Biology subjects

Rangarajan, S.

Publications and source records attributed to Rangarajan, S..

5 recordsLinked to original sources

bioRxiv2026

Customizable bead-based platform for EV surface protein immunocapture and detection in plasma

Extracellular vesicles (EVs) are increasingly recognized as valuable biomarkers for liquid biopsy because they carry molecular signatures that reflect their cells of origin. However, EV isolation remains a challenge, and existing EV immunocapture platforms are constrained by nonspecific binding, limited flexibility, and insufficient sensitivity. Here, we present a customizable bead-based platform for the isolation and detection of specific EV populations directly from plasma. The platform combines bead-immobilized capture antibodies with fluorescent detection antibodies, enabling sensitive and multiplexed EV profiling. Optimization of bead conjugation chemistry improved antibody immobilization and reduced nonspecific background signals, enhancing assay specificity and performance. Using breast cancer-derived EVs expressing fluorescent protein, we demonstrate the detection of tumor-derived EVs in plasma at concentrations as low as 1E9 EVs/ml. Furthermore, the assay distinguished HER2- and TROP2-high from low EV populations, demonstrating its ability to detect clinically relevant differences in biomarker expression. The workflow is compatible with direct EV analysis in the unprocessed plasma samples, thereby reducing sample preparation requirements and assay complexity. These findings establish a sensitive, flexible, and scalable platform for EV biomarker analysis. The approach overcomes several limitations of current commercial bead-based technologies and may facilitate the development of customizable multiplexed EV-based assays for cancer detection, patient stratification, and disease monitoring.

cancer biology↗

bioRxiv2024

Siglec-F Deficiency Prevents Fibrosis After Bleomycin-Induced Acute Lung Injury

Injury to the lungs causes acute inflammation that can lead to pathological lung fibrosis. Airspace macrophages (AMs) are critical for repair of injured tissue, but they can contribute fibrosis through mechanisms that are incompletely understood. Siglecs are expressed by immune cells. In mice, Siglec-F is chiefly expressed AMs where it is considered inflammosuppressive. We hypothesized that its deletion would worsen lung injury and fibrosis in response to intratracheal bleomycin challenge. We evaluated Siglec-F expression and function in mice challenged with bleomycin on days 7, 14, and 21 post-challenge (2.5 U/kg). AMs were the predominant inflammatory cells at all timepoints, and they included resident (RAM) and recruited (RecAM) subsets. Siglec-F deficiency prevented fibrosis than in Siglecf-/- mouse lungs, as evident from biochemical and histologic readouts. We performed RNAseq on pooled RAMs and RecAMs from wild type and Siglec-F deficient mice. Lung fibrosis 21 d after bleomycin challenge was associated with differentially expressed genes (DEGs) related to cholesterol synthesis and metabolism. In AMs from healthy lung lavage fluid and idiopathic pulmonary fibrosis patient tissues, the human paralogs Siglec-7 and Siglec-9 were expressed. Findings here identify novel mechanisms that control protective and detrimental functions of AMs after lung injury.

physiology↗

bioRxiv2023

A mobile intron facilitates interference competition between co-infecting viruses

Mobile introns containing homing endonucleases are widespread in nature and have long been assumed to be selfish elements that provide no benefit to the host organism. These genetic elements are common in viruses, but whether they confer a selective advantage is unclear. Here we studied a mobile intron in bacteriophage {Phi}PA3 and found its homing endonuclease gp210 contributes to viral competition by interfering with the virogenesis of co-infecting phage {Phi}KZ. We show that gp210 targets a specific sequence in its competitor {Phi}KZ, preventing the assembly of progeny viruses. This work reports the first demonstration of how a mobile intron can be deployed to engage in interference competition and provide a reproductive advantage. Given the ubiquity of introns, this selective advantage likely has widespread evolutionary implications in nature.

microbiology↗

bioRxiv2022

Next-generation large-scale binary protein interaction network for Drosophila

Generating reference maps of the interactome networks underlying most cellular functions can greatly illuminate genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. Here, we applied state-of-the-art experimental and bioinformatics methods to identify high-confidence binary protein-protein interactions (PPIs) for Drosophila melanogaster. We performed four all-by-all yeast two-hybrid (Y2H) screens of >10,000 Drosophila proteins, resulting in the FlyBi dataset of 8,723 PPIs among 2,939 proteins. As part of this effort, we tested subsets of our data and data from previous PPI datasets using an orthogonal assay, which allowed us to normalize data quality across datasets. Next, we integrated our FlyBi data with previous PPI data, resulting in an expanded, high-confidence binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6,511 proteins. These data are accessible through the Molecular Interaction Search Tool (MIST) and other databases. To assess the utility of the PPI resource, we used novel interactions from the FlyBi dataset to generate an autophagy interaction network that we validated in vivo using two different autophagy-related assays. We found that deformed wings (dwg) encodes a protein that is both a regulator and a target of autophagy. Altogether, the resources generated in this project provide a strong foundation for building high-confidence new hypotheses regarding protein networks and function.

cell biology↗

bioRxiv2019

Towards an ″assayome″ for binary interactome mapping

Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose the concept of an "assayome" for binary protein-protein interaction (PPI) mapping as an optimal combination of assays and/or assay versions that maximizes detection of true positive interactions, while avoiding detection of random protein pairs. We engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions differing by protein expression systems and tagging configurations. The resulting N2H assayome recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our assayome concept should be applicable to systematic mapping of other biological landscapes.

systems biology↗