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Protection of telomeres 1a (POT1a) is a telomere binding protein. A decrease of POT1a is related to myeloid-skewed haematopoiesis with ageing, suggesting that protection of telomeres is essential to sustain multi-potency. Since mesenchymal stem cells (MSCs) are a constituent of the hematopoietic niche in bone marrow, their dysfunction is associated with haematopoietic failure. However, the importance of telomere protection in MSCs has yet to be elucidated. Here, we show that genetic deletion of POT1a in MSCs leads to intracellular accumulation of fatty acids and excessive ROS and DNA damage, resulting in impaired osteogenic-differentiation. Furthermore, MSC-specific POT1a deficient mice exhibited skeletal retardation due to reduction of IL-7 producing bone lining osteoblasts. Single-cell gene expression profiling of bone marrow from POT1a deficient mice revealed that B-lymphopoiesis was selectively impaired. These results demonstrate that bone marrow microenvironments composed of POT1a deficient MSCs fail to support B-lymphopoiesis, which may underpin age-related myeloid-bias in haematopoiesis., (© 2023. Springer Nature Limited.)

FOXG1 is a critical transcription factor in human brain where loss-of-function mutations cause a severe neurodevelopmental disorder, while increased FOXG1 expression is frequently observed in glioblastoma. FOXG1 is an inhibitor of cell patterning and an activator of cell proliferation in chordate model organisms but different mechanisms have been proposed as to how this occurs. To identify genomic targets of FOXG1 in human neural progenitor cells (NPCs), we engineered a cleavable reporter construct in endogenous FOXG1 and performed chromatin immunoprecipitation (ChIP) sequencing. We also performed deep RNA sequencing of NPCs from two females with loss-of-function mutations in FOXG1 and their healthy biological mothers. Integrative analyses of RNA and ChIP sequencing data showed that cell cycle regulation and Bone Morphogenic Protein (BMP) repression gene ontology categories were over-represented as FOXG1 targets. Using engineered brain cell lines, we show that FOXG1 specifically activates SMAD7 and represses CDKN1B. Activation of SMAD7 which inhibits BMP signaling may be one way that FOXG1 patterns the forebrain, while repression of cell cycle regulators such as CDKN1B may be one way that FOXG1 expands the NPC pool to ensure proper brain size. Our data reveal novel mechanisms on how FOXG1 may control forebrain patterning and cell proliferation in human brain development., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

There is a wealth of data indicating human bone marrow contains skeletal stem cells (SSC) with the capacity for osteogenic, chondrogenic and adipogenic differentiation. However, current methods to isolate SSCs are restricted by the lack of a defined marker, limiting understanding of SSC fate, immunophenotype, function and clinical application. The current study applied single-cell RNA-sequencing to profile human adult bone marrow populations from 11 donors and identified novel targets for SSC enrichment. Spherical nucleic acids were used to detect these mRNA targets in SSCs. This methodology was able to rapidly isolate potential SSCs found at a frequency of <1 in 1,000,000 in human bone marrow, with the capacity for tri-lineage differentiation in vitro and ectopic bone formation in vivo. The current studies detail the development of a platform to advance SSC enrichment from human bone marrow, offering an invaluable resource for further SSC characterisation, with significant therapeutic impact therein., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Kabuki syndrome is frequently caused by loss-of-function mutations in one allele of histone 3 lysine 4 (H3K4) methyltransferase KMT2D and is associated with problems in neurological, immunological and skeletal system development. We generated heterozygous KMT2D knockout and Kabuki patient-derived cell models to investigate the role of reduced dosage of KMT2D in stem cells. We discovered chromosomal locus-specific alterations in gene expression, specifically a 110 Kb region containing Synaptotagmin 3 (SYT3), C-Type Lectin Domain Containing 11A (CLEC11A), Chromosome 19 Open Reading Frame 81 (C19ORF81) and SH3 And Multiple Ankyrin Repeat Domains 1 (SHANK1), suggesting locus-specific targeting of KMT2D. Using whole genome histone methylation mapping, we confirmed locus-specific changes in H3K4 methylation patterning coincident with regional decreases in gene expression in Kabuki cell models. Significantly reduced H3K4 peaks aligned with regions of stem cell maps of H3K27 and H3K4 methylation suggesting KMT2D haploinsufficiency impact bivalent enhancers in stem cells. Preparing the genome for subsequent differentiation cues may be of significant importance for Kabuki-related genes. This work provides a new insight into the mechanism of action of an important gene in bone and brain development and may increase our understanding of a specific function of a human disease-relevant H3K4 methyltransferase family member., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The future of single cell diversity screens involves ever-larger sample sizes, dictating the need for higher throughput methods with low analytical noise to accurately describe the nature of the cellular system. Current approaches are limited by the Poisson statistic, requiring dilute cell suspensions and associated losses in throughput. In this contribution, we apply Dean entrainment to both cell and bead inputs, defining different volume packets to effect efficient co-encapsulation. Volume ratio scaling was explored to identify optimal conditions. This enabled the co-encapsulation of single cells with reporter beads at rates of ∼1 million cells per hour, while increasing assay signal-to-noise with cell multiplet rates of ∼2.5% and capturing ∼70% of cells. The method, called Pirouette coupling, extends our capacity to investigate biological systems.

