Reno Allison, Tang Jianan, Sudbeck Madeline, Custodio Precious Fe, Baldus Brandi, McLaughlin Elizabeth, Peng Fei, Xiao Hai
Department of Bioengineering, Clemson University, Clemson, SC, USA.
Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA.
bioRxiv. 2024 Aug 30:2024.08.29.610252. doi: 10.1101/2024.08.29.610252.
One of the most common techniques found in a cell biology or tissue engineering lab is the cytotoxicity assay. This can be performed using a variety of different dyes and stains and various protocols to result in a clear indication of dead and live cells within a culture to quantify the viability of a culture and monitor for sudden drops or increases in viability by a drug, material, viral vector, etc introduced into the culture. This assay helps cell biologists determine the health of their culture and what toxicity added substances may add to the culture and whether they are appropriate and safe to use with human cells. However, many of the dyes and stains used for this process are eventually toxic to cells, rendering the cells useless after testing and preventing real time monitoring of the same culture over a period of hours or days. Computation biology is moving cell biology towards novel and innovative techniques such as in silico labeling and dye free labeling using deep learning algorithms. In this work, we investigate whether it is feasible to train a Resnet CNN model to detect morphological changes in human cells that indicate cell death in order to classify cells as live or dead without utilizing a stain or dye. This work also aims to train one CNN model to count all cells regardless of viability status to get a total cell count, and then one CNN model that specifically identifies and counts all of the dead cells for an accurate dead and live cell total by utilizing both pieces of data to determine a general viability percentage for the culture. Additionally, this work explores the use of various image enhancements to understand if this process helps or impedes the deep learning models in their detection of total cells and dead cells.
细胞生物学或组织工程实验室中最常见的技术之一是细胞毒性测定。这可以使用多种不同的染料和染色剂以及各种方案来进行，以明确指示培养物中的死细胞和活细胞，从而量化培养物的活力，并监测引入培养物中的药物、材料、病毒载体等导致的活力突然下降或增加。该测定有助于细胞生物学家确定其培养物的健康状况，以及添加物质可能给培养物带来的毒性，以及它们与人类细胞一起使用是否合适和安全。然而，用于此过程的许多染料和染色剂最终对细胞有毒，在测试后使细胞无用，并阻止在数小时或数天内对同一培养物进行实时监测。计算生物学正在将细胞生物学推向新的创新技术，如使用深度学习算法的计算机模拟标记和无染料标记。在这项工作中，我们研究训练一个Resnet卷积神经网络（CNN）模型来检测人类细胞中指示细胞死亡的形态变化，以便在不使用染色剂或染料的情况下将细胞分类为活细胞或死细胞是否可行。这项工作还旨在训练一个CNN模型来对所有细胞进行计数，而不考虑活力状态以获得细胞总数，然后训练一个CNN模型，通过利用这两部分数据来确定培养物的总体活力百分比，从而专门识别和计数所有死细胞，以准确获得死细胞和活细胞总数。此外，这项工作探索了使用各种图像增强技术，以了解这一过程对深度学习模型检测总细胞和死细胞有帮助还是有阻碍。
