Activity-Dependent Remodeling of Corticostriatal Axonal Boutons During Motor Learning.

Motor skill learning induces long-lasting synaptic plasticity at not only the inputs, such as dendritic spines1-4, but also at the outputs to the striatum of motor cortical neurons5,6. However, very little is known about the activity and structural plasticity of corticostriatal axons during learning in the adult brain. Here, we used longitudinal in vivo two-photon imaging to monitor the activity and structure of thousands of corticostriatal axonal boutons in the dorsolateral striatum in awake mice. We found that learning a new motor skill induces dynamic regulation of axonal boutons. The activities of motor corticostriatal axonal boutons exhibited selectivity for rewarded movements (RM) and un-rewarded movements (UM). Strikingly, boutons on the same axonal branches showed diverse responses during behavior. Motor learning significantly increased the fraction of RM boutons and reduced the heterogeneity of bouton activities. Moreover, motor learning-induced profound structural dynamism in boutons. By combining structural and functional imaging, we identified that newly formed axonal boutons are more likely to exhibit selectivity for RM and are stabilized during motor learning, while UM boutons are selectively eliminated. Our results highlight a novel form of plasticity at corticostriatal axons induced by motor learning, indicating that motor corticostriatal axonal boutons undergo dynamic reorganization that facilitates the acquisition and execution of motor skills.

System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning.

The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca2+ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca2+ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca2+ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.

Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells.

The functional performance of immune cells relies on a complex transcriptional regulatory network. The three-dimensional structure of chromatin can affect chromatin status and gene expression patterns, and plays an important regulatory role in gene transcription. Currently available techniques for studying chromatin spatial structure include chromatin conformation capture techniques and their derivatives, chromatin accessibility sequencing techniques, and others. Additionally, the recently emerged deep learning technology can be utilized as a tool to enhance the analysis of data. In this review, we elucidate the definition and significance of the three-dimensional chromatin structure, summarize the technologies available for studying it, and describe the research progress on the chromatin spatial structure of dendritic cells, macrophages, T cells, B cells, and neutrophils.

Single-cell transcriptomic analysis reveals the landscape of epithelial-mesenchymal transition molecular heterogeneity in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a prevalent and highly lethal malignant disease. The epithelial-mesenchymal transition (EMT) is crucial in promoting ESCC development. However, the molecular heterogeneity of ESCC and the potential inhibitory strategies targeting EMT remain poorly understood. In this study, we analyzed high-resolution single-cell transcriptome data encompassing 209,231 ESCC cells from 39 tumor samples and 16 adjacent samples obtained from 44 individuals. We identified distinct cell populations exhibiting heterogeneous EMT characteristics and identified 87 EMT-associated molecules. The expression profiles of these EMT-associated molecules showed heterogeneity across different stages of ESCC progression. Moreover, we observed that EMT primarily occurred in early-stage tumors, before lymph node metastasis, and significantly promoted the rapid deterioration of ESCC. Notably, we identified SERPINH1 as a potential novel marker for ESCC EMT. By classifying ESCC patients based on EMT gene sets, we found that those with high EMT exhibited poorer prognosis. Furthermore, we predicted and experimentally validated drugs targeting ESCC EMT, including dactolisib, docetaxel, and nutlin, which demonstrated efficacy in inhibiting EMT and metastasis in ESCC. Through the integration of scRNA-seq, RNA-seq, and TCGA data with experimental validation, our comprehensive analysis elucidated the landscape of EMT during the entire course of ESCC development and metastasis. These findings provide valuable insights and a reference for refining ESCC clinical treatment strategies.

U-RISC: An Annotated Ultra-High-Resolution Electron Microscopy Dataset Challenging the Existing Deep Learning Algorithms.

Connectomics is a developing field aiming at reconstructing the connection of the neural system at the nanometer scale. Computer vision technology, especially deep learning methods used in image processing, has promoted connectomic data analysis to a new era. However, the performance of the state-of-the-art (SOTA) methods still falls behind the demand of scientific research. Inspired by the success of ImageNet, we present an annotated ultra-high resolution image segmentation dataset for cell membrane (U-RISC), which is the largest cell membrane-annotated electron microscopy (EM) dataset with a resolution of 2.18 nm/pixel. Multiple iterative annotations ensured the quality of the dataset. Through an open competition, we reveal that the performance of current deep learning methods still has a considerable gap from the human level, different from ISBI 2012, on which the performance of deep learning is closer to the human level. To explore the causes of this discrepancy, we analyze the neural networks with a visualization method, which is an attribution analysis. We find that the U-RISC requires a larger area around a pixel to predict whether the pixel belongs to the cell membrane or not. Finally, we integrate the currently available methods to provide a new benchmark (0.67, 10% higher than the leader of the competition, 0.61) for cell membrane segmentation on the U-RISC and propose some suggestions in developing deep learning algorithms. The U-RISC dataset and the deep learning codes used in this study are publicly available.

Siamese Network-Based All-Purpose-Tracker, a Model-Free Deep Learning Tool for Animal Behavioral Tracking.

Accurate tracking is the basis of behavioral analysis, an important research method in neuroscience and many other fields. However, the currently available tracking methods have limitations. Traditional computer vision methods have problems in complex environments, and deep learning methods are hard to be applied universally due to the requirement of laborious annotations. To address the trade-off between accuracy and universality, we developed an easy-to-use tracking tool, Siamese Network-based All-Purpose Tracker (SNAP-Tracker), a model-free tracking software built on the Siamese network. The pretrained Siamese network offers SNAP-Tracker a remarkable feature extraction ability to keep tracking accuracy, and the model-free design makes it usable directly before laborious annotations and network refinement. SNAP-Tracker provides a "tracking with detection" mode to track longer videos with an additional detection module. We demonstrate the stability of SNAP-Tracker through different experimental conditions and different tracking tasks. In short, SNAP-Tracker provides a general solution to behavioral tracking without compromising accuracy. For the user's convenience, we have integrated the tool into a tidy graphic user interface and opened the source code for downloading and using (https://github.com/slh0302/SNAP).