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Your search keyword '"Reategui, Eduardo"' showing total 11 results
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11 results on '"Reategui, Eduardo"'

1. Dual targeted extracellular vesicles regulate oncogenic genes in advanced pancreatic cancer

Author

Chiang, Chi-Ling, Ma, Yifan, Hou, Ya-Chin, Pan, Junjie, Chen, Sin-Yu, Chien, Ming-Hsien, Zhang, Zhi-Xuan, Hsu, Wei-Hsiang, Wang, Xinyu, Zhang, Jingjing, Li, Hong, Sun, Lili, Fallen, Shannon, Lee, Inyoul, Chen, Xing-Yu, Chu, Yeh-Shiu, Zhang, Chi, Cheng, Tai-Shan, Jiang, Wen, Kim, Betty Y. S., Reategui, Eduardo, Lee, Robert, Yuan, Yuan, Liu, Hsiao-Chun, Wang, Kai, Hsiao, Michael, Huang, Chi-Ying F., Shan, Yan-Shen, Lee, Andrew S., and James Lee, L.

Published
2023
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4. Phase 2 of extracellular RNA communication consortium charts next-generation approaches for extracellular RNA research

Author

Mateescu, Bogdan, Jones, Jennifer C., Alexander, Roger P., Alsop, Eric, An, Ji Yeong, Asghari, Mohammad, Boomgarden, Alex, Bouchareychas, Laura, Cayota, Alfonso, Chang, Hsueh-Chia, Charest, Al, Chiu, Daniel T., Coffey, Robert J., Das, Saumya, De Hoff, Peter, deMello, Andrew, D’Souza-Schorey, Crislyn, Elashoff, David, Eliato, Kiarash R., Franklin, Jeffrey L., Galas, David J., Gerstein, Mark B., Ghiran, Ionita H., Go, David B., Gould, Stephen, Grogan, Tristan R., Higginbotham, James N., Hladik, Florian, Huang, Tony Jun, Huo, Xiaoye, Hutchins, Elizabeth, Jeppesen, Dennis K., Jovanovic-Talisman, Tijana, Kim, Betty Y.S., Kim, Sung, Kim, Kyoung-Mee, Kim, Yong, Kitchen, Robert R., Knouse, Vaughan, LaPlante, Emily L., Lebrilla, Carlito B., Lee, L. James, Lennon, Kathleen M., Li, Guoping, Li, Feng, Li, Tieyi, Liu, Tao, Liu, Zirui, Maddox, Adam L., McCarthy, Kyle, Meechoovet, Bessie, Maniya, Nalin, Meng, Yingchao, Milosavljevic, Aleksandar, Min, Byoung-Hoon, Morey, Amber, Ng, Martin, Nolan, John, De Oliveira Junior, Getulio P., Paulaitis, Michael E., Phu, Tuan Anh, Raffai, Robert L., Reátegui, Eduardo, Roth, Matthew E., Routenberg, David A., Rozowsky, Joel, Rufo, Joseph, Senapati, Satyajyoti, Shachar, Sigal, Sharma, Himani, Sood, Anil K., Stavrakis, Stavros, Stürchler, Alessandra, Tewari, Muneesh, Tosar, Juan P., Tucker-Schwartz, Alexander K., Turchinovich, Andrey, Valkov, Nedyalka, Van Keuren-Jensen, Kendall, Vickers, Kasey C., Vojtech, Lucia, Vreeland, Wyatt N., Wang, Ceming, Wang, Kai, Wang, ZeYu, Welsh, Joshua A., Witwer, Kenneth W., Wong, David T.W., Xia, Jianping, Xie, Ya-Hong, Yang, Kaichun, Zaborowski, Mikołaj P., Zhang, Chenguang, Zhang, Qin, Zivkovic, Angela M., and Laurent, Louise C.

Published
2022
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5. Exosomal mRNAs for Angiogenic–Osteogenic Coupled Bone Repair

Author

Ma, Yifan, primary, Sun, Lili, additional, Zhang, Jingjing, additional, Chiang, Chi‐ling, additional, Pan, Junjie, additional, Wang, Xinyu, additional, Kwak, Kwang Joo, additional, Li, Hong, additional, Zhao, Renliang, additional, Rima, Xilal Y., additional, Zhang, Chi, additional, Zhang, Anan, additional, Liu, Yutong, additional, He, Zirui, additional, Hansford, Derek, additional, Reategui, Eduardo, additional, Liu, Changsheng, additional, Lee, Andrew S., additional, Yuan, Yuan, additional, and Lee, Ly James, additional

Published
2023
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6. Extracellular Vesicular Analysis of Glypican 1 mRNA and Protein for Pancreatic Cancer Diagnosis and Prognosis.

