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Jiang N, Saftics A, Romano E, Ghaeli I, Resto C, Robles V, Das S, Van Keuren-Jensen K, Seewaldt VL, Jovanovic-Talisman T. Multiparametric profiling of HER2-enriched extracellular vesicles in breast cancer using Single Extracellular VEsicle Nanoscopy. J Nanobiotechnology 2024; 22:589. [PMID: 39342336 PMCID: PMC11438238 DOI: 10.1186/s12951-024-02858-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 09/14/2024] [Indexed: 10/01/2024] Open
Abstract
BACKGROUND Patients with HER2-positive breast cancer can significantly benefit from HER2-directed therapy - such as the monoclonal antibody trastuzumab. However, some patients can develop therapy resistance or change HER2 status. Thus, we urgently need new, noninvasive strategies to monitor patients frequently. Extracellular vesicles (EVs) secreted from tumor cells are emerging as potential biomarker candidates. These membrane-delimited nanoparticles harbor molecular signatures of their origin cells; report rapidly on changes to cellular status; and can be frequently sampled from accessible biofluids. RESULTS Using Single Extracellular VEsicle Nanoscopy (SEVEN) platform that combines affinity isolation of EVs with super-resolution microscopy, here we provide multiparametric characterization of EVs with ~ 8 nm precision and molecular sensitivity. We first interrogated cell culture EVs affinity-enriched in tetraspanins CD9, CD63, and CD81; these transmembrane proteins are commonly found on EV membranes. SEVEN robustly provided critical parameters of individual, tetraspanin-enriched EVs: concentration, size, shape, molecular cargo content, and heterogeneity. Trastuzumab-resistant cells (vs. trastuzumab-sensitive) secreted more EVs. Additionally, EVs from trastuzumab-resistant cells had lower tetraspanin density and higher HER2 density. We also evaluated EVs affinity-enriched in HER2; we found that these EVs (vs. tetraspanin-enriched) were larger and more elongated. We further optimized analytical sample processing to assess a rare population of HER2-enriched EVs from patient plasma. In breast cancer patients with elevated HER2 protein expression (vs. controls), HER2-enriched EVs had distinct characteristics including typically increased number of tetraspanin molecules and larger size. Importantly, these EVs were on average 25-fold more abundant compared to no cancer controls. CONCLUSIONS SEVEN revealed unique characteristics of HER2-enriched EVs in cultured cells and complex biological fluid. In combination with current clinical approaches, this method is well poised to support precise therapeutic decisions.
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Affiliation(s)
- Nan Jiang
- Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Andras Saftics
- Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Eugenia Romano
- Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Ima Ghaeli
- Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Cristal Resto
- Deprtment of Population Sciences, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Vanessa Robles
- Deprtment of Population Sciences, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Saumya Das
- Cardiology Division and Corrigan Minehan Heart Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Victoria L Seewaldt
- Deprtment of Population Sciences, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Tijana Jovanovic-Talisman
- Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA.
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Long X, Wei J, Fang Q, Yuan X, Du J. Single-cell RNA sequencing reveals the transcriptional heterogeneity of Tbx18-positive cardiac cells during heart development. Funct Integr Genomics 2024; 24:18. [PMID: 38265516 DOI: 10.1007/s10142-024-01290-6] [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: 10/17/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 01/25/2024]
Abstract
The T-box family transcription factor 18 (Tbx18) has been found to play a critical role in regulating the development of the mammalian heart during the primary stages of embryonic development while the cellular heterogeneity and landscape of Tbx18-positive (Tbx18+) cardiac cells remain incompletely characterized. Here, we analyzed prior published single-cell RNA sequencing (scRNA-seq) mouse heart data to explore the heterogeneity of Tbx18+ cardiac cell subpopulations and provide a comprehensive transcriptional landscape of Tbx18+ cardiac cells during their development. Bioinformatic analysis methods were utilized to identify the heterogeneity between cell groups. Based on the gene expression characteristics, Tbx18+ cardiac cells can be classified into a minimum of two distinct cell populations, namely fibroblast-like cells and cardiomyocytes. In terms of temporal heterogeneity, these cells exhibit three developmental stages, namely the MEM stage, ML_P0 stage, and P stage Tbx18+ cardiac cells. Furthermore, Tbx18+ cardiac cells encompass several cell types, including cardiac progenitor-like cells, cardiomyocytes, and epicardial/stromal cells, as determined by specific transcriptional regulatory networks. The scRNA-seq results revealed the involvement of extracellular matrix (ECM) signals and epicardial epithelial-to-mesenchymal transition (EMT) in the development of Tbx18+ cardiac cells. The utilization of a lineage-tracing model served to validate the crucial function of Tbx18 in the differentiation of cardiac cells. Consequently, these findings offer a comprehensive depiction of the cellular heterogeneity within Tbx18+ cardiac cells.
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Affiliation(s)
- Xianglin Long
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Jiangjun Wei
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Qinghua Fang
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Xin Yuan
- Department of Nephrology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
| | - Jianlin Du
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
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Paas-Oliveros E, Hernández-Lemus E, de Anda-Jáuregui G. Computational single cell oncology: state of the art. Front Genet 2023; 14:1256991. [PMID: 38028624 PMCID: PMC10663273 DOI: 10.3389/fgene.2023.1256991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.
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Affiliation(s)
- Ernesto Paas-Oliveros
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Investigadores por Mexico, Conahcyt, Mexico City, Mexico
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