Nils Otto

FIM, a Novel FTIR-Based Imaging Method for High Throughput Locomotion Analysis

We designed a novel imaging technique based on frustrated total internal reflection (FTIR) to obtain high resolution and high contrast movies. This FTIR-based Imaging Method (FIM) is suitable for a wide range of biological applications and a wide range of organisms. It operates at all wavelengths permitting the in vivo detection of fluorescent proteins. To demonstrate the benefits of FIM, we analyzed large groups of crawling Drosophila larvae. The number of analyzable locomotion tracks was increased by implementing a new software module capable of preserving larval identity during most collision events. This module is integrated in our new tracking program named FIMTrack which subsequently extracts a number of features required for the analysis of complex locomotion phenotypes. FIM enables high throughput screening for even subtle behavioral phenotypes. We tested this newly developed setup by analyzing locomotion deficits caused by the glial knockdown of several genes. Suppression of kinesin heavy chain (khc) or rab30 function led to contraction pattern or head sweeping defects, which escaped in previous analysis. Thus, FIM permits forward genetic screens aimed to unravel the neural basis of behavior.

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

The analysis of neuronal network function requires a reliable measurement of behavioral traits. Since the behavior of freely moving animals is variable to a certain degree, many animals have to be analyzed, to obtain statistically significant data. This in turn requires a computer assisted automated quantification of locomotion patterns. To obtain high contrast images of almost translucent and small moving objects, a novel imaging technique based on frustrated total internal reflection called FIM was developed. In this setup, animals are only illuminated with infrared light at the very specific position of contact with the underlying crawling surface. This methodology results in very high contrast images. Subsequently, these high contrast images are processed using established contour tracking algorithms. Based on this, we developed the FIMTrack software, which serves to extract a number of features needed to quantitatively describe a large variety of locomotion characteristics. During the development of this software package, we focused our efforts on an open source architecture allowing the easy addition of further modules. The program operates platform independent and is accompanied by an intuitive GUI guiding the user through data analysis. All locomotion parameter values are given in form of csv files allowing further data analyses. In addition, a Results Viewer integrated into the tracking software provides the opportunity to interactively review and adjust the output, as might be needed during stimulus integration. The power of FIM and FIMTrack is demonstrated by studying the locomotion of Drosophila larvae.

FIMTrack: An open source tracking and locomotion analysis software for small animals

Imaging and analyzing the locomotion behavior of small animals such as Drosophila larvae or C. elegans worms has become an integral subject of biological research. In the past we have introduced FIM, a novel imaging system feasible to extract high contrast images. This system in combination with the associated tracking software FIMTrack is already used by many groups all over the world. However, so far there has not been an in-depth discussion of the technical aspects. Here we elaborate on the implementation details of FIMTrack and give an in-depth explanation of the used algorithms. Among others, the software offers several tracking strategies to cover a wide range of different model organisms, locomotion types, and camera properties. Furthermore, the software facilitates stimuli-based analysis in combination with built-in manual tracking and correction functionalities. All features are integrated in an easy-to-use graphical user interface. To demonstrate the potential of FIMTrack we provide an evaluation of its accuracy using manually labeled data. The source code is available under the GNU GPLv3 at https://github.com/i-git/FIMTrack and pre-compiled binaries for Windows and Mac are available at http://fim.uni-muenster.de.

Interactions among Drosophila larvae before and during collision.

In populations of Drosophila larvae, both, an aggregation and a dispersal behavior can be observed. However, the mechanisms coordinating larval locomotion in respect to other animals, especially in close proximity and during/after physical contacts are currently only little understood. Here we test whether relevant information is perceived before or during larva-larva contacts, analyze its influence on behavior and ask whether larvae avoid or pursue collisions. Employing frustrated total internal reflection-based imaging (FIM) we first found that larvae visually detect other moving larvae in a narrow perceptive field and respond with characteristic escape reactions. To decipher larval locomotion not only before but also during the collision we utilized a two color FIM approach (FIM2c), which allowed to faithfully extract the posture and motion of colliding animals. We show that during collision, larval locomotion freezes and sensory information is sampled during a KISS phase (german: Kollisions Induziertes Stopp Syndrom or english: collision induced stop syndrome). Interestingly, larvae react differently to living, dead or artificial larvae, discriminate other Drosophila species and have an increased bending probability for a short period after the collision terminates. Thus, Drosophila larvae evolved means to specify behaviors in response to other larvae.

Quantifying subtle locomotion phenotypes of Drosophila larvae using internal structures based on FIM images

Quantitative analysis of behavioral traits requires precise image acquisition and sophisticated image analysis to detect subtle locomotion phenotypes. In the past, we have established Frustrated Total Internal Reflection (FTIR) to improve the measurability of small animals like insects. This FTIR-based Imaging Method (FIM) results in an excellent foreground/background contrast and even internal organs and other structures are visible without any complicated imaging or labeling techniques. For example, the trachea and muscle organizations are detectable in FIM images. Here these morphological details are incorporated into phenotyping by performing cluster analysis using histogram-based statistics for the first time. We demonstrate that FIM enables the precise quantification of locomotion features namely rolling behavior or muscle contractions. Both were impossible to quantify automatically before. This approach extends the range of FIM applications by enabling advanced automatic phenotyping for particular locomotion patterns.

