基于明场成像与深度学习的无标记细胞计数与活力预测
Label-free cell counting and viability prediction with brightfield imaging and deep learning
Amir Reza Vazifeh · Christian Zeigler · Sornanathan Meyyappan · Richard Jeske · Jason W. Fleischer
中文摘要
细胞活力评估是细胞培养系统的核心需求,在生物制药生产和临床试验中应用关键。传统上通过向样本添加膜不通透性染料(即染色)来测量,利用染料区分膜受损细胞与完整细胞。然而染色存在若干局限:(a)化学试剂会干扰被测细胞的正常生理过程;(b)对于膜完整性仅部分丧失的细胞,难以明确判定其活力状态;(c)使用荧光染料时,光漂白会随时间降低测量精度;(d)染色无法原位或实时进行。研究表明:(1)明场成像下捕获的已染色细胞包含足以区分活细胞与死细胞的图像特征;(2)未染色明场成像细胞与已染色细胞呈现相似的图像特征,使基于染色细胞训练的模型可推广至未染色样本。据此报告 ViabiLens 的开发与验证,这是一款面向无标记细胞活力分析的 AI 辅助软件。ViabiLens 将用于定位单个细胞的细胞检测模型与用于活/死预测的卷积神经网络(CNN)分类器相结合,并配备基于 UMAP 的交互式可视化浏览器,以探索样本中各细胞。在涵盖广泛活力条件的中国仓鼠卵巢(CHO)细胞上,以基于荧光的参考测量为基准,ViabiLens 在未染色样本上达到 2.68% 的平均绝对误差。同时发布了一个面向无标记细胞活力分析的基准数据集以促进后续研究。
关键要点
- 01问题:传统染色法测定细胞活力存在化学干扰、判定模糊、光漂白、无法原位实时等四大局限。
- 02方法:基于明场成像与深度学习构建 ViabiLens,融合细胞检测 CNN 与活/死分类 CNN,并配套 UMAP 交互可视化。
- 03关键发现:已染色细胞的明场图像蕴含活/死区分特征,且未染色的明场图像与染色图像特征相似。
- 04结果:在 CHO 细胞上,对未染色样本的活力预测平均绝对误差为 2.68%,相对荧光参考测量精度较高。
- 05贡献:公开无标记细胞活力分析基准数据集,促进该方向后续研究。
解读
尚无解读。
原始英文摘要
arXiv:2610.10473v1 Announce Type: new Abstract: 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/.