Machine Learning of Hematopoietic Stem Cell Divisions from Paired Daughter Cell Expression Profiles Reveals Effects of Aging on Self-Renewal

Age-related changes in the hematopoietic stem cell pool revealed via quantifying the balance of symmetric and asymmetric divisions

Hematopoietic stem cells (HSCs) are somatic stem cells that continuously generate lifelong supply of blood cells through a balance of symmetric and asymmetric divisions. It is well established that the HSC pool increases with age. However, not much is known about the underlying cause for these observed changes. Here, using a novel method combining single-cell ex vivo HSC expansion with mathematical modeling, we quantify HSC division types (stem cell-stem cell (S-S) division, stem cell-progenitor cell (S-P) division, and progenitor cell-progenitor cell (P-P) division) as a function of the aging process. Our time-series experiments reveal how changes in these three modes of division can explain the increase in HSC numbers with age. Contrary to the popular notion that HSCs divide predominantly through S-P divisions, we show that S-S divisions are predominant throughout the lifespan of the animal, thereby expanding the HSC pool. We, therefore, provide a novel mathematical model-based experimental validation for reflecting HSC dynamics in vivo.

Mitochondria pleiotropism in stem cell senescence: Mechanisms and therapeutic approaches

Aging is a complex biological process characterized by a progressive decline in cellular and tissue function, ultimately leading to organismal aging. Stem cells, with their regenerative potential, play a crucial role in maintaining tissue homeostasis and repair throughout an organism's lifespan. Mitochondria, the powerhouses of the cell, have emerged as key players in the aging process, impacting stem cell function and contributing to age-related tissue dysfunction. Here are discuss the mechanisms through which mitochondria influence stem cell fate decisions, including energy production, metabolic regulation, ROS signalling, and epigenetic modifications. Therefore, this review highlights the role of mitochondria in driving stem cell senescence and the subsequent impact on tissue function, leading to overall organismal aging and age-related diseases. Finally, we explore potential anti-aging therapies targeting mitochondrial health and discuss their implications for promoting healthy aging. This comprehensive review sheds light on the critical interplay between mitochondrial function, stem cell senescence, and organismal aging, offering insights into potential strategies for attenuating age-related decline and promoting healthy longevity.

Decoding Aging Hallmarks at the Single-Cell Level

Organismal aging exhibits wide-ranging hallmarks in divergent cell types across tissues, organs, and systems. The advancement of single-cell technologies and generation of rich datasets have afforded the scientific community the opportunity to decode these hallmarks of aging at an unprecedented scope and resolution. In this review, we describe the technological advancements and bioinformatic methodologies enabling data interpretation at the cellular level. Then, we outline the application of such technologies for decoding aging hallmarks and potential intervention targets and summarize common themes and context-specific molecular features in representative organ systems across the body. Finally, we provide a brief summary of available databases relevant for aging research and present an outlook on the opportunities in this emerging field.

Ageing and rejuvenation of tissue stem cells and their niches

Most adult organs contain regenerative stem cells, often organized in specific niches. Stem cell function is critical for tissue homeostasis and repair upon injury, and it is dependent on interactions with the niche. During ageing, stem cells decline in their regenerative potential and ability to give rise to differentiated cells in the tissue, which is associated with a deterioration of tissue integrity and health. Ageing-associated changes in regenerative tissue regions include defects in maintenance of stem cell quiescence, differentiation ability and bias, clonal expansion and infiltration of immune cells in the niche. In this Review, we discuss cellular and molecular mechanisms underlying ageing in the regenerative regions of different tissues as well as potential rejuvenation strategies. We focus primarily on brain, muscle and blood tissues, but also provide examples from other tissues, such as skin and intestine. We describe the complex interactions between different cell types, non-cell-autonomous mechanisms between ageing niches and stem cells, and the influence of systemic factors. We also compare different interventions for the rejuvenation of old regenerative regions. Future outlooks in the field of stem cell ageing are discussed, including strategies to counter ageing and age-dependent disease.

