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. 2025 Jan 28;36(2):br5. doi: 10.1091/mbc.E23-07-0268

Optogenetic control of receptor-mediated growth cone dynamics in neurons

Stephen R Tymanskyj 1,†, Althea Escorce 1, Siddharth Karthikeyan 1, Le Ma 1,*
Editor: Stephanie Gupton2
PMCID: PMC11809317  PMID: 39705378

Abstract

Development of neuronal connections is spatially and temporally controlled by extracellular cues which often activate their cognate cell surface receptors and elicit localized cellular responses. Here, we demonstrate the use of an optogenetic tool to activate receptor signaling locally to induce actin-mediated growth cone remodeling in neurons. Based on the light-induced interaction between Cryptochrome 2 (CRY2) and CIB1, we generated a bicistronic vector to co-expresses CRY2 fused to the intracellular domain of a guidance receptor and a membrane-anchored CIB1. When expressed in primary neurons, activation of the growth inhibitory PlexA4 receptor induced growth cone collapse, while activation of the growth stimulating TrkA receptor increased growth cone size. Moreover, local activation of either receptor not only elicited the predicted response in light-activated growth cones but also an opposite response in neighboring no-light-exposed growth cones of the same neuron. Finally, this tool was used to reorient growth cones toward or away from the site of light activation and to stimulate local actin polymerization for branch initiation along axonal shafts. These studies demonstrate the use of an optogenetic tool for precise spatial and temporal control of receptor signaling in neurons and support its future application in investigating cellular mechanisms of neuronal development and plasticity.

  • To form nerve connections, axons are guided by extracellular cues that activate specific cell surface receptors to reorganize cellular structures such as the cytoskeleton. As such signaling events are restricted to parts of neurons, studying the cellular response with precise spatial and temporal controls is often experimentally challenging.

  • The authors tested a new way to activate receptor signaling using light sensitive proteins. Using newly created reagents, they were able to activate guidance receptor signaling and induce localized actin assembly/disassembly and growth cone formation/collapse.

  • This study demonstrates the use of localized light control of receptor signaling in axonal development and provides a new tool to examine cellular dynamics in response to extracellular regulation of neuronal development and plasticity.

INTRODUCTION

During development, cells are influenced by extracellular cues that modulate intracellular processes, such as cytoskeletal dynamics and membrane trafficking (Basson, 2012). This is achieved through specific membrane surface receptors, which elicit localized cellular responses that are seen in many cell types (Sonnen and Janda, 2021). One prime example is the morphogenesis of axons, the long processes of nerve cells, in the developing nervous system. It involves three related steps, axon growth, guidance, and branching (Kalil et al., 2000; Gibson and Ma, 2011; Lewis et al., 2013; Kalil and Dent, 2014; Sainath and Gallo, 2015), which are led by the growth cone, a highly motile structure at the tip of an axon (Lowery and Van Vactor, 2009; Dent et al., 2011). Growth cone motility is influenced by extracellular cues, either stimulatory or inhibitory, and mediated by cell surface guidance receptors that activate signaling pathways to reorganize the actin and microtubule cytoskeleton (Bashaw and Klein, 2010; Gomez and Letourneau, 2014; Omotade et al., 2017; Miller and Suter, 2018; McCormick and Gupton, 2020; Zang et al., 2021). This reorganization in response to extracellular stimuli is responsible for guiding the axon toward or away from the cue, a process that ultimately leads to the formation of properly connected neuronal circuits.

Past studies of growth cone dynamics have relied on focal application of extracellular molecules, which creates diffusive gradients (such as pipet and Dunn's chamber) (Ming et al., 1997; Yam et al., 2009) or substrate boundaries (such as patterned surfaces and coated beads) (Gallo and Letourneau, 1998; Knoll et al., 2007). Studies using these tools have provided a wealth of information on the cellular mechanisms controlling growth cone dynamics required for axon growth, guidance, and branching (Hines et al., 2012; Winkle et al., 2016; Omotade et al., 2017; McCormick and Gupton, 2020) and have identified key intracellular signaling mediators, such as cAMP, calcium, and Rho family GTPases that mediate these processes (Gomez and Zheng, 2006; Tojima et al., 2011; Nicol and Gaspar, 2014; Stankiewicz and Linseman, 2014). Although local manipulation of these pathways or the cytoskeleton has been achieved in several studies (Buck and Zheng, 2002; Akiyama et al., 2009; Averaimo et al., 2016), direct activation of receptors with higher degrees of spatial and temporal control could help enhance our understanding of the precise mechanisms linking extracellular stimuli to cellular dynamics, such as actin polymerization.

The development of optogenetic tools for protein interactions in the past decade has provided a new venue to study cellular dynamics in a highly controlled manner (Tischer and Weiner, 2014; Rogers and Muller, 2020). These tools are based on several light-sensitive proteins that can alter conformation and protein-protein interaction after exposure to light of specific wavelengths (Tischer and Weiner, 2014). One notable tool uses CRY2, a protein from Arabidopsis thaliana, which homodimerizes to form oligomers and heterodimerizes with its binding partner CIB1 upon blue light exposure (Kennedy et al., 2010). CRY2 has been used in various cellular contexts, including direct manipulations of actin and microtubule dynamics (Bourke et al., 2018; Johnson and Toettcher, 2018; Wittmann et al., 2020; Tan et al., 2022), and has provided a new strategy to control receptor signaling. Indeed, previous studies have demonstrated the use of CRY2 in controlling guidance receptor signaling. In one study (Endo et al., 2016), CRY2 was fused to the C-terminus of the DCC receptor, allowing the use of light-induced CRY2 homodimerization to activate DCC and control neurite growth direction. Other studies have utilized a similar approach to manipulate neurotrophin signaling (Chang et al., 2014; Duan et al., 2018). In these studies, blue light was used to control the recruitment and signaling of the Trk receptor at the cell surface through the dimerization of the full-length receptor fused to CRY2 or through the recruitment of the intracellular signaling domain (ICD) of the receptor to membrane-anchored CIB1, which led to increased cell survival and neurite outgrowth. Although these studies demonstrate the feasibility of using CRY2 to control guidance receptors, it has not been tested whether optogenetic activation can be used to study subcellular responses, such as actin reorganization and growth cones remodeling.

