Lysis of human erythrocytes due to Piezo1-dependent cytosolic calcium overload as a mechanism of circulatory removal.

Hematopoietic stem cells surrender organelles during differentiation, leaving mature red blood cells (RBC) devoid of transcriptional machinery and mitochondria. The resultant absence of cellular repair capacity limits RBC circulatory longevity, and old cells are removed from circulation. The specific age-dependent alterations required for this apparently targeted removal of RBC, however, remain elusive. Here, we assessed the function of Piezo1, a stretch-activated transmembrane cation channel, within subpopulations of RBC isolated based on physical properties associated with aging. We subsequently investigated the potential role of Piezo1 in RBC removal, using pharmacological and mechanobiological approaches. Dense (old) RBC were separated from whole blood using differential density centrifugation. Tolerance of RBC to mechanical forces within the physiological range was assessed on single-cell and cell population levels. Expression and function of Piezo1 were investigated in separated RBC populations by monitoring accumulation of cytosolic Ca2+ and changes in cell morphology in response to pharmacological Piezo1 stimulation and in response to physical forces. Despite decreased Piezo1 activity with increasing cell age, tolerance to prolonged Piezo1 stimulation declined sharply in older RBC, precipitating lysis. Cell lysis was immediately preceded by an acute reversal of density. We propose a Piezo1-dependent mechanism by which RBC may be removed from circulation: Upon adherence of these RBC to other tissues, they are uniquely exposed to prolonged mechanical forces. The resultant sustained activation of Piezo1 leads to a net influx of Ca2+, overpowering the Ca2+-removal capacity of specifically old RBC, which leads to reversal of ion gradients, dysregulated cell hydration, and ultimately osmotic lysis.

Altered expression response upon repeated gene repression in single yeast cells.

Cells must continuously adjust to changing environments and, thus, have evolved mechanisms allowing them to respond to repeated stimuli. While faster gene induction upon a repeated stimulus is known as reinduction memory, responses to repeated repression have been less studied so far. Here, we studied gene repression across repeated carbon source shifts in over 1,500 single Saccharomyces cerevisiae cells. By monitoring the expression of a carbon source-responsive gene, galactokinase 1 (Gal1), and fitting a mathematical model to the single-cell data, we observed a faster response upon repeated repressions at the population level. Exploiting our single-cell data and quantitative modeling approach, we discovered that the faster response is mediated by a shortened repression response delay, the estimated time between carbon source shift and Gal1 protein production termination. Interestingly, we can exclude two alternative hypotheses, i) stronger dilution because of e.g., increased proliferation, and ii) a larger fraction of repressing cells upon repeated repressions. Collectively, our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression. The computational results of our study can serve as the starting point for experimental follow-up studies.

Generative artificial intelligence empowers digital twins in drug discovery and clinical trials.

INTRODUCTION: The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties. AREAS COVERED: The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials. EXPERT OPINION: The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.

RedTell: an AI tool for interpretable analysis of red blood cell morphology.

Introduction: Hematologists analyze microscopic images of red blood cells to study their morphology and functionality, detect disorders and search for drugs. However, accurate analysis of a large number of red blood cells needs automated computational approaches that rely on annotated datasets, expensive computational resources, and computer science expertise. We introduce RedTell, an AI tool for the interpretable analysis of red blood cell morphology comprising four single-cell modules: segmentation, feature extraction, assistance in data annotation, and classification. Methods: Cell segmentation is performed by a trained Mask R-CNN working robustly on a wide range of datasets requiring no or minimum fine-tuning. Over 130 features that are regularly used in research are extracted for every detected red blood cell. If required, users can train task-specific, highly accurate decision tree-based classifiers to categorize cells, requiring a minimal number of annotations and providing interpretable feature importance. Results: We demonstrate RedTell's applicability and power in three case studies. In the first case study we analyze the difference of the extracted features between the cells coming from patients suffering from different diseases, in the second study we use RedTell to analyze the control samples and use the extracted features to classify cells into echinocytes, discocytes and stomatocytes and finally in the last use case we distinguish sickle cells in sickle cell disease patients. Discussion: We believe that RedTell can accelerate and standardize red blood cell research and help gain new insights into mechanisms, diagnosis, and treatment of red blood cell associated disorders.