The study presents a novel approach for quantifying cellular interactions in digital pathology using deep learning-based image cytometry. Traditional methods struggle with the diversity and heterogeneity of cells within tissues. To address this, we introduce the Spatial Interaction Potential (SIP) and the Co-Localization Index (CLI), leveraging deep learning classification probabilities. SIP assesses the potential for cell-to-cell interactions, similar to an electric field, while CLI incorporates distances between cells, accounting for dynamic cell movements. Our approach enhances traditional methods, providing a more sophisticated analysis of cellular interactions. We validate SIP and CLI through simulations and apply them to colorectal cancer specimens, demonstrating strong correlations with actual biological data. This innovative method offers significant improvements in understanding cellular interactions and has potential applications in various fields of digital pathology.
Cameraless High-throughput 3D Imaging Flow Cytometry
对理解细胞表型的巨大异质性的日益增长的需求推动了成像流式细胞术（IFC）的发展，其将流式细胞术的特征与荧光和明场显微镜相结合。IFC将流式细胞术的通量和统计优势与基于真实或计算图像以及常规流式细胞术指标离散测量事件的能力相结合。现有IFC系统的局限性在于，不管检测方法如何，仅获得二维（2D）细胞图像。在没有断层扫描三维（3D）分辨率的情况下，投影问题仍然存在：将3D信息折叠到2D图像上，限制了对细胞表型至关重要的点计数或共定位的可靠性。在这里，我们提出了一个解决方案的投影问题：三维成像流式细胞术（3D-IFC），一个高通量的三维细胞成像仪的基础上光学切片显微镜。我们结合正交光片扫描照明与我们以前的时空变换检测产生3D细胞图像重建从一个无相机的单像素光电探测器读出。我们进一步证明了这种能力，通过共同捕获的3D荧光和无标记的侧散射图像的单细胞在流中的速度为0.2 m s-1，对应于约500个细胞每秒的吞吐量与60,000体素（随后调整大小为106体素）为每个细胞图像在小于1微米的分辨率在X，Y和Z尺寸。需要改进的高通量成像工具来在免疫学、肿瘤学、细胞和基因治疗以及药物发现领域中识别表型-基因型异质性。
Pose-Free 3D Quantitative Phase Imaging of Flowing Cellular Populations
1 School of Instrumentation Science \(\&\) Optoelectronics Engineering, Beihang University, Beijing, 2 College of Future Technology, Peking University, Beijing, China 3 National Biomedical Imaging Center, Peking University, Beijing, China 4 Department of Obstetrics；Gynecology, Peking University People’s Hospital, Beijing, China 5 Peking University Third Hospital, Department of Radiation Oncology, Beijing, China. 6 Peking University Third Hospital, Cancer Center, Beijing, China The authors contributed equally to this work. The corresponding authors ( , ).
scI2CL: Effectively Integrating Single-cell Multi-omics by Intra- and Inter-omics Contrastive Learning
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）
HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference
Centre for Artificial Intelligence and Robotics（人工智能与机器人中心）；Hong Kong Institute of Science and Innovation（香港创新科学研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Engineering, Westlake University（西湖大学工程学院）
