Nature declared single-cell multimodal omics to be the 2019 Method of the Year. This follows the entrenchment of single-cell transcriptome analyses as household tools in biological and clinical research. Single cell transcriptome analyses offer unprecedented access to study cellular heterogeneity and tissue organization, but they remain limited. They provide information solely about the transcriptomic state of a cell. They also come at the expense of spatial information, which prevents accurate reconstruction and analysis of cellular microenvironments.
Single-cell multimodal omics integrate two or more types of complementary omic data, enabling deeper insight into cell function and organization. One such method, CITE-seq single-cell RNA sequencing, allows researchers to simultaneously study protein and RNA expression, but does not provide spatial information. To overcome this, the Camara lab at the University of Pennsylvania recently developed a method for combining CITE-seq data with ultra-high multiplex immunohistochemistry (mIHC) data obtained with CODEX®. This approach, called Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA), maps the CITE-seq transcriptome to spatially resolved CODEX mIHC data, while retaining the single cell spatial resolution that is provided by CODEX. With this approach it is now possible to conduct routine spatial multi-omics analysis in situ and at single cell resolution.
We spoke to Oliver Braubach, PhD, Senior Applications Scientist at Akoya Biosciences, to learn more about this technology and what it means for CODEX users.
The CODEX Instrument
Q: For those who are unfamiliar, what is CODEX?
The CODEX Workflow
Q: What is CITE-Seq?
CITE-Seq (cellular indexing of transcriptomes and epitopes by sequencing) is a high throughput single-cell RNA sequencing method that captures cell phenotypic information as encoded by cell surface proteins. This method uses oligonucleotide-labeled antibodies to initially ‘capture’ or ‘tag’ cells, prior to them being analyzed with compatible single cell sequencing technology. During sequencing, cellular protein and transcriptome states are concurrently measured, which provides researchers with a multi-omic output. However, because tissues are dissociated, spatial information is lost, and the data are no longer in situ.
Q: How does STvEA integrate CITE-Seq with CODEX multiplex IHC data?
STvEA integrates the ‘protein space’ that is measured in both CODEXand CITE-seq experiments. If both experiments are conducted with the same antibody panel it is possible to measure surface expression of the same antigens, provided that the same cell populations exist in both CODEXand CITE-seq experiments. Via sophisticated clustering and correlation analyses, STvEA recognized this similarity, and organizes cells into discrete populations. The transcriptome information from the CITE-seq experiment can then be overlaid onto CODEX data, which in turn complement the analysis with single-cell spatial resolution. The result is a true multi-omic dataset with single cell resolution in intact tissue samples.
Image Source: Govek et al (2019) / CC-BY-NC-ND 4.0.
Q: Why is it useful to be able to combine single-cell transcriptomics with CODEX multiplex IHC?
Single-cell RNA sequencing is today’s gold standard for studies of cellular heterogeneity and organization. However, single-cell RNA sequencing does not provide spatial context. Spatial context is key to understanding cellular functions, so additional research would be needed to resolve function. The combination of CODEX® and CITE-seq data overcomes this problem by adding spatial context. In addition, CODEX® and CITE-seq experiments provide proteome data, which cannot be resolved via single-cell transcriptome analyses.
Q: What are the applications of this tool?
The applications of this tool are wide ranging and complementary to existing omics research agendas that exist in virtually every space of biological science. If I was to name a few specific areas that could benefit from this tool, then I would say immunology, cancer research, neuroscience and developmental biology.
Q: How can researchers get started with STvEA?
The first step would be to gain access to CODEX and CITE-seq technologies.
The Camara lab has released the STvEA algorithms in an R package available for download on GitHub.
They’ve also published an interactive web-based tool which allows you to view gene and protein data mapped on three murine splenic sections profiled with CODEX.
The preprint describing this method is available from bioRxiv.
