Image Tools for Computational Cellular Barcoding and Automated Annotation

用于计算细胞条形码和自动注释的图像工具

• 批准号: 10367874
• 负责人: STEVEN M FINKBEINER
• 金额: $ 41.35万
• 依托单位: J. DAVID GLADSTONE INSTITUTES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-19 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367874
• 关键词: Address; Algorithms; Anatomy; Animal Model; Artificial Intelligence; Automated Annotation; Bar Codes; Basic Science; Bioinformatics; Biological; Biological Markers; Biomedical Research; Brain; Cell Separation; Cells; Cellular biology; Central Nervous System Diseases; Characteristics; Chemicals; Collection; Communities; Complex; Complex Mixtures; Computers; Computing Methodologies; Consumption; Cultured Cells; Data; Data Analyses; Data Science; Data Set; Detection; Foundations; Genomics; Goals; Gold; Human; Image; Imaging Device; Imaging technology; Immobilization; Individual; Intervention; Investigation; Label; Learning; Letters; Link; Machine Learning; Manuals; Measurement; Memory; Microscope; Microscopy; Modeling; Monitor; Morphology; Nerve Degeneration; Neurites; Neurons; Neurosciences; Nightmare; Ownership; Phenotype; Physiology; Proteomics; Quality Control; Readability; Reading; Research; Research Personnel; Research Project Grants; Resolution; Sampling; Slice; Stimulus; Techniques; Technology; Testing; Time; Tissues; To specify; United States National Library of Medicine; Validation; Variant; Work; algorithm development; base; biomedical informatics; cell behavior; cell motility; cell type; cellular imaging; computerized tools; cost; data curation; data repository; experimental study; fluorescence imaging; image processing; in silico; interest; large datasets; machine learning algorithm; meetings; next generation; repository; response; robotic microscopy; screening; synaptogenesis; tool

PROJECT SUMMARY With technological breakthroughs in high-throughput single-cell imaging and screening, we can precisely monitor native cell behavior in response to diverse stimuli. Improvements in resolution and new detection capacity further enrich the recording from each cell. Many image-processing steps that help to extract the full breadth of the recording can be automated to a throughput comparable to the imaging itself. However, because biological samples can be complex and nonhomogeneous, it is valuable to specify subsets of cells when addressing downstream biological questions. With the amount of data that can now be generated from high-throughput measurements, this subset-selection step is a significant bottleneck to obtaining a quantitative result about the biological sample. Currently, the gold standard to reliably filter through cell data is manual annotation by a technician. This approach is costly and time-consuming, creating a significant bottleneck to answering important biological questions. To overcome this bottleneck, we will develop tools to automate annotation with three unique approaches: chemical annotation, annotation amplification, and cellular barcoding. Chemical annotation will deliver a computer-readable cell label via an additional biomarker. Annotation amplification will use small, curated datasets to generate large ones. Cellular barcoding will identify pixel-based signatures to uniquely identify individual cells. Once annotation is addressed computationally, relevant cells can be classified in-line with the acquisition. We can then produce a large annotated dataset. Both the computational tools and data repository will be shared with the scientific community as a validation set for new models and as a foundation for algorithms that could be developed across research groups studying cells with fluorescence imaging. The goal of this work is to generate the technology and define the experimental-computational methods that automate the highly manual steps of cell curation through a strong interplay between wet-lab and machine-learning techniques. The technology we propose is relevant to a broad scope of high-throughput measurement applications, because it enables curating samples computationally rather than experimentally.

项目摘要 通过高通量单细胞成像和筛选的技术突破，我们可以精确监视 响应多种刺激的天然细胞行为。进一步改善分辨率和新的检测能力 丰富每个单元格的记录。许多图像处理步骤有助于提取完整的广度 录制可以自动化到与成像本身相当的吞吐量。但是，因为生物学 样品可能是复杂且非均匀的，在解决问题时指定细胞的子集很有价值 下游生物学问题。现在可以从高通量产生的数据量 测量值，此子集选择步骤是获得有关获得定量结果的重要瓶颈 生物样品。当前，通过单元数据可靠过滤的黄金标准是手动注释 技术员。这种方法是昂贵且耗时的，为回答重要的瓶颈创造了重要的瓶颈 生物学问题。为了克服这种瓶颈，我们将开发工具以自动化注释与三个独特 方法：化学注释，注释放大和细胞条形码。化学注释会 通过附加的生物标志物提供计算机可读的单元格标签。注释放大将使用小， 策划的数据集生成大型数据集。蜂窝条形码将识别基于像素的特征 识别单个细胞。一旦在计算上解决注释，可以将相关单元格分类为 与收购。然后，我们可以生成一个大注释数据集。计算工具和数据 存储库将与科学界共享，以作为新模型的验证设置，并作为基础 可以在研究细胞荧光成像的细胞之间开发的算法。目标 这项工作是为了生成技术并定义了自动化的实验计算方法 通过湿行和机器学习技术之间的强相互作用，高度手动的细胞策展步骤。 我们提出的技术与广泛的高通量测量应用程序有关，因为 它可以通过计算而不是实验性地策划样品。