Biology subjects

Dan, S.

Publications and source records attributed to Dan, S..

15 recordsLinked to original sources

bioRxiv2026

PIANO: Probabilistic Inference Autoencoder Networks for multi-Omics enables robust generative modeling of gene expression and scales single-cell integration to 100 million cells

Single-cell RNA technologies enable the routine acquisition of transcriptomic atlases. However, these molecular profiles are influenced by overlapping sources of variation. Since these covariates confound comparisons, data integration is the first step in most analyses. Three challenges remain: correcting strong batch effects, scaling to millions of cells, and modeling how covariates influence gene expression. To address these challenges, we developed PIANO: Probabilistic Inference Autoencoder Networks for multi-Omics, a deep learning framework whose central feature is a generative model of gene expression data. Additionally, PIANO achieves robust integrations and trains 10x faster than previous methods. PIANO accurately integrates single-cell data across species and across single-cell and spatial transcriptomics modalities. As practical applications, PIANO models spatially-resolved gene expression during Alzheimer's disease progression in human brains and integrates over 100 million cancer cells to model drug perturbations. In summary, PIANO's integration and generative modeling capabilities will empower novel insights for countless future studies.

bioinformatics↗

bioRxiv2026

Enzyme activity as an actionable axis for small-molecule precision oncology

Current precision oncology--molecular targeted therapies and immunotherapies--relies on genomic or expressed biomarkers, yet most cancer patients remain ineligible for these treatments. Here, we establish enzyme activity as an actionable and orthogonal axis for precision cancer medicine. Strategic activity-based screening of mouse organs and human clinical specimens with a panel of enzyme-reactive fluorescence probes identified {beta}-galactosidase 1 (GLB1) and {beta}-hexosaminidases (HEX) as broadly elevated tumor-selective biomarkers. Leveraging these activities, we developed 7-ethyl-10-hydroxycamptothecin (SN38)-based GLB1-and HEX-reactive prodrugs. These prodrugs exhibited dramatically reduced systemic toxicities and improved therapeutic windows, compared to a clinically used SN38-based prodrug, irinotecan (CPT-11). Both prodrugs demonstrated activity-dependent therapeutic efficacy, affording a dramatic reduction of tumor volumes across multiple in vivo models, including a subcutaneous patient-derived xenograft (PDX) of lung squamous cell carcinoma that lacked genetic alterations targeted by current precision medicine. Furthermore, this strategy is broadly applicable across various cytotoxic payloads, establishing a generalizable platform for small-molecule precision medicines. Our results define an enzyme-targeting paradigm for precision oncology, in which fluorescence probes serve as companion diagnostic tools to guide development and selection of appropriately targeted prodrugs, which are expected to provide safer and more efficacious treatment options for cancer patients with elevated enzyme activities.

cancer biology↗

bioRxiv2025

Cross-species consensus atlas of the primate basal ganglia

The basal ganglia (BG) are conserved brain regions essential for motor control, learning, emotion, and cognition, and are implicated in neurological and psychiatric disease. Yet a unified cross-species taxonomy of BG cell types is lacking, limiting translation of BG circuit mechanisms, interpretation of human genetic risk, and development of cell type-targeted tools. We present a multiomic consensus atlas of 1.8 million nuclei from human, macaque, and marmoset spanning eight BG structures. Integrating cross-species gene expression, open chromatin, and spatial profiling enables definition of conserved and divergent cell types. Alignment to existing mouse and human atlases identifies 61 homologous cell types conserved over 80 million years. We identify a STRd D2 StrioMat Hybrid medium spiny neuron (MSN) type with molecular, electrophysiological, and morphological features that clarify hybrid MSN identities. Comparative cis-regulatory analysis reveals conserved sequence grammars that encode cell identity and inform viral targeting strategies, providing a foundational resource for BG evolution, function, and disease.

neuroscience↗

bioRxiv2025

A cross-species spatial transcriptomic atlas of the human and non-human primate basal ganglia