In this study, we expanded the use of the CRY2-CIB1 heterodimerization system (Duan et al., 2018) by creating a bicistronic vector system to co-express a CRY2-fused ICD and a membrane-bound CIB1. We demonstrated that this tool works well for both growth-stimulating and growth-inhibiting receptors. Using an actin marker (Riedl et al., 2008) to monitor growth cone behaviors, we showed that light-induced receptor activation could alter growth cone dynamics, rapidly converting growth cones from growing to collapsing or vice versa. We also showed that focal activation of one growth cone not only elicited the anticipated response but also induced an opposite response in the nearby unstimulated growth cone. Further, focal light activation could reorient growth cones as well as stimulate localized actin reorganization needed for branch initiation. Thus, our study demonstrates the use of optogenetic activation of guidance receptors to modulate cellular responses with both spatial and temporal precision in neurons. Our study also provides a new tool that can be combined with other markers to study cell biological problems in neuronal development and plasticity.

RESULTS AND DISCUSSION

Design and validation of optogenetic constructs for guidance receptors

We designed an IRES2-based bicistronic expression system to express both a CRY2-fused ICD and a membrane-associated CIB1 (CIB1-CAAX) from a single plasmid with a CMV promoter. Based on previously developed reagents (Duan et al., 2018), we generated an iTrk-mCherry-Opto construct to co-express mCherry-tagged CRY2-iTrk and EGFP-tagged membrane CIB1 (CIB1-EGFP-CAAX) via a linker sequence containing the internal ribosomal entry site (IRES2) (Figure 1A). Using the same design, we generated an optogenetic construct to express CYR2-fused ICD of PlexinA4 (iPlex-mCherry-Opto), a receptor involved in semaphorin-mediated growth cone collapse (Winberg et al., 1998; Tamagnone et al., 1999) (Figure 1A). When expressed in cells, exposure to blue light should cause a conformational change of CRY2 in the ICD fusion protein, leading to its heterodimerization with CIB1-CAAX for recruitment to the plasma membrane and subsequent homodimerization/oligomerization to elicit downstream responses (Figure 1B). As a control (Ctrl-mCherry-Opto), we used the same DNA construct design but omitted the ICD (Figure 1A).

FIGURE 1:

FIGURE 1:

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Validation of the optogenetic constructs in COS cells. (A–B) Schematic drawings of the design of the bicistronic optogenetic constructs (A) and the domain structures in the expressed proteins (B). Fusion proteins containing CRY2 (CRY; blue), mCherry (mCh) fluorescent protein, and a receptor ICD (Trk-ICD or Plex-ICD) are expressed from an IRES2 vector (iTrkA-mCherry-Opto or iPlex-mCherry-Opto), which allows for simultaneous expression of the CIB1-CAAX-EGFP fusion protein that is localized to the plasma membrane via the CAAX prenylation site. In the presence of blue light, CRY-mCherry-ICD is recruited and activated at the plasma membrane via the heterodimerization of CRY2-CIB1 and homodimerization of CRY2. The Ctrl-mCherry-Opto construct expressing only CRY-mCherry without any ICD is used as control. (C) Time course of experiments: imaging mCherry at 560 nm (green bar) for a total of 7 min with blue light exposure at 488 nm (blue bar) between 1 and 2 min. (D) Images of COS cells expressing Ctrl-mCherry-Opto, iPlex-mCherry-Opto, or iTrk-mCherry-Opto show mCherry (left three columns) and CIB1-EGFP signal (last column). Fluorescent images are shown in the top row (gray) and the corresponding pseudocolored images are shown in the bottom row (with a 16-color heatmap, blue for low and red for high signal). The mCherry signal can be seen accumulating at the periphery after blue light exposure (arrows). (E–G) Change of mCherry signal at the cell membrane over the time of the experiment (C) based on the ratio of the membrane signal over the signal in the center of the cell of the three Opto constructs. Signal ratios are normalized to time 0 with the thick lines representing the average and the shaded areas representing SEM. Ctrl-mCherry-Opto, n = 17; iPlex-mCherry-Opto n = 14, iTrk-mCherry-Opto, n = 11. Scale bar = 10 µm.

To validate these constructs, we transfected COS cells and examined the membrane localization of mCherry and EGFP-tagged fusion proteins in live cells. Cells were imaged for mCherry first for 1 min, followed by 1 min of blue light activation/EGFP imaging accompanied by mCherry imaging, and followed by an additional 5 min of post-blue light imaging of mCherry (Figure 1C). Before blue light exposure, the mCherry signal from all three constructs was diffused throughout the cell (Figure 1D, mCherry/Pre, row 1,3,5,). However, after blue light (488 nm) exposure for 1 min, the mCherry signal rapidly accumulated at the cell periphery (Figure 1D, +blue light), similar to the membrane-enriched EGFP signal from CIB1-CAAX (Figure 1D, right, EGFP/Post), indicating the association of the CRY2-fusion protein with CIB1-CAAX. Such accumulation can be best visualized by pseudocolors that represent mCherry signal intensities, which showed a redistribution of diffuse mCherry signals within the cell to the cell membrane after light exposure (Figure 1D, rows 2,4,6, and arrows). As quantified by the change in the mCherry signal at the plasma membrane, the increase in membrane association appeared as rapidly as 2 s after light exposure, peaking at ∼ 20 s with an increase of 2.6-, 2.5-, and 2.7-fold at the periphery for Ctrl-mCherry-Opto, iPlex-mCherry-Opto and iTrk-mCherry-Opto, respectively (Figure 1, E–G), while the CIBN-EGFP signal remained unchanged upon blue light activation (data not shown). Post activation, the mCherry signal of Ctrl-Opto decreased over time (Figure 1E), consistent with previous studies demonstrating a relatively slow dissociation of CRY2 from CIB (Benedetti et al., 2018). Interestingly, the mCherry signal of iPlex-Opto remained at the membrane for at least 5 min while that of iTrk-Opto dropped to the preactivation level, suggesting that activation of either receptor signaling may alter the dissociation kinetics (Saxena et al., 2005). Nonetheless, these constructs that express two protein components of the optogenetic system off a single IRES2 plasmid provide a reliable way to use light to recruit receptor ICDs to plasma membranes in cells.

