3D Particle Tracking for Noninvasive In Vivo Analysis of Synaptic Microtubule Dynamics in Dendrites and Neuromuscular Junctions of Drosophila

Vivian  T. Chou; Hunkar  Gizem Yesilyurt; Hoyin Lai; Jennifer B. Long; Mercedes Arnes; Kamal Obbad; Michael Jones; Hideki Sasaki; Luciano  A.G. Lucas; Sam Alworth; James  Shih-Jong Lee; David Van Vactor

doi:10.3791/61159

JoVE Journal > Neuroscience

Neuroscience

3D Particle Tracking for Noninvasive In Vivo Analysis of Synaptic Microtubule Dynamics in Dendrites and Neuromuscular Junctions of Drosophila

Published: May 12, 2020

doi:

10.3791/61159

Vivian T. Chou*¹, Hunkar Gizem Yesilyurt*¹, Hoyin Lai*², Jennifer B. Long¹, Mercedes Arnes¹, Kamal Obbad¹, Michael Jones², Hideki Sasaki², Luciano A.G. Lucas², Sam Alworth², James Shih-Jong Lee², David Van Vactor¹

¹Blavatnik Institute of Cell Biology and Program in Neuroscience,Harvard Medical School, ²DRVision Technologies LLC

Summary

This study presents a noninvasive intravital neuronal imaging strategy combined with a new software strategy to achieve automated, unbiased tracking and analysis of in vivo microtubule (MT) plus-end dynamics in the sensory dendrites and the neuromuscular junctions of Drosophila.

Abstract

Microtubules (MTs) play critical roles in neuronal development, but many questions remain about the molecular mechanisms of their regulation and function. Furthermore, despite progress in understanding postsynaptic MTs, much less is known about the contributions of presynaptic MTs to neuronal morphogenesis. In particular, studies of in vivo MT dynamics in Drosophila sensory dendrites yielded significant insights into polymer-level behavior. However, the technical and analytical challenges associated with live imaging of the fly neuromuscular junction (NMJ) have limited comparable studies of presynaptic MT dynamics. Moreover, while there are many highly effective software strategies for automated analysis of MT dynamics in vitro and ex vivo, in vivo data often necessitate significant operator input or entirely manual analysis due to inherently inferior signal-to-noise ratio in images and complex cellular morphology. To address this, this study optimized a new software platform for automated and unbiased in vivo particle detection. Multiparametric analysis of live time-lapse confocal images of EB1-GFP labeled MTs was performed in both dendrites and the NMJ of Drosophila larvae and found striking differences in MT behaviors. MT dynamics were furthermore analyzed following knockdown of the MT-associated protein (MAP) dTACC, a key regulator of Drosophila synapse development, and identified statistically significant changes in MT dynamics compared to wild type. These results demonstrate that this novel strategy for the automated multiparametric analysis of both pre- and postsynaptic MT dynamics at the polymer-level significantly reduces human-in-the-loop criteria. The study furthermore shows the utility of this method in detecting distinct MT behaviors upon dTACC-knockdown, indicating a possible future application for functional screens of factors that regulate MT dynamics in vivo. Future applications of this method may also focus on elucidating cell type and/or compartment-specific MT behaviors, and multicolor correlative imaging of EB1-GFP with other cellular and subcellular markers of interest.

Introduction

Cells organize to form functional structures through the coordination of intra- and intercellular changes via morphogenesis. A remarkable example of morphogenesis is the development of the highly specialized neuronal structure. Neurons display remarkable polarization, in which they extend two structurally and functionally distinct types of processes, dendrites and axons¹, which can achieve immense lengths. The complexity of neuronal development arises not only from the sheer size of dendrites and axons but also from the difficulty in forming their intricately branched geometries²^,³. Neuronal morphogenesis and its consequences in learning and memory⁴ motivate the ongoing investigation of both its genetic control and the underlying cell biological mechanisms. Such mechanisms include, but are not limited to, intracellular membrane transport and the many cytoskeletal rearrangements needed for changes in neuronal morphology¹^,²^,³.

Studies of neuronal morphogenesis have produced a variety of advanced visualization techniques. Static methods, such as electron microscopy or fluorescence microscopy of fixed probes, are widely used to perform high-resolution morphological and structural analysis. However, besides the artifacts that are inevitable to any preservation method, static visualization cannot capture the dynamic changes that underpin morphogenesis. Thus, many pivotal insights originated from time-lapse fluorescence microscopy of living tissues. Early work by Lichtman and colleagues⁵^,⁶^,⁷ utilized in vivo imaging of the mammalian nervous system to investigate axon regeneration/degeneration, organization of synaptic components, and long-range axonal transport. Furthermore, seminal studies in primary neuronal explants were critical to establishing the importance of microtubule (MT) dynamics to axonal elongation and motility⁸^,⁹. Crucially, early neuronal explant studies established the use of fluorescently-tagged end-binding family proteins (EBs) to gain invaluable insights into MT plus-end dynamics in developing neurons at the level of individual MT polymers¹⁰. These studies arose from observations that the EB family member EB1 preferentially localizes to MT plus ends¹¹ in S. cerevisiae¹² and in cultured cells¹³. Since then, EB1 and other plus tip tracking proteins (+TIPs)¹⁴^,¹⁵ have been widely used in in vivo studies of MT dynamic instability¹⁶, including in the context of neuronal development¹⁷.

