synaptic events evoke NMDA receptor-mediated release of calcium from internal stores in hippocampal dendritic spines,” Neuron 22, 115–124 (1999). 10.1016/S0896-6273(00)80683-2 [DOI] [PubMed] [Google Scholar]
  • 55.Pozzo Miller L. D., et al. , “Ca2+ release from intracellular stores induced by afferent stimulation of CA3 pyramidal neurons in hippocampal slices,” J. Neurophysiol. 76, 554–562 (1996). 10.1152/jn.1996.76.1.554 [DOI] [PubMed] [Google Scholar]
  • 56.Callaway J. C., Ross W. N., “Frequency-dependent propagation of sodium action potentials in dendrites of hippocampal CA1 pyramidal neurons,” J. Neurophysiol. 74, 1395–1403 (1995). 10.1152/jn.1995.74.4.1395 [DOI] [PubMed] [Google Scholar]
  • 57.Markram H., Helm P. J., Sakmann B., “Dendritic calcium transients evoked by single back-propagating action potentials in rat neocortical pyramidal neurons,” J. Physiol. 485(1), 1–20 (1995). 10.1113/jphysiol.1995.sp020708 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Svoboda K., et al. , “In vivo dendritic calcium dynamics in neocortical pyramidal neurons,” Nature 385, 161–165 (1997). 10.1038/385161a0 [DOI] [PubMed] [Google Scholar]
  • 59.Sun X. R., et al. , “Fast GCaMPs for improved tracking of neuronal activity,” Nat. Commun. 4, 2170 (2013). 10.1038/ncomms3170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Badura A., et al. , “Fast calcium sensor proteins for monitoring neural activity,” Neurophotonics 1, 025008 (2014). 10.1117/1.NPh.1.2.025008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Rose T., et al. , “Putting a finishing touch on GECIs,” Front. Mol. Neurosci. 7, 88 (2014). 10.3389/fnmol.2014.00088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Thestrup T., et al. , “Optimized ratiometric calcium sensors for functional in vivo imaging of neurons and T lymphocytes,” Nat. Methods 11, 175–182 (2014). 10.1038/nmeth.2773 [DOI] [PubMed] [Google Scholar]
  • 63.Storace D. A., et al. , “Monitoring brain activity with protein voltage and calcium sensors,” Sci. Rep. 5, 10212 (2015). 10.1038/srep10212 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Akerboom J., et al. , “Optimization of a GCaMP calcium indicator for neural activity imaging,” J. Neurosci. 32, 13819–13840 (2012). 10.1523/JNEUROSCI.2601-12.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Zhang Y., et al. , “Fast and sensitive GCaMP calcium indicators for imaging neural populations,” Nature 615, 884–891 (2023). 10.1038/s41586-023-05828-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Chen T. W., et al. , “Ultrasensitive fluorescent proteins for imaging neuronal activity,” Nature 499, 295–300 (2013). 10.1038/nature12354 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Wachowiak M., et al. , “Optical dissection of odor information processing in vivo using GCaMPs expressed in specified cell types of the olfactory bulb,” J. Neurosci. 33, 5285–5300 (2013). 10.1523/JNEUROSCI.4824-12.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Li J., et al. , “Engineering of NEMO as calcium indicators with large dynamics and high sensitivity,” Nat. Methods 20, 918–924 (2023). 10.1038/s41592-023-01852-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Dana H., et al. , “High-performance calcium sensors for imaging activity in neuronal populations and microcompartments,” Nat. Methods 16, 649–657 (2019). 10.1038/s41592-019-0435-6 [DOI] [PubMed] [Google Scholar]
  • 70.Daigle T. L., et al. , “A suite of transgenic driver and reporter mouse lines with enhanced brain-cell-type targeting and functionality,” Cell 174, 465–480.e22 (2018). 10.1016/j.cell.2018.06.035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Madisen L., et al. , “Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance,” Neuron 85, 942–958 (2015). 10.1016/j.neuron.2015.02.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Baker B. J., et al. , “Three fluorescent protein voltage sensors exhibit low plasma membrane expression in mammalian cells,” J. Neurosci. Methods 161, 32–38 (2007). 10.1016/j.jneumeth.2006.10.005 [DOI] [PubMed] [Google Scholar]
