Biology subjects

Nguyen, T. T.

Publications and source records attributed to Nguyen, T. T..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Glia phagocytose neuronal sphingolipids to infiltrate developing synapses

The complex morphologies of mature neurons and glia emerge through profound rearrangements of cell membranes during development. Despite being integral components of these membranes, it is unclear whether lipids might actively sculpt these morphogenic processes. By analyzing lipid levels in the developing fruit fly brain, we discover dramatic increases in specific sphingolipids coinciding with neural circuit establishment. Disrupting this sphingolipid bolus via genetic perturbations of sphingolipid biosynthesis and catabolism leads to impaired glial autophagy. Remarkably, glia can obtain sphingolipid precursors needed for autophagy by phagocytosing neurons. These precursors are then converted into specific long-chain ceramide phosphoethanolamines (CPEs), invertebrate analogs of sphingomyelin. These lipids are essential for glia to arborize and infiltrate the brain, a critical step in circuit maturation that when disrupted leads to reduced synapse numbers. Taken together, our results demonstrate how spatiotemporal tuning of sphingolipid metabolism during development plays an instructive role in programming brain architecture. HighlightsO_LIBrain sphingolipids (SLs) remodel to very long-chain species during circuit maturation C_LIO_LIGlial autophagy requires de novo SL biosynthesis coordinated across neurons and glia C_LIO_LIGlia evade a biosynthetic blockade by phagolysosomal salvage of neuronal SLs C_LIO_LICeramide Phosphoethanolamine is critical for glial infiltration and synapse density C_LI

neuroscience↗

bioRxiv2025

Antibody responses against bacterial glycans affinity mature and diversify in germinal centers.

Anti-carbohydrate antibodies (Abs) play crucial roles in pathogen control, but their generation remains poorly understood. By studying responses to Streptococcus pyogenes in humans, we reveal that the glycan-targeted response shifts from IgM towards IgG and IgA memory with age and antigen exposure across blood, spleen, and tonsils. Both natural colonization and controlled human infection with S. pyogenes increased class-switched B cells, with evidence of within-clone switching. Glycan-specific B cells readily participated in germinal center (GC) responses and showed robust somatic hypermutation despite a molecular signature consistent with receiving reduced T cell help. We conclude that mucosal pathogen encounters elicit glycan responses that class-switch, evolve and diversify through the GC. These findings reveal how age and infection history can influence the quality, quantity, and isotype use of glycan-specific B cells, with implications for the design and schedule of glycan-containing vaccines.

immunology↗

bioRxiv2025

Detecting Temporal Changes in Skin Surrogate Model Using Hyperspectral Imaging

Advancements in biomedical imaging have increasingly explored innovative models for studying human skin conditions. Recent research highlights a biochemical connection between banana peels and human skin, particularly through the enzyme tyrosinase, which plays a key role in pigmentation and oxidative stress. Studies have shown that, much like human skin, banana peels undergo oxidative browning due to tyrosinase activity. Leveraging this similarity, we have developed imaging techniques to track biochemical changes in bananas as a potential surrogate model for the detection of skin changes such as sun spots or melanoma. This paper introduces the application of hyperspectral imaging (HSI) to analyse the spectral properties of banana peels as a method for studying pigmentation disorders and oxidative damage. We present a HSI system designed to capture spectral reflectance characteristics across the 440 nm to 900 nm range, with a hyperspectral cube resolution of 956x952x96. Our approach enables a more targeted spectral assessment by dynamically selecting and investigating specific regions in banana samples over multiple days. Preliminary spectral analysis demonstrates the systems capability to detect reflectance variations linked to biochemical changes such as ripeness, bruising, and oxidative stress--factors that parallel pigmentation alterations in human skin. However, direct hyperspectral imaging can be resource intensive, making it less accessible for applications in dermatology and mobile health diagnostics. To address this, we conducted preliminary experiments to reconstruct 27-band hyperspectral representations in the VIS spectrum from RGB images. The observed spectral variations and successful reconstruction results confirm the potential of HSI for capturing biochemical changes and provide a foundation for future biomedical imaging research, particularly in melanoma detection and dermatology-related applications.