The basal ganglia are interconnected subcortical nuclei with complex topographical organization that orchestrate goal-directed behaviors and are implicated in neurodegenerative movement disorders. We generated a cellular-resolution, spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, sampling over one million cells in each species. By integrating spatial data with a cross-species, consensus snRNA-seq cell type taxonomy, this atlas reveals conserved principles of molecular organization within and across structures. The cellular architecture is complex but highly stereotyped, with gene expression gradients superimposed onto discrete compartments. Extensive spatial sampling illuminates 3D gradients of molecular organization in the striatum and reveals cell type-specific core and shell compartments in the primate internal globus pallidus, which is conserved with mouse. This unified, cross-species spatial transcriptomic atlas will be a foundational resource for characterizing the molecular and functional organization of the basal ganglia and their roles in health and disease.

neuroscience↗

bioRxiv2025

Spatial patterning of transcriptional and regulatory programs in the primate subcortex

Mammalian brain cell identity is shaped by intrinsic factors and external context. We present a spatially resolved transcriptomic and gene regulatory atlas of cell types across all subcortical regions in a primate, the common marmoset. Dense sampling and cross-species integration revealed spatially precise neuronal assemblies, including in complex midbrain and diencephalic structures. Chromatin accessibility and transcriptional identity are spatially tuned within and across subcortical structures; spatial gradients within hippocampal subfields are orchestrated by graded transcription factors acting through graded enhancers. The primate-expanded thalamic GABAergic population shares transcriptional and regulatory syntax with conserved midbrain populations, reflecting an evolutionary adaptation compared with rodents. Similar regional expression across cell types can arise by distinct regulatory architectures, as for telencephalic astrocytes and neurons. Conversely, distant cell types can share regulatory programs despite divergent identities: striatal GABAergic medium spiny neurons and telencephalic glutamatergic neurons share a postsynaptic regulatory program despite divergent lineage, region, and neurotransmitter identity.

neuroscience↗

bioRxiv2025

Fast Optimization of Robust Transcriptomics Embeddings using Probabilistic Inference Autoencoder Networks for multi-Omics

Advances in single-cell genomics technologies enable the routine acquisition of atlases with millions of cells. These datasets often include multiple covariates, such as donors, sequencing platforms, developmental timepoints, and species, which provide new opportunities for discovery and new challenges. To mitigate unwanted sources of variation, dataset integration is the starting point for most analyses. However, existing methods struggle with integrating large complex datasets. To address these limitations, we developed PIANO, a variational autoencoder framework that uses a negative binomial generalized linear model for stronger batch correction, and code compilation for ten times faster training than existing tools. We first demonstrate performant integration compared to commonly used methods on single-species datasets. We then show PIANO enables superior analyses of multiple atlases, solving challenging integration tasks across sequencing platforms, development, and species, while simultaneously preserving desired biological signals. Our contributions include a novel, high-performance integration method and recommendations for integration applications.

neuroscience↗

bioRxiv2025

A Clinically Aligned Murine Model of Electroconvulsive Stimulation Reverses Social Aversion and Displays Fear Memory Impairment After Chronic Social Defeat Stress

Electroconvulsive therapy (ECT) is the most effective treatment for patients with major depression, bipolar depression, mania, catatonia, and schizophrenia. Nonetheless, the mechanisms underlying its therapeutic effects largely remain unknown. While previous preclinical studies have noted a role for neurotropic signaling, neurogenesis, and alterations in monoamine neurotransmitter systems, these models were largely conducted using procedures that deviate from clinical practice. Therefore, we sought to develop a clinically relevant murine model of ECT, referred to as electroconvulsive stimulation (ECS) in animals, which more closely aligns with current clinical approaches to better explore its mechanisms. Using the well-established chronic social defeat stress (CSDS) paradigm, known to negatively impact reward processes, we investigated whether the behavioral changes after CSDS could be reversed following a clinically related course of ECS. Additionally, we observed induction of plasticity-related genes in the nucleus accumbens (NAcc) and amygdala, regions responsible for reward and fear-related memory, respectively. Lastly, we investigated ECS-related changes in the NAcc with bulk RNA-sequencing. Pathway analysis demonstrated cellular changes primarily involved in neuroplasticity and regulating cell migration and differentiation. Therefore, utilizing our novel and clinically relevant model of ECS, we have begun to elucidate mechanisms that contribute to ECTs therapeutic outcomes by examining murine behavior and RNA from brain regions associated with stress-induced states that model anxiety and depression and the effects of ECS.

