使用BD®OMICS-One-Protein-Panels进行CITE-seq对肺肿瘤微环境的整合转录组学和蛋白质组学表征

The tumor microenvironment (TME) is a heterogeneous and continuously evolving system. It is composed of malignant cells, numerous infiltrating immune cells, stromal cells, blood vessels, and extracellular matrix. The interactions of tumor and non-tumor cells produce an immune-reactive milieu, altering cellular phenotypes and function, thus contributing to tumor growth and progression or antitumor immunity. Profiling the components and alterations in the TME at high resolution is crucial to identify factors influencing cancer progression or evaluating the efficacy of immunotherapies. Advancements of single cell multiomics techniques provide powerful means to scrutinize the tumor and TME at high resolution, shedding light on discrete cell subsets and their potential functions. Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) enables concurrent single-cell RNA sequencing alongside surface protein profiling by using oligonucleotide-tagged antibodies. By combining proteomic and transcriptomic data from the same cell, CITE-seq is an especially powerful approach for phenotyping discrete cell populations, especially suitable for the TME. Here we applied CITE-seq to clinically relevant human lung cancer samples. A single cel suspension was obtained with BD Horizon™ Dri Tumor & Tissue Dissociation Reagent and then frozen with BD®OG-Cryopreservation Buffer, a non-cross-linking buffer. At another location, CITE-seq was conducted on the samples with the BD OMICS-One™ WTA Next Assay as well as the BD®OMICS-One-Protein-Panels, which are lyophilized and pre-titrated oligonucleotide-tagged antibody panels. Non-supervised clustering of transcriptomic and proteomic data revealed 12 major cell clusters, including infiltrating lymphocytes (TILs), resident and infiltrating immune cells, as well as non-immune intrinsic tumor cells. Over 50 protein epitopes were detected and used to identify tumor intrinsic cells with their expression of well published protein signatures of non-small cell lung cancer. Differential gene and protein expression profiles distinguish distinct tissue-resident alveolar-macrophages from infiltrating hematogenous macrophages. Moreover, T cell gene sets of naive, activation/stimulation, and exhaustion states were used to identify the functional heterogeneity of TILs. We successfully detected over fifty epitopes in this sample, enabling the identification of cancer specific cellular states, varied immune functions and potential interactions with other cells, when integrated with transcriptomic data. We demonstrate an end-to-end workflow from sample collection, storage, single-cell capture, sequencing, and analysis to characterize TME heterogeneity and further understand tumor biology. Not for use in diagnostic or therapeutic procedures.©2025 BD. All rights reserved.