Drosophila is a powerful model for in vivo imaging studies of MT dynamics during neuronal development due to the vast genetic and imaging tools available for fly studies¹⁸^,¹⁹ as well as the similarities in structure and function between Drosophila and vertebrate neurons¹. A key early study of the neuromuscular junction (NMJ) of Drosophila larvae performed repeated noninvasive imaging of a fluorescent membrane marker through the translucent cuticle of intact animals to document presynaptic terminal morphogenesis²⁰. Using a similar method to image whole, live Drosophila larvae, an initial demonstration of subcellular, particle-level analysis of processive movement of motor cargos in the axons was provided²¹. More recently, meticulous studies by Rolls and colleagues in the sensory dendrites of intact Drosophila larvae²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷ characterized postsynaptic MT plus-end dynamics by performing particle tracking and analysis of green fluorescent protein (GFP)-tagged EB1. Such studies in Drosophila²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷ and other systems²⁸^,²⁹^,³⁰^,³¹^,³² have significantly advanced understanding of single-polymer behavior of MT plus ends in the dendrites of developing neurons³³.

Despite the impressive in vivo studies of postsynaptic MT dynamics²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷^,²⁸^,²⁹^,³⁰^,³¹, there have been far fewer comparable studies of presynaptic MT dynamics at the developing axon terminal. MT dynamics at the Drosophila larval NMJ has been studied using fluorescent speckle microscopy (FSM) and fluorescence recovery after photobleaching (FRAP)³⁴. These techniques evaluate the overall tubulin kinetics but not the behavior of individual MT plus ends. As of this writing, there has been one sole investigation of individual MT plus ends at the Drosophila NMJ: This study combined live time-lapse imaging with manual analysis of kymographs to characterize a population of dynamic, EB1-GFP labeled "pioneering MTs" that appeared distinct from a broader population of stabilized MTs³⁵. This lack of research on presynaptic MT dynamics may be due at least in part to anatomy: While it is relatively straightforward to obtain images of dendrites due to their proximity to the larval cuticle, NMJs are obstructed by other tissues, making it challenging to acquire images with sufficient signal-to-noise ratio for particle-level analysis. Nonetheless, given the well-established importance of the presynaptic MTs to synaptic morphogenesis and stabilization³⁶, as well as their links to neurodevelopmental and neurodegenerative disorders³⁷, bridging this gap between understanding of pre- and postsynaptic MTs is likely to yield invaluable insights.

An additional challenge to the analysis of in vivo MT dynamics in general, in contrast to in vitro or ex vivo analysis, is the limited automated software tools that can extract dynamics parameters from in vivo data. Presently, one of the most popular and powerful techniques for analysis of +TIP-labeled MT plus ends is plusTipTracker³⁸^,³⁹, a MATLAB-based software that allows automated tracking and analysis of multiple dynamics parameters. Notably, plusTipTracker measures not only MT growth but also shrinkage and rescues: while +TIP labels such as EB1-GFP only associate with growing plus ends, plusTipTracker can algorithmically infer shrinkage rates and rescue events. However, while plusTripTracker has been very successfully applied to many contexts, including previous multiparametric analysis of ex vivo MT dynamics in Drosophila S2 cells⁴⁰, plusTipTracker is not optimal for analysis of in vivo data given their lower signal-to-noise ratio. As a result, in vivo studies of plus-end dynamics at dendrites²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷ and at the NMJ³⁵ of Drosophila have relied on manual generation and analysis of kymographs using software such as ImageJ⁴¹, or on semiautomated strategies that involve numerous human-in-the-loop components.

