Implementing high dimensional reductional analysis on histocytometric data

In the previous protocol article (Munoz-Erazo, Schmidt, Shinko, Eccles, et al., 2022), we demonstrated construction of a histocytometry pipeline that is capable of both segmenting highly-aggregated cell populations and retaining the original intensity data range of the input microscopic images. In the protocol article presented here, using the output from the aforementioned protocol article, we demonstrate how to phenotype the data using the high dimensional reductional analysis technique opt-t-SNE (optimized t-distributed Stochastic Neighbor Embedding) and compare it to traditional manual gating. Additional, we present a support protocol illustrating the advantage of the inclusion of cell junction/membrane marker in accurately segmenting highly-aggregated cell populations in ilastik. In the previous protocol article (Munoz-Erazo, Schmidt, Shinko, Eccles, et al., 2022), we demonstrated construction of a histocytometry pipeline that is capable of both segmenting highly-aggregated cell populations and retaining the original intensity data range of the input microscopic images. In the protocol article presented here, using the output from the aforementioned protocol article, we demonstrate how to phenotype the data using the high dimensional reductional analysis technique opt-t-SNE (optimized t-distributed Stochastic Neighbor Embedding) and compare it to traditional manual gating. Additional, we present a support protocol illustrating the advantage of the inclusion of cell junction/membrane marker in accurately segmenting highly-aggregated cell populations in ilastik.在先前的方案论文（Munoz-Erazo、Schmidt、Shinko、Eccles等人，2022）中，我们已构建了一套组织细胞计量学（histocytometry）流程，该流程可实现高度聚集细胞群的分割，同时保留输入显微图像的原始强度数据范围。在本文呈现的方案中，我们借助前述方案论文的输出结果，演示了如何利用高维降维分析技术优化t分布随机邻域嵌入（opt-t-SNE, optimized t-distributed Stochastic Neighbor Embedding）对数据进行表型分析，并将其与传统手动门控进行对比。此外，我们还提供了一项辅助方案，用以说明在ilastik中加入细胞连接/膜标记物，可在高度聚集细胞群的精准分割中发挥优势。在先前的方案论文（Munoz-Erazo、Schmidt、Shinko、Eccles等人，2022）中，我们已构建了一套组织细胞计量学（histocytometry）流程，该流程可实现高度聚集细胞群的分割，同时保留输入显微图像的原始强度数据范围。在本文呈现的方案中，我们借助前述方案论文的输出结果，演示了如何利用高维降维分析技术优化t分布随机邻域嵌入（opt-t-SNE, optimized t-distributed Stochastic Neighbor Embedding）对数据进行表型分析，并将其与传统手动门控进行对比。此外，我们还提供了一项辅助方案，用以说明在ilastik中加入细胞连接/膜标记物，可在高度聚集细胞群的精准分割中发挥优势。