Biology subjects

Momenzadeh, A.

Publications and source records attributed to Momenzadeh, A..

7 recordsLinked to original sources

bioRxiv2026

Robotic perturbation proteomics and AI agents enable scalable drug mechanism discovery

Large-scale mass spectrometry-based proteomic screening could reveal cellular mechanisms of drug action at systems resolution but remains limited by experimental complexity and the difficulty of extracting insight from high-dimensional datasets. Here, we describe an end-to-end platform that combines semi-automated sample preparation, rapid LC-MS/MS, and AI agent-based data analysis to enable scalable proteomic screening. In a screen of 172 compounds in HepG2 cells, we generated 1,232 proteomes with more than 8,700 quantified proteins in approximately three weeks. Agentic AI reduced data analysis and interpretation time to less than one day while translating proteomic measurements into structured mechanism-oriented summaries and experimentally testable hypotheses. Guided by this framework, we validated: (1) a cholesterol-lowering effect of methylene blue in vitro and (2) an association between loratadine exposure and increased circulating iron in matched electronic health record analyses. This work establishes a scalable platform for generating proteomic drug perturbation data and automatically converting that data into mechanistic insights and candidate translational hypotheses using AI.

systems biology↗

bioRxiv2025

Single Cell Proteomics Reveals Novel Cell Phenotypes in Marfan Mouse Aneurysm

BackgroundSingle-cell omics technology is a powerful tool in biomedical research. However, single cell proteomics has lagged due to an inability to amplify peptides in a similar fashion to nucleotide strings. Single cell proteomics is important because proteins are the main functional unit in cells, and they often poorly correlate with mRNA quantities. In this paper we describe the first single cell proteomic analysis of complex tissue, comparing aneurysmal and normal mouse aorta from males and females. We also compare and integrate our single cell proteomic profiles with a matching single cell transcriptomics dataset. MethodsWe compared single cell proteomes between male and female, wild-type and Fbn1C1041G/+ Marfan mice (N=3 per group). Individual cells from mouse aortic root single cell suspensions were deposited in 384 well plates and subjected to ultra-sensitive nanoflow liquid chromatography-ion mobility-time of flight-mass spectrometry. The data were analyzed with leiden clustering to identify cell types. Statistical analyses were performed to detect differential proteins within cell types and multi-omics analysis integrated single cell proteomics with published single cell RNA-seq. ResultsWe identified all major aortic cell types including 7 distinct smooth muscle cell subtypes. The proportion of these cells varied based on sex and the Fbn1C1041G/+ genotype. Differentially expressed proteins between male and female in addition to wild-type and Marfan samples uncovered enhanced endothelial to mesenchymal transition patterns in endothelial cells from male Marfan mice. Comparisons between single cell RNA and single cell proteomic profiles showed similarities in major subtypes but not smooth muscle cell subtypes. Multi-omics analysis of these two single cell platforms demonstrated a potential novel role for smooth muscle cell derived angiotensin signaling in the Marfan phenotype. ConclusionsSingle cell proteomics identified new subpopulations of vascular smooth muscles cells and novel cell type specific protein signatures related to sex differences and aneurysm formation. AbbreviationsNext generation sequencing (NGS), Mass spectrometer (MS), Single cell proteomics by Mass Spectrometry (ScOPE-MS), Marfans syndrome (MFS), Fibrillin 1 (FBN1), Transforming growth factor {beta} (TGF{beta}), Smooth muscle cell (SMC), Single cell proteomic (scProteomic), Differentially expressed proteins (DEPs), Wild-type (WT), Hanks balanced salt solution (HBSS), Fetal bovine serum (FBS), Dulbeccos Modified Eagle Medium (DMEM), Data-independent acquisition parallel accumulation-serial fragmentation (DIA-PASEF), Magnetic assisted cell sorted (MACS), Single Cell Analysis in Python (Scanpy), Kyoto Encyclopedia of Genes and Genomes (KEGG), Principal component analysis (PCA), Uniform manifold projection (UMAP), Single cell transcriptomic (scTranscriptomic), Smoothelin (Smtn), Transgelin (Tagln), Myosin heavy chain 11 (Myh11), Platelet endothelial cell adhesion molecule 1 (Pecam1), Dipeptidase 1 (Dpep1), Uncoupling protein 1 (Ucp1), Low-density lipoprotein receptor-related protein (Lrp1), DNA ligase 3 (Lig3), Capsaicin channel transient receptor potential vanilloid 1 (Trpv1), Endothelial to mesenchymal transition (endMT), Intercellular adhesion molecule 1 (Icam1), Intercellular adhesion molecule 2 (Icam2), Endothelial cell-selective adhesion molecule (Esam), Calponin 1 (Cnn1), Vimentin (Vim), Zinc finger E-box-binding homeobox 1 (Zeb1), Snail family transcriptional repressor 1 (Snai1), Tropomyosin alpha-4 chain (Tpm4), Angiotensin converting enzyme (Ace)

