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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 Htb1 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. Source code: https://github.com/SchmollerLab/Cell_ACDC

Spheroids have become principal three-dimensional models to study cancer, developmental processes, and drug efficacy. Single-cell analysis techniques have emerged as ideal tools to gauge the complexity of cellular responses in these models. However, the single-cell quantitative assessment based on 3D-microscopic data of the subcellular distribution of fluorescence markers, such as the nuclear/cytoplasm ratio of transcription factors, has largely remained elusive. For spheroid generation, ultra-low attachment plates are noteworthy due to their simplicity, compatibility with automation, and experimental and commercial accessibility. However, it is unknown whether and to what degree the plate type impacts spheroid formation and biology. This study developed a novel AI-based pipeline for the analysis of 3D-confocal data of optically cleared large spheroids at the wholemount, single-cell, and sub-cellular levels. To identify relevant samples for the pipeline, automated brightfield microscopy was employed to systematically compare the size and eccentricity of spheroids formed in six different plate types using four distinct human cell lines. This showed that all plate types exhibited similar spheroid-forming capabilities and the gross patterns of growth or shrinkage during 4 days after seeding were comparable. Yet, size and eccentricity varied systematically among specific cell lines and plate types. Based on this prescreen, spheroids of HaCaT keratinocytes and HT-29 cancer cells were further assessed. In HaCaT spheroids, the in-depth analysis revealed a correlation between spheroid size, cell proliferation, and the nuclear/cytoplasm ratio of the transcriptional coactivator, YAP1, as well as an inverse correlation with respect to cell differentiation. These findings, yielded with a spheroid model and at a single-cell level, corroborate earlier concepts of the role of YAP1 in cell proliferation and differentiation of keratinocytes in human skin. Further, the results show that the plate type may influence the outcome of experimental campaigns and that it is advisable to scan different plate types for the optimal configuration during a specific investigation.

The paper aims to clarify the meaning of the word ἰχνηλάτης, «inquirer», which Plutarch of Chaironeia employs in his Dialogue on Love (Amat. 762 A). Although the corresponding verb ἰχνηλατέω occurs several times in Philo, the use of the substantive ἰχνηλάτης in a theological context seems to be genuinely Plutarchean, and it will return in the following centuries in the works of some Christian theologians. The article examines all the philosophical and religious implications of the use of the word in Plutarch, by finally considering the role of the ἰχνηλάτης within his theological conceptions.

To maintain stable DNA concentrations, proliferating cells need to coordinate DNA replication with cell growth. For nuclear DNA, eukaryotic cells achieve this by coupling DNA replication to cell-cycle progression, ensuring that DNA is doubled exactly once per cell cycle. By contrast, mitochondrial DNA replication is typically not strictly coupled to the cell cycle, leaving the open question of how cells maintain the correct amount of mitochondrial DNA during cell growth. Here, we show that in budding yeast, mitochondrial DNA copy number increases with cell volume, both in asynchronously cycling populations and during G1 arrest. Our findings suggest that cell-volume-dependent mitochondrial DNA maintenance is achieved through nuclear-encoded limiting factors, including the mitochondrial DNA polymerase Mip1 and the packaging factor Abf2, whose amount increases in proportion to cell volume. By directly linking mitochondrial DNA maintenance to nuclear protein synthesis and thus cell growth, constant mitochondrial DNA concentrations can be robustly maintained without a need for cell-cycle-dependent regulation. Mitochondrial DNA is critical for cell function, but how growing cells maintain stable concentrations is unclear. Seel et al. find that cells couple mitochondrial DNA copy number directly to cell volume through nuclear-encoded limiting factors, whose amount increases with cell volume.

The mitochondrial genome (mtDNA) encodes essential subunits of the electron transport chain and ATP synthase. Mutations in these genes impair oxidative phosphorylation, compromise mitochondrial ATP production and cellular energy supply, and can cause mitochondrial diseases. These consequences highlight the importance of mtDNA quality control (mtDNA-QC), the process by which cells selectively maintain intact mtDNA to preserve respiratory function. Here, we developed a high-throughput flow cytometry assay for Saccharomyces cerevisiae to track mtDNA segregation in cell populations derived from heteroplasmic zygotes, in which wild-type (WT) mtDNA is fluorescently labeled and mutant mtDNA remains unlabeled. Using this approach, we observe purifying selection against mtDNA lacking subunits of complex III ( COB ), complex IV ( COX2 ) or the ATP synthase ( ATP6 ), under fermentative conditions that do not require respiratory activity. By integrating cytometric data with growth assays, qPCR-based mtDNA copy-number measurements, and simulations, we find that the decline of mtDNA Δ atp6 in populations derived from heteroplasmic zygotes is largely explained by the combination of its reduced mtDNA copy number—biasing zygotes toward higher contributions of intact mtDNA—and the proliferative disadvantage of cells carrying this variant. In contrast, the loss of mtDNA Δ cob and mtDNA Δ cox2 cannot be explained by growth defects and copy-number asymmetries alone, indicating an additional intracellular selection against these mutant genomes when intact mtDNA is present. In heteroplasmic cells containing both intact and mutant mtDNA, fluorescent reporters revealed local reductions in ATP levels and membrane potential ( Δ Ψ ) near mutant genomes, indicating spatial heterogeneity in mitochondrial physiology that reflects local mtDNA quality. Disruption of the respiratory chain by deletion of nuclear-encoded subunits ( RIP1 , COX4 ) abolished these physiological gradients and impaired mtDNA-QC, suggesting that local bioenergetic differences are required for selective recognition. Together, our findings support a model in which yeast cells assess local respiratory function as a proxy for mtDNA integrity, enabling intracellular selection for functional mitochondrial genomes.

Chromatin architecture is a fundamental mediator of genome function. Fasting is a major environmental cue across the animal kingdom, yet how it impacts three-dimensional (3D) genome organization is unknown. Here we show that fasting induces an intestine-specific, reversible and large-scale spatial reorganization of chromatin in Caenorhabditis elegans . This fasting-induced 3D genome reorganization requires inhibition of the nutrient-sensing mTOR pathway, acting through the regulation of RNA Pol I, but not Pol II nor Pol III, and is accompanied by remodelling of the nucleolus. By uncoupling the 3D genome configuration from the animal’s nutritional status, we find that the expression of metabolic and stress-related genes increases when the spatial reorganization of chromatin occurs, showing that the 3D genome might support the transcriptional response in fasted animals. Our work documents a large-scale chromatin reorganization triggered by fasting and reveals that mTOR and RNA Pol I shape genome architecture in response to nutrients. Al-Refaie et al. show that fasting induces spatial reorganization of chromatin and formation of chromatin rings in an mTORC1- and RNA Pol I-dependent manner in the C. elegans intestine.

Artificial intelligence (AI) has revolutionized many fields, including bioimage analysis. Powerful AI-based tools enable the analysis of large image datasets with unprecedented accuracy. However, easy-to-use tools are needed to allow experimentalists without specialised technical knowledge to leverage AI to its full potential. Here, we discuss our efforts to develop an intuitive and open-source bioimage analysis software that enables scientists to easily access state-of-the-art AI models.