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In This Article

  • Summary
  • Abstract
  • Introduction
  • Protocol
  • النتائج
  • Discussion
  • Disclosures
  • Acknowledgements
  • Materials
  • References
  • Reprints and Permissions

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

  1. 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.
  2. Obtain or generate flies with the MT marker under control of a UAS promoter (e.g., UAS-EB1-GFP).
  3. 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.
  4. 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.
  5. 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

  1. 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.
  2. 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.
  3. 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).
  4. 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

  1. Fill a container (e.g., a 6 well plate) with 1x PBS.
  2. Collect 3rd 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).
  3. 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.
  4. 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.
  5. 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).
  6. Seal the chamber with petroleum jelly or nail polish.

4. Time-lapse confocal imaging of live samples

  1. Prepare confocal microscope and the 60x objective lens with oil immersion. Place the sample on the stage (Figure 1A,B).
  2. 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.
  3. 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).
  4. 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.
  5. Initiate image acquisition. EB1 comets are recognizable as bright, motile punctae.
  6. Refer to previously published protocols for additional details and guidelines on fluorescence live imaging57.

5. Software-based image processing and analysis

  1. 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.
  2. 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.
  3. 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.
  4. 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.

النتائج

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

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 inhe...

Disclosures

The authors Hoyin Lai, Michael Jones, Hideki Sasaki, Luciano A.G. Lucas, Sam Alworth (formerly), and James Shih-Jong Lee are employees of DRVision Technologies LLC, which produces the software used in this protocol.

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

NameCompanyCatalog NumberComments
1.5 mL microcentrifuge tubeEppendorf21008-959Sample preparation
1000 µL TipOne pipette tipsUSA Scientific1111-2721Sample preparation
200 µL TipOne pipette tipsUSA Scientific1120-8710Sample preparation
221-Gal4 fliesBloomington Drosophila Stock Center (US)26259Drosophila genetics/crosses
60x Objective LensNikonPlan Apo 60x OilImage acquisition
6-well plateBD Falcon353224Sample preparation
AgarMoorAgar41084Drosophila food
AiviaDRVision LLCOptimized as part of this study
Chloroform (stabilized with amylenes)Sigma-AldrichC2432Sample preparation
CO2 blowgun (for selection of flies for crosses)Genesee54-104Drosophila genetics/crosses
CO2 bubbler (for selection of flies for crosses)Genesee59-180Drosophila genetics/crosses
Cooled CCD cameraHamamatsuORCA-R2Image acquisition
CornmealGenesee62-101Drosophila food
Distilled WaterDrosophila food
Double-sided tapeScotchSample preparation
Drosophila vialsGenesee32-109Drosophila food
Droso-plugs (foam plugs for vials)Genesee59-200Drosophila food
Dumont #5 Biologie Inox ForcepsFine Science Tools11252-20Sample preparation
elaV-Gal4;UAS-EB1-GFP;UAS-Dcr2 fliesGift of Melissa Rolls (Penn State University)N/ADrosophila genetics/crosses
Ethanol (95%)VWR75811-022Drosophila food
Fiber optic illuminator/light source for stereomicroscopeNikonNI-150Sample preparation
Flypad (for selection of flies for crosses)Genesee59-172Drosophila genetics/crosses
Forma Environmental Chamber/IncubatorThermoFisher3940Drosophila genetics/crosses
Halocarbon oil 700Sigma-AldrichH8898Sample preparation
Immersion OilNikonMXA22168Image acquisition
Kimwipe Delicate WipesFisher Scientific34120Sample preparation
Laser Merge ModuleSpectral Applied ResearchLMM-5Image acquisition
Light Source for ConfocalLumencorSOLA 54-10021Image acquisition
MetaMorph Microscopy Automation & Image Analysis SoftwareMolecular DevicesImage acquisition
Micro Cover Glasses, Square, No. 1 1/2 (#1.5)VWR48366-205Sample preparation
Motorized inverted microscope with Perfect Focus SystemNikonTI-ND6-PFS-SImage acquisition
Motorized stage and shuttersPriorProscan IIIImage acquisition
Multi-purpose scissorsScotchMMM1428Sample preparation
Nail PolishSally Hansen784179032016 074170382839Sample preparation
Optical FilterChromaET480/40mImage acquisition
P1000 PipetmanGilsonF123602Sample preparation
P200 PipetmanGilsonF123601Sample preparation
PBS (10X) ph 7.4ThermoFisher70011044Sample preparation
Propionic AcidFisherA258-500Drosophila food
Spinning disk confocal scanner unitYokagawaCSU-X1Image acquisition
StereomicroscopeNikonSMZ800NSample preparation
Sugar (Sucrose)Genesee62-112Drosophila food
Superfrost SlideVWR48311-600Sample preparation
TegoseptGenesee20-258Drosophila food
UAS-dtacc-RNAi fliesVienna Drosophila Resource Center (Vienna, Austria)VDRC-101439Drosophila genetics/crosses
Vaseline petroleum jellyWB MasonDVOCB311003Sample preparation
Winsor & Newton Brush Regency Gold 520, Size 0Staples5012000Drosophila genetics/crosses
YeastVWRTorula Yeast IC90308580Drosophila food
Yokogawa dichroic beamsplitterSemrockDi01-T405/488/568/647-13x15x0.5Image acquisition

