Author response: Juxtacellular opto-tagging of hippocampal CA1 neurons in freely moving mice
2021-12-29

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Neural circuits are made of a vast diversity of neuronal cell types. While immense progress has been made in classifying neurons based on morphological, molecular, and functional properties, understanding how this heterogeneity contributes to brain function during natural behavior has remained largely unresolved. In the present study, we combined the juxtacellular recording and labeling technique with optogenetics in freely moving mice. This allowed us to selectively target molecularly defined cell classes for in vivo single-cell recordings and morphological analysis. We validated this strategy in the CA1 region of the mouse hippocampus by restricting Channelrhodopsin expression to Calbindin-positive neurons. Directly versus indirectly light-activated neurons could be readily distinguished based on the latencies of light-evoked spikes, with juxtacellular labeling and post hoc histological analysis providing ‘ground-truth’ validation. Using these opto-juxtacellular procedures in freely moving mice, we found that Calbindin-positive CA1 pyramidal cells were weakly spatially modulated and conveyed less spatial information than Calbindin-negative neurons – pointing to pyramidal cell identity as a key determinant for neuronal recruitment into the hippocampal spatial map. Thus, our method complements current in vivo techniques by enabling optogenetic-assisted structure–function analysis of single neurons recorded during natural, unrestrained behavior. Editor's evaluation This study presents a major technical advance by recording from genetically identified neurons in freely moving mice. This method is applied to the hippocampus to determine circuit specific synaptic interaction in vivo and to compare behavioral correlates of genetically-defined cell types. This technique paves the way for future studies aiming to relate genetics, circuits, and neuronal coding in freely moving animals. https://doi.org/10.7554/eLife.71720.sa0 Decision letter eLife's review process Introduction Since the seminal work of Ramón y Cajal it has become clear that neuronal circuits consist of a large variety of neuronal elements. Such diversity encompasses virtually all aspects of neuronal biology, including morphological, genetic, physiological, and functional properties. Over recent years, major advances have been made toward the systematic classification of neuronal cell types (Ascoli et al., 2008; Scala et al., 2020; Tasic et al., 2016; Zeisel et al., 2015). From these research lines, it has become increasingly clear that a combinatorial approach – integrating morphological, molecular, electrophysiological, and functional properties – is required for neuronal cell-type classification. As inventories of cell types within brain circuits are beginning to emerge, the next challenge is to understand how these cell types contribute to neural circuit computations during behavior. Bridging this gap is a methodological challenge, since current in vivo techniques for neural circuit analysis – for example optogenetic tagging and Ca2+ imaging (Anikeeva et al., 2011; Ghosh et al., 2011; Lima et al., 2009) – provide only limited information about the recorded neuronal elements. These methods largely rely upon the genetically restricted expression of Channelrhodopsin (ChR2) or Ca2+ indicators – for example via Cre driver lines (Adesnik, 2018; Gafford and Ressler, 2016; Kravitz et al., 2013). In the case of ‘optogenetic tagging’, recordings are performed with a dual optical and electrical probe (referred to as ‘optrode’) and short-latency spikes elicited upon illumination provide information about the identity of the recorded units. One limitation of this approach is that ‘cell types’ are defined by single features, like genetic marker expression or projection target. Additional structural features – like morphology, local and long-range axonal projections, and gene expression profiles – are not accessible with these techniques, thereby making it difficult to map neuronal activity (assessed in vivo) to the multidimensional cell-type classification schemes (being developed in vitro). In vivo single-cell identification techniques can in principle provide the necessary anatomical resolution for multidimensional classification of the recorded neurons. For example, the