Bianco Vittorio, D'Agostino Massimo, Pirone Daniele, Giugliano Giusy, Mosca Nicola, Di Summa Maria, Scerra Gianluca, Memmolo Pasquale, Miccio Lisa, Russo Tommaso, Stella Ettore, Ferraro Pietro
CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli, Napoli, 80078, Italy.
Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Via S. Pansini 5, Naples, 80131, Italy.
Small Methods. 2023 Nov;7(11):e2300447. doi: 10.1002/smtd.202300447. Epub 2023 Sep 5.
In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitative phase imaging approach, is the lack of specificity compared to fluorescence microscopy, thus restraining its huge potentialities in single-cell analysis and diagnostics. Remarkable results in introducing specificity are obtained through artificial intelligence (AI), but only for adherent cells. However, accessing the 3D fluorescence ground truth and obtaining accurate voxel-level co-registration of image pairs for AI training is not viable for high-throughput cytometry. The recent statistical inference approach is a significant step forward for label-free specificity but remains limited to cells' nuclei. Here, a generalized computational strategy based on a self-consistent statistical inference to achieve intracellular multi-specificity is shown. Various subcellular compartments (i.e., nuclei, cytoplasmic vacuoles, the peri-vacuolar membrane area, cytoplasm, vacuole-nucleus contact site) can be identified and characterized quantitatively at different phases of the cells life cycle by using yeast cells as a biological model. Moreover, for the first time, virtual reality is introduced for handling the information content of multi-specificity in single cells. Full fruition is proofed for exploring and interacting with 3D quantitative biophysical parameters of the identified compartments on demand, thus opening the route to a metaverse for 3D microscopy.
流入相衬断层扫描技术可在细胞计数系统中提供无标记细胞的三维折射率。与任何定量相成像方法一样，其主要局限性在于与荧光显微镜相比缺乏特异性，从而限制了其在单细胞分析和诊断中的巨大潜力。通过人工智能（AI）在引入特异性方面取得了显著成果，但仅适用于贴壁细胞。然而，对于高通量细胞计数来说，获取三维荧光基本事实并获得用于AI训练的图像对的准确体素级配准是不可行的。最近的统计推断方法在无标记特异性方面向前迈出了重要一步，但仍仅限于细胞核。在此，展示了一种基于自洽统计推断以实现细胞内多特异性的广义计算策略。通过使用酵母细胞作为生物学模型，可以在细胞生命周期的不同阶段对各种亚细胞区室（即细胞核、细胞质液泡、液泡周围膜区域、细胞质、液泡 - 细胞核接触位点）进行定量识别和表征。此外，首次引入虚拟现实来处理单细胞中多特异性的信息内容。证明了按需探索和交互已识别区室的三维定量生物物理参数的充分实现，从而为三维显微镜的元宇宙开辟了道路。