Optogenetic constructs can be used to control growth cone dynamics with blue light in neurons

We next tested these optogenetic constructs in neurons by examining growth cone dynamics using mRuby-Lifeact (referred to as Lifeact thereafter), a commonly used marker for filamentous actin (Riedl et al., 2008). To provide an extra imaging channel, we removed mCherry from the above optogenetic constructs to generate Ctrl-Opto, iTrk-Opto, and iPlex-Opto (Figure 2A). We cotransfected embryonic DRG neurons with these Opto constructs along with Lifeact and iRFP670, a far-red fluorescent protein (Shcherbakova and Verkhusha, 2013; Tymanskyj et al., 2018) used to search for transfected cells without nonspecific activation of the optogenetic system by blue light.

FIGURE 2:

FIGURE 2:

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Analysis of growth cone dynamics after global activation by light. (A) Schematic drawings of the optogenetic constructs omitting the mCherry tag. (B) Neuronal growth cones exposed to blue light can potentially transition from a growing state (star) to continuing to grow or a collapsed state (square). (C) Time course of experiments: imaging Lifeact at 560 nm (green bar, Pre) for 10 min followed by blue light activation at 488 nm (blue bar) and Lifeact imaging (green bar) for 10 min. (D) Pseudocolored Lifeact images using a mpl-inferno heatmap of DRG neuron growth cones expressing either Ctrl-Opto, iPlex-Opto, or iTrk-Opto, at different timepoints before blue light activation (pre), and during pulsed blue light activation (+ Blue light). (E) Percentage of growth cones transitioning from growing to collapsing after blue light exposure n=3 or 4. (F) The ratio of growth cone areas after blue light exposure over the area before exposure in an independent experiment. Ctrl-Opto n = 7; iPlex-Opto n = 8; iTrk-opto n = 6. One-way ANOVA. ns,  not significant, *p < 0.05; **p < 0.01;*** p < 0.001. Scale bar = 10 µm.

To establish the baseline of growth cone dynamics, we first examined cells expressing Ctrl-Opto with Lifeact and iRFP670. We monitored growth cone dynamics by imaging Lifeact every 5 s for 10 min first and then exposed growth cones to blue light while simultaneously imaging Lifeact (Figure 2, B and C). Before any blue light exposure, growth cones were highly dynamic, with filopodia and lamellipodia undergoing bouts of growth and retraction (Figure 2D, top row; Supplemental Movie S1 [left]). After growth cones were exposed to a whole field of blue light, no obvious changes in dynamic behavior were observed in Ctrl-Opto transfected cells that were identified by the CIB1-CAAX-EGFP signal (insert, green). Filopodia and lamellipodia remained highly dynamic, constantly forming, growing, and retracting, with only 29 ± 8% of growth cones collapsing or collapsed (Figure 2E; Supplemental Movie S2 [left]), similar to the collapse rate when neurons were not exposed to light (data not shown). In addition, we measured the area of the growth cone pre-and post-blue light and found an averaged ratio of around 1 (Figure 2F), consistent with the notion that Ctrl-Opto expression does not alter growth cone dynamics after blue light exposure.

Movie S1.

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Actin dynamics of DRG growth cones before blue light exposure. Pseudocolored Lifeact movies of DRG growth cones expressing CIB1 (left), iPlex‐Opto (middle), or iTrk‐Opto (right). The movie is created from images taken every 2 seconds for a total of 10 minutes before blue light exposure. The movie is shown at 30 fps.

Movie S2.

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Changes in actin dynamics of DRG growth cones expressing CIB1, iPlex‐Opto and iTrk‐Opto in the presence of blue light. Pseudocolored Lifeact movies of DRG neurons expressing CIB1 (left), iPlex‐Opto (middle), or iTrk‐Opto (right). Growth cones were imaged for Lifeact while simultaneously exposed to blue light every 2 seconds for a total of 10 minutes. iPlex‐Opto growth cones undergo transition to collapsed while CIB1 and iTrk‐Opto remain dynamic. The movie is shown at 30 fps.

Using the same imaging and blue light activation scheme, we examined neurons expressing iPlex-Opto. Similar to Ctrl-Opto, these neurons had highly dynamic growth cones before any blue light exposure as visualized by Lifeact (Figure 2D; middle row, Supplemental Movie S1 [middle]). However, upon blue light exposure, growth cones exhibited signs of collapse, as filopodia became less dynamic, highly elongated, or retracted completely, and lamellipodia were lost (Supplemental Movie S2 [middle]). As a result, 10 min after light exposure, all growth cones imaged were collapsed or collapsing (Figure 2E, p=0.0003), with a 53% reduction in the growth cone area (Figure 2F, p=0.0419), suggesting that blue light is sufficient to activate iPlex-Opto and induce growth cone collapse. For comparison, growth cones expressing iTrk-Opto remained highly dynamic after blue light exposure, with fewer (11%, p=0.1991) collapsing (Figure 2, D and E; Supplemental Movie S1 and S2 [right]) and a 60% increase (p=0.0050)  in growth cone area. This result not only confirms that the blue light itself had no negative effect on growth cone dynamics but also suggests that activation of TrkA signaling could increase growth cone size.

We also tested the iPlex-Opto tool in cultured cortical neurons, which share many of the same signaling pathways as DRG sensory neurons (Yaron et al., 2005; Tran et al., 2009; Mlechkovich et al., 2014). Based on the same experimental and imaging paradigm, axonal growth cones of cortical neurons expressing Ctrl-Opto from a hSyn promoter exhibited dynamic filopodia and lamellipodia (Supplemental Figure S1A), with only 21% of growth cones transitioning toward a collapsed morphology and an average ratio of around 1 for growth cone area post- over pre-blue light exposure (Supplemental Figure S1B,C). However, neurons that expressed iPlex-Opto lost dynamic growth cones after blue light exposure, as lamellipodia disappeared and filopodia either retracted or in the process of retraction (Supplemental Figure S1A). Consequently, 87% of growth cones transitioned toward a collapsed state and exhibited an averaged area ratio of 0.77, representing a 23% decrease in growth cone area after light exposure (Supplemental Figure S1B,C). Combined, these experiments demonstrate that the ICD-Opto system can be used to alter growth cone dynamics in conjunction with exposure of blue light in different neuronal types.