References

  1. Rolls, M. M. Neuronal polarity in Drosophila: Sorting out axons and dendrites. Developmental Neurobiology. 71 (6), 419-429 (2011).
  2. Jan, Y. N., Jan, L. Y. Branching out: Mechanisms of dendritic arborization. Nature Reviews Neuroscience. 11 (5), 316-328 (2010).
  3. Lewis, T. L., Courchet, J., Polleux, F. Cell biology in neuroscience: Cellular and molecular mechanisms underlying axon formation, growth, and branching. Journal of Cell Biology. 202 (6), 837-848 (2013).
  4. Kandel, E. R. The Molecular Biology of Memory Storage: A Dialogue Between Genes and Synapses. Science. 294 (5544), 1030-1038 (2001).
  5. Turney, S. G., Lichtman, J. W. Chapter 11: Imaging Fluorescent Mice In Vivo Using Confocal Microscopy. Methods in Cell Biology. 89 (8), 309-327 (2008).
  6. McCann, C. M., Lichtman, J. W. In vivo imaging of presynaptic terminals and postsynaptic sites in the mouse submandibular ganglion. Developmental Neurobiology. 68 (6), 760-770 (2008).
  7. Turney, S. G., Walsh, M. K., Lichtman, J. W. In vivo imaging of the developing neuromuscular junction in neonatal mice. Cold Spring Harbor Protocols. 7 (11), 1166-1176 (2012).
  8. Tanaka, E., Ho, T., Kirschner, M. W. The role of microtubule dynamics in growth cone motility and axonal growth. Journal of Cell Biology. 128 (1-2), 139-155 (1995).
  9. Tanaka, E. M., Kirschner, M. W. Microtubule behavior in the growth cones of living neurons during axon elongation. Journal of Cell Biology. 115 (2), 345-363 (1991).
  10. Stepanova, T., et al. Visualization of microtubule growth in cultured neurons via the use of EB3-GFP (end-binding protein 3-green fluorescent protein). Journal of Neuroscience. 23 (7), 2655-2664 (2003).
  11. Tirnauer, J. S., Bierer, B. E. EB1 proteins regulate microtubule dynamics, cell polarity, and chromosome stability. Journal of Cell Biology. 149 (4), 761-766 (2000).
  12. Schwartz, K., Richards, K., Botstein, D. BIM1 encodes a microtubule-binding protein in yeast. Molecular Biology of the Cell. 8 (12), 2677-2691 (1997).
  13. Juwana, J. P., et al. EB/RP gene family encodes tubulin binding proteins. International Journal of Cancer. 81 (2), 275-284 (1999).
  14. Akhmanova, A., Steinmetz, M. O. Tracking the ends: a dynamic protein network controls the fate of microtubule tips. Nature Reviews Molecular Cell Biology. 9 (4), 309-322 (2008).
  15. Akhmanova, A., Steinmetz, M. O. Control of microtubule organization and dynamics: Two ends in the limelight. Nature Reviews Molecular Cell Biology. 16 (12), 711-726 (2015).
  16. Mitchison, T., Kirschner, M. Dynamic Instability of microtubule growth. Nature. 312 (15), 237-242 (1984).
  17. Van De Willige, D., Hoogenraad, C. C., Akhmanova, A. Microtubule plus-end tracking proteins in neuronal development. Cellular and Molecular Life Sciences. 73 (10), 2053-2077 (2016).
  18. Rebollo, E., Karkali, K., Mangione, F., Martín-Blanco, E. Live imaging in Drosophila: The optical and genetic toolkits. Methods. 68 (1), 48-59 (2014).
  19. Bier, E. Drosophila, the golden bug, emerges as a tool for human genetics. Nature Reviews Genetics. 6 (1), 9-23 (2005).
