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Diploid transgenic organisms are either hemi- or homozygous. Genetic assays are, therefore, required to identify the genotype. Our AGameOfClones vector concept uses two clearly distinguishable transformation markers embedded in interweaved, but incompatible Lox site pairs. Cre-mediated recombination leads to hemizygous individuals that carry only one marker. In the following generation, heterozygous descendants are identified by the presence of both markers and produce homozygous progeny that are selected by the lack of one marker. We prove our concept in Tribolium castaneum by systematically creating multiple functional homozygous transgenic lines suitable for long-term fluorescence live imaging. Our approach saves resources and simplifies transgenic organism handling. Since the concept relies on the universal Cre-Lox system, it is expected to work in all diploid model organisms, for example, insects, zebrafish, rodents and plants. With appropriate adaptions, it can be used in knock-out assays to preselect homozygous individuals and thus minimize the number of wasted animals.
Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.
Membrane proteins frequently assemble into higher order homo- or hetero-oligomers within their natural lipid environment. This complex formation can modulate their folding, activity as well as substrate selectivity. Non-disruptive methods avoiding critical steps, such as membrane disintegration, transfer into artificial environments or chemical modifications are therefore essential to analyze molecular mechanisms of native membrane protein assemblies. The combination of cell-free synthetic biology, nanodisc-technology and non-covalent mass spectrometry provides excellent synergies for the analysis of membrane protein oligomerization within defined membranes. We exemplify our strategy by oligomeric state characterization of various membrane proteins including ion channels, transporters and membrane-integrated enzymes assembling up to hexameric complexes. We further indicate a lipid-dependent dimer formation of MraY translocase correlating with the enzymatic activity. The detergent-free synthesis of membrane protein/nanodisc samples and the analysis by LILBID mass spectrometry provide a versatile platform for the analysis of membrane proteins in a native environment.
Compartmental models are the theoretical tool of choice for understanding single neuron computations. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. We illustrate this for a novel, highly detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. This work sets a new benchmark for detailed compartmental modeling. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. We discuss possible T2N application in degeneracy studies.