Locomotion is a fundamental phenotypic metric in C. elegans. In order to fully characterize locomotory phenotypes, various automated nematode tracking algorithms and software have been proposed and built (Buckingham and Sattelle, 2009; Fang-Yen et al., 2010; Pierce-Shimomura et al., 2008; Tsechpenakis et al., 2008). While each of these platforms has contributed in a significant way to our understanding of locomotion, a simple, quantitative, and universal tool for measuring multiple parameters of C. elegans locomotion is still lacking. In order to make characterizing locomotory phenotypes more precise, standardized and easier we integrated non-invasive video microscopy, MATLAB-based image analysis algorithms, and fluid mechanics principles into a Biomechanical Profiling Platform (BMP) to quantify C. elegans locomotion (Figure 1). Using BMP we can quantify 18 distinct features that describe C. elegans body shape, swimming patterns (kinematics) and tissue material properties (biomechanics) (Figure 2). In many cases, standard assays of motility are insufficient to describe motility defects, whereas multi-parametric comparisons reveal clear differences.

To make our integrated platform accessible to all users regardless of computational background, we derived a simple user interface for automated analysis of swimming movies (Figure 3).  After defining the location of a family of sequentially named .tif images (multiple independent recordings can be analyzed and summary statistics are reported), the acquisition parameters (i.e., spatial resolution (µ/pixel) and recording speed (frames per second) are entered (Figure 3a).  Users can adjust body and skeleton threshold values to obtain the best fit to their recording conditions (Figure 3b).  After definition, users see the progress of analysis, including summary kinematics, biomechanics, skeletonization, and curvature plots (Figure 3c).  Upon completion of the analysis, summary statistics for each of the 18 parameters are shown (Figure 3d) and users can review each individual recording for accuracy using trajectory plots, curvature analysis, and/or a skeleton browser.  Data are output as .txt files for further analysis.

We have developed a free, user-friendly, and quantitative solution for integrative analysis of C. elegans locomotion. Our method uses equipment commonly found in C. elegans research labs, making it easy and inexpensive to implement.  The ability to quantitatively analyze phenotypes regardless of image acquisition methods should allow comparison of data between laboratories. Full manuscript, BMP MATLAB scripts as well as detailed instructions and sample files (Krajacic et al., 2012) are available for download at www.genetics.org.

Dosage compensation equalizes X chromosome gene expression between the sexes. In Caenorhabditis elegans, the dosage compensation complex (DCC) binds both hermaphrodite X chromosomes and downregulates expression from each two-fold. Documented in a recent paper, we found that repressive H4K20me1 is enriched on hermaphrodite X chromosomes in a sex- and DCC-dependent manner (Wells et al., 2012). H4K20me1 levels are regulated by the conserved methyltransferases SET-1 (PR-SET7 homolog) and SET-4 (SUV4-20 homolog). PR-SET7 is a monomethylase, while SUV4-20 is a di- and tri-methylase, in other systems, and our data support conservation of these roles in C. elegans (Wells et al., 2012). We hypothesized that knockdown of both set-1 and set-4 would cause a derepressed chromatin state in developing worms.

To look for this effect, we employed a modified xol-1 suppression assay (Miller et al., 1988) using a him-8(e1489); xol-1(y9) sex-1(y263) strain in which males die due to the inappropriate dosage compensation allowed by a xol-1 mutation. A mutation in sex-1 sensitizes the worms to dosage compensation defects. Male viability is rescued by depleting factors important for dosage compensation function.

Simultaneous RNAi of multiple factors is known to be inefficient (Shapira and Tan, 2008; Michael Wells, unpublished observations). Several studies have employed sequential RNAi strategies to overcome this issue (Shapira et al., 2006; Checci and Engebrecht, 2011). Using sequential RNAi, it was possible to knock down a non-essential gene (set-4), then an essential gene in the same pathway (set-1), in the rescue assay (Wells et al., 2012). Length of exposure to each RNAi can be varied to obtain the desired level of knockdown by changing growing temperature and when in development worms are transferred.

Two-generation set-1 RNAi treatment led to less than 5% viability in this strain, precluding counts. Simultaneous knockdown with a mixture of set-1 and set-4 RNAi-expressing bacteria showed insignificant male rescue. Two-generation set-4 knockdown led to 4.5% rescue, as compared to less than 1% with empty vector and 54% with dpy-27, a DCC member, RNAi; finally, set-4 followed by set-1 RNAi led to a remarkable 20% rescue of male viability (Wells et al., 2012).

The rescued males grew up to become adults of a smaller size than WT males, but possessed a well-developed tail and a single gonad arm (data not shown). We showed that these rescued males are indeed XO using X chromosome FISH in meiotic anaphase sperm nuclei (Fig. 1A), where the single X lags behind separating autosomes. Male rescue assay results indicate that DCC function is compromised in these animals (Miller et al., 1988); however, we observe strong DCC localization to X (Fig. 1A), suggesting that loss of repression is occurring downstream of DCC loading. set-1 RNAi weakened X enrichment of H4K20me1, while set-4 RNAi led to robust, uniform H4K20me1 staining across the nucleus. Following set-4 set-1 sequential RNAi, H4K20me1 levels in both sexes were low and similar across the entire nucleus (Fig. 1A), possibly due to loss of X enrichment (set-4 RNAi) and greatly reduced monomethylation (set-1 RNAi).

These data indicate that loss of set-1 and set-4 caused a disruption in dosage compensation-mediated repression, downstream of DCC loading, sufficient for males to survive to the adult stage. We have since continued to exploit this robust sequential RNAi method to study other essential factors, and believe that sequential RNAi could be used in many useful contexts.