  20. Zito, K., Parnas, D., Fetter, R. D., Isacoff, E. Y., Goodman, C. S. Watching a synapse grow: noninvasive confocal imaging of synaptic growth in Drosophila. Neuron. 22 (4), 719-729 (1999).
  21. Miller, K. E., et al. Direct observation demonstrates that Liprin-alpha is required for trafficking of synaptic vesicles. Current Biology. 15 (7), 684-689 (2005).
  22. Rao, K., et al. Spastin, atlastin, and ER relocalization are involved in axon but not dendrite regeneration. Molecular Biology of the Cell. 27 (21), 3245-3256 (2016).
  23. Hill, S. E., et al. Development of dendrite polarity in Drosophila neurons. Neural Development. 7, 34 (2012).
  24. Stone, M. C., Nguyen, M. M., Tao, J., Allender, D. L., Rolls, M. M. Global up-regulation of microtubule dynamics and polarity reversal during regeneration of an axon from a dendrite. Molecular Biology of the Cell. 21 (5), 767-777 (2010).
  25. Mattie, F. J., et al. Directed microtubule growth, +TIPs, and kinesin-2 are required for uniform microtubule polarity in dendrites. Current Biology. 20 (24), 2169-2177 (2010).
  26. Stone, M. C., Roegiers, F., Rolls, M. M. Microtubules Have Opposite Orientation in Axons and Dendrites of Drosophila Neurons. Molecular Biology of the Cell. 19 (10), 4122-4129 (2008).
  27. Rolls, M. M., et al. Polarity and intracellular compartmentalization of Drosophila neurons. Neural Development. 2, 7 (2007).
  28. Hu, X., Viesselmann, C., Nam, S., Merriam, E., Dent, E. W. Activity-dependent dynamic microtubule invasion of dendritic spines. Journal of Neuroscience. 28 (49), 13094-13105 (2008).
  29. Merriam, E. B., et al. Dynamic microtubules promote synaptic NMDA receptor-dependent spine enlargement. PLoS One. 6 (11), 27688 (2011).
  30. Hu, X., et al. BDNF-induced increase of PSD-95 in dendritic spines requires dynamic microtubule invasions. Journal of Neuroscience. 31 (43), 15597-15603 (2011).
  31. Merriam, E. B., et al. Synaptic regulation of microtubule dynamics in dendritic spines by calcium, F-actin, and drebrin. Journal of Neuroscience. 33 (42), 16471-16482 (2013).
  32. Jaworski, J., et al. Dynamic Microtubules Regulate Dendritic Spine Morphology and Synaptic Plasticity. Neuron. 61 (1), 85-100 (2009).
  33. Dent, E. W. Of microtubules and memory: Implications for microtubule dynamics in dendrites and spines. Molecular Biology of the Cell. 28 (1), 1-8 (2017).
  34. Yan, Y., Broadie, K. In vivo assay of presynaptic microtubule cytoskeleton dynamics in Drosophila. Journal of Neuroscience Methods. 162 (1-2), 198-205 (2007).
  35. Pawson, C., Eaton, B. A., Davis, G. W. Formin-dependent synaptic growth: evidence that Dlar signals via Diaphanous to modulate synaptic actin and dynamic pioneer microtubules. Journal of Neuroscience. 28 (44), 11111-11123 (2008).
  36. Ruiz-Cañada, C., Budnik, V. Synaptic cytoskeleton at the neuromuscular junction. International Review of Neurobiology. 75 (6), 217-236 (2006).
  37. Bodaleo, F. J., Gonzalez-Billault, C. The presynaptic microtubule cytoskeleton in physiological and pathological conditions: lessons from Drosophila Fragile X syndrome and hereditary spastic paraplegias. Frontiers in Molecular Neuroscience. 9, 60 (2016).