This study presents an experimental and analytical workflow that reduces the experimental and analytical overhead required to perform noninvasive polymer-level analysis of presynaptic MT dynamics in both sensory dendrites and the motor axon terminal of Drosophila third-instar larvae. The protocol utilizes immobilized, intact larvae and therefore avoids injuries known to trigger stress responses as well as other nonphysiological conditions that might perturb in vivo MT dynamics. To label dynamic MT plus-ends, EB1-GFP is pan-neuronally expressed using the Gal4/UAS system⁴², allowing visualization of MTs at both dendrites and NMJ with a single driver. While some early steps are inevitably subject to human decision-making, such as the selection of animal specimens and identification of regions to image, the steps following data acquisition are largely automated. Crucially, optimization of a new software enabled automated, unbiased analysis requiring minimal human input. While other particle tracking methods are available⁴³^,⁴⁴^,⁴⁵, this study utilizes a proprietary software because it was algorithmically well-suited to address the particular challenges of this particular dataset. The software is now available to users for a variety of applications. Specifically, the use of coherence-enhancing diffusion filtering⁴⁶ is integral to automated segmentation and background removal, and custom algorithms are implemented specifically to automate particle detection and tracking. This strategy could effectively handle the low signal-to-noise ratio inherent to the data in this study, as well as other challenges, such as movement of EB1-GFP comets through different focal planes. While it is not feasible to exhaustively test the performance of this software against all other particle analysis software, the performance of the present strategy equaled or approached the standard human performance. Furthermore, to the authors’ knowledge, there has been no other software specifically trained on in vivo data from sensory dendrites and the presynaptic terminal. Given that the performance of image analysis algorithms is often highly specific to the data they were designed for and that generalized computer vision is not yet possible, it is expected that training the described software to the specific in vivo data of interest is the most algorithmically sound approach.

Given the extensive work on dendritic MTs²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷ as well as the consistent quality of data that can be acquired from this system, the image acquisition and software analysis strategy was first validated in Drosophila sensory dendrites. Importantly, it was found in dendrites that the use of different neuronal Gal4 drivers, even in otherwise identical wild type backgrounds, results in significant differences in EB1-GFP dynamics due to differences in genetic background, emphasizing the importance of using a single Gal4 driver for consistent results. This strategy was next used for multiparametric analysis of EB1-GFP dynamics at the presynaptic terminal of the NMJ. To further illustrate the investigative value of this method, this imaging and software strategy was used to assess both pre- and postsynaptic EB1-GFP dynamics following knockdown of dTACC, the Drosophila homolog of the highly conserved TACC (transforming acidic coiled coil) family⁴⁷^,⁴⁸. Prior work in Drosophila S2 cells⁴⁰, as well as work by Lowery and colleagues in the Xenopus growth cone⁴⁹^,⁵⁰^,⁵¹, has shown that TACC family members regulates MT plus-end dynamics. Furthermore, recently reported evidence from confocal and super-resolution immunofluorescence imaging showed that dTACC is a key regulator of presynaptic MTs during neuronal morphogenesis⁵², raising the question of whether dTACC regulates live MT dynamics. This report demonstrates a method that can indeed detect differences in live MT behaviors upon dTACC knockdown. Thus, this study presents an in vivo method that can effectively identify and characterize key regulators of MT dynamics within the developing neuron, particularly in the presynaptic compartment.

Protocol

1. Generation of Drosophila specimens

Select a suitable MT plus-end marker. This study utilized GFP-tagged EB1, a well-characterized plus-end marker with a strong, clear signal¹¹^,¹². Alternatives include other +TIPs such as EB3¹⁰^,¹³, CLASP/Orbit⁵³, and CLIP-170⁵⁴.
Obtain or generate flies with the MT marker under control of a UAS promoter (e.g., UAS-EB1-GFP).
Choose the appropriate tissue-specific Gal4-driver. This study used the pan-neuronal driver elaV-Gal4⁵⁸^,⁵⁹ to drive expression in both sensory dendrites and at the NMJ and 221-Gal4⁶⁰^,⁶¹ for dendrite-specific expression.
Raise flies using standard fly husbandry techniques⁵⁵^,⁵⁶. It is recommended that flies be kept in humidified incubators at 25 °C for optimal Gal4/UAS expression.
Using standard fly genetic techniques⁵⁵^,⁵⁶, perform crosses to generate flies to express the MT plus-end marker in the desired cells/tissues.
NOTE: For any Gal4-driver and -transgene combination, the experimental design should include proof-of-concept and validation experiments to characterize the system and avoid artifacts from overexpression.

2. Equipment setup

Set up a workstation, including the flies, anesthetic reagents, slide construction materials, stereomicroscope, and illumination source, close to the confocal microscope (i.e., in the same room) to minimize the time spent between sample preparation and imaging to prolong the health and viability of the larvae.
Prepare the anesthetic by mixing a 9% chloroform mixture (0.1 mL of chloroform and 1.0 mL of halocarbon oil) in a 1.5 mL microcentrifuge tube. To avoid separation, mix well by inverting the tube prior to preparing each new slide.
Prepare the glass slide: Cut four strips of double-sided tape (~15 mm wide). Line up two of the pieces on the glass slide, leaving a space of ~5 mm in between the strips. Layer the remaining two pieces on top of the first two to double the thickness of the tape (Figure 1C).
Add a large drop (~100 µL) of chloroform/oil mixture onto the glass slide in the 5 mm space between the tape pieces (Figure 1C).

