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Comparing Single-Cell, Spatial, and In Situ Sequencing Technologies

Psomagen Blog

Comparing Single-Cell, Spatial, and In Situ Sequencing Technologies


Psomagen is committed to providing researchers access to technologies for every stage of their experiment.  Whether you are in the discovery phase of basic research or the targeted phase of translational research, there is a solution at Psomagen. But with the rise of third-generation technologies and new approaches, it can be difficult to know which option is right for your research question. 

In this article, we break down the differences between single-cell, spatial, and in situ sequencing technologies, with examples of how the technologies have been used in disease research.  

Why Single-Cell Sequencing? 

The quick answer: Bulk sequencing data are wrong. 

Bulk sequencing collects all RNAs from all cells. However, each cell type has a different function, role, and cell state. This means that each cell has a different transcriptomic pattern. Immune cells have different roles compared to cardiomyocytes. A cell transitioning to apoptosis or an epithelial cell transitioning to a mesenchymal cell rely on very different RNA expression content. Bulk sequencing averages these transcriptomes, yielding a profile that is not truly representative of any particular cell in the sample.  

In contrast, single-cell sequencing allows us to separate out individual cells and sequence their transcriptomes to identify exactly which RNA came from which cell.  We can then determine cell types and compare the expression within one cell type to another. This is a valuable tool in understanding cell state transition and disease response. 

This technology has been of particular interest in cancer studies. Tumors are a heterogeneous mix of normal and cancerous cells. The tumor microenvironment (TME) surrounding the tumor is similarly complex. When we pool the RNA for a bulk sequencing project, we are essentially muting the impacts of the cancer cells.

Single-cell sequencing allows us to look at the specific patterns of cancer cells compared to the specific patterns of healthy cells.  We can determine which genes are up and down regulated in one type versus another and identify pathways which may be activated or deactivated.  

While bulk sequencing can identify these patterns, more subtle changes are lost in the noise. Not all cell types are involved in disease response.  Being able to target pathways contributing to disease reduces unwanted treatment side effects and provides more effective treatment options.

In a 2021 research project, a group of oncologists from Guangzhou, China used single-cell sequencing on 11,866 T-cells of oral squamous cell carcinoma. Past projects have used bulk sequencing, which did not allow researchers to explore tumor heterogeneity. Within single-cell, this project identified 14 tumor T-cell populations within tumor tissue and 5 T-cell populations in the surrounding tissues. Upregulation of two of these T-cell types were linked to tumor immunosuppression. Insights like these are promising breakthroughs in developing effective treatment strategies.  

Why Spatial Biology?

The quick answer: In vivo tissues are not arranged by cell type.

Single-cell sequencing provides single-cell resolution of cells, but does not provide that information in a spatial context. We lose information like cell-to-cell interactions and immune infiltration. 

The aim of spatial transcriptomics is to understand the complex structure of tissue and intercellular communication. Intercellular communication allows cells to respond to external stimuli through the release of ligands or cell signaling molecules. 

These molecules can bind to cell surface proteins on nearby cells or pass through gap junctions in neighboring cells. The transmission of molecules triggers an intracellular response allowing the target cell to regulate metabolism, affect development, or produce other necessary responses to a changing environment. Because these changes can happen locally or at a distance, spatial context becomes important in understanding the response mechanism.

One of the most common uses for spatial transcriptomics is understanding the tumor microenvironment. A tumor can contain both cancerous and healthy cells. Single-cell sequencing can easily distinguish between the two and even provide an approximate ratio of cancerous to healthy cells. 

What it cannot determine, however, is how those cancer cells have grown within the environment. Much cancer research is centered around using the body’s natural immune system to target and destroy cancer cells, but often the immune cells cannot penetrate the tumor effectively. By studying the environment of the tumor, researchers can understand if immune cells are penetrating and how far the tumor cells have spread into healthy tissue.  

Meylan, et. al. showed the importance of tertiary lymphoid structures as sites of B cell enrichment in tumors and corresponding to positive clinical outcomes in renal cell carcinoma.  VIsium spatial analysis identified 8 immune and stromal populations within the TME, particularly expressed in the TLS, along with genes consistent with B cell maturation lineage, all the way to plasma cell development.  Following patient outcomes, researchers were able to elucidate that the increased presence of plasma cells in the TLS, which were carried on fibroblastic tracks to disseminate in the TME, led to more favorable outcomes, suggesting that TLS induction in tumors may lead to better immunotherapeutic responses.

Why In Situ Sequencing?

The quick answer: Spatial transcriptomics is not subcellular.

Spatial transcriptomics relies on patterned capture probes laid out on a slide which will bind to:

  1. RNA transcripts in tissue laid on top of them (polyA capture method) or 
  2. RNA probes which bind to complementary transcript regions on the tissue (probe based capture method)

These probes can be used as templates for library generation for downstream sequencing.  

Regardless of the resolution of these patterned probes, spatial transcriptomics cannot provide true single-cell resolution. Tissue is not organized in a gridded fashion. Therefore, there is always the chance for a capture probe to occur at the site of intercellular junctions such that captured transcripts could come from two or more different cells.  

In situ sequencing, however, does not require downstream library preparation and sequencing. Instead, it images the transcript targets directly in the tissue.  Capture probes can be fluorescently labeled and imaged so that their signals create a fluorescent “barcode” whose order (or sequence) can be read to identify the corresponding transcript. 

In situ sequencing is often used as a validation for discovery methods such as single-cell or spatial transcriptomics.  These two technologies are considered “whole transcriptome” as they attempt to interrogate all mRNA species within a cell or tissue.  Even probe-based options tend to represent the majority of the transcriptome (i.e. 18k genes for human, 21k genes for mouse).  From this data, researchers identify targets or pathways of interest and can then design or use more focused panels for in situ methods which include only genes which are relevant to the disease state.  

In a 2020 Cell paper, mouse and human in situ sequencing combined with spatial transcriptomics was used to identify cellular alterations in Alzheimer's disease. In situ technology used on 10 coronal sections (4 mouse, 6 human) was able to confirm spatial observations and predictions at the cellular level. They found that amyloid plaques induce strong cellular changes in all cell types found in their niche. Amyloid plaques have historically not been considered active manipulators of the disease state, making this an important breakthrough in our understanding of the disease.

Single-cell sequencing, spatial biology, and in situ technologies are important tools for disease research. Selecting the correct approach (or combination of approaches) for your research is an important step in generating strong, actionable data.

Cover image courtesy of NanoString.