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The naked mole-rat is a subterranean rodent, approximately the size of a mouse, renowned for its exceptional longevity (>30 years) and remarkable resistance to cancer. To explore putative mechanisms underlying the cancer resistance of the naked mole-rat, we investigated the regulation and function of the most commonly mutated tumor suppressor, TP53, in the naked mole-rat. We found that the p53 protein in naked mole-rat embryonic fibroblasts (NEFs) exhibits a half-life more than ten times in excess of the protein’s characterized half-life in mouse and human embryonic fibroblasts. We determined that the long half-life of the naked mole-rat p53 protein reflects protein-extrinsic regulation. Relative to mouse and human p53, a larger proportion of naked mole-rat p53 protein is constitutively localized in the nucleus prior to DNA damage. Nevertheless, DNA damage is sufficient to induce activation of canonical p53 target genes in NEFs. Despite the uniquely long half-life and unprecedented basal nuclear localization of p53 in NEFs, naked mole-rat p53 retains its canonical tumor suppressive activity. Together, these findings suggest that the unique stabilization and regulation of the p53 protein may contribute to the naked mole-rat’s remarkable resistance to cancer.

Partial pluripotent reprogramming can reverse features of aging in mammalian cells, but the impact on somatic identity and the necessity of individual reprogramming factors remain unknown. Here, we used single-cell genomics to map the identity trajectory induced by partial reprogramming in multiple murine cell types and dissected the influence of each factor by screening all Yamanaka Factor subsets with pooled single-cell screens. We found that partial reprogramming restored youthful expression in adipogenic and mesenchymal stem cells but also temporarily suppressed somatic identity programs. Our pooled screens revealed that many subsets of the Yamanaka Factors both restore youthful expression and suppress somatic identity, but these effects were not tightly entangled. We also found that a multipotent reprogramming strategy inspired by amphibian regeneration restored youthful expression in myogenic cells. Our results suggest that various sets of reprogramming factors can restore youthful expression with varying degrees of somatic identity suppression. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Transient induction of pluripotent reprogramming factors has been reported to reverse some features of aging in mammalian cells and tissues. However, the impact of transient reprogramming on somatic cell identity programs and the necessity of individual pluripotency factors remain unknown. Here, we mapped trajectories of transient reprogramming in young and aged cells from multiple murine cell types using single cell transcriptomics to address these questions. We found that transient reprogramming restored youthful gene expression in adipocytes and mesenchymal stem cells but also temporarily suppressed somatic cell identity programs. We further screened Yamanaka Factor subsets and found that many combinations had an impact on aging gene expression and suppressed somatic identity, but that these effects were not tightly entangled. We also found that a transient reprogramming approach inspired by amphibian regeneration restored youthful gene expression in aged myogenic cells. Our results suggest that transient pluripotent reprogramming poses a neoplastic risk, but that restoration of youthful gene expression can be achieved with alternative strategies.

Annotating cell identities is a common bottleneck in the analysis of single-cell genomics experiments. Here, we present scNym, a semisupervised, adversarial neural network that learns to transfer cell identity annotations from one experiment to another. scNym takes advantage of information in both labeled data sets and new, unlabeled data sets to learn rich representations of cell identity that enable effective annotation transfer. We show that scNym effectively transfers annotations across experiments despite biological and technical differences, achieving performance superior to existing methods. We also show that scNym models can synthesize information from multiple training and target data sets to improve performance. We show that in addition to high accuracy, scNym models are well calibrated and interpretable with saliency methods.

Annotating cell identities is a common bottleneck in the analysis of single cell genomics experiments. Here, we present scNym, a semi-supervised, adversarial neural network that learns to transfer cell identity annotations from one experiment to another. scNym takes advantage of information in both labeled datasets and new, unlabeled datasets to learn rich representations of cell identity that enable effective annotation transfer. We show that scNym effectively transfers annotations across experiments despite biological and technical differences, achieving performance superior to existing methods. We also show that scNym models can synthesize information from multiple training and target datasets to improve performance. In addition to high performance, we show that scNym models are well-calibrated and interpretable with saliency methods.

Skeletal muscle experiences a decline in lean mass and regenerative potential with age, in part due to intrinsic changes in progenitor cells. However, it remains unclear if age-related changes in progenitors persist across a differentiation trajectory or if new age-related changes manifest in differentiated cells. To investigate this possibility, we performed single cell RNA-seq on muscle mononuclear cells from young and aged mice and profiled muscle stem cells (MuSCs) and fibro/adipose progenitors (FAPs) after differentiation. Differentiation increased the magnitude of age-related change in MuSCs and FAPs, but also masked a subset of age-related changes present in progenitors. Using a dynamical systems approach and RNA velocity, we found that aged MuSCs follow the same differentiation trajectory as young cells, but stall in differentiation near a commitment decision. Our results suggest that age-related changes are plastic across differentiation trajectories and that fate commitment decisions are delayed in aged myogenic cells.