neuroscience↗

bioRxiv2025

GASTON-Mix: a unified model of spatial gradients and domains using spatial mixture-of-experts

MotivationGene expression varies across a tissue due to both the organization of the tissue into spatial domains, i.e. discrete regions of a tissue with distinct cell type composition, and continuous spatial gradients of gene expression within different spatial domains. Spatially resolved transcriptomics (SRT) technologies provide high-throughput measurements of gene expression in a tissue slice, enabling the characterization of spatial gradients and domains. However, existing computational methods for quantifying spatial variation in gene expression either model only spatial domains - and do not account for continuous gradients of expression - or require restrictive geometric assumptions on the spatial domains and spatial gradients that do not hold for many complex tissues. ResultsWe introduce GASTON-Mix, a machine learning algorithm to identify both spatial domains and spatial gradients within each domain from SRT data. GASTON-Mix extends the mixture-of-experts (MoE) deep learning framework to a spatial MoE model, combining the clustering component of the MoE model with a neural field model that learns a separate 1-D coordinate ("isodepth") within each domain. The spatial MoE is capable of representing any geometric arrangement of spatial domains in a tissue, and the isodepth coordinates define continuous gradients of gene expression within each domain. We show using simulations and real data that GASTON-Mix identifies spatial domains and spatial gradients of gene expression more accurately than existing methods. GASTON-Mix reveals spatial gradients in the striatum and lateral septum that regulate complex social behavior, and GASTON-Mix identifies localized spatial gradients of hypoxia and TNF- signaling in the tumor microenvironment.

bioinformatics↗

bioRxiv2024

A novel epigenetic clock for rhesus macaques unveils an association between early life adversity and epigenetic age acceleration

Because DNA methylation changes reliably with age, machine learning models called epigenetic clocks can estimate an individuals age based on their DNA methylation profile. This epigenetic measure of age can deviate from ones true age, and the difference between the epigenetic age and true age, known as epigenetic age acceleration (EAA), has been found to directly correlate with morbidity and mortality in adults. Emerging evidence suggests that EAA is also associated with aberrant health outcomes in children, making epigenetic clocks useful tools for studying aging and development. We developed two highly accurate epigenetic clocks for the rhesus macaque, utilizing 1,008 blood samples from 690 macaques between 2 days and 23.4 years of age with diverse genetic backgrounds and exposure to environmental conditions. The first clock, which is trained on all samples, achieves a Pearson correlation between true age and predicted age of 0.983 and median absolute error of 0.210 years. To study phenotypes during development, the second clock is optimized for macaques younger than 6 years and achieves a Pearson correlation of 0.974 and a median absolute error of 0.148 years. Using the latter clock, we investigated whether epigenetic aging is affected by early life adversity in the form of infant maltreatment. Our data suggests that maltreatment and increased hair cortisol levels are associated with epigenetic age acceleration right after the period of maltreatment.

neuroscience↗

bioRxiv2024

Spatial transcriptomics reveals modulation of transcriptional networks across brain regions after auditory threat conditioning

