Core Viewpoint - The article discusses the significant advancements in liver organoid research, highlighting the challenges of replicating the complex multicellular structure and function of adult human liver in vitro [1][2]. Group 1: Challenges in Liver Organoid Models - A core challenge in creating adult liver organoids is maintaining the multicellular complexity found in real human livers, which consist of various cell types working together [2][4]. - Existing models, such as primary human hepatocytes (PHH) and iPSC-derived liver organoids, face limitations in long-term functionality and physiological relevance [4][6]. Group 2: Breakthrough in Human Liver Assemblage - A research team from the Max Planck Institute successfully created a human liver "assembloid" model that replicates the multicellular structure and functional characteristics of the periportal region of the liver [7]. - This model was developed using fresh liver tissues from 28 adult donors, ensuring stability and enhancing clinical relevance [12]. Group 3: Advancements in Cell Culture Techniques - The research team optimized the culture system to support the long-term expansion of human liver cells, achieving a mature liver-like function [14][16]. - The model demonstrated the ability to replicate liver structures, including bile duct networks, and showed significant improvements in drug metabolism and other liver functions [17][19]. Group 4: Disease Modeling Capabilities - The human liver assembloid can simulate both healthy and diseased states, successfully inducing early cholangiopathy-like changes, which provides a novel model for studying cholangiopathy and related liver diseases [22]. Group 5: Future Prospects - This research fills a gap in existing 3D human liver models by enhancing structural complexity, cellular diversity, and functional maturity, with potential applications in drug screening, toxicity assessment, and personalized medicine [24]. - Future developments may include integrating additional cell types to create a more comprehensive human liver model that closely resembles real human physiology [24].

Core Viewpoint - Shenzhen Medical Academy of Research and Translation (SMART) aims to pave new paths in future medical science by fostering original innovation and cultivating a top talent team to address urgent challenges in public health [3]. Group 1: Research Focus - Infection and immunity are key research directions for SMART and its sister institution, Shenzhen Bay Laboratory (SZBL), with a focus on areas such as microbial pathogenicity, host-pathogen interactions, T cell and B cell biology, viral immunology, and systems immunology [5]. Group 2: Recruitment Positions and Qualifications - Senior Principal Investigator (Senior PI) candidates should hold a tenured professor position or equivalent at a renowned university or research institution, have recognized academic achievements, and demonstrate international reputation and influence [8]. - Principal Investigator (PI) candidates must possess outstanding innovative research capabilities and show potential to become leaders in their field, along with a strong record in teaching and mentoring [11]. - Junior Principal Investigator (Junior PI) candidates should have a PhD and postdoctoral experience, with academic achievements comparable to assistant professors at well-known institutions [11]. Group 3: Benefits and Compensation - Qualified candidates will receive internationally competitive salaries, laboratory and office space, and access to advanced core facilities and interdisciplinary academic environments. SMART will assist in applying for various talent awards and subsidies [10]. - Additional benefits include insurance, housing fund, annual health check-ups, paid annual leave, and educational support for children. For international staff, SMART will help with work permits, residence permits, and foreign tax incentives [10].

