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基于荧光的肿瘤球体定量与空间分析:一种预测患者特异性治疗反应的潜在工具。

Fluorescence-Based Quantitative and Spatial Analysis of Tumour Spheroids: A Proposed Tool to Predict Patient-Specific Therapy Response.

作者信息

Spoerri Loredana, Gunasingh Gency, Haass Nikolas K

机构信息

The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia.

出版信息

Front Digit Health. 2021 May 28;3:668390. doi: 10.3389/fdgth.2021.668390. eCollection 2021.

DOI:10.3389/fdgth.2021.668390
PMID:34713141
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8521823/
Abstract

Tumour spheroids are widely used to pre-clinically assess anti-cancer treatments. They are an excellent compromise between the lack of microenvironment encountered in adherent cell culture conditions and the great complexity of animal models. Spheroids recapitulate intra-tumour microenvironment-driven heterogeneity, a pivotal aspect for therapy outcome that is, however, often overlooked. Likely due to their ease, most assays measure overall spheroid size and/or cell death as a readout. However, as different tumour cell subpopulations may show a different biology and therapy response, it is paramount to obtain information from these distinct regions within the spheroid. We describe here a methodology to quantitatively and spatially assess fluorescence-based microscopy spheroid images by semi-automated software-based analysis. This provides a fast assay that accounts for spatial biological differences that are driven by the tumour microenvironment. We outline the methodology using detection of hypoxia, cell death and PBMC infiltration as examples, and we propose this procedure as an exploratory approach to assist therapy response prediction for personalised medicine.

摘要

肿瘤球体被广泛用于临床前评估抗癌治疗。它们是贴壁细胞培养条件下缺乏微环境与动物模型极大复杂性之间的绝佳折衷方案。球体概括了肿瘤内微环境驱动的异质性,这是治疗结果的一个关键方面,但往往被忽视。可能由于其简便性,大多数检测将球体的总体大小和/或细胞死亡作为读数进行测量。然而,由于不同的肿瘤细胞亚群可能表现出不同的生物学特性和治疗反应,从球体中的这些不同区域获取信息至关重要。我们在此描述一种通过基于软件的半自动分析对基于荧光的显微镜球体图像进行定量和空间评估的方法。这提供了一种快速检测方法,可考虑到由肿瘤微环境驱动的空间生物学差异。我们以缺氧检测、细胞死亡和外周血单核细胞浸润为例概述该方法,并将此程序作为一种探索性方法,以协助个性化医疗的治疗反应预测。

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