Mutations in HPRT1, a gene encoding a rate-limiting enzyme for purine salvage, cause Lesch-Nyhan disease which is characterized by self-injury and motor impairments. We leveraged stem cell and genetic engineering technologies to model the disease in isogenic and patient-derived forebrain and midbrain cell types. Dopaminergic progenitor cells deficient in HPRT showed decreased intensity of all developmental cell-fate markers measured. Metabolic analyses revealed significant loss of all purine derivatives, except hypoxanthine, and impaired glycolysis and oxidative phosphorylation. real-time glucose tracing demonstrated increased shunting to the pentose phosphate pathway for de novo purine synthesis at the expense of ATP production. Purine depletion in dopaminergic progenitor cells resulted in loss of RHEB, impairing mTORC1 activation. These data demonstrate dopaminergic-specific effects of purine salvage deficiency and unexpectedly reveal that dopaminergic progenitor cells are programmed to a high-energy state prior to higher energy demands of terminally differentiated cells., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Epigenetic memories play an important part in regulating stem cell identities. Tools from the theory of non-Markov processes may help us understand these memories better and develop a more integrated view of stem cell fate and function., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Hematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, it has proven difficult to generate functional hematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we explored single-cell RNA sequencing (scRNAseq) in combination with single-cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors and erythroid-, megakaryocyte-, and leukocyte-committed progenitors, and we identified CD44, CD326, ICAM2/CD9, and CD18, respectively, as markers of these progenitors. Using an artificial neural network that we trained on scRNAseq derived from human fetal liver, we identified a wide range of hPSC-derived HSPCs phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded, and was completely missing in the data set that had been generated using cells selected on the basis of CD43 expression. By comparing the single-cell transcriptome of in vitro-generated HSC-like cells with those generated within the fetal liver, we identified transcription factors and molecular pathways that can be explored in the future to improve the in vitro production of HSCs., (© 2020 by The American Society of Hematology.)

Changes in stem cell activity may underpin aging. However, these changes are not completely understood. Here, we combined single-cell profiling with machine learning and in vivo functional studies to explore how hematopoietic stem cell (HSC) divisions patterns evolve with age. We first trained an artificial neural network (ANN) to accurately identify cell types in the hematopoietic hierarchy and predict their age from single-cell gene-expression patterns. We then used this ANN to compare identities of daughter cells immediately after HSC divisions and found that the self-renewal ability of individual HSCs declines with age. Furthermore, while HSC cell divisions are deterministic and intrinsically regulated in young and old age, they are variable and niche sensitive in mid-life. These results indicate that the balance between intrinsic and extrinsic regulation of stem cell activity alters substantially with age and help explain why stem cell numbers increase through life, yet regenerative potency declines., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning-the branch of machine learning that concerns passing information from one domain to another-can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

Modern single cell experiments have revealed unexpected heterogeneity in apparently functionally 'pure' cell populations. However, we are still lacking a conceptual framework to understand this heterogeneity. Here, we propose that cellular memories-changes in the molecular status of a cell in response to a stimulus, that modify the ability of the cell to respond to future stimuli-are an essential ingredient in any such theory. We illustrate this idea by considering a simple age-structured model of stem cell proliferation that takes account of mitotic memories. Using this model we argue that asynchronous mitosis generates heterogeneity that is central to stem cell population function. This model naturally explains why stem cell numbers increase through life, yet regenerative potency simultaneously declines.

Cell fate decisions are controlled by complex intracellular molecular regulatory networks. Studies increasingly reveal the scale of this complexity: not only do cell fate regulatory networks contain numerous positive and negative feedback loops, they also involve a range of different kinds of nonlinear protein-protein and protein-DNA interactions. This inherent complexity and nonlinearity makes cell fate decisions hard to understand using experiment and intuition alone. In this primer, we will outline how tools from mathematics can be used to understand cell fate dynamics. We will briefly introduce some notions from dynamical systems theory, and discuss how they offer a framework within which to build a rigorous understanding of what we mean by a cell "fate", and how cells change fate. We will also outline how modern experiments, particularly high-throughput single-cell experiments, are enabling us to test and explore the limits of these ideas, and build a better understanding of cellular identities. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Cell Fates Models of Systems Properties and Processes > Cellular Models., (© 2019 The Authors. WIREs Systems Biology and Medicine published by Wiley Periodicals, Inc.)

The molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the "average" pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells-corresponding to naïve and formative pluripotent states and an early primitive endoderm state-and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell's response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities.

During acute myelosuppression or thrombocytopenia, bone marrow (BM) hematopoietic cells respond rapidly to replenish peripheral blood platelets. While the cytokine thrombopoietin (Thpo) both regulates platelet production and maintains HSC potential, whether Thpo controls megakaryocyte (Mk)-lineage differentiation of HSCs is unclear. Here, we show that Thpo rapidly upregulates mitochondrial activity in HSCs, an activity accompanied by differentiation to an Mk lineage. Moreover, in unperturbed hematopoiesis, HSCs with high mitochondrial activity exhibit Mk-lineage differentiation in vitro and myeloid lineage-biased reconstitution in vivo. Furthermore, Thpo skewed HSCs to express the tetraspanin CD9, a pattern correlated with mitochondrial activity. Mitochondria-active HSCs are resistant to apoptosis and oxidative stress upon Thpo stimulation. Thpo-regulated mitochondrial activity associated with mitochondrial translocation of STAT3 phosphorylated at serine 727. Overall, we report an important role for Thpo in regulating rapid Mk-lineage commitment. Thpo-dependent changes in mitochondrial metabolism prime HSCs to undergo direct differentiation to an Mk lineage., (Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.)

Pluripotent stem cells can self-renew in culture and differentiate along all somatic lineages in vivo. While much is known about the molecular basis of pluripotency, the mechanisms of differentiation remain unclear. Here, we profile individual mouse embryonic stem cells as they progress along the neuronal lineage. We observe that cells pass from the pluripotent state to the neuronal state via an intermediate epiblast-like state. However, analysis of the rate at which cells enter and exit these observed cell states using a hidden Markov model indicates the presence of a chain of unobserved molecular states that each cell transits through stochastically in sequence. This chain of hidden states allows individual cells to record their position on the differentiation trajectory, thereby encoding a simple form of cellular memory. We suggest a statistical mechanics interpretation of these results that distinguishes between functionally distinct cellular "macrostates" and functionally similar molecular "microstates" and propose a model of stem cell differentiation as a non-Markov stochastic process., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

A number of important pluripotency regulators, including the transcription factor Nanog, are observed to fluctuate stochastically in individual embryonic stem cells. By transiently priming cells for commitment to different lineages, these fluctuations are thought to be important to the maintenance of, and exit from, pluripotency. However, because temporal changes in intracellular protein abundances cannot be measured directly in live cells, fluctuations are typically assessed using genetically engineered reporter cell lines that produce a fluorescent signal as a proxy for protein expression. Here, using a combination of mathematical modeling and experiment, we show that there are unforeseen ways in which widely used reporter strategies can systematically disturb the dynamics they are intended to monitor, sometimes giving profoundly misleading results. In the case of Nanog, we show how genetic reporters can compromise the behavior of important pluripotency-sustaining positive feedback loops, and induce a bifurcation in the underlying dynamics that gives rise to heterogeneous Nanog expression patterns in reporter cell lines that are not representative of the wild-type. These findings help explain the range of published observations of Nanog variability and highlight the problem of measurement in live cells., (Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Pluripotent stem cells (PSCs) are a popular model system for investigating development, tissue regeneration, and repair. Although much is known about the molecular mechanisms that regulate the balance between self-renewal and lineage commitment in PSCs, the spatiotemporal integration of responsive signaling pathways with core transcriptional regulatory networks are complex and only partially understood. Moreover, measurements made on populations of cells reveal only average properties of the underlying regulatory networks, obscuring their fine detail. Here, we discuss the reconstruction of regulatory networks in individual cells using novel single-cell transcriptomics and proteomics, in order to expand our understanding of the molecular basis of pluripotency, including the role of cell-cell variability within PSC populations, and ways in which networks may be controlled in order to reliably manipulate cell behavior., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Polymersome nanoparticles (PMs) are attractive candidates for spatio-temporal controlled delivery of therapeutic agents. Although many studies have addressed cellular uptake of solid nanoparticles, there is very little data available on intracellular release of molecules encapsulated in membranous carriers, such as polymersomes. Here, we addressed this by developing a quantitative assay based on the hydrophilic dye, fluorescein. Fluorescein was encapsulated stably in PMs of mean diameter 85 nm, with minimal leakage after sustained dialysis. No fluorescence was detectable from fluorescein PMs, indicating quenching. Following incubation of L929 cells with fluorescein PMs, there was a gradual increase in intracellular fluorescence, indicating PM disruption and cytosolic release of fluorescein. By combining absorbance measurements with flow cytometry, we quantified the real-time intracellular release of a fluorescein at a single-cell resolution. We found that 173 ± 38 polymersomes released their payload per cell, with significant heterogeneity in uptake, despite controlled synchronisation of cell cycle. This novel method for quantification of the release of compounds from nanoparticles provides fundamental information on cellular uptake of nanoparticle-encapsulated compounds. It also illustrates the stochastic nature of population distribution in homogeneous cell populations, a factor that must be taken into account in clinical use of this technology.