Author

Li, Hong, Chiang, Chi‐Ling, Kwak, Kwang Joo, Wang, Xinyu, Doddi, Sital, Ramanathan, Lakshmi V., Cho, Sun M., Hou, Ya‐Chin, Cheng, Tai‐Shan, Mo, Xiaokui, Chang, Yueh‐Shih, Chang, Hui‐Lan, Cheng, Weiming, Tsai, Wei‐Ni, Nguyen, Luong T. H., Pan, Junjie, Ma, Yifan, Rima, Xilal Y., Zhang, Jingjing, and Reategui, Eduardo

Subjects
CANCER diagnosis, CANCER prognosis, GENE expression, EXTRACELLULAR vesicles, PANCREATIC duct
Abstract

Detecting pancreatic duct adenocarcinoma (PDAC) in its early stages and predicting late‐stage patient prognosis undergoing chemotherapy is challenging. This work shows that the activation of specific oncogenes leads to elevated expression of mRNAs and their corresponding proteins in extracellular vesicles (EVs) circulating in blood. Utilizing an immune lipoplex nanoparticle (ILN) biochip assay, these findings demonstrate that glypican 1 (GPC1) mRNA expression in the exosomes‐rich (Exo) EV subpopulation and GPC1 membrane protein (mProtein) expression in the microvesicles‐rich (MV) EV subpopulation, particularly the tumor associated microvesicles (tMV), served as a viable biomarker for PDAC. A combined analysis effectively discriminated early‐stage PDAC patients from benign pancreatic diseases and healthy donors in sizable clinical from multiple hospitals. Furthermore, among late‐stage PDAC patients undergoing chemotherapy, lower GPC1 tMV‐mProtein and Exo‐mRNA expression before treatment correlated significantly with prolonged overall survival. These findings underscore the potential of vesicular GPC1 expression for early PDAC screenings and chemotherapy prognosis. [ABSTRACT FROM AUTHOR]

Published
2024
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7. ICAM-1-decorated extracellular vesicles loaded with miR-146a and Glut1 drive immunomodulation and hinder tumor progression in a murine model of breast cancer.

Author

Duarte-Sanmiguel, Silvia, Salazar-Puerta, Ana I., Panic, Ana, Dodd, Daniel, Francis, Carlie, Alzate-Correa, Diego, Ortega-Pineda, Lilibeth, Lemmerman, Luke, Rincon-Benavides, Maria A., Dathathreya, Kavya, Lawrence, William, Ott, Neil, Zhang, Jingjing, Deng, Binbin, Wang, Shipeng, Santander, Sandra P., McComb, David W., Reategui, Eduardo, Palmer, Andre F., and Carson, William E.

Published
2023
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8. ICAM-1-decorated extracellular vesicles loaded with miR-146a and Glut1drive immunomodulation and hinder tumor progression in a murine model of breast cancerElectronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3bm00573a

Author

Duarte-Sanmiguel, Silvia, Salazar-Puerta, Ana I., Panic, Ana, Dodd, Daniel, Francis, Carlie, Alzate-Correa, Diego, Ortega-Pineda, Lilibeth, Lemmerman, Luke, Rincon-Benavides, Maria A., Dathathreya, Kavya, Lawrence, William, Ott, Neil, Zhang, Jingjing, Deng, Binbin, Wang, Shipeng, Santander, Sandra P., McComb, David W., Reategui, Eduardo, Palmer, Andre F., Carson, William E., Higuita-Castro, Natalia, and Gallego-Perez, Daniel

