We propose a simple, cost-effective method for synchronized phase contrast and fluorescence video acquisition in live samples. Counter-phased pulses of phase contrast illumination and fluorescence excitation light are synchronized with the exposure of the two fields of an interlaced camera sensor. This results in a video sequence in which each frame contains both exposure modes, each in half of its pixels. The method allows real-time acquisition and display of synchronized and spatially aligned phase contrast and fluorescence image sequences that can be separated by de-interlacing in two independent videos. The method can be implemented on any fluorescence microscope with a camera port without needing to modify the optical path.

Many animals perceive odorant molecules by collecting information from ensembles of olfactory neurons, where each neuron uses receptors that are tuned to recognize certain odorant molecules with different binding affinity. Olfactory systems are able, in principle, to detect and discriminate diverse odorants using combinatorial coding strategies. We have combined microfluidics and multineuronal imaging to study the ensemble-level olfactory representations at the sensory periphery of the nematode Caenorhabditis elegans. The collective activity of C. elegans chemosensory neurons reveals high-dimensional representations of olfactory information across a broad space of odorant molecules. We reveal diverse tuning properties and dose-response curves across chemosensory neurons and across odorants. We describe the unique contribution of each sensory neuron to an ensemble-level code for volatile odorants. We show that a natural stimuli, a set of nematode pheromones, are also encoded by the sensory ensemble. The integrated activity of the C. elegans chemosensory neurons contains sufficient information to robustly encode the intensity and identity of diverse chemical stimuli.

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

Behavioral plasticity helps humans and animals to achieve their goals by adapting their behaviors to different environments. Although behavioral plasticity is ubiquitous, many innate species-specific behaviors, such as mating, are often assumed to be stereotyped and unaffected by plasticity or learning, especially in invertebrates. Here, we describe a novel case of behavioral plasticity in the nematode C. elegans – under a different set of naturalistic conditions the male uses a unique, previously undescribed set of behavioral steps for mating. Under standard lab conditions (agar plates with bacterial food), the male performs parallel mating, a largely two-dimensional behavioral strategy where his body and tail remain flat on the surface and slide alongside the partner ‘s body from initial contact to copulation. But when placed in liquid medium, the male performs spiral mating, a distinctly three-dimensional behavioral strategy where he winds around the partner ’s body in a helical embrace. The performance of spiral mating does not require a long-term change in growing conditions but it does improve with experience. This experience-dependent improvement involves a critical period – a time window around the L4 to early adult stage, which coincides with the development of most male-specific neurons. We tested several wild isolates of C. elegans and other Caenorhabditis species and found that most were capable of parallel mating on surfaces and spiral mating in liquids. We suggest that two- and three-dimensional mating strategies in Caenorhabditis are plastic, conditionally expressed phenotypes conserved across the genus, and which can be genetically “fixed ” in some species.