Background: Hematopoiesis within the bone marrow (BM) is a complex and tightly regulated process predominantly influenced by immune factors. Aging, diabetes, and obesity are significant contributors to BM niche damage, which can alter hematopoiesis and lead to the development of clonal hematopoiesis of intermediate potential (CHIP). Genetic/epigenetic alterations during aging could influence BM niche reorganization for hematopoiesis or clonal hematopoiesis. CHIP is driven by mutations in genes such as Tet2, Dnmt3a, Asxl1, and Jak2, which are associated with age-related hematological malignancies. Objective: This literature review aims to provide an updated exploration of the functional aspects of BM niche cells within the hematopoietic microenvironment in the context of age-related hematological malignancies. The review specifically focuses on how immunological stressors modulate different signaling pathways that impact hematopoiesis. Methods: An extensive review of recent studies was conducted, examining the roles of various BM niche cells in hematopoietic stem cell (HSC) trafficking and the development of age-related hematological malignancies. Emphasis was placed on understanding the influence of immunological stressors on these processes. Results: Recent findings reveal a significant microheterogeneity and temporal stochasticity of niche cells across the BM during hematopoiesis. These studies demonstrate that niche cells, including mesenchymal stem cells, osteoblasts, and endothelial cells, exhibit dynamic interactions with HSCs, significantly influenced by the BM microenvironment as the age increases. Immunosurveillance plays a crucial role in maintaining hematopoietic homeostasis, with alterations in immune signaling pathways contributing to the onset of hematological malignancies. Novel insights into the interaction between niche cells and HSCs under stress/aging conditions highlight the importance of niche plasticity and adaptability. Conclusion: The involvement of age-induced genetic/epigenetic alterations in BM niche cells and immunological stressors in hematopoiesis is crucial for understanding the development of age-related hematological malignancies. This comprehensive review provides new insights into the complex interplay between niche cells and HSCs, emphasizing the potential for novel therapeutic approaches that target niche cell functionality and resilience to improve hematopoietic outcomes in the context of aging and metabolic disorders. Novelty statement: This review introduces novel concepts regarding the plasticity and adaptability of BM niche cells in response to immunological stressors and epigenetics. It proposes that targeted therapeutic strategies aimed at enhancing niche cell resilience could mitigate the adverse effects of aging, diabetes, and obesity on hematopoiesis and clonal hematopoiesis. Additionally, the review suggests that understanding the precise temporal and spatial dynamics of niche-HSC interactions and epigenetics influence may lead to innovative treatments for age-related hematological malignancies. [ABSTRACT FROM AUTHOR]

One of the fundamental questions in developmental biology is how a cell is specified to differentiate as a specialized cell type. Traditionally, plant cell types were defined based on their function, location, morphology, and lineage. Currently, in the age of single-cell biology, researchers typically attempt to assign plant cells to cell types by clustering them based on their transcriptomes. However, because cells are dynamic entities that progress through the cell cycle and respond to signals, the transcriptome also reflects the state of the cell at a particular moment in time, raising questions about how to define a cell type. We suggest that these complexities and dynamics of cell states are of interest and further consider the roles signaling, stochasticity, cell cycle, and mechanical forces play in plant cell fate specification. Once established, cell identity must also be maintained. With the wealth of single-cell data coming out, the field is poised to elucidate both the complexity and dynamics of cell states. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRs) play a pivotal role in a variety of biological processes including stem cell differentiation and function. Human foetal femur derived skeletal stem cells (SSCs) display enhanced proliferation and multipotential capacity indicating excellent potential as candidates for tissue engineering applications. This study has examined the expression and role of miRs in human foetal femur derived SSC differentiation along chondrogenic and osteogenic lineages. Cells isolated from the epiphyseal region of the foetal femur expressed higher levels of genes associated with chondrogenesis while cells from the foetal femur diaphyseal region expressed higher levels of genes associated with osteogenic differentiation. In addition to the difference in osteogenic and chondrogenic gene expression, epiphyseal and diaphyseal cells displayed distinct miRs expression profiles. miR-146a was found to be expressed by human foetal femur diaphyseal cells at a significantly enhanced level compared to epiphyseal populations and was predicted to target various components of the TGF-β pathway. Examination of miR-146a function in foetal femur cells confirmed regulation of protein translation of SMAD2 and SMAD3, important TGF-β and activin ligands signal transducers following transient overexpression in epiphyseal cells. The down-regulation of SMAD2 and SMAD3 following overexpression of miR-146a resulted in an up-regulation of the osteogenesis related gene RUNX2 and down-regulation of the chondrogenesis related gene SOX9. The current findings indicate miR-146a plays an important role in skeletogenesis through attenuation of SMAD2 and SMAD3 function and provide further insight into the role of miRs in human skeletal stem cell differentiation modulation with implications therein for bone reparation.

