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Updated: Mar 19, 2026
Micromanipulation of Circulating Tumor Cells for Downstream Molecular Analysis and Metastatic Potential Assessment
Published on: May 14, 2019
Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in
A Grant Schissler1, Qike Li1, James L Chen2
1Center for Biomedical Informatics and Biostatistics (CB2) Graduate Interdisciplinary Program in Statistics Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.
Motivation:
As 'omics' biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples.
Results:
In response to these characteristics and limitations in current single-cell RNA-sequencing methodology, we introduce an analytic framework that models transcriptome dynamics through the analysis of aggregated cell-cell statistical distances within biomolecular pathways. Cell-cell statistical distances are calculated from pathway mRNA fold changes between two cells. Within an elaborate case study of circulating tumor cells derived from prostate cancer patients, we develop analytic methods of aggregated distances to identify five differentially expressed pathways associated to therapeutic resistance. Our aggregation analyses perform comparably with Gene Set Enrichment Analysis and better than differentially expressed genes followed by gene set enrichment. However, these methods were not designed to inform on differential pathway expression for a single cell. As such, our framework culminates with the novel aggregation method, cell-centric statistics (CCS). CCS quantifies the effect size and significance of differentially expressed pathways for a single cell of interest. Improved rose plots of differentially expressed pathways in each cell highlight the utility of CCS for therapeutic decision-making.
Availability And Implementation:
http://www.lussierlab.org/publications/CCS/ CONTACT: yves@email.arizona.edu or piegorsch@math.arizona.edu
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
This study introduces a new computational framework for analyzing single-cell RNA sequencing data, enabling better detection of disease markers in small cell populations like circulating tumor cells.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Single-cell 'omics' technologies offer deep molecular insights but face analytical challenges in detecting dysregulations and interpreting mRNA expression for clinical applications.
- Current single-cell RNA sequencing (scRNA-seq) methods struggle with analyzing small cell populations, such as circulating tumor cells (CTCs) from patient blood samples.
Purpose of the Study:
- To develop an advanced analytical framework for scRNA-seq data that addresses limitations in detecting single-cell dysregulations and interpreting functional insights.
- To create a method capable of analyzing small cell populations and providing functional interpretation of transcriptome dynamics for precision medicine.
Main Methods:
- Developed a framework modeling transcriptome dynamics using aggregated cell-cell statistical distances within biomolecular pathways.
- Calculated cell-cell statistical distances from pathway mRNA fold changes.
- Introduced cell-centric statistics (CCS) to quantify differential pathway expression for individual cells.
Main Results:
- Identified five differentially expressed pathways associated with therapeutic resistance in prostate cancer CTCs.
- The aggregation analyses demonstrated comparable performance to Gene Set Enrichment Analysis (GSEA) and outperformed differential gene expression followed by GSEA.
- The novel CCS method effectively quantifies pathway expression effects and significance at the single-cell level.
Conclusions:
- The proposed framework and CCS method enhance the analysis of scRNA-seq data, particularly for small cell populations like CTCs.
- CCS provides a powerful tool for identifying key pathways in individual cells, aiding in therapeutic decision-making and advancing precision medicine.
