Department of Research Support, Yonsei Biomedical Research Institute, Yonsei University College of Medicine, Seoul, Korea.
Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea.
Yonsei Med J. 2024 Sep;65(9):544-555. doi: 10.3349/ymj.2023.0639.
By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems.
This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data. The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed.
We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 and CD8 T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset.
In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.
通过利用蛋白质和 mRNA 表达模式，我们可以识别更详细和多样化的免疫细胞，深入了解癌症生态系统中复杂的免疫景观。
本研究通过从基因表达综合数据库中获取外周血单核细胞（PBMCs）的公开可用的转录组和表位细胞索引测序（CITE-seq）数据来进行。共分析了 94674 个总细胞，其中 32412 个是 T 细胞。数据中包含 228 个蛋白质特征和 16262 个 mRNA 特征。使用 Seurat 软件包进行质量控制和预处理，进行主成分分析，并使用 Uniform Manifold Approximation and Projection 进行聚类可视化。分析 CITE-seq 中的蛋白质和 mRNA 水平。
我们观察到在蛋白质水平生成的聚类中，一部分 T 细胞的划分效果更好。通过鉴定与 CD4 和 CD8 蛋白高度相关的 mRNA 标志物，并使用流式细胞术对 CD26 和 CD99 标志物进行交叉验证，我们发现 PBMC 中 CD4 和 CD8 T 细胞的区分效果更好。加权最近邻聚类结果鉴定了一个以前未观察到的 T 细胞亚群。
在这项研究中，我们使用 CITE-seq 数据证实了蛋白质表达模式可用于更精确地识别细胞。这些发现将有助于我们更好地了解未来免疫细胞的异质性，并为健康和疾病中免疫反应的复杂性提供有价值的见解。
