AI based system for longitudinal, repeated measure analyses of freely moving C. elegans worms
AI based system for longitudinal, repeated measure analyses of freely moving C. elegans worms
批准号:
10258638
负责人:
JACOB R GLASER
金额:
$45.0万
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:
2021-07-09 至 2022-07-08
关键词:
AcetylcholineAddressAgarAgingAlzheimer&aposs DiseaseAnimal BehaviorAnimal ModelAppearanceArtificial IntelligenceBehaviorBehavioralBehavioral ResearchBiological AssayBiotechnologyBrainBrain DiseasesCaenorhabditis elegansClassificationCollaborationsComplexComputer softwareDataDevelopmentDiseaseDopamineEukaryotaFeasibility StudiesGlutamatesGoalsHumanImageIndividualLaboratoriesLegal patentLightingLocomotionLongevityLongitudinal StudiesMarket ResearchMassachusettsMeasuresMicroscopeMolecularMotionMusNamesNational Institute of Mental HealthNematodaNeurodegenerative DisordersNeurodevelopmental DisorderNeurosciencesNeurotransmittersPathologicPharmacologic SubstancePhasePopulation AnalysisPreparationPsyche structureRattusResearchResearch DesignResearch PersonnelRodentSchizophreniaSchoolsSerotoninSpeedStrategic PlanningSystemTechnologyTestingTimeToxicologyTraumatic Brain InjuryValidationVisual FieldsWorkanalytical methodautism spectrum disorderbasebehavioral studyconvolutional neural networkdesigndigitaldrug discoveryfightingfree behaviorgamma-Aminobutyric Acidhigh throughput screeninginnovationintelligent algorithmlongitudinal analysisneural network architectureneurodevelopmentneuropsychiatric disorderneurotransmitter releasenext generationnovelnovel therapeuticspreventprototypereceptorsocialusability
中文摘要
摘要
该项目旨在开发WormInvestigator™,这是一个新的、高度创新的系统,用于执行自动化、
高通量和纵向研究线虫自由移动和社交的行为
在琼脂平板上相互作用(以下简称:“自由移动的蠕虫”)跨越多个时间点,延长时间
(例如,多天)具有重复措施设计。第一阶段的工作将集中于论证
我们正在申请专利的新型蠕虫识别器™技术--能够执行基于图像的自动
在一组自由移动的蠕虫中识别单个线虫(自由移动的数字标记
移动蠕虫“)。第二阶段的工作将集中于创建WormInvestigator的完整功能
商业版。WormRecognizer固有的创新将成为启用游戏的基础-
改变领域的创新-执行高吞吐量纵向重复测量设计的能力
从离散、非连续分析自由活动线虫的运动等行为
视频序列。与具有独立组的研究设计相比,重复测量设计提供了
更强大的统计能力和跟踪随时间变化的效果的可能性。具体地说,重复测量设计
因为分析自由移动的蠕虫的运动和其他行为将使研究人员能够明确地评估
特定行为与先前行为相关联的可能性,如果不重复,这是不可能的
在恒定光照下测量设计或不切实际的连续成像和跟踪。蠕虫识别器
将利用深度卷积神经网络(CNN)架构执行自动识别
在不同时间点记录的多组自由移动的蠕虫视频中同一蠕虫的轨迹;
在本申请的准备过程中产生了令人鼓舞的试点数据。线虫越来越多地被用作
研究中的模式生物,专注于复杂行为和病理基础的大脑机制
其变化,包括对神经发育、阿尔茨海默病、自闭症、精神分裂症和
创伤性脑损伤。因此,WormInvestigator将在各种智力方面实现显著进步
使用线虫作为模式生物的神经科学应用。具体地说,线虫表达的
在高等真核生物中发现的许多神经递质和相关受体,包括人类,
使线虫在(高通量)筛选下一代治疗精神疾病的药物方面具有极大的吸引力
阿尔茨海默病等疾病,以及依赖神经递质释放调节的疾病
比如精神分裂症的下一代治疗。我们将进行广泛的可行性研究,产品
与专家神经学家密切合作,对WormInvestigator进行验证和可用性研究。市场
在准备本申请期间进行的研究表明,WormInvestigator将扩大使用
秀丽隐杆线虫是许多目前不使用线虫的实验室的模式生物。一项与之竞争的技术是
不可用。我们预计WormInvestigator的全球市场规模将超过300个系统。
英文摘要
Abstract
This project aims to develop WormInvestigator™, a novel, highly innovative system for performing automated,
high-throughput and longitudinal studies of the behavior of C. elegans worms freely moving and socially
interacting on agar plates (hereafter: "freely moving worms") across multiple time points over extended times
(e.g., multiple days) with repeated measures designs. Work in Phase I will focus on demonstrating feasibility of
our novel, patent pending, WormRecognizer™ technology – the ability to perform automatic, image-based
identification of individual C. elegans worms within a group of freely moving worms ("digital tagging of freely
moving worms"). Work in Phase II will focus on creating the full functionality of WormInvestigator for the
commercial release. The innovation inherent in WormRecognizer will serve as the basis for enabling a game-
changing innovation in the field – the ability to perform high throughput longitudinal, repeated measures design
analyses of locomotion and other behavior of freely moving C. elegans worms from discrete, non-continuous
video sequences. Compared to study designs that have independent groups repeated measures designs offer
more statistical power and the possibility to track an effect over time. Specifically, repeated measures designs
for analyzing locomotion and other behavior of freely moving worms will allow researchers to definitively assess
the likelihood that a particular behavior is associated with a prior behavior, which is impossible without repeated
measures designs or impractical continuous imaging and tracking under constant illumination. WormRecognizer
will leverage the Deep Convolutional Neural Network (CNN) architecture to perform automatic identification of
the tracks of the same worm in videos of groups of freely moving worms recorded at different time points;
encouraging pilot data were generated during preparation of this application. C. elegans is increasingly used as
a model organism in research focusing on brain mechanisms underlying complex behaviors and pathological
alterations thereof, including research into neurodevelopment, Alzheimer's disease, autism, schizophrenia and
traumatic brain injury. Thus, WormInvestigator will enable significant advancements in various mental
neuroscience applications that use C. elegans as a model organism. Specifically, the fact that C. elegans express
many of the neurotransmitters and associated receptors that are found in higher eukaryotes, including humans,
makes C. elegans highly attractive for the (high throughput) screening of next generation therapeutics for mental
diseases such as Alzheimer's disease, as well as for disorders that rely on neurotransmitter release modulation
such as next generation treatments for schizophrenia. We will perform extensive feasibility studies, product
validation and usability studies of WormInvestigator in close collaboration with expert neuroscientists. Market
research performed during preparation of this application indicated that WormInvestigator will expand the use of
C. elegans as a model organism to many laboratories that do not currently use them. A competing technology is
not available. We anticipate the global market size for WormInvestigator to be more than 300 systems.
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