Signaling crosstalk between competing growth cones of the same neuron revealed by localized optogenetic activation

To demonstrate the use of the optogenetic tool for local activation of receptors, we next asked whether we could alter growth cone dynamics with restricted blue light exposure using a 20-µm pinhole. Using the same DNA combination for transfection, we determined the growth cone morphology based on time-lapse movies of Lifeact and designated it as either growing (G) or collapsing (C). To obtain a full spectrum of responses, we obtained the change in morphology of a growth cone pre-and post-blue light exposure (activated) and compared it with those in neurons that were never exposed to blue light (baseline) (Figure 3A).

FIGURE 3:

FIGURE 3:

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Growth dynamics are altered after focal activation by light. (A) Schematic drawing of the experimental setup to analyze actin dynamics in growth cones activated by lights locally. One group of neurons were exposed to blue light (activated) and another group of neurons were never exposed to blue light (baseline, −light). For activated neurons, only one of the two growth cones at a branch junction was exposed to blue light using a pinhole for 5 min (+light; blue circle), and the other was never exposed to light (−light). (B) Time course of experiments for + light (left) and −light (right) conditions: growth cones were imaged for Lifeact for 5 min (green bar, Pre), exposed to blue lights for 5 min (blue bar) or not (empty space), and then imaged again for Lifeact for 5 min (green bar, Post). (C) Pseudocolored images (with a mpl-inferno heatmap) of Lifeact in growth cones expressing Ctrl-Opto, iPlex-Opto, or iTrk-Opto at 5 min right before blue light exposure (Pre) and the subsequent times (10, 11.5, 13, and 15 min) after blue light activation (Post). Images shown here are growth cones exposed with light in activated neurons and growth cones without light exposure in baseline neurons. Blue circles depict the region of light exposure. (D–I) Statistical analysis based on conditional logistic regression of the transitions from growing to collapsing (G→C, D–F) or collapsing to growing (C→G, G–I) of light (+) or no light (−) exposed growth cones in activated neurons or no-light growth cones (base) in baseline neurons expressing Ctrl-Opto (D,G), iPlex-Opto (E,H), or iTrk-Opto (F,I). Activated: Ctrl-Opto n = 21; iPlex-Opto n = 28; iTrk-Opto n = 31; Baseline: Ctrl-Opto n = 20; iPlex-Opto n = 20; iTrk-Opto n = 22 from three independent replicates. ns, not significant; * p < 0.05; ** p < 0.01; and *** p < 0.001. Scale bar = 20 µm.

Using this paradigm (Figure 3, A and B), we found that dynamic growth cones expressing Ctrl-Opto exhibited little response to blue light (Figure 3C, top two rows). For the baseline neurons with no blue light exposure, the majority (76%) of growth cones that were growing in the first 5 min remained dynamic when imaged between 10 and 15 min (G→G), while ∼24% of them collapsed (G→C). These probabilities did not change when the growth cones were exposed to blue light for 5 min (activated), as 77% of them remained dynamic and 23% transitioned to collapsed within the 10-min imaging window (Figure 3D, blue vs. gray bar). In contrast, growth cones expressing iPlex-Opto showed a collapsing response when exposed to blue light (Figure 3C, middle two rows). Of those growth cones that were initially dynamic, 65% collapsed (G→C) when exposed to light while only 8% collapsed in the absence of light (Figure 3E, blue vs. gray bar). This represents a 23 × increase in the G→C odds, or the probability of G→C over the probability of G→G, between light and no-light activation for iPlex-Opto (p = 0.001), as compared with Ctrl-Opto that has an odds ratio (OR) of 0.95 (p = 0.999, Table 1), which is consistent with the growth inhibitory signaling of PlexinA4 (Mlechkovich et al., 2014).

TABLE 1:

Statistical analysis based on conditional logistic regression of growth cone dynamics after localized growth cone activation.

G->C transitions
(A) L+ of Activated vs. Baseline (B) L− of Activated vs. Baseline (C) L+ vs. L−
DNA L+ Baseline Effect of light on L+ DNA L− Baseline Effect of light on L−
N n % N n % OR (95% CI) P   N n % N n % OR (95% CI) P P
Ctrl 13 3 23.1% 25 6 24.0% 0.95 (0.13–5.71) 0.999 Ctrl 14 3 21.4% 25 6 24.0% 0.87 (0.12– 5.12) 0.999 0.999
iPlex 20 14 70.0% 24 2 8.3% 23.2 (3.85– 266) 0.001 iPlex 19 6 31.6% 24 2 8.3% 4.88 (0.73– 56.42) 0.121 0.036
iTrk 20 0 0.0% 31 6 19.4% 0.16 (0–0.92) 0.041 iTrk 21 13 61.9% 31 6 19.4% 6.48 (1.66– 28.81) 0.005 0.001
C->G transitions
(D) L+ of Activated vs. Baseline (E) L− of Activated vs. Baseline (F) L+ vs. L−
DNA L+ Baseline Effect of light on L+ DNA L− Baseline Effect of light on L−
N n % N n % OR (95% CI) P   N n % N n % OR (95% CI) P P
Ctrl 8 3 37.5% 15 1 6.7% 7.54 (0.48– 471) 0.206 Ctrl 7 1 14.3% 15 1 6.7% 2.24 (0.03– 196) 0.999 0.677
iPlex 8 0 0.0% 16 3 18.8% 0.47 (0– 3.40) 0.277 iPlex 9 5 55.6% 16 3 18.8% 5.01 (0.65–49.04) 0.15 0.020
iTrk 11 10 90.9% 13 1 7.7% 75.32 (5.03– 999) 0.001 iTrk 10 3 30.0% 13 1 7.7% 4.78 (0.31– 292) 0.34 0.013
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Transitions of growth cone dynamics from growing to collapsing (G->C, top) or vice versa (C->G, bottom) in DRG neurons transfected with Ctrl-Opto, iPlex-Opto, or iTrk-Opto were compared between different conditions. Growth cones activated by light (L+) from activated neurons were compared with growth cones in neurons never exposed to light in the baseline condition (A and D), while neighboring growth cones not activated by the light (L−) from activated neurons were compared with growth cones in the baselines (B and E). N = Total n number; n = number undergoing transition. The Odds (OR) was calculated from the probability of either transition (%) with the 95% confidence interval (CI) and the p value listed. The transition ORs were further compared between L+ and L− growth cones with the p value listed in the last column (C and F).

