Deciphering Gene Expression in 3D with Spatial Transcriptomics

Omnya Mohamed Izzeldin
March 8, 2024

Understanding gene expression within its spatial context has long been a challenge in molecular biology. Traditional methods lack the precision to capture the spatial intricacies of gene activity within tissues and organs. However, recent advancements in technology have given rise to spatial transcriptomics, a groundbreaking field offering unprecedented insights into the spatial organization of gene expression patterns within biological systems.

Spatial transcriptomics transcends traditional gene expression analysis, unraveling spatial heterogeneity within biological systems. It sheds light on molecular mechanisms underlying diseases like cancer and neurodegenerative disorders, facilitating targeted therapeutic interventions. Additionally, it enhances our understanding of developmental processes and tissue morphogenesis, offering insights into biological development and evolution.

Currently, its driving excitement with its transformative potential and technological advancements. Innovations like spatially barcoded RNA sequencing enable unprecedented spatial resolution and sensitivity in gene expression analysis. Integrating spatial transcriptomic data with other omics modalities promises comprehensive insights into cellular organization and function, revolutionizing our understanding of health and disease.

What is Spatial Transcriptomics?

Spatial transcriptomics merges high-throughput sequencing with spatial information, enabling researchers to map gene expression profiles directly onto tissue sections. This approach provides a holistic view of gene expression across different tissue regions, surpassing the limitations of bulk sample analysis. Polly houses harmonized Spatially Resolved Transcriptomics (SRT) datasets, facilitates analysis, including the identification of spatially variable genes and tissue segmentation. Polly houses spatial transcriptomics datasets in two formats: unfiltered raw counts and custom processed counts, depending on the researcher's needs.The power of harmonized data helps reduce the challenges faced with handling spatial transcriptomics data.

Advantages of Spatial Transcriptomics in R&D

1. Comprehensive Spatial Profiling

Traditional transcriptomic analyses often provide insights into gene expression patterns at the bulk level, averaging out signals from different cell types within a tissue. Spatial transcriptomics preserves gene expression's spatial context, allowing direct mapping onto tissue sections for detailed insights into tissue architecture and cellular interactions, overcoming limitations of bulk-level analyses and offering a nuanced understanding of spatial heterogeneity.

2. Identification of Spatially Regulated Genes

One of the key advantages of spatial transcriptomic it enables the identification of genes with spatially variable expression within tissues, offering insights into the cellular organization and regulatory mechanisms. This understanding is essential for deciphering tissue development, homeostasis, and disease progression.

3. Insights into Disease Mechanisms

Spatial transcriptomics offers a powerful approach to studying disease mechanisms by revealing spatially localized changes in gene expression within diseased tissues. This provides insights into disease heterogeneity, identifies key molecular drivers, and informs targeted therapeutic interventions, particularly in tumors, neurodegenerative disorders, and autoimmune conditions

4. Drug Discovery and Development

Spatial transcriptomics accelerates drug discovery by pinpointing spatially regulated drug targets within tissues and evaluating their expression in disease microenvironments. This enables the design of targeted therapies tailored to specific tissue regions and optimizes drug delivery strategies for enhanced efficacy.

Applications of Spatial Transcriptomics in R&D

1. Pharmaceuticals and Biotechnology

Spatial transcriptomics revolutionizes pharmaceutical and biotechnology industries by offering insights into tissue-specific gene expression patterns, aiding in the discovery of new drug targets and assessing drug efficacy in tissue microenvironments. This accelerates drug development, enabling the selection of precise therapies and optimizing drug delivery strategies for improved outcomes. Furthermore, spatial transcriptomics informs drug metabolism studies, enhancing therapeutic effectiveness

2. Biomedical Research

In biomedical research, spatial transcriptomics illuminates molecular mechanisms in physiological and pathological processes. By revealing spatial gene expression patterns, it unravels biological complexity, identifies disease drivers, and informs targeted diagnostics and therapies for conditions like cancer, neurodegeneration, and autoimmunity.

3. Precision Medicine

Spatial transcriptomics revolutionizes precision medicine by characterizing tissue-specific gene expression, facilitating the identification of molecular subtypes and patient stratification. This enables personalized treatment strategies, considering spatial disease heterogeneity, and ultimately enhances treatment outcomes and patient care.

Challenges and Considerations with Spatial Transcriptomics Data

1. Data Complexity and Interpretation

Spatial transcriptomics generates vast amounts of data, resulting in increased complexity during analysis and interpretation. Integrating spatial information with gene expression profiles requires sophisticated computational algorithms and bioinformatics tools. Furthermore, deciphering spatially resolved gene expression patterns necessitates expertise in data visualization and statistical analysis. Researchers must address these challenges to accurately interpret spatial transcriptomic data and extract meaningful biological insights.