  38. Applegate, K. T., et al. PlusTipTracker: Quantitative image analysis software for the measurement of microtubule dynamics. Journal of Structural Biology. 176 (2), 168-184 (2011).
  39. Matov, A., et al. Analysis of microtubule dynamic instability using a plus-end growth marker. Nature Methods. 7 (9), 761-768 (2010).
  40. Long, J. B., et al. Multiparametric analysis of CLASP-interacting protein functions during interphase microtubule dynamics. Molecular and Cellular Biology. 33 (8), 1528-1545 (2013).
  41. Schneider, C. A., Rasband, W. S., Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nature Methods. 9 (7), 671-675 (2012).
  42. Brand, A. H., Perrimon, N. Targeted gene expression as a means of altering cell fates and generating dominant phenotypes. Development. 118 (2), 401-415 (1993).
  43. Ma, Y., Wang, X., Liu, H., Wei, L., Xiao, L. Recent advances in optical microscopic methods for single-particle tracking in biological samples. Analytical and Bioanalytical Chemistry. 411 (19), 4445-4463 (2019).
  44. Shen, H., et al. Single Particle Tracking: From Theory to Biophysical Applications. Chemical Reviews. 117 (11), 7331-7376 (2017).
  45. Zwetsloot, A. J., Tut, G., Straube, A. Measuring microtubule dynamics. Essays in Biochemistry. 62 (6), 725-735 (2018).
  46. Weickert, J. Coherence-Enhancing Diffusion Filtering. International Journal of Computer Vision. 31 (2-3), 111-127 (1999).
  47. Peset, I., Vernos, I. The TACC proteins: TACC-ling microtubule dynamics and centrosome function. Trends in Cell Biology. 18 (8), 379-388 (2008).
  48. Hood, F. E., Royle, S. J. Pulling it together. Bioarchitecture. 1 (3), 105-109 (2011).
  49. Lucaj, C. M., et al. Xenopus TACC1 is a microtubule plus-end tracking protein that can regulate microtubule dynamics during embryonic development. Cytoskeleton. 72 (5), 225-234 (2015).
  50. Nwagbara, B. U., et al. TACC3 is a microtubule plus end-tracking protein that promotes axon elongation and also regulates microtubule plus end dynamics in multiple embryonic cell types. Molecular Biology of the Cell. 25 (21), 3350-3362 (2014).
  51. Rutherford, E. L., et al. Xenopus TACC2 is a microtubule plus end-tracking protein that can promote microtubule polymerization during embryonic development. Molecular Biology of the Cell. 27 (20), 3013-3020 (2016).
  52. Chou, V. T., Johnson, S., Long, J., Vounatsos, M. dTACC restricts bouton addition and regulates microtubule organization at the Drosophila neuromuscular junction. Cytoskeleton. 77 (1-2), 4-15 (2019).
  53. Maiato, H., et al. Human CLASP1 Is an Outer Kinetochore Component that Regulates Spindle Microtubule Dynamics. Cell. 113 (7), 891-904 (2003).
  54. Komarova, Y. A., Vorobjev, I. A., Borisy, G. G. Life cycle of MTs persistent growth in the cell interior , asymmetric transition frequencies and effects of the cell boundary. Journal of Cell Science. 115, 3527-3539 (2002).
  55. Greenspan, R. J. . Fly pushing: The theory and practice of Drosophila genetics. , (2004).