Interestingly, growth cones expressing iTrk-Opto showed an opposite response, with no dynamic growth cones collapsed after light exposure, as compared with 19.4% for baseline growth cones (Figure 3F, blue vs. gray bar), leading to a 6.25-fold decrease in the G→C odds (OR = 0.16, p = 0.041, Table 1). This indicates that activation of TrkA signaling could prevent the basal level of conversion from growth to retraction as suggested Figure 2. The light effect on iTrkA signaling was stronger when collapsing growth cones were analyzed. Here, 90.9% of growth cones had C→G conversion after blue light activation as compared with 7.7% for baseline growth cones (Figure 3I, blue vs. gray bar), resulting in a 75-fold increase in C→G odds (p = 0.001, Table 1), which is consistent with the role of TrkA signaling in promoting axonal growth (Duan et al., 2018). For comparison, the C→G transition in growth cones expressing Ctrl-Opto or iPlex-Opto had some but insignificant differences between blue light activation and no light baseline (Figure 3, G and H, blue vs. gray bars, and OR = 7.54 or 0.47, p = 0.206 or 0.277, Table 1). These analyses further demonstrate the control of growth cone dynamics via local manipulation of TrkA or PlexA4 signaling.

Because localized light activation can alter growth cone dynamics, we wished to test whether such local signaling could propagate to influence other regions of axons. Thus, we next examined neighboring growth cones that were not exposed to light (−light) but connected with an activated growth cone (+light) of the same neuron via a shared branch junction in the above experiment (Figure 3A). As expected, dynamic growth cones expressing Ctrl-Opto had a similar probability of G→C or C→G conversion regardless of whether their neighboring growth cones were activated by light or not (OR = 0.87 or 2.24, p = 0.999 or 0.999, Table 1) (Figure 3, D and G, red vs. gray bars). Interestingly, growth cones expressing iPlex-Opto had a small increase in G→C conversion when neighboring growth cones were activated, but this increase is not significantly different from the no-light baseline (OR = 4.88, p = 0.121, Table 1, Figure 3E, red vs. gray bar). Moreover, the change in the G→C odds for nonactivated (−light) growth cones is significantly smaller (p = 0.036) than that of the activated (+light) growth cones, suggesting that there was no growth-inhibiting signaling spillover from activated to nonactivated growth cones (Figure 3E, red/gray vs. blue/gray). Similarly, the C→G conversion for (−light) growth cones expressing iTrk-Opto is not significantly different from the baseline (OR = 4.78, p = 0.34, Table 1, Figure 3I, red vs. gray bar), but is significantly smaller (p = 0.013) than that of activated (+light) growth cones (Figure 3I, red/gray vs. blue/gray), again suggesting that the same signaling response did not pass from activated (+light) to the neighboring nonactivated (−light) growth cones.

However, in iTrk-Opto expressing neurons, we did find that the neighboring (−light) growth cones have opposite responses. For example, dynamic (−light) growth cones that were not exposed to light had a large and significant increase in collapse (G→C) in neurons expressing iTrk-Opto when neighboring growth cones were activated (61.9 vs. 19.4% of baseline control, OR = 6.48, p = 0.005, Figure 3F, red vs. gray bar). This difference is also significantly different from the G→C responses in (+light) growth cones that were exposed to light (p = 0.001, Figure 3F, red/gray vs. blue/gray). Because TrkA signaling normally leads to growth stimulation, the observation of an opposite G→C response in the neighboring non-activated growth cones suggests that an opposing signal was sent from activated (+light) growth cones to the neighboring (−light) growth cones of the same neuron. The same opposite effect was seen in iPlex-Opto-expressing neurons. Here, the C→G odds for (−light) growth cones increased over the baseline control (OR = 5.01, Figure 3H, red vs. gray bar), while the C→G odds for (+light) growth cones decreased (OR = 0.47, Figure 3H, blue vs. gray bar). Although neither change appears significant over the baseline, the pairwise comparison reveals a significant difference (p = 0.02, Figure 3H, red/gray vs. blue/gray) with an increase in the C→G response in (−light) growth cones, which is opposite of the normal collapsing response elicited by iPlex-Opto in (+light) growth cones. Taken together, these comparisons demonstrate signaling cross-talk between growth cones in the same neurons.

Our study demonstrates the application of the optogenetic system in controlling receptor activity locally in growth cones. Due to the high degree of spatial control, this system allows us to uncover signaling cross-talk, that is, signals received in one growth cone can elicit opposing responses in another growth cone of the same neuron. It validates the recent use of the system to examine transport regulation at branch junctions in response to growth cone signaling (Tymanskyj et al., 2022). Since axonal branches are refined during synaptic development via the interaction of individual growth cones from the same neuron with localized positive/negative guidance cues (Shen and Cowan, 2010; Vanderhaeghen and Cheng, 2010; Riccomagno and Kolodkin, 2015), the ICD-Opto system can be used to explore the relationship of neighboring growth cones and investigate the cellular mechanisms of branch competition and axonal plasticity.

Restricted optogenetic activation induced growth cone reorientation

We next asked whether the optogenetic tool can be used to control growth cone directionality. Following the same expression paradigm in DRG neurons above, growth cones were imaged for 5 min to establish their dynamic morphology based on Lifeact. Growth cones were then exposed to blue light via a slit every 10 s for 5 min (Figure 4B). The slit was positioned on one side of the growth cone (with reference to the main axon), splitting the growth cone into two areas, a large one linked to the main axon (L: Figure 4A, red) and a small one (S: Figure 4A, purple). Growth cone areas on either side of the slit (L and S) were measured before and after blue light exposure. The ratio of the two areas (L/S) was used to determine the change in growth cone orientation. A decrease in the ratio would indicate that the growth cone extended toward the side with light activation, while an increase would suggest that the growth cone moved away from the light.