3. Preparation of larval samples for imaging

Fill a container (e.g., a 6 well plate) with 1x PBS.
Collect 3^rd instar larvae from the fly vial using forceps or a similar instrument. Identify larvae at the proper stage by their crawling behavior and by the presence of 9–12 prominent, serrated mouth hooks. Use a stereomicroscope to assist in staging larvae (Figure 1D).
Place a larva in the PBS and move it gently to wash off any remains of food or other debris. Dry the larva gently on a delicate tissue.
Anesthetize the larva by placing it into chloroform/oil drop on the slide from section 2 (Figure 1C).
NOTE: The dorsal/ventral orientation of the larva is not critical because both sensory and motor neurons can be detected through the translucent cuticle by setting the microscope stage to the proper focal plane, regardless of the orientation of the specimen.
Place a #1.5 coverslip on top. Adhere the coverslip to the tape by applying gentle pressure, thus immobilizing the larva without damaging it (Figure 1C).
Seal the chamber with petroleum jelly or nail polish.

4. Time-lapse confocal imaging of live samples

Prepare confocal microscope and the 60x objective lens with oil immersion. Place the sample on the stage (Figure 1A,B).
Use the acquisition software to configure experiments.
NOTE: For this study, each imaging series was acquired at a single focal plane as opposed to a z-stack.
1. Set the time-lapse duration to 30 s at an interval of 2 s, for a total of 16 frames.
2. Set laser exposure and intensity to ensure sufficient signal while avoiding saturation and photobleaching.
3. For EB1-GFP imaging, the 488 nm laser was set to an exposure time of 100 ms and intensity of 30%. These values may vary for different uses of this protocol and should be modified empirically.
Use the eyepieces of the microscope to find the larva in widefield-green illumination. Find the dendrites or NMJs by adjusting the stage slowly. Do not expose larva to illumination (widefield or confocal) for longer than necessary.
1. Dendrites appear as thin bright-green webs of nerves easily distinguishable from thick long axon bundles (Figure 1E).
2. NMJs appear as groups of bright-green individual boutons, approximately 5 µm in diameter, at the ends of thick long axon bundles that diverge from the nerve cord (Figure 1F).
Using the live camera feed, quickly focus on the region of interest using 488 nm illumination. Immediately stop illumination once the proper focus is found to avoid phototoxicity.
Initiate image acquisition. EB1 comets are recognizable as bright, motile punctae.
Refer to previously published protocols for additional details and guidelines on fluorescence live imaging⁵⁷.

5. Software-based image processing and analysis

Analyze each video file individually. Within software (user interface shown in Figure 2), select File | Import | Image Sequence and drag TIF files in the box that appears. Preview the video.
Under the Detection Parameters menu, tune the software parameters to ensure detection of only clearly visible punctae and avoid detection of spurious objects. For instance, reducing particle intensity results in greater sensitivity of software to punctae but increases potential false positives. The precise values of the parameters will vary empirically. Descriptions of the Detection and Tracking Parameters are available from the authors upon request.
Apply the Neuron Particle Tracking recipe to analyze the image using the From beginning button (blue arrow, Figure 2B). The software will output results for the tracking parameters listed in Table 1 to the Results Spreadsheet (green box, Figure 2B). For ease of later analysis and interpretation, the results can be stored in spreadsheet software using the Export function found in the Results Spreadsheet section.
Navigate the cursor to puncta detected in the previous step and left-click to select or deselect. Multiple puncta can be selected simultaneously using Ctrl + left-click.
NOTE: Depending on the project aims and applications, additional heuristics may be used to filter the punctae. For instance, punctae with a lifetime of fewer than 8–10 s (4–5 frames) might be omitted because they do not present sufficient information about the entire growth event. The need for such heuristics will vary empirically. Additional details on software functionality are available from the authors upon request.

Representative Results

Flies were raised from stable stocks that constitutively express the UAS-EB1-GFP transgene either pan-neuronally (elaV-Gal4; UAS-EB1-GFP)⁵⁸^,⁵⁹ or in sensory neurons (221-Gal4; UAS-EB1-GFP)⁶⁰^,⁶¹. EB1 was chosen for this study because it specifically localizes to growing ends and dissociates immediately upon pause and shrinkage¹⁴^,¹⁵ and has been shown through multiple studies, including in Drosophila²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷^,³⁵, to be a robust marker that does not have significant detrimental effects on the underlying biology of the organism. Imaging of wandering third-instar larvae was performed on an inverted spinning disc confocal microscope following the preparation of intact samples (Figure 1A–C). Larvae were staged based on behavior (active crawling along vial walls) and the presence of large, extended mouth hooks with 9–12 teeth (Figure 1D). Each image series was acquired at a single focal plane. Sensory dendrites superficially located near the larval cuticle (Figure 1E) were imaged to provide comparisons with published data²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷^,³⁵, while NMJs located at deeper image planes on the surface of body wall muscle within the animal (Figure 1F) were imaged to define presynaptic MT dynamics parameters.