  • 73.Rhee J. K., et al. , “Biophysical parameters of GEVIs: considerations for imaging voltage,” Biophys. J. 119, 1–8 (2020). 10.1016/j.bpj.2020.05.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Jung A., et al. , “Linker length and fusion site composition improve the optical signal of genetically encoded fluorescent voltage sensors,” Neurophotonics 2, 021012 (2015). 10.1117/1.NPh.2.2.021012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Sepehri Rad M., Cohen L. B., Baker B. J., “Conserved amino acids residing outside the voltage field can shift the voltage sensitivity and increase the signal speed and size of Ciona based GEVIs,” Front. Cell Dev. Biol. 10, 868143 (2022). 10.3389/fcell.2022.868143 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Jung A., et al. , “Modulating the voltage-sensitivity of a genetically encoded voltage indicator,” Exp. Neurobiol. 26, 241–251 (2017). 10.5607/en.2017.26.5.241 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Lee S., et al. , “Improving a genetically encoded voltage indicator by modifying the cytoplasmic charge composition,” Sci. Rep. 7, 8286 (2017). 10.1038/s41598-017-08731-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Kralj J. M., et al. , “Electrical spiking in Escherichia coli probed with a fluorescent voltage-indicating protein,” Science 333, 345–348 (2011). 10.1126/science.1204763 [DOI] [PubMed] [Google Scholar]
  • 79.Bando Y., et al. , “Comparative evaluation of genetically encoded voltage indicators,” Cell Rep. 26, 802–813.e4 (2019). 10.1016/j.celrep.2018.12.088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Milosevic M., et al. , “In vitro testing of voltage indicators: Archon1, ArcLightD, ASAP1, ASAP2s, ASAP3b, Bongwoori-Pos6, BeRST1, FlicR1 and Chi-VSFP-Butterfly,” eneuro, 7, ENEURO.0060-0020 (2020). 10.1523/ENEURO.0060-20.2020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Sepehri Rad M., et al. , “Monitoring voltage fluctuations of intracellular membranes,” Sci. Rep. 8, 6911 (2018). 10.1038/s41598-018-25083-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Bando Y., Wenzel M., Yuste R., “Simultaneous two-photon imaging of action potentials and subthreshold inputs in vivo,” Nat. Commun. 12, 7229 (2021). 10.1038/s41467-021-27444-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Guerrero G., et al. , “Tuning FlaSh: redesign of the dynamics, voltage range, and color of the genetically encoded optical sensor of membrane potential,” Biophys. J. 83, 3607–3618 (2002). 10.1016/S0006-3495(02)75361-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Ataka K., Pieribone V. A., “A genetically targetable fluorescent probe of channel gating with rapid kinetics,” Biophys. J. 82, 509–516 (2002). 10.1016/S0006-3495(02)75415-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Sakai R., et al. , “Design and characterization of a DNA‐encoded, voltage‐sensitive fluorescent protein,” Eur. J. Neurosci. 13, 2314–2318 (2001). 10.1046/j.0953-816x.2001.01617.x [DOI] [PubMed] [Google Scholar]
  • 86.Dimitrov D., et al. , “Engineering and characterization of an enhanced fluorescent protein voltage sensor,” PLoS One 2, e440 (2007). 10.1371/journal.pone.0000440 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Murata Y., et al. , “Phosphoinositide phosphatase activity coupled to an intrinsic voltage sensor,” Nature 435, 1239–1243 (2005). 10.1038/nature03650 [DOI] [PubMed] [Google Scholar]
  • 88.Kang B. E., Baker B. J., “Pado, a fluorescent protein with proton channel activity can optically monitor membrane potential, intracellular pH, and map gap junctions,” Sci. Rep. 6, 23865 (2016). 10.1038/srep23865 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Sung U., et al. , “Developing fast fluorescent protein voltage sensors by optimizing FRET interactions,” PLoS One 10, e0141585 (2015). 10.1371/journal.pone.0141585 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Tsutsui H., et al. , “Improving membrane voltage measurements using FRET with new fluorescent proteins,” Nat. Methods 5, 683–685 (2008). 10.1038/nmeth.1235 [DOI] [PubMed] [Google Scholar]
  • 91.Jin L., et al. , “Single action potentials and subthreshold electrical events imaged in neurons with a fluorescent protein voltage probe,” Neuron 75, 779–785 (2012). 10.1016/j.neuron.2012.06.040 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92.Piao H. H., et al. , “Combinatorial mutagenesis of the voltage-sensing domain enables the optical resolution of action potentials firing at 60 Hz by a genetically encoded fluorescent sensor of membrane potential,” J. Neurosci. 35, 372–385 (2015). 10.1523/JNEUROSCI.3008-14.2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Ilagan R. P., et al. , “A new bright green-emitting fluorescent protein: engineered monomeric and dimeric forms,” FEBS J. 277, 1967–1978 (2010). 10.1111/j.1742-4658.2010.07618.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Han Z., et al. , “Fluorescent protein voltage probes derived from ArcLight that respond to membrane voltage changes with fast kinetics,” PLoS One 8, e81295 (2013). 10.1371/journal.pone.0081295 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Han Z., et al. , “Mechanistic studies of the genetically encoded fluorescent protein voltage probe ArcLight,” PLoS One 9, e113873 (2014). 