FIGURE 4:

FIGURE 4:

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Restricted blue light exposure reorients growth cones. (A) Schematic drawings of the growth cone reorientation assay. A growth cone was divided into two regions separated by blue light from a slit (blue dashed line) placed parallel to the axon shaft. This resulted in one large region (L: red) and one small region (S: purple). After blue light exposure, the area of the regions was measured in relation to the site of activation to allow for quantification of reorientation of the growth cone. (B) Time course of reorientation assay, in which the growth cone was imaged for Lifeact for 5 min (green bar), activated by blue light via the slit for 5 min (blue bar), and then imaged for Lifeact again for 5 min (green bar). (C) Pseudocolored images (with a mpl-inferno heatmap) of Lifeact in neurons expressing Ctrl-Opto, iPlex-Opto, or iTrk-Opto pre (0 min), during (5, 10 min), and, post (15 min) blue light exposure. Blue dashed lines represent the location of the slit for blue light exposure. (D) Quantification of the normalized area ratio of the small/large region of the growth cone. (Ctrl-Opto n = 16, iPlex-Opto n = 11; iTrk-Opto n = 16). Student t-test: ns, not significant; *p < 0.05; and ***p < 0.001. Scale bar = 20 µm.

In growth cones expressing Ctrl-Opto, this restricted light exposure did not affect the overall growth cone orientation (Figure 4, C and D). This can be demonstrated by the normalized ratio of the two areas pre and post light activation, which shows no difference with an average ratio of 1.01 (p=0.7639), despite having a large distribution of growth cone movement, both toward and away from the site of activation, suggesting a random reorientation of the growth cone (Figure 4D). However, growth cones expressing iPlex-Opto showed a significant area change away from the side of activation, as shown by a significant reduction of the normalized area ratio to 0.64 (Figure 4D, p = 0.0145). In contrast, growth cones expressing iTrk-Opto produced the opposite response with an increase of the normalized area ratio to 1.61 (p = 0.0006), indicating an increased movement toward the side of the growth cone exposed to blue light (Figure 4D). Combined, these experiments demonstrate the use of optogenetic constructs to reorient growth cones toward or away from restricted sites of light exposure.

Growth cone reorientation represents the initial response to extracellular cues, which have been traditionally presented either as a soluble gradient or a substrate boundary. Because the Opto system confers the precise positional control of light activation in relation to the growth cone, our data suggests the possibility of using light to turn the growth cone. We envision the use of computer-assisted control of the blue light location to achieve a robust turning response of a growing axon in the future.

Local optogenetic activation to study actin dynamics and branch formation along axons

Previous work has shown that local presentation of NGF, the ligand for TrkA, is able to induce the formation of actin patches along the DRG axon shaft, a key prerequisite for local actin remodeling to induce filopodia formation and the subsequent initiation of a new branch (Gallo and Letourneau, 1998; Spillane et al., 2012). We next asked whether we could achieve similar local activation of NGF signaling and actin reorganization using the iTrk-Opto system.

We first tested localized light activation along DRG axons expressing the optogenetic constructs along with Lifeact (Figure 5A). We imaged Lifeact every 5 s for 5 min, then exposed a small region of the axon (using a 20-µm circle) to pulsed blue light for 5 min (+light; blue; Figure 5, A and B), followed by imaging of Lifeact for 5 min (green box, Figure 5B). To determine whether the activation was local, we also monitored Lifeact ∼40 µm away from the activated region of the same axon within the same field (Figure 5A, −light; red box).

FIGURE 5:

FIGURE 5:

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Local stimulation of actin dynamics along the axonal shaft by light. (A) Schematic drawing to show the regions along the axon where actin dynamics were analyzed before and after blue light exposure locally (+light; blue circle). Nearby regions without blue light exposure (red box) were used for comparison. (B) Time course of experiments: imaging for Lifeact (green bar) for 5 min, followed by blue light activation for 5 min (blue bar), and then Lifeact imaging for 5 min (green bar, Post). (C) Pseudocolored images (mpl heatmap) of Lifeact in axons expressing Ctrl-Opto, iPlex-Opto, or iTrk-Opto before (Pre, 5 min) and after (Post, 10, 11, 13, and 15 min) blue light exposure. Blue circles denote regions that were exposed to blue light. (D–F) The time course of Lifeact signals was measured in +light (blue) and −light (red) regions and normalized to time 0. Blue blocks indicate the period of local light activation when Lifeact was not imaged. (G–I) Quantification of relative actin signals for the 5 min before light activation and 5 min post light activation in +light and −light regions of the axonal shafts. (Ctrl-Opto n = 6; iTrk-Opto n = 5; iPlex-Opto n = 6) Student t-test: ns, not significant; and **p < 0.01. Scale bar = 20 µm.

In the Ctrl-Opto control condition, we saw no change in the average actin signal in either exposed or nonexposed (+ light or −light) regions pre- and post-exposure to blue light (Figure 5C; top row, Supplemental Movie S3 and S4 [left]). This can be demonstrated by examining the time course of the relative Lifeact signal in the two regions that were normalized to the first timepoint (Figure 5D, blue and red lines). This can also be shown by comparing the average Lifeact signal during the 5 min preactivation and postactivation period for either region. Before blue light exposure, the average normalized signal remained relatively constant for both regions; after local light exposure, there was no significant change in the normalized signals (Figure 5G), indicating no difference in net actin assembly. In contrast, neurons expressing iTrk-Opto exhibited robust actin assembly along the axon shaft in response to local blue light (Figure 5C, middle row, Supplemental Movie S3 [right]). Before light exposure, both exposed and nonexposed (+light and −light) regions showed comparable levels of Lifeact signal (Figure 5E). However, immediately after blue light exposure, the exposed (+light) regions showed a continued increase in actin signals while the non-exposed (−light) region did not, as represented by the time course of signal traces (Figure 5E) and the difference in the normalized Lifeact signal for pre and post light exposure (Figure 5H). This increase in Lifeact signals after light activation at the site of exposure is accompanied by an increase in filopodia formation and/or lamellipodia formation specifically (Figure 5C, middle row, Supplemental Movie S4 [right]), which did not happen in the nonexposed regions, supporting the local effect. For comparison, local activation of axons expressing iPlex-Opto did not elicit any response to blue light (Figure 5C, bottom row; Figure 5, F and I; Supplemental Movie S3 and S4 [middle]), consistent with the suppression of actin assembly by PlexinA4 signaling (Alto and Terman, 2017).