Following image acquisition as described in the protocol above automated, unbiased analysis of the EB1-GFP comets was performed (Figure 2), producing measurements for nine dynamic parameters (Table 1). Statistical analysis, including exploratory data analysis and hypothesis testing, was performed in MATLAB. It was noted through data visualization and the Anderson-Darling test that the data contained non-normally distributed values. Thus, to avoid making assumptions about the underlying distribution of the data, all hypothesis testing was performed using the nonparametric Wilcoxon-Mann-Whitney test.

EB1-GFP dynamics under the control of both the elaV-Gal4 and 221-Gal4 drivers were compared in otherwise equivalent wild type backgrounds (Figure 3). Interestingly, there were highly significant differences (P < 0.005) in several measured parameters (e.g., mean acceleration, sinuosity, growth length). While EB1 is not generally expected to disrupt native MT biology to an adverse degree¹⁰^,⁵⁴, MTs are nevertheless highly sensitive to perturbations in EB1 expression⁶²^,⁶³. The differences observed between the two drivers could arise from their distinct expression patterns: elaV-Gal4 could drive UAS-EB1-GFP expression pan-neuronally as well as in neuronal progenitors and glia⁵⁸^,⁵⁹ while 221-Gal4 could drive expression solely in sensory dendrites⁶⁰^,⁶¹. Differences in UAS-EB1-GFP expression could also be due to different temporal onset of elaV-Gal4 and 221-Gal4, or any number of other variations in the genetic background of the two driver lines. To avoid any artifacts from these and other potentially confounding factors, all further experiments in both dendrites and at the NMJ were carried out using only the elaV-Gal4 pan-neuronal driver.

This method was first validated in sensory dendrites (Figure 4), and the entire protocol was repeated at the NMJ (Figure 5). To assess the potential of this strategy for investigating the role of specific molecules on MT dynamics, EB1-GFP dynamics were compared between wild type controls and animals expressing UAS-dtacc-RNAi. dTACC was chosen because it is a known regulator of MT plus-end dynamics⁴⁰^,⁴⁹^,⁵⁰^,⁵¹ in other systems, and also based on recent evidence that it regulates presynaptic MTs at the Drosophila NMJ⁵². To enhance dtacc-RNAi expression, elaV-GAL4 was also used to express UAS-Dcr2, an endonuclease that promotes processing of long dsRNAs to siRNAs.

Upon reduction of dTACC expression to ~50%⁵², significant changes in EB1-GFP dynamics were found in both dendrites (Figure 4) and at the NMJ (Figure 5). Notably, the effects of dTACC knockdown in dendrites closely resembled the effects of dTACC knockdown previously observed in S2 cells⁴⁰. In contrast, striking differences were observed between dendrites and the NMJ upon dTACC knockdown. While loss of dTACC affected seven parameters in dendrites, and three parameters at the NMJ, all but two of the parameters (max comet velocity and sinuosity) were unique to either dendrites or NMJ. Furthermore, while sinuosity was affected by dTACC loss in both contexts, the effect was opposite between dendrites (increase) and the NMJ (decrease). Thus, this protocol can not only identify significant differences in MT dynamics between genetic backgrounds but can also demonstrate distinct roles for a single MT regulator in different contexts.

Figure 1: Experimental setup. (A) Schematic and (B) actual example of imaging setup. Anesthetized, whole-mount larvae were imaged on an inverted spinning disc confocal microscope. (C) Example of slide preparation using third-instar larvae. (D) Larvae were staged by their crawling behavior and by the presence of 9–12 prominent, serrated mouth hooks. Imaging was performed on (E) sensory neuron dendrites, which have a relatively superficial location close to the outer cuticle, and (F) the presynaptic terminal of the NMJ, which is located deeper within the animal. Scale bar = 2 µm. Please click here to view a larger version of this figure.

Figure 2: Demonstration of software-based dendrite and NMJ analysis. (A) Summary of the automated analysis processing pipeline. A common issue of typical morphological approaches to background removal is the enhancement of image signal along the edges of small and narrow structures (e.g., dendrites). To address this, a coherence-enhancing diffusion filter⁴⁶ was applied to the raw image to extract the whole dendrite/NMJ structure as background and to isolate the EB1 comets on the image. This approach enabled identification and tracking of the comets even where the contrast between the background structure and the EB1 comet was low. (B) Workflow integration by the software interface allows the user to 1) optimize analysis parameters for a given image, and 2) review the analysis. The blue arrow highlights the button used to run the recipe, and the green box indicates the spreadsheet with analysis results. Additional details on software functionality are available from the authors upon request. Please click here to view a larger version of this figure.

