Single-Cell Transcriptomics Fuels Cell-Cell Communication Insights

Cell-cell communication (CCC) is crucial for the smooth functioning of tissues and required for organisms to remain healthy—many diseases are known to involve defects in cell-cell interactions (CCIs). Most CCC is governed by receptor-ligand interactions (LLIs), where cell A sends a ligand “message” that is received upon binding to a receptor expressed by cell B. Cells have evolved manifold means of ligand-receptor signaling to communicate in different circumstances. Cells send autocrine signals to themselves, and use gap junction signals to interact with neighboring cells to which they are physically connected via gap junction channels. Cells also interact with nearby and distant neighbors using paracrine and endocrine signals, respectively.

Because technical limitations make studying CCC in vivo nearly impossible thus far, researchers are developing other approaches to unlocking these important signaling events. Recent advances in single-cell transcriptomics make it possible to approximate the expression levels of individual molecules (e.g., ligands and receptors) in individual cells. In addition, emerging spatial biology techniques can measure cellular and sometimes subcellular positional information across tissues, correlating with gene expression data. This article discusses how developments in single-cell RNAseq are fueling research in cell-cell communication.

Cell atlasing in CCC

The likelihood that cells interact with one another increases with their physical proximity, so mapping the spatial environs and the neighboring cell types is valuable for predicting CCIs. New spatial biology methods have led to recent advances in cell atlasing, such that atlases of many tissue types are now available online. For example, Vizgen’s MERSCOPE^® platform can characterize individual cell types across a tissue region, making it well-suited for cell atlasing projects.

MERSCOPE is a single-cell spatial transcriptomics imaging platform that can profile hundreds to thousands of genes in situ with single-cell resolution. It uses a technique known as MERFISH (multiplexed error-robust fluorescence in situ hybridization), which combines single-cell transcriptomics with spatial biology. It was recently instrumental in efforts to create the first high-resolution cell atlas of the mouse brain, as detailed by multiple papers in the December 2023 issue of Nature.

In one of these papers, a Harvard research group predicted cell-cell interactions across the entire mouse brain using MERSCOPE and spatial proximity analysis. “This is the first comprehensive cell-cell interaction analysis across the brain, and MERFISH data allowed researchers to better understand cell-type specific interactions between several hundred pairs of molecularly defined cell types and predict potential ligand and receptor interactions,” says Jiang He, Scientific Co-founder and Senior Director of Scientific Affairs at Vizgen.

CCC in the tumor microenvironment

Although MERSCOPE is a targeted platform, it holds predictive power because it routinely integrates single-cell sequencing data for transcriptome-wide spatial imaging. “This means that researchers are able to predict the spatial location and copy number of RNA transcripts for genes not measured by MERSCOPE,” says He. “With the integrated data, researchers can study more genes and their expression patterns in greater detail, such as the ligand-receptor interactome with spatial context.” One example, He notes, is the ability to study pairs of LRIs in neighboring cells within the tumor microenvironment, such as the interaction between the T cell receptor PD-1, and its ligand PD-L1—an important target of cancer immunotherapies based on immune checkpoint blockade.

Researchers at Massachusetts General Hospital recently used MERSCOPE to study changes in the tumor microenvironment that occur during lung cancer treatment with a PD-1 immune checkpoint blockade therapy. They found that tissue samples containing particular immunity “hub” structures—a localized area of immune cells and chemokines that attract them—were more strongly associated with better responses to PD-1 blockade therapy. The researchers then used neighborhood analysis to unravel which immune cell types were closely interacting within the immunity hub.

He notes that Vizgen’s platform plays another important role in interaction studies: documenting cell boundaries. “To study cell-cell interaction, a method that can accurately identify the boundary of a cell is crucial because effective cell segmentation is critical for downstream bioinformatic analysis,” He adds. “The MERSCOPE platform offers a cell boundary staining kit that can stain the plasma membrane of cells in tissue to give that ground truth.”

Low-tech with high-throughput results

Larger scale atlasing projects require higher throughput studies to confirm CCIs. ScaleBio offers simple yet clever methodology that boosts throughput with multiplexed scRNA-seq using common lab equipment, making the workflow more accessible and affordable. “The assay has consistently obtained high-quality data from a variety of difficult tissue sample types, which is crucial for cell-cell interaction experiments,” says Melanie Masuda, Director of Market Development for Scale Biosciences.

ScaleBio’s patented combinatorial indexing technology labels cells (or nuclei) in repeated split-pooling fashion. Using iterations of labeling, pooling, and splitting of isolated cells in suspension, their method adds a combinatorial labeling code to each individual cell that is cell-specific. These cell-specific codes go along for the ride through the sequencing process and are then demultiplexed after sequencing. This makes it possible to sequence individual cells simultaneously. For example, the ScaleBio™ Single Cell Sequencing Kit supports the sequencing of 125,000 cells at once.

Scaling up single-cell sequencing is essential for greater insights into the connections between interacting cells. “Ligand-receptor interactions (LRI), as the dominant mechanism for CCIs, need to be profiled to build high-confidence databases,” says Masuda. “Given the large number of LRIs and the location-specific interactions, our high-sample multiplexing allows for this to be done at scale while removing batch effects.” She adds that ScaleBio’s gene expression outputs correlate with spatial gene expression data.

Matsuda notes that perturbing signaling pathways to assess redundancies and dependencies would be a logical next step. However, “[the analysis of the many samples and cells required for this is] beyond the aims of most tools today,” says Masuda. “While there are many bioinformatics tools to assess cell-cell interactions in a variety of ways, the inherent complexities of the analysis are limited on throughput and will need to be improved for atlasing.” Future work in the field may include the development of bioinformatics tools that can handle more easily the burgeoning output of greater multiplexing.