juxtacellular method (Pinault, 1996; Pinault, 1994) allows recording of single neurons in awake behaving animals, along with post hoc morphological analysis and molecular phenotyping of the recorded cells (Averkin et al., 2016; Diamantaki et al., 2018; Katona et al., 2014; Lapray et al., 2012; Tang et al., 2014b; Valero et al., 2015). However, a significant limitation of these methods is that neurons are typically sampled by ‘blind’ procedures; hence, recordings cannot be targeted to a predefined cell class, which makes it extremely challenging – if not impossible – to efficiently sample sparse neuronal elements within a given circuit. To circumvent this limitation, an elegant approach has been recently developed by combining single-cell identification techniques with optogenetic tagging (Katz et al., 2013; Muñoz et al., 2014). This approach enabled ChR2-assisted targeting of genetically defined cell classes; in addition, by means of juxtacellular labeling and post hoc morphological/molecular analysis, it also provided access to the multidimensional neuronal features required for precise cell-type classification (Muñoz et al., 2017). In its current form however, the applicability of this method is limited to mechanically stable preparations (e.g., anesthetized or head-restrained animals), thereby preventing structure–function analysis in freely moving animals, engaged in ethologically relevant behaviors. In the present study, we aimed at bridging this gap by combining the single-cell opto-tagging method (Katz et al., 2013; Muñoz et al., 2014) with the juxtacellular procedures that we have recently established in freely moving mice (Diamantaki et al., 2018). This approach enabled us to efficiently target juxtacellular recording and labeling to predefined, genetically tagged cell classes in awake animals, engaged in natural exploratory behavior. We demonstrate the technical feasibility of this approach in the dorsal CA1 of the mouse hippocampus. During behavior, the activity of ‘place cells’ in this region (O’Keefe, 1976) is thought to contribute to the encoding of spatial and episodic experiences, and thus to form the neural basis of ‘memory engrams’ (Tonegawa et al., 2018). Anatomically – despite its relatively simple organization – the CA1 has a remarkably diverse cellular composition. In vitro and in vivo work has distinguished more than 20 different morphofunctional interneuronal classes (Bezaire and Soltesz, 2013; Freund and Buzsáki, 1998; Klausberger and Somogyi, 2008), and recent transcriptomic studies point to a seemingly complex heterogeneity among pyramidal neurons, with at least 15 classes being identified purely based on gene expression data (Yao et al., 2021). At present it is unclear how these distinct cell types contribute to neural activity during behavior. As test ground for our technique, we focused on a well-defined dimension of pyramidal cell diversity, namely along the radial (deep-superficial) axis of the dorsal CA1 (deep, closer to stratum oriens; superficial, closer to stratum radiatum). Indeed, a growing body of evidence indicates that the anatomical location of the neurons along this axis correlates with in vivo activity patterns (Cohen et al., 2017; Danielson et al., 2016; Fattahi et al., 2018; Geiller et al., 2017; Li et al., 2017; Mizuseki et al., 2011; Sharif et al., 2021; Valero et al., 2015; for review, see Preston-Ferrer and Burgalossi, 2018; Soltesz and Losonczy, 2018). We thus took advantage of the expression of Calbindin (Calb1) – which is selective for superficially located pyramidal neurons – for testing our juxtacellular opto-tagging procedures in freely behaving mice. We found that, while animals explored a familiar environment, Calb1-negative pyramidal neurons were preferentially recruited into the place cell map, while Calb1-positive pyramidal cells were only weakly spatially modulated. These data are thus consistent with the emerging idea of an asymmetric recruitment of CA1 pyramidal cells types into the hippocampal representation. Results To genetically target Calb1-positive CA1 pyramidal neurons, we took advantage of the Calb1cre driver line (Daigle et al., 2018; Nigro et al., 2018). As expected from the distribution of Calb1-positive neurons within the CA1 pyramidal layer, injections of AAV-CAG-flex-GFP in Calb1cre mice resulted in selective labeling of superficial pyramidal cells (Figure 1A) along with scattered, putative interneurons