Figure 3: Comparison of elaV- and 221-Gal4 drivers in wild type control dendrites. To determine the effects of Gal4-dependent UAS-EB1-GFP expression levels on EB1-GFP dynamics, elaV-Gal4; UAS-EB1-GFP and 221-Gal4; UAS-EB1-GFP were expressed in a w¹¹¹⁸ control background. Highly significant differences were observed in mean acceleration, sinuosity, and growth length. ** P < 0.005, Wilcoxon-Mann-Whitney-test; error bars indicate ± SEM; number of NMJs quantified indicated on graph. Please click here to view a larger version of this figure.

Figure 4: Neuronal RNAi-knockdown of TACC affected EB1-GFP dynamics in sensory dendrites. (A) Representative time-lapse images of EB1-GFP comet dynamics in control elaV-Gal4; UAS-EB1-GFP; UAS-Dcr2 x w¹¹¹⁸sensory dendrites. Image series on the right shows a detailed view of the region indicated by the box in the image on the left. In each panel, the solid white arrow indicates the position of the EB1-GFP comet at the most recent timepoint, while the hollow arrow indicates the original position of the comet at t = 0 s. (B) Comparison of EB1-GFP dynamics in elaV-Gal4;UAS-EB1-GFP; UAS-Dcr2 x w¹¹¹⁸and elaV-Gal4; UAS-EB1-GFP; UAS-Dcr2 x UAS-tacc-rnai dendrites. Knockdown of dTACC significantly affected all dynamics parameters other than mean acceleration and growth lifetime. * P < 0.05, ** P < 0.005, Wilcoxon-Mann-Whitney-test; error bars indicate ± SEM; number of NMJs quantified indicated on graph; scale bar = 1 µm. Please click here to view a larger version of this figure.

Figure 5: Neuronal RNAi-knockdown of TACC affected EB1-GFP dynamics at the NMJ. (A) Representative time-lapse images of EB1-GFP comet dynamics at the presynaptic terminal of control elaV-Gal4; UAS-EB1-GFP; UAS-Dcr2 x w¹¹¹⁸NMJs. Image series on the right shows detailed view of the region indicated by the box in the image on the left. In each panel, the solid white arrow indicates the position of the EB1-GFP comet at the most recent timepoint, while the hollow arrow indicates the original position of the comet at t = 0 s. (B) Comparison of EB1-GFP dynamics at elaV-Gal4; UAS-EB1-GFP; UAS-Dcr2 x w¹¹¹⁸and elaV-Gal4; UAS-EB1-GFP; UAS-Dcr2 x UAS-tacc-rnai NMJs. Knockdown of dTACC significantly affected max velocity, mean acceleration, and sinuosity. * P < 0.05, ** P < 0.005, Wilcoxon-Mann-Whitney-test; error bars indicate ± SEM; number of NMJs quantified indicated on graph; scale bar = 1 µm. Please click here to view a larger version of this figure.

Tracking Parameter	Description
Mean Comet Velocity	average of the detected track velocity (scalar) over the lifetime of the track
Max Comet Velocity	highest value of track velocity (scalar) detected over the lifetime of the track
Straight Line Velocity	growth length divided by the growth lifetime
Mean Acceleration	average of the rate of change of detected track velocity (scalar) over the lifetime of the track
Sinuosity	growth length divided by path length
Mean Square Displacement	sum of the particle displacement squared at all time points divided by growth lifetime
Growth Length	straight line distance between the starting frame position and ending frame position of the track
Path Length	total distance traveled by the track
Growth Lifetime	total length (in time) of the detected track

Table 1: Plus-end dynamic parameters analyzed

Discussion

This paper discusses a protocol to perform noninvasive intravital imaging of MT dynamics in the dendrites and at the NMJ of during development. Human input is required during the experimental steps, such as in selecting animals to image, and may introduce bias in the data collection process that cannot be reasonably removed. Thus, a key goal of the protocol is to minimize bias wherever possible by performing automated analysis with a new software (section 5) that was optimized to handle the low signal-to-noise ratio inherent to in vivo data. Importantly, the algorithms used in this study allow machine-based particle detection, kymograph generation, and track analysis, reducing the need for human input compared to traditional methods. Regardless, users of the software should assess the results and set the tracking and detection parameters to filter out false positives (e.g., aggregates or noise). These modifications must be empirically adjusted based on each user’s unique data and use. It is also worth noting that the analysis presented here is not completely equivalent to that possible with plusTipTracker: While it is possible to infer shrinkage and rescue events with plusTipTracker, the algorithms in the current software cannot perform such measurements. Furthermore, because each imaging series is acquired at a single focal plane, movement of EB1 comets in the z axis cannot be fully captured using the current protocol. Nonetheless, despite these limitations, given the considerable constraints on data quality that are inherent to in vivo data, this method makes progress towards achieving automated, reproducible data analysis in vivo.