2. Technical Limitations

Spatial transcriptomics techniques face technical limitations that can impact data quality and resolution. Challenges such as spatial resolution, sensitivity, and throughput may vary depending on the chosen experimental platform. Additionally, sample preparation methods, tissue handling protocols, and imaging techniques can introduce artifacts or biases that need to be carefully controlled. Addressing these technical limitations is essential to ensure the reliability and reproducibility of spatial transcriptomic data.

3. Resource and Expertise Requirements

Spatial transcriptomics requires significant resources and expertise to conduct experiments and analyze data effectively. Access to specialized equipment, such as high-resolution imaging systems and next-generation sequencing platforms, is essential for generating spatially resolved gene expression data. Moreover, expertise in molecular biology, bioinformatics, and computational biology is necessary to design experiments, process samples, and interpret results accurately. Collaborative efforts and interdisciplinary collaborations can help overcome resource and expertise barriers in spatial transcriptomics research.

Streamline Spatial Transcriptomics Analysis for Enhanced Insights and Impact on Polly

Elucidata’s Solutions

Polly addresses the challenges of spatial transcriptomics as researchers gain access to harmonized Spatially Resolved Transcriptomics (SRT) datasets, empowering them to identify spatially variable genes and perform tissue segmentation.

Polly seamlessly imports SRT data from diverse public sources, provided that raw counts matrix, spatial coordinates, imaging data, and metadata are available. Currently, Spatial datasets on Polly are available in two formats: unfiltered raw counts and custom processed counts, depending on the researcher's needs.

Spatial transcriptomics datasets on Polly represent curated collections of biologically and statistically comparable samples. Each dataset is uniquely identified and named based on the public database from which it was ingested.Polly supports sources from Gene Expression Omnibus, Single Cell Portal, Zenodo, CZI-CellxGene, and publications.

Deciphering Gene Expression in 3D with Spatial Transcriptomics
Spacial Integrated Analysis on Polly

How are the Spatial Datasets Streamlined on Polly?

1. Data Retrieval: Raw counts and spatial data are fetched from the source either by direct download from provided links or manual downloading.

2. Data Preparation: A JSON file containing essential input parameters is generated to guide subsequent pipeline steps.

3. Creation of h5ad: The pipeline begins by generating the initial h5ad file, ensuring the presence of raw counts, spatial coordinates, and required image files. Checks are performed to validate data integrity, with the pipeline halting if discrepancies are detected.

4. Metadata Curation: Curated metadata is added at the sample/cell level, enhancing dataset completeness and interpretability. Quality control metrics are computed and appended to the h5ad file, facilitating data quality assessment.

5. Spatial Embedding: An X_spatial embedding is incorporated into the h5ad file's obsm slot, enabling spatial visualization within cellXgene.

6. Finalization: The fully processed h5ad file, containing QC metrics, curated metadata, raw counts matrix, spatial coordinates, and H&E images, is saved for analysis and exploration.

This comprehensive pipeline ensures the seamless integration and processing of spatial datasets on Polly, empowering researchers with reliable and curated data for spatial transcriptomics.

Impact and RoI

  1. Addresses limitation of low resolution Visium data.
  2. Useful for mapping/ validation of tissues’ cell-type composition.
  3. Useful for understanding cell-type localization patterns.
  4. Combined with spatial dimension, can enhance cell-cell interaction analyses.


Spatial transcriptomics has emerged as a groundbreaking approach, providing invaluable insights into the spatial organization of gene expression within biological systems. With Polly, Elucidata's innovative solution, researchers gain access to harmonized Spatially Resolved Transcriptomics (SRT) datasets, enabling the identification of spatially variable genes and tissue segmentation. By streamlining data processing and analysis, Polly addresses challenges in spatial transcriptomics, empowering researchers to unravel the complexities of gene expression dynamics and accelerate discoveries in biomedical research.

Polly's harmonization  platform facilitates seamless integration of spatial datasets from diverse public sources, simplifying data retrieval and processing. With its user-friendly interface and advanced analytical tools, Polly empowers researchers to explore spatial gene expression patterns and their implications for disease mechanisms and therapeutic interventions. By revolutionizing spatial transcriptomics analysis, Polly enables researchers to unlock novel insights into cellular organization and function, driving impactful discoveries in molecular biology and beyond.

Connect with us or reach out to us at info@elucidata.io to learn more.

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