Movie S3.

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Actin dynamics in DRG axons prior to blue light exposure. Pseudocolored Lifeact movies of DRG axons expressing either CIB1 (left), iPlex‐Opto (middle), or iTrk‐Opto (right). The movie is from created from images taken every 5 seconds for a total of 5 minutes, and shown at 30 fps.

Movie S4.

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Changes in actin dynamics after localized blue light exposure. Pseudocolored Lifeact movies of DRG axons expressing CIB1 (left), iPlex‐Opto (middle) or iTrk‐Opto (right). The movie is taken right after exposed to localized blue light every 5 seconds for a total of 5 minutes and shown at 30 fps. Local activation of iTrk‐Opto induced the formation of lamellipodia at the sight of activation.

We asked whether the local activation was required to stimulate actin remodeling, or whether whole field illumination along the axon could drive the formation of multiple sites of actin polymerization, a process mimicking bath application of NGF to the neuron. We used the same imaging setup as described in Figure 5B but exposed the whole field to blue light covering approximately 100 µm of the axon (Supplemental Figure S2, A and B). Interestingly, unlike local activation, wider exposure did not result in the same lamellipodia/filopodia formation anywhere along the axon, instead only resulting in the rearrangement of actin patches without changing the overall signal from preactivation levels (Supplemental Figure S2C). The data suggest that concentrated local activation is required to induce strong changes in actin remodeling.

As local activation of iTrk-Opto is sufficient to induce the formation of filopodia and lamellipodia, we next asked whether these sites would ever go on to form a branch with longer activation. To address this, we performed local activation of iTrk-Opto for 25 min using a slit to illuminate a region that was perpendicular to the axon (Supplemental Figure S2, D and E) and provided a potential track for growth of a newly formed branch. Like the 5 min activation described above, no change in actin signal was seen at the sites of activation in axons expressing Ctrl-Opto (Supplemental Figure S2F), but a significant localized and restricted increase in actin polymerization was found in axons expressing iTrk-Opto (Supplemental Figure S2G). However, despite extensive actin polymerization at the site of activation, these regions never extend out to form a new branch, suggesting that besides local signaling, other spatial and temporal regulation is needed to generate a new branch. This is consistent with the notion that the formation of a new axonal branch involves not just actin reorganization (Kalil and Dent, 2014; Armijo-Weingart and Gallo, 2017) but also the coordinated regulation of microtubule dynamics, membrane trafficking, and mitochondria fusion/fission (Lewis et al., 2013; Winkle et al., 2016; Bodakuntla et al., 2021). Using additional markers and long-term light activation, the iTrk-Opto tool provides a controllable experimental system to investigate the cellular mechanisms for NGF-induced branch formation.

In summary, our proof-of-concept study demonstrates the feasibility of using the ICD-Opto platform to modulate receptor activity and growth cone dynamics with a high degree of spatial and temporal control. Although we focused on TrkA and PlexA4 here, this system can easily incorporate the ICD of any receptor that uses clustering to activate downstream signaling. In addition to Lifeact, this system can be combined with other cellular markers to examine various subcellular changes, such as cytoskeletal reorganization and membrane trafficking. Thus, we envision the wide use of the system to investigate cellular dynamics in many processes that are essential for neuronal development and plasticity.

MATERIALS AND METHODS

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Animals

Timed pregnant Long-Evans or Sprague Dawley rats were obtained from Charles River and used in accordance with the Guidelines for the Care and Use of Laboratory Animals of the National Institutes of Health and the approved IACUC protocol (#01560) of the Thomas Jefferson University. Vaginal plug dates were designated as E0. Embryos of both sexes were combined for each experiment.

DNA constructs

The optogenetic vectors were generated based on CRY2-mCherry-iTrkA and CIB1-EGFP-CAAX described previously (Duan et al., 2018) (gifts from Dr. Bianxiao Cui). First, CIB1-EGFP-CAAX was subcloned to replace EGFP behind IRES2 in the pIRES2-EGFP vector (Clonetech) using BstX1 and BsrG1. CRY2-mCherry-iTrkA was then inserted before IRES2 using XhoI/EcoRI to generate the iTrk-mCherry-Opto construct. For iTrk-Opto, mCherry was first removed from CRY2-mCherry-iTrkA by replacing it with an oligo adaptor using BsrG1 and Mfe1 and then subcloning to the IRES2 vector as above. iPlex-mCherry-Opto was done by first PCR amplification of iPlex from full-length PlexA4 (gift from Dr. Avraham Yaron (Mlechkovich et al., 2014)) and then subcloning into iTrk-mCherry-Opto to replace iTrk using triple ligation. iPlex-Opto was created similarly by replacing iTrk in iTrk-Opto with iPlex. Ctrl-mCherry-Opto and Ctrl-Opto were created from iTrk-mCherry-Opto or iTrk-Opto respectively using a similar strategy to replace iTrk with an oligo adaptor and triple ligation. Lifeact was created by subcloning mRuby-Lifeact from pRFPruby-N1-Lifeact (a gift from Dr. Roland Wedlich-Soldner) into a pCAGGS-vector using EcoRI and NotI (Xia et al., 2013). To express in cortical neurons, both Ctrl-Opto and iPlex-Opto were moved into a pAAV-hSYN vector (Addgene #26973) using the XhoI/MfeI sites. The cytosolic fill iRFP670 was generated as described in (Tymanskyj et al., 2018). All constructs and their sequence maps will be available for sharing upon publication.

