Mikhail Kislin

DYNC1H1 encodes the cytoplasmic dynein heavy chain, a key motor protein involved in intracellular transport and neural development. While mutations in DYNC1H1 are known to cause a range of neurodevelopmental and motor disorders, the molecular mechanisms linking genotype to phenotype remain poorly defined. Here, we investigated how a patient‐derived missense mutation in the motor domain of DYNC1H1 (c.9052C>T; P3018S) affects brain metabolism and behavior using a heterozygous knock‐in mouse model. Behavioral phenotyping revealed increased locomotor activity without significant changes in sociability or novelty‐seeking behavior. To uncover potential molecular correlates of this hyperactive phenotype, we performed label‐free quantitative proteomics on cerebrum and cerebellum tissue from male and female mice. Over 80 mitochondrial proteins exhibited differential abundance in HET mice relative to WT controls, particularly in pathways related to oxidative phosphorylation and carbohydrate metabolism. These proteomic signatures were more pronounced in the cerebrum and showed sex‐specific patterns. Our findings support the hypothesis that dysregulated mitochondrial metabolism contributes to the behavioral phenotype observed in DYNC1H1 HET mice, and they provide a molecular framework for understanding how cytoplasmic dynein mutations may lead to neurodevelopmental disorders. Summary: Cytoplasmic dynein is a multi‐subunit motor protein complex essential for intracellular cargo transport and proper neural function. Mutations in DYNC1H1 are increasingly recognized as a cause of pediatric motor and cognitive disorders, yet the molecular underpinnings of these phenotypes are not fully understood. The P3018S mutation in DYNC1H1 disrupts the motor domain and has been associated with abnormal neuronal migration, cortical malformations, and developmental delay. In this study, we used a knock‐in mouse model carrying the P3018S mutation to explore how dynein dysfunction affects brain metabolism and behavior. Our proteomic analysis of cerebrum and cerebellum samples revealed widespread differential abundance of mitochondrial proteins, particularly those involved in oxidative phosphorylation and carbohydrate metabolism. These findings align with prior behavioral and metabolic observations of increased activity and energy expenditure in heterozygous mice. By stratifying data by sex and brain region, we identified molecular patterns that may underlie the distinct neurobehavioral profiles observed in DYNC1H1‐related disorders. This study provides a valuable resource linking genotype, proteomic phenotype, and behavior, and lays the groundwork for future therapeutic strategies aimed at modulating mitochondrial metabolism in affected individuals.

Despite perinatal damage to the cerebellum being one of the highest risk factors for later being diagnosed with autism spectrum disorder (ASD), it is not yet clear how the cerebellum might influence the development of cerebral cortex and whether this co-developmental process is distinct between neurotypical and ASD children. Leveraging a large structural brain MRI dataset of neurotypical children and those diagnosed with ASD, we examined whether structural variation in cerebellar tissue across individuals was correlated with neocortical variation during development, including the thalamus as a coupling factor. We found that the thalamus plays a distinct role in moderating cerebro-cerebellar structural coordination in ASD. Notably, structural coupling between cerebellum, thalamus, and neocortex was strongest in younger childhood and waned by early adolescence, mirroring a previously undescribed trajectory of behavioral development between ASD and neurotypical children. Complementary functional connectivity analyses likewise revealed atypical connectivity between cerebellum and neocortex in ASD. This relationship was particularly prominent in a model of cerebellar structure predicting functional connectivity, where ASD and neurotypical children showed divergent patterns. Interestingly, these functional-structural relationships became more prominent with age, while structural effects were most prominent earlier in childhood, and showed significant lateralization. This pattern may suggest a developmental sequence where early uncoordinated structural growth amongst regions is followed by increasingly atypical functional synchronization. These findings provide multimodal evidence in the living brain for a cerebellar diaschisis model of autism, where both increased cerebellar-cerebral structural coupling and altered functional connectivity in cerebro-cerebellar pathways contribute to the ontogeny of this neurodevelopmental disorder.

