Biology subjects

Alizadeh, E.

Publications and source records attributed to Alizadeh, E..

7 recordsLinked to original sources

bioRxiv2025

High-Resolution Spatial Transcriptomics Reveals Fibroblast and Neuroimmune Microenvironments in Endometriosis Lesions

Endometriosis is a chronic, systemic, inflammatory disease characterized by the presence of endometrium-like tissue growing outside of the uterus. One of its main symptoms is chronic pain and inflammation leading to a decreased quality of life. This is a common disease, as at least one in ten female-born individuals have endometriosis. Yet the understanding of the mechanisms that drive pain symptoms and disease progression remain poorly defined. This study establishes the precise spatial transcriptomic cartography of human ovarian and peritoneal lesions, two of the most commonly found lesions. We identified shared spatial features across lesion types, including immune cell infiltration, fibroblast specific compartments surrounding epithelial glands, and distinct distributions of neuronal and macrophage subsets. We precisely defined sensory neuronal subtypes, and mapped their spatial location relative to immune cells. We further validate the epithelial-neuronal interactome, using an in vitro 3D model of peripheral sensory brain organoids co-cultured with human endometriosis epithelial and fibroblast cells. By mapping spatial cellular interactions and identifying conserved features across lesion types linked to pain, our study provides emerging insights into endometriosis pathophysiology, paving the way for the development of novel targeted therapeutic strategies.

cell biology↗

bioRxiv2024

Cell-based high-content approach for SARS-CoV-2 neutralization identifies unique monoclonal antibodies and PI3K pathway inhibitors.

The sudden rise of the SARS-CoV-2 virus and the delay in the development of effective therapeutics to mitigate it made evident a need for ways to screen for compounds that can block infection and prevent further pathogenesis and spread. Yet, identifying effective drugs efficacious against viral infection and replication with minimal toxicity for the patient can be difficult. Monoclonal antibodies were shown to be effective, yet as the SARS-CoV-2 mutated, these antibodies became ineffective. Small molecule antivirals were identified using pseudovirus constructs to recapitulate infection in non-human cells, such as Vero E6 cells. However, the impact was limited due to poor translation of these compounds in the clinical setting. This is partly due to the lack of similarity of screening platforms to the in vivo physiology of the patient and partly because drugs effective in vitro showed dose-limiting toxicities. In this study, we performed two high-throughput screens in human lung adenocarcinoma cells with authentic SARS-CoV-2 virus to identify both monoclonal antibodies that neutralize the virus and clinically useful kinase inhibitors to block the virus and prioritize minimal host toxicity. Using high-content imaging combined with single-cell and multidimensional analysis, we identified antibodies and kinase inhibitors that reduce virus infection without affecting the host. Our screening technique uncovered novel antibodies and overlooked kinase inhibitors (i.e. PIK3i, mTORi, multiple RTKi) that could be effective against SARS-CoV-2 virus. Further characterization of these molecules will streamline the repurposing of compounds for the treatment of future pandemics and uncover novel mechanisms viruses use to hijack and infect host cells.

pharmacology and toxicology↗

bioRxiv2024

Inferring single-cell spatial gene expression with tissue morphology via explainable deep learning

Deep learning models trained with spatial omics data uncover complex patterns and relationships among cells, genes, and proteins in a high-dimensional space. State-of-the-art in silico spatial multi-cell gene expression methods using histological images of tissue stained with hematoxylin and eosin (H&E) allow us to characterize cellular heterogeneity. We developed a vision transformer (ViT) framework to map histological signatures to spatial single-cell transcriptomic signatures, named SPiRiT. SPiRiT predicts single-cell spatial gene expression using the matched H&E image tiles of human breast cancer and whole mouse pup, evaluated by Xenium (10x Genomics) datasets. Importantly, SPiRiT incorporates rigorous strategies to ensure reproducibility and robustness of predictions and provides trustworthy interpretation through attention-based model explainability. SPiRiT model interpretation revealed the areas, and attention details it uses to predict gene expressions like marker genes in invasive cancer cells. In an apple-to-apple comparison with ST-Net, SPiRiT improved the predictive accuracy by 40%. These gene predictions and expression levels were highly consistent with the tumor region annotation. In summary, SPiRiT highlights the feasibility to infer spatial single-cell gene expression using tissue morphology in multiple-species.

bioinformatics↗

bioRxiv2023

Efficacy of Drpitor1a, a Dynamin-Related Protein 1 inhibitor, in Pulmonary Arterial Hypertension