located primarily within the stratum oriens (not shown). Figure 1 with 1 supplement see all Download asset Open asset Juxtacellular opto-tagging of Calb1-positive CA1 pyramidal cells in anesthetized mice. (A) Left, a schematic of a mouse coronal brain section showing the injection site of AAV-CAG-flex-GFP in Calb1cre mice. Right, epifluoresence image showing Green Fluorescent Protein (GFP) expression (green) in the superficial pyramidal cell layer (blue, DAPI). SO, stratum oriens; Pyr, stratum pyramidale; SR, stratum radiatum; SLM, stratum lacunosum moleculare; ML, molecular layer of dentate gyrus. Scale bar = 100 μm. (B) Left, a schematic of a mouse coronal brain section showing the juxtacellular opto-tagging recording configuration. Right, a confocal image showing the expression of mCherry coexpressed with ChR2 (red) in the superficial Calb1-positive pyramidal layer (green). Scale bar = 20 μm. (C) A representative light-responsive pyramidal neuron, recorded in vivo. Top, epifluorescence images showing Calb1 (green), ChR2 (red), and Neurobiotin labeling of the neuron (white). Middle, z-stack projection of the same neuron (inverted signal). The arrowhead indicates a branching point of the primary apical dendrite. Bottom, high-pass filtered juxtacellular spike trace, showing short-latency spike responses upon pulses of blue light (5 ms, indicated in blue). Scale bar = 20 μm. (D) Bottom, high-pass filtered juxtacellular spike traces of a representative neuron responding to 25 Hz light-pulse stimuli. Top, high-magnification view on a single train (25 Hz, 10 pulses). (E) Raster plot (top) and peristimulus time histogram (bottom) showing the spike latency to the light stimulus for the neuron shown in C. The average latency is indicated. (F) Histogram of average spike latencies of all identified Calb1-positive pyramidal neurons in CA1 (n = 10). Figure 1—source data 1 Juxtacellular opto-tagging data, average spike latencies (source data for panels E and F). https://cdn.elifesciences.org/articles/71720/elife-71720-fig1-data1-v1.xlsx Download elife-71720-fig1-data1-v1.xlsx To record the activity from single Calb1-positive CA1 pyramidal neurons, we selectively expressed ChR2 in Calb1-positive CA1 neurons by injecting AAV-hSyn1-flex-ChR2-mCherry in the dorsal CA1 of Calb1cre mice. We then employed the juxtacellular opto-tagging configuration (also known as ‘optopatcher’; Katz et al., 2013; Muñoz et al., 2014) – consisting of a 50-μm-core diameter optic fiber, connected to a blue laser source, placed inside the glass electrode (Figure 1B) – for recording single CA1 neurons in anesthetized animals. Once a recording was established from a putative CA1 pyramidal neuron, low-power (0.1–1 mW) brief blue light pulses (5 ms) were delivered via the optic fiber to test for light-evoked spike responses. Subsequently, the neuron was labeled via juxtacellular procedures. One representative recording is shown in Figure 1C–E. This neuron was located in the superficial pyramidal layer and its apical dendrite displayed a proximal branching point (see Figure 1C) – a characteristic morphological correlate of superficial CA1 pyramidal neurons (Bannister and Larkman, 1995; Li et al., 2017). This neuron was confirmed to be positive for the marker Calb1 as well as ChR2 (Figure 1C). Indeed, blue light pulses could reliably drive short-latency spikes (mean latency = 2.1 ± 0.12 ms; Figure 1D, E) thus confirming the Calb1-positive identity of the recorded cell. Altogether, we recorded and labeled 19 neurons in anesthetized mice. In all cases where short-latency spiking responses were observed (mean latency <4 ms; Figure 1F) the neurons were confirmed to be Calb1-positive by immunohistochemical analysis (n = 10), therefore validating our opto-tagging strategy. Seven additional nonresponsive neurons were also juxtacellularly labeled and confirmed to be Calb1-negative pyramidal cells (not shown). In two cases, we recorded the activity of light-responsive, narrow-waveform neurons (spike peak-to-trough <0.4 ms, see Ding et al., 2020; Preston-Ferrer et al., 2016) which were positive for Calb1 immunoreactivity, and were thus classified as putative Calb1-positive interneurons (see Figure 1—figure supplement 1A–C; these neurons were not included in further analysis). One known challenge of optogenetic tagging experiments is to reliably distinguish directly versus indirectly excited neurons (e.g., Beyeler et al., 2016; Zutshi et