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The indirect flight muscles (IFMs) are the largest muscles in the fly, making up the bulk of the adult thorax. IFMs in Drosophila are generated during pupariation by fusion of hundreds of muscle precursor cells (myoblasts) with larval muscle templates (myotubes). Prominent features, including the large number of fusion events, the structural similarity to vertebrate muscles, and the amenability to the powerful genetic techniques of the Drosophila system make the IFMs an attractive system to study muscle cell fusion. Here we describe methods for live imaging of IFMs, both in intact pupae, and in isolated IFMs ex-vivo. The protocols elaborated upon here were used in the manuscript by (Segal et al., 2016).
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[Abstract] The indirect flight muscles (IFMs) are the largest muscles in the fly, making up the bulk of the adult thorax. IFMs in Drosophila are generated during pupariation by fusion of hundreds of muscle precursor cells (myoblasts) with larval muscle templates (myotubes). Prominent features, including the large number of fusion events, the structural similarity to vertebrate muscles, and the amenability to the powerful genetic techniques of the Drosophila system make the IFMs an attractive system to study muscle cell fusion. Here we describe methods for live imaging of IFMs, both in intact pupae, and in isolated IFMs ex-vivo. The protocols elaborated upon here were used in the manuscript by (Segal et al., 2016).
Keywords: Myoblast fusion, Live imaging, Indirect flight muscle, Drosophila, Muscle, ex-vivo culture
[Background] While Drosophila embryonic muscles have long been an established model system for the study of muscle development (Volk, 1999; Chen and Olson, 2004; Abmayr et al., 2008; Richardson et al., 2008) the adult Drosophila indirect flight muscles (IFMs), which form during pupal stages, have emerged in recent years as a complementary system to address cell-biological processes during myogenesis (Dutta, 2006; Oas et al., 2014; Weitkunat et al., 2014; Shwartz et al., 2016). Their large size, ample fusion events, structural similarity to vertebrate muscles, and amenability to powerful genetic techniques of the Drosophila system make the IFMs an attractive system to study muscle development. Historically, study of IFM development has been limited compared to embryonic muscle for several reasons. First, classic genetic approaches are difficult to implement in IFMs due to functional requirements earlier in development for many of the genes potentially involved in muscle development in the adult and to the syncytial nature of muscles, which restricts the usefulness of clonal analysis. Relatively recent advances in the available tools utilizing the GAL4-UAS system allow circumvention of these limitations, by expressing RNAi in a tissue specific manner. The power of this approach was demonstrated in a comprehensive screen for genes involved in pupal myogenesis (Schnorrer et al., 2010), and has been successfully implemented in several recent studies of myoblast fusion in IFMs (Mukherjee et al., 2011; Gildor et al., 2012; Dhanyasi et al., 2015; Segal et al., 2016). In addition, the technical challenges associated with dissection, accessibility, and visualization of IFMs have been overcome by advances in techniques and technology (Weitkunat and Schnorrer, 2014; Segal et al., 2016). This protocol is an expanded version of the methods used in the manuscript by (Segal et al., 2016), and is intended to contribute to the growing repertoire of techniques for study of IFMs. Here we describe methods for live imaging of IFMs, both in intact pupae, and in isolated IFMs ex-vivo. While previous work focused on stages of myotube growth via fusion (18-22 h after puparium formation [APF], 25 °C), these methods are readily applicable to other stages of IFM myogenesis, starting at 12 h APF onwards. Imaging of intact pupae can be suitable for studies of developmental processes which span several hours, while imaging of ex-vivo cultures is intended to better visualize finer structural details and dynamic behaviors over shorter time periods (e.g., 1 h). Parts of this protocol are variations on (Weitkunat and Schnorrer, 2014).
Materials and Reagents
Equipment
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Procedure
Data analysis
In general, visualization over time of fluorescent markers of interest can provide insight of dynamic behavior simply by observation of time-lapse of the acquired data. Time-lapse movies of single slices or maximum intensity projections of the data were created using Zen software (ZEISS). For example, a time-lapse movie of a single optical slice from an ex-vivo culture of dissected IFMs expressing Lifeact-GFP to label actin fibers (Video 1) displays myoblasts with actin-based protrusions emanating from their edges, which appear to be entangled within one another. The myoblasts are surprisingly immobile over time.
Quantification of length, frequency, etc. of specific features may also be performed in this system. For example, Segal et al. 2016 measured the lengths of filopodia in IFMs expressing the membrane marker Gap-GFP (Ritzenthaler et al., 2000). In this case, dynamics over time were not necessary, so a z-stack at a single timepoint was acquired (Figure 4). Using Zen Black software, a subset of z-slices were selected to include the filopodia of the myotube but exclude background from neighboring myotubes. Then, a maximum intensity projection (MIP) of the subset of slices was made. An area of interest was selected and cropped (Figure 4, red box), and brightness was adjusted to highlight the filopodia. The cropped raw data was transferred to Zen Blue software, for measurement of filopodia lengths. The output of lengths of filopodia was transferred to Excel for analysis. Statistical analysis of differences in filopodia length between experimental groups was done in R software, but could be done on any statistical software. Figure 4. Analysis of filopodia length as in Segal et al., 2016. See text for details.
Notes
Acknowledgments
This work was done under the supervision of and with guidance from Prof. Benny Shilo and Dr. Eyal Schejter, at Weizmann Institute of Science in Rehovot, Israel. This work was supported by a grant from the Israel Science Foundation. Parts of this protocol were adapted from (Weitkunat and Schnorrer, 2014). This protocol was used in (Segal et al., 2016).
References
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