SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Spatially resolved transcriptomics (SRT) characterizes cell function in situ by integrating transcriptional profiles and location.
• Single-molecule SRT methods require accurate aggregation of mRNA molecules into cells, a challenge for traditional segmentation-based approaches.
• Emerging cell-segmentation-free methods offer improved performance but lack accessibility due to computational and programming requirements.

Purpose of the Study:

• To introduce SSAM-lite, a novel tool designed for accessible, segmentation-free cell-typing of SRT data.
• To provide a graphical user interface (GUI) for rapid analysis directly within a web browser.
• To enable researchers without specialized computational skills or hardware to analyze single-molecule SRT data.

Main Methods:

• Development of SSAM-lite, a lightweight, portable tool with a GUI.
• Implementation of a segmentation-free approach for cell aggregation in SRT data.
• Local execution on standard hardware (e.g., laptop) with interactive parameter optimization.

Main Results:

• SSAM-lite enables rapid, segmentation-free cell-typing of SRT data via an easy-to-use web interface.
• Analysis of mouse somatosensory cortex tissue took under a minute on modest hardware.
• The tool runs locally, requires no specialized hardware or programming expertise, and can be run offline.

Conclusions:

• SSAM-lite democratizes the analysis of single-molecule SRT data by offering a portable, lightweight, and user-friendly solution.
• The tool significantly lowers the barrier to entry for researchers interested in spatial transcriptomics.
• SSAM-lite empowers a broader scientific audience to investigate and analyze in situ cellular information.