Spatial Transcriptomics

Achieve TRUE Single-Cell Resolution with Stereo-Seq and DNBSEQ™ technology

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What is Spatial Transcriptomics?

Spatial transcriptomics reveals cell localization and gene expression in intact tissue samples

While single-cell RNA-Seq (scRNA-Seq) can provide the novel insights of cell tissue, it has its own limitations. First, it requires to release viable cells from whole tissue without any damages or stress, making it harder to recover certain cell types, e.g. neurons in the brain. Additionally, spatial information, such as the relationship between cells and their neighboring cells within a tissue, is missing.

The latest groundbreaking technology, Spatial transcriptomics, can fill this gap by discovering localization of cell types and their associated gene expression within an intact tissue sample.

In 2021, the spatial-resolved transcriptomics was named “Method of the Year 2020” by Nature Methods. This spatial transcriptomics technology allows scientists to obtain gene expression information in a spatial dimension, furthering the understanding of diseases and accelerating drug or therapy discovery.

Learn more
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Science, 2022 “Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration”

Why Choose Stereo-seq for Your Spatial Transcriptomics?

Stereo-seq technology achieves TRUE single-cell resolution

Stereo-seq (SpaTial Enhanced Resolution Omics-sequencing) technology enables:

  • Unparalleled field-of-view and subcellular resolution.
  • Simultaneous transcriptome analysis at multiple levels: tissue-wide, cellular, subcellular, and molecular.
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Ultimate Spatial Transcriptomics Workflow

Complete Genomics is the only company, offering the complete spatial transcriptomics workflow solutions.

While Stereo-seq technology captures mRNA directly within tissue using a chip-based method and reconstructs cellular spatial locations through spatial coordination barcoding (Coordinate ID, CID). Meanwhile, DNBSEQ sequencing platforms provide fast, flexible, and cost-effective sequencing workflow.

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The Benefits of Stereo-seq Technology

True Single-Cell Resolution

Mapping with 400 spots per 100 µm²

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Large Capture Area

Capturing area up to 13 cm x 13 cm

Species Agnostic

Support diverse tissue types and species

True Single-cell Resolution

Stereo-seq enables unbiased transcriptome capturing at nanoscale resolution

With unbeatable nanoscale of spot size in 0.22 µm in diameter, Stereo-seq technology offers researchers a novel tool to explore spatial biology with unprecedented true single cell resolution at 0.5 µm , enabling simultaneous transcriptome study and analysis at tissue-, cellular-, subcellular- and molecular level from fresh frozen, FFPE, fixed frozen tissues.

Stereo-seq DNB chips have spots with a diameter of 0.22 µm and a center-to-center distance of 0.5 µm which provides up to 400 spots per 100 µm2, thereby allowing whole transcriptome mapping at true single cell resolution.

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Stereo-seq vs Competitors

CompanyProduct name​Spot size (µm)​Center-to-center
distance (µm)​		Capture areaSpeciesSample Type
STOmicsStereo-seq transcriptomics0.22​0.5​From 5 mm x 5 mm
Up to 132 mm x 132 mm		AllFresh Frozen
Fixed Frozen​
Stereo-seq OMNI0.220.510 mm x 10 mmAllFFPE
10x Genomics​Visium55​100​5 mm x 5 mm
11 mm x 11 mm		Human
Mouse​		Fresh Frozen
Fixed Frozen
FFPE
Visium HD​2​26.5 mm x 6.5 mmHuman
Mouse​		FFPE
Curio Bioscience​Seeker​10​10​3 mm x 3 mm
10 mm x 10 mm		AllFresh Frozen

Large Capture Area

Stereo-Seq accommodates larger tissue samples by offering a centimeter-level field of view

The standard chip size for Stereo-Seq is 1 cm x 1 cm and can be expanded up to 13 cm x 13 cm. Enlarging the capture area allows the measurement of whole mammalian embryos, human organs, or model organisms.

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Demonstration of Stereo-seq chip at different sizes

Species Agnostic

Stereo-seq technology supports diverse tissue types and species, and works with Fresh/Fixed frozen and FFPE samples

Stereo-seq Transcriptomics Solution utilizes a capture method for poly-adenylated mRNA capture which supports a wide range of tissue types and species including human, animals, and plants.

