Editorial: Decoding cell fate: the critical roles of extracellular vesicles
Bibliographic record
Abstract
Once considered passive byproducts of cellular metabolism, extracellular vesicles (EVs) have emerged as finely tuned intercellular communication messengers that orchestrate biological processes, such as immune responses and disease progression (Kumar et al., 2024;Yanez-Mo et al., 2015;Yim et al., 2020). These nano-and micro-sized vesicles range from 30-5,000 nanometers and mainly include exosomes, microvesicles, and apoptotic bodies; they are secreted by prokaryotic and eukaryotic cells, transporting a wide range of biologically active molecules, such as proteins, lipids, and nucleic acids (Mobarak et al., 2024).While traditional studies have focused primarily on intracellular pathways and direct environmental stressors as key regulators of cell fate, this Research Topic shifts the lens toward the emerging influence of EVs in dictating whether a cell survives or dies and how disease can be treated. Recent advances suggest that EVs are not merely passive information conveyors but active participants in the dynamic balance between cellular resilience and vulnerability (Xia et al., 2022;Sulek, 2024). They mediate protective functions under stress, modulate autophagy, apoptosis, and immune responses, and have been implicated in a range of pathophysiological conditions, such as neurodegeneration, cancer, fibrosis, and autoimmune disease (Wilczak et al., 2024;Zhang et al., 2025).This Topic has collected one Brief Research Report article and three review articles. Gade et al. (Gade et al., 2025) reported paradigm-shifting original findings related to secretory mitophagy. Under oxidative stress, when damaged mitochondria exceed the degradation capacity of the lysosome, cancer cells adopt a surprising survival mechanism. Rather than undergoing degradation via the lysosome, dysfunctional mitochondria are expelled out of the cell through EVs enriched with the mitophagy regulator PINK1 (PTEN (phosphatase and tensin homolog deleted on chromosome 10)-induced kinase 1). The EV-mediated removal of "toxic" defective mitochondria can help prevent cancer cells from dying. The study identified a novel, EV-mediated resilience pathway in tumor cells that could be exploited to sensitize cancer cells to oxidative therapies.Longfei et al. (Longfei et al., 2025) EVs have become hot research topics in disease diagnosis and treatment. Articles collected in this Research Topic highlight the multifaceted roles of EVs in cellular decision-making. From cancer cell survival, antioxidant defense, and immune modulation to OA and SSc treatment, EVs are more than vesicular bystanders-they are functional effectors capable of tipping the scales between degeneration and regeneration, as well as disease progression and therapeutic success.EVs from parental cells can promote cell survival or death in recipient cells (Sanwlani and Gangoda, 2021) (Figure 1). This Research Topic reveals a new mechanism that parental cells can use their EVs to transport "toxic" intracellular components (e.g., damaged mitochondria) outside to prolong survival. More studies are needed to evaluate whether different "toxic" elements can be expelled out of the cell to support survival.EVs can also play a dual role (promoting or worsening) in disease treatment, depending upon the context (Figure 1). They could have opposite effects on treating the same disease (e.g., OA) if derived from different sources of cells in the same organism. Regarding the same disease (e.g., OA), EVs from healthy cells (e.g., healthy chondrocytes) could help its treatment, whereas those from "diseased" cells (e.g., degenerative chondrocytes) do not. This observation might be extrapolated to other diseases, although more studies are needed to verify the hypothesis. When EVs are derived from the same type of cells (e.g., synovial fibroblasts), their components (e.g., the types of miRNAs) could contribute to different outcomes (e.g., promoting cartilage repair or damage). The possibility of using TCM-derived EVs for OA treatment suggests that EVs from different kingdoms or species of organisms can be explored to treat human diseases.As the field progresses, understanding the precise bioactive cargo, delivery mechanisms, and contextspecific effects of EVs will be essential. The promise they hold across disciplines, from oncology to rheumatology to regenerative medicine, invites a rethinking of how we approach complex diseases.The Topic provides an overview of the current breakthroughs and lays a compelling foundation for future research that positions EVs at the center of next-generation diagnostics and therapies.FIGURE 1 Graphical model of the effects of extracellular vesicles on cell survival or death and disease treatment. The effects are context-dependent on the types of (parental and recipient) cells, the types of extracellular vesicles (EVs), the composition of EVs, and the disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".