发表机构
Princeton University; Waters Corporation（普林斯顿大学; 沃特世公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于明场成像和深度学习的无标记细胞活力分析方法ViabiLens，通过检测和分类模型实现准确预测，在CHO细胞上误差仅2.68%，并发布基准数据集。
AI 中文摘要
细胞活力评估是细胞培养系统中的核心需求，在生物制药生产和药物开发中具有关键应用。传统上，通过向样本中添加不能透过细胞膜的染料（称为染色过程）来测量细胞活力，该方法能够区分受损的细胞膜与完整的细胞膜。然而，染色存在若干局限性：(a) 化学试剂可能干扰所测细胞的正常细胞过程；(b) 对于膜完整性仅部分受损的单个细胞，通常难以明确判定其活力；(c) 使用荧光染色时，光漂白可能随时间推移削弱测量准确性；(d) 染色无法在原位或实时进行。在此，我们证明：(1) 在明场成像下捕获的染色细胞包含足够的信息以区分活细胞和死细胞；(2) 在未染色明场成像下捕获的细胞表现出与其染色对应物相似的图像特征，使得在染色细胞上训练的模型能够泛化到未染色细胞。随后，我们报告了ViabiLens的开发与验证，这是一种用于无标记细胞活力分析的人工智能辅助软件。ViabiLens结合了用于定位单个细胞的细胞检测模型和用于活/死预测的卷积神经网络（CNN）分类器，并配有一个基于UMAP的交互式查看器，用于可视化和探索样本中的单个细胞。在涵盖广泛活力条件的中国仓鼠卵巢（CHO）细胞上评估，ViabiLens在未染色样本上相对于荧光参考测量实现了2.68%的平均绝对误差。我们还发布了一个用于无标记细胞活力分析的基准数据集，以促进未来研究，可在以下网址获取：此https URL。
英文摘要
Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development. Conventionally, it is measured by adding membrane-impermeable dyes to a sample (a process called staining), which allows compromised cell membranes to be distinguished from intact ones. However, staining has several limitations: (a) chemical agents can perturb normal cellular processes of the cells being measured, (b) it is often ambiguous to assign viability to individual cells whose membrane integrity is only partially compromised. (c) photobleaching can undermine measurement accuracy over time when using fluorescent stains, and (d) staining cannot be performed in situ or in real time. Here, we show that (1) stained cells captured under brightfield imaging contain sufficient information to distinguish live and dead cells, and (2) cells captured under unstained brightfield imaging exhibit similar image features to their stained counterparts, enabling models trained on stained cells to generalize to unstained ones. We then report the development and validation of ViabiLens, an AI-assisted software for label-free cell viability analysis. The ViabiLens combines a cell detection model for localizing individual cells with a convolutional neural network (CNN) classifier for live/dead prediction, paired with an interactive UMAP-based viewer for visualizing and exploring individual cells across the sample. Evaluated on Chinese Hamster Ovary (CHO) cells spanning a wide range of viability conditions, ViabiLens achieves a mean absolute error of 2.68\% on unstained samples against fluorescence-based reference measurements. We also release a benchmark dataset for label-free cell viability analysis to facilitate future research, available at https://amirrezavazifeh.github.io/ViabiLens-Project-Page/.
