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dotdotdot quantifies multiplex single-molecule fluorescent in situ hybridization (smFISH) data in complex tissues to enable single-nucleus RNA transcript measurement and spatial gene expression analysis.

Key Features:

• Multiplexing Capacity: Enables simultaneous detection of multiple RNA transcripts within single nuclei to increase smFISH multiplexing capacity and support RNA sequencing and spatial transcriptomic validation.
• Autofluorescence Mitigation: Masks autofluorescence from tissue components such as lipofuscin in spectral imaging to reduce background signal.
• Quantification and Differential Expression Analysis: Performs precise transcript quantification in single nuclei and supports differential expression analysis, demonstrated on activity-regulated gene expression in mouse neurons.
• Spatial Gene Expression Profiling: Profiles spatial gene expression in complex tissues, including analyses of human dorsolateral prefrontal cortex (DLPFC) samples.
• Machine Learning Integration: Integrates machine learning algorithms to predict cell types from smFISH data for downstream cell type-specific expression analysis.

Scientific Applications:

• Neuroscience: Analyze activity-regulated gene expression and cellular heterogeneity in mouse neurons and human dorsolateral prefrontal cortex (DLPFC).
• RNA-seq and Spatial Transcriptomics Validation: Validate RNA sequencing and spatial transcriptomic analyses through direct multiplex smFISH quantification.
• Spatially Resolved Tissue Analysis: Enable spatially resolved transcript analysis in heterogeneous tissues by mitigating autofluorescence and quantifying multiplexed smFISH signals.

Methodology:

Processes spectral imaging data with algorithms that mask lipofuscin autofluorescence, perform single-nucleus RNA transcript quantification, conduct differential expression analysis, and apply machine learning for cell type prediction.