High-throughput high-resolution microscopy for phenotypic drug discovery applications
High-throughput high-resolution microscopy for phenotypic drug discovery applications
批准号:
10654145
负责人:
Aniruddha Ray
金额:
$45.15万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-01 至 2026-07-31
关键词:
3-DimensionalAddressAntineoplastic AgentsAntitumor Drug Screening AssaysApoptosisApoptoticArtificial IntelligenceAutophagocytosisAwardBiochemicalBiological AssayBiophysicsBuffersBullaBypassCOVID-19 pandemicCancer ModelCell DeathCell Death InductionCell Death ProcessCell NucleusCell modelCellsCellular MorphologyCellular biologyCessation of lifeCharacteristicsChemicalsChemotherapy and/or radiationClassificationClimateClinicClinical TrialsConsumptionCoupledDevelopmentDevicesDiagnosticDoxorubicinDrug ScreeningFirst Generation College StudentsGoalsHolographyHydrogen PeroxideImageIncubatedInduction of ApoptosisInhibition of ApoptosisInstitutionLabelLaboratoriesLightingMalignant NeoplasmsMeasurementMediatingMembraneMethodsMicroscopeMicroscopyMitoticModelingMolecularMorphologyMulti-Drug ResistanceNecrosisNeoplasm MetastasisOrganellesPathway interactionsPatientsPharmaceutical PreparationsPharmacologyPhasePhenotypePhysiologicalProcessPrognosisPropertyPublic HealthResearchResearch PersonnelResistanceResistance developmentResolutionRuptureScreening procedureSeriesSpeedTechnologyTestingTimeToxic effectTrainingTreatment FailureUnderrepresented MinorityUniversitiesVacuoleVisualizationanti-canceranticancer researchcancer cellcancer drug resistancecancer imagingcell typechemotherapycollegeconvolutional neural networkcostcost effectivecytotoxicitydeep learningdisadvantaged backgrounddrug discoveryeconomic disparityexperienceexperimental studyhigh resolution imaginghigh throughput screeningimaging systemimprovedminority communitiesmortalitynovelnovel anticancer drugnovel strategiesnovel therapeuticsparticlephenotypic dataresponsescreeningsimulationskillstherapeutic candidatetime usetooltwo-dimensionalultravioletundergraduate student
中文摘要
摘要:多药耐药(MDR)是癌症化疗失败的主要原因之一,也是公众关注的主要问题
英文摘要
Abstract: Multidrug resistance (MDR) is a major cause of chemotherapy failure in cancer and a major public
health concern. Often, MDR cancers are aggressive, metastatic, and have poor prognoses. In addition, MDR
cancer is highly resistant to treatments that induce conventional programmed cell death, such as chemotherapy
and radiation. For the purpose of combating apoptosis mediated MDR, new drug discoveries are being directed
towards therapies that induce apoptosis-inhibiting processes, such as necroptosis, autophagy, paraptosis,
methuosis, and ferroptosis. When optimizing new chemical molecules during the early phases of drug discovery,
two key questions need to be addressed: a) the ability of the drug to kill cancer cells, and b) the mechanism by
which the drug kills cancer cells. At present, conventional biochemical assays and high-definition imaging are
the only methods for studying these processes, but they are time consuming, costly, and require skilled experts,
thus limiting their utility to a small number of laboratories.
We propose a paradigm-altering phenotypic screening tool that identifies cell death mechanisms in real time
using high-resolution widefield microscopy coupled with deep learning. First, we propose to develop a low-cost
widefield holographic microscope, with multi-wavelength illumination, including ultraviolet (UV), to enable high
content screening without external labeling, at high resolution that exceeds the diffraction limit. We will achieve
this by integrating lens-less holographic microscopy with microparticle array-based imaging substrates that will
allow us to image thousands of live cells per test. UV illumination may provide extra information about nuclei and
other organelles of cells, even though it is used sparingly. Using time lapse images of cancer cells, a 3D-
convolutional neural network will be trained to identify different morphological features, such as shrinking,
blebbing, vacuoles and membrane ruptures, associated with different cell death processes. During the incubation
step, results will be obtained in real time, using the pre-trained network for automated classification of the cell
death process. Using this approach, new anti-cancer drug molecules and their intermediates can be screened
at high-throughput without requiring any further processing or labeling. A successful completion of this project
will result in an affordable, compact, high-content screening tool that can be used for many different applications,
in addition to phenotypic screening. In particular, during this Covid-19 crisis, which has highlighted the need for
high throughput diagnostics, drug screening, and therapy tools.
By implementing this AREA award, we will significantly strengthen University of Toledo's research climate and
provide undergraduate students with a unique interdisciplinary training experience in biophysics, microscopy,
imaging, deep learning, cell biology, and pharmacology.
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