The Ol1mpiad: concordance of behavioural faculties of stage 1 and stage 3 Drosophila larvae.

ABSTRACT Mapping brain function to brain structure is a fundamental task for neuroscience. For such an endeavour, the Drosophila larva is simple enough to be tractable, yet complex enough to be interesting. It features about 10,000 neurons and is capable of various taxes, kineses and Pavlovian conditioning. All its neurons are currently being mapped into a light-microscopical atlas, and Gal4 strains are being generated to experimentally access neurons one at a time. In addition, an electron microscopic reconstruction of its nervous system seems within reach. Notably, this electron microscope-based connectome is being drafted for a stage 1 larva – because stage 1 larvae are much smaller than stage 3 larvae. However, most behaviour analyses have been performed for stage 3 larvae because their larger size makes them easier to handle and observe. It is therefore warranted to either redo the electron microscopic reconstruction for a stage 3 larva or to survey the behavioural faculties of stage 1 larvae. We provide the latter. In a community-based approach we called the Ol1mpiad, we probed stage 1 Drosophila larvae for free locomotion, feeding, responsiveness to substrate vibration, gentle and nociceptive touch, burrowing, olfactory preference and thermotaxis, light avoidance, gustatory choice of various tastants plus odour–taste associative learning, as well as light/dark–electric shock associative learning. Quantitatively, stage 1 larvae show lower scores in most tasks, arguably because of their smaller size and lower speed. Qualitatively, however, stage 1 larvae perform strikingly similar to stage 3 larvae in almost all cases. These results bolster confidence in mapping brain structure and behaviour across developmental stages. Summary: A community-based survey of the behavioural faculties of stage 1 Drosophila larvae, providing a resource for relating these behavioural faculties to the upcoming connectome of their nervous system.

Integration of Parallel Opposing Memories Underlies Memory Extinction

Summary Accurately predicting an outcome requires that animals learn supporting and conflicting evidence from sequential experience. In mammals and invertebrates, learned fear responses can be suppressed by experiencing predictive cues without punishment, a process called memory extinction. Here, we show that extinction of aversive memories in Drosophila requires specific dopaminergic neurons, which indicate that omission of punishment is remembered as a positive experience. Functional imaging revealed co-existence of intracellular calcium traces in different places in the mushroom body output neuron network for both the original aversive memory and a new appetitive extinction memory. Light and ultrastructural anatomy are consistent with parallel competing memories being combined within mushroom body output neurons that direct avoidance. Indeed, extinction-evoked plasticity in a pair of these neurons neutralizes the potentiated odor response imposed in the network by aversive learning. Therefore, flies track the accuracy of learned expectations by accumulating and integrating memories of conflicting events.

A MULTI-PURPOSE WORM TRACKER BASED ON FIM

The analysis of behavioural traits of Caenorhabditis elegans is an important method for understanding neuromuscular functions and diseases. Since C. elegans is a small and translucent animal which conducts a variety of complex movement patterns many different imaging and tracking protocols are used for different behavioural traits. Thus a unified multi-purpose imaging and tracking system for multiple behavioural assays would be favourable to improve statistical strength and comparability. Here we present a novel worm tracking toolbox based on the FIM (Frustrated total internal reflection (FTIR) based Imaging Method) system incorporating a variety of different behavioural assays into a single imaging and tracking setup. First, we apply the FTIR-based imaging method to C. elegans, thus we are able to improve the overall image quality compared to state of the art recording techniques. This method is easy to use and can be utilised to image animals during crawling on agar and trashing in water. Second, we extended the existing FIMTrack software to extract skeleton-based posture and motion features of multiple worms with very high accuracy in a comparatively large field-of-view. Third, we integrated a variety of different assays into this system. We carried out chemotaxis assays both with attractant and repellent chemicals. A novel electrotaxis dome compatible with FIM allows locomotion analyses that are not corrupted by random aberrations in unrestricted movement. Additionally, the FIM based worm tracker is able to analyse thrashing behaviour of multiple worms automatically with a high accuracy. Finally we demonstrate the capacity of the FIM based worm tracker to observe GFP signals in C. elegans worms. We tested our new C. elegans tracking suite with mutant strains of the ubiquitin-fold modifier 1 (Ufm1) cascade. We identified intermediate chemosensory phenotypes in Ufm1 cascade mutants which were previously undetected.

Automatic non-invasive heartbeat quantification of Drosophila pupae

The importance of studying model organisms such as Drosophila melanogaster has significantly increased in recent biological research. Amongst others, Drosophila can be used to study heart development and heartbeat related diseases. Here we propose a method for automatic in vivo heartbeat detection of Drosophila melanogaster pupae based on morphological structures which are recorded without any dissection using FIM imaging. Our approach is easy-to-use, has low computational costs, and enables high-throughput experiments. After automatically segmenting the heart region of the pupa in an image sequence, the heartbeat is indirectly determined based on intensity variation analysis. We have evaluated our method using 47,631 manually annotated frames from 29 image sequences recorded with different temporal and spatial resolutions which are made publicly available. We show that our algorithm is both precise since it detects more than 95% of the heartbeats correctly as well as robust since the same standardized set of parameters can be used for all sequences. The combination of FIM imaging and our algorithm enables a reliable heartbeat detection of multiple Drosophila pupae while simultaneously avoiding any time consuming preparation of the animals.