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Related Experiment Video

Updated: Jun 26, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Emily Laubscher¹, Xuefei Wang², Nitzan Razin²

¹Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA 91125, USA.

|May 16, 2024

Summary

This summary is machine-generated.

Polaris simplifies spatial transcriptomics analysis using deep learning for accurate gene expression quantification. This turnkey pipeline supports various imaging techniques, making complex data analysis accessible.

Keywords:

deep learning image analysis spatial transcriptomics spot detection

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Image-based spatial transcriptomics offers gene expression insights with spatial context.
Current analysis pipelines are often complex and require manual optimization.

Purpose of the Study:

To introduce Polaris, an automated analysis pipeline for image-based spatial transcriptomics.
To enable accurate single-cell gene expression quantification from diverse spatial transcriptomics datasets.

Main Methods:

Polaris integrates deep-learning models for cell segmentation and spot detection.
A probabilistic gene decoder is employed for precise gene expression quantification.
The pipeline is designed for multiplexed error-robust FISH (MERFISH), sequential fluorescence in situ hybridization (seqFISH), and in situ RNA sequencing (ISS).

Main Results:

Polaris provides a unified and automated solution for spatial transcriptomics data analysis.
The pipeline accurately quantifies single-cell gene expression from various experimental techniques.
Deep-learning components enhance cell segmentation and spot detection efficiency.

Conclusions:

Polaris offers a user-friendly, turnkey solution for analyzing complex spatial transcriptomics data.
The software facilitates more accessible and accurate gene expression studies in spatial contexts.
Polaris is available as part of the DeepCell software library.