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:
- 金额:$ 41.35万
- 依托单位:
- 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:
- 关键词:AddressAlgorithmsAnatomyAnimal ModelArtificial IntelligenceAutomated AnnotationBar CodesBasic ScienceBioinformaticsBiologicalBiological MarkersBiomedical ResearchBrainCell SeparationCellsCellular biologyCentral Nervous System DiseasesCharacteristicsChemicalsCollectionCommunitiesComplexComplex MixturesComputersComputing MethodologiesConsumptionCultured CellsDataData AnalysesData ScienceData SetDetectionFoundationsGenomicsGoalsGoldHumanImageImaging DeviceImaging technologyImmobilizationIndividualInterventionInvestigationLabelLearningLettersLinkMachine LearningManualsMeasurementMemoryMicroscopeMicroscopyModelingMonitorMorphologyNerve DegenerationNeuritesNeuronsNeurosciencesNightmareOwnershipPhenotypePhysiologyProteomicsQuality ControlReadabilityReadingResearchResearch PersonnelResearch Project GrantsResolutionSamplingSliceStimulusTechniquesTechnologyTestingTimeTissuesTo specifyUnited States National Library of MedicineValidationVariantWorkalgorithm developmentbasebiomedical informaticscell behaviorcell motilitycell typecellular imagingcomputerized toolscostdata curationdata repositoryexperimental studyfluorescence imagingimage processingin silicointerestlarge datasetsmachine learning algorithmmeetingsnext generationrepositoryresponserobotic microscopyscreeningsynaptogenesistool
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.
项目摘要
随着高通量单细胞成像和筛选技术的突破,我们可以精确地监测
天然细胞对不同刺激的反应。分辨率的提高和新的检测能力进一步提高
丰富每个细胞的记录。许多图像处理步骤有助于提取
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
样本可以是复杂的和非均匀的,在寻址时指定细胞的子集是有价值的。
下游生物问题。随着现在可以从高吞吐量
在测量中,该子集选择步骤是获得关于测量的定量结果的显著瓶颈。
生物样本目前,可靠地过滤单元格数据的黄金标准是由
技师这种方法既昂贵又耗时,给回答重要问题造成了严重的瓶颈。
生物问题。为了克服这个瓶颈,我们将开发工具来自动化注释,
方法:化学注释、注释扩增和细胞条形码化。化学注释将
通过另外的生物标志物递送计算机可读的细胞标记。注释放大将使用小的,
策展数据集以生成大型数据集。细胞条形码将识别基于像素的签名,
识别单个细胞。一旦通过计算解决注释问题,就可以对相关细胞进行在线分类
与收购。然后,我们可以生成一个大型的带注释的数据集。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
这些算法可以在研究荧光成像细胞的研究小组中开发出来。目标
这项工作的重点是生成技术,并定义自动化的实验计算方法。
通过湿实验室和机器学习技术之间的强大相互作用,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相关,因为
它使得能够通过计算而不是通过实验来管理样本。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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STEVEN M FINKBEINER其他文献
STEVEN M FINKBEINER的其他文献
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{{ truncateString('STEVEN M FINKBEINER', 18)}}的其他基金
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:
10552638 - 财政年份:2022
- 资助金额:
$ 41.35万 - 项目类别:
Role of central and peripheral immune crosstalk in FTD-Grn neurodegeneration
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10514263 - 财政年份:2022
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Cell and Network Disruptions and Associated Pathogenenesis in Tauopathy and Down Syndrome
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- 批准号:
9974319 - 财政年份:2020
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Cell and Network Disruptions and Associated Pathogenenesis in Tauopathy and Down Syndrome
Tau 蛋白病和唐氏综合症的细胞和网络破坏及相关发病机制
- 批准号:
10377486 - 财政年份:2020
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Cell and Network Disruptions and Associated Pathogenenesis in Tauopathy and Down Syndrome
Tau 蛋白病和唐氏综合症的细胞和网络破坏及相关发病机制
- 批准号:
10601035 - 财政年份:2020
- 资助金额:
$ 41.35万 - 项目类别:
Cell and Network Disruptions and Associated Pathogenenesis in Tauopathy and Down Syndrome
Tau 蛋白病和唐氏综合症的细胞和网络破坏及相关发病机制
- 批准号:
10599756 - 财政年份:2020
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$ 41.35万 - 项目类别:
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10651757 - 财政年份:2019
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10439255 - 财政年份:2019
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Understanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer Disease
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