LNP drug delivery image data

Data to accompany the manuscript "Deep learnings models for lipid-nanoparticle-based drug delivery"Abstract:Large-scale time-lapse microscopy experiments are useful to understand delivery and expression in RNA-based therapeutics. The resulting data has high dimensionality and high (but sparse) information content, making it challenging and costly to store and process. Early prediction of experimental outcome enables intelligent data management and decision making. We start from time-lapse data of HepG2 cells exposed to lipid-nanoparticles loaded with mRNA for expression of green fluorescent protein (GFP). We hypothesize that it is possible to predict if a cell will express GFP or not based on cell morphology at time-points prior to GFP expression. Here we present results on per-cell classification (GFP expression/no GFP expression) and regression (level of GFP expression) using three different approaches. In the first approach we use a convolutional neural network extracting per-cell features at each time point. We then utilize the same features combined with: a long-short-term memory (LSTM) network encoding temporal dynamics (approach 2); and time-series feature extraction using the python package tsfresh followed by principal component analysis and gradient boosting machines (approach 3), to reach a final classification or regression result. Application of the three approaches to a previously unanalyzed test set of cells showed good predictive performance of all three approaches but that accounting for the temporal dynamics via LSTMs or tsfresh led to significantly improved performance. The predictions made by the LSTM and tsfresh applications were not significantly different. The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high content imaging.Python code:https://github.com/pharmbio/phil_LNP_modelling

随《基于脂质纳米颗粒的药物递送深度学习模型》一文的配套数据。摘要：大规模时间间隔显微摄影实验有助于理解基于RNA的治疗药物递送与表达。由此产生的数据具有高维度和高（但稀疏）的信息含量，使得存储和处理变得极具挑战性和成本高昂。提前预测实验结果能够实现智能数据管理和决策。本研究从暴露于携带mRNA以表达绿色荧光蛋白（GFP）的脂质纳米颗粒的HepG2细胞的时间间隔数据入手。我们假设，基于细胞在GFP表达前的时间点形态，可以预测细胞是否表达GFP。在此，我们展示了使用三种不同方法进行的单细胞分类（GFP表达/非GFP表达）和回归（GFP表达水平）的结果。在第一种方法中，我们使用卷积神经网络在每个时间点提取单细胞特征。随后，我们利用相同的特征结合以下方法：长短期记忆（LSTM）网络编码时间动态（方法2）；以及使用python包tsfresh进行的时间序列特征提取，随后进行主成分分析和梯度提升机（方法3），以获得最终的分类或回归结果。将这三种方法应用于之前未分析的细胞测试集，显示了所有三种方法良好的预测性能，但通过LSTM或tsfresh考虑时间动态的方法导致了显著性能提升。LSTM和tsfresh应用的预测结果无显著差异。结果表明，在研究药物递送时，考虑时间动态具有显著优势。Python代码：https://github.com/pharmbio/phil_LNP_modelling