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#cellbiology

2 posts2 participants0 posts today
Public
Victoria Stuart 🇨🇦 🏳️‍⚧️

Cells are swapping their mitochondria: Health implications?
nature.com/articles/d41586-025
news.ycombinator.com/item?id=4

Researchers are studying why the energy factories are moving between cells and whether the process can be harnessed to treat cancer and other diseases.

Power/potential of mitochondria transfer
nature.com/articles/s41586-023

Tunneling nanotube
en.wikipedia.org/wiki/Tunnelin
Role: intercellular transfer
en.wikipedia.org/wiki/Tunnelin

www.nature.comCells are swapping their mitochondria. What does this mean for our health?Researchers are studying why the energy factories are moving between cells and whether the process can be harnessed to treat cancer and other diseases.
#mitochondria#MolecularBiology#CellBiology
Public
Nicola Romanò

Our new preprint is now out!

Dynamic transcriptional heterogeneity in pituitary corticotrophs

biorxiv.org/content/10.1101/20

We analysed publicly available single-cell RNA sequencing data of pituitary gland tissue and looked at corticotrophs, cells that are central to mediate stress responses.

We identified several transcriptional states in these cells that are related to how they respond to stress. Cells are able to transition between these states and this might be helpful for them to respond to stress coming at unpredictable times.

We also highlight issues related to using scRNAseq to look at functional subpopulations of cells.

bioRxiv · Dynamic transcriptional heterogeneity in pituitary corticotrophsA large body of evidence has shown that corticotrophs, the anterior pituitary cells central to the generation of hormonal stress responses, exhibit heterogeneous functional behavior, suggesting the presence of functional sub-populations of corticotrophs. We investigated whether this was the case at the transcriptomic level by conducting a comprehensive analysis of scRNA-seq datasets from rodent pituitary cells. We envisaged two alternative scenarios, one where robust subtypes of corticotrophs exist, and the other where these subpopulations were only transient states, possibly transitioning into one another. Our findings suggest that corticotrophs transition between multiple transcriptional states rather than existing as rigidly defined subpopulations. We employed marker gene-based comparisons and whole transcriptome label transfer approaches to analyze transcriptional signatures across datasets. Marker-based clustering revealed strikingly low similarity in the identified subpopulations across datasets. This analysis evidenced the presence of transcriptional states with different functional relevance, related to different stages of hormonal signalling. Similarly, the label transfer approach, which considers non-linear interactions across the entire transcriptome showed that transcriptional states could be detected across independent datasets. This classification relied on broader gene expression patterns rather than conventional marker genes, reinforcing the notion of continuous rather than discrete cell states. Furthermore, trajectory analysis by RNA velocity indicated dynamic transitions between transcriptional states, suggesting the presence of transcriptional mechanisms facilitating rapid recruitment of corticotrophs in response to physiological demands. Our findings align with evidence from other endocrine cell types, such as lactotrophs and pancreatic β-cells, where hormone secretion is linked to fluctuating transcriptional activity. The observed transitions in corticotroph states suggest a mechanism allowing flexible hormonal responses to unpredictable and time-varying stressful events. Additionally, this study highlights the challenges associated with scRNA-seq methodologies, including data sparsity, batch effects, and pseudoreplication, underscoring the need for rigorous experimental design and reproducibility in single-cell transcriptomics research. These insights contribute to a broader understanding of pituitary cell plasticity and endocrine adaptation mechanisms. ### Competing Interest Statement The authors have declared no competing interest.
#scrnaseq#stress#physiology
Public
Stephen Royle

Very interesting preprint from Jurgen Klingauf and colleagues demonstrating that synaptic vesicle retrieval is clathrin-mediated.

The debate over the mechanism has been going since the 1970s. In recent years, ultra-fast endocytosis has become accepted. In this paper it's argued that if you look at retrieval of cargo, rather than membrane, then it is mainly driven by clathrin.

biorxiv.org/content/10.1101/20

bioRxiv · A slow and clathrin-dependent mode of compensatory endocytosis in hippocampal boutons at near-physiological temperatureThe mode of presynaptic compensatory endocytosis has been controversial for decades. Recently, an ultrastructural study on synaptic membrane retrieval following synaptic vesicle fusion revealed an ultrafast mechanism of endocytosis in mouse hippocampal synapses at physiological temperature. Using live cell imaging of single-vesicle exo- and endocytosis in mouse hippocampal synapses, combined with accelerated optogenetic acidification of synaptic vesicles, we found that compensatory endocytosis occurs mostly through a slow clathrin-dependent and actin-independent mode at both room and physiological temperatures. ### Competing Interest Statement The authors have declared no competing interest.
#neuroscience#CellBiology
Public
CSBJ

🧬 Can AI decode the hidden language of cells?

This review article explores how machine learning (ML) is revolutionizing the analysis of complex cellular systems, helping researchers dissect how cells respond to genetic, chemical, and environmental perturbations.

🔗 Machine learning to dissect perturbations in complex cellular systems. Computational and Structural Biotechnology Journal, DOI: doi.org/10.1016/j.csbj.2025.02

📚 CSBJ: csbj.org

Machine learning to dissect perturbations in complex cellular systems. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.02.028
#AI#MachineLearning#CellBiology
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