Objective.Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform patterns-rhythmic and irregular oscillations and transient patterns of sharp waves or spikes-are potential phenotypical biomarkers, reflecting genotype-specific neural activity. This is especially relevant to diagnosing epilepsy without direct seizure observations, which is common in clinical settings, as well as in animal models, which often have subtle neurological phenotypes without overt epilepsy. Herein, we investigate genotypic prediction from long-term EEG signals of freely behaving mice belonging to six groups defined by the presence or absence of a neurological disease-genotype (TSC1gene knockout) in three different inbred strains with distinct genetic backgrounds.Approach.We propose a machine learning approach to predict the genotypes of individual mice from the occurrence counts of waveforms that approximate short windows of the EEG. That is, a dictionary of waveforms is optimized to approximate windows from each genotype, and the vectors of waveform occurrence counts are the features for predicting genotypes via logistic regression models.Main results.Across two-fold cross-validation of the waveform dictionary learning, and leave-one-individual-out genotype prediction, we find that waveform counts pooled over multiple hour segments enable reliable prediction of mouse strain with an accuracy of 70% (95% CI 62-78) compared to chance rate of 38%. For two of the three strains, DBA2 and C57B6, strain-specific classifiers reliably determined the epilepsy-genotype (TSC1gene knockout) with accuracies of 86% (95% CI 70-101) and 67% (95% 55-79), respectively. None of the mice of these strains had evidence of overt seizures or EEG-based seizure detection. In comparison, a state-of-the-art time-series classification approach (Hydra) enables higher strain classification at 98%, comparableTSC1-genotype prediction for the two strains (86% and 71% respectively), but the method is not interpretable.Significance.The methodologies and results show the potential of EEG waveforms as interpretable phenotypes and bag-of-waves as a feature representation for identifying epilepsy genotypes.

Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality despite major advances in pharmacological, devices, and surgical care. Gene editing technologies have introduced a transformative approach to correct pathogenic variants and modulate disease pathways. This review highlights recent progress in editing technologies that are currently or may soon be applied to address cardiovascular disorders, summarizes recent preclinical and clinical studies that demonstrate improved precision and efficacy, and examines technical and translational challenges that must be overcome for broader clinical application. We summarize preclinical advances, including refined target selection, improved delivery strategies, and enhanced therapeutic efficiency. We highlight emerging technologies that aim to overcome longstanding constraints such as limited vector cargo capacity, protospacer-adjacent motif (PAM) incompatibility, chromatin accessibility, suboptimal editing efficiency, and off-target activity. We also summarize the increasing clinical experience with in-vivo editing - particularly using lipid nanoparticle (LNP) and adeno-associated virus (AAV)-based platforms - that has also revealed important safety considerations, including vector immunogenicity, systemic inflammation, and organ-specific toxicities. Despite rapid progress, successful clinical translation of gene and base editing for CVD continues to hinge on two central challenges: efficient and precise delivery and mitigation of immunogenicity and toxicity from both delivery vectors and gene-editing enzymes. Although next-generation editors and targeted delivery systems have expanded the scope of feasible cardiovascular applications, overcoming these biological barriers remains the critical step toward achieving well tolerated, durable, one-time genomic therapies. Continued innovation in vector engineering, tissue-selective delivery, and immunologic risk mitigation will be essential for advancing editing technologies into cardiovascular care.

Next-generation sequencing has transformed cancer care by providing essential insights for diagnosis, prognosis, and treatment. However, variability in testing timing, reporting practices, and interpretation challenges limits its clinical impact. This article highlights key opportunities to optimize somatic reporting, emphasizing the importance of timely testing throughout the cancer care continuum to maximize the diagnostic and therapeutic relevance of findings. Technical factors such as test design, sequencing depth, and the use of liquid biopsy substantially influence result accuracy and interpretation, underscoring the need for careful integration with clinical history. Standardized reporting practices that clearly delineate diagnostic, prognostic, and therapeutic findings can enhance the clinical utility of next-generation sequencing results. Streamlined formats and curated clinical trial data further support actionable decision making. Additionally, direct patient engagement and education are essential for empowering patients to navigate genomic testing and make informed decisions about their care. By leveraging multidisciplinary tumor boards, decision-support tools, and emerging artificial intelligence technologies, clinicians can better navigate the complexities of somatic reports. Standardization and clarity in reporting are critical to advancing precision oncology, empowering providers and patients to make informed treatment decisions and improve outcomes.

