Having identified spatial patterns in cellular organization and gene expression, it is further of interest to characterize how cells coordinate activity through i) their (physical) community context, or cell-cell interactions (CCI); and, ii) protein-mediated signaling, or cell-cell communication (CCC).
Mechanisms underlying inter-cellular interaction and communication have been classified as autocrine, juxtacrine, paracrine, and endocrine to refer to intra-cellular, contact-dependent, close- and long-range signaling events, respectively (Armingol et al. 2020). Paracrine events, for instance, depend on diffusion of signaling molecules between proximal cells; endocrine events are mediated by molecules (typically hormones) that travel between distal cells through extracellular fluids.
In scRNA-seq data, CCI/C is commonly studied through ligand-receptor (LR) pairs, but corresponding tools are restricted to gene expression only; e.g., CellphoneDB (Efremova et al. 2020). However, incorporating spatial information should enrich this type of analysis because distances between cells play a key role in inter-cellular signaling.
Methods developed for CCI/C analysis of spatial (transcript)omics data consider cellular distances to adjust interactions predicted from expression, and typically aim at quantifying spatial co-expression of LRs (that is, between proximal cells). Such analyses need not be limited to LRs, however.
In principle, any CCC tool may be turned into a spatial one by:
pre-filtering: select a spatial neighborhood,
and apply CCC inference to selected observations.
post-filtering: apply CCC inference,
and filter results based on spatial proximity.
The reason we might choose to take one of these approaches is because CCC modeling frameworks designed for single cell data are typically less complex. Consequently, they tend to be more interpretable, easier and faster to run.
library(dplyr)
library(tidyr)
library(scater)
library(scuttle)
library(CCPlotR)
library(ggplot2)
library(ggspavis)
library(patchwork)
library(reticulate)
library(BiocParallel)
library(zellkonverter)
library(SpatialExperiment)
# load data from previous chapter
# (past quality control & clustered)
spe <- readRDS("img-spe_cl.rds")
# specify whether/how to
# perform parallelization
bp <- MulticoreParam(4)Here we run COMMOT (Cang et al. 2023), an optimal transport-based approach that models competition between sender and receiver signals, can account for multimeric interactions, considers cell-cell distances, and is linked to databases of signalling interactions and pathways.
Since COMMOT is available as a Python package, we can access it from R using reticulate with a Conda environment (see Chapter 7).
We first set up a Python environment. Here, we do this by re-using the Conda environment created in Chapter 7, in order to speed up the book build time. Alternatively, a new environment could also be set up instead (see Chapter 7).
We also install the commot package and additional dependencies from PyPI.
Alternatively, one could set up a conda environment (or similar) outside of R, following the authors’ installation instructions for COMMOT, and activate it with reticulate::use_condaenv().
# re-use environment from Python interoperability chapter
# using reticulate functions
env <- "env-ccc"
conda_clone(env, clone = "py-interop")## [1] "/root/.local/share/r-miniconda/envs/env-ccc/bin/python"use_condaenv(env, required = TRUE)
# install additional Python packages from PyPI
conda_install(
envname = env,
packages = c("commot==0.0.3", "anndata==0.12.2", "pandas==2.3.2", "numpy==1.26.4"),
pip = TRUE
)## [1] "'commot==0.0.3'" "'anndata==0.12.2'" "'pandas==2.3.2'"
## [4] "'numpy==1.26.4'"First, we retrieve ligand-receptor interactions (and a lot more!) from CellChatDB (Jin et al. 2021) using COMMOT’s ligand_receptor_database() function.
## ligand receptor pathway type
## 0 TGFB1 TGFBR1_TGFBR2 TGFb Secreted Signaling
## 1 TGFB2 TGFBR1_TGFBR2 TGFb Secreted Signaling
## 2 TGFB3 TGFBR1_TGFBR2 TGFb Secreted Signaling
## 3 TGFB1 ACVR1B_TGFBR2 TGFb Secreted Signaling
## 4 TGFB1 ACVR1C_TGFBR2 TGFb Secreted Signaling
## 5 TGFB2 ACVR1B_TGFBR2 TGFb Secreted SignalingWhile we obtain 1939 interactions from above, the vast majority of genes involved are missing from our panel. Filtering for those that are fully covered, we are left with very few:
# interaction types &
# number of interactions
table(db$type)##
## Cell-Cell Contact ECM-Receptor Secreted Signaling
## 319 421 1199## [1] 983## ligand receptor pathway type
## 711 CXCL12 CXCR4 CXCL Secreted Signaling
## 882 PTN SDC4 PTN Secreted Signaling
## 905 EDN1 EDNRB EDN Secreted Signaling
## 1640 PTPRC MRC1 CD45 Cell-Cell Contact
## 1644 CD80 CD274 CD80 Cell-Cell Contact
## 1646 CD80 CTLA4 CD80 Cell-Cell Contact
## 1648 CD86 CTLA4 CD86 Cell-Cell Contact
## 1657 CDH1 CDH1 CDH Cell-Cell Contact
## 1671 CD69 KLRB1 CLEC Cell-Cell Contact
## 1834 NCAM1 NCAM1 NCAM Cell-Cell Contact
## 1887 CD274 PDCD1 PD-L1 Cell-Cell Contact
## 1888 PDCD1LG2 PDCD1 PDL2 Cell-Cell Contact
## 1889 PECAM1 PECAM1 PECAM1 Cell-Cell ContactTo reduce runtime, we will restrict analysis to a 2mm\(2\) tissue section:
# subset data to features being considered
ss <- strsplit(db$receptor, "_")
rs <- sapply(ss, .subset, 1)
sub <- spe[unique(c(db$ligand, rs)), ]
# assign spatial coordinates to 'spatial'
# reduced dimension slot (required by 'COMMOT')
xy <- spatialCoords(sub)
reducedDim(sub, "spatial") <- xy
# get indices of cells that fall
# into a (2mm) x (2mm) window
cs <-
xy[, 1] >= 1500 & xy[, 1] <= 3500 &
xy[, 2] >= 2000 & xy[, 2] <= 4000
# print number of genes x cells left
dim(sub <- sub[, cs]) ## [1] 19 15003We now perform run CCC inference, using zellkonverter’s SCE2AnnData function to convert our spe in R to an AnnData object in Python. Note that in real settings (e.g., lots of interactions across lots of cells), this can be parallelized, e.g., across fields of view (FOVs) or tissue sections.
