Rigid anatomical mapping is a necessity in current neuroscience research. Here, we introduce DMC-BrainMap, an open-source napari plugin designed as a user-friendly tool for streamlined processing and whole-brain analysis of anatomical data. Its core functionalities include all steps after image acquisition, i.e., preprocessing of images, registration of images to a reference atlas, segmentation of different anatomical features, and data analysis/visualization. DMC-BrainMap can be applied to histological data obtained from a variety of model organisms at different developmental stages to map a diverse range of features. We demonstrate the utility of DMC-BrainMap by mapping and quantifying the location of cell bodies, axonal densities, injection sites, optical fiber and Neuropixels tracts, (single-cell) spatial transcriptomics, as well as neuron morphology data in mice, rats, and zebrafish. By eliminating the need for programming by the user, DMC-BrainMap provides an easy-to-use tool for increased rigor, reproducibility, and data sharing in neuroscientific research involving animal models.

Brown algae represent the third most complex lineage to have independently evolved multicellularity, distinct from plants and animals. Yet, functional studies of their development and evolution have been limited by the absence of efficient genome editing tools. Here, we present a robust, high-efficiency, and transgene-free CRISPR-based genome editing platform applicable across four ecologically and biotechnologically important brown algal species. Using Ectocarpus as a model, we optimized a polyethylene glycol (PEG)-mediated ribonucleoprotein (RNP) delivery system that achieves reproducible editing across multiple loci without cloning or specialized equipment. As proof of concept, we recreated the hallmark imm mutant phenotype by precisely editing the IMMEDIATE UPRIGHT (IMM) locus. APT/2-fluoroadenine (2-FA) selection further enhanced specificity with minimal false positives. The method was easily transferable to other species, including kelps. This platform now enables functional genomics in brown algae, providing powerful tools for investigating development, life cycle regulation, and the independent evolution of complex multicellularity.

SummarySimple behavioral tests like the forced swim test (FST) and tail suspension test (TST) are widely used to assess depression-like behaviors in rodents, primarily measuring immobility time. However, this approach can oversimplify behavioral readouts and obscure cognitive processes driving behavior, leaving the relationship between increased immobility and cognitive biases unclear. Here, we developed the SwimStruggleTracker (SST) to extract fine-grained behavioral trajectories and integrate computational modeling to systematically analyze behavior. Our findings show that behavior in the FST and TST follows reinforcement learning principles involving learning, consequence perception, and decision-making. Notably, the cognitive processes underlying behavior differ between the two tests, challenging the assumption that they are interchangeable for cross-validation. Regression analyses identify distinct behavior phases: early behavior is primarily influenced by learning-related factors, while later stages are more affected by consequence sensitivity. These findings suggest that traditional analyses may underestimate the role of learning and overemphasize consequence sensitivity.

SummaryCalcium fluorescence imaging enables us to investigate how individual neurons of live animals encode sensory input or drive specific behaviors. Extracting and interpreting large-scale neuronal activity from imaging data are crucial steps in harnessing this information. A significant challenge arises from uncorrectable tissue deformation, which disrupts the effectiveness of existing neuron segmentation methods. Here, we propose an open-source software, DynamicNeuronTracker (DyNT), which generates dynamic neuron masks for deforming and/or incompletely registered 3D calcium imaging data using patch-matching iterations. We demonstrate that DyNT accurately tracks densely populated neurons under positional jitters. DyNT also includes automated statistical analyses for interpreting neuronal responses to multiple sequential stimuli. We applied DyNT to analyze the responses of pheromone-sensing neurons in mice to controlled stimulation. We found that four bile acids and four sulfated steroids activated 15 subpopulations of sensory neurons with distinct combinatorial response profiles, revealing a strong bias toward detecting sulfated estrogen and pregnanolone.

SummaryCysteine oxidative modifications are critical signaling events regulating cellular functions, but their low abundance and dynamic nature pose technical challenges. We developed the SICyLIA-TMT workflow, which sequentially labels reduced and reversibly oxidized cysteines with light and heavy iodoacetamide (IAA) within the same sample. The inclusion of tandem mass tags (TMTs) enables simultaneous quantification of oxidative modification dynamics and protein levels across multiple conditions using micrograms of material. To improve the detection of low-abundance oxidized cysteines, a dedicated TMT channel serves as a carrier for heavy IAA-labeled peptides (SICyLIA-cTMT), enhancing quantification and enabling precise stoichiometry calculations. We demonstrate the workflow’s applicability to cultured cells and full organs under stress. SICyLIA-cTMT achieves unprecedented depth and accuracy in redox proteome analysis while reducing mass spectrometry time. Combining SICyLIA-TMT with latest mass spectrometry technologies further halves the acquisition time without compromising coverage, improving throughput and enabling comprehensive studies of oxidative signaling.

SummaryMitochondria are central to cardiomyocyte function, and their spatial organization regulates nuclear signaling and gene transcription, holding potential for novel cardioprotective interventions. We developed a transmission electron microscopy platform optimized for resolving mitochondrial subpopulations and nuclear architecture in adult cardiomyocytes. This approach reliably captures longitudinal sections containing the center of the nucleus and perinuclear regions, enabling consistent imaging of subcellular nanostructures, assessment of pharmacological effects within the same organism, and visualization of extracellular vesicles carrying dysfunctional mitochondria. Integrated with an analysis workflow employing machine learning-based segmentation for annotation, the method allows automated quantification of mitochondrial and nuclear architecture and positioning. Using Drp1-deficient mice with impaired mitochondrial fission, we demonstrate this tool’s ability to uncover nanoscale remodeling of mitochondria and nuclei under stress. Our platform overcomes challenges in electron microscopy analysis, providing a powerful resource to interrogate mitochondrial-nuclear dynamics in cardiac (patho)physiology. These insights will inform therapeutic targeting of bioenergetic failure.