bioengineering↗

bioRxiv2025

Deep Learning-based Framework for Mycobacterium tuberculosis Bacterial Growth Detection for Antimicrobial Susceptibility Testing

Tuberculosis (TB) kills more people annually than any other pathogen. Resistance is an ever-increasing global problem, not least because diagnostics remain challenging and access limited. 96-well broth microdilution plates offer one approach to high-throughput phenotypic testing, but they can be challenging to read. Automated Mycobacterial Growth Detection Algorithm (AMyGDA) is a software package that uses image processing techniques to read plates, but struggles with plates that exhibit low growth or images of low quality. We developed a new framework, TMAS (TB Microbial Analysis System), which leverages state-of-the-art deep learning models to detect M. tuberculosis growth in images of 96-well microtiter plates. TMAS is designed to measure Minimum Inhibitory Concentrations (MICs) rapidly and accurately while differentiating between true bacterial growth and artefacts. Using 4,018 plate images from the CRyPTIC (Comprehensive Resistance Prediction for Tuberculosis: An International Consortium) dataset to train models and refine the framework, TMAS achieved an essential agreement of 98.8%. TMAS offers a reliable, automated and complementary evaluation to support expert interpretation, potentially improving accuracy and efficiency in tuberculosis drug susceptibility testing (DST). Author summaryTuberculosis (TB) is one of the worlds leading causes of death from infectious diseases, with drug resistance making treatment increasingly difficult. Accurate and timely drug susceptibility testing (DST) is essential to determine which antibiotics remain effective against a given TB strain. A widely used DST method involves 96-well broth microdilution plates, where bacteria are exposed to different antibiotic concentrations to determine the minimum inhibitory concentration (MIC), the lowest concentration that fully prevents bacterial growth. However, manually interpreting these plates is time-consuming and prone to human error, while existing automated methods often struggle with imaging artifacts such as condensation, shadows, and contamination. In this study, we developed the Tuberculosis Microbial Analysis System (TMAS), a machine learning-based tool designed to automate MIC determination from plate images. Training on advanced deep learning models, TMAS can learn to detect bacterial growth with high precision, distinguishing true growth from artefacts such as shadows, bubbles, sediment, condensation and contamination. When tested on a large dataset, TMAS outperformed existing automated methods in accuracy, reliability and efficiency. By reducing the reliance on manual interpretation, TMAS has the potential to streamline TB diagnostics, improve efficiency in laboratories, and improve access to high-quality DST, particularly in resource-limited settings.

microbiology↗

bioRxiv2025

Integrative Epigenomic Landscape of Alzheimer's Disease Brains Reveals Oligodendrocyte Molecular Perturbations Associated with Tau

Alzheimers disease (AD) brains are characterized by neuropathologic and biochemical changes that are highly variable across individuals. Capturing epigenetic factors that associate with this variability can reveal novel biological insights into AD pathophysiology. We conducted an epigenome-wide association study of DNA methylation (DNAm) in 472 AD brains with neuropathologic measures (Braak stage, Thal phase, and cerebral amyloid angiopathy score) and brain biochemical levels of five proteins (APOE, amyloid-{beta} (A{beta})40, A{beta}42, tau, and p-tau) core to AD pathogenesis. Using a novel regional methylation (rCpGm) approach, we identified 5,478 significant associations, 99.7% of which were with brain tau biochemical measures. Of the tau-associated rCpGms, 93 had concordant associations in external datasets comprising 1,337 brain samples. Integrative transcriptome-methylome analyses uncovered 535 significant gene expression associations for these 93 rCpGms. Genes with concurrent transcriptome-methylome perturbations were enriched in oligodendrocyte marker genes, including known AD risk genes such as BIN1, myelination genes MYRF, MBP and MAG previously implicated in AD, as well as novel genes like LDB3. We further annotated the top oligodendrocyte genes in an additional 6 brain single cell and 2 bulk transcriptome datasets from AD and two other tauopathies, Picks disease and progressive supranuclear palsy (PSP). Our findings support consistent rCpGm and gene expression associations with these tauopathies and tau-related phenotypes in both bulk brain tissue and oligodendrocyte clusters. In summary, we uncover the integrative epigenomic landscape of AD and demonstrate tau-related oligodendrocyte gene perturbations as a common potential pathomechanism across different tauopathies.

