Core Insights - Spatial transcriptomics is transforming the understanding of cellular interactions and functions by providing measurable data on "where cells are, what they are doing, and who they are interacting with" [1] - The future competition in spatial omics will focus on constructing interpretable spatial coordinate systems and establishing evidence chains linking location, interaction, signal, and phenotype [1] Course Features - Comprehensive teaching covering the entire workflow of spatial transcriptomics, including data structure, quality control, annotation, and visualization, with supportive R/Python code templates [6] - One-on-one guidance tailored to individual datasets (Xenium/VisiumHD/MERFISH), ensuring practical application of learned skills [6] - Integration of AI tools and detailed breakdown of Nature articles to enhance understanding of biological significance behind the code [7] - In-depth learning of source code and project structure to enable customization and application of published methods to personal research [7] - Live teaching sessions with recorded materials and ongoing Q&A support to ensure effective learning [7] Course Schedule - The first session will run from January 10 to February 2026, consisting of thirteen classes held on weekends, focusing on both detailed instruction and Q&A sessions [8] Course Core Modules - The first class will focus on understanding the narrative framework of Nature articles and how to apply these insights to personal research [9] - Subsequent classes will cover data processing, spatial statistics, cell segmentation, and the reproduction of figures from published studies, emphasizing practical skills and reproducibility [10][12][14][16][18][20][22][24][26][28][30][32][34][35][37] Course Outcomes - Participants will achieve the ability to reproduce high-quality figures from top journals, understanding the input data, key steps, and validation processes involved [46] - Mastery of core methodologies in spatial transcriptomics, enabling the construction of interpretable spatial coordinate systems and the visualization of cellular interactions and signaling pathways [46]

Core Viewpoint - The article discusses the limitations of current spatial transcriptomics (ST) technologies, which primarily operate in two dimensions, and introduces a new computational framework called SpatialZ that enables the reconstruction of dense 3D cell atlases from sparse 2D slices, significantly enhancing the understanding of biological functions and tissue organization [2][3][10]. Group 1: Limitations of Current Technologies - Current ST technologies are limited to 2D observations, making it difficult to capture the continuous gradients of gene expression and the intricate cellular interactions within organs [2]. - The compromise in sampling density along the Z-axis due to high experimental costs leads to significant gaps in data, resulting in a fragmented view of biological tissues [2]. Group 2: Introduction of SpatialZ - SpatialZ is a novel computational framework that integrates optimal transport theory to generate virtual slices between sparse real slices, facilitating the transition from discrete 2D data to dense 3D maps [3][5]. - The framework has successfully constructed a digital mouse brain atlas containing over 38 million cell gene expressions and 3D coordinates, marking a significant advancement in the field of life sciences [3][8]. Group 3: Methodology of SpatialZ - SpatialZ employs a four-step algorithm for high-fidelity 3D reconstruction, including spatial alignment, position generation, cell state inference, and expression profile inference [5]. - The methodology ensures that the generated cells not only have accurate spatial positioning but also reflect the biological states and microenvironment characteristics [5]. Group 4: Validation and Performance - The reliability of SpatialZ was validated using mouse visual cortex data, showing that it accurately restored missing intermediate layer information and maintained high consistency with ground truth data [6][7]. - The framework demonstrated improved correlation and statistical significance compared to unprocessed sparse sampling data, effectively addressing structural information gaps caused by sparse sampling [7]. Group 5: Broader Applications - SpatialZ's underlying logic is highly generalizable, allowing its application in spatial proteomics, spatial metabolomics, and other multi-omics data, providing new perspectives for complex disease research [9]. - The framework has been successfully applied to human breast cancer tissue imaging mass cytometry data, correcting expression anomalies caused by tissue loss or technical artifacts, thus aiding in spatial screening for tumor immunotherapy targets [9]. Group 6: Conclusion - SpatialZ represents a breakthrough in computational methods, bridging the gap from single-cell analysis to organ-level digitalization, and offers a standardized digital reference for neuroscience research [10]. - The framework opens new possibilities for constructing comprehensive 3D spatial maps across modalities, organs, and species, potentially leading to new discoveries in developmental biology, neuroscience, and oncology [10].

Core Insights - A new Siamese clustering framework named scSiameseClu has been proposed by a research team from institutions including the Chinese Academy of Sciences and Northeast Agricultural University, aimed at interpreting single-cell RNA sequencing (scRNA-seq) data effectively [1][4][5] - The framework addresses challenges in scRNA-seq data analysis, particularly the issues of representation collapse and the need for clearer cell population classification [1][4][5] Summary by Sections Introduction to scRNA-seq - Traditional bulk RNA sequencing focuses on average gene expression at the population level, potentially masking the characteristics of rare cells [1] - Single-cell RNA sequencing (scRNA-seq) captures comprehensive genetic information from individual cells, revealing hidden complexities [1] Challenges in scRNA-seq Data - scRNA-seq data is characterized by high noise, sparsity, and dimensionality, leading to issues such as "insufficient graph construction" and "representation collapse" even in advanced methods like graph neural networks (GNNs) [2][4] scSiameseClu Framework - The scSiameseClu framework integrates three key modules: Dual Augmentation, Siamese Fusion, and Optimal Transport Clustering, designed to capture and refine complex intercellular information [4][5][9] - The framework has shown superior performance in clustering and other biological tasks compared to state-of-the-art methods [5] Performance Evaluation - The performance of scSiameseClu was evaluated on seven real scRNA-seq datasets, which included samples from both mice and humans, covering various cell types [7][8] - The framework demonstrated significant advantages in clustering metrics such as Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI) [14][15] Key Modules of scSiameseClu - **Dual Augmentation Module**: Enhances robustness against noise by simulating natural fluctuations in gene expression and generating augmented adjacency matrices for cell graphs [11] - **Siamese Fusion Module**: Integrates refined gene expression and cell graph matrices to learn robust and meaningful representations, improving clustering performance [12] - **Optimal Transport Clustering**: Aligns and corrects predicted distributions to ensure balanced clustering and avoid collapse [13] Experimental Results - The framework's performance was validated through extensive experiments, including comparisons with nine advanced benchmark models, showing consistent superiority across various datasets [14][15] - In downstream tasks, scSiameseClu accurately identified cell types and their marker genes, achieving over 90% similarity with gold standard references [15][17] Conclusion - The introduction of scSiameseClu represents a significant advancement in computational biology, effectively addressing long-standing challenges in cell heterogeneity analysis [20] - The framework exemplifies the integration of computer science methodologies with life sciences, paving the way for future innovations in the field [20]