The potential gains from advances in high-throughput experimental molecular biology techniques are commonly not fully realized since these techniques often produce more data than can be easily organized and visualized. To address these problems, GATE (Grid-Analysis of Time-Series Expression) was developed. GATE is an integrated software platform for the analysis and visualization of high-dimensional time-series datasets, which allows flexible interrogation of time-series data against a wide range of databases of prior knowledge, thus linking observed molecular dynamics to potential genetic, epigenetic, and signaling mechanisms responsible for observed dynamics. This article provides a brief guide to using GATE effectively.

A number of key regulators of mouse embryonic stem (ES) cell identity, including the transcription factor Nanog, show strong expression fluctuations at the single-cell level. The molecular basis for these fluctuations is unknown. Here we used a genetic complementation strategy to investigate expression changes during transient periods of Nanog downregulation. Employing an integrated approach that includes high-throughput single-cell transcriptional profiling and mathematical modelling, we found that early molecular changes subsequent to Nanog loss are stochastic and reversible. However, analysis also revealed that Nanog loss severely compromises the self-sustaining feedback structure of the ES cell regulatory network. Consequently, these nascent changes soon become consolidated to committed fate decisions in the prolonged absence of Nanog. Consistent with this, we found that exogenous regulation of Nanog-dependent feedback control mechanisms produced a more homogeneous ES cell population. Taken together our results indicate that Nanog-dependent feedback loops have a role in controlling both ES cell fate decisions and population variability.

Miniaturization and parallelization of cell culture procedures are in focus of research in order to develop test platforms with low material consumption and increased standardization for toxicity and drug screenings. The cultivation in hanging drops (HDs) is a convenient and versatile tool for biological applications and represents an interesting model system for the screening applications due to its uniform shape, the advantageous gas supply, and the small volume. However, its application has so far been limited to non-adherent and aggregate forming cells. Here, we describe for the first time the proof-of-principle regarding the adherent cultivation of human embryonic stem cells in HD. For this microcarriers were added to the droplet as dynamic cultivation surfaces resulting in a maintained pluripotency and proliferation capacity for 10 days. This enables the HD technique to be extended to the cultivation of adherence-dependent stem cells. Also, the possible automation of this method by implementation of liquid handling systems opens new possibilities for miniaturized screenings, the improvement of cultivation and differentiation conditions, and toxicity and drug development.

Transcriptional noise is proposed to participate in cell fate changes, but contributions to mammalian cell differentiation systems, including cancer, remain associative. Cancer evolution is driven by genetic variability, with modulatory or contributory participation of epigenetic variants. Accumulation of epigenetic variants enhances transcriptional noise, which can facilitate cancer cell fate transitions. Acute myeloid leukaemia (AML) is an aggressive cancer with strong epigenetic dependencies, characterized by blocked differentiation. It constitutes an attractive model to probe links between transcriptional noise and malignant cell fate regulation. Gcn5/KAT2A is a classical epigenetic transcriptional noise regulator. Its loss increases transcriptional noise and modifies cell fates in stem and AML cells. By reviewing the analysis of KAT2A-depleted pre-leukaemia and leukaemia models, I discuss that the net result of transcriptional noise is diversification of cell fates secondary to alternative transcriptional programmes. Cellular diversification can enable or hinder AML progression, respectively, by differentiation of cell types responsive to mutations, or by maladaptation of leukaemia stem cells. KAT2A-dependent noise-responsive genes participate in ribosome biogenesis and KAT2A loss destabilizes translational activity. I discuss putative contributions of perturbed translation to AML biology, and propose KAT2A loss as a model for mechanistic integration of transcriptional and translational control of noise and fate decisions. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'. [ABSTRACT FROM AUTHOR]

Background: Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process. Results: In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (scRT-qPCR and scRNA-seq). Conclusions: We identified that the gene transcription profiles of differentiating sister cells are more similar to each other than to those of non-related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister cells than between self-renewing sister cells. Furthermore, a progressive increase in this divergence from first generation to second generation was observed when comparing differentiating cousin cells to self renewing cousin cells. Our results are in favor of a gradual erasure of transcriptional memory during the differentiation process. [ABSTRACT FROM AUTHOR]