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Using Multiplexed Multi-Omics to Study Spatial Heterogeneity in Ovarian Cancer

Studying the spatial organization of all the cells in the niche or neighborhood of a tumor is crucial to fully understand how cancers progress. Since proximity speaks to cellular activity, the spatial positioning of cells certainly plays a role in modulating clinical outcomes. However, most platforms used to reconstruct the tumor ecosystem (TE) fail to include spatial context in the three-dimensional (3D) space of a solid tumor with single-cell resolution, and thus lack information on cell-cell or cell-extracellular matrix interactions.

49 min

sammy_ferri-borgogno_multi-omics

SAMMY FERRI-BORGOGNO

Instructor
MD Anderson Cancer Center

WATCH THE WEBINAR
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Breakthrough in Spatial Transcriptomics
with Stereo-seq

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Systematic comparison of sequencing-based spatial transcriptomic methods

July 4, 2024

This study benchmarks 11 spatial transcriptomic methods, including Stereo-seq, to provide critical insights into performance, assisting researchers in selecting appropriate platforms and establishing evaluation standards for spatial transcriptomic analysis.

Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex

July 12, 2023

Utilizing Stereo-seq, this study maps the macaque cortex at single-cell resolution, uncovering primate-specific cell types and their regional distributions to offer a valuable resource for understanding brain function, development, and disease.

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An invasive zone in human liver cancer identified by Stereo-seq promotes hepatocyte–tumor cell crosstalk, local immunosuppression and tumor progression

June 19, 2023

Using Stereo-seq, a novel tumor border invasive zone was identified and characterized in liver cancer patients displaying strong immunosuppression and severely damaged hepatocytes which may aid in the understanding of disease progression as well as the development of novel therapeutic strategies for solid tumors.

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Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration

September 2, 2022

Stereo-seq was used to study axolotl brain development and regeneration, uncovering 33 cell types during development and 28 during regeneration. The single-cell resolution data across multiple developmental and regenerative stages provide valuable insights into brain repair mechanisms.

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays

May 4, 2022

A spatiotemporal transcriptomic atlas of mouse organogenesis was generated with Stereo-seq in order to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues, and visualize gene expression localization related to developmental disorders.

Featured in the Journal Cell

Spatiotemporal Transcriptomic Atlas of Mouse Organogenesis Using DNA Nanoball-patterned Arrays

Cell. 2022; 185: P1777-1792.E21, May 12, 2022

“Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq).

We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.”

https://doi.org/10.1016/j.cell.2022.04.003

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Image source: https://www.cell.com/

FAQ

Spatial transcriptomics is a technology that allows gene expression patterns within tissue samples to be mapped while preserving spatial information.

Unlike traditional RNA-seq, which loses spatial context during sample preparation, spatial transcriptomics techniques like Stereo-seq capture the location of transcripts within the tissue architecture.

This enables researchers to study gene expression in the context of cellular organization and tissue microenvironments.

Stereo-seq utilizes DNA nanoball (DNB) patterned arrays to achieve high-resolution spatial barcoding at a 500 nm resolution. This nanoscale precision allows for identifying and localizing RNA transcripts at subcellular levels.

The technology combines this high-resolution capture with unbiased whole-transcriptome profiling, enabling researchers to map gene expression patterns with true single-cell resolution across large tissue areas.

A 1 cm x 1 cm Stereo-seq chip has 400 million spots. During sequencing, each spot holds a single DNA nanoball (DNB).

No, Stereo-seq workflows do not require a separate specialized instrument for spatial transcriptomics analysis. The technology is designed to be compatible with standard laboratory equipment, making it accessible to many research facilities.

Here’s what you need for a standard Stereo-seq experiment:

DNBSEQ Sequencing Platforms: This is the core sequencing instrument required for Stereo-seq analysis. It’s not a separate spatial instrument; the same sequencer is used for other NGS applications.

Standard laboratory equipment including:

  • Fluorescent microscope with a 10x objective and tile scanning capabilities*
  • Thermocycler or incubator for larger chip sizes
  • Pipettes
  • Qubit or other fluorometric quantification method
  • Agilent TapeStation or Bioanalyzer for quality control

*A fluorescent microscope (for fluorescent staining) or epi-brightfield microscope (for H&E staining) with a 10x objective and the ability to perform tile scanning and stitching. For more information on specifications, please feel free to contact us.

