Right Place At The Right Time: Positional Multiomics

Science, especially biology, is all about conducting experiments to arrive at conclusions. Experiments may involve multiple methods and stretch over multiple days/months to make the necessary observations. The nature of methods is always evolving, with scientists inventing newer methods using the technology available at the time. Sequencing the human genome was a landmark, but also an extremely expensive achievement. Over time, the price of sequencing has reduced drastically, and the field has evolved rapidly, covering every aspect of the central dogma and also going beyond them, as seen in, transcriptomics (RNA), proteomics (protein), epigenomics (epigenetics), TCR repertoire (due to different antigen exposures), etc. 

Another massive leap was the advent of single-cell RNA sequencing (scRNAseq), where the RNA content of each cell could be separately sequenced. This provided specific and high-resolution information on the true nature of each cell of the tissue of interest. All the mentioned methods still could not provide us with the topology (the arrangement of cells when in tissue) information. Spatial transcriptomics solved this! It could retain spatial information about the cells while providing relatively high-resolution data of the transcriptome. This makes it the ideal technique for researchers focused on solving complex microenvironments, like tumors. 

Despite the high resolution of the transcriptome obtained through methods relying on RNA, quantifying protein expression is the main step for validation. Using fluorescent-tagged antibodies has remained the gold standard for validation, through methods like immunofluorescence and flow cytometry. So, it is not surprising that the acquisition of transcriptomic and proteomic data together is highly sought after. In our article on various OMICS technologies from last year, we covered a method that could do this: CITE-seq (where CITE stands for Cellular Indexing of Transcriptomes and Epitopes by Sequencing). In this method, the cells are stained with antibodies that are tagged with oligonucleotides instead of fluorescent antibodies. Hence, when scRNAseq is done, the indexed antibodies are also read, providing protein readouts along with transcriptomic data. In this article we take a look at technologies that go one step ahead, and bring simultaneous protein and RNA readouts while maintaining positional information in tissue and protein complexes:

1. Spatial PrOtein and Transcriptome Sequencing (SPOTS)

Ben-Chetrit et al. in their short communication applied CITE-seq’s DNA barcoded antibodies to spatial transcriptomics. Using SPOTS, they carried out high throughput spatial transcriptomics accompanied by up to 30 barcoded antibodies. Using antibodies that target marker proteins, regions of dominant cell types in a particular part of the tissue slide could be easily identified and also correlated with the transcriptomic signatures. In the spleen stained with several immune cell markers, the protein and mRNA abundance were also similar, although macrophages were an exception. The researchers also tested SPOTS in mouse breast cancer sections, where the subtypes of immune cells are not as well defined as in the spleen. Due to the multimodal integration (protein and mRNA signals together), a feature of the SPOTS technique, clustering, and characterization of cells was largely improved while retaining the topology of the microenvironment.

2. Prox-Seq

 Vistain et al. modified the barcoded antibodies such that two antibodies in proximity would ligate/attach to each other and show a ligated signal during analysis. This modification was in the form of a connector region at the end of two probes of each antibody (targeting a specific protein). When two complementary probes of the same or different targets are in proximity (~54 – ~73 nm), they get ligated through the connector, forming the aforementioned PLA (proximity-ligation assay) product. Hence, Prox-seq integrates the ability to detect hundreds of protein-protein complexes with existing whole-cell transcriptomics. 

They demonstrated the use of Prox-seq in identifying previously known and unknown protein complexes in immune cells. When peripheral blood mononuclear cells (PBMCs) were stained with a panel of 38 markers focusing on T cells, along with known complexes and homodimers like CD3-CD3, CD3-CD8, CD3-CD4, etc., one of the previously unknown interactions between CD9 and CD8 was also identified only in CD8+ T cells. Using the transcriptome, the nature of these cells could be further investigated. The CD8+ T cells with high CD9-CD9 interaction and no CD9-CD8 interaction had upregulation of lymphocyte activation markers. On the other hand, cells that did have the CD9-CD8 complex had high expression of markers for naïve T cells. Through this combined exploratory analysis, new complexes to identify naïve T cells could be identified. 

They also used Prox-seq on macrophages stimulated by TLR ligands (LPS and PAM) at different time points. They could see changes in different protein complex formations temporally and correlate them with the nature of the cell. They showed how the TLR2-TLR2 homodimer appeared after 2 hours of LPS stimulation, but disappeared by 12 hours, while for PAM, the homodimer was only formed at around 12 hours. This data signified the importance of temporality in the formation of the TLR2-TLR2 complex. Moreover, this complex was also previously unknown to occur in LPS or PAM-induced macrophages.

While the two studies discussed here to attempt to make simultaneous protein and RNA sequencing possible, efforts of multimodal integration from varying datasets are also being explored. The company, 10X genomics, recently revealed their new technology, Xenium, which detects fewer genes than Visium, but at a much higher resolution while also not destroying the tissue. This means, using Xenium, the tissue can first be sequenced spatially and then validated using immunofluorescence or H&E staining. All these methods, despite being novel and limitless in their scope, are still extremely expensive and the skill required to process these data often requires specialized labor. But hopefully, with time, these methods will get cheaper and more accessible across the world.   


Vistain L, Van Phan H, Keisham B, Jordi C, Chen M, Reddy ST, Tay S. Quantification of extracellular proteins, protein complexes and mRNAs in single cells by proximity sequencing. Nat Methods. 2022 Dec;19(12):1578-1589. doi: 10.1038/s41592-022-01684-z.

Ben-Chetrit N, Niu X, Swett AD, Sotelo J, Jiao MS, Stewart CM, Potenski C, Mielinis P, Roelli P, Stoeckius M, Landau DA. Integration of whole transcriptome spatial profiling with protein markers. Nat Biotechnol. 2023 Jan 2. doi: 10.1038/s41587-022-01536-3.

High resolution mapping of the breast cancer tumor microenvironment using integrated single cell, spatial and in situ analysis of FFPE tissue | bioRxiv

Article author: Kevin Merchant. Kevin is a PhD student at Helmholtz Munich, working at the intersection of computational biology and drug development against Idiopathic pulmonary fibrosis. He aims to simplify latest research so that it becomes accessible to all.

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