Prior research has demonstrated genome-wide transcriptional changes related to fear and anxiety across species, often focusing on individual brain regions or cell types. However, the extent of gene expression differences across brain regions and how these changes interact at the level of transcriptional connectivity remains unclear. To address this, we performed spatial transcriptomics RNAseq analyses in an auditory threat conditioning paradigm in mice. We generated a spatial transcriptomic atlas of a coronal mouse brain section covering cortical and subcortical regions, corresponding to histologically defined regions. Our finding revealed widespread transcriptional responses across all brain regions examined, particularly in the medial and lateral habenula, and the choroid plexus. Network analyses highlighted altered transcriptional connectivity between cortical and subcortical regions, emphasizing the role of steroidogenic factor 1. These results provide new insights into the transcriptional networks involved in auditory threat conditioning, enhancing our understanding of molecular and neural mechanisms underlying fear and anxiety disorders.

neuroscience↗

bioRxiv2023

Dimensional clinical phenotyping using post-mortem brain donor medical records: Association with neuropathology

INTRODUCTIONTransdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to postmortem work, as assessment of newly developed phenotypic concepts needs to rely on existing records. METHODSWe adapted well-validated methodologies to compute NIMH research domain criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether RDoC cognitive domain scores were associated with hallmark Alzheimers disease (AD) neuropathological measures. RESULTSOur results confirm an association of EHR-derived cognitive scores with hallmark neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal ({beta}=0.38, p=0.0004), parietal ({beta}=0.35, p=0.0008), temporal ({beta}=0.37, p=0. 0004) and occipital ({beta}=0.37, p=0.0003) lobes. DISCUSSIONThis proof of concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from postmortem EHR.

neuroscience↗

bioRxiv2021

The spatio-temporal program of liver zonal regeneration

The liver carries a remarkable ability to regenerate rapidly after acute zonal damage. Single-cell approaches are necessary to study this process, given the spatial heterogeneity of multiple liver cell types. Here, we use spatially-resolved single cell RNA sequencing (scRNAseq) to study the dynamics of mouse liver regeneration after acute acetaminophen (APAP) intoxication. We find that hepatocytes proliferate throughout the liver lobule, creating the mitotic pressure required to repopulate the necrotic pericentral zone rapidly. A subset of hepatocytes located at the regenerating front transiently up-regulate fetal-specific genes, including Afp and Cdh17, as they reprogram to a pericentral state. Zonated endothelial, hepatic-stellate cell (HSC) and macrophage populations are differentially involved in immune recruitment, proliferation and matrix remodeling. We observe massive transient infiltration of myeloid cells, yet stability of lymphoid cell abundance, in accordance with global decline in antigen presentation. Our study provides a resource for understanding the coordinated programs of zonal liver regeneration.

cell biology↗

bioRxiv2021

Biomorphogenic feedbacks and the spatial organisation of a dominant grass steer dune development

Nature-based solutions to mitigate the impact of future climate change depend on restoring biological diversity and natural processes. Coastal foredunes represent the most important natural flood barriers along coastlines worldwide, but their area has been squeezed dramatically because of a continuing urbanisation of coastlines, especially in Europe. Dune development is steered by the development of vegetation in interaction with sand fluxes from the beach. Marram grass (Calamagrostis arenaria, formerly Ammophila arenaria) is the main dune building species along most European coasts, but also in other continents where the species was introduced. Engineering of coastal dunes, for instance by building dunes in front of dikes, needs to be based on a solid understanding of the species interactions with the environment. Only quantitative approaches enable the further development of mechanistic models and coastal management strategies that encapsulate these biomorphogenic interactions. We here provide a quantitative review of the main biotic and physical interactions that affect marram grass performance, their interactions with sand fluxes and how they eventually shape dune development. Our review highlights that the species spatial organisation is central to dune development. We further demonstrate this importance by means of remote sensing and a mechanistic model and provide an outlook for further research on the use of coastal dunes as a nature-based solution for coastal protection.

ecology↗

bioRxiv2020

A putative long noncoding RNA-encoded micropeptide maintains cellular homeostasis in pancreatic β-cells