10.1371/journal.pone.0113873 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.Chamberland S., et al. , “Fast two-photon imaging of subcellular voltage dynamics in neuronal tissue with genetically encoded indicators,” Elife 6, e25690 (2017). 10.7554/eLife.25690 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.St-Pierre F., et al. , “High-fidelity optical reporting of neuronal electrical activity with an ultrafast fluorescent voltage sensor,” Nat. Neurosci. 17, 884–889 (2014). 10.1038/nn.3709 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Liu Z., et al. , “Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy,” Cell 185, 3408–3425.e29 (2022). 10.1016/j.cell.2022.07.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Platisa J., et al. , “High-speed low-light in vivo two-photon voltage imaging of large neuronal populations,” Nat. Methods 20, 1095–1103 (2023). 10.1038/s41592-023-01820-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Junge W., Witt H. T., “On the ion transport system of photosynthesis—investigations on a molecular level,” Zeitschr. Naturforsch. B 23, 244–254 (1968). 10.1515/znb-1968-0222 [DOI] [PubMed] [Google Scholar]
  • 101.Kralj J. M., et al. , “Optical recording of action potentials in mammalian neurons using a microbial rhodopsin,” Nat. Methods 9, 90–95 (2012). 10.1038/nmeth.1782 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Maclaurin D., et al. , “Mechanism of voltage-sensitive fluorescence in a microbial rhodopsin,” Proc. Natl. Acad. Sci. U. S. A. 110, 5939–5944 (2013). 10.1073/pnas.1215595110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Hochbaum D. R., et al. , “All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins,” Nat. Methods 11, 825–833 (2014). 10.1038/nmeth.3000 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.Gong Y., et al. , “Imaging neural spiking in brain tissue using FRET-opsin protein voltage sensors,” Nat. Commun. 5, 3674 (2014). 10.1038/ncomms4674 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105.Zou P., et al. , “Bright and fast multicoloured voltage reporters via electrochromic FRET,” Nat. Commun. 5, 4625 (2014). 10.1038/ncomms5625 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106.Kannan M., et al. , “Dual-polarity voltage imaging of the concurrent dynamics of multiple neuron types,” Science 378, eabm8797 (2022). 10.1126/science.abm8797 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Kannan M., et al. , “Fast, in vivo voltage imaging using a red fluorescent indicator,” Nat. Methods 15, 1108–1116 (2018). 10.1038/s41592-018-0188-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108.Deal P. E., et al. , “Covalently tethered rhodamine voltage reporters for high speed functional imaging in brain tissue,” J. Am. Chem. Soc. 142, 614–622 (2019). 10.1021/jacs.9b12265 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Liu P., et al. , “Fluorogenic targeting of voltage-sensitive dyes to neurons,” J. Am. Chem. Soc. 139, 17334–17340 (2017). 10.1021/jacs.7b07047 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110.Villette V., et al. , “Ultrafast two-photon imaging of a high-gain voltage indicator in awake behaving mice,” Cell 179, 1590–1608.e23 (2019). 10.1016/j.cell.2019.11.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.Cao G., et al. , “Genetically targeted optical electrophysiology in intact neural circuits,” Cell 154, 904–913 (2013). 10.1016/j.cell.2013.07.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112.Raccuglia D., et al. , “Presynaptic GABA receptors mediate temporal contrast enhancement in drosophila olfactory sensory neurons and modulate odor-driven behavioral kinetics,” eNeuro 3, ENEURO.0080-16.2016 (2016). 10.1523/ENEURO.0080-16.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 113.Raccuglia D., et al. , “Network-specific synchronization of electrical slow-wave oscillations regulates sleep drive in drosophila,” Curr. Biol. 29, 3611–3621.e3 (2019). 10.1016/j.cub.2019.08.070 [DOI] [PubMed] [Google Scholar]
