The diversification of methods for studying cell-cell interactions and communication

Abstract

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

Figure 1.. Methodological advancement of cell-cell interaction research.

(a) A wide range of computational tools have been developed to infer cell-cell interactions from gene expression. Recent innovations have expanded the capabilities of such analyses to account for finer resolution of single cell interactions, spatial information, larger datasets, and deeper information. (b) Experimental methods have expanded the power of conventional methods to increase the throughput for tracking cell-cell interactions.

Figure 2.. Phylogenetic tree of computational tools for inferring cell-cell interactions.

There has been an evolution of computational tools to infer CCIs, derived from a ‘root’ of core tools. From this root, methods have become more specialised to address specific opportunities (grey arrows in the centre). Main branches growing from the centre represent the predominant features of tools (“core tools”, “finer”, “deeper”, “broader”, “more localised”, or “other improvements”). Coloured boxes indicate secondary features of each fate or sub-group of tools (coloured shades). A total of 105 tools are displayed here (see Supplementary Table 1 for further details, including summaries and repository availability). Tools are grouped as follows: (i) The “core tools” rely on core or similar scoring functions and are general frameworks for CCI analysis. (ii) Other branches capture tools with predominant features such as those associated with Fig. 1a (secondary features from different branches are further shown in Supplementary Table 1). (iii) The “other improvements” branch highlights tools whose predominant features are not in Fig. 1a (such as implementing interactive interfaces, enabling benchmarking, and focusing on multiple ligand-receptor interactions (LRIs) simultaneously to infer CCIs). (iv) Sub-branches and leaves are sorted by similarities in underlying algorithms, secondary features, or date of publication, defining the distinct sub-groups (coloured boxes and shades). A tool name is marked with an asterisk (*) if published as a preprint upon writing this review. Multiple versions of the same tool are treated separately if they include different features and were published in separate articles. DE, differentially expressed; LR, ligand–receptor; ST, spatial transcriptomics

Figure 3.. New features and analyses performed by next-generation computational tools.

(a) Analysing cell-cell interactions (CCIs) at single-cell resolution enables one to identify ligand-receptor interactions (LRIs) that define heterogeneity of cell pairs (markers) and helps obtain more precise annotations related to their communication mechanisms. (b) Similarly, analysis of interactions between single cells (circles) can reveal phenotypic heterogeneity (different colours) that is not associated with cell annotations. (c,d) Tools that account for spatial transcriptomics explore local neighbourhoods of cells so they can spatially visualise signalling activities (c), and infer directionality of signalling pathways given the spatial distribution of receptors and/or diffusion of ligands (d). (e) Strategies integrating metabolite- or small-compound-based LRIs depend on expression of enzymes catalysing their production or consumption. (f) The inclusion of gene-regulatory networks and receptor-downstream transcription factors (TFs) can be used to infer signalling feedback loops between two cells, helping to capture interconnected layers of intracellular activity. (g,h) Next-generation tools also enable comparisons of multiple conditions either pairwise to detect differential CCI changes (g) or simultaneously to identify trends or patterns of CCIs (such as factors from factorization methods) (h).

Figure 4.. Major approaches in next-generation experimental methods for studying cell-cell interactions.

Sequencing technologies have enabled the measurement of single-cell transcriptomes while also (a) tracking barcodes passed between physically interacting cells (such as those delivered through transfection or by engineered viruses) or (b) directly isolating interacting cells by generating multiplets (such as by using fluorescence-activated cell sorting (FACS) or microfluidics). Thus, cell-cell contact networks can be built while inferring the ligand-receptor interactions (LRI) used by these cells. (c,d) Cell-cell interactions (CCIs) between proximal cells can be evaluated by labelling methods either relying on (c) enzymes catalysing the binding of a probe to an acceptor to tag the LRIs in a contact-dependent way or (d) catalysts inducing a highly-reactive state of a diffusible tag (with a radius that depends on its half-life) to label LRIs in a contact-independent way. (e) Synthetic receptors can be devised to induce a transcriptional response of interest within a receiver cell each time they interact with a specific ligand produced by a sender cell. For example, a membrane-bound green fluorescent protein (GFP) in a sender cell can be used with a synthetic receptor made from an anti-GFP (a-GFP) nanobody and a Notch receptor. Such synthetic receptors can help track sender-receiver interactions in vivo.

Figure 5.. Challenges and opportunities for future enhancement of methods for cell-cell interaction research.

(a) Aligning single cells across multiple conditions is difficult because each sample inherently differs in cell number, which challenges cell-cell interaction (CCI) comparisons across conditions with single-cell resolution. Without correction, this case would lead to the pigeonhole principle (that is, not all cells can be assigned a one-to-one alignment from one condition to another). (b) Combinations of ligand-receptor interactions (LRIs) given their different protein variants also shape the expression of downstream genes, leading to different transcriptional responses for similar CCIs. By incorporating this information into CCI research, one may improve predictions and capture new biological insights of signalling activities. (c) Generating ground-truth data has been a major challenge for years; however, to address this, curated databases of real interactions are being developed, helping to benchmark and tune computational tools. Furthermore, community-organised efforts will be key for generating gold-standard data using next-generation experimental methods. (d) Including sub-cellular localization of ligands and receptors could refine predictions of interacting cells. (e) Most experimental methods are limited to in situ and in vitro deployment, with little work conducted in vivo. Further advances will be obtained with experimental methods that allow simultaneous evaluation of many LRIs and libraries of engineered ligands and receptors for tracking interactions.