al., 2018). The short latency of our light-responsive neurons (Figure 1F) together with post hoc validation by juxtacellular labeling (Figure 1C), provide strong support for direct light activation. To further support these observations and benchmark our opto-tagging strategy, we tested our approach with presynaptic input activation. To this end, we injected AAV-CAG-ChR2-mCherry in the CA3 of wild-type mice (Figure 2A). By these means, we obtained strong labeling of CA3 axon terminals (Schaffer collaterals), extending into the CA1 strata radiatum and oriens (Figure 2B). We then juxtacellularly recorded from single postsynaptic CA1 pyramidal neurons while simultaneously activating presynaptic CA3 terminals. A representative recording from a light-responsive identified CA1 pyramidal neuron is shown in Figure 2B–D. In this neuron, photostimulation at ~1.3 mW was sufficient for inducing suprathreshold spiking (Figure 2C), although with lower efficiency and longer latencies (7.4 ± 0.8 ms) as compared to the directly light-activated neurons (see Figure 1C–F). At the population level, the response latencies of indirectly light-activated CA1 neurons were significantly longer than those of directly light-activated CA1 Calb1-positive cells (direct light activation, mean latency = 2.7 ± 0.3 ms, n = 10; indirect light activation, mean latency = 7.0 ± 1.8 ms, n = 11; p = 0.0010; Figure 2E), thereby further validating our latency-based criteria for cell-type classification. Figure 2 Download asset Open asset Juxtacellular opto-activation of Schaffer collaterals and mossy fiber inputs onto postsynaptic neurons in anesthetized mice. (A) Left, schematic of a mouse coronal brain section showing the injection site of an opsin-expressing viral vector (AAV-CAG-ChR2-mCherry) in the CA3 region. Right, schematic showing the opto-activation of Schaffer collateral inputs combined with juxtacellular recording from single CA1 pyramidal neurons. (B) Confocal images showing a light-responsive CA1 pyramidal neuron (recording shown in C, D) labeled in vivo. Right panel, high-magnification view on the neuron (white, Neurobiotin, Nb) relative to the labeled Schaffer collaterals (red, ChR2-mCherry) and Calb1 staining (green). Scale bars = 100 μm (left), 20 μm (right). (C) High-pass filtered juxtacellular trace (bottom) for the neuron shown in B. Note the partial spiking responses to the light pulses (3 Hz, ~1.3 mW). Top, high magnification showing long-latency spike responses upon pulses of blue light (5 ms, indicated in blue). Scale bar = 2 mV, 2 s. (D) Raster plot (top) and peristimulus time histogram (bottom) showing the spike latency to the light stimuli for the CA1 pyramidal neuron shown in B. The average latency is indicated. (E) Histogram of average spike latencies of CA1 neurons (n = 11) showing spiking responses to Schaffer collateral input activation. The average latency is indicated. (F) Left, schematic of a mouse coronal brain section showing the injection site of recombinase-dependent opsin-expressing viral vectors (e.g., AAV-EF1a-flex-ChR2-eYFP, AAV-hSyn1-flex-ChR2-mCherry, or AAV-hSyn1-flex-oChIEF-TdTomato) in the dentate gyrus of Calb1cre mice. Right top, schematic showing the opto-activation of mossy fiber inputs combined with juxtacellular recording from single CA3 pyramidal neurons. Right bottom, epifluorescence image showing eYFP signal (pseudocolored to red for display purposes) following the injection of AAV-CAG-flex-eYFP. Note the labeling of mossy fibers (red) along the transverse CA3 axis. DG, dentate gyrus; SL, stratum lucidum; SO, stratum oriens; Pyr, stratum pyramidale; SR, stratum radiatum; SLM, stratum lacunosum moleculare; ML, molecular layer of dentate gyrus. Scale bar = 200 μm. (G) Epifluorescence image showing a nonresponsive (Cell 1) and a responsive (Cell 2) CA3a pyramidal neuron, recorded (as in F) along the same electrode penetration and labeled in vivo. Left panel, high magnification on the somata of the two neurons (green, Nb) relative to the labeled mossy terminals (red, oChIEF-TdTomato). Scale bars = 100 μm (left inset), 200 μm (right). (H) Representative recordings from the nonresponsive (Cell 1) and responsive (Cell 2) CA3a pyramidal neurons shown in G. The recording form Cell 1 (top, gray) shows the absence of spiking, but increasing amplitude of the negative local field potential (LPF) deflection during the stimulus train (25 Hz, 5-ms pulse duration) − consistent with