genomics↗

bioRxiv2024

Epigenetic reprogramming induced by Acetyl-CoA and SAM depletion is an evolutionarily-ancient path to malignant growth

Despite the vast diversity of life forms and living histories, it appears that all branches of Metazoa face the challenge of tumor growth. Contrary to human tumors, which take years to form, tumors in short-living species can arise within days without accumulating multiple mutations, raising the question whether the paths to tumorigenesis in diverse species have any commonalities. In a fly tumor model caused by loss of cell polarity genes, we first identified the rise of a glycolytic cell population over time, resembling the Wartburg effect observed in human tumors. We further identified two key metabolic changes in these fly tumors. First, a systemic depletion of acetyl-CoA leads to a reduction in histone acetylation levels and stochastic silencing of actively-transcribed genes. Second, defects in the methionine cycle cause a systemic depletion of S-Adenosyl methionine, which further reduces histone methylation levels and causes stochastic activation of transposons. Perturbation of the methionine metabolic process strongly inhibits tumor growth. Finally, to understand the evolutionary origin of tumorigenesis, we performed comparative studies of fly and human tumors, and identified human tumors that exhibit metabolic signatures similar to those of fly tumors. We found that human tumors with high metabolic similarity to fly tumors have a lower mutational load, younger patient age, and lower DNA methylation levels. This study suggests that tumorigenesis processes have a deep evolutionary origin and highlights that depletion of key metabolites is an evolutionarily-ancient driving force for tumorigenesis.

developmental biology↗

bioRxiv2024

High-dimensional confounding in causal mediation: a comparison study of double machine learning and regularized partial correlation network

In causal mediation analyses, of interest are the direct or indirect pathways from exposure to an outcome variable. For observation studies, massive baseline characteristics are collected as potential confounders to mitigate selection bias, possibly approaching or exceeding the sample size. Accordingly, flexible machine learning approaches are promising in filtering a subset of relevant confounders, along with estimation using the efficient influence function to avoid overfitting. Among various confounding selection strategies, two attract growing attention. One is the popular debiased, or double machine learning (DML), and another is the penalized partial correlation via fitting a Gaussian graphical network model between the confounders and the response variable. Nonetheless, for causal mediation analyses when encountering high-dimensional confounders, there is a gap in determining the best strategy for confounding selection. Therefore, we exemplify a motivating study on the human microbiome, where the dimensions of mediator and confounders approach or exceed the sample size to compare possible combinations of confounding selection methods. By deriving the multiply robust causal direct and indirect effects across various hypotheses, our comprehensive illustrations offer methodological implications on how the confounding selection impacts the final causal target parameter estimation while generating causality insights in demystifying the "gut-brain axis". Our results highlighted the practicality and necessity of the discussed methods, which not only guide real-world applications for practitioners but also motivate future advancements for this crucial topic in the era of big data.

microbiology↗

bioRxiv2024

Impaired S-nitrosylation of Cx43 prevents arrhythmogenicity and myocardial injury upon cardiac stress in Duchenne Muscular Dystrophy