No, a DNBSEQ Sequencing Platform is required. Sequence barcodes are added to the cDNA synthesis step during the library prep workflow. These barcodes are specific to DNB sequencers and incompatible with other next-generation sequencers.

The optimal sequencing depth for Stereo-seq experiments varies depending on the chip type and size. Researchers may need to adjust the sequencing depth based on their specific experimental design and objectives.

Here are our general guidelines.
 
Standard Stereo-seq chip (1 cm x 1 cm): Recommended sequencing depth: 1.5 billion reads
 
Stereo-seq OMNI chip (1 cm x 1 cm): Recommended sequencing depth: 4 billion reads
 
Consultation with our technical support team can help determine optimal sequencing depth based on our experimental design and objectives. Contact Technical Support

What factors influence the required sequencing depth?

  • Chip size: Larger chip areas may require proportionally more reads
  • Sample complexity: Tissues with higher cellular diversity might benefit from increased sequencing depth
  • Research goals: Studies focusing on rare cell types or low-abundance transcripts may need deeper sequencing

Spatial transcriptomics data analysis involves several steps to process and interpret large volumes of spatially resolved gene expression data. Specialized analysis pipelines have been developed to address the unique challenges of spatial transcriptomics data and streamline the workflow.

For Stereo-seq data, two free essential software tools are used:

  • Stereo-seq Analysis Workflow (SAW): This is a Linux-based pipeline tool that forms the foundation of Stereo-seq data analysis. SAW processes the raw sequencing data from Stereo-seq libraries to generate gene expression matrices. These matrices contain crucial information about which genes are expressed and where within the tissue sample.
  • StereoMap: This software is used for downstream analysis and visualization of the spatial gene expression data. It likely provides tools for clustering, differential expression analysis, and creating spatial maps of gene expression patterns. 

These software tools are designed to handle the high-dimensional nature of spatial transcriptomics data and provide researchers with insights into the spatial organization of gene expression within tissues. Both SAW and StereoMap are freely accessible, allowing researchers to analyze their Stereo-seq data comprehensively.

Analyzing spatial transcriptomics data presents several computational challenges:

  • Large data volumes: High-resolution spatial data generates massive datasets, requiring efficient storage and processing solutions.
  • Integration with other data types: Combining spatial transcriptomics with histology images or single-cell RNA-seq data requires sophisticated integration methods.
  • Spatial statistics: Developing and applying appropriate statistical methods for analyzing spatially resolved gene expression data.
  • Cell type identification: Accurately identifying cell types based on gene expression patterns while considering spatial context.
  • Visualization: Creating effective ways to visualize complex spatial gene expression data in 2D and 3D.

Yes, Stereo-seq enables multiplexing, but the extent of multiplexing depends on the specific workflow being used:
 
Stereo-seq with Multiplex Immunofluorescence (mIF):

  • This workflow has been tested with up to 3 antibodies plus a nuclear stain, utilizing a total of 4 channels.
  • Due to spectral considerations, it is recommended that no more than 3 antibodies be used in the Stereo-seq mIF workflow.

Stereo-CITE:

  • This advanced version of the technology allows for much higher levels of multiplexing.
  • Over 100 antibodies can be multiplexed together in a single Stereo-CITE experiment.


 The difference in multiplexing capacity between the Stereo-seq mIF and Stereo-CITE workflows reflects the distinct technologies used for protein detection. While mIF relies on fluorescence imaging, it is limited by spectral overlap, Stereo-CITE uses oligonucleotide-tagged antibodies that can be detected through sequencing, allowing for much higher multiplexing capabilities.
 
Both approaches enable the detection of cellular and cell surface proteins and markers.

Yes, Stereo-seq data can be analyzed using popular single-cell analysis tools, including Seurat. Be sure to consider:

Data compatibility: Stereo-seq data can be formatted to be compatible with Seurat and other widely used analysis tools.

Format conversion: To use Stereo-seq data with these tools, you’ll need to perform a format conversion. The SAW (Stereo-seq Analysis Workflow) operation manual provides detailed instructions for format conversion, and the StereoPy documentation provides additional guidance.

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