PO.TB04.07 · 肿瘤生物学

利用自发荧光的定量评估分析肿瘤球体的结构异质性

Structural heterogeneity of tumor spheroids using quantitative assessment of autofluorescence

海报缩略图:利用自发荧光的定量评估分析肿瘤球体的结构异质性
编号 3419 展板 24 时间 4/20 02:00–05:00 区域 Section 28 主讲 Debanjan Chakroborty, PhD
分会场 In Vitro Models 1: 2D and 3D
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作者与单位 Authors & Affiliations

Prabhat Suman, Sooraj Kakkat, Joel F. Andrews, Chandrani Sarkar, Dhananjay T. Tambe, Debanjan Chakroborty

University of South Alabama, Mobile, AL

摘要 Abstract

中文摘要
引言:肿瘤内异质性是公认的肿瘤进展和治疗耐药发展的促成因素。传统取样和批量测序等程序往往无法捕捉血管性肿瘤内景观。在此,我们引入一种新颖的分析工作流程,利用定量3D自发荧光成像来检查由乳腺癌细胞(BCC)和成纤维细胞(MEF和CAF)生成的乳腺癌细胞球体的结构和细胞组织。这一创新方法能够通过基于荧光的体积成像和计算工具表征多细胞肿瘤球体的结构。 方法:通过将4T1(小鼠BCC)与MEF或CAF以2:1比例共培养创建肿瘤球体。使用24孔AggreWell板(STEMCELL Technologies)生成球体,并使用共聚焦显微镜采集Z-stack图像。使用Google Colab云平台和多个Python库(包括OpenCV和scikit-image)开发定制软件。以EGFP绿色和红色荧光通道采集Z-stack图像,并在每个z层使用两个通道的最大强度进行合并。为聚焦于大尺度对象,使用高斯滤波器对图像进行滤波,并使用Otsu算法转换为二值图像。然后估计球体的荧光强度加权质心。相对于该质心,计算三个径向函数:平均强度、标准差和变异系数。使用对数-对数回归拟合,计算平均值与标准差之间的幂律关系,并确定每种类型球体的Taylor指数。 结果与结论:我们的结果确定了与MEF、CAF或BCC一起培养的球体之间球体内异质性的显著差异。A组(4T1和MEF)球体的Taylor指数b约为1,表明类泊松分布。然而,对于B组球体(4T1和CAF),指数更大,表明超泊松聚集。拟合指数(平均值±SD)为A组b = 1.12 ± 0.05,B组b = 1.38 ± 0.04。这些较高指数的球体表明更大的径向异质性和荧光信号分布不均。Taylor指数b > 1意味着标准差增长快于平均值,即平均强度越大,球体中更密集和更稀疏区域越极端,可能在球体内形成耐药生态位。我们的研究结果意义重大,表明这种无标记、非侵入性方法在不久的将来可用于检测肿瘤间异质性。这些见解可启发未来研究探索肿瘤微环境内的变化,并设计针对实体瘤中治疗耐药生态位的新策略。
查看英文原文 English abstract
Introduction: Intratumor heterogeneity is a well-recognized contributor to tumor progression and the development of treatment resistance. Procedures like traditional sampling and bulk sequencing often fall short of capturing the vascular intratumoral landscape. Here, we introduce a novel analytical workflow that uses quantitative 3D autofluorescence imaging to examine the structure and cellular organization of breast cancer cells (BCC) spheroids generated from BCCs and fibroblasts (MEFs and CAFs). This innovative approach enables characterization of structure in multicellular tumor spheroids via fluorescence-based volumetric imaging and computational tools. Methods: Tumor spheroids were created by co-culturing 4T1 (mouse BCC) with MEFs or CAFs in a 2:1 ratio. The spheroids were generated using 24-well AggreWell plates (STEMCELL Technologies), and Z-stack images were taken using a confocal microscope. Custom software was developed using the Google Colab cloud platform and various Python libraries, including OpenCV and scikit-image. Z-stack images were acquired with the EGFP green and red fluorescence channels, and combined at each z-level using the maximum intensity of the two channels. To focus on large-scale objects, the image was filtered using a Gaussian filter and converted to a binary image using the Otsu algorithm. The fluorescent-intensity-weighted centroid of the spheroid was then estimated. With respect to this centroid, three radial functions were computed: mean intensity, standard deviation, and coefficient of variation. Using a log-log regression fit, the power-law relationship between the mean and standard deviation was computed, and Taylor's exponent for each type of spheroid was determined. Results and Conclusion: Our results identify a significant difference in intra-spheroidal heterogeneity between spheroids grown with MEFs, CAFs, or BCCs. The Taylor's exponent, b, for group A (4T1 and MEFs) spheroids was about 1, indicating a Poisson-like distribution. However, for Group B spheroids (4T1 and CAFs), the exponent was larger, suggesting a super-Poisson aggregation. The fitted exponents (mean ± SD) were b = 1.12 ± 0.05 for the A group and b = 1.38 ± 0.04 for the B group. These higher-exponent spheroids indicate greater radial heterogeneity and uneven distribution of the fluorescent signal. A Taylor's exponent b > 1 signifies that the standard deviation grows faster than the mean. i.e., the greater the mean intensity, the more extreme were the denser and rarer regions of the spheroid, potentially creating drug-resistance niches within the spheroid. Our findings are significant and indicate that this label-free, noninvasive method can be used to detect intertumoral heterogeneity in the near future. These insights can inspire future studies to explore changes within the tumor microenvironment and design new strategies targeting treatment-resistant niches in solid tumors.
利益披露 Disclosure
P. Suman, None.. S. Kakkat, None.. J. F. Andrews, None.. C. Sarkar, None.. D. T. Tambe, None.. D. Chakroborty, None.

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