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

基本信息

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
项目类别：
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

项目摘要

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.

摘要：多药耐药（MDR）是癌症化疗失败的主要原因，也是公众关注的焦点。健康问题。通常，耐多药癌症具有侵袭性、转移性，并且预后较差。此外，耐多药癌症对诱导传统程序性细胞死亡的治疗（例如化疗）具有高度抵抗力和辐射。为了对抗细胞凋亡介导的多药耐药性，新药的发现正在被引导针对诱导细胞凋亡抑制过程的疗法，例如坏死性凋亡、自噬、细胞凋亡、和铁死亡。在药物发现的早期阶段优化新化学分子时，需要解决两个关键问题：a）药物杀死癌细胞的能力，b）其机制该药物可以杀死癌细胞。目前，常规生化检测和高清成像研究这些过程的唯一方法，但它们既耗时又昂贵，并且需要熟练的专家，从而将其实用性限制在少数实验室。我们提出了一种改变范式的表型筛选工具，可以实时识别细胞死亡机制使用高分辨率宽视野显微镜与深度学习相结合。首先，我们建议开发一种低成本的宽视场全息显微镜，具有多波长照明，包括紫外线 (UV)，可实现高无需外部标记即可以超过衍射极限的高分辨率进行内容筛选。我们将实现这是通过将无透镜全息显微镜与基于微粒阵列的成像基底相结合来实现的让我们能够在每次测试中对数千个活细胞进行成像。紫外线照射可以提供有关原子核和细胞的其他细胞器，尽管它很少被使用。使用癌细胞的延时图像，3D- 卷积神经网络将被训练来识别不同的形态特征，例如收缩、与不同的细胞死亡过程相关的起泡、液泡和膜破裂。孵化期间步骤，将使用预先训练的网络对细胞进行自动分类，实时获得结果死亡过程。利用这种方法，可以筛选新的抗癌药物分子及其中间体高通量，无需任何进一步处理或标记。本项目的顺利完成将产生一种经济实惠、紧凑、高内涵的筛选工具，可用于许多不同的应用，除了表型筛选之外。特别是在这场 Covid-19 危机期间，突显了我们需要高通量诊断、药物筛选和治疗工具。通过实施该 AREA 奖项，我们将显着加强托莱多大学的研究氛围和为本科生提供生物物理学、显微镜、成像、深度学习、细胞生物学和药理学。