Connexin-43 (Cx43) plays a critical role in the propagation of action potentials and cardiac contractility. In healthy cardiomyocytes, Cx43 is mainly located at the intercalated disk; however, Cx43 remodeling is observed in cardiac pathologies and is linked with arrhythmogenesis and sudden cardiac death. Using a mouse model of Duchenne muscular dystrophy (DMD), we previously demonstrated that Cx43 localizes to the lateral side of dystrophic cardiomyocytes, forming undocked hemichannels. {beta}-adrenergic signaling-induced cardiac stress promotes S-nitrosylation and the opening of undocked Cx43 hemichannels leading to disrupted cardiac membrane excitability and deadly arrhythmogenic behaviors. To establish the direct role of S-nitrosylated Cx43 in DMD cardiomyopathy, we generated knockin DMDmdx mice with reduced levels of S-nitrosylated Cx43, by replacing cysteine 271 with a serine in one Cx43 of the unique site for S-nitrosylation of Cx43 (DMDmdx:C271S+/-). Immunofluorescence analysis revealed that cardiac Cx43 lateralization in DMDmdx:C271S+/- mice was similar to DMDmdx mice, indicating that the genetic modification did not prevent Cx43 remodeling. Upon isoproterenol treatment, DMDmdx mice displayed a higher incidence of arrhythmogenic events when compared to DMDmdx:C271S+/- mice, which more closely resemble wild-type mice. Optical mapping imaging in isolated hearts showed that DMDmdx mice displayed aberrant Ca2+ signaling and prolonged action potentials, which is restored in DMDmdx:C271S+/- mice. Isoproterenol treatment evoked severe myocardial injury in DMDmdx mice, which was significantly attenuated in DMDmdx:C271S+/- mice. Notably, DMDmdx mice treated with Gap19, a Cx43 hemichannel blocker, exhibited cardioprotection against myocardial injury. We concluded that S-nitrosylation of Cx43 proteins is a fundamental NO-mediated mechanism involved in arrhythmias and myocardial injury in DMDmdx, occurring through the opening of hemichannels following {beta}-adrenergic stress.

physiology↗

bioRxiv2024

Tensor Kernel Learning for Classification of Alzheimer's Conditions using Multimodal Data

Early, timely, and accurate assessment of Alzheimers disease (AD), particularly at its earlier stage - mild cognitive impairment (MCI) -, is central to detecting, managing, and potentially treating the disease. The biological underpinnings of AD, however, is multifaceted, from genetic variations, abnormal protein accumulation, to irregular brain functions and structure. A joint analysis of these data, therefore, may offer potentially new insights about AD-related biomarkers and AD prediction. But such explorations must confront the complexity of these data: heterogeneity, multimodality and high-dimensionality. Here, to address these challenges, we propose a new machine-learning method, namely the tensor kernel learning (TKL), leveraging tensor methods and kernel learning, to enhance AD assessment by enhancing multi-modal data integration. More specifically, TKL first uses CP/PARAFAC decomposition and graph diffusion to fuse multiple kernels learned from four complementary data modalities (MRI, PET, CSF, and SNP data). We then used a supervised kernel for a kernel SVM classifier to identify potential patients. To evaluate the effectiveness of TKL, we apply it to data from the Alzheimers Disease Neuroimaging Initiative (ADNI; the sample size n = 331 subjects), including cognitively normal individuals (CN), MCI subjects, and AD patients. TKL improves AD classification performance in both linear and nonlinear combinations, achieving accuracies of 91.31% for CN vs. AD, 81.45% for CN vs. MCI, and 78.27% for AD vs. MCI, compared to 85.48%, 70.89%, and 73.51% using the best single modality. Additionally, TKL reveals clearer, more structured patterns in the data, enhancing interpretation and understanding of the relationships among different modalities. Detailed implementation details of our method can be found at: https://github.com/thanhvd18/Tensor-Kernel-Learning-matlab.

neuroscience↗

bioRxiv2024

Extracellular rRNA profiling reveals the sinking and cell lysis dynamics of marine microeukaryotes

Marine plankton communities consist of numerous species, and their composition and physiological states are closely linked to ecosystem functions. Understanding biogeochemical cycles requires measuring taxon-specific mortality due to viral lysis or sloppy feeding, as the dissolved organic matter released contributes to rapid nutrient recycling and long-term carbon sequestration following microbial transformation. This study introduces a quantitative and comprehensive analysis of microeukaryotes in the dissolved constituents of seawater by using Mortality by Ribosomal Sequencing (MoRS) method. Our experimental pipeline successfully recovered 83% of cell-free rRNA. The ratio of cell-free rRNA to cell-associated rRNA was more than 10-fold higher in the mesopelagic layer than in the upper epipelagic layer, suggesting the mesopelagic zone as a hotspot for eukaryotic cell lysis. Many protist lineages, including phytoplankton such as haptophytes, are less susceptible to cell lysis in the epipelagic layer yet are actively lysed in the mesopelagic zone. Notably, over 86% of the significantly lysed species in the mesopelagic layer showed a habitat preference for the epipelagic layer. These findings indicate that sinking from the surface and lysis in the mesopelagic are prevalent dynamics for various eukaryotes.