Core Viewpoint - The article discusses a groundbreaking technology developed by Tsinghua University, known as IMEE, which allows for long-term observation of cellular interactions in the developing mammalian brain, providing new insights into brain development and potential implications for understanding neurodevelopmental disorders [2][3]. Group 1: IMEE Technology Overview - IMEE (intravital imaging of externally immobilized embryos) is a high-stability, multi-angle, long-duration imaging technique that enables the observation of dynamic interactions between inhibitory neurons, blood vessels, and microglia in the embryonic mouse brain [3]. - This technology overcomes previous limitations in live imaging of embryonic neural development, allowing for continuous observation of mouse embryos from E10.5 to E16.5 for over 8 hours without affecting normal development [8]. Group 2: Neuronal Migration Patterns - The research team observed different migration patterns of excitatory neurons in the embryonic brain, including multipolar migration, locomotion, and somal translocation, with notable differences in switching speeds between these modes [10]. - Inhibitory neurons exhibited complex migration behaviors, taking two main pathways: the marginal zone (MZ) with random movement and the subventricular zone (SVZ) with organized directional movement [11]. Group 3: Cellular Interactions - Neurons interact frequently with surrounding cells during migration, establishing three types of contacts with the vascular system, which significantly affect their migration speed and direction [13]. - The study highlights the role of the EphA4/ephrinB signaling pathway in regulating neuron-vascular interactions, where disruption of this pathway leads to abnormal behaviors in neurons [13]. Group 4: Microglial Function - Microglia, the brain's resident immune cells, begin their protective roles during the embryonic stage, with two types identified based on their relationship with blood vessels: vascular-associated microglia (CAM) and parenchymal microglia (PCM) [15]. - Upon brain injury, microglia migrate towards the damage site at an average speed of 2.3 μm/min, with a maximum speed of 10.8 μm/min, demonstrating their active role in immune response and repair [15]. Group 5: Future Research Implications - IMEE technology not only addresses critical issues in neurodevelopment research but also serves as a powerful platform for future studies, compatible with various genetic markers and manipulation tools [19]. - This advancement opens new avenues for real-time observation of how genetic and environmental factors influence brain development, potentially aiding in early diagnosis and intervention of related diseases [19].

Core Viewpoint - The recent study by the Military Medical Research Institute reveals complex cognitive processes in simple rodent depression tests, providing new insights into the cognitive mechanisms underlying depressive-like behaviors [1][6]. Group 1: Research Background - The study focuses on advancements in understanding the neural circuits and molecular mechanisms related to mental disorders, particularly depression, using rodent models [3]. - Traditional methods like Forced Swim Test (FST) and Tail Suspension Test (TST) have been widely used to assess depressive-like behaviors in rodents, but they have limitations in capturing cognitive distortions and information processing anomalies [3][4]. Group 2: Methodology and Findings - The research team developed an automated tool called Swim Struggle Tracker (SST) to capture behavioral trajectories with fine temporal resolution and analyze the cognitive processes driving these behaviors through computational modeling [4]. - Results indicate that behaviors in FST and TST follow reinforcement learning principles, with different cognitive processes involved in each test, challenging the assumption that these tests can be interchangeably used for cross-validation [4][6]. - Regression analysis identified distinct behavioral phases, showing that early behaviors are influenced more by learning-related factors, while later stages are more sensitive to consequences, suggesting traditional analyses may underestimate the role of learning [4][6]. Group 3: Implications - The study emphasizes the importance of analyzing complete behavioral trajectories to understand depressive-like behaviors better and provides a theoretical foundation for developing more precise animal behavior analysis methods and antidepressant treatment strategies [6].

Core Viewpoint - The article discusses the revolutionary impact of Smart Spatial Omics (S2-omics) technology in biomedical research, which optimizes region selection for spatial omics experiments, enhancing molecular analysis while preserving tissue structure [2][19]. Group 1: Challenges in Spatial Omics - Spatial omics platforms like Xenium, Visium HD, and CosMx provide single-cell gene expression data but are costly, with sample costs reaching up to $7,000, and have limited tissue capture areas [6]. - Traditional region selection relies heavily on pathologists' subjective experience, leading to labor-intensive processes and variability in results across different laboratories [6][5]. Group 2: S2-omics Overview - S2-omics utilizes AI models to extract features from H&E stained images, simulating molecular heterogeneity to guide experimental design [8]. - The workflow consists of three steps: 1. Feature extraction from tissue images, segmenting them into 8μm×8μm superpixels to capture cellular morphology and tissue architecture [8]. 2. Automatic selection of regions of interest (ROI) based on a scoring system that balances coverage and diversity, allowing user-defined parameters [8]. 3. Prediction of molecular information for unmeasured areas based on selected regions, providing a comprehensive "virtual preview" of the tissue [9][11]. Group 3: Practical Applications - In a gastric cancer sample experiment, S2-omics selected a region covering 7 key tissue clusters, achieving prediction accuracies of 73.8% for cell types and 72.8% for community labels [13]. - In a colon cancer study, S2-omics covered 89.3% of the cells selected by experts while reducing blank areas, thus capturing critical structures more effectively [14]. - For kidney samples, S2-omics optimized the layout of views, successfully capturing glomeruli structures and enhancing data continuity and interpretability [15]. Group 4: Flexibility and Efficiency - S2-omics allows users to specify "positive priors" (e.g., focusing on tumor clusters) or "negative priors" (e.g., ignoring muscle areas), adjusting selection strategies accordingly [16]. - The system can automatically determine the optimal number of regions needed, as demonstrated in breast cancer samples where it identified two 2mm×2mm regions sufficient for capturing heterogeneity [17]. Group 5: Implications for Research - The introduction of S2-omics marks a significant step towards standardization and reproducibility in spatial omics experiments, reducing costs and subjective bias while empowering subsequent experimental designs through virtual predictions [19].