Abstract

Tumor-associated immune cells play a crucial role in cancer progression. Myeloid-derived suppressor cells (MDSCs), for example, are immature innate immune cells that infiltrate the tumor to exert immunosuppressive activity and protect cancer cells from the host's immune system and/or cancer-specific immunotherapies. While tumor-associated immune cells have emerged as a promising therapeutic target, efforts to counter immunosuppression within the tumor niche have been hampered by the lack of approaches that selectively target the immune cell compartment of the tumor, to effectively eliminate “tumor-protecting” immune cells and/or drive an “anti-tumor” phenotype. Here we report on a novel nanotechnology-based approach to target tumor-associated immune cells and promote “anti-tumor” responses in a murine model of breast cancer. Engineered extracellular vesicles (EVs) decorated with ICAM-1 ligands and loaded with miR-146a and Glut1, were biosynthesized (in vitroor in vivo) and administered to tumor-bearing mice once a week for up to 5 weeks. The impact of this treatment modality on the immune cell compartment and tumor progression was evaluated viaRT-qPCR, flow cytometry, and histology. Our results indicate that weekly administration of the engineered EVs (i.e., ICAM-1-decorated and loaded with miR-146a and Glut1) hampered tumor progression compared to ICAM-1-decorated EVs with no cargo. Flow cytometry analyses of the tumors indicated a shift in the phenotype of the immune cell population toward a more pro-inflammatory state, which appeared to have facilitated the infiltration of tumor-targeting T cells, and was associated with a reduction in tumor size and decreased metastatic burden. Altogether, our results indicate that ICAM-1-decorated EVs could be a powerful platform nanotechnology for the deployment of immune cell-targeting therapies to solid tumors.

Published
2023
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9. Deep autoencoder as an interpretable tool for Raman spectroscopy investigation of chemical and extracellular vesicle mixtures.

Author

Kazemzadeh M, Martinez-Calderon M, Otupiri R, Artuyants A, Lowe M, Ning X, Reategui E, Schultz ZD, Xu W, Blenkiron C, Chamley LW, Broderick NGR, and Hisey CL

Abstract

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool that provides valuable insight into the molecular contents of chemical and biological samples. However, interpreting Raman spectra from complex or dynamic datasets remains challenging, particularly for highly heterogeneous biological samples like extracellular vesicles (EVs). To overcome this, we developed a tunable and interpretable deep autoencoder for the analysis of several challenging Raman spectroscopy applications, including synthetic datasets, chemical mixtures, a chemical milling reaction, and mixtures of EVs. We compared the results with classical methods (PCA and UMAP) to demonstrate the superior performance of the proposed technique. Our method can handle small datasets, provide a high degree of generalization such that it can fill unknown gaps within spectral datasets, and even quantify relative ratios of cell line-derived EVs to fetal bovine serum-derived EVs within mixtures. This simple yet robust approach will greatly improve the analysis capabilities for many other Raman spectroscopy applications., Competing Interests: There are no conflicts to declare., (© 2024 Optica Publishing Group.)

Published
2024
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10. Light-induced Extracellular Vesicle Adsorption.

Author

Hisey CL, Rima XY, Doon-Ralls J, Nagaraj CK, Mayone S, Nguyen KT, Wiggins S, Dorayappan KDP, Selvendiran K, Wood D, Hu C, Patel D, Palmer A, Hansford D, and Reategui E

Abstract

The role of extracellular vesicles (EVs) in human health and disease has garnered considerable attention over the past two decades. However, while several types of EVs are known to interact dynamically with the extracellular matrix and there is great potential value in producing high-fidelity EV micropatterns, there are currently no label-free, high-resolution, and tunable platform technologies with this capability. We introduce Light-induced Extracellular Vesicle Adsorption (LEVA) as a powerful solution to rapidly advance the study of matrix- and surface-bound EVs and other particles. The versatility of LEVA is demonstrated using commercial GFP-EV standards, EVs from glioblastoma bioreactors, and E. coli outer membrane vesicles (OMVs), with the resulting patterns used for single EV characterization, single cell migration on migrasome-mimetic trails, and OMV-mediated neutrophil swarming. LEVA will enable rapid advancements in the study of matrix- and surface-bound EVs and other particles, and should encourage researchers from many disciplines to create novel diagnostic, biomimetic, immunoengineering, and therapeutic screening assays.

Published
2024
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11. Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures.

Author

Kazemzadeh M, Martinez-Calderon M, Otupiri R, Artuyants A, Lowe MM, Ning X, Reategui E, Schultz ZD, Xu W, Blenkiron C, Chamley LW, Broderick NGR, and Hisey CL

Abstract

Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.

Published
2023
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