  • 114.Matzke A. J., Matzke M., “Expression and testing in plants of ArcLight, a genetically-encoded voltage indicator used in neuroscience research,” BMC Plant Biol. 15, 245 (2015). 10.1186/s12870-015-0633-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 115.Borden P. Y., et al. , “Genetically expressed voltage sensor ArcLight for imaging large scale cortical activity in the anesthetized and awake mouse,” Neurophotonics 4, 031212 (2017). 10.1117/1.NPh.4.3.031212 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 116.Jimenez-Martin J., et al. , “Cholinergic modulation of sensory processing in awake mouse cortex,” Sci. Rep. 11, 17525 (2021). 10.1038/s41598-021-96696-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 117.Cleland T. A., et al. , “Sequential mechanisms underlying concentration invariance in biological olfaction,” Front. Neuroeng. 4, 21 (2011). 10.3389/fneng.2011.00021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 118.Zak J. D., et al. , “Antagonistic odor interactions in olfactory sensory neurons are widespread in freely breathing mice,” Nat. Commun. 11, 3350 (2020). 10.1038/s41467-020-17124-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 119.Buck L., Axel R., “A novel multigene family may encode odorant receptors: a molecular basis for odor recognition,” Cell 65, 175–187 (1991). 10.1016/0092-8674(91)90418-X [DOI] [PubMed] [Google Scholar]
  • 120.Malnic B., et al. , “Combinatorial receptor codes for odors,” Cell 96, 713–723 (1999). 10.1016/S0092-8674(00)80581-4 [DOI] [PubMed] [Google Scholar]
  • 121.Duchamp-Viret P., Chaput M. A., Duchamp A., “Odor response properties of rat olfactory receptor neurons,” Science 284, 2171–2174 (1999). 10.1126/science.284.5423.2171 [DOI] [PubMed] [Google Scholar]
  • 122.Ma M., Shepherd G. M., “Functional mosaic organization of mouse olfactory receptor neurons,” Proc. Natl. Acad. Sci. U. S. A. 97, 12869–12874 (2000). 10.1073/pnas.220301797 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 123.Mombaerts P., et al. , “Visualizing an olfactory sensory map,” Cell 87, 675–686 (1996). 10.1016/S0092-8674(00)81387-2 [DOI] [PubMed] [Google Scholar]
  • 124.Takeuchi H., Sakano H., “Neural map formation in the mouse olfactory system,” Cell Mol. Life Sci. 71, 3049–3057 (2014). 10.1007/s00018-014-1597-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 125.Bozza T., et al. , “Odorant receptor expression defines functional units in the mouse olfactory system,” J. Neurosci. 22, 3033–3043 (2002). 10.1523/JNEUROSCI.22-08-03033.2002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 126.Feinstein P., et al. , “Axon guidance of mouse olfactory sensory neurons by odorant receptors and the beta2 adrenergic receptor,” Cell 117, 833–846 (2004). 10.1016/j.cell.2004.05.013 [DOI] [PubMed] [Google Scholar]
  • 127.Price J. L., Powell T. P., “The mitral and short axon cells of the olfactory bulb,” J. Cell Sci. 7, 631–651 (1970). 10.1242/jcs.7.3.631 [DOI] [PubMed] [Google Scholar]
  • 128.Igarashi K. M., et al. , “Parallel mitral and tufted cell pathways route distinct odor information to different targets in the olfactory cortex,” J. Neurosci. 32, 7970–7985 (2012). 10.1523/JNEUROSCI.0154-12.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 129.Nagayama S., Homma R., Imamura F., “Neuronal organization of olfactory bulb circuits,” Front. Neural Circuits 8, 98 (2014). 10.3389/fncir.2014.00098 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 130.Parrish-Aungst S., et al. , “Quantitative analysis of neuronal diversity in the mouse olfactory bulb,” J. Comp. Neurol. 501, 825–836 (2007). 10.1002/cne.21205 [DOI] [PubMed] [Google Scholar]
  • 131.Najac M., et al. , “Monosynaptic and polysynaptic feed-forward inputs to mitral cells from olfactory sensory neurons,” J. Neurosci. 31, 8722–8729 (2011). 10.1523/JNEUROSCI.0527-11.2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 132.Gire D. H., et al. , “Mitral cells in the olfactory bulb are mainly excited through a multistep signaling path,” J. Neurosci. 32, 2964–2975 (2012). 10.1523/JNEUROSCI.5580-11.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 133.De Saint Jan D., et al. , “External tufted cells drive the output of olfactory bulb glomeruli,” J. Neurosci. 29, 2043–2052 (2009). 10.1523/JNEUROSCI.5317-08.2009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 134.McGann J. P., “Presynaptic inhibition of olfactory sensory neurons: new mechanisms and potential functions,” Chem. Sens. 38, 459–474 (2013). 10.1093/chemse/bjt018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 135.Aungst J. L., et al. , “Centre-surround inhibition among olfactory bulb glomeruli,” Nature 426, 623–629 (2003). 10.1038/nature02185 [DOI] [PubMed] [Google Scholar]
  • 136.Rall W., et al. , “Dendrodendritic synaptic pathway for inhibition in the olfactory bulb,” Exp. Neurol. 14, 44–56 (1966). 10.1016/0014-4886(66)90023-9 [DOI] [PubMed] [Google Scholar]
  • 137.Shepherd G. M., Chen W. R., Greer C. A., Olfactory Bulb. In the Synaptic Organization of the Brain, pp. 165–216, Oxford University Press, New York: (2004). [Google Scholar]
