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U6 small nuclear RNA (U6 snRNA), a critical spliceosome component primarily found in the nucleus, plays a vital role in RNA splicing. Our previous study, using the simian immunodeficiency virus (SIV) macaque model, revealed an increase of U6 snRNA in plasma extracellular vesicles (EVs) in acute retroviral infection. Given the limited understanding of U6 snRNA dynamics across cells and EVs, particularly in SIV infection, this research explores U6 snRNA trafficking and its association with splicing proteins in the nucleus, cytoplasm, and EVs. We observed a redistribution of U6 snRNA from the nucleus to EVs post-infection, accompanied by distinct protein profile changes and alterations in nucleic acid metabolism and spliceosome pathways. In addition, U6 machinery proteins changed in cells and EVs in a contrasting manner. The redistribution of U6 and related proteins we observed could be part of a viral strategy to redirect host splicing machinery, suggesting that U6 may have regulatory roles and be part of retroviral infection signature. 

Cells interact as dynamically evolving ecosystems. While recent single-cell and spatial multi-omics technologies quantify individual cell characteristics, predicting their evolution requires mathematical modeling. We propose a conceptual framework—a cell behavior hypothesis grammar—that uses natural language statements (cell rules) to create mathematical models. This enables systematic integration of biological knowledge and multi-omics data to generate in silico models, enabling virtual “thought experiments” that test and expand our understanding of multicellular systems and generate new testable hypotheses. This paper motivates and describes the grammar, offers a reference implementation, and demonstrates its use in developing both de novo mechanistic models and those informed by multi-omics data. We show its potential through examples in cancer and its broader applicability in simulating brain development. This approach bridges biological, clinical, and systems biology research for mathematical modeling at scale, allowing the community to predict emergent multicellular behavior. 

The crystal structure of the light-gated anion channel Gt ACR1 reported in our previous Research Article (Li et al., 2019) revealed a continuous tunnel traversing the protein from extracellular to intracellular pores. We proposed the tunnel as the conductance channel closed by three constrictions: C1 in the extracellular half, mid-membrane C2 containing the photoactive site, and C3 on the cytoplasmic side. Reported here, the crystal structure of bromide-bound Gt ACR1 reveals structural changes that relax the C1 and C3 constrictions, including a novel salt-bridge switch mechanism involving C1 and the photoactive site. These findings indicate that substrate binding induces a transition from an inactivated state to a pre-activated state in the dark that facilitates channel opening by reducing free energy in the tunnel constrictions. The results provide direct evidence that the tunnel is the closed form of the channel of Gt ACR1 and shed light on the light-gated channel activation mechanism. 

Abstract Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year‐on‐year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non‐vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its ‘Minimal Information for Studies of Extracellular Vesicles’, which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly. 

The collective intelligence of online communities often depends on implicit forms of coordination, given the fluidity of membership and the lack of traditional hierarchies and associated incentive structures. This coordination drives knowledge production. Studying temporal dynamics may help elucidate how coordination happens. Specifically, the rate of interaction with an artifact such as a Wikipedia page can function as a signal that affects future interactions. Many activities can be characterized as bursty, meaning activity is not evenly spread or random, but is instead concentrated. This study analyzes 3,260 Wikipedia articles and shows that the coordination pattern in the Wikipedia community is mostly bursty. More importantly, the extent of burstiness affects article quality. This work highlights the important role temporal dynamics can play in the coordination process in online communities, and how it can affect the quality of knowledge production.