Nanobiosensorics Laboratory, Institute of Technical Physics and Materials Science, HUN-REN Centre for Energy Research, Konkoly-Thege út 29-33, Budapest H-1121, Hungary.
Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H-1111, Hungary.
ACS Sens. 2024 Nov 22;9(11):5815-5827. doi: 10.1021/acssensors.4c01139. Epub 2024 Jul 31.
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.
目前，人们越来越需要简单易用、非侵入性且快速的工具，以便在单细胞水平上以足够的通量识别和分离各种细胞类型或亚型。通常，根据细胞的直接生物学活性选择细胞将是有利的。这些步骤在免疫疗法、再生医学、癌症诊断和有效治疗中至关重要。如今，活细胞选择程序包括某种形式的生物分子标记或其他侵入性措施，这些措施可能会影响细胞功能或对细胞造成损害。在这项研究中，我们首先通过结合相差显微镜的高空间分辨率和共振波导光栅（RWG）生物传感器的粘附动力学记录能力，介绍了一种高度精确的单细胞分割方法。我们提出了一种分类工作流程，该流程将基于 RWG 的生物传感器的粘附动力学数据和相差显微镜的高精度空间分辨率的测量数据中分离和分类单细胞。该方法已通过两种功能化涂层记录的一个健康细胞和六个癌细胞类型进行了测试。该数据集包含每个表面超过 5000 个单细胞样本，总共有超过 12000 个样本。我们比较并评估了使用两种表面（纤连蛋白和非涂层）和不同分割策略以及应用于分类器的不同测量时间范围的分类性能。整体分类性能达到近 95%，最佳模型表明，我们的概念验证方法可以适应实际的自动诊断用例。无标记测量技术不会影响细胞功能，直接测量细胞活性，并且可以通过改变传感器涂层轻松调整为特定应用。这些特性使其适用于需要进一步处理选定细胞的应用。