  • 138.Wachowiak M., et al. , “Inhibition [corrected] of olfactory receptor neuron input to olfactory bulb glomeruli mediated by suppression of presynaptic calcium influx,” J. Neurophysiol. 94, 2700–2712 (2005). 10.1152/jn.00286.2005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 139.Berkowicz D. A., Trombley, P. Q. “Dopaminergic modulation at the olfactory nerve synapse,” Brain Res. 855, 90–99 (2000). 10.1016/S0006-8993(99)02342-2 [DOI] [PubMed] [Google Scholar]
  • 140.Boyd A. M., et al. , “Cortical feedback control of olfactory bulb circuits,” Neuron 76, 1161–1174 (2012). 10.1016/j.neuron.2012.10.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 141.Kapoor V., et al. , “Activation of raphe nuclei triggers rapid and distinct effects on parallel olfactory bulb output channels,” Nat. Neurosci. 19, 271–282 (2016). 10.1038/nn.4219 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 142.Markopoulos F., et al. , “Functional properties of cortical feedback projections to the olfactory bulb,” Neuron 76, 1175–1188 (2012). 10.1016/j.neuron.2012.10.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 143.Petzold G. C., Hagiwara A., Murthy V. N., “Serotonergic modulation of odor input to the mammalian olfactory bulb,” Nat. Neurosci. 12, 784–791 (2009). 10.1038/nn.2335 [DOI] [PubMed] [Google Scholar]
  • 144.Rothermel M., et al. , “Cholinergic inputs from Basal forebrain add an excitatory bias to odor coding in the olfactory bulb,” J. Neurosci. 34, 4654–4664 (2014). 10.1523/JNEUROSCI.5026-13.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 145.Rothermel M., Wachowiak M., “Functional imaging of cortical feedback projections to the olfactory bulb,” Front. Neural Circuits 8, 73 (2014). 10.3389/fncir.2014.00073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 146.Martelli C., Storace D. A., “Stimulus driven functional transformations in the early olfactory system,” Front. Cell. Neurosci. 15, 684742 (2021). 10.3389/fncel.2021.684742 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 147.Moran A. K., Eiting T. P., Wachowiak M., “Dynamics of glutamatergic drive underlie diverse responses of olfactory bulb outputs,” eNeuro 8, ENEURO.0110-21.2021 (2021). 10.1523/ENEURO.0110-21.2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 148.Storace D. A., Cohen L. B., “Measuring the olfactory bulb input-output transformation reveals a contribution to the perception of odorant concentration invariance,” Nat. Commun. 8, 81 (2017). 10.1038/s41467-017-00036-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 149.Storace D. A., Cohen L. B., “The mammalian olfactory bulb contributes to the adaptation of odor responses: a second perceptual computation carried out by the bulb,” eNeuro 8, ENEURO.0322-21.2021 (2021). 10.1523/ENEURO.0322-21.2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 150.Storace D. A., Cohen L. B., Choi Y., “Using genetically encoded voltage indicators (GEVIs) to study the input-output transformation of the mammalian olfactory bulb,” Front. Cell Neurosci. 13, 342 (2019). 10.3389/fncel.2019.00342 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 151.Vincis R., et al. , “Dense representation of natural odorants in the mouse olfactory bulb,” Nat. Neurosci. 15, 537–539 (2012). 10.1038/nn.3057 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 152.Economo M. N., Hansen K. R., Wachowiak M., “Control of mitral/tufted cell output by selective inhibition among olfactory bulb glomeruli,” Neuron 91, 397–411 (2016). 10.1016/j.neuron.2016.06.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 153.Kay L. M., Laurent G., “Odor- and context-dependent modulation of mitral cell activity in behaving rats,” Nat. Neurosci. 2, 1003–1009 (1999). 10.1038/14801 [DOI] [PubMed] [Google Scholar]
  • 154.Rinberg D., Koulakov A., Gelperin A., “Sparse odor coding in awake behaving mice,” J. Neurosci. 26, 8857–8865 (2006). 10.1523/JNEUROSCI.0884-06.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 155.Shusterman R., et al. , “Precise olfactory responses tile the sniff cycle,” Nat. Neurosci. 14, 1039–1044 (2011). 10.1038/nn.2877 [DOI] [PubMed] [Google Scholar]
  • 156.Niessing J., Friedrich R. W., “Olfactory pattern classification by discrete neuronal network states,” Nature 465, 47–52 (2010). 10.1038/nature08961 [DOI] [PubMed] [Google Scholar]
  • 157.Rubin B. D., Katz L. C., “Optical imaging of odorant representations in the mammalian olfactory bulb,” Neuron 23, 499–511 (1999). 10.1016/S0896-6273(00)80803-X [DOI] [PubMed] [Google Scholar]
