1
|
Wang S, Zhou Y, Ding K, Ding ZQ, Zhang W, Liu Y. High-throughput and multimodal profiling of antigen-specific T cells with a droplet-based cell-cell interaction screening platform. Biosens Bioelectron 2025; 267:116815. [PMID: 39348735 DOI: 10.1016/j.bios.2024.116815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 09/04/2024] [Accepted: 09/24/2024] [Indexed: 10/02/2024]
Abstract
Identifying antigen-specific T cells from tumor-infiltrating lymphocytes is essential for designing effective T cell immunotherapies. Traditional methods can detect antigen-specific T cells but struggle with high-throughput screening and multimodal profiling simultaneously. To address this issue, we developed DropCCI, a new strategy that transfers antigen information to co-incubated T cells for high-throughput, non-contaminated multimodal profiling. In DropCCI, droplets encapsulated DNA barcodes and antigen-loaded antigen-presenting cells (APCs), while click chemistry-modified T cells were injected into these droplets to capture free barcodes and acquire the corresponding antigen information. Following cell-cell interaction, APCs were removed via streptavidin-biotin conjugation, to prevent contamination. The resulting T cells underwent single-cell omics sequencing for comprehensive profiling of their antigen specificity, transcriptome, and genomics accurately. This click-chemistry method allowed detection of antigen-specific T cells without lysing APCs, avoiding cross-cell contamination and enabling low-noise multimodal profiling of primary T cells. With a completion time within 12 h and no requirement for complex equipment, DropCCI provides unbiased single-cell sequencing results that offer a comprehensive understanding of anti-tumor T cell responses. The concept of DropCCI holds great promise not only for advancing the field of T cell immunotherapy but also for its potential application in studying other cell-cell interactions.
Collapse
Affiliation(s)
- Shiyu Wang
- Department of Neurology and Cell Biology, School of Life Science, Xuzhou Medical University, Xuzhou, 221002, China.
| | - Yan Zhou
- Department of Neurology and Cell Biology, School of Life Science, Xuzhou Medical University, Xuzhou, 221002, China; Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, China
| | - Ke Ding
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China
| | | | - Wenjie Zhang
- Department of Hepatobiliary Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
| | - Yang Liu
- Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, 518107, China.
| |
Collapse
|
2
|
Antunes DA, Baker BM, Cornberg M, Selin LK. Editorial: Quantification and prediction of T-cell cross-reactivity through experimental and computational methods. Front Immunol 2024; 15:1377259. [PMID: 38444853 PMCID: PMC10912571 DOI: 10.3389/fimmu.2024.1377259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 02/05/2024] [Indexed: 03/07/2024] Open
Affiliation(s)
- Dinler A. Antunes
- Department of Biology and Biochemistry, University of Houston, Houston, TX, United States
| | - Brian M. Baker
- Department of Chemistry and Biochemistry, and Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States
| | - Markus Cornberg
- Department of Gastroenterology, Hepatology, Infectious Diseases and Endocrinology, Hannover Medical School, Hannover, Germany
- Centre for Individualized Infection Medicine (CiiM), c/o CRC Hannover, Hannover, Germany
- German Center for Infection Research (DZIF), Partner-site Hannover-Braunschweig, Hannover, Germany
| | - Liisa K. Selin
- Department of Pathology, University of Massachusetts Medical School, Worcester, MA, United States
| |
Collapse
|
3
|
Qi J, Zhu H, Li Y, Guan X, He Y, Ren G, Guo Q, Liu L, Gu Y, Dong X, Liu Y. Creation of a High-Throughput Microfluidic Platform for Single-Cell Transcriptome Sequencing of Cell-Cell Interactions. SMALL METHODS 2023; 7:e2300730. [PMID: 37712212 DOI: 10.1002/smtd.202300730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 08/21/2023] [Indexed: 09/16/2023]
Abstract
Cell-cell interaction is one of the major modalities for transmitting information between cells and activating the effects of functional cells. However, the construction of high-throughput analysis technologies from cell omics focusing on the impact of interactions of functional cells on targets has been relatively unexplored. Here, they propose a droplet-based microfluidic platform for cell-cell interaction sequencing (c-c-seq) and screening in vitro to address this challenge. A class of interacting cells is pre-labeled using cell molecular tags, and additional single-cell sequencing reagents are introduced to quickly form functional droplet mixes. Lastly, gene expression analysis is used to deduce the impact of the interaction, while molecular sequence tracing identifies the type of interaction. Research into the active effect between antigen-presenting cells and T cells, one of the most common cell-to-cell interactions, is crucial for the advancement of cancer therapy, particularly T cell receptor-engineered T cell therapy. As it allows for high throughput, this platform is superior to well plates as a research platform for cell-to-cell interactions. When combined with the next generation of sequencing, the platform may be able to more accurately evaluate interactions between epitopes and receptors and verify their functional relevance.
Collapse
Affiliation(s)
- Jingyu Qi
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | | | - Yijian Li
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiangyu Guan
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ying He
- Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Guanhua Ren
- China National Institute of Standardization, Beijing, 100191, China
| | - Qiang Guo
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | | | - Ying Gu
- BGI Research, Shenzhen, 518083, China
| | - Xuan Dong
- BGI Research, Shenzhen, 518083, China
- Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, Shenzhen, 518083, China
| | - Ya Liu
- BGI Research, Shenzhen, 518083, China
| |
Collapse
|