Biology subjects

Amanullah, M.

Publications and source records attributed to Amanullah, M..

3 recordsLinked to original sources

bioRxiv2026

Isolation and Characterization of Extracellular Vesicles from Mouse Retina Tissue

This protocol provides a standardized workflow for the isolation of extracellular vesicles (EVs) from mouse retinal tissue, and includes an assessment of EV size and concentration, marker expression, and EV visualization in accordance with the International Society for Extracellular Vesicles Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines. Most retinal EV studies rely on cell culture, which may not fully capture in vivo biology. Our approach more accurately reflects physiological and pathological EV states in vivo by enabling the extraction of EVs from intact retinal tissue. This method addresses a key gap in the field by providing a reproducible and rigorous protocol for studying retinal EVs in a biologically relevant context.

cell biology↗

bioRxiv2025

Lignature: A Comprehensive Database of Ligand Signatures to Predict Cell-Cell Communication

Ligand-receptor interactions mediate intercellular communication, inducing transcriptional changes that regulate physiological and pathological processes. Ligand-induced transcriptomic signatures can be used to predict active ligands; however, the absence of a comprehensive set of ligand-response signatures has limited their practical application in predicting ligand-receptor interactions. To bridge this gap, we developed Lignature, a curated database encompassing intracellular transcriptomic signatures for 362 human ligands, significantly expanding the repertoire of ligands with available intracellular response signatures. Lignature compiles signatures from published transcriptomic datasets and established resources such as CytoSig and ImmuneDictionary, generating both gene- and pathway-based signatures for each ligand. We applied Lignature to predict active ligands driving transcriptomic changes in controlled in vitro experiments and real-world single-cell sequencing datasets. Lignature outperformed existing methods such as NicheNet, achieving higher accuracy in identifying active ligands at both the gene and pathway levels. These results establish Lignature as a robust platform for ligand signaling inference, providing a powerful tool to explore ligand-receptor interactions across diverse experimental and physiological contexts.

bioinformatics↗

bioRxiv2024

Single-Cell Proteomic and Transcriptomic Characterization of Drug-Resistant Prostate Cancer Cells Reveals Molecular Signatures Associated with Morphological Changes

This study delves into the proteomic intricacies of drug-resistant cells (DRCs) within prostate cancer, which are known for their pivotal roles in therapeutic resistance, relapse, and metastasis. Utilizing single-cell proteomics (SCP) with an optimized high-throughput Data Independent Acquisition (DIA) approach with the throughput of 60 sample per day, we characterized the proteomic landscape of DRCs in comparison to parental PC3 cells. This optimized DIA method allowed for robust and reproducible protein quantification at the single-cell level, enabling the identification and quantification of over 1,300 proteins per cell on average. Distinct proteomic sub-clusters within the DRC population were identified, closely linked to variations in cell size. The study uncovered novel protein signatures, including the regulation of proteins critical for cell adhesion and metabolic processes, as well as the upregulation of surface proteins and transcription factors pivotal for cancer progression. Furthermore, by integrating SCP and single-cell RNA-seq (scRNA-seq) data, we identified six upregulated and ten downregulated genes consistently altered in drug-treated cells across both SCP and scRNA-seq platforms. These findings underscore the heterogeneity of DRCs and their unique molecular signatures, providing valuable insights into their biological behavior and potential therapeutic targets.

cancer biology↗