# convert 'SpatialExperiment' (R) to
# 'AnnData' (Python) using 'zellkonverter'
ad <- SCE2AnnData(sub, X_name="logcounts")
# run CCC inference using 'COMMOT'
ct <- import("commot")
ct$tl$spatial_communication(ad,
database_name="CellChatDB",
dis_thr=10,
df_ligrec=db,
heteromeric=TRUE,
pathway_sum=TRUE,
heteromeric_rule="min",
heteromeric_delimiter="_")
# extract 'data.frame's containing
# sender & receiver CCC estimates
ccc <- list(
s=ad$obsm["commot-CellChatDB-sum-sender"],
r=ad$obsm["commot-CellChatDB-sum-receiver"])
# add CCC inference results as cell metadata
for (df in ccc) colData(sub)[names(df)] <- dfAn important consideration is the distance threshold to impose on CCC inference (argument dis_thr in the code chunk below). Ideally, this decision is based on a biological expectation on cell size and signalling range. Estimates on the distance over which immune cell signaling can occur, for example, measured interactions on length scales of 80-120\(\mu\)m (Oyler-Yaniv et al. 2017).
Alternatively, we can arrive at a data-driven estimate by inspecting the distribution of \(k\)th-NN distances for a sensible \(k\) that, in this context, determines the number of cells to consider as potential communication partners:
Visualizing the above data across the tissue, we will typically observe that expression and signalling values give similar spatial patterns, but the latter will be restricted to where ligand and receptor-expressing cells co-localize.
For simplicity, we pick a single interaction (CXCL12-CXCR4 signalling); here, s and r correspond to sender and receiver signal, respectively:
# specify colors for sender/receiver signals
pal <- list(
s=c("grey90", "gold", "black"),
r=c("grey90", "turquoise", "black"))
# get variables names of the form: x, s-x-y, r-x-y, y
.f <- \(i, j) c(i, paste(c("s","r"), i,j, sep="-"), j)
xs <- .f("CXCL12", "CXCR4")
# visualize expression & signaling side-by-side
lapply(seq_along(xs), \(.) {
typ <- ifelse(. < 3, "s", "r")
plotCoords(sub, point_size=0.5,
annotate=xs[.], assay_name="logcounts") +
scale_color_gradientn(typ, colors=pal[[typ]])
}) |>
wrap_plots(nrow=1, guides="collect") &
theme(legend.key.width=unit(0.5, "lines"))
Let’s compare gene expression values with cell-cell signalling estimates:
es <- t(as.matrix(logcounts(sub)))
df <- data.frame(colData(sub), es)
ggplot(df, aes(CXCL12, s.CXCL12.CXCR4)) +
ggplot(df, aes(CXCR4, r.CXCL12.CXCR4)) +
plot_layout(nrow=1) &
geom_point(shape=16, stroke=0, size=1, alpha=0.2) &
geom_abline(intercept=0, slope=1, linewidth=0.4, col="red") &
coord_equal() & theme_bw() & theme(panel.grid.minor=element_blank())
We can observe that x- and y-values are similar for only a subset of cells. A portion of cells expresses CXCL12/CXCR4, but takes on 0 signalling values. In between, signalling estimates fall below the corresponding expression values.
This makes sense, knowing that COMMOT considers physical cell-cell distances. I.e., some cells express CXCL12/CXCR4, but are too distant from cells expressing CXCR4/CXCL12, and are thus deemed to not be involved in signalling. Meanwhile, there is a penalty on signalling in proportion to the distance between sender and receiver cells, as well as competition between proximal cells, yielding non-zero but lower-than-expression values.