RationaleDynamin-related protein 1 (Drp1), a large GTPase, mediates mitochondrial fission. Increased Drp1-mediated fission permits accelerated mitosis, contributing to hyperproliferation of pulmonary artery smooth muscle cells (PASMC), which characterizes pulmonary arterial hypertension (PAH). We developed a Drp1 inhibitor, Drpitor1a, and tested its ability to regress PAH. ObjectivesAssess Drpitor1as efficacy and toxicity in: a)normal and PAH human PASMC (hPASMC); b)normal rats versus rats with established monocrotaline (MCT)-induced PAH. MethodsDrpitor1as effects on recombinant and endogenous Drp1-GTPase activity, mitochondrial fission, and cell proliferation were studied in hPASMCs (normal=3; PAH=5). Drpitor1as pharmacokinetics and tissue concentrations were measured (n=3 rats/sex). In a pilot study (n=3-4/sex/dose), Drpitor1a (1mg/kg/48-hours, intravenous) reduced adverse PA remodeling only in females. Consequently, we compared Drpitor1a to vehicle in normal (n=6 versus 8) and MCT-PAH (n=9 and 11) females, respectively. Drpitor1a treatment began 17-days post-MCT with echocardiography and cardiac catheterization performed 28-29 days post-MCT. ResultsDrpitor1a inhibited recombinant and endogenous Drp1 GTPase activity, which was increased in PAH hPASMC. Drpitor1a inhibited mitochondrial fission and proliferation and induced apoptosis, in PAH hPASMC but not normal hPASMC. Drpitor1a tissue levels were higher in female versus male RVs. In MCT-PAH females, Drpitor1a regressed PA obstruction, lowered pulmonary vascular resistance, and improved RV function, without hematologic, renal, or hepatic toxicity. ConclusionsDrpitor1a inhibits Drp1 GTPase, reduces mitochondrial fission, and inhibits cell proliferation in PAH hPASMC. Drpitor1a caused no toxicity in MCT-PAH and had no significant effect on normal rats or hPASMCs. Drpitor1a is a potential PAH therapeutic which displays an interesting therapeutic sexual dimorphism.

pharmacology and toxicology↗

bioRxiv2022

Feedback in the β-catenin destruction complex imparts bistability and cellular memory

Wnt ligands are considered classical morphogens, for which the strength of the cellular response is proportional to the concentration of the ligand. Herein, we show an emergent property of bistability arising from feedback among the Wnt destruction complex proteins that target the key transcriptional co-activator {beta}-catenin for degradation. Using biochemical reconstitution, we identified positive feedback between the scaffold protein Axin and the kinase GSK3. Theoretical modeling of this feedback between Axin and GSK3 predicted that the activity of the destruction complex exhibits bistable behavior. We experimentally confirmed these predictions by demonstrating that cellular cytoplasmic {beta}-catenin concentrations exhibit an "all-or-none" response with sustained memory (hysteresis) of the signaling input. This bistable behavior was transformed into a graded response and memory was lost through inhibition of GSK3. These findings provide a mechanism for establishing decisive, switch-like cellular response and memory upon Wnt pathway stimulation. One Sentence SummaryPositive feedback within the {beta}-catenin destruction complex gives rise to bistability and memory in response to Wnt stimulation, imparting signal transduction accuracy and insulation.

cell biology↗

bioRxiv2018

Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation

--Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the methods accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

systems biology↗

bioRxiv2018

A comprehensive suite of measures to quantify cell shape and actin organization

A number of recent studies have shown that cell shape and cytoskeletal texture can be used as sensitive readouts of the physiological state of the cell. However, utilization of this information requires the development of quantitative measures that can describe relevant aspects of cell shape. In this paper we develop a toolbox, TISMorph, to calculate set of quantitative measures to address this need. Some of the measures introduced here are used previously and others are new and have desirable properties for shape and texture quantification of cells. These measures, broadly classifiable into the categories of textural, spreading and irregularity measures, are tested by using them to discriminate between osteosarcoma cell lines treated with different cytoskeletal drugs. We find that even though specific classification tasks often rely on a few measures, these are not the same between all classification tasks, thus requiring the use of the entire suite of measures for classification and discrimination. We provide detailed descriptions of the measures, well as TISMorph package to implement them. Image based quantitative analysis has the potential to become a new field of biological data (\"image-omics\"), providing quantitative insight into cellular processes.

bioengineering↗