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Related Experiment Video

Updated: Jun 24, 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

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.

Lyla Atta^1,2, Kalen Clifton^1,2, Manjari Anant^2,3

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.

|June 12, 2024

Summary

This summary is machine-generated.

Normalization methods in imaging-based spatially resolved transcriptomics (im-SRT) can skew results. Using non-gene count normalization or representative gene panels improves data reliability for accurate biological interpretation.

Keywords:

Differential expression Normalization Scaling factor Spatial transcriptomics

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Area of Science:

Transcriptomics
Spatial Biology
Bioinformatics

Background:

Imaging-based spatially resolved transcriptomics (im-SRT) allows high-throughput gene and location profiling in fixed tissues.
Normalization is crucial to correct technical variations and reveal true biological signals in im-SRT data.

Purpose of the Study:

To investigate how different normalization methods and gene panels affect im-SRT data analysis and interpretation.
To assess the impact of normalization-induced biases on downstream analyses.

Main Methods:

Simulated im-SRT gene panels overrepresenting specific tissue regions or cell types were used.
Normalization methods based on detected gene counts per cell were compared with non-gene count-based methods (e.g., cell volume/area).

Main Results:

Normalization methods using gene counts per cell differentially altered gene expression magnitudes in a region- and cell type-specific manner.
These normalization-induced effects led to false positives and negatives in differential gene expression and spatially variable gene analyses.
Non-gene count-based normalization approaches did not exhibit these biases.

Conclusions:

Non-gene count-based normalization methods are recommended when feasible for im-SRT analysis.
Evaluating gene panel representativeness is crucial before applying gene count-based normalization.
The choice of normalization method and gene panel significantly impacts the biological interpretation of im-SRT data.