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New Progress in Extracellular Vesicles

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Dr. Jorge Sanz-Ros

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1. Freshage Research Group, Department of Physiology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain
2. Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable-Instituto de Salud Carlos III (CIBERFES-ISCIII), INCLIVA, 28029 Madrid, Spain
3. Department of Cardiology, Hospital Universitari i Politècnic La Fe, 46026 Valencia, Spain
Interests: aging; intercellular communication; extracellular vesicles; stem cells; regenerative medicine

Special Issue Information

Dear Colleagues,

The aim of the Special Issue "New Progress in Extracellular Vesicles" is to provide an up-to-date overview of the latest advances in the field of extracellular vesicles (EVs). EVs are small, membrane-bound structures that are released by cells and carry a wide range of biological molecules, including proteins, lipids and nucleic acids. In recent years, there has been a growing interest in understanding the roles of EVs in several biological processes, such as intercellular communication, immunity, or specific diseases such as cancer.

This Special Issue will cover a broad range of topics related to EV research, including the isolation and characterization of EVs, the mechanisms of EV biogenesis and uptake, and the functional roles of EVs in disease pathogenesis and therapeutic applications, with a focus on regenerative medicine. Articles that highlight new techniques and technologies for studying EVs, such as single-particle analysis and high-throughput sequencing, are also welcome.

Overall, the Special Issue provides a comprehensive and timely overview of the rapidly evolving field of EV research and will be of interest to researchers from different fields, including cell biology, immunology, oncology and drug discovery.

Dr. Jorge Sanz-Ros
Guest Editor

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Published Papers (2 papers)

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23 pages, 5513 KiB

Open AccessArticle

Exosomal Preconditioning of Human iPSC-Derived Cardiomyocytes Beneficially Alters Cardiac Electrophysiology and Micro RNA Expression

by Øystein Røsand, Jianxiang Wang, Nathan Scrimgeour, Gurdeep Marwarha and Morten Andre Høydal

Int. J. Mol. Sci. 2024, 25(15), 8460; https://doi.org/10.3390/ijms25158460 - 2 Aug 2024

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Abstract

Experimental evidence, both in vitro and in vivo, has indicated cardioprotective effects of extracellular vesicles (EVs) derived from various cell types, including induced pluripotent stem cell-derived cardiomyocytes. The biological effects of EV secretion, particularly in the context of ischemia and cardiac electrophysiology, remain to be fully explored. Therefore, the goal of this study was to unveil the effects of exosome (EXO)-mediated cell–cell signaling during hypoxia by employing a simulated preconditioning approach on human-induced pluripotent stem cell-derived cardiomyocytes (hIPSC-CMs). Electrophysiological activity of hIPSC-CMs was measured using a multielectrode array (MEA) system. A total of 16 h of hypoxic stress drastically increased the beat period. Moreover, hIPSC-CMs preconditioned with EXOs displayed significantly longer beat periods compared with non-treated cells after 16 h of hypoxia (+15.7%, p < 0.05). Furthermore, preconditioning with hypoxic EXOs resulted in faster excitation–contraction (EC) coupling compared with non-treated hIPSC-CMs after 16 h of hypoxia (−25.3%, p < 0.05). Additionally, microRNA (miR) sequencing and gene target prediction analysis of the non-treated and pre-conditioned hIPSC-CMs identified 10 differentially regulated miRs and 44 gene targets. These results shed light on the intricate involvement of miRs, emphasizing gene targets associated with cell survival, contraction, apoptosis, reactive oxygen species (ROS) regulation, and ion channel modulation. Overall, this study demonstrates that EXOs secreted by hIPSC-CM during hypoxia beneficially alter electrophysiological properties in recipient cells exposed to hypoxic stress, which could play a crucial role in the development of targeted interventions to improve outcomes in ischemic heart conditions. Full article

(This article belongs to the Special Issue New Progress in Extracellular Vesicles)