Calpains are cysteine proteases that control cell fate transitions whose loss of function causes severe, pleiotropic phenotypes in eukaryotes. Although mainly considered as modulatory proteases, human calpain targets are directed to the N-end rule degradation pathway. Several such targets are transcription factors, hinting at a gene-regulatory role. Here, we analyze the gene-regulatory networks of the moss Physcomitrium patens and characterize the regulons that are misregulated in mutants of the calpain DEFECTIVE KERNEL1 (DEK1). Predicted cleavage patterns of the regulatory hierarchies in five DEK1-controlled subnetworks are consistent with a pleiotropic and regulatory role during cell fate transitions targeting multiple functions. Network structure suggests DEK1-gated sequential transitions between cell fates in 2D-to-3D development. Our method combines comprehensive phenotyping, transcriptomics and data science to dissect phenotypic traits, and our model explains the protease function as a switch gatekeeping cell fate transitions potentially also beyond plant development. Examination of the cell fate gene-regulatory networks of the moss Physcomitrium patens reveals regulons that exhibit misregulation in mutants for the calpain DEFECTIVE KERNEL1 (DEK1) gene. [ABSTRACT FROM AUTHOR]

Cells modify their internal organization during continuous state transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5–14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes. Synopsis: The prediction certainty of machine learning classification models can be used as a continuous measurement to quantitatively monitor single cell state transitions, as demonstrated for myoblast differentiation during muscle fiber formation. Live imaged single myoblast continuous differentiation states are computationally derived from motility and actin dynamics. The model distinguishes between cells that differentiated but failed to fuse to predict molecules specifically involved in fusion, as well as changes in actin dynamics. Mass spectrometry supports these in silico predictions and suggests novel fusion and maturation regulators downstream of differentiation. p38 is essential for the transition from terminal differentiation to fusion. The prediction certainty of machine learning classification models can be used as a continuous measurement to quantitatively monitor single cell state transitions, as demonstrated for myoblast differentiation during muscle fiber formation. [ABSTRACT FROM AUTHOR]

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We extended the cross-domain structure-preserving projection toward out-of-sample prediction. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO among the most influential genes composing the latent space for integration. Subsequently, during the alignment of latent spaces, each composed of species-specific genes, it is possible to identify functional annotations of genes missing from public orthology databases. We evaluated our approach with four different single-cell sequencing datasets focusing on cell-type prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms related methods working without prior knowledge when predicting unseen cell types based on other species' data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets. [ABSTRACT FROM AUTHOR]

Within-host infection dynamics of Omicron dramatically differs from previous variants of SARS-CoV-2. However, little is still known about which parameters of virus-cell interplay contribute to the observed attenuated replication and pathogenicity of Omicron. Mathematical models, often expressed as systems of differential equations, are frequently employed to study the infection dynamics of various viruses. Adopting such models for results of in vitro experiments can be beneficial in a number of aspects, such as model simplification (e.g., the absence of adaptive immune response and innate immunity cells), better measurement accuracy, and the possibility to measure additional data types in comparison with in vivo case. In this study, we consider a refinement of our previously developed and validated model based on a system of integro-differential equations. We fit the model to the experimental data of Omicron and Delta infections in Caco-2 (human intestinal epithelium model) and Calu-3 (lung epithelium model) cell lines. The data include known information on initial conditions, infectious virus titers, and intracellular viral RNA measurements at several time points post-infection. The model accurately explains the experimental data for both variants in both cell lines using only three variant- and cell-line-specific parameters. Namely, the cell entry rate is significantly lower for Omicron, and Omicron triggers a stronger cytokine production rate (i.e., innate immune response) in infected cells, ultimately making uninfected cells resistant to the virus. Notably, differences in only a single parameter (e.g., cell entry rate) are insufficient to obtain a reliable model fit for the experimental data. [ABSTRACT FROM AUTHOR]

We study phylogenetic signal present in syntactic information by considering the syntactic structures data from Longobardi (Linguist Anal 41:517–557, 2017), Collins (Syntactic structures of the world’s language: a cross-linguistic database. 27 September (2010), Colloquium: , 2010), Ceolin et al. (Front Psychol 11:2384, 2020) and Koopman (SSWL syntactic structures of the world’s languages: an open-ended database for the linguistic community and by the linguistic community. mit 50, 12. , 2011). Focusing first on the general Markov models, we explore how well the the syntactic structures data conform to the hypothesis required by these models. We do this by comparing derived phylogenetic trees against trees agreed on by the linguistics community. We then interpret the methods of Ceolin et al. (2020) as an infinite sites evolutionary model and compare the consistency of the data with this alternative. The ideas and methods discussed in the present paper are more generally applicable than to the specific setting of syntactic structures, and can be used in other contexts, when analyzing consistency of data with against hypothesized evolutionary models. [ABSTRACT FROM AUTHOR]

