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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要凭借高通量单细胞成像和筛选的技术突破，我们可以精确监测原生细胞响应不同刺激的行为。分辨率和新检测能力的进一步提高丰富每个细胞的记录。许多图像处理步骤有助于提取完整的图像记录可以自动化到与成像本身相当的吞吐量。然而，由于生物样本可能是复杂且非均质的，在寻址时指定细胞子集很有价值下游生物学问题。现在可以通过高吞吐量生成大量数据测量，这个子集选择步骤是获得有关测量的定量结果的一个重要瓶颈生物样本。目前，可靠地过滤单元格数据的黄金标准是由人工注释技术员。这种方法成本高昂且耗时，为回答重要问题造成了严重瓶颈。生物学问题。为了克服这个瓶颈，我们将开发具有三个独特功能的自动化注释工具方法：化学注释、注释放大和细胞条形码。化学注释将通过额外的生物标记物提供计算机可读的细胞标签。注释放大将使用小，整理数据集以生成大型数据集。蜂窝条形码将识别基于像素的签名，以唯一地识别识别单个细胞。一旦注释通过计算得到解决，相关细胞就可以在线分类随着收购。然后我们可以生成一个大型带注释的数据集。计算工具和数据存储库将与科学界共享，作为新模型的验证集和基础可以在通过荧光成像研究细胞的研究小组之间开发的算法。目标这项工作的重点是生成技术并定义实验计算方法，使通过湿实验室和机器学习技术之间的强大相互作用，实现高度手动的细胞管理步骤。我们提出的技术与广泛的高通量测量应用相关，因为它可以通过计算而不是实验来管理样本。