Gene Expression. Visualized.

Uncover the ‘Where’ for
every ‘What’ with Spatial
Transcriptomics Technology

The relationship between cells and their relative locations within a tissue sample can be critical to understanding disease pathology. Spatial transcriptomics is a groundbreaking technology that allows scientists to measure all the gene activity in a tissue sample and map where the activity is occurring. Already this technology is leading to new discoveries that will prove instrumental in helping scientists gain a better understanding of biological processes and disease.

 
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Explore Gene Expression in the Context of the Tissue Microenvironment

Innovation in spatial gene expression technologies is enabling scientists to get a holistic understanding of cells in their morphological context. In this presentation, you will hear first-hand from 10x Genomics’ scientists about ground-breaking improvements to the technology and exciting applications showcased by users.

A fresh-frozen tissue section is imaged for histological purposes and placed on an array containing capture probes that bind to RNA. Tissue is fixed and permeabilized to release RNA to bind to adjacent capture probes, allowing for the capture of gene expression information. cDNA is then synthesized from captured RNA and sequencing libraries prepared. The libraries are then sequenced and data visualized to determine which genes are expressed, and where, as well as in what quantity.

How it Works 1
1. Tissue Fixation
How it Works 2
2. Permeabilization
How it Works 3
3. cDNA Synthesis
How it Works 4
4. Library Construction
How it Works 5
5. Sequencing
How it Works 6
6. Data Visualization

The data generated by Spatial Transcriptomics technology allows you to choose any gene of interest and display its spatially resolved expression on the original tissue section. Since all mRNAs are captured, you are not limited to visualizing only a single gene but can choose any number of genes in any combination to view and analyze together.

Visualization Controls

Use the sliders under the tissue image to adjust how you visualize and combine the tissue image and the gene expression data. Colors represent clusters identified by differentially expressed genes.

Change map spot opacity:

Change map cluster cloud opacity:

Gene Identification

By placing the pointer above a gene name within the table, spots in the tissue image will be colored based on the expression of that gene. Alternatively, by placing the pointer above a value within the table, you can observe the expression of a specific gene with the spots from an individual cluster highlighted.
Discover More

For any application involving sectioning tissue – morphological and proteomic information are incredibly important in resolving biology. Layer unbiased gene expression data onto your images and access a new level of information. This complement to your stain of interest will help you understand tissue microenvironments like never before.

Turnkey Solution

Most traditional sectioning techniques and samples are supported with fresh-frozen tissues. Follow 10x Visium’s easy to follow protocols for a shorter workflow that takes your sample through to sequence-ready libraries.

Standard Tools

Sectioning, H&E, and imaging can all be done with your own existing infrastructure. We supply the array slides, assay,and instructions for sequencing and analysis. We make it easy for you to operationalize tools in your current infrastructure.

Easy, Informative Data Analysis

Our Cell Ranger tool allows you to analyze Visium’s data outputs in the context of the tissue H&E image. We provide you the flexibility of how you analyze your data with a simple user interface

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Gain a holisitc understanding of gene expression
in the tissue microenvironment

See how Spatial Transcriptomics technology is being used by researchers to elevate the understanding of complex transcriptional landscapes within a spatial context, which is essential for the understanding of biology and complex disease.

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Spatially Resolved Transcriptomics Enables Dissection of Genetic Heterogeneity in Stage III Cutaneous Malignant Melanoma
Thrane K et al. Cancer Research. 2018
Spatial Gene Expression assay was used to better understand melanoma lymph node metastases which revealed a complex transcriptional landscape in a localized context; may help in better understanding the multiple components of melanoma tumor progression and therapy outcome
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Spatiotemporal Dynamics of Molecular Pathology in Amyotrophic Lateral Sclerosis
Maniatis et al. Science. 2019
Used spatial RNA sequencing to define transcriptomic changes in different regions of the spinal cord of a mouse ALS model and a postmortem human ALS spinal cord. Identified disease-associated pathways and established the key steps in motor neuron degeneration observed in ALS
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Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl PL et al. Science. 2016
Analyzed patterns of RNA expression in mouse brain and human breast cancer enabling a new way of analyzing all gene expression while preserving tissue morphology with implications in diagnostics, research and novel target identification
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Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity
Berglund E et al. Nature Commun. 2018
Profiled gene expression in an unbiased manner using Spatial Transcriptomics technology in prostate cancer tissue. Extracted distinct expression profiles for various tissue components, including immune infiltrates and tumor

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