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Spatial Transcriptomics Services

Neorabio provides spatial transcriptomics services designed to enable analysis of gene expression within intact tissue architecture. Unlike bulk or dissociated single-cell approaches, spatial transcriptomics preserves positional information, allowing transcriptional activity to be interpreted in the context of tissue organization and microenvironmental structure. The feasibility of mapping genome-wide gene expression directly within tissue sections was first demonstrated in pioneering spatial transcriptomics studies by Ståhl et al. (2016), which established spatially resolved transcript profiling as a foundational method for investigating tissue heterogeneity, development, and disease-associated spatial regulation.

About Service

Neorabio's spatial transcriptomics services are built on an integrated workflow combining standardized tissue handling, spatially barcoded transcript capture, next-generation sequencing, and controlled computational analysis. Experimental procedures emphasize tissue preservation, sectioning consistency, and capture efficiency to maintain spatial fidelity. In data processing and interpretation, Neorabio follows analytical principles aligned with community-validated spatial transcriptomics workflows, in which spatial quality control, expression normalization, and region-level annotation are treated as interdependent determinants of biological interpretability.

Our Scope

● Spatial Gene Expression Profiling
● Genome-wide measurement of transcript abundance within preserved tissue sections
● Region and Domain-specific Analysis
● Identification of spatially defined transcriptional domains and tissue regions
● Integration with Single-cell Data
● Mapping dissociated cell populations back to their spatial tissue context

Applications

● Analysis of tissue organization and spatial gene expression patterns
● Investigation of cellular microenvironments and local cell–cell interactions
● Developmental and differentiation studies within intact tissues
● Exploration of spatial heterogeneity in disease states
● Integration of spatial transcriptomics with single-cell and multi-omics data

Workflow

Exploratory Consultation → Tissue Handling & Spatial Design Alignment → Spatial Library Preparation & Sequencing → Spatial Expression Mapping & Quality Control → Tissue-Level Interpretation & Integration → Final Report Delivery & Review Discussion

References

Ståhl, P. L., et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 2016, 353(6294): 78–82. DOI: 10.1126/science.aaf2403
Asp, M., Bergenstråhle, J., Lundeberg, J. Spatially resolved transcriptomes—next generation tools for tissue exploration. Nature Reviews Genetics, 2020, 21(8): 449–466. DOI: 10.1038/s41576-020-0211-4
Regev, A., et al. The Human Cell Atlas. eLife, 2017, 6: e27041. DOI: 10.7554/eLife.27041
Dries, R., et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biology, 2021, 22: 78. DOI: 10.1186/s13059-021-02286-2

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Project execution at Neorabio emphasizes traceable data processing, spatial consistency assessment, and transparent reporting across experimental and computational stages. To support biologically meaningful interpretation, spatial expression patterns can be integrated with single-cell reference profiles using deconvolution and mapping strategies that have been widely applied in tissue atlas construction, including approaches developed in large-scale spatial and single-cell integration efforts. In addition, spatial pattern detection and region-specific expression analysis are guided by statistical frameworks for spatial autocorrelation and domain identification.

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