Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742, USA.
Robert E. Fischell Institute for Biomedical Devices, University of Maryland, College Park, MD, 20742, USA.
Small. 2021 Jun;17(23):e2100491. doi: 10.1002/smll.202100491. Epub 2021 Apr 25.
Microfluidic encapsulation of cells/tissues in hydrogel microcapsules has attracted tremendous attention in the burgeoning field of cell-based medicine. However, when encapsulating rare cells and tissues (e.g., pancreatic islets and ovarian follicles), the majority of the resultant hydrogel microcapsules are empty and should be excluded from the sample. Furthermore, the cell-laden hydrogel microcapsules are usually suspended in an oil phase after microfluidic generation, while the microencapsulated cells require an aqueous phase for further culture/transplantation and long-term suspension in oil may compromise the cells/tissues. Thus, real-time on-chip selective extraction of cell-laden hydrogel microcapsules from oil into aqueous phase is crucial to the further use of the microencapsulated cells/tissues. Contemporary extraction methods either require labeling of cells for their identification along with an expensive detection system or have a low extraction purity (<≈30%). Here, a deep learning-enabled approach for label-free detection and selective extraction of cell-laden microcapsules with high efficiency of detection (≈100%) and extraction (≈97%), high purity of extraction (≈90%), and high cell viability (>95%) is reported. The utilization of deep learning to dynamically analyze images in real time for label-free detection and on-chip selective extraction of cell-laden hydrogel microcapsules is unique and may be valuable to advance the emerging cell-based medicine.
微流控技术将细胞/组织包封在水凝胶微胶囊中，在新兴的基于细胞的医学领域引起了极大的关注。然而，当包封稀有细胞和组织（例如胰岛和卵巢滤泡）时，大多数所得水凝胶微胶囊是空心的，应将其从样本中排除。此外，微流控生成后，载细胞的水凝胶微胶囊通常悬浮在油相中，而微囊化的细胞需要水相进行进一步培养/移植，并且在油中长期悬浮可能会损害细胞/组织。因此，实时在芯片上将载细胞的水凝胶微胶囊从油相中选择性地提取到水相对于进一步使用微囊化的细胞/组织至关重要。目前的提取方法要么需要对细胞进行标记以进行识别，同时需要昂贵的检测系统，要么提取纯度低（<≈30%）。在这里，报告了一种基于深度学习的无标记检测和高效（检测≈100%，提取≈97%）、高纯度（提取≈90%）和高细胞活力（>95%）的载细胞微胶囊的选择性提取方法。利用深度学习实时动态分析图像进行无标记检测和载细胞水凝胶微胶囊的芯片上选择性提取是独特的，可能对推进新兴的基于细胞的医学具有重要价值。