COS cell and neuronal culture

COS cells were transfected using a TransMax system (Tymanskyj et al., 2018) with various constructs after plating on glass bottom dishes (MatTek) and subject to live imaging described below. Primary rat DRG neuronal cultures were performed as described previously (Zhao et al., 2009; Tymanskyj et al., 2017). Briefly, DRGs were dissected out from E16/17 rat embryos, washed once in HBSS, and incubated at 37°C with 0.25% trypsin for 10–15 min. Trypsin-treated DRGs were resuspended in L15 medium plus 10% horse serum and then mechanically triturated with a fire-polished glass pipette. Dissociated rat DRG neurons (∼7.5 × 105 cells) were transfected with ∼1–2 µg plasmid DNA by nucleofection (Lonza) using reagent P3 and the CU-133 program. Neurons were then plated at ∼30,000 cells in glass-bottom dishes coated with 10 µg/ml poly-d-lysine and 10 µg/ml laminin, and cultured in F12 medium (with N3 supplement, 40 mM glucose, and 25 ng/ml NGF) in a humidified incubator at 37°C and 5% CO2. Dissociated cortical neurons were generated from embryonic E16/17 rat cortical tissue, as described previously (Hruska et al., 2018; Hruska et al., 2022). Briefly, cortices were dissected out and then digested in a papain and cysteine solution for 3–4 min before trituration using a fire-polished glass pipette. Neurons were transfected with DNA using Lipofectamine 2000 in OptiMEM before plating onto glass-bottom dishes coated with 10 µg/ml PDL and 1 µg/ml laminin at a density of 150,000 neurons per 18 mm glass surface. 90 min after plating, the transfection solution was washed off and replaced by fresh Neurobasal media supplemented with B27, Glutamax, and Pen/Strep.

Live cell imaging and optogenetic activation

Dishes of COS cells or DRG neurons were mounted on a heated humidified chamber (OkoLab) equilibrated to 37°C with 5% CO2 on an inverted microscope (Zeiss Axiovert 200) 24–48 h after plating, while cortical neurons were imaged 72–120 h after plating. Fluorescent images were acquired from live cells that were selected based on iRFP670 expression using a 100 × apochromatic objective (NA = 1.4) and a W1 Yokogawa spinning disk system equiped with an EMCCD camera (Cascade 512, Photometrics) and 488 nm, 560 nm, or 640 nm lasers that were controlled by the Metamorph software.

For optogenetic experiments, whole field activation was achieved by exposing cells to 488 nm wavelength of light for 100 ms at 20% laser power, every 5 s for 10 min. For global activation along the axon, blue light (488 nm filter) from a mercury lamp (100 W) was pulsed with an exposure of 100 ms every 10 s for 5 min using a shutter controlled by the Metamorph software.

Local activation was achieved by creating a circular spot in the center of the imaging field using an adjustable pinhole in the conjugating plane of the light path and blue light (488 nm filter) from a mercury lamp (100 W) with pulse exposure of 100 ms every 10 s for 5 min 20-µm wide circular spots were either centered on a growth cone or along the axon generating. To examine whether local activation would lead to branch formation, a 50-µm slit was substituted for the pinhole to generate a 2-µm wide light slit at 100x magnification.The slit was positioned perpendicular to the axon to allow blue light exposure for 100 ms every 30 s for 30 min. To assay growth cone turning, a similar setup was used, with the 2 µm slit being positioned on one side of the growth cone.

Image analysis, quantification, and statistical analysis

All image analyses were conducted using Fiji. Fluorescence images were pseudocolored to show intensity levels using a16 colour (mCherry) or mpl-inferno (Lifeact) look up table. The fluorescence signal intensity in COS cells was measured by drawing three small boxes (30–40 µm2) at the edge of the cell, the center of the cell, and in an area with no signal used as a background. Mean gray value for each time frame for mCherry signal was measured. Background signal was subtracted from both center and peripheral gray values, and then these were ratioed and plotted for each timepoint.

The Lifeact signal in axon shafts was measured in a 20-µm circle drawn around the regions exposed to blue light, or the no-light region located 40 µm+ away. Mean gray values were recorded for each frame and plotted over time after the background gray value was subtracted. For the time course plot, the average signal was measured at each time point and normalized to the pre-signals averaged from the 5-min imaging window.

Growth cone dynamics were classified as either dynamic/growing or collapsed/collapsing based on time-lapsed movies. Dynamic growth cones were defined as growth cones having either lamellipodia formation or having multiple dynamic filopodia which were constantly extending and retracting. Collapsed/collapsing growth cones were those that had limited or no lamellipodia formation, an absence of filopodia, or filopodia that were not extending and remained immobile.

For the growth cone reorientation analysis, the growth cone was divided up into two regions on either side of the slit of activation creating an uneven distribution of two regions, large and small. Their areas were measured before and after activation. An area ratio between the small and large region was calculated at different time points.

Data are presented as mean ± SEM. Statistical analysis was performed in Prism 9.0 software (GraphPad). Data normality was tested for each dataset using the Kolmogorov–Smirnov test. For two sample comparisons, parametric data were analyzed by the two-sided t test, and nonparametric data by the two-sided Mann–Whitney test. Multiple parametric samples were compared using one-way ANOVA with the Tukey's post hoc. For correlative analysis of growth cone dynamics with light exposure, conditional logistic regression with exact confidence intervals and p-values was performed. For changes in fluorescent signals at the membrane, nonlinear regression was used to plot the time course. The statistical tests, sample number (n), experimental replicates, and p-values are described in the figure legends or main text. p-values smaller than 0.05 are represented by asterisks: * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001, and p-values >= 0.05 are considered not significant (ns).

Supplementary Material

mbc-36-br5-s001.pdf (662.3KB, pdf)

ACKNOWLEDGMENTS

We thank members of the Le Ma lab for the discussion throughout the study. We thank Drs. Bianxiao Cui, Roland Wedlich-Soldner, and Avraham Yaron for sharing DNA constructs, Jefferson Weinberg ALS Center for sharing the nucleofection system, and Constantine Daskalakis for statistical analysis and consultation. This work was supported by NIH grants to L.M. (NS112504 and NS114247).

Abbreviations used:

ICD

intracellular domain

CRY2

Cryptochrome 2

CIB1

cryptochrome-interacting basic-helix-loop-helix.

Footnotes

This article was published online ahead of print in MBoC in Press (http://www.molbiolcell.org/cgi/doi/10.1091/mbc.E23-07-0268) on December 20, 2024.

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