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Figure 1

Figure 1
Exosomes (EXOs) were isolated with a Total Exosome Isolation kit and analyzed using a Nanosight NS300. Nanoparticle tracing analysis (NTA) analysis showed particle size and concentration distribution for EXOs secreted during hypoxia. Particle sizes were captured five times for 60 s for every sample. The mean diameter of EXOs secreted under hypoxic conditions was measured to be 167.4 nm (±25.6 nm). The mean concentration of secreted EXOs was measured to be 9.2 × 107 EXO/mL (±2.0 × 107 EXO/mL). Data were analyzed using the NanoSight Software NTA 3.2 Dev Build 3.2.16. Black line shows particle concentration while red error bars indicate ± SEM. Full article ">Figure 2
hIPSC-CM electrophysiological activity was recorded in vitro on an MEA system. (A) Experimental setup of the MEA recordings, EXO preconditioning, and hypoxic stimulation. A representative image of the recorded MEA activity map shows hIPSC-CM beat rate at baseline (B) and after 16 h of hypoxia (C). Saturation was set to 80 bpm. (D) hIPSC-CMs displayed a significant increase in beat period after 16 h of hypoxia. Furthermore, cells preconditioned with EXOs exhibited increased beat period compared with non-treated cells. (E) The 16 h of hypoxia significantly increased excitation–contraction (EC)-coupling for the control group. Interestingly, preconditioning with hypoxic EXOs resulted in faster EC-coupling compared with non-treated CMs after 16 h of hypoxia. (F) Field potential duration (FPD)for both preconditioned and non-treated hIPSC was significantly increased after 16 h of hypoxia. There were no significant changes in FPD between the two experimental groups. (G) EXO preconditioning did not affect the beat amplitude of contraction after 16 h of hypoxia. The were no significant changes in (H) FPD spike slope or (I) FPD spike amplitude between the control and preconditioned groups after hypoxia. (J) Visual representation of MEA recordings of hIPSC-CM beat period for the control group (blue) and EXO-treated group (yellow) at baseline and after 16 h of hypoxia for the control group (green) and the EXO-treated group (red). (K) Representative visualizations of cardiomyocyte contractions from the electrophysiological MEA recordings show hIPSC-CM contractions at baseline and after 16 h of hypoxia for both preconditioned and non-preconditioned hIPSC-CMs. Data were analyzed using one-way ANOVA multiple comparison analysis or t-test. Data from the MEA analysis are expressed as mean of raw values or %-change from baseline ± SEM (n = 20). Raw values were calculated from the mean recordings from 3 of the 8 electrodes at the time of baseline and post-hypoxic stimulus. * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001; ns: not significant (p > 0.05). Full article ">Figure 3
MEA recordings show electrophysiological changes during 16 h of hypoxia for control (blue) and EXO-preconditioned hIPSC-CMs (red). (A) The beat period was increased for both groups of hIPSC-CMs shortly after initiation and throughout the 16 h of hypoxic stress. (B) FPD increased for both control and preconditioned hIPSC-CMs during hypoxia. FPD was significantly increased in cells preconditioned with EXOs at 8 h of hypoxia compared to non-treated cells. Continuous MEA recordings showed no significant differences in the spike slope (C) or spike amplitude (D) of FPD between the control and preconditioned groups throughout the 16 h of hypoxia. Data were analyzed using mixed-effects analysis and Šídák’s multiple comparisons test. Data from the MEA analysis are expressed as mean of raw values or %-change from baseline ± SEM (n = 20). Raw values were calculated from the mean recordings from 3 of the 8 electrodes once every hour during the 16 h of hypoxic stimulus. Full article ">Figure 4