Because the quality of the raw data is also paramount regardless of the capabilities of the analysis software, optimal specimen preparation is also a critical consideration. While every effort must be made to reduce deleterious effects on the larva (e.g., using intact samples), stresses such as chloroform and phototoxicity are inevitable. Thus, care should be made when setting imaging parameters to ensure specimen health, based both on general guidelines⁵⁷ and empirical monitoring. It was found that expediting the experimental steps by working quickly and having a sample preparation workstation in the same room as the confocal microscope (section 2) helped to mitigate sample deterioration and prolong larval viability. Another key aspect of the method is to find a chloroform dosage that is sufficiently potent but not overly harmful. Nevertheless, it should be noted that general anesthetization may influence MT dynamics⁶⁴. The use of nonchemical methods of immobilizing larvae, such as adhering larvae to an agarose pad²⁵ or microfluidic chambers⁶⁵^,⁶⁶, may further improve results by circumventing the potential side effects of anesthetization.

Another crucial step is the method by which EB1-GFP is expressed using the Gal4/UAS system to label MT dynamics (section 1). As already noted, it is imperative to maintain a consistent genetic background throughout all comparisons due to the potential for multiple confounding variables that can influence EB1-GFP dynamics (Figure 3). A further consideration when using Gal4/UAS or other similar targeted expression systems is the effect of overexpression on endogenous MT dynamics. Thus, a possible future improvement would be to utilize knock-in fluorescent tags to avoid gain-of-function artifacts, although at present, Gal4/UAS remains a very widely used method in studies of live MT dynamics in Drosophila²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷^,³⁵. An issue regarding the use of fluorescently-tagged +TIPs to keep in mind is the potential ectopic effect of the tag on +TIP function. Thus, any novel fusion constructs should be validated through rescue experiments, and data analysis and interpretation should be performed with these points in mind.

Significant effects on multiple MT dynamics parameters were observed upon knockdown of dTACC in both dendrites and at the NMJ. This demonstrates that this method may be a potential screening tool for regulators of synaptic MT dynamics and moreover identifies a potential role for dTACC in dendrites. While the role of presynaptic dTACC in the development of the motor axon terminal has been established⁵², the roles of postsynaptic dTACC are unknown. Thus, future studies may focus on role of postsynaptic dTACC, either in sensory dendrites and/or in the muscle of the NMJ.

Key differences were noted in the effects of dTACC knockdown on MT dynamics in sensory dendrites and the NMJ, indicating clear biological differences between the two contexts. This raises the question of whether MT dynamics differs between neuronal types, between distinct compartments of a single neuron, or both. The differences observed between dendrites and the NMJ might reflect differences between sensory and motor neurons but could also indicate differences between dendritic and axonal compartments, independent of the neuronal type. Because the focus in the present study was on developing a robust methodology rather than comprehensive characterization of neuronal MT dynamics, analysis of motor neuron dendrites or the axon terminals of sensory neurons has not yet been performed. Due to their less accessible location within the animal, these structures are more challenging to image and analyze compared to the structures presently discussed. Future efforts will focus on applying this optimized protocol to improve imaging of less accessible regions to enable studies of compartment- and cell-type differences in MT dynamics.

Conceivably, this in vivo imaging and analysis strategy will be of value to researchers interested in a detailed understanding of the dynamic MT behaviors during the critical stages of neuronal development. A key future innovation would be multicolor imaging through coexpression of EB1-GFP with other markers, such as those that label the cell membrane (i.e., CD8⁶⁷, myristol⁶⁸), the actin cytoskeleton (i.e., moesin⁶⁹, LifeAct⁷⁰), and other structures of interest. This would allow correlative analysis of the spatiotemporal interactions of MTs with other key cellular structures. While such multicolor imaging has been used to study MT-actin interactions in the neuronal growth cone⁷¹^,⁷², it has not been employed in dendrites or the presynaptic axon terminal. Thus, developing a comparable method for in vivo Drosophila studies will be a significant addition to the imaging toolkit for understanding the role of MTs in the broader context of neuronal development.

Disclosures

The authors have nothing to disclose.

