Department of Bioengineering, Stanford University School of Medicine, Stanford, CA, USA.
Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Sci Adv. 2023 Jan 20;9(3):eadd1166. doi: 10.1126/sciadv.add1166.
Although literature suggests that resistance to TNF inhibitor (TNFi) therapy in patients with ulcerative colitis (UC) is partially linked to immune cell populations in the inflamed region, there is still substantial uncertainty underlying the relevant spatial context. Here, we used the highly multiplexed immunofluorescence imaging technology CODEX to create a publicly browsable tissue atlas of inflammation in 42 tissue regions from 29 patients with UC and 5 healthy individuals. We analyzed 52 biomarkers on 1,710,973 spatially resolved single cells to determine cell types, cell-cell contacts, and cellular neighborhoods. We observed that cellular functional states are associated with cellular neighborhoods. We further observed that a subset of inflammatory cell types and cellular neighborhoods are present in patients with UC with TNFi treatment, potentially indicating resistant niches. Last, we explored applying convolutional neural networks (CNNs) to our dataset with respect to patient clinical variables. We note concerns and offer guidelines for reporting CNN-based predictions in similar datasets.
虽然文献表明,溃疡性结肠炎(UC)患者对肿瘤坏死因子抑制剂(TNFi)治疗的耐药性部分与炎症区域的免疫细胞群有关,但相关的空间背景仍然存在很大的不确定性。在这里,我们使用高度多重免疫荧光成像技术 CODEX 对 29 名 UC 患者和 5 名健康个体的 42 个组织区域的 42 个组织进行了公共浏览的炎症组织图谱构建。我们分析了 1710973 个空间分辨的单细胞上的 52 个生物标志物,以确定细胞类型、细胞-细胞接触和细胞邻居。我们观察到细胞的功能状态与细胞邻居有关。我们进一步观察到,在接受 TNFi 治疗的 UC 患者中存在一部分炎症细胞类型和细胞邻居,这可能表明存在耐药性生态位。最后,我们探讨了在我们的数据集上应用卷积神经网络(CNN)的问题,以及与患者临床变量的关系。我们注意到报告基于 CNN 的预测的相关问题,并为类似数据集提供了指南。
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