AI Insight
Researchers developed ViabiLens, an AI-powered tool that uses brightfield microscopy imaging and deep learning to count cells and assess their viability without chemical staining. The system employs a cell detection model combined with a convolutional neural network classifier to distinguish live from dead cells, achieving a mean absolute error of 2.33% when tested on Chinese Hamster Ovary cells compared to traditional fluorescence-based measurements. The approach overcomes limitations of conventional staining methods including cellular perturbation, ambiguous results for partially compromised cells, photobleaching, and inability to perform real-time in situ measurements.
Why it matters
This technology could significantly improve biopharmaceutical manufacturing and drug development processes by enabling continuous, non-invasive monitoring of cell cultures in real time. Eliminating the need for chemical stains reduces costs, prevents interference with cellular processes, and allows for more accurate longitudinal studies of cell health without destroying samples.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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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 tool for label-free cell viability analysis. 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 2.33% on unstained samples against fluorescence-based reference measurements. The demo of ViabiLens software is available at https://amirrezavazifeh.github.io/ViabiLens-Project-Page/.
Source: Label-free cell counting and viability prediction with brightfield imaging and deep learning