Biology subjects

Amouzgar, M.

Publications and source records attributed to Amouzgar, M..

6 recordsLinked to original sources

bioRxiv2026

APOE4 Drives Uniquely Dysfunctional Human Microglial States in Alzheimer's Disease

Variation in APOE, notably the {varepsilon}4 allele, profoundly shapes risk and severity of late-onset Alzheimers disease (AD), yet how it remodels human microglial states remains unresolved. We combine spatially resolved proteomic profiling with single-nuclear multiomic analyses to define microglial organization across APOE3/3 and APOE4/4 genotypes in AD. Quantifying condition-associated variation across the cellular manifold reveals a continuous landscape of microglial states. APOE4/4 shifts cells toward terminal states marked by loss of homeostatic identity, metabolic disruption, and incomplete acquisition of disease-associated programs. We identify an APOE4/4-enriched population in AD that exhibits inflammatory signaling without effective metabolic or phagocytic engagement, localizing to niches of gliosis and senescence, and coupled to chronic stress adaptation programs. Together with evidence that APOE4/4 potentiates the activation threshold of nascent microglia, these findings establish a unified framework for human microglial state change, linking genetic risk to spatial and molecular organization of immune responses in the AD brain. Graphical Abstract.APOE4/4 in Alzheimers disease reshapes microglial fate along continuous trajectories characterized by proteomic, transcriptional, and epigenetic programs consistent with chronic stress adaptation, alongside distinct composite spatial niches comprised of astrocytic gliosis and cellular senescence. O_FIG O_LINKSMALLFIG WIDTH=168 HEIGHT=200 SRC="FIGDIR/small/733295v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@b2afa4org.highwire.dtl.DTLVardef@12978f8org.highwire.dtl.DTLVardef@1c4e46dorg.highwire.dtl.DTLVardef@170e44d_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

Cell Cycle Sensing Shapes Human T Cell Fate and Exhaustion Programs

Cell cycle (CC) dynamics are reflected in diverse T cell processes such as TCR activation, expansion, contraction, differentiation, senescence, anergy, and exhaustion; linking CC behaviors to functional and dysfunctional T cell states in development and disease. Progression through CC checkpoints is also tightly linked to cell fate decisions across development. Yet, much remains unknown about the connection between CC sensing and T cell differentiation programs. To disentangle the relationship across T cell state, time-since-activation, receptor signaling, division, and CC, we leverage high-throughput single-cell mass cytometry for parallel measurement of these diverse biological states. By modulating CC progression and receptor signaling with inhibitors as well as tonic signaling Chimeric Antigen Receptor (CAR) models of T cell exhaustion, we reveal that earlier G1/S CC programs crosstalk with receptor signaling to control T cell fate, and that exhaustion programs are downstream to aberrant, S-G2 phase CC arrest signatures in tonic CAR signaling in vitro, in situ, and in vivo across human cancers in association with CD8 T-lymphocyte dysfunction.

immunology↗

bioRxiv2025

A deep single cell mass cytometry approach to capture canonical and noncanonical cell cycle states

The cell cycle (CC) is involved in diverse cell processes like cell differentiation, immune cell expansion, and tumorigenesis but current single-cell (sc) strategies study CC as: (i) coarse phases, (ii) are based on transcriptomic data, (iii) leverage imaging modalities that are not extensible outside adherent cell culture, or (iv) do not enable high-throughput, single-cell multiplexing. To solve this, we developed an expanded, metal-tagged antibody approach for Mass Cytometry (MC) with 48 CC-related molecules that more deeply phenotypes the diversity of scCC states. Using cytometry by time of flight (CyTOF), we quantified CC states across various suspension and adherent cell lines and stimulated primary human T cells. Our approach captures the diversity of scCC states, including atypical molecular signatures that do not meet the canonical definitions of CC phases in the literature. Multiplexing with pharmacologically-induced CC arrest reveals that drug-induced CC perturbations can exacerbate reported noncanonical states and induce previously unobserved states. Notably, cells escaping CC inhibitor action in primary cells demonstrated more aberrant CC states compared to their untreated counterparts. Overall, our approach enables deeper phenotyping of CC biology that generalizes to diverse cell systems and can simultaneously multiplex experimental perturbation & disease systems, or integrate with other MC measurement platforms.

