Quality control for single-cell analysis of high-plex tissue profiles using CyLinter.

Area of Science:

• Biomedical Imaging
• Computational Biology
• Cancer Research

Background:

• High-plex spatial profiling enables detailed analysis of tumor microenvironments.
• Image-based methods provide subcellular protein distribution data from millions of cells.
• Tissue imaging is susceptible to artifacts from preparation, acquisition, and processing.

Purpose of the Study:

• To address the impact of imaging artifacts on single-cell data analysis in high-plex spatial profiling.
• To introduce CyLinter, a software tool for identifying and removing imaging artifacts.
• To demonstrate CyLinter's ability to improve biological interpretation of spatial profiling data.

Main Methods:

• Development of an interactive quality control software tool named CyLinter.
• Implementation of artifact detection algorithms within CyLinter.
• Application of CyLinter to analyze high-plex spatial profiling data, including archival specimens.

Main Results:

• Imaging artifacts significantly compromise the accuracy of single-cell data analysis.
• CyLinter effectively identifies and removes data points associated with various imaging artifacts.
• Improved single-cell analysis and biological interpretation were achieved using CyLinter, especially on older samples.

Conclusions:

• Artifacts in high-plex spatial profiling data present a major challenge for accurate biological interpretation.
• CyLinter provides a robust solution for quality control in spatial profiling, enhancing data reliability.
• The tool is particularly valuable for re-analyzing historical tissue specimens, unlocking new insights from archival data.