the expected facilitation of neurotransmitter release at mossy terminals. Scale bars = 2 mV, 100 ms. The recording from Cell 2 (middle and bottom, black) shows partial spiking responses to low and high-frequency light pulses (3 and 40 Hz, 5 ms). Note the increase of the local field potential (LFP) amplitude (indicated with an arrow) during the stimulus trains. Scale bars = 2 mV, 500 ms (middle trace); 2 mV, 50 ms (bottom trace). (I) Raster plot (top) and peristimulus time histogram (bottom) showing the spike latency to the light stimuli for Cell 2 shown in G,H. 40 Hz light stimulus trains were excluded because of a high probability of spiking failure (see details in Materials and Methods). The average latency is indicated. (J) Boxplots showing comparison of latencies between directly activated CA1 cells (n = 10), indirectly activated CA3 (via mossy fiber photostimulation, n = 7), and CA1 cells (via Schaffer collateral photostimulation, n = 11). Significant p values after multiple group comparison are indicated (Kruskal–Wallis, p = 0.00006). Red lines indicate medians. Outliers are shown as crosses. Figure 2—source data 1 Juxtacellular opto-tagging data, average spike latencies (source data for panels D,E and I,J). https://cdn.elifesciences.org/articles/71720/elife-71720-fig2-data1-v1.xlsx Download elife-71720-fig2-data1-v1.xlsx The above data indicate that synaptically activated neurons can be reliably discriminated from directly light-activated cells, at least when ‘weak and unreliable’ synapses – like typical cortical synapses, including Schaffer terminals (Csicsvari et al., 2000; Sayer et al., 1990; Sayer et al., 1989) – are activated. As additional challenge, we next asked whether our latency-based criteria would still be valid under conditions of ‘strong and reliable’ synaptic activation. To this end, we performed a subset of experiments on the hippocampal mossy fiber synapse, which is among the strongest and most reliable connections in the nervous system (Henze et al., 2002; Neubrandt et al., 2018; Vyleta et al., 2016). We selectively expressed ChR2 in dentate gyrus granule cells by injecting AAV-hSyn1-flex-ChR2-mCherry (or AAV-hSyn1-flex-oChIEF-TdTomato) in the dentate gyrus of Calb1cre mice (Figure 2F). By these means, we obtained strong labeling of presynaptic mossy fibers terminals, extending into the CA3 field (Figure 2F, G). We then juxtacellularly recorded from single postsynaptic CA3 pyramidal neurons while simultaneously activating presynaptic mossy fibers at different light stimulation frequencies (e.g., 3–40 Hz; see details in Materials and Methods). Two representative recordings from a nonresponsive and a responsive identified CA3 pyramidal neuron are shown in Figure 2G–I. In the first neuron, no spiking activity was observed upon light illumination; however, a prominent light-induced facilitation of local field potential amplitude was observed – consistent with the known facilitation of neurotransmitter release at mossy terminal (Capogna, 1998; Vyleta et al., 2016; Zucca et al., 2017). In the second neuron, photostimulation was sufficient for inducing suprathreshold spiking (Figure 2H), whose probability increased as a function of stimulus number, consistent with presynaptic facilitation of mossy inputs. Notably, the response latencies of synaptically activated CA3 neurons (mean latency = 8.1 ± 2.4 ms, n = 7) were significantly longer than that of directly light-activated CA1 Calb1-positive cells (Figure 2J). Altogether, this dataset shows the technical feasibility of combining the opto-juxtacellular approach for single-cell recording, and presynaptic input the longer spike latencies in indirectly activated neurons compared to the directly activated (Figure further the of our latency-based opto-tagging on these we took advantage of the juxtacellular opto-tagging approach for the in vivo activity patterns of Calb1-positive and Calb1-negative CA1 pyramidal neurons. To this end, we juxtacellular opto-tagging procedures (Figure 1) to freely moving mice. were with (see Figure Diamantaki et al., and a 50-μm-core optic fiber was within the glass The of a fiber strategy, together with a system (see Figure supplement details in Materials and allowed us to multiple within animals. Figure with 2 see all Download asset Open asset Juxtacellular opto-tagging of single CA1 pyramidal neurons in freely moving mice. (A) of the recording for juxtacellular opto-tagging in freely