ecology↗

bioRxiv2024

RealtimeDecoder: A fast software module for online clusterless decoding

Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing neural activity. That in turn allows experimenters to test hypotheses about the role of that content in driving downstream activity patterns and behaviors. While multiple real-time systems have been developed, they are typically implemented in C++ and are locked to a specific data acquisition system, making them difficult to adapt to new experiments. Here we present a Python software system that implements online clusterless decoding using state space models in a manner independent of data acquisition systems. The parallelized system processes neural data with temporal resolution of 6 ms and median computational latency <50 ms for medium- to large-scale (32+ tetrodes) rodent hippocampus recordings without the need for spike sorting. It also executes auxiliary functions such as detecting sharp wave ripples from local field potential (LFP) data. Performance is similar to state-of-the-art solutions which use compiled programming languages. We demonstrate this system use in a rat behavior experiment in which the decoder allowed closed loop neurofeedback based on decoded hippocampal spatial representations . This system provides a powerful and easy-to-modify tool for real-time feedback experiments.

neuroscience↗

bioRxiv2024

Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer's Disease and Older Controls in Multi-Ethnic Populations

INTRODUCTIONMulti-omics studies in Alzheimers disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked African Americans (AA) and Latin Americans (LA), who are disproportionately affected by AD. METHODSTo bridge this gap, Accelerating Medicines Partnership in AD (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors. RESULTSWe generated multi-omics data and curated and harmonized phenotypic data from AA (n=306), LA (n=326), or AA and LA (n=4) brain donors plus Non-Hispanic White (n=252) and other (n=20) ethnic groups, to establish a foundational dataset enriched for AA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures. DISCUSSIONInclusion of traditionally underrepresented groups in multi-omics studies is essential to discover the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD.

neuroscience↗

bioRxiv2024

A stress-dependent TDP-43 SUMOylation program preserves neuronal function

Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are overwhelmingly linked to TDP-43 dysfunction. Mutations in TDP-43 are rare, indicating that the progressive accumulation of exogenous factors - such as cellular stressors - converge on TDP-43 to play a key role in disease pathogenesis. Post translational modifications such as SUMOylation play essential roles in response to such exogenous stressors. We therefore set out to understand how SUMOylation may regulate TDP-43 in health and disease. We find that TDP-43 is regulated dynamically via SUMOylation in response to cellular stressors. When this process is blocked in vivo, we note age-dependent TDP-43 pathology and sex-specific behavioral deficits linking TDP-43 SUMOylation with aging and disease. We further find that SUMOylation is correlated with human aging and disease states. Collectively, this work presents TDP-43 SUMOylation as an early physiological response to cellular stress, disruption of which may confer a risk for TDP-43 proteinopathy.

neuroscience↗

bioRxiv2024

AMRomics: a scalable workflow to analyze large microbial genome collection

Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, a minimalized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license.

bioinformatics↗

bioRxiv2024

Cell aspect ratio is a mechanical winning strategy in microbial competition

Bacterial competition shapes community architecture, yet a universally conserved determinant remains elusive. We show that cell aspect ratio -a simple morphological feature- confers a competitive advantage. Using growth-based range expansion experiments, we show that longer bacteria conquer the expanding front, even when initially in minority. Using an agent-based model of dividing bacteria, to isolate the effect of aspect ratio, we reveal that the takeover mechanism is collective alignment: groups of locally aligned bacteria form "nematic arms" bridging the central region of the colony to the expanding front. Once at the front, bacteria align parallel to it and block shorter bacteria from access to nutrients and space. We confirm this observation with single-cell experiments and further generalise our findings by introducing a generic continuum model of alignment-dominated competition, explaining both experimental and cell-based model observations. Moreover, we extend our predictions to spherical range expansions and confirm the competitive advantage, even though the effect is less pronounced than in surface-attached colonies. Our results uncover a simple, yet hitherto overlooked, mechanical mechanism determining the outcome of bacterial competition, which is potentially ubiquitous among various bacteria. Current advances in genetic engineering enable aspect ratio tuning as a mechanism with broad implications for biofilm control.