Time to membrane re-polarization extracted from local extracellular action potential (LEAP) data before and after 16 h of hypoxia. (A) APD30 is significantly increased after 16 h of hypoxia for both cell groups. However, EXO preconditioning did not significantly alter APD30. (B) The 16 h of hypoxia did significantly increase APD50 for both groups. Preconditioned hIPSC-CMs exhibited no significant change in APD50 compared with non-treated hIPSC-CMs. (C) The 16 h of hypoxia significantly increased APD90 for non-treated hIPSC-CMs but not for EXO-preconditioned cells. Furthermore, EXO preconditioning did not significantly alter ADP90. Representative traces from the MEA recordings show hIPSC-CM LEAP at baseline (D) and after 16 h of hypoxia (E) for both preconditioned and non-preconditioned hIPSC-CMs. Data were analyzed using one-way ANOVA multiple-comparisons analysis. Data from the MEA analysis are expressed as mean of raw values ± SEM (n = 6–8). Raw values were calculated from the mean recordings from 3 of the 8 electrodes at the time of baseline and post-hypoxic stimulus. * p ≤ 0.05; ** p ≤ 0.01; ns: not significant (p > 0.05). Full article ">Figure 5
The Gene Ontology (GO) enrichment dot plot depicts the top 30 enriched GO terms for all significantly expressed miRNAs identified in the results. The Y-axis represents enriched terms categorized as biological process (colored in orange), cellular component (colored in blue), and molecular function (colored in green). Terms are ranked based on p-adjusted value. The X-axis denotes the Rich factor for each term. The Rich factor is defined as the ratio of input genes annotated in a term to all genes annotated in the same term. A higher Rich factor indicates a greater degree of enrichment. The size of the dot indicates the number of genes enriched in each term. The color represents the statistical significance of the enrichment, with a brighter shade of color denoting greater significance. Full article ">Figure 6
The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation plot depicts the terms that are significantly enriched in various cell pathways, as identified in the KEGG database. The enriched terms are displayed along the Y-axis, with the BRITE hierarchy parent term in bold black and subordinate terms in various colors. A bar following each term indicates the number of genes enriched in each KEGG term, with the X-axis indicating the number of genes enriched in each term. Full article ">Figure 7
The Sankey diagram illustrates the differential expression of various miRs, regulating their predicted gene targets and the subsequent possible impact on cells, as interpretated in the experiment setting. The diagram comprises three columns with flow paths connecting various strata of the columns. The first column contains miR names, with tile colors indicating expression levels: red for upregulation and blue for downregulation. The flow ribbon color between the miR and Gene Target columns, as well as the tiles in the Impact column, indicate the inverse effect of miR expression: blue for inhibition (blue flow and tiles) and red for promotion (red flow and tiles) of target genes. Grey tiles in the Gene Target column indicate the unknown effects of inhibition or promotion, as they are predicted to be impacted by miRs with mixed effects. The flow ribbon between the Gene Target and Impact columns, as well as the tiles in the Impact column, are colored based on the effects of gene regulation on the cell. Detrimental effects are indicated in purple, while beneficial effects are shown in green. Full article ">

Abstract

Seminal plasma contains a heterogeneous population of extracellular vesicles (sEVs) that remains poorly characterized. This study aimed to characterize the lipidomic profile of two subsets of differently sized sEVs, small (S-) and large (L-), isolated from porcine seminal plasma by size-exclusion chromatography and characterized by an orthogonal approach. High-performance liquid chromatography–high-resolution mass spectrometry was used for lipidomic analysis. A total of 157 lipid species from 14 lipid classes of 4 major categories (sphingolipids, glycerophospholipids, glycerolipids, and sterols) were identified. Qualitative differences were limited to two cholesteryl ester species present only in S-sEVs. L-sEVs had higher levels of all quantified lipid classes due to their larger membrane surface area. The distribution pattern was different, especially for sphingomyelins (more in S-sEVs) and ceramides (more in L-sEVs). In conclusion, this study reveals differences in the lipidomic profile of two subsets of porcine sEVs, suggesting that they differ in biogenesis and functionality. Full article

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