  • 158.Vincis R., et al. , “Sensory-evoked intrinsic imaging signals in the olfactory bulb are independent of neurovascular coupling,” Cell Rep. 12, 313–325 (2015). 10.1016/j.celrep.2015.06.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 159.Wachowiak M., Cohen L. B., “Correspondence between odorant-evoked patterns of receptor neuron input and intrinsic optical signals in the mouse olfactory bulb,” J. Neurophysiol. 89, 1623–1639 (2003). 10.1152/jn.00747.2002 [DOI] [PubMed] [Google Scholar]
  • 160.Soucy E. R., et al. , “Precision and diversity in an odor map on the olfactory bulb,” Nat. Neurosci. 12, 210–220 (2009). 10.1038/nn.2262 [DOI] [PubMed] [Google Scholar]
  • 161.Blasdel G. G., Salama G., “Voltage-sensitive dyes reveal a modular organization in monkey striate cortex,” Nature 321, 579–585 (1986). 10.1038/321579a0 [DOI] [PubMed] [Google Scholar]
  • 162.Grinvald A., et al. , “Functional architecture of cortex revealed by optical imaging of intrinsic signals,” Nature 324, 361–364 (1986). 10.1038/324361a0 [DOI] [PubMed] [Google Scholar]
  • 163.Chery R., et al. , “Imaging odor-evoked activities in the mouse olfactory bulb using optical reflectance and autofluorescence signals,” J. Vis. Exp. 56, e3336 (2011). 10.3791/3336 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 164.Uytingco C. R., Puche A. C., Munger S. D., “Using intrinsic flavoprotein and NAD(P)H imaging to map functional circuitry in the main olfactory bulb,” PLoS One 11, e0165342 (2016). 10.1371/journal.pone.0165342 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 165.Spors H., Grinvald A., “Spatio-temporal dynamics of odor representations in the mammalian olfactory bulb,” Neuron 34, 301–315 (2002). 10.1016/S0896-6273(02)00644-X [DOI] [PubMed] [Google Scholar]
  • 166.Friedrich R. W., “Calcium imaging in the intact olfactory system of zebrafish and mouse,” Cold Spring Harb. Protoc. 2014, 310–316 (2014). 10.1101/pdb.prot081166 [DOI] [PubMed] [Google Scholar]
  • 167.Bozza T., et al. , “In vivo imaging of neuronal activity by targeted expression of a genetically encoded probe in the mouse,” Neuron 42, 9–21 (2004). 10.1016/S0896-6273(04)00144-8 [DOI] [PubMed] [Google Scholar]
  • 168.Kass M. D., et al. , “Changes in olfactory sensory neuron physiology and olfactory perceptual learning after odorant exposure in adult mice,” Chem. Sens. 41, 123–133 (2016). 10.1093/chemse/bjv065 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 169.McGann J. P., et al. , “Odorant representations are modulated by intra- but not interglomerular presynaptic inhibition of olfactory sensory neurons,” Neuron 48, 1039–1053 (2005). 10.1016/j.neuron.2005.10.031 [DOI] [PubMed] [Google Scholar]
  • 170.Banerjee A., et al. , “An interglomerular circuit gates glomerular output and implements gain control in the mouse olfactory bulb,” Neuron 87, 193–207 (2015). 10.1016/j.neuron.2015.06.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 171.Blauvelt D. G., et al. , “Distinct spatiotemporal activity in principal neurons of the mouse olfactory bulb in anesthetized and awake states,” Front. Neural Circuits 7, 46 (2013). 10.3389/fncir.2013.00046 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 172.Fletcher M. L., et al. , “Optical imaging of postsynaptic odor representation in the glomerular layer of the mouse olfactory bulb,” J. Neurophysiol. 102, 817–830 (2009). 10.1152/jn.00020.2009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 173.Ogg M. C., et al. , “Olfactory bulb acetylcholine release dishabituates odor responses and reinstates odor investigation,” Nat. Commun. 9, 1868 (2018). 10.1038/s41467-018-04371-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 174.Fletcher M. L., “Analytical processing of binary mixture information by olfactory bulb glomeruli,” PLoS One 6, e29360 (2011). 10.1371/journal.pone.0029360 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 175.Inagaki S., et al. , “Widespread inhibition, antagonism, and synergy in mouse olfactory sensory neurons in vivo,” Cell Rep. 31, 107814 (2020). 10.1016/j.celrep.2020.107814 [DOI] [PubMed] [Google Scholar]