A culture platform to study quiescent hematopoietic stem cells following genome editing

Other than genetically engineered mice, few reliable platforms are available for the study of hematopoietic stem cell (HSC) quiescence. Here we present a platform to analyze HSC cell cycle quiescence by combining culture conditions that maintain quiescence with a CRISPR-Cas9 genome editing system optimized for HSCs. We demonstrate that preculture of HSCs enhances editing efficiency by facilitating nuclear transport of ribonucleoprotein complexes. For post-editing culture, mouse and human HSCs edited based on non-homologous end joining and cultured under low-cytokine, low-oxygen, and high-albumin conditions retain their phenotypes and quiescence better than those cultured under the proliferative conditions. Using this approach, HSCs regain quiescence even after editing by homology-directed repair. Our results show that low-cytokine culture conditions for gene-edited HSCs are a useful approach for investigating HSC quiescence ex vivo.

Studying Epigenetics of Cardiovascular Diseases on Chip Guide

Epigenetics is defined as the study of inheritable changes in the gene expressions and phenotypes that occurs without altering the normal DNA sequence. These changes are mainly due to an alteration in chromatin or its packaging, which changes the DNA accessibility. DNA methylation, histone modification, and noncoding or microRNAs can best explain the mechanism of epigenetics. There are various DNA methylated enzymes, histone-modifying enzymes, and microRNAs involved in the cause of various CVDs (cardiovascular diseases) such as cardiac hypertrophy, heart failure, and hypertension. Moreover, various CVD risk factors such as diabetes mellitus, hypoxia, aging, dyslipidemia, and their epigenetics are also discussed together with CVDs such as CHD (coronary heart disease) and PAH (pulmonary arterial hypertension). Furthermore, different techniques involved in epigenetic chromatin mapping are explained. Among these techniques, the ChIP-on-chip guide is explained with regard to its role in cardiac hypertrophy, a final form of heart failure. This review focuses on different epigenetic factors that are involved in causing cardiovascular diseases.

A primer on machine learning techniques for genomic applications

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous "omic" data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available.

Symmetric and asymmetric activation of hematopoietic stem cells

PURPOSE OF REVIEW

Hematopoietic stem cells (HSCs) are in an inactive quiescent state for most of their life. To replenish the blood system in homeostasis and after injury, they activate and divide. HSC daughter cells must then decide whether to return to quiescence and metabolic inactivity or to activate further to proliferate and differentiate and replenish lost blood cells. Although the regulation of HSC activation is not well understood, recent discoveries shed new light on involved mechanisms including asymmetric cell division (ACD).

RECENT FINDINGS

HSC metabolism has emerged as a regulator of cell fates. Recent evidence suggests that cellular organelles mediating anabolic and catabolic processes can be asymmetrically inherited during HSC divisions. These include autophagosomes, mitophagosomes, and lysosomes, which regulate HSC quiescence. Their asymmetric inheritance has been linked to future metabolic and translational activity in HSC daughters, showing that ACD can regulate the balance between HSC (in)activity.

SUMMARY

We discuss recent insights and remaining questions in how HSCs balance activation and quiescence, with a focus on ACD.

Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

The application of machine learning (ML) in bioprinting has attracted considerable attention recently. Many have focused on the benefits and potential of ML, but a clear overview of how ML shapes the future of three-dimensional (3D) bioprinting is still lacking. Here, it is proposed that two missing links, Big Data and Digital Twin, are the key to articulate the vision of future 3D bioprinting. Creating training databases from Big Data curation and building digital twins of human organs with cellular resolution and properties are the most important and urgent challenges. With these missing links, it is envisioned that future 3D bioprinting will become more digital and in silico, and eventually strike a balance between virtual and physical experiments toward the most efficient utilization of bioprinting resources. Furthermore, the virtual component of bioprinting and biofabrication, namely, digital bioprinting, will become a new growth point for digital industry and information technology in future.

Evaluation of Arai et al.: What Can Differences across Daughter Cells Tell Us about Stem Cell Renewal?

One snapshot of the peer review process for "Machine learning of hematopoietic stem cell divisions from paired daughter cell expression profiles reveals effects of aging on self-renewal" (Arai et al., 2020).