Acknowledgements

We thank our colleagues in the Van Vactor lab and at DRVision in addition to Drs. Max Heiman, Pascal Kaeser, David Pellman, and Thomas Schwarz for helpful discussion. We thank Dr. Melissa Rolls for generously providing the elaV-Gal4; UAS-EB1-GFP; UAS-Dcr2 and 221-Gal4; UAS-EB1-GFP stocks used in this study. We thank Drs. Jennifer Waters and Anna Jost at the Nikon Imaging Center at Harvard for light microscopy expertise. This work is funded by the National Institutes of Health (F31 NS101756-03 to V.T.C., SBIR 1R43MH100780-01D to J.S.L.).

Materials

1.5 mL microcentrifuge tube	Eppendorf	21008-959	Sample preparation
1000 µL TipOne pipette tips	USA Scientific	1111-2721	Sample preparation
200 µL TipOne pipette tips	USA Scientific	1120-8710	Sample preparation
221-Gal4 flies	Bloomington Drosophila Stock Center (US)	26259	Drosophila genetics/crosses
60x Objective Lens	Nikon	Plan Apo 60x Oil	Image acquisition
6-well plate	BD Falcon	353224	Sample preparation
Agar	MoorAgar	41084	Drosophila food
Aivia	DRVision LLC		Optimized as part of this study
Chloroform (stabilized with amylenes)	Sigma-Aldrich	C2432	Sample preparation
CO2 blowgun (for selection of flies for crosses)	Genesee	54-104	Drosophila genetics/crosses
CO2 bubbler (for selection of flies for crosses)	Genesee	59-180	Drosophila genetics/crosses
Cooled CCD camera	Hamamatsu	ORCA-R2	Image acquisition
Cornmeal	Genesee	62-101	Drosophila food
Distilled Water			Drosophila food
Double-sided tape	Scotch		Sample preparation
Drosophila vials	Genesee	32-109	Drosophila food
Droso-plugs (foam plugs for vials)	Genesee	59-200	Drosophila food
Dumont #5 Biologie Inox Forceps	Fine Science Tools	11252-20	Sample preparation
elaV-Gal4;UAS-EB1-GFP;UAS-Dcr2 flies	Gift of Melissa Rolls (Penn State University)	N/A	Drosophila genetics/crosses
Ethanol (95%)	VWR	75811-022	Drosophila food
Fiber optic illuminator/light source for stereomicroscope	Nikon	NI-150	Sample preparation
Flypad (for selection of flies for crosses)	Genesee	59-172	Drosophila genetics/crosses
Forma Environmental Chamber/Incubator	ThermoFisher	3940	Drosophila genetics/crosses
Halocarbon oil 700	Sigma-Aldrich	H8898	Sample preparation
Immersion Oil	Nikon	MXA22168	Image acquisition
Kimwipe Delicate Wipes	Fisher Scientific	34120	Sample preparation
Laser Merge Module	Spectral Applied Research	LMM-5	Image acquisition
Light Source for Confocal	Lumencor	SOLA 54-10021	Image acquisition
MetaMorph Microscopy Automation & Image Analysis Software	Molecular Devices		Image acquisition
Micro Cover Glasses, Square, No. 1 1/2 (#1.5)	VWR	48366-205	Sample preparation
Motorized inverted microscope with Perfect Focus System	Nikon	TI-ND6-PFS-S	Image acquisition
Motorized stage and shutters	Prior	Proscan III	Image acquisition
Multi-purpose scissors	Scotch	MMM1428	Sample preparation
Nail Polish	Sally Hansen	784179032016 074170382839	Sample preparation
Optical Filter	Chroma	ET480/40m	Image acquisition
P1000 Pipetman	Gilson	F123602	Sample preparation
P200 Pipetman	Gilson	F123601	Sample preparation
PBS (10X) ph 7.4	ThermoFisher	70011044	Sample preparation
Propionic Acid	Fisher	A258-500	Drosophila food
Spinning disk confocal scanner unit	Yokagawa	CSU-X1	Image acquisition
Stereomicroscope	Nikon	SMZ800N	Sample preparation
Sugar (Sucrose)	Genesee	62-112	Drosophila food
Superfrost Slide	VWR	48311-600	Sample preparation
Tegosept	Genesee	20-258	Drosophila food
UAS-dtacc-RNAi flies	Vienna Drosophila Resource Center (Vienna, Austria)	VDRC-101439	Drosophila genetics/crosses
Vaseline petroleum jelly	WB Mason	DVOCB311003	Sample preparation
Winsor & Newton Brush Regency Gold 520, Size 0	Staples	5012000	Drosophila genetics/crosses
Yeast	VWR	Torula Yeast IC90308580	Drosophila food
Yokogawa dichroic beamsplitter	Semrock	Di01-T405/488/568/647-13x15x0.5	Image acquisition

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