systems biology↗

bioRxiv2024

Cell Storage Conditions Impact Single-Cell Proteomic Landscapes

Single-cell transcriptomics (SCT) has revolutionized our understanding of cellular heterogeneity, yet the emergence of single-cell proteomics (SCP) promises a more functional view of cellular dynamics. A challenge is that not all mass spectrometry facilities can perform SCP, and not all labs have access to cell sorting equipment required for SCP, which together motivate an interest in sending bulk cell samples through the mail for sorting and SCP analysis. Shipping requires cell storage, which has an unknown impact on SCP results. This study investigates the impact of cell storage conditions on the proteomic landscape at the single-cell level utilizing a Data-Independent Acquisition (DIA) coupled with Parallel Accumulation Serial Fragmentation (diaPASEF). Three storage conditions were compared in 293T cells: (1) 37{degrees}C (control), (2) 4{degrees}C overnight, and (3) - 80{degrees}C storage followed by liquid nitrogen preservation. Either cold or frozen storage induced significant alterations in cell diameter, elongation, and proteome composition. By elucidating how cell storage conditions alter cellular morphology and proteome profiles, this study contributes foundational technical information about SCP sample preparation and data quality.

cell biology↗

bioRxiv2023

Simultaneous Multi-Omics Analysis by Direct Infusion Mass Spectrometry (SMAD-MS)

Combined multi-omics analysis of proteomics, metabolomics, and lipidomics requires separate liquid chromatography-mass spectrometry (LC-MS), which limits throughput and increases costs, hindering the application of mass spectrometry-based multi-omics to large-scale analyses. Here, we present single-injection multi-omics analysis by direct infusion (SMAD), an integrated platform leveraging ion mobility mass spectrometry and self-developed software tools to enable single injection multi-omics analysis without liquid chromatography. SMAD allows quantification of over 9,000 metabolite m/z features and over 1,300 proteins from the same sample in less than five minutes. We validated the efficiency and reliability of SMAD with three case studies. (1) mouse macrophages after M1/M2 polarization and senescence, (2) a pilot drug screen in human cells, and (3) large-scale high-throughput drug screening of mammalian cells in 96-well plates. Finally, relationships between proteomic and metabolomic data are discovered by machine learning and validated.

systems biology↗

bioRxiv2023

Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease

BackgroundDescending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. MethodsThis study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using 5-fold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. ResultsOf the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p-value <0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important correlated groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. ConclusionsWe conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

systems biology↗

bioRxiv2023

Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious requiring two hours of mass spectrometry time per single muscle fiber; 50 fibers would take approximately four days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 minutes total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 hours. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Sixty-five proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, muscle structure and regulation. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

biochemistry↗

bioRxiv2022

Parallelization with Dual-Trap Single-Column Configuration Maximizes Throughput of Proteomic Analysis

Proteomic analysis on the scale that captures population and biological heterogeneity over hundreds to thousands of samples requires rapid mass spectrometry methods which maximize instrument utilization (IU) and proteome coverage while maintaining precise and reproducible quantification. To achieve this, a short liquid chromatography gradient paired to rapid mass spectrometry data acquisition can be used to reproducibly profile a moderate set of analytes. High throughput profiling at a limited depth is becoming an increasingly utilized strategy for tackling large sample sets but the time spent on loading the sample, flushing the column(s), and re-equilibrating the system reduces the ratio of meaningful data acquired to total operation time and IU. The dual-trap single-column configuration presented here maximizes IU in rapid analysis (15 min per sample) of blood and cell lysates by parallelizing trap column cleaning and sample loading and desalting with analysis of the previous sample. We achieved 90% IU in low micro-flow (9.5 {micro}L/min) analysis of blood while reproducibly quantifying 300-400 proteins and over 6,000 precursor ions. The same IU was achieved for cell lysates, in which over 4,000 proteins (3,000 at CV below 20%) and 40,000 precursor ions were quantified at a rate of 15 minutes/sample. Thus, deployment of this dual-trap single column configuration enables high throughput epidemiological blood-based biomarker cohort studies and cell-based perturbation screening.

biochemistry↗