moving mice from Diamantaki et al., 2018). (B) Raster (top) and peristimulus time (bottom) showing responses from a Calb1-positive and Calb1-negative neuron to the light stimulus (indicated in blue). Note the short-latency spiking of the Calb1-positive neurons (mean latency and the absence of response in the Calb1-negative cell. (C) of the of a Calb1-negative CA1 pyramidal neuron, recorded in a freely moving mouse (recording shown in Scale bar = 50 μm. (D) Epifluorescence image showing Calb1 staining (green) and the juxtacellular labeled CA1 neuron for the neurons shown in C. Scale bar = 20 μm. (E) map (top) and plot (bottom) for the neuron shown in as in but for a representative Calb1-positive CA1 pyramidal (I) of all Calb1-negative cells (n = that the criteria for spatial analysis (see Materials and Methods). the relative to the projection of the are to along the The bar on the indicates for from the (J) as in (I) for all Calb1-positive cells (n = that the criteria for spatial analysis (see Materials and Methods). Boxplots showing spatial information and for Calb1-negative (n = as in and Calb1-positive (n = as in CA1 neurons. p values are indicated lines indicate medians. Outliers are shown as crosses. Figure data 1 properties of Calb1-positive and Calb1-negative neurons (source data for panels and Figure supplement Download Using these juxtacellular opto-tagging we the activity of single CA1 pyramidal neurons while mice explored a familiar neurons included in the present study displayed low Hz; average ± Hz; n = and complex spikes ± n = – features with cell identity and identified neurons were classified as pyramidal neurons (see Materials and thus further confirming our classification As in the anesthetized dataset (Figure neurons recorded in behaving mice were classified as Calb1-positive if short-latency spikes ms) were reliably by the light pulse (n = Figure In a subset of juxtacellular labeling was performed and the identity of the recorded neuron was via Calb1 (Figure were classified as Calb1-negative neurons (n = if not to the light stimulus or were located in the pyramidal layer and were negative for Calb1 expression (see Materials and Methods). In two cases, we recorded from light-responsive, putative Calb1-positive which at high during Hz average see Figure 1—figure supplement these neurons were not included in further analysis). During spatial Calb1-positive and Calb1-negative pyramidal cells displayed average ± Hz, n = ± Hz, n = p = and to spike ± n = ± n = p = However, these two cell types remarkably in the of spatial While the large of Calb1-negative neurons displayed spatially which were the (Figure the spiking activity of Calb1-positive neurons was less spatially modulated (Figure (n = Calb1-negative and n = Calb1-positive neurons included in the spatial see details in and Methods). To these we spatial information et al., and the – a of how the place field is relative to the recording et al., Indeed, we found that Calb1-positive neurons conveyed significantly less spatial information ± n = ± n = p = and displayed a ± n = ± n = p = (Figure a more compared to Calb1-negative To further these we performed a spatial analysis by the for for the two of recording (Figure supplement Indeed, in line with spatial (Figure spatial in Calb1-positive neurons was significantly less stable than in Calb1-negative cells (mean ± n = ± n = p = see Figure supplement Altogether, these are in line with observations et al., 2016; Geiller et al., 2017; Mizuseki et al., 2011; et al., 2016; Sharif et al., and indicate that in the dorsal place cells are preferentially by pyramidal neurons, while Calb1-positive neurons are only weakly spatially modulated. Discussion A of neural circuits the of neuronal elements to in vivo neural and behavior. Significant have been aimed at neuronal diversity within brain However, to methodological it is still largely whether and how the neuronal cell classes – typically identified in vitro – contribute to in vivo activity during natural behavior. The of has neural circuit By restricting the expression of ChR2 (or Ca2+ to a specific subset of neurons, it has become to in vivo activity patterns to the neuronal elements. In freely behaving animals, the of recordings with optogenetic tagging (e.g., et al., 2011; Lima et al., 2009) has become a approach for neural circuit However, while the of the method is are two to be cell-type identification is since it is from