Micropeptides (microproteins) encoded by transcripts previously annotated as long noncoding RNA (IncRNAs) are emerging as important mediators of fundamental biological processes in health and disease. Here we applied two computational tools to identify putative micropeptides encoded by lncRNAs that are expressed in the human pancreas. We experimentally verified one such micropeptide encoded by a {beta}-cell- and neural cell-enriched lncRNA TUNAR (also known as TUNA, HI-LNC78 or LINC00617). We named this highly conserved 48-amino-acid micropeptide Beta cell- and Neural cell-regulin (BNLN). BNLN contains a single-pass transmembrane domain and localized at the endoplasmic reticulum in pancreatic {beta}-cells. Overexpression of BNLN lowered ER calcium levels, increased cytosolic calcium levels, and maintained ER homeostasis in response to high glucose challenge. To determine the physiological and pathological roles of BNLN, we assessed the BNLN expression in islets from mice fed with a high-fat diet and a regular diet, and found that BNLN is suppressed by diet-induced obesity (DIO). Conversely, overexpression of BNLN elevated glucose-stimulated insulin secretion in INS-1 cells. Lastly, BNLN overexpression enhanced insulin secretion in islets from lean and obese mice as well as from humans. Taken together, our study provides the first evidence that lncRNA-encoded micropeptides play a critical role in pancreatic {beta}-cell function and provides a foundation for future comprehensive analyses of micropeptide function and pathophysiological impact on diabetes.

molecular biology↗

bioRxiv2020

Dynamic metabolic reprogramming in dendritic cells: an early response to influenza infection that is essential for effector function

Infection with the influenza virus triggers an innate immune response aimed at initiating the adaptive response to halt viral replication and spread. However, the metabolic response fueling the molecular mechanisms underlying changes in innate immune cell homeostasis remain undefined. Thus, we compared the metabolic response of dendritic cells to that of those infected with active and inactive influenza A virus or treated with toll like receptor agonists. While influenza infects dendritic cells, it does not productively replicate in these cells, and therefore metabolic changes upon infection may represent an adaptive response on the part of the host cells. Using quantitative mass spectrometry along with pulse chase substrate utilization assays and metabolic flux measurements, we found global metabolic changes 17 hours post infection, including significant changes in carbon commitment via glycolysis and glutaminolysis, as well as ATP production via TCA cycle and oxidative phosphorylation. Influenza infection of dendritic cells led to a metabolic phenotype, distinct from that induced by TLR agonists, with significant resilience in terms of metabolic plasticity. We identified Myc as one transcription factor modulating this response. Restriction of either Myc activity or mitochondrial substrates resulted in significant changes in the innate immune functions of dendritic cells, including reduced motility and T cell activation. Transcriptome analysis of inflammatory dendritic cells isolated following influenza infection showed similar metabolic reprogramming occurs in vivo. Thus, early in the infection process dendritic cells respond with global metabolic restructuring that is present in lung DC 9 days following infection and impacts their effector function, suggesting that metabolic switching in dendritic cells plays a vital role in initiating the immune response to influenza infection. Author SummaryIn response to influenza infection we found that dendritic cells, cells that are critical in mounting an effective immune response, undergo a profound metabolic shift. They alter the concentration and location of hundreds of proteins, including c-MYC, mediating a shift to a highly glycolytic phenotype that is also flexible in terms of fueling respiration. Dendritic cells initiate the immune response to influenza and activate the adaptive response allowing viral clearance and manifesting immune memory for protection against subsequent infections. We found that limiting access to specific metabolic pathways or substrates diminished key immune functions. Previously we described an immediate, fixed, hypermetabolic state in infected respiratory epithelial cells. We now show the metabolic responses of epithelial and dendritic cells are distinct. Here, we also demonstrate that dendritic cells tailor their metabolic response to the pathogen or TLR stimulus. This metabolic reprogramming occurs rapidly in vitro and it is sustained in inflammatory dendritic cells in vivo for at least 9 days following influenza infection. Thus, drugs targeting metabolism are likely to have cell- and pathogen-specific activities in the context of infection. These studies open the possibility of modulating the immune response to viral infection via customizing metabolic therapy to enhance or diminish the function of specific cells.

immunology↗