PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
Physik-Institut, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.
Commun Biol. 2024 Feb 6;7(1):154. doi: 10.1038/s42003-024-05839-w.
Mapping the cellular refractive index (RI) is a central task for research involving the composition of microorganisms and the development of models providing automated medical screenings with accuracy beyond 95%. These models require significantly enhancing the state-of-the-art RI mapping capabilities to provide large amounts of accurate RI data at high throughput. Here, we present a machine-learning-based technique that obtains a biological specimen's real-time RI and thickness maps from a single image acquired with a conventional color camera. This technology leverages a suitably engineered nanostructured membrane that stretches a biological analyte over its surface and absorbs transmitted light, generating complex reflection spectra from each sample point. The technique does not need pre-existing sample knowledge. It achieves 10 RI sensitivity and sub-nanometer thickness resolution on diffraction-limited spatial areas. We illustrate practical application by performing sub-cellular segmentation of HCT-116 colorectal cancer cells, obtaining complete three-dimensional reconstruction of the cellular regions with a characteristic length of 30 μm. These results can facilitate the development of real-time label-free technologies for biomedical studies on microscopic multicellular dynamics.
绘制细胞折射率（RI）图谱是涉及微生物组成和开发模型的核心任务，这些模型提供了超过 95%准确率的自动化医学筛查。这些模型需要显著提高现有的 RI 映射能力，以便以高通量提供大量准确的 RI 数据。在这里，我们提出了一种基于机器学习的技术，该技术可以从常规彩色相机获取的单个图像中实时获得生物样本的 RI 和厚度图谱。该技术利用了经过适当设计的纳米结构膜，该膜在其表面拉伸生物分析物并吸收透射光，从而从每个样本点产生复杂的反射光谱。该技术不需要预先存在的样本知识。它在衍射极限的空间区域上实现了 10 RI 的灵敏度和亚纳米厚度分辨率。我们通过对 HCT-116 结直肠癌细胞进行亚细胞分割来说明实际应用，获得了具有 30 μm 特征长度的细胞区域的完整三维重建。这些结果可以促进实时无标记技术的发展，用于对微观多细胞动力学的生物医学研究。