Many critical issues arise when training deep neural networks using limited biological datasets. These include overfitting, exploding/vanishing gradients and other inefficiencies which are exacerbated by class imbalances and can affect the overall accuracy of a model. There is a need to develop semi-supervised models that can reduce the need for large, balanced, manually annotated datasets so that researchers can easily employ neural networks for experimental analysis. In this work, Iterative Pseudo Balancing (IPB) is introduced to classify stem cell microscopy images while performing on the fly dataset balancing using a student-teacher meta-pseudo-label framework. In addition, multi-scale patches of multi-label images are incorporated into the network training to provide previously inaccessible image features with both local and global information for effective and efficient learning. The combination of these inputs is shown to increase the classification accuracy of the proposed deep neural network by 3 % over baseline, which is determined to be statistically significant. This work represents a novel use of pseudo-labeling for data limited settings, which are common in biological image datasets, and highlights the importance of the exhaustive use of available image features for improving performance of semi-supervised networks. The proposed methods can be used to reduce the need for expensive manual dataset annotation and in turn accelerate the pace of scientific research involving non-invasive cellular imaging. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Human embryonic stem cells (hESCs) have become an ideal cell source for the ex vivo generation of megakaryocyte (MK) and platelet products for clinical applications. However, an ongoing challenge is to establish scalable culture systems to maximize the yield of stem cell‐derived MKs that release platelets. We defined a specific dynamic 3D manufacturing system in a baffled‐flow manner that could remarkably facilitate megakaryopoiesis and increase the yield of platelet‐producing MKs from hESCs within a 12‐day induction period. Additionally, an increased number of >16N ploidy MKs, proplatelets, and platelets were generated from induced cells harvested on Day 12 using the specific dynamic culture method. The specific dynamic culture method significantly enhanced endothelium‐to‐haematopoietic transition and early haematopoiesis. More importantly, MK fate was significantly facilitated in a specific dynamic manner during early haematopoiesis. Mechanistically, this dynamic culture significantly enhanced mitochondrial function via the oxidative phosphorylation pathway and caused differentiation skewing of hESCs toward megakaryopoiesis. This study can aid in the automatic and scalable production of MKs from stem cells using baffled‐flow bioreactors and assist in the manufacturing of hESC‐derived MK and platelet products. [ABSTRACT FROM AUTHOR]

This paper presents an account of the nature of stem cells based on the philosophical concept of disposition. It is argued that stem cells can be conceived as dispositional objects, and adopting this attitude allows overcoming some of the controversies surrounding the nature of stemness (most notably, the state vs. entity debate) because it offers a framework that accommodates the lessons from different theories. Additionally, the account is simultaneously useful for interpreting stem cell experiments and guiding potential interventions. The account shows how different levels, both molecular and emergent network-level, play the primary causal role in explaining some empirical results, and hence they suggest that the explanations can be mechanistic or topological, respectively. The realization that any of these levels may play a more prominent causal role than another allows suggesting interventions at the genetic, molecular and population levels. [ABSTRACT FROM AUTHOR]

With the continuous increase of the elderly population, there is an urgency to understand and develop relevant treatments for Alzheimer's disease and related dementias (ADRD). In tandem with this, the prevalence of health inequities continues to rise as disadvantaged communities fail to be included in mainstream research. The neural exposome poses as a relevant mechanistic approach and tool for investigating ADRD onset, progression, and pathology as it accounts for several different factors: exogenous, endogenous, and behavioral. Consequently, through the neural exposome, health inequities can be addressed in ADRD research. In this paper, we address how the neural exposome relates to ADRD by contributing to the discourse through defining how the neural exposome can be developed as a tool in accordance with machine learning. Through this, machine learning can allow for developing a greater insight into the application of transferring and making sense of experimental mouse models exposed to health inequities and potentially relate it to humans. The overall goal moving beyond this paper is to define a multitude of potential factors that can increase the risk of ADRD onset and integrate them to create an interdisciplinary approach to the study of ADRD and subsequently translate the findings to clinical research., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