Core Insights - The research from Harvard University reveals a dual mechanism by which stress leads to hair loss, providing new insights into autoimmune diseases [1] Group 1: Mechanism of Hair Loss - The first mechanism identified is "immediate hair loss," triggered by the sympathetic nervous system's response to stress, resulting in the release of high levels of norepinephrine, which can kill rapidly proliferating cells in hair follicles [1] - The second mechanism involves the destruction of hair follicles by norepinephrine, which leads the body to perceive the damaged tissue as foreign, triggering an immune response that activates CD8+ T cells to attack hair follicles, potentially causing recurrent hair loss with lasting effects [1] Group 2: Implications for Autoimmune Diseases - This discovery is significant for understanding autoimmune diseases, as conditions like type 1 diabetes, lupus, and multiple sclerosis often require external triggers, with stress potentially being one of them [1]

Core Insights - The research team from the Swiss Federal Institute of Technology in Lausanne has developed a new three-dimensional organoid called "Budoid," which exhibits key features of developing limbs, including symmetry breaking and early cartilage formation [1][2] Group 1: Research and Development - The study published in the journal "Science Advances" highlights the importance of chemical signaling between different cell types during the early stages of embryonic development, particularly in limb formation [1] - Previous organoid models focused mainly on mesodermal cells, neglecting the role of the Apical Ectodermal Ridge (AER) and other ectodermal cells in guiding limb formation [2] - The "Budoid" was created using mixed cell populations derived from mouse embryonic stem cells, which naturally formed structures resembling AER, superficial ectoderm, and mesoderm cells, covering all major cell types involved in limb development [2] Group 2: Applications and Implications - "Budoid" provides a novel platform for exploring difficult-to-study areas in embryonic development, such as how cells coordinate behavior, how early structures develop, and how cartilage forms [2] - The implications of this research extend beyond basic science, potentially aiding in congenital disease modeling, testing chemicals that may impair limb development, and advancing regenerative medicine applications [2] - The new stem cell-based approach offers a more ethical alternative to traditional animal experiments in biomedical research, allowing for the reproduction of key embryonic tissue features without the need for large numbers of embryos [3]

撰文丨王聪 编辑丨王多鱼 排版丨水成文 哺乳动物的大脑是一个错综复杂的神经元回路网络，由数十亿个神经元通过数以万亿计的突触相互连接而 成。在这个复杂的结构中，单个神经元在形态、转录身份和功能作用方面表现出显著的异质性。揭开大脑 的组织原则和功能连接性需要具备单细胞分辨率的方法学，以捕捉这种多样性。 基因标记、高分辨率成像和计算分析方面的最新进展，极大地促进了在哺乳动物系统中对整个大脑神经元 形态的重建工作。这些进展为了解介观尺度 （即细胞水平） 的连接性和支撑大脑功能的结构模式提供了关 键见解。然而，要想生成具有单神经元分辨率的全面大规模脑图谱，仍需在 标记准确度和成像效率方面取 得进一步创新。 近日，北京生命科学研究所/清华大学生物医学交叉研究院 林睿 实验室与北京脑科学与类脑研究所 罗敏敏 实验室合作，在 Cell 子刊 Neuron 上发表了题为： Ultrabright Chemical Labeling Enables Rapid Neural Connectivity Profiling in Large Tissue Samples 的研究论文，该论文还被选为当期 封面论文 。 该研究提出了一种 ...