  56. Hales, K. G., Korey, C. A., Larracuente, A. M., Roberts, D. M. Genetics on the fly: A primer on the drosophila model system. Genetics. 201 (3), 815-842 (2015).
  57. Waters, J. C. Live-cell fluorescence imaging. Methods in Cell Biology. 114, 125-150 (2007).
  58. Berger, C., Renner, S., Lüer, K., Technau, G. M. The commonly used marker ELAV is transiently expressed in neuroblasts and glial cells in the Drosophila embryonic CNS. Developmental Dynamics. 236 (12), 3562-3568 (2007).
  59. Robinow, S., White, K. Characterization and spatial distribution of the ELAV protein during Drosophila melanogaster development. Journal of Neurobiology. 22 (5), 443-461 (1991).
  60. Parrish, J. Z., Kim, M. D., Lily, Y. J., Yuh, N. J. Genome-wide analyses identify transcription factors required for proper morphogenesis of Drosophila sensory neuron dendrites. Genes and Development. 20 (7), 820-835 (2006).
  61. Grueber, W. B., Jan, L. Y., Jan, Y. N. Different levels of the homeodomain protein cut regulate distinct dendrite branching patterns of Drosophila multidendritic neurons. Cell. 112 (6), 805-818 (2003).
  62. Zhang, T., et al. Microtubule plus-end binding protein EB1 is necessary for muscle cell differentiation, elongation and fusion. Journal of Cell Science. 122 (9), 1401-1409 (2009).
  63. Yang, C., et al. EB1 and EB3 regulate microtubule minus end organization and Golgi morphology. Journal of Cell Biology. 216 (10), 3179-3198 (2017).
  64. Allison, A., Nunn, J. Effect of general anaesthetics on microtubules. Lancet. 292 (7582), 1326-1329 (1968).
  65. Mondal, S., Ahlawat, S., Koushika, S. P. Simple microfluidic devices for in vivo imaging of C. elegans, drosophila and zebrafish. Journal of Visualized Experiments. (67), e3780 (2012).
  66. Mishra, B., et al. Using microfluidics chips for live imaging and study of injury responses in Drosophila larvae. Journal of Visualized Experiments. (84), e50998 (2014).
  67. Lee, T., Luo, L. Mosaic analysis with a repressible neurotechnique cell marker for studies of gene function in neuronal morphogenesis. Neuron. 22 (5), 451-461 (1999).
  68. Resh, M. D. Fatty acylation of proteins: New insights into membrane targeting of myristoylated and palmitoylated proteins. Biochimica et Biophysica Acta. 1451 (1), 1-16 (1999).
  69. Edwards, K. A., Demsky, M., Montague, R. A., Weymouth, N., Kiehart, D. P. GFP-moesin illuminates actin cytoskeleton dynamics in living tissue and demonstrates cell shape changes during morphogenesis in Drosophila. Developmental Biology. 191 (1), 103-117 (1997).
  70. Riedl, J., et al. Lifeact a versatile marker to visualize F-actin. Nature Methods. 5 (7), 605-607 (2008).
  71. Dent, E. W., Kalil, K. Axon Branching Requires Interactions between Dynamic Microtubules and Actin Filaments. Journal of Neuroscience. 21 (24), 9757-9769 (2001).
  72. Schaefer, A. W., Kabir, N., Forscher, P. Filopodia and actin arcs guide the assembly and transport of two populations of microtubules with unique dynamic parameters in neuronal growth cones. Journal of Cell Biology. 158 (1), 139-152 (2002).

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