Summary Skeletal muscle experiences a decline in lean mass and regenerative potential with age, in part due to intrinsic changes in progenitor cells. However, it remains unclear how age-related changes in progenitors manifest across a differentiation trajectory. Here, we perform single-cell RNA sequencing (RNA-seq) on muscle mononuclear cells from young and aged mice and profile muscle stem cells (MuSCs) and fibro-adipose progenitors (FAPs) after differentiation. Differentiation increases the magnitude of age-related change in MuSCs and FAPs, but it also masks a subset of age-related changes present in progenitors. Using a dynamical systems approach and RNA velocity, we find that aged MuSCs follow the same differentiation trajectory as young cells but stall in differentiation near a commitment decision. Our results suggest that differentiation reveals latent features of aging and that fate commitment decisions are delayed in aged myogenic cells in vitro.

Single cell RNA sequencing (scRNA-seq) enables transcriptional profiling at the resolution of individual cells. These experiments measure features at the level of transcripts, but biological processes of interest often involve the complex coordination of many individual transcripts. It can therefore be difficult to extract interpretable insights directly from transcript-level cell profiles. Latent representations which capture biological variation in a smaller number of dimensions are therefore useful in interpreting many experiments. Variational autoencoders (VAEs) have emerged as a tool for scRNA-seq denoising and data harmonization, but the correspondence between latent dimensions in these models and generative factors remains unexplored. Here, we explore training VAEs with modifications to the objective function (i.e.β-VAE) to encourage disentanglement and make latent representations of single cell RNA-seq data more interpretable. Using simulated data, we find that VAE latent dimensions correspond more directly to data generative factors when using these modified objective functions. Applied to experimental data of stimulated peripheral blood mononuclear cells, we find better correspondence of latent dimensions to experimental factors and cell identity programs, but impaired performance on cell type clustering.Publication StatusThis pre-print represents the final output of a preliminary research direction and will not be updated or published in an archival journal. We are happy to discuss future directions we believe to be promising with any interested researchers.

Murine muscle stem cells (MuSCs) experience a transition from quiescence to activation that is required for regeneration, but it remains unknown if the trajectory and dynamics of activation change with age. Here, we use time-lapse imaging and single cell RNA-seq to measure activation trajectories and rates in young and aged MuSCs. We find that the activation trajectory is conserved in aged cells, and we develop effective machine-learning classifiers for cell age. Using cell-behavior analysis and RNA velocity, we find that activation kinetics are delayed in aged MuSCs, suggesting that changes in stem cell dynamics may contribute to impaired stem cell function with age. Intriguingly, we also find that stem cell activation appears to be a random walk-like process, with frequent reversals, rather than a continuous linear progression. These results support a view of the aged stem cell phenotype as a combination of differences in the location of stable cell states and differences in transition rates between them., Highlighted Article: Aged muscle stem cells display delayed activation dynamics, but retain a youthful activation trajectory, suggesting that changes to cell state dynamics might contribute to aging pathology.

Murine muscle stem cells (MuSCs) experience a transition from quiescence to activation that is required for regeneration, but it remains unclear if the transition states and rates of activation are uniform across cells, or how features of this process may change with age. Here, we use timelapse imaging and single cell RNA-seq to measure activation trajectories and rates in young and aged MuSCs. We find that the activation trajectory is conserved in aged cells, and develop effective machine learning classifiers for cell age. Using cell behavior analysis and RNA velocity, we find that activation kinetics are delayed in aged MuSCs, suggesting that changes in stem cell dynamics may contribute to impaired stem cell function with age. Intriguingly, we also find that stem cell activation appears to be a random walk like process, with frequent reversals, rather than a continuous, linear progression. These results support a view of the aged stem cell phenotype as a combination of differences in the location of stable cell states and differences in transition rates between them.Summary StatementWe find that aged muscle stem cells display delayed activation dynamics, but retain a youthful activation trajectory, suggesting that changes to cell state dynamics may contribute to aging pathology.