  • 176.Kass M. D., et al. , “Fear learning enhances neural responses to threat-predictive sensory stimuli,” Science 342, 1389–1392 (2013). 10.1126/science.1244916 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 177.Ogg M. C., Bendahamane M., Fletcher M. L., “Habituation of glomerular responses in the olfactory bulb following prolonged odor stimulation reflects reduced peripheral input,” Front. Mol. Neurosci. 8, 53 (2015). 10.3389/fnmol.2015.00053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 178.Ross J. M., Fletcher M. L., “Learning-dependent and -independent enhancement of mitral/tufted cell glomerular odor responses following olfactory fear conditioning in awake mice,” J. Neurosci. 38, 4623–4640 (2018). 10.1523/JNEUROSCI.3559-17.2018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 179.Platisa J., et al. , “Voltage imaging in the olfactory bulb using transgenic mouse lines expressing the genetically encoded voltage indicator ArcLight,” Sci. Rep. 12, 1875 (2022). 10.1038/s41598-021-04482-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 180.Passini M. A., et al. , “Intraventricular brain injection of adeno-associated virus type 1 (AAV1) in neonatal mice results in complementary patterns of neuronal transduction to AAV2 and total long-term correction of storage lesions in the brains of beta-glucuronidase-deficient mice,” J. Virol. 77, 7034–7040 (2003). 10.1128/JVI.77.12.7034-7040.2003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 181.Vucinic D., Cohen L. B., Kosmidis E. K., “Interglomerular center-surround inhibition shapes odorant-evoked input to the mouse olfactory bulb in vivo,” J. Neurophysiol. 95, 1881–1887 (2006). 10.1152/jn.00918.2005 [DOI] [PubMed] [Google Scholar]
  • 182.Spors H., et al. , “Temporal dynamics and latency patterns of receptor neuron input to the olfactory bulb,” J. Neurosci. 26, 1247–1259 (2006). 10.1523/JNEUROSCI.3100-05.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 183.Sobel E. C., Tank D. W., “Timing of odor stimulation does not alter patterning of olfactory bulb unit activity in freely breathing rats,” J. Neurophysiol. 69, 1331–1337 (1993). 10.1152/jn.1993.69.4.1331 [DOI] [PubMed] [Google Scholar]
  • 184.Doving K. B., “Response properties of neurones in the rat olfactory bulb to various parameters of odour stimulation,” Acta Physiol. Scand. 130, 285–298 (1987). 10.1111/j.1748-1716.1987.tb08139.x [DOI] [PubMed] [Google Scholar]
  • 185.Cang J., Isaacson J. S., “In vivo whole-cell recording of odor-evoked synaptic transmission in the rat olfactory bulb,” J. Neurosci. 23, 4108–4116 (2003). 10.1523/JNEUROSCI.23-10-04108.2003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 186.Mayford M., et al. , “Control of memory formation through regulated expression of a CaMKII transgene,” Science 274, 1678–1683 (1996). 10.1126/science.274.5293.1678 [DOI] [PubMed] [Google Scholar]
  • 187.Li J., et al. , “Odorant receptor gene choice is reset by nuclear transfer from mouse olfactory sensory neurons,” Nature 428, 393–399 (2004). 10.1038/nature02433 [DOI] [PubMed] [Google Scholar]
  • 188.Nagai Y., Sano H., Yokoi M., “Transgenic expression of Cre recombinase in mitral/tufted cells of the olfactory bulb,” Genesis 43, 12–16 (2005). 10.1002/gene.20146 [DOI] [PubMed] [Google Scholar]
  • 189.Kohwi M., et al. , “A subpopulation of olfactory bulb GABAergic interneurons is derived from Emx1- and Dlx5/6-expressing progenitors,” J. Neurosci. 27, 6878–6891 (2007). 10.1523/JNEUROSCI.0254-07.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 190.Firestein S., Picco C., Menini A., “The relation between stimulus and response in olfactory receptor cells of the tiger salamander,” J. Physiol. 468, 1–10 (1993). 10.1113/jphysiol.1993.sp019756 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 191.Kurahashi T., Menini A., “Mechanism of odorant adaptation in the olfactory receptor cell,” Nature 385, 725–729 (1997). 10.1038/385725a0 [DOI] [PubMed] [Google Scholar]
  • 192.Menini A., Picco C., Firestein S., “Quantal-like current fluctuations induced by odorants in olfactory receptor cells,” Nature 373, 435–437 (1995). 10.1038/373435a0 [DOI] [PubMed] [Google Scholar]
  • 193.Reisert J., Matthews H. R., “Adaptation of the odour-induced response in frog olfactory receptor cells,” J. Physiol. 519(3), 801–813 (1999). 10.1111/j.1469-7793.1999.0801n.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 194.Reisert J., Matthews H. R., “Response properties of isolated mouse olfactory receptor cells,” J. Physiol. 530, 113–122 (2001). 10.1111/j.1469-7793.2001.0113m.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 195.Reisert J., Matthews H. R., “Responses to prolonged odour stimulation in frog olfactory receptor cells,” J. Physiol. 534, 179–191 (2001). 10.1111/j.1469-7793.2001.t01-1-00179.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 196.Uchida N., Mainen Z. F., “Odor concentration invariance by chemical ratio coding,” Front. Syst. Neurosci. 1, 3 (2007). 10.3389/neuro.06.003.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 197.Gross-Isseroff R., Lancet D., “Concentration-dependent changes of perceived odor quality,” Chem. Sens. 13, 191–204 (1988). 10.1093/chemse/13.2.191 [DOI] [Google Scholar]
