Behavioral evidence of olfactory imprinting during embryonic and larval stages in lake sturgeon.

Many migratory fishes are thought to navigate to natal streams using olfactory cues learned during early life stages. However, direct evidence for early-life olfactory imprinting is largely limited to Pacific salmon, and other species suspected to imprint show life history traits and reproductive strategies that raise uncertainty about the generality of the salmonid-based conceptual model of olfactory imprinting in fishes. Here, we studied early-life olfactory imprinting in lake sturgeon (Acipenser fulvescens), which have a life cycle notably different from Pacific salmon, but are nonetheless hypothesized to home via similar mechanisms. We tested one critical prediction of the hypothesis that early-life olfactory imprinting guides natal homing in lake sturgeon: that exposure to odorants during early-life stages results in increased activity when exposed to those odorants later in life. Lake sturgeon were exposed to artificial odorants (phenethyl alcohol and morpholine) during specific developmental windows and durations (limited to the egg, free-embryo, exogenous feeding larvae and juvenile stages), and later tested as juveniles for behavioral responses to the odorants that were demonstrative of olfactory memory. Experiments revealed that lake sturgeon reared in stream water mixed with artificial odorants for as little as 7 days responded to the odorants in behavioral assays over 50 days after the initial exposure, specifically implicating the free-embryo and larval stages as critical imprinting periods. Our study provides evidence for olfactory imprinting in a non-salmonid fish species, and supports further consideration of conservation tactics such as stream-side rearing facilities that are designed to encourage olfactory imprinting to targeted streams during early life stages. Continued research on lake sturgeon can contribute to a model of olfactory imprinting that is more generalizable across diverse fish species and will inform conservation actions for one of the world's most imperiled fish taxonomic groups.

Determination of thyroid hormones in lake sturgeon (Acipenser fulvescens) tissues at different developmental stages using a sensitive liquid chromatography-mass spectrometry method.

Thyroid hormones (TH) are known to play an important role in the growth and development of vertebrates. In fish species, TH regulates the larval-juvenile metamorphosis, and is crucial for development during early life stages. Monitoring the variations in TH levels at different life stages can provide insights into the regulation of metamorphosis and fish development. In this study, we developed an extremely sensitive method for the quantification of thyroxine (T4), triiodothyronine (T3), and reverse-triiodothyronine (rT3), in lake sturgeon (Acipenser fulvescens) tissues from eggs, free embryos, larvae, and juveniles. The target compounds were extracted by an enzymatic digestion method, followed by protein precipitation. Further cleanup was performed by liquid-liquid extraction (LLE) and solid phase extraction (SPE) using SampliQ OPT cartridges. The liquid-chromatography tandem mass spectrometry (LC-MS/MS) method used to quantify TH compounds showed remarkably high sensitivity with the limit of detection (LOD) and the limit of quantification (LOQ) ranging from < 1 pg/mL to 10 pg/mL and linearity in the range of 10-50,000 pg/mL. This method was validated for tissue samples across several early developmental stages and was checked for intra- and inter-day accuracy (78.3-111.2 %) and precision (0.1-4.9 %), matrix effect (75.4-134.1 %), and recovery (41.2-69.0 %). The method was successfully applied for the quantification and comparison of T4, T3 and rT3 in hatchery raised lake sturgeon samples collected at unique time points (i.e., days post fertilization dpf) including fertilized eggs (11 dpf), free embryos (14 dpf), larvae (22 dpf), juveniles (40 dpf) and older juveniles (74 dpf). With modifications, this method could be applied to other species important for agriculture or conservation.

Diverse partial reprogramming strategies restore youthful gene expression and transiently suppress cell identity.

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.

Differentiation reveals latent features of aging and an energy barrier in murine myogenesis.

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.

Semisupervised adversarial neural networks for single-cell classification.

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.

Deep Convolutional and Recurrent Neural Networks for Cell Motility Discrimination and Prediction.

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.

Unprovoked Stabilization and Nuclear Accumulation of the Naked Mole-Rat p53 Protein.

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.

Aging induces aberrant state transition kinetics in murine muscle stem cells.

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.

Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging.

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.

Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance.

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.