The Q-system is a binary expression system that works well across species. Here we report the development and demonstrate applications of a split-QF system that drives strong expression in , is repressible by QS and inducible by a small non-toxic molecule quinic acid. The split-QF system is fully compatible with existing split-GAL4 and split-LexA lines, thus greatly expanding the range of possible advanced intersectional experiments and anatomical, physiological and behavioural assays in and in other organisms.

Sensory cortices are active in the absence of external sensory stimuli. To understand the nature of this ongoing activity, we used two-photon calcium imaging to record from over 10,000 neurons in the visual cortex of mice awake in darkness while monitoring their behavior videographically. Ongoing population activity was multidimensional, exhibiting at least 100 significant dimensions, some of which were related to the spontaneous behaviors of the mice. The largest single dimension was correlated with the running speed and pupil area, while a 16-dimensional summary of orofacial behaviors could predict ~45% of the explainable neural variance. Electrophysiological recordings with 8 simultaneous Neuropixels probes revealed a similar encoding of high-dimensional orofacial behaviors across multiple forebrain regions. Representation of motor variables continued uninterrupted during visual stimulus presentation, occupying dimensions nearly orthogonal to the stimulus responses. Our results show that a multidimensional representation of motor state is encoded across the forebrain, and is integrated with visual input by neuronal populations in primary visual cortex.

Background

Dendritic spines are tiny protrusions found along the dendrites of neurons, and their number is a measure of the density of synaptic connections. Altered density and morphology is observed in several pathologies, and spine formation as well as morphological changes correlate with learning and memory. The detection of spines in microscopy images and the analysis of their morphology is therefore a prerequisite for many studies. We have developed a new open-source, freely available, plugin for ImageJ/FIJI, called Spot Spine, that allows detection and morphological measurements of spines in three dimensional images.

Method

Local maxima are detected in spine heads, and the intensity distribution around the local maximum is computed to perform the segmentation of each spine head. Spine necks are then traced from the spine head to the dendrite. Several parameters can be set to optimize detection and segmentation, and manual correction gives further control over the result of the process.

Results

The plugin allows the analysis of images of dendrites obtained with various labeling and imaging methods. Quantitative measurements are retrieved including spine head volume and surface, and neck length.

Conclusion

The plugin and instructions for use are available at https://imagej.net/plugins/spot-spine.

Unveiling the genetic profiles of spatially distinguished cells is an important aspect in many areas of brain research, as the genetic identity contains information about a cell’s physiological properties and internal state. On top of this, knowledge of the genetic details of each cell can reveal structural organization within tissue. As image-based spatial transcriptomics moves toward applications in tissues with dense cellular packing, accurate assignment of detected mRNA transcripts ("spots") to correct segmented cells becomes increasingly difficult, rendering simple methods insufficient with many incorrect assignments to neighboring cells. Here we introduce SpotDMix, a statistical model for assigning spots to cells by modeling spots as coming from a mixture model of distributions matching segmented cell shapes, with assignment probabilities and shape parameters optimized using the Expectation Maximization algorithm. Performance is assessed and compared against several simple methods in various scenarios on both surrogate data and larval zebrafish data. In all tested scenarios SpotDMix outperforms the simple methods on all evaluated metrics, including individual transcript assignment accuracy, total assigned number of spots per cell error and cell type classification. Further, SpotDMix produces a higher degree of exclusivity between genes which are known to not or rarely co-express.