High-throughput high-resolution microscopy for phenotypic drug discovery applications

用于表型药物发现应用的高通量高分辨率显微镜

• 批准号: 10654145
• 负责人: Aniruddha Ray
• 金额: $ 45.15万
• 依托单位: UNIVERSITY OF TOLEDO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10654145
• 关键词: 3-Dimensional; Address; Antineoplastic Agents; Antitumor Drug Screening Assays; Apoptosis; Apoptotic; Artificial Intelligence; Autophagocytosis; Award; Biochemical; Biological Assay; Biophysics; Buffers; Bulla; Bypass; COVID-19 pandemic; Cancer Model; Cell Death; Cell Death Induction; Cell Death Process; Cell Nucleus; Cell model; Cells; Cellular Morphology; Cellular biology; Cessation of life; Characteristics; Chemicals; Chemotherapy and/or radiation; Classification; Climate; Clinic; Clinical Trials; Consumption; Coupled; Development; Devices; Diagnostic; Doxorubicin; Drug Screening; First Generation College Students; Goals; Holography; Hydrogen Peroxide; Image; Incubated; Induction of Apoptosis; Inhibition of Apoptosis; Institution; Label; Laboratories; Lighting; Malignant Neoplasms; Measurement; Mediating; Membrane; Methods; Microscope; Microscopy; Mitotic; Modeling; Molecular; Morphology; Multi-Drug Resistance; Necrosis; Neoplasm Metastasis; Organelles; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacology; Phase; Phenotype; Physiological; Process; Prognosis; Property; Public Health; Research; Research Personnel; Resistance; Resistance development; Resolution; Rupture; Screening procedure; Series; Speed; Technology; Testing; Time; Toxic effect; Training; Treatment Failure; Underrepresented Minority; Universities; Vacuole; Visualization; anti-cancer; anticancer research; cancer cell; cancer drug resistance; cancer imaging; cell type; chemotherapy; college; convolutional neural network; cost; cost effective; cytotoxicity; deep learning; disadvantaged background; drug discovery; economic disparity; experience; experimental study; high resolution imaging; high throughput screening; imaging system; improved; minority communities; mortality; novel; novel anticancer drug; novel strategies; novel therapeutics; particle; phenotypic data; response; screening; simulation; skills; therapeutic candidate; time use; tool; two-dimensional; ultraviolet; undergraduate student

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.

摘要：多药耐药性（MDR）是癌症和主要公众化学疗法失败的主要原因 健康问题。通常，MDR癌症是侵略性，转移性的，预后较差。此外，MDR 癌症对诱导常规程序性细胞死亡的治疗高度抗性，例如化学疗法 和辐射。为了打击凋亡介导的MDR，正在定向新药物发现 采取诱导抑制细胞凋亡过程的疗法，例如坏死，自噬，popaptosis， 甲基病和铁凋亡。在药物发现的早期阶段优化新的化学分子时， 需要解决两个关键问题：a）药物杀死癌细胞的能力，b）通过 药物杀死癌细胞。目前，常规的生化测定和高清成像是 研究这些过程的唯一方法，但它们耗时，昂贵，需要熟练的专家， 因此将其效用限制在少数实验室中。 我们提出了一个改变范式的表型筛查工具，该工具实时识别细胞死亡机制 使用高分辨率的广场显微镜结合深度学习。首先，我们建议开发低成本 广场全息显微镜，具有多波长照明，包括紫外线（UV），以使高高 在没有外部标签的情况下进行内容筛选，以高分辨率超过衍射极限。我们将实现 通过将无镜头全息显微镜与基于微粒阵列的成像底物相结合 让我们每次测试对数千个活细胞进行映像。紫外线照明可能会提供有关核和核的额外信息 其他细胞的细胞器，即使很少使用。使用癌细胞的延时图像，一个3D- 卷积神经网络将经过训练，以识别不同的形态特征，例如收缩， 与不同的细胞死亡过程有关的爆炸，液泡和膜破裂。在孵化期间 步骤，将使用预训练的网络实时获得结果，以自动分类 死亡过程。使用这种方法，可以筛选新的抗癌药物分子及其中间体 在高通量时，无需进行任何进一步的处理或标签。这个项目成功完成 将产生一种负担得起的，紧凑的高内心筛选工具，可用于许多不同的应用程序， 除表型筛查外。特别是，在这场19009危机期间，它强调了需要 高通量诊断，药物筛查和治疗工具。 通过执行该领域奖，我们将大大加强托莱多大学的研究环境和 为本科生提供独特的生物物理学，显微镜检查的跨学科培训经验 成像，深度学习，细胞生物学和药理学。