From a biological perspective, we might be less interested signaling at the single cell-level. Instead, we could explore (for example but not limited to):
In essence, CCC results correspond to another assay, and we can compare them the same way we would compare gene expression across subpopulations, niches, regions of interest, sections, etc.
In order to more easily investigate this, we can reformat estimates for sender and receiver signals from CCC inference into a SingleCellExperiment object where rows = interactions and columns = cells.
## class: SingleCellExperiment
## dim: 26 15003
## metadata(0):
## assays(2): s r
## rownames(26): CD69-KLRB1 CD80-CTLA4 ... PECAM1 PTN
## rowData names(0):
## colnames(15003): 4303 4305 ... 107719 107722
## colData names(69): cell_id transcript_counts ... r-PECAM1 r-PTN
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):Next, we summarize CCC results for each subpopulation (separately for sender and receiver estimates) and visualize subpopulation-level results in a heatmap, using SCE-friendly functions for aggregation and visualization available through packages like scuttle and scater.
# aggregate estimates by cluster
# (separately for sender/receiver)
mu <- aggregateAcrossCells(sce,
ids=sce$Leiden,
statistics="mean",
use.assay.type=assayNames(sce))
# visualize scales values across clusters
for (. in assayNames(mu)) {
plotHeatmap(mu, exprs_values=.,
features=grep("-", rownames(sce)),
main=switch(., s="sender", r="receiver"),
show_colnames=TRUE, center=TRUE, scale=TRUE)
}
The CCPlotR (Ennis, Ó Broin, and Szegezdi 2023) package provides a visualization suit for CCC analysis results, including graphs and circos plots. These rely on a generic format, where each row contains the source and target cluster, sender and receiver feature, as well as an interaction score. It is not straightforward how to best arrive at the latter; naively, we can simply average across sender and receiver estimates, either at the single cell- or cluster-level, to obtain such a score:
# average across cluster-level sender/receiver
# averages to obtain i(nteraction) scores
ks <- expand.grid(ks <- colnames(mu), ks)
cs <- split(colnames(sce), sce$Leiden)
lr <- grepl("-", rownames(sce))
lr <- lr & !grepl("total", rownames(sce))
ss <- strsplit(rownames(sce), "-")
l <- sapply(ss, .subset, 1)
r <- sapply(ss, .subset, 2)
df <- mapply(
i=ks[, 1], j=ks[, 2],
SIMPLIFY=FALSE, \(i, j) {
source <- sce[lr, cs[[i]]]
target <- sce[lr, cs[[j]]]
sr <- cbind(
rowMeans(assay(source, "s")),
rowMeans(assay(target, "r")))
data.frame(
source=paste(i), target=paste(j),
ligand=l[lr], receptor=r[lr],
score=rowMeans(sr))
}) |> do.call(what=rbind)
rownames(df) <- NULL
head(df)## source target ligand receptor score
## 1 1 1 CD69 KLRB1 0.00000000
## 2 1 1 CD80 CTLA4 0.00000000
## 3 1 1 CD80 CD274 0.00000000
## 4 1 1 PECAM1 PECAM1 0.01452581
## 5 1 1 CD274 PDCD1 0.00000000
## 6 1 1 CDH1 CDH1 2.10121365We are now set to generate a range of visualizations using CCPlotR, e.g., a circos plot where the width of the links represents the total number of interactions between clusters. However, because this is only a toy example (few genes and cells), we will refrain from any biological interpretation.
cc_circos(df, n_top_ints=100)
On a final note, inference is arguably much more complex (and results less straightforward to interpret) when a higher-plex panel is being considered; i.e., when overlapping and multimeric interactions are involved, which did not apply in the example provided here. Nevertheless, we hope that readers can appreciate the conceptual difference between expression data and CCC results, and will be able to implement similar reticulate approaches to incorporate methods living in Python into their analysis pipelines.
Liu, Sun, and Wang (2022) compare methods developed for CCC analysis of spatially resolved data with non-spatial approaches.
mistyR (Tanevski et al. 2022) distinguishes between an ‘intrinsic’ view to capture how markers within a location influence one another; a ‘juxtaview’ to model local cellular niche (by relating expression between neighboring cells); and, a ‘paraview’ to capture tissue structure effects (by relating expression within a given radius).
Giotto (Chen2023-Giotto-Suite?) detects increased co-expression of LR pairs in neighboring cells of different types via comparison to randomly shuffled cell locations (permutation test).
CellChat (Jin, Plikus, and Nie 2025) quantifies CCC signalling probability between two groups of cells using a simplified mass action-based model, which incorporates core LR interaction with potential multimeric structure and modulation by cofactors.
SpatialDM (Li et al. 2023) uses a bivariate extension of Moran’s \(I\) – a measure of spatial autocorrelation (cf., Chapter 29) – to test LR pairs in close-range distance against independence.
SpaOTsc (Cang and Nie 2020) relies on optimal transport to segregate signal in a spatial region into LRs and their downstream genes, yielding information on regulatory flows between cells.