Label-Free Single-Cell Cancer Classification from the Spatial Distribution of Adhesion Contact Kinetics.

IF 8.2 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Balint Beres, Kinga Dora Kovacs, Nicolett Kanyo, Beatrix Peter, Inna Szekacs, Robert Horvath
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Abstract

There is an increasing need for simple-to-use, noninvasive, and rapid tools to identify and separate various cell types or subtypes at the single-cell level with sufficient throughput. Often, the selection of cells based on their direct biological activity would be advantageous. These steps are critical in immune therapy, regenerative medicine, cancer diagnostics, and effective treatment. Today, live cell selection procedures incorporate some kind of biomolecular labeling or other invasive measures, which may impact cellular functionality or cause damage to the cells. In this study, we first introduce a highly accurate single-cell segmentation methodology by combining the high spatial resolution of a phase-contrast microscope with the adhesion kinetic recording capability of a resonant waveguide grating (RWG) biosensor. We present a classification workflow that incorporates the semiautomatic separation and classification of single cells from the measurement data captured by an RWG-based biosensor for adhesion kinetics data and a phase-contrast microscope for highly accurate spatial resolution. The methodology was tested with one healthy and six cancer cell types recorded with two functionalized coatings. The data set contains over 5000 single-cell samples for each surface and over 12,000 samples in total. We compare and evaluate the classification using these two types of surfaces (fibronectin and noncoated) with different segmentation strategies and measurement timespans applied to our classifiers. The overall classification performance reached nearly 95% with the best models showing that our proof-of-concept methodology could be adapted for real-life automatic diagnostics use cases. The label-free measurement technique has no impact on cellular functionality, directly measures cellular activity, and can be easily tuned to a specific application by varying the sensor coating. These features make it suitable for applications requiring further processing of selected cells.

Abstract Image

根据粘附接触动力学的空间分布进行无标记单细胞癌症分类
现在越来越需要简单易用、非侵入性的快速工具,以足够的通量在单细胞水平上识别和分离各种细胞类型或亚型。通常情况下,根据细胞的直接生物活性来选择细胞会更有优势。这些步骤对于免疫疗法、再生医学、癌症诊断和有效治疗至关重要。如今,活细胞筛选程序都包含某种生物分子标记或其他侵入性措施,这可能会影响细胞功能或对细胞造成损伤。在本研究中,我们首先介绍了一种高精度的单细胞分割方法,它将相位对比显微镜的高空间分辨率与共振波导光栅(RWG)生物传感器的粘附动力学记录能力相结合。我们介绍了一种分类工作流程,该流程结合了从基于共振波导光栅的生物传感器捕获的测量数据中对单细胞进行半自动分离和分类的方法,前者用于捕获粘附动力学数据,后者用于捕获高精度空间分辨率的相位对比显微镜数据。使用两种功能化涂层记录的一种健康细胞和六种癌细胞类型对该方法进行了测试。数据集包含每个表面的 5000 多个单细胞样本和总共 12000 多个样本。我们比较并评估了使用这两种表面(纤连蛋白和无涂层)进行分类的效果,我们的分类器采用了不同的分割策略和测量时间跨度。最佳模型的整体分类性能接近 95%,这表明我们的概念验证方法可用于实际生活中的自动诊断应用案例。无标记测量技术对细胞功能没有影响,可直接测量细胞活动,并可通过改变传感器涂层轻松调整以适应特定应用。这些特点使其适用于需要对选定细胞进行进一步处理的应用。
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来源期刊
ACS Sensors
ACS Sensors Chemical Engineering-Bioengineering
CiteScore
14.50
自引率
3.40%
发文量
372
期刊介绍: ACS Sensors is a peer-reviewed research journal that focuses on the dissemination of new and original knowledge in the field of sensor science, particularly those that selectively sense chemical or biological species or processes. The journal covers a broad range of topics, including but not limited to biosensors, chemical sensors, gas sensors, intracellular sensors, single molecule sensors, cell chips, and microfluidic devices. It aims to publish articles that address conceptual advances in sensing technology applicable to various types of analytes or application papers that report on the use of existing sensing concepts in new ways or for new analytes.
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