  • 198.Rokni D., et al. , “An olfactory cocktail party: figure-ground segregation of odorants in rodents,” Nat. Neurosci. 17, 1225–1232 (2014). 10.1038/nn.3775 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 199.Asahina K., et al. , “A circuit supporting concentration-invariant odor perception in Drosophila,” J. Biol. 8, 9 (2009). 10.1186/jbiol108 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 200.Homma R., et al. , “Perceptual stability during dramatic changes in olfactory bulb activation maps and dramatic declines in activation amplitudes,” Eur. J. Neurosci. 29, 1027–1034 (2009). 10.1111/j.1460-9568.2009.06644.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 201.Cleland T. A., et al. , “Relational representation in the olfactory system,” Proc. Natl. Acad. Sci. U. S. A. 104, 1953–1958 (2007). 10.1073/pnas.0608564104 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 202.Cleland T. A., “Early transformations in odor representation,” Trends Neurosci. 33, 130–139 (2010). 10.1016/j.tins.2009.12.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 203.Meredith M., “Patterned response to odor in mammalian olfactory bulb: the influence of intensity,” J. Neurophysiol. 56, 572–597 (1986). 10.1152/jn.1986.56.3.572 [DOI] [PubMed] [Google Scholar]
  • 204.Orbach H. S., Cohen L. B., “Optical monitoring of activity from many areas of the in vitro and in vivo salamander olfactory bulb: a new method for studying functional organization in the vertebrate central nervous system,” J. Neurosci. 3, 2251–2262 (1983). 10.1523/JNEUROSCI.03-11-02251.1983 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 205.London J. A., Zecević D., Cohen L. B., “Simultaneous optical recording of activity from many neurons during feeding in Navanax,” J. Neurosci. 7, 649–661 (1987). 10.1523/JNEUROSCI.07-03-00649.1987 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 206.Antić S., Zecević D., “Optical signals from neurons with internally applied voltage-sensitive dyes,” J. Neurosci. 15, 1392–1405 (1995). 10.1523/JNEUROSCI.15-02-01392.1995 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 207.Djurisic M., et al. , “Voltage imaging from dendrites of mitral cells: EPSP attenuation and spike trigger zones,” J. Neurosci. 24, 6703–6714 (2004). 10.1523/JNEUROSCI.0307-04.2004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 208.Popovic M. A., et al. , “Electrical behaviour of dendritic spines as revealed by voltage imaging,” Nat. Commun. 6, 8436 (2015). 10.1038/ncomms9436 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 209.Chakrabarty P., et al. , “Capsid serotype and timing of injection determines AAV transduction in the neonatal mice brain,” PLoS One 8, e67680 (2013). 10.1371/journal.pone.0067680 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 210.Adam Y., et al. , “Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics,” Nature 569, 413–417 (2019). 10.1038/s41586-019-1166-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 211.Denk W., Strickler J. H., Webb W. W., “Two-photon laser scanning fluorescence microscopy,” Science 248, 73–76 (1990). 10.1126/science.2321027 [DOI] [PubMed] [Google Scholar]
  • 212.Frostig R. D., In vivo optical imaging of brain function, 2nd ed., CRC Press; (2009). [PubMed] [Google Scholar]
  • 213.Akemann W., et al. , “Two-photon voltage imaging using a genetically encoded voltage indicator,” Sci. Rep. 3, 2231 (2013). 10.1038/srep02231 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 214.Ahrens K. F., et al. , “Two-photon scanning microscopy of in vivo sensory responses of cortical neurons genetically encoded with a fluorescent voltage sensor in rat,” Front. Neural Circuits 6, 15 (2012). 10.3389/fncir.2012.00015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 215.Platisa J., Pieribone V. A., “Genetically encoded fluorescent voltage indicators: are we there yet?” Curr. Opin. Neurobiol. 50, 146–153 (2018). 10.1016/j.conb.2018.02.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 216.Homma R., et al. , “Narrowly confined and glomerulus-specific onset latencies of odor-evoked calcium transients in the juxtaglomerular cells of the mouse main olfactory bulb,” eNeuro 6, ENEURO.0387-18.2019 (2019). 10.1523/ENEURO.0387-18.2019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 217.Dana H., et al. , “Sensitive red protein calcium indicators for imaging neural activity,” Elife 5, e12727 (2016). 10.7554/eLife.12727 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Data Availability Statement

All data described in this manuscript are available from the authors upon request.


Articles from Neurophotonics are provided here courtesy of Society of Photo-Optical Instrumentation Engineers

RESOURCES