Using low-cost automated tracking microscopes, we have generated a behavioral database for 305 C. elegans strains, including 76 mutants with no previously described phenotype. The database consists of 9,203 short videos segmented to extract behavior and morphology features that are available online for further analysis. The database also includes summary statistics for 702 measures with statistical comparisons to wild-type controls so that phenotypes can be identified and understood by users.

Visible phenotypes based on locomotion and posture have played a critical role in understanding the molecular basis of behavior and development in Caenorhabditis elegans and other model organisms. However, it is not known whether these human-defined features capture the most important aspects of behavior for phenotypic comparison or whether they are sufficient to discover new behaviors. Here we show that four basic shapes, or eigenworms, previously described for wild-type worms, also capture mutant shapes, and that this representation can be used to build a dictionary of repetitive behavioral motifs in an unbiased way. By measuring the distance between each individual's behavior and the elements in the motif dictionary, we create a fingerprint that can be used to compare mutants to wild type and to each other. This analysis has revealed phenotypes not previously detected by real-time observation and has allowed clustering of mutants into related groups. Behavioral motifs provide a compact and intuitive representation of behavioral phenotypes.phenotyping | imaging | ethology | nematode T he study of unconstrained spontaneous behavior is the core of ethology, and it has also made significant contributions to behavioral genetics in model organisms. A powerful approach has been the careful expert observation of mutants to identify those with visible locomotor phenotypes, as demonstrated for many model organisms (1-6). However, as with most manually scored experiments, subjectivity can reduce reproducibility, whereas subtle quantitative changes or those that happen on very short or long time-scales are likely to be missed. Furthermore, manual observations are not scalable, and this has led to a widening gap between our ability to sequence and manipulate genomes and our ability to assess the effects of genetic variation and mutation on behavior.Several recent reports describe systems that begin to address this gap by automatically recording and quantifying spontaneous behavior in animals ranging from worms (7-15) to flies (16-19), fish (20, 21), and mice (22,23). The advantage of these approaches is that they provide a means to quantify movement parameters such as velocity precisely and in some cases to automatically detect predefined behaviors based on a manually annotated training data set. This automated analysis eliminates some of the problems of a purely manual approach, but it still relies on preselected behavioral parameters that may not be optimal for phenotypic comparisons and precludes the discovery of new behaviors that have not already been observed by eye. An alternative approach is to use unsupervised learning, which attempts to use the inherent structure of a data set to identify informative patterns; to do this, we first needed to extract worm postures from movie data and have as compact and complete a representation of worm behavior as possible.

The Green Fluorescent Protein (GFP) has been tremendously useful in investigating cell architecture, protein localization, and protein function. Recent developments in transgenesis and genome editing methods now enable working with fewer transgene copies and, consequently, with physiological expression levels. However, lower signal intensity might become a limiting factor. The recently developed mNeonGreen protein is a brighter alternative to GFP in vitro. The goal of the present study was to determine how mNeonGreen performs in vivo in Caenorhabditis elegans—a model used extensively for fluorescence imaging in intact animals. We started with a side-by-side comparison between cytoplasmic forms of mNeonGreen and GFP expressed in the intestine, and in different neurons, of adult animals. While both proteins had similar photostability, mNeonGreen was systematically 3–5 times brighter than GFP. mNeonGreen was also used successfully to trace endogenous proteins, and label specific subcellular compartments such as the nucleus or the plasma membrane. To further demonstrate the utility of mNeonGreen, we tested transcriptional reporters for nine genes with unknown expression patterns. While mNeonGreen and GFP reporters gave overall similar expression patterns, low expression tissues were detected only with mNeonGreen. As a whole, our work establishes mNeonGreen as a brighter alternative to GFP for in vivo imaging in a multicellular organism. Furthermore, the present research illustrates the utility of mNeonGreen to tag proteins, mark subcellular regions, and describe new expression patterns, particularly in tissues with low expression.