biophysics↗

bioRxiv2023

Propagation of a rapid cell-to-cell H2O2 signal over long distances in a monolayer of cardiomyocyte cells

Cell-to-cell communication plays a cardinal role in the biology of multicellular organisms. H2O2 is an important cell-to-cell signaling molecule involved in the response of mammalian cells to wounding and other stimuli. We previously identified a signaling pathway that transmits wound-induced cell-to-cell H2O2 signals within minutes over long distances, measured in centimeters, in a monolayer of cardiomyocytes. Here we report that this long-distance H2O2 signaling pathway is accompanied by enhanced accumulation of cytosolic H2O2 and altered redox state in cells along its path. We further show that it requires the production of superoxide, as well as the function of gap junctions, and that it is accompanied by changes in the abundance of hundreds of proteins in cells along its path. Our findings highlight the existence of a unique and rapid long-distance H2O2 signaling pathway that could play an important role in different inflammatory responses, wound responses/healing, cardiovascular disease, and/or other conditions. HighlightsO_LIWounding induces an H2O2 cell-to-cell signal in a monolayer of cardiomyocytes. C_LIO_LIThe cell-to-cell signal requires H2O2 and O2{middle dot}- accumulation along its path. C_LIO_LIThe signal propagates over several centimeters changing the redox state of cells. C_LIO_LIChanges in the abundance of hundreds of proteins accompanies the signal. C_LIO_LIThe cell-to-cell signal requires paracrine and juxtacrine signaling. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/572374v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@3b3806org.highwire.dtl.DTLVardef@1db37fcorg.highwire.dtl.DTLVardef@138bdd7org.highwire.dtl.DTLVardef@377402_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Physical activity regulates the immune response to breast cancer by a hematopoietic stem cell-autonomous mechanism

Physical activity is a modifiable lifestyle factor that is associated with a decreased risk for the development of breast cancer. While the exact mechanisms for the reduction in cancer risk due to physical activity are largely unknown, it is postulated that the biological reduction in cancer risk is driven by improvements in inflammation and immune function with exercise. Hematopoietic stem cells (HSCs) are the progenitor for all of the cells of the immune system and are involved in cancer immunosurveillance through differentiation into cytotoxic cell population. In this study, we investigate the role of physical activity (PA) in a spontaneously occurring model of breast cancer over time, with a focus on tumor incidence, circulating and tumor-infiltrating immune cells as well gene expression profiles of tumors and hematopoietic stem cells. Furthermore, we show that, in addition to a direct effect of PA on the immune cells of tumor-bearing mice, PA reduces the oxidative stress in HSCs of wildtype and tumor-bearing mice, and by doing so, alters the differentiation of the HSCs towards T cells in order to enhance cancer immunosurveillance.

cancer biology↗

bioRxiv2023

A distinct class of hematopoietic stem cells develop from the human yellow bone marrow

During aging, adipose tissue within the bone marrow expands while the trabecular red marrow contracts. The impact of these changes on blood cell formation remains unclear. To address this question, we performed single-cell and single-nuclei transcriptomic analysis on adipose-rich yellow bone marrow (BMY) and adipose-poor trabecular red marrow (BMR) from human subjects undergoing lower limb amputations. Surprisingly, we discovered two distinct hematopoietic niches, in which BMY contains a higher number of monocytes and progenitor cells expressing genes associated with inflammation. To further investigate these niches, we developed an in-vitro organoid system that maintains features of the human bone marrow. We find cells from BMY are distinct in their expression of the leptin receptor, and respond to leptin stimulation with enhanced proliferation, leading to increased monocyte production. These findings suggest that the age-associated expansion of bone marrow adipose tissue drives a pro-inflammatory state by stimulating monocyte production from a spatially distinct, leptin-responsive hematopoietic stem/progenitor cell population. SignificanceThis study reveals that adipose tissue within the human bone marrow is a niche for hematopoietic stem and progenitor cells that can give rise to pro-inflammatory monocytes through leptin signaling. Expansion of bone marrow adipose tissue with age and stress may thus underlie inflammageing.

developmental biology↗