Genome biology · 2025 · 17 citations · 50 references
Life SciencesMolecular BiologyOncologyMolecular OncologyTranscriptomicsTumor MicroenvironmentCancer TranscriptomicsCell-type-specific TranscriptomicsSpatial Single-cell AnalysisSpatial TranscriptomicsTranscriptomic ProfilingTumor CharacterizationCell SegmentationImage-based TranscriptomicsIntra-tumor HeterogeneitySingle-cell Spatial ProfilingSpatial Cell SegmentationSpatial Transcriptomics ComparisonTumor Heterogeneity AssessmentTumor Microenvironment Mapping
BACKGROUND: Spatial transcriptomics technologies are revolutionizing our understanding of intra-tumor heterogeneity and the tumor microenvironment by revealing single-cell molecular profiles within their spatial tissue context. The rapid development of spatial transcriptomics methods, each with unique characteristics, makes it challenging to select the most suitable technology for specific research objectives. Here, we compare four imaging-based approaches-RNAscope HiPlex, Molecular Cartography, Merscope, and Xenium-alongside Visium, a sequencing-based method. These technologies were employed to study cryosections of medulloblastoma with extensive nodularity (MBEN), a tumor chosen for its distinct microanatomical features. RESULTS: Our analysis reveals that automated imaging-based spatial transcriptomics methods are well-suited to delineate the intricate MBEN microanatomy and capture cell-type-specific transcriptome profiles. We devise approaches to compare the sensitivity and specificity of different methods, along with their unique attributes, to guide method selection based on the research objective. Furthermore, we demonstrate how reimaging slides after the spatial transcriptomics analysis can significantly improve cell segmentation accuracy and integrate additional transcript and protein readouts, expanding the analytical possibilities and depth of insight. CONCLUSIONS: This study underscores important distinctions between spatial transcriptomics technologies and offers a framework for evaluating their performance. Our findings support informed decisions regarding methods and outline strategies to improve the resolution and scope of spatial transcriptomic analyses, ultimately advancing spatial transcriptomics applications in solid tumor research.
50
SciPy 1.0: fundamental algorithms for scientific computing in Python
Nature Methods · 2020 · 37.9K citations · Full text
Comprehensive Integration of Single-Cell Data
Tim Stuart, Andrew Butler, Paul Hoffman et al. · Cell · 2019 · 16.9K citations · Full text
Fast, sensitive and accurate integration of single-cell data with Harmony
Ilya Korsunsky, Nghia Millard, Jean Fan et al. · Nature Methods · 2019 · 10.5K citations · Full text