Core Insights - The article discusses a recent study revealing the neuroimmune mechanisms behind social withdrawal during illness, suggesting that this behavior is an active choice driven by specific neurons in the brain rather than a passive response to physical discomfort [2][20]. Group 1: Research Background - Traditional views suggest that social withdrawal during illness is a passive reaction due to discomfort, but evolutionary biologists propose it may serve adaptive purposes, such as preventing disease spread and conserving energy [6]. - The research team from MIT and Harvard Medical School conducted experiments to explore the neural mechanisms underlying this behavior, focusing on cytokines as messengers between the immune and nervous systems [6]. Group 2: Key Molecule - IL-1β - In a large-scale behavioral screening, the study found that among 21 cytokines tested, only IL-1β uniquely induced social withdrawal in mice [8]. - The experimental design allowed mice to explore a runway, showing that those treated with IL-1β exhibited significant social withdrawal compared to normal mice [8]. Group 3: Identifying the "Loneliness Switch" - The study identified that IL-1β's main receptor, IL-1R1, is highly expressed in the dorsal raphe nucleus (DRN), a key source of serotonin neurons that regulate social behavior [12]. - Over 90% of IL-1R1-expressing neurons in the DRN are serotonin neurons, challenging the traditional view that serotonin primarily promotes social behavior [12]. Group 4: Causal Relationship Verification - The research confirmed that activating IL-1R1 neurons led to social withdrawal even without immune challenges, while inhibiting these neurons prevented social withdrawal induced by IL-1β [14][15]. - Gene knockout experiments showed that specifically knocking out IL-1R1 in DRN neurons completely blocked IL-1β-induced social withdrawal without affecting motor suppression [16]. Group 5: Real-World Implications - The study's findings were validated in a natural social environment, where IL-1β-treated mice actively isolated themselves from companions, demonstrating that social withdrawal is a conscious choice during illness [18]. - Both peripheral and central IL-1β contribute to this process, creating a self-reinforcing cycle that prolongs social withdrawal, with microglia playing a crucial role [18]. Group 6: Broader Implications - This research provides insights into the neuroimmune interactions that may help understand social withdrawal in certain mental disorders, such as depression, which often accompanies inflammatory states [20]. - The findings highlight the complexity of the dialogue between the brain and immune system, suggesting that the desire for solitude during illness is a biologically sophisticated self-protection strategy shaped by natural selection [20].

Core Insights - The iPEX technology developed by the teams from Westlake University and Kiryl D. Piatkevich enables scientists to visualize the spatial distribution of hundreds to thousands of proteins within biological tissues, providing a new tool for biomedical research and disease mechanism exploration [1][2] Group 1: Technology Overview - iPEX technology integrates three core techniques: protein anchoring in hydrogel, tissue expansion, and mass spectrometry imaging, addressing the limitations of existing spatial proteomics technologies [1] - The sensitivity of iPEX is reported to be 10 to 100 times higher than traditional spatial proteomics methods, with effective pixel sizes ranging from 1 to 5 micrometers, allowing detection of 600 to 1500 proteins per sample [1][2] Group 2: Methodology - The iPEX process involves four main steps: 1. Anchoring proteins in a hydrogel network to prevent diffusion and remove interfering substances 2. Linear expansion of tissues by 3 to 7 times while maintaining structural integrity 3. Performing in situ enzymatic digestion and mass spectrometry imaging 4. Utilizing a self-developed computational workflow to analyze data and automatically identify tissue structures and specific proteins [2] Group 3: Applications and Future Prospects - The iPEX technology has been validated in various tissues, organoids, and disease models, demonstrating its versatility and effectiveness in reconstructing clear tissue layers and identifying specific proteins in mouse retina studies [2] - Future applications of iPEX may include aiding scientists in directly observing protein distribution in fine tissues and providing new pathways for early diagnosis and treatment of diseases such as Alzheimer's [2]