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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

From Cell-Free Transcriptomes to Single-Cell Landscapes: Biomarker Discovery and Originating Cell Alteration Analysis

Wenxiang Zhang^1,2,3, Wenjing Zhang^1,2,3, Hang Wei⁴

¹Shenzhen Clinical Research Center for Trauma treatment, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|March 12, 2026

Summary

This summary is machine-generated.

CellFreeGMF identifies cell-free RNA (cfRNA) biomarkers and their cellular origins. This tool analyzes functional changes in originating cells, advancing precision medicine through cfRNA profiling.

Keywords:

cfRNA biomarker identification cfRNA originating cells clinical sample diagnostic classification graph matrix factorization

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Cell-free RNA (cfRNA) profiling holds promise for clinical diagnostics and precision medicine.
Current cfRNA studies often lack detailed cellular origin analysis due to bulk-level data limitations.
Understanding cfRNA biomarker-originating cells is crucial for interpreting functional alterations.

Purpose of the Study:

To develop a computational tool, CellFreeGMF, for cfRNA biomarker discovery.
To enable diagnosis classification and analyze alterations in cfRNA originating cells.
To investigate functional changes in cfRNA-originating cells during disease using cell-cell communication analysis.

Main Methods:

Graph matrix factorization approach implemented in CellFreeGMF.
Analysis of cell-cell communication networks.
Validation on diverse cell-free RNA transcriptome clinical datasets.

Main Results:

CellFreeGMF successfully identified cfRNA biomarkers and their cellular origins.
In pancreatic ductal adenocarcinoma (PDAC), cfRNA origins were traced to myeloid and T-cell populations.
Significant transcriptomic differences were observed in these cell populations between disease and normal states.

Conclusions:

CellFreeGMF provides a robust method for identifying cfRNA biomarkers and their cellular origins.
The tool elucidates pathophysiological changes in cfRNA-originating cells.
CellFreeGMF facilitates the integration of cfRNA analysis into clinical workflows for precision medicine.