Biology subjects

Chakrabortty, S. K.

Publications and source records attributed to Chakrabortty, S. K..

4 recordsLinked to original sources

bioRxiv2025

Prostate-specific EV capture with sufficient RNA yield to enable transcriptomic profiling

Prostate-specific antigen (PSA) screening has reduced prostate cancer (PCa) mortality but suffers from limited specificity, contributing to unnecessary biopsies and overdiagnosis of indolent disease. There is a critical need for biofluid-based biomarkers that improve the precision of PCa detection. Extracellular vesicles (EVs) offer a promising platform for noninvasive diagnostics, as they carry molecular cargo reflective of their tissue of origin. The ExoDx Prostate IntelliScore (EPI) test, a urine-based EV assay, is currently the only commercial EV diagnostic for clinically significant (cs)PCa, but its performance may be constrained by contamination from renal and bladder-derived EVs. To address this, we developed Exosome Diagnostics Depletion and Enrichment (EDDE), a novel immunocapture-based method for isolating prostate-derived EVs with high specificity. By targeting Prostate Specific Membrane Antigen (PSMA), we optimized EDDE to selectively enrich prostate EVs from post-DRE urine and recover sufficient RNA for transcriptomic analysis. Throughout development, we implemented a quantitative framework to track EV stoichiometry and assess depletion efficiency and yield, enabling rigorous optimization of the workflow. Our findings demonstrate that PSMA EDDE enriches prostate-specific EVs and yields RNA quantities compatible with sequencing. This platform enhances the specificity of EV-based biomarker discovery and holds promise for determining if tissue-specific EV biomarkers contribute advantages over bulk EVs.

cancer biology↗

bioRxiv2025

Extracellular Vesicle Gene Expression Enables Sensitive Detection of Colorectal Neoplasia

BACKGROUND & AIMSExtracellular vesicles (EVs), including exosomes, are emerging as promising carriers of disease-specific biomarkers due to their molecular cargo reflective of cellular origin. While cell-free DNA (cfDNA) methylation assays have recently been developed for colorectal cancer (CRC) screening and perform well for cancer detection, they show limited sensitivity for advanced adenomas (AA), a key precursor in the CRC pathway. To address this gap, we sought to determine whether other blood-based analytes, specifically EV-derived long and small RNA transcriptomes and EV proteomes, could improve detection of AA and early-stage CRC. In a prospective cohort, we performed a head-to-head comparison of EV transcriptomics, EV proteomics, and their combinations against cfDNA methylation, all measured from the same patient cohort, enabling a direct performance benchmark and identification of the most promising modality for larger-scale CRC screening studies. METHODSWe prospectively collected pre-colonoscopy plasma samples from 220 participants across three clinical sites. EVs were isolated and profiled using long RNA-seq, small RNA-seq, and Olink-based proteomics. cfDNA was analyzed for methylation patterns. Analyses were conducted according to a statistical analysis plan pre-specified before unblinding. Machine learning models were developed under nested cross-validation to evaluate sensitivity for detecting AA and CRC at a fixed specificity of 91%, consistent with clinical screening benchmarks. RESULTSEV-derived gene expression on long RNA demonstrated the highest sensitivity for detecting AA as well as CRC: 54.8% (95% CI, 26.4%-75.6%) for AA and 94.1% (95% CI, 79.2%-100%) for CRC. This outperformed cfDNA methylation (33.6% [95% CI, 9.7%-60.3%] for AA, 81.3% [95% CI, 64.6%- 93.9%] for CRC) and other EV-based modalities. In addition, for small RNA the sensitivities were 42.6% (95% CI, 35.9%-47.4%) for AA, and 78.9% (95% CI, 69.2%-83.0%) for CRC, while for proteomics the sensitivities were 30.0% (95% CI, 13.9%-40.0%) for AA, and 64.2% (95% CI, 43.6%-87.2%) for CRC. Transcriptomic profiles revealed progressive enrichment of hallmarks of cancer pathways, including apoptosis and epithelial-mesenchymal transition, across disease stages. Multiomic integration did not improve performance beyond EV transcriptomics alone. CONCLUSIONSBy directly comparing multiple EV-based and cfDNA analytes within the same patient cohort, we found that EV transcriptomics delivers the strongest diagnostic performance for both AA and CRC. This rigorous benchmarking approach allows clear prioritization of the most promising modality guiding the design of larger validation studies and accelerating development of next-generation, blood-based CRC screening tools.

genomics↗

bioRxiv2025

Salivary Extracellular Vesicle RNA Profiling Reveals Biomarkers for Sjogrens

Sjogrens is a chronic autoimmune disease affecting exocrine glands and is subclassified into SSA-positive (SSA+) and SSA-negative (SSA-) subtypes, with a complex diagnostic journey and an average diagnostic delay of almost 4 years. While SSA+ cases can be detected via serological testing, current assays lack specificity. For SSA-patients, no non-invasive diagnostic tools exist, and diagnosis often requires invasive lip biopsy. A saliva-based liquid biopsy capable of diagnosing both subtypes is therefore of high clinical interest. However, saliva poses challenges due to its abundant oral microbiome, which complicates unbiased biomarker discovery. In this study, we present a novel RNA sequencing workflow that efficiently depletes microbial content, enabling deep profiling of long RNAs within salivary extracellular vesicles (EVs). This approach identified both known and novel RNA biomarkers capable of diagnosing SSA+ and SSA-subtypes with high sensitivity and specificity. Moreover, we uncovered distinct RNA signatures that allow molecular stratification of Sjogrens subtypes. Pathway analysis in SSA+ cases revealed enrichment of immune and glandular pathways consistent with prior tissue-based studies, supporting the utility of salivary EVs as a non-invasive surrogate for tissue biopsy. Importantly, our data provides new molecular insights into the under-characterized SSA-subtype, laying the foundation for future mechanistic studies and facilitating their broader inclusion in clinical trials.

genomics↗

bioRxiv2025

Deep Profiling of EV Long RNAs Reveals Biofluid-Specific Transcriptomes and Splicing Landscapes

RNA profiling of extracellular vesicles (EVs) from human biofluids has historically been limited to small RNA species, with long RNAs--such as mRNA exons and long non-coding RNAs--remaining largely underexplored. Moreover, the dominance of hematopoietic-derived EVs in complex fluids like plasma has posed significant challenges for detecting low-abundance, tissue-specific transcripts. Here, we establish foundational transcriptomic maps of long RNAs in EVs from plasma, urine, and cerebrospinal fluid (CSF) using ultra-deep whole transcriptome sequencing (WTS), revealing both fluid-specific and shared expression and splicing signatures. We then introduce a targeted RNA capture method that enriches for all protein-coding and long non-coding transcripts, dramatically enhancing sensitivity for gene and splice variant detection. Applying this approach to brain-specific transcripts, we achieve >85-fold enrichment of target gene expression and, on average, 3.1-fold increase in detected splice junctions per gene compared to untargeted WTS. As a proof of concept, we apply this brain-targeted RNA panel to EVs from plasma in a Parkinsons disease cohort of 40 plasma samples and compare its performance to exome sequencing as well as untargeted WTS. This work advances EV transcriptomics into the long RNA domain and establishes a framework for high-sensitivity, noninvasive biomarker profiling across tissues and diseases.

genomics↗