Group 1 - The article discusses various innovative technologies showcased at the Data Expo, highlighting advancements in artificial intelligence and user interaction [1][22] - Xiaomi's AI assistant, Xiao Ai, integrates with smart devices to provide personalized services through voice commands, enhancing user experience [4] - JD.com's 3D product display technology allows users to interact with products in a virtual environment, improving online shopping experiences [8] Group 2 - Flexible display technology, introduced at the expo, offers design freedom and superior image quality, applicable in various devices from smartphones to medical equipment [13] - The 360 Smart Firewall utilizes AI to enhance network security by detecting threats and providing a user-friendly management interface [17] - Super microcomputers, a cutting-edge concept, leverage tiny computing units for efficient energy use and potential applications in health monitoring and environmental sensing [21] Group 3 - Big data genetic prediction technology analyzes genomic data to forecast individual health risks and responses to medications, promoting personalized healthcare [27]

Core Insights - The presentation by Associate Professor Zhang Shugang from Ocean University of China focuses on the construction and application of an intelligent protein computing system, highlighting breakthroughs in traditional protein research challenges [1][3][4]. Group 1: Traditional Challenges in Protein Research - Proteins play a crucial role in biological functions but face challenges such as high structural analysis costs, delayed functional annotation, and low efficiency in novel protein design [1][3]. - The demand for understanding complex protein characteristics has increased, necessitating innovative approaches to overcome these challenges [1][3]. Group 2: AI-Driven Innovations - The introduction of AI technologies has revolutionized protein research, exemplified by the awarding of the 2024 Nobel Prize in Chemistry for breakthroughs in AI-driven protein structure prediction and design [3][4]. - The intelligent protein computing system enables significant advancements in large-scale functional annotation, interaction prediction, and 3D structure modeling, providing new technical pathways for drug discovery and life system simulation [1][3]. Group 3: Key Breakthroughs in Protein Computing - The core tasks of intelligent protein computing include: 1. **Protein Structure Prediction**: AlphaFold's models have achieved unprecedented accuracy in predicting protein structures, with AlphaFold2 providing atomic-level precision and AlphaFold3 extending capabilities to predict interactions with various biomolecules [4][5]. 2. **Functional Annotation**: The team has developed methods to automate protein function annotation using deep learning, significantly increasing the scale of data processed and improving prediction accuracy [6][7]. 3. **Interaction Prediction**: A self-developed model has been created to enhance the prediction of protein interactions, achieving over 95% accuracy in specific applications [16][20]. 4. **Protein Design**: The potential for designing new proteins has been demonstrated, with innovative approaches being explored for applications in vaccine development and cancer treatment [22]. Group 4: Multiscale Modeling in Life Systems - The research emphasizes the importance of multiscale modeling in understanding complex life systems, integrating various biological scales from molecular to cellular levels [23]. - The team has proposed a comprehensive modeling framework that encompasses multiple research points, aiming for a holistic simulation of life systems [23].

Core Insights - Chen Runsheng is a pioneer in non-coding gene research and a participant in the Human Genome Project, which is one of the largest life science projects globally [1][2] - He emphasizes that the future of AI in China lies not in the quantity of chips but in the density of intelligent computing [1] Group 1: Contributions to Genomics - Under Chen's leadership, China became the sixth country globally to possess large-scale genome sequencing capabilities [2][6] - In 1999, China joined the International Human Genome Project, taking on the task of sequencing approximately 30 million base pairs of the human chromosome 3 short arm, which represented 1% of the entire project [6] - Chen's team innovated sequencing methods, completing their tasks two years ahead of schedule, demonstrating significant advancements in genomic research [6] Group 2: Discoveries in Non-Coding DNA - Chen discovered that only 2%-3% of the human genome encodes proteins, while 97% consists of non-coding sequences previously deemed "junk DNA" [6][7] - His team focused on these non-coding regions, leading to the identification of new disease-related loci, particularly in cancer research [7] Group 3: Open Science and Collaboration - Since 1993, Chen's team has established a comprehensive database of 640,000 non-coding molecular information, which they chose to share openly with the global scientific community [7] - Chen believes that science is a collective human contribution and emphasizes the importance of sharing research findings for the advancement of knowledge [7] Group 4: AI and Future Innovations - Chen has been involved in AI research since the late 1980s, applying artificial neural networks to predict coding genes [8] - His current work involves integrating traditional Chinese medicine data into AI models, aiming to create a platform that merges different medical perspectives [8] - He advocates for viewing AI not merely as a tool but as a new center for innovation, which could lead to more creative possibilities in research and development [8]