Non-canonical myeloid cell populations are increasingly recognized as critical regulators of inflammation in neuroimmunological disease. Here, we investigate the role of alternatively activated neutrophils (aaN) in limiting encephalitogenic T cell responses during experimental autoimmune encephalomyelitis (EAE), a widely used model of multiple sclerosis. Arginase-1–expressing aaN were identified and characterized within central nervous system (CNS) infiltrates during EAE using flow cytometry, single-cell RNA sequencing, and fluorescent in situ hybridization (RNAscope) combined with immunohistochemistry. The immunomodulatory properties of aaN were evaluated in vitro using CD4⁺ T cell suppression assays and in vivo by adoptive transfer of ex vivo–generated aaN during the preclinical phase following encephalitogenic T-cell injection. aaN were consistently detected within the CNS throughout EAE and spatially co-localized with encephalitogenic T cells. Transcriptomic profiling of aaN revealed enrichment of pathways associated with regulation of T cell activation and immune suppression. CNS-derived aaN potently inhibited CD4⁺ T cell proliferation in vitro. Therapeutic augmentation of this population, via adoptive transfer of ex vivo–generated aaN into mice following the injection of encephalitogenic T cells, delayed clinical EAE onset, markedly reduced the accumulation of pathogenic T cells within CNS lesions, and significantly enhanced neuronal survival. Mechanistically, ex vivo–generated aaN suppressed T cell responses through a contact-dependent, PD-L1–independent pathway, indicating a previously unrecognized mode of neutrophil-mediated immunoregulation. These findings identify aaN as a previously underappreciated immunoregulatory population within the inflamed CNS that restrains pathogenic T cell responses and limits neuroinflammation during EAE. Collectively, our data support the therapeutic potential of strategies that augment aaN activity, including autologous aaN-based cell therapy or interventions that promote CNS homing, polarization, and persistence of endogenous aaN, as novel approaches for disease modification in multiple sclerosis.

The common marmoset is a New World monkey (NWM) commonly used as a model organism to investigate questions in primate evolution and human disease, including Alzheimer’s and other neurodegenerative diseases, as well as neuropsychiatric disorders. Here we present the first telomere-to-telomere (T2T) reference genome for the common marmoset, adding over 88 Mb of sequence and resolving challenging genomic regions. An additional near-T2T assembly from a second unrelated individual yields a total of four high-quality haplotypes for analysis. The improved contiguity and accuracy of these assemblies enable unprecedented insights into complex and rapidly evolving genomic regions such as centromeres, sex chromosomes, ribosomal DNA (rDNA) structure, and the major histocompatibility complex (MHC). We fully resolved all marmoset centromeres, uncovering dimeric alpha satellites with chromosomal specificity and stratified inactive layers documenting ancestral centromere turnover. We assembled six acrocentric autosomes with gene-poor, satellite-rich short arms and provide evidence that most of them can harbor rDNA and all of them share large pseudo-homologous regions (PHRs). The Y chromosome, but not the X chromosome, carries active rDNA and PHRs, and the rDNA copy number is sexually dimorphic. Chromosomes that share PHRs also share closely related centromeric satellite DNA, supporting a model of ongoing recombinational exchange between heterologous chromosomes facilitated by rDNA. We discovered multiple novel, marmoset-specific MHC genes that are predicted to protect against pathogens encountered in its environment. Leveraging this complete reference, we further identified over 500 transcribed genes with transcript models or expansions specific to the marmoset lineage. Together with additional long-read marmoset assemblies, these genomes were used to construct a marmoset pangenome, providing a robust reference framework for short-read mapping across diverse individuals. This resource will improve the utility of the common marmoset as a biomedical model organism and fill key gaps in our understanding of primate evolution.

In cancer and chronic infection, CD8 T cell exhaustion is hallmarked by expression of inhibitory receptors such as PD1, TIM3, LAG3 and others1-3. Thus, inhibitory molecule focus has been limited to cell-surface proteins. Here we evaluate the surface lipid metabolite phosphatidylserine (PS) as a regulator of exhaustion. PS primarily localizes to the inner plasma membrane of live cells but iswell known to be externalized to the outer membrane during cell death. The role of exposed PS on live immune cells is less clear. We show that viable, antigen-specific CD8 T cells externalize PS during lymphocytic choriomeningitis virus (LCMV) infection. T cell activation induced initial PS exposure, and chronic antigen stimulation sustained externalization. Transcriptomic and lipidomic analyses also identified PS accumulation in exhausted CD8 T cells. To evaluate a role for exposed PS in exhaustion, we treated LCMV chronically infected mice with a PS-targeting antibody (mch1N11)4 and found that it expanded LCMV-specific CD8 responses. PD1+TCF1+ stem-like CD8 T cells downregulated quiescence-associated gene modules and increased proliferation after antibody treatment, highlighting an inhibitory role for PS. Mechanistically, exposed PS on T cells functioned extrinsically to suppress dendritic cell immunostimulatory phenotypes, in turn limiting CD8 T cell responses. PS-targeting antibody with anti-PDL1 synergized to increase CD8 responses and improve viral control. Finally, we show that PD1+ CD8 T cells from human tumours can also expose PS. In summary, we detail CD8 T cell PS biology and provide insight into a mechanism by which exposed PS functions as a 'non-classical' extrinsic inhibitory molecule in exhaustion.