The cerebellum regulates nonmotor behavior, but the routes of influence are not well characterized. Here we report a necessary role for the posterior cerebellum in guiding a reversal learning task through a network of diencephalic and neocortical structures, and in flexibility of free behavior. After chemogenetic inhibition of lobule VI vermis or hemispheric crus I Purkinje cells, mice could learn a water Y-maze but were impaired in ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (including thalamus and habenula) and crus I (including hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. To identify functional networks, we used correlated variation in c-Fos activation within each group. Lobule VI inactivation weakened within-thalamus correlations, while crus I inactivation divided neocortical activity into sensorimotor and associative subnetworks. In both groups, high-throughput automated analysis of whole-body movement revealed deficiencies in across-day behavioral habituation to an open-field environment. Taken together, these experiments reveal brainwide systems for cerebellar influence that affect multiple flexible responses.

Cellular diversification is critical for specialized functions of the brain including learning and memory¹. Single-cell RNA sequencing facilitates transcriptomic profiling of distinct major types of neuron^2,3,4, but the divergence of transcriptomic profiles within a neuronal population and their link to function remain poorly understood. Here we isolate nuclei tagged⁵ in specific cell types followed by single-nucleus RNA sequencing to profile Purkinje neurons and map their responses to motor activity and learning. We find that two major subpopulations of Purkinje neurons, identified by expression of the genes Aldoc and Plcb4, bear distinct transcriptomic features. Plcb4⁺, but not Aldoc⁺, Purkinje neurons exhibit robust plasticity of gene expression in mice subjected to sensorimotor and learning experience. In vivo calcium imaging and optogenetic perturbation reveal that Plcb4⁺ Purkinje neurons have a crucial role in associative learning. Integrating single-nucleus RNA sequencing datasets with weighted gene co-expression network analysis uncovers a learning gene module that includes components of FGFR2 signalling in Plcb4⁺ Purkinje neurons. Knockout of Fgfr2 in Plcb4⁺ Purkinje neurons in mice using CRISPR disrupts motor learning. Our findings define how diversification of Purkinje neurons is linked to their responses in motor learning and provide a foundation for understanding their differential vulnerability to neurological disorders.

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal.

Background: Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD. Methods: We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. Results: Both Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Limitations: Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. Conclusions: Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.

The cerebellum regulates nonmotor behavior, but the routes by which it exerts its influence are not well characterized. Here we report a necessary role for the posterior cerebellum in guiding a reversal learning task, acting through a network of diencephalic and neocortical structures. After chemogenetic inhibition of Purkinje cells in lobule VI or crus I, mice could learn a water Y-maze task but were impaired in their ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic regions and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (including thalamus and habenula) and crus I (including hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. To identify functional networks, we used correlated variation in c-Fos activation within each test group. Lobule VI inactivation weakened within-thalamus correlations, while crus I inactivation divided neocortical activity into well-separated sensorimotor and associative subnetworks. In both groups, high-throughput automated analysis of complex whole-body movement revealed deficiencies in across-day adaptation to an open field environment. Neither perturbation affected gait, within-day open-field adaptation, or location preference. Taken together, these experiments reveal brainwide systems for cerebellar influence that can affect multiple flexible responses. Significance statement: The cerebellum, a part of all vertebrate brains, provides feedback to the rest of the brain on a split-second basis in response to unexpected events. The consequent changes can range from minute adjustments in movement to broad changes in behavior. In mice, we investigated cerebellar regions that help guide flexible behavior. Inactivation of these regions prevented mice from responding flexibly to a changing Y-shaped maze. We used light-sheet microscopy of c-fos protein expression to map brainwide effects of different stages of learning and cerebellar inactivation. Inactivation caused changes in thalamocortical activity that were similar in pattern, but opposite in sign, from normal learning. Lobule VI inactivation weakened thalamic functional networks, while crus I inactivation divided sensorimotor from associative neocortical networks. The same inactivation also impaired multiday adaptation to an open arena, as tracked using machine vision. Our work shows how the cerebellum’s influence over flexible response can be mediated by a widespread brain network.

Background: Repetitive action, resistance to environmental change, and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here, we aimed at investigating behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice, both mutations are found in the clinical conditions and have been associated with ASD. We observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. Methods: Here, we use advances in computer vision and deep learning, a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple time scales using a single popular behavioral assay, the open field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. Results: Both Cntnap2 knockout and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure-to-adapt took the form of maintained ambling, turning, and locomotion, and an overall decrease in grooming. Adaptation in Cntnap2 knockout mice more broadly resembled that of wild-type. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Limitations: Genetic risk factors for autism are numerous, and here we tested only two. Our pipeline was only applied to conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. Conclusions: Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as a quantitative diagnosis criteria.