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., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Droplet microfluidics is a rapidly advancing area of microfluidic technology, which offers numerous advantages for cell analysis, such as isolation and accumulation of signals, by confining cells within droplets. However, controlling cell numbers in droplets is challenging due to the uncertainty of random encapsulation which result in many empty droplets. Therefore, more precise control techniques are needed to achieve efficient encapsulation of cells within droplets. Here, an innovative microfluidic droplet manipulation platform had been developed, which employed positive pressure as a stable and controllable driving force for manipulating fluid within chips. The air cylinder, electro-pneumatics proportional valve, and the microfluidic chip were connected through a capillary, which enabled the formation of a fluid wall by creating a difference in hydrodynamic resistance between two fluid streams at the channel junction. Lowering the pressure of the driving oil phase eliminates hydrodynamic resistance and breaks the fluid wall. Regulating the duration of the fluid wall breakage controls the volume of the introduced fluid. Several important droplet microfluidic manipulations were demonstrated on this microfluidic platform, such as sorting of cells/droplets, sorting of droplets co-encapsulated cells and hydrogels, and active generation of droplets encapsulated with cells in a responsive manner. The simple, on-demand microfluidic platform was featured with high stability, good controllability, and compatibility with other droplet microfluidic technologies. [ABSTRACT FROM AUTHOR]

Background: Single-cell sequencing (sc-Seq) experiments are producing increasingly large data sets. However, large data sets do not necessarily contain large amounts of information. Results: Here, we formally quantify the information obtained from a sc-Seq experiment and show that it corresponds to an intuitive notion of gene expression heterogeneity. We demonstrate a natural relation between our notion of heterogeneity and that of cell type, decomposing heterogeneity into that component attributable to differential expression between cell types (inter-cluster heterogeneity) and that remaining (intra-cluster heterogeneity). We test our definition of heterogeneity as the objective function of a clustering algorithm, and show that it is a useful descriptor for gene expression patterns associated with different cell types. Conclusions: Thus, our definition of gene heterogeneity leads to a biologically meaningful notion of cell type, as groups of cells that are statistically equivalent with respect to their patterns of gene expression. Our measure of heterogeneity, and its decomposition into inter- and intra-cluster, is non-parametric, intrinsic, unbiased, and requires no additional assumptions about expression patterns. Based on this theory, we develop an efficient method for the automatic unsupervised clustering of cells from sc-Seq data, and provide an R package implementation. [ABSTRACT FROM AUTHOR]

Aging is a progressive multifaceted functional decline of a biological system. Chronic age-related conditions such as neurodegenerative diseases are leading causes of death worldwide, and they are becoming a pressing problem for our society. To address this global challenge, there is a need for novel, safe, and effective rejuvenation therapies aimed at reversing age-related phenotypes and improving human health. With gene expression being a key determinant of cell identity and function, and in light of recent studies reporting rejuvenation effects through genetic perturbations, we propose an age reversal strategy focused on reprogramming the cell transcriptome to a youthful state. To this end, we suggest using transcriptomic data from primary human cells to predict rejuvenation targets and develop high-throughput aging assays, which can be used in large perturbation screens. We propose neural cells as particularly relevant targets for rejuvenation due to substantial impact of neurodegeneration on human frailty. Of all cell types in the brain, we argue that glutamatergic neurons, neuronal stem cells, and oligodendrocytes represent the most impactful and tractable targets. Lastly, we provide experimental designs for anti-aging reprogramming screens that will likely enable the development of neuronal age reversal therapies, which hold promise for dramatically improving human health. [ABSTRACT FROM AUTHOR]

Bone defect (BD) caused by trauma, infection, congenital defects, or neoplasia is a major cause of physical limitation. Distraction osteogenesis (DO) is a highly effective procedure for bone regeneration, while the concrete mechanism remains unknown. In this study, canine DO and BD models of the mandible were established. The results of micro–computed tomography and histological staining revealed that DO led to an increased mineralized volume fraction and robust new bone formation; in contrast, BD demonstrated incomplete bone union. Mesenchymal stem cells (MSCs) from DO and BD calluses were isolated and identified. Compared with BD-MSCs, DO-MSCs were found to have a stronger osteogenic capability. Single-cell RNA sequencing analysis was further performed to comprehensively define cell differences between mandibular DO and BD calluses. Twenty-six clusters of cells representing 6 major cell populations were identified, including paired related homeobox 1–expressing MSCs (PRRX1 +MSCs), endothelial cells (ECs), T cells, B cells, neutrophils, and macrophages. Interestingly, 2 subpopulations in PRRX1 +MSCs in the DO group were found to express the marker of neural crest cells (NCCs) and were associated with the process of epithelial–mesenchymal transition. The immunofluorescence assay was performed to further corroborate these results in vivo and in vitro, experimentally validating that continuous distraction maintained the PRRX1 +MSCs in an embryonic-like state. Finally, we used CRISPR/Cas9 to knock out (KO) PRRX1 in the context of DO, which significantly blunted the capability of jawbone regeneration, resulting in a diminished NCC-like program and reduction of new bone volume. In addition, the ability of osteogenesis, cell migration, and proliferation in cultured PRRX1 KO MSCs was inhibited. Taken together, this study provides a novel, comprehensive atlas of the cell fates in the context of DO regeneration, and PRRX1 +MSCs act essential roles. [ABSTRACT FROM AUTHOR]