Manual frailty index (FI) assessment in mice is a strong predictor of morbidity and mortality, and is frequently used in mechanistic and translational geroscience. However, it is labor-intensive, requires expert training, and is vulnerable to scorer variability. We previously developed a visual frailty index (vFI) that objectively predicts age and frailty using expert-defined, supervised behavioral features extracted from open-field videos. However, relying solely on human-defined features may miss subtle, latent behavioral signatures of aging. Here, we test whether unsupervised behavioral discovery using Keypoint-MoSeq (KPMS) could uncover these hidden signatures and improve the prediction of aging-related outcomes. Using a large dataset of isogenic C57BL/6J (B6J) and genetically diverse Diversity Outbred (DO) mice, we find that unsupervised features are highly predictive of chronological age, biological frailty, and the proportion of life lived. Notably, while supervised features overall outperformed unsupervised features in these tasks, combining both feature sets yielded the highest predictive accuracy across all outcomes. Despite these improvements, models trained on either feature set failed to generalize across strains, confirming that behavioral manifestations of aging are strongly population-specific. These findings demonstrate that supervised and unsupervised machine vision provide complementary information, establishing a highly sensitive, scalable, and non-invasive framework for objective and scalable geroscience in rodents.

The aim of this White Paper is to establish a foundational framework for research, technological development, and regulation in the emerging field of stem cell-based embryo models (SCBEMs). These models, generated from Pluripotent Stem Cells, are designed to recapitulate essential events in early stages of human development. They have the potential to illuminate the early stages of embryo development and implantation and hold promise as an avenue to address global health challenges, including infertility and pregnancy loss, congenital, neonatal and adult conditions, and the need for organ transplants. While SCBEMs are not a substitute for human embryos, their tractability for large-scale analysis and their abilities to model the earliest stages of embryonic development suggest that they will have a significant impact on reproductive biology and regenerative medicine. But SCBEMs do not just raise novel scientific questions; they pose ethical and legal questions that need to be addressed. The paper stems from a meeting of a core group of researchers that met at the Institut Pasteur in Paris in November 2024 and represents the views of an extended group that has worked to elaborate the documents as a consensus for the field. Here, we provide a framework to guide research in this new field. We do this by summarizing the state of the science, assessing current SCBEM research in relation to its primary future applications and addressing the need for continued ethical and regulatory oversight associated with this new field.

Humanized APOE targeted-replacement (TR) mice are essential tools for studying apoE isoform effects in Alzheimer’s disease (AD) and other apoE-related disorders. Despite their widespread use, existing APOE mouse models, generated with different gene targeting strategies, have not been directly compared in terms of apoE isoform expression, lipid profiles, and transcriptomic signatures. Such differences could impact how we interpret APOE genotype-related outcomes, as well as related underlying molecular mechanisms. We conducted a comprehensive molecular comparison of humanized APOE mouse models from three sources: Taconic Biosciences (TAC), the Cure Alzheimer’s Fund (CAF), and The Jackson Laboratory (JAX). We assessed apoE protein and transcript levels, peripheral plasma lipid composition, and bulk brain transcriptomics. ApoE isoform levels were evaluated by biochemical and proteomic measurements. Peripheral lipids, including low-density lipoprotein (LDL), high-density lipoprotein (HDL), cholesterol, and triglycerides, were also measured. We employed complementary bioinformatics analyses to evaluate brain transcriptomes and identify differentially expressed genes (DEGs) and networks based on source, APOE genotype, and sex. We found that apoE isoforms exhibited differential levels among the three sources in the brain, liver, and plasma. Peripheral lipoproteins and lipids, including LDL, HDL, cholesterol, and triglycerides, also showed distinct concentrations in each source and genotype. Importantly, we identified distinct brain transcriptional signatures among these mouse models, which were influenced by source, APOE genotype, and sex. Finally, our analysis revealed specific differentially expressed genes and pathways impacted by source, genotype, and sex. Our findings highlight APOE genotype- and source-dependent variations in apoE isoform levels, lipid profiles, and molecular pathways. This study underscores the importance of consistency and caution in choosing and utilizing humanized APOE mouse models, offering molecular insights into key apoE-related outcomes.