Cells in culture display diverse motility behaviors that may reflect differences in cell state and function, providing motivation to discriminate between different motility behaviors. Current methods to do so rely upon manual feature engineering. However, the types of features necessary to distinguish between motility behaviors can vary greatly depending on the biological context, and it is not always clear which features may be most predictive in each setting for distinguishing particular cell types or disease states. Convolutional neural networks (CNNs) are machine learning models allowing for relevant features to be learned directly from spatial data. Similarly, recurrent neural networks (RNNs) are a class of models capable of learning long term temporal dependencies. Given that cell motility is inherently spacio-temporal data, we present an approach utilizing both convolutional and long- short-term memory (LSTM) recurrent neural network units to analyze cell motility data. These RNN models provide accurate classification of simulated motility and experimentally measured motility from multiple cell types, comparable to results achieved with hand-engineered features. The variety of cell motility differences we can detect suggests that the algorithm is generally applicable to additional cell types not analyzed here. RNN autoencoders based on the same architecture are capable of learning motility features in an unsupervised manner and capturing variation between myogenic cells in the latent space. Adapting these RNN models to motility prediction, RNNs are capable of predicting muscle stem cell motility from past tracking data with performance superior to standard motion prediction models. This advance in cell motility prediction may be of practical utility in cell tracking applications.

Aging is a pleiotropic process affecting many aspects of mammalian physiology. Mammals are composed of distinct cell type identities and tissue environments, but the influence of these cell identities and environments on the trajectory of aging in individual cells remains unclear. Here, we performed single-cell RNA-seq on >50,000 individual cells across three tissues in young and old mice to allow for direct comparison of aging phenotypes across cell types. We found transcriptional features of aging common across many cell types, as well as features of aging unique to each type. Leveraging matrix factorization and optimal transport methods, we found that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating. These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.

BackgroundAging is a pleiotropic process affecting many aspects of organismal and cellular physiology. Mammalian organisms are composed of a constellation of distinct cell type and state identities residing within different tissue environments. Due to technological limitations, the study of aging has traditionally focused on changes within individual cell types, or the aggregate changes across cell types within a tissue. The influence of cell identity and tissue environment on the trajectory of aging therefore remains unclear.ResultsHere, we perform single cell RNA-seq on >50,000 individual cells across three tissues in young and aged mice. These molecular profiles allow for comparison of aging phenotypes across cell types and tissue environments. We find transcriptional features of aging common across many cell types, as well as features of aging unique to each type. Leveraging matrix factorization and optimal transport methods, we compute a trajectory and magnitude of aging for each cell type. We find that cell type exerts a larger influence on these measures than tissue environment.ConclusionIn this study, we use single cell RNA-seq to dissect the influence of cell identity and tissue environment on the aging process. Single cell analysis reveals that cell identities age in unique ways, with some common features of aging shared across identities. We find that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating. These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.

Cell populations display heterogeneous and dynamic phenotypic states at multiple scales. Similar to molecular features commonly used to explore cell heterogeneity, cell behavior is a rich phenotypic space that may allow for identification of relevant cell states. Inference of cell state from cell behavior across a time course may enable the investigation of dynamics of transitions between heterogeneous cell states, a task difficult to perform with destructive molecular observations. Cell motility is one such easily observed cell behavior with known biomedical relevance. To investigate heterogenous cell states and their dynamics through the lens of cell behavior, we developed Heteromotility, a software tool to extract quantitative motility features from timelapse cell images. In mouse embryonic fibroblasts (MEFs), myoblasts, and muscle stem cells (MuSCs), Heteromotility analysis identifies multiple motility phenotypes within the population. In all three systems, the motility state identity of individual cells is dynamic. Quantification of state transitions reveals that MuSCs undergoing activation transition through progressive motility states toward the myoblast phenotype. Transition rates during MuSC activation suggest non-linear kinetics. By probability flux analysis, we find that this MuSC motility state system breaks detailed balance, while the MEF and myoblast systems do not. Balanced behavior state transitions can be captured by equilibrium formalisms, while unbalanced switching between states violates equilibrium conditions and would require an external driving force. Our data indicate that the system regulating cell behavior can be decomposed into a set of attractor states which depend on the identity of the cell, together with a set of transitions between states. These results support a conceptual view of cell populations as dynamical systems, responding to inputs from signaling pathways and generating outputs in the form of state transitions and observable motile behaviors., Author summary Cells in a population are not all alike. The differences between cells impact how each cell responds to stimuli, with implications for development and disease. How do cells change between these functionally important states over time? This question is difficult to answer using molecular biology, because these methods destroy cells in order to observe them, preventing us from observing the same cell more than once. Cells in culture display a diverse set of motility behaviors that can be observed non-destructively over time. We have developed software to quantify these behaviors, and used this tool to identify cell motility states and quantify the changes in cell motility states over time. Using this approach, we identify that cancerous fibroblasts behave similarly to normal fibroblasts, but prefer some behaviors to others. This suggests cancerous transformation may lead to changes between states, rather than just creating new ones. Applying this analysis to muscle stem cells, we measure the rate of change from the stem cell to progenitor state for the first time. We are also able to determine if changes in behavior state are externally driven or random, demonstrating the utility of observing cell behaviors to study changes in cells over time.