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Comparative analysis of multiplexed in situ gene expression profiling technologies.

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    Standard sensitivity metrics for in situ gene expression profiling are unreliable due to off-target molecular artifacts. This study introduces new metrics to control for artifacts, aiding technology selection and data interpretation.

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    Area of Science:

    • Molecular Biology
    • Bioinformatics
    • Neuroscience

    Background:

    • In situ multiplexed gene expression profiling is crucial for understanding cellular behavior.
    • Numerous technologies exist, but direct comparison is challenging.
    • Existing sensitivity metrics are confounded by off-target molecular artifacts.

    Approach:

    • Benchmarking six in situ gene expression profiling methods using mouse brain data.
    • Investigating sources of molecular artifacts and their impact on specificity.
    • Developing novel metrics to quantify and control for these artifacts.

    Key Points:

    • Standard sensitivity metrics (e.g., unique molecules per cell) are not directly comparable across different in situ datasets.
    • Off-target molecular artifacts significantly impact the specificity of gene expression profiling.
    • False positives from molecular artifacts can distort spatially-aware differential expression analysis.

    Conclusions:

    • Novel metrics are essential for accurate evaluation and comparison of in situ technologies.
    • Careful consideration of molecular artifacts is required for reliable interpretation of spatial transcriptomics data.
    • This work provides guidance for selecting, processing, and interpreting in situ spatial technologies.