Quantitative Analysis of Cell-Free RNA at Attomolar Level Using CRISPR/Cas Digital Imaging Platform DOI
Y Li,

Fenglei Quan,

Yige Wu

et al.

Analytical Chemistry, Journal Year: 2024, Volume and Issue: 96(43), P. 17362 - 17369

Published: Oct. 16, 2024

Quantitative analysis of cell-free RNA (cfRNA) in plasma sample can be used for screening, diagnosing, and prognosticating multiple diseases. Here, we report a quantitative CRISPR/Cas digital imaging platform (qCasdip) the detection various cfRNAs, including circular RNAs miRNAs, clinical samples at attomolar (aM) level without need preamplification. Digital counting strategy provides qCasdip ability with linear range 10

Language: Английский

Detection of reproducible liver cancer specific ligand-receptor signaling in blood DOI Creative Commons
Aram Safrastyan, Damian Wollny

Frontiers in Bioinformatics, Journal Year: 2025, Volume and Issue: 4

Published: Jan. 9, 2025

Cell-cell communication mediated by ligand-receptor interactions (LRI) is critical to coordinating diverse biological processes in homeostasis and disease. Lately, our understanding of these has greatly expanded through the inference cellular communication, utilizing RNA extracted from bulk tissue or individual cells. Considering challenge obtaining biopsies for approaches, we considered potential studying cell-free obtained blood. To test feasibility this approach, used BulkSignalR algorithm across 295 samples compared LRI profiles multiple cancer types healthy donors. Interestingly, detected specific reproducible LRIs particularly blood liver patients We found an increase magnitude hepatocyte interactions, notably autocrine patients. Additionally, a robust panel 30 cancer-specific presents bridge linking pathogenesis discernible markers. In summary, approach shows plausibility detecting builds upon transcriptomes.

Language: Английский

Citations

0
Comprehensive evaluation of methods for identifying tissues or cell types of origin of the plasma cell-free transcriptome DOI Creative Commons
Tingyu Yang,

Yulong Qin,

Shuo Yan

et al.

PeerJ, Journal Year: 2025, Volume and Issue: 13, P. e19241 - e19241

Published: April 17, 2025

Plasma cell-free RNA (cfRNA) is derived from cells in various tissues and organs throughout the body reflects physiological pathological conditions. Identifying origins of cfRNA essential for comprehending its variations. Only a few tools are designed deconvolution, most studies have relied on traditional bulk methods. In this study, we employed human tissue cell transcriptomic data as reference sets evaluated performance seven deconvolution methods cfRNA. We compared analysis results types origin plasma chose to use single-cell sequencing (scRNA-seq) conduct further evaluation Subsequently, assessed accuracy robustness by utilizing simulated generated scRNA-seq. also methods’ real analyzing correlation between predicted proportions corresponding clinical indicators. Moreover, effectiveness revealing impacts diseases cancer classification models based they provided. summary, our study provides valuable insights into analysis, enhancing potential biomedical research.

Language: Английский

Citations

0
The impact of liquid biopsy in breast cancer: Redefining the landscape of non-invasive precision oncology. DOI Creative Commons
Shaivy Malik, Sufian Zaheer

The Journal of Liquid Biopsy, Journal Year: 2025, Volume and Issue: unknown, P. 100299 - 100299

Published: May 1, 2025

Language: Английский

Citations

0
Pathway-enhanced Transformer-based robust model for quantifying cell types of origin of cell-free transcriptome DOI Open Access

Shuo Yan,

Xuetao Tian,

Yulong Qin

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 28, 2024

Abstract Analyzing cell types of origin cell-free RNA can enhance the resolution liquid biopsies, thereby deepening understanding molecular and cellular changes in development disease processes. Existing deconvolution methods typically rely on meticulously curated gene expression profiles or employ deep neural network with vast complex solution spaces that are difficult to interpret. These approaches overlook synergistic co-expression effects among genes biological signaling pathways, compromising their generalizability robustness. we developed ‘Deconformer’, a Transformer-based model integrates pathways at embedding stage, address these issues. Compared popular multiple datasets, Deconformer demonstrates superior performance robustness, is capable tracking developmental process fetal placenta. Additionally, pathway-level interpretability offers new insights into crosstalk, dependencies, other interactions within supporting further discoveries. We posit represents significant advancement precise analysis transcriptome. It holds promise describing progression severity level accuracy, focusing contributions originating pathway dependencies. This has potential catalyze non-invasive diagnostic tools our underlying biology diseases.

Language: Английский

Citations

1
Quantitative Analysis of Cell-Free RNA at Attomolar Level Using CRISPR/Cas Digital Imaging Platform DOI
Y Li,

Fenglei Quan,

Yige Wu

et al.

Analytical Chemistry, Journal Year: 2024, Volume and Issue: 96(43), P. 17362 - 17369

Published: Oct. 16, 2024

Quantitative analysis of cell-free RNA (cfRNA) in plasma sample can be used for screening, diagnosing, and prognosticating multiple diseases. Here, we report a quantitative CRISPR/Cas digital imaging platform (qCasdip) the detection various cfRNAs, including circular RNAs miRNAs, clinical samples at attomolar (aM) level without need preamplification. Digital counting strategy provides qCasdip ability with linear range 10

Language: Английский

Citations

0