cell biology↗

bioRxiv2025

Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence

Gliomas are among the most lethal cancers, with limited treatment options. To uncover hallmarks of therapeutic escape and tumor microenvironment (TME) evolution, we applied spatial proteomics, transcriptomics, and glycomics to 670 lesions from 310 adult and pediatric patients. Single-cell analysis shows high B7H3+ tumor cell prevalence in glioblastoma (GBM) and pleomorphic xanthoastrocytoma (PXA), while most gliomas, including pediatric cases, express targetable tumor antigens in less than 50% of tumor cells, potentially explaining trial failures. Longitudinal samples of isocitrate dehydrogenase (IDH)-mutant gliomas reveal recurrence driven by tumor-immune spatial reorganization, shifting from T-cell and vasculature-associated myeloid cell-enriched niches to microglia and CD206+ macrophage-dominated tumors. Multi-omic integration identified N-glycosylation as the best classifier of grade, while the immune transcriptome best predicted GBM survival. Provided as a community resource, this study opens new avenues for glioma targeting, classification, outcome prediction, and a baseline of TME composition across all stages.

cancer biology↗

bioRxiv2023

Autophagy counters inflammation-driven glycolytic impairment in aging hematopoietic stem cells

Aging of the hematopoietic system promotes various blood, immune and systemic disorders and is largely driven by hematopoietic stem cell (HSC) dysfunction (1). Autophagy is central for the benefits associated with activation of longevity signaling programs (2), and for HSC function and response to nutrient stress (3,4). With age, a subset of HSCs increases autophagy flux and preserves some regenerative capacity, while the rest fail to engage autophagy and become metabolically overactivated and dysfunctional (4). However, the signals that promote autophagy in old HSCs and the mechanisms responsible for the increased regenerative potential of autophagy-activated old HSCs remain unknown. Here, we demonstrate that autophagy activation is an adaptive survival response to chronic inflammation in the aging bone marrow (BM) niche (5). We find that inflammation impairs glucose metabolism and suppresses glycolysis in aged HSCs through Socs3-mediated impairment of AKT/FoxO-dependent signaling. In this context, we show that inflammation-mediated autophagy engagement preserves functional quiescence by enabling metabolic adaptation to glycolytic impairment. Moreover, we demonstrate that transient autophagy induction via a short-term fasting/refeeding paradigm normalizes glucose uptake and glycolytic flux and significantly improves old HSC regenerative potential. Our results identify inflammation-driven glucose hypometabolism as a key driver of HSC dysfunction with age and establish autophagy as a targetable node to reset old HSC glycolytic and regenerative capacity. One-Sentence SummaryAutophagy compensates for chronic inflammation-induced metabolic deregulation in old HSCs, and its transient modulation can reset old HSC glycolytic and regenerative capacity.

cell biology↗

bioRxiv2022

Supervised dimensionality reduction for exploration of single-cell data by Hybrid Subset Selection - Linear Discriminant Analysis

Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction enables visualization of data by representing cells in two-dimensional plots that capture the structure and heterogeneity of the original dataset. Visualizations contribute to human understanding of data and are useful for guiding both quantitative and qualitative analysis of cellular relationships. Existing algorithms are typically unsupervised, utilizing only measured features to generate manifolds, disregarding known biological labels such as cell type or experimental timepoint. Here, we repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling users to tailor visualizations to separate specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this flexible, computationally-efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes, such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality reduction algorithms and illustrate its utility and versatility for exploration of single-cell mass cytometry, transcriptomics and chromatin accessibility data.

bioinformatics↗