Development of machine learning software to quantitatively map telomere induced senescence in tissue sections during aging
Development of machine learning software to quantitatively map telomere induced senescence in tissue sections during aging
批准号:
10376395
负责人:
Joao Passos
金额:
$55.65万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-21 至 2023-08-31
关键词:
3-DimensionalAddressAgeAgingAtlasesBiologicalBiopsyCell AgingCell NucleusCellsChronicCollaborationsCommunitiesComputer softwareComputing MethodologiesCytometryDNA DamageDataDetectionDevelopmentDiseaseEmerging TechnologiesFluorescence MicroscopyFunctional disorderGenerationsHourHumanImageImpairmentInterventionKnowledgeLaboratoriesLengthMachine LearningMapsMeasurementMeasuresMediatingMethodsMitochondriaMorphologic artifactsMusNaturePhenotypePlayProcessProteinsResearchResourcesRoleSignal TransductionSignaling ProteinSpatial DistributionStainsSuspensionsTechnologyTimeTissuesTreatment Efficacyage relatedbaseclinical practicedeep learningextracellularhuman tissueimaging modalityinnovationinnovative technologiesinsightlearning networkmicroscopic imagingmultiplexed imagingneural networknext generationprognostic toolresponsesenescencesingle cell technologytelomeretissue resource
中文摘要
项目总结/摘要
细胞衰老是衰老的支柱,是衰老和年龄相关疾病的关键驱动力。
端粒在细胞衰老中起着重要作用。当端粒变得非常短,
一旦受损,它们就会引发DNA损伤反应(DDR),从而导致衰老。本实验室
开发了复杂的方法来检测组织中的衰老细胞,
端粒和DDR之间的联系此外,我们最近开发了SenoQuant软件,
这简化了端粒相关病灶(TAF)的测量,减少了从
几周到几小时我们现在建议应用机器学习等新兴技术,
通过深度学习,在人体组织切片中更准确、更稳健地映射TAF。这将
解决了TAF分析固有的几个挑战,包括细胞核检测,染色伪影,
和量化时间。此外,与组织映射中心合作,我们将
定制SenoQuant以分析特定的人体组织。最后,我们建议将TAF
与多重成像方法,如成像质谱细胞计数法(IMC),允许检测
多种衰老标志物同时存在我们预计这项技术将
极大地推进了人体组织中衰老细胞的空间分辨映射,并将成为
是衰老和细胞衰老社区的重要资源。
英文摘要
PROJECT SUMMARY/ABSTRACT
Cellular senescence is a pillar of aging, acting as a key driver of aging and age-related diseases.
Telomeres play a major role in cellular senescence. When telomeres become critically short or
damaged, they elicit a DNA damage response (DDR) that drives senescence. Our laboratory
developed sophisticated methods to detect senescent cells in tissues based on the co-localization
between telomeres and the DDR. Furthermore, we recently developed SenoQuant, a software
that simplifies the measure of Telomere associated foci (TAF), reducing quantification time from
weeks to hours. We now propose to apply emerging technologies such as machine learning and
deep learning to map TAF more accurately and robustly in human tissue sections. This will
address several challenges inherent to TAF analysis, including nuclei detection, staining artifacts,
and quantification time. Furthermore, in collaboration with the Tissue Mapping Centers we will
tailor SenoQuant to the analysis of specific human tissues. Finally, we propose to integrate TAF
with multiplexed imaging methods such as Imaging Mass Cytometry (IMC), allowing the detection
of multiple senescence markers in tissues simultaneously. We anticipate that this technology will
greatly advance the spatially-resolved mapping of senescent cells in human tissues and will be a
great resource for the aging and cell senescence community.
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专著(0)
科研奖励(0)
会议论文
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负责人:Joao Passos
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依托单位:
Development of machine learning software to quantitatively map telomere induced senescence in tissue sections during aging
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The role of sub-lethal mitochondrial apoptotic stress in cellular senescence
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批准号:10225590
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依托单位:
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批准号:10180804
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资助金额:$23.07万
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财政年份:2016
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负责人:Joao Passos
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依托单位:
Postdoctoral Training Program for Research on Aging
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批准号:10454837
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项目类别:
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资助金额:$11.25万
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财政年份:2016
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负责人:Joao Passos
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依托单位:
海外基金