Cellular enlargement-A new hallmark of aging?

Years of important research has revealed that cells heavily invest in regulating their size. Nevertheless, it has remained unclear why accurate size control is so important. Our recent study using hematopoietic stem cells (HSCs) in vivo indicates that cellular enlargement is causally associated with aging. Here, we present an overview of these findings and their implications. Furthermore, we performed a broad literature analysis to evaluate the potential of cellular enlargement as a new aging hallmark and to examine its connection to previously described aging hallmarks. Finally, we highlight interesting work presenting a correlation between cell size and age-related diseases. Taken together, we found mounting evidence linking cellular enlargement to aging and age-related diseases. Therefore, we encourage researchers from seemingly unrelated areas to take a fresh look at their data from the perspective of cell size.Peer reviewe

Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC

Background: High-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed. Results: We present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae, histone Htbl concentrations decrease with replicative age. Conclusions: Cell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis.Peer reviewe

Women in STEM Becoming Independent: Our Shared Motivation and Enthusiasm Are Our Driving Force

This year at JEM, we are highlighting women in science by sharing their stories and amplifying their voices. In this Viewpoint, we hear from a cross section of women, across multiple research fields, discussing their science and the process of setting up a lab as an independent researcher

Cell size is a determinant of stem cell potential during aging

Stem cells are remarkably small. Whether small size is important for stem cell function is unknown. We find that hematopoietic stem cells (HSCs) enlarge under conditions known to decrease stem cell function. This decreased fitness of large HSCs is due to reduced proliferation and was accompanied by altered metabolism. Preventing HSC enlargement or reducing large HSCs in size averts the loss of stem cell potential under conditions causing stem cell exhaustion. Last, we show that murine and human HSCs enlarge during aging. Preventing this age-dependent enlargement improves HSC function. We conclude that small cell size is important for stem cell function in vivo and propose that stem cell enlargement contributes to their functional decline during aging.Peer reviewe

Salmonella Transiently Reside in Luminal Neutrophils in the Inflamed Gut

Enteric pathogens need to grow efficiently in the gut lumen in order to cause disease and ensure transmission. The interior of the gut forms a complex environment comprising the mucosal surface area and the inner gut lumen with epithelial cell debris and food particles. Recruitment of neutrophils to the intestinal lumen is a hallmark of non-typhoidal Salmonella enterica infections in humans. Here, we analyzed the interaction of gut luminal neutrophils with S. enterica serovar Typhimurium (S. Tm) in a mouse colitis model.Upon S. Tm(wt) infection, neutrophils transmigrate across the mucosa into the intestinal lumen. We detected a majority of pathogens associated with luminal neutrophils 20 hours after infection. Neutrophils are viable and actively engulf S. Tm, as demonstrated by live microscopy. Using S. Tm mutant strains defective in tissue invasion we show that pathogens are mostly taken up in the gut lumen at the epithelial barrier by luminal neutrophils. In these luminal neutrophils, S. Tm induces expression of genes typically required for its intracellular lifestyle such as siderophore production iroBCDE and the Salmonella pathogenicity island 2 encoded type three secretion system (TTSS-2). This shows that S. Tm at least transiently survives and responds to engulfment by gut luminal neutrophils. Gentamicin protection experiments suggest that the life-span of luminal neutrophils is limited and that S. Tm is subsequently released into the gut lumen. This "fast cycling" through the intracellular compartment of gut luminal neutrophils would explain the high fraction of TTSS-2 and iroBCDE expressing intra- and extracellular bacteria in the lumen of the infected gut. In conclusion, live neutrophils recruited during acute S. Tm colitis engulf pathogens in the gut lumen and may thus actively engage in shaping the environment of pathogens and commensals in the inflamed gut

Asymmetric Segregation of Aged Spindle Pole BodiesDuring Cell Division: Mechanisms and RelevanceBeyond Budding Yeast?

Asymmetric cell division generates cell diversity and contributes to cellular aging and rejuvenation. Here, we review the molecular mechanisms enabling budding yeast to recognize spindle pole bodies (SPB, centrosome equivalent) based on their age, and guide their non‐random mitotic segregation: SPB inheritance requires the distinction of old from new SPBs and is regulated by the SPB‐inheritance network (SPIN) and the mitotic exit network (MEN). The SPIN marks the pre‐existing SPB as old and the MEN recognizes these marks translating them into spindle orientation. We next revisit other molecules and structures that partition depending on their age rather than their abundance at mitosis as, for example, DNA, centrosomes, mitochondria, and histones in yeast and other systems. The recurrence of this differential behavior suggests a functional significance for numerous cell types, which we then discuss. We conclude that non‐random segregation may facilitate asymmetric cell fate determination and thereby indirectly aging and rejuvenation.ISSN:0265-9247ISSN:1521-187

Editorial : Cell size regulation: molecular mechanisms and physiological importance

Non peer reviewe

Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC

Background: High-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed. Results: We present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae, histone Htbl concentrations decrease with replicative age. Conclusions: Cell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis.Peer reviewe