15  Human Heart MerFISH Analysis

TipLearning Objectives
  • Go through standard analysis steps for preprocessed Merfish data
  • Understand how to visualize and interpret merfish data
  • Basic interpretation of markers and spatial patterns

For this example, we will be using the preprocessed Merfish dataset of the human heart again. The dataset contains spatially resolved gene expression data for various cell types in the heart tissue. As there is no image associated with the data, this is basically a spatially resolved single-cell dataset. We will be using single cell functions in Seurat to analyse and visualise the data. The spatial coordinates are defined by dimensional reduction “spatial”, which we will use for visualisation instead of UMAP.

# Load the required libraries and the data
library(Seurat)
library(paletteer)

merfish <- readRDS("data/human_heart_merfish/overall_merfish.rds")
#Visualize the structure of the heart in the MERFISH data
DimPlot(merfish, reduction = "spatial", group.by = "communities", cols = paletteer_d("ggthemes::Tableau_20"))

As you can see, the data is already annotated with cluster identities and tissue types. We can use this information to explore the spatial distribution of different cell types in the heart tissue. However, there are three full slices of the heart in the dataset, so we will focus on the middle slice for more detailed visualization.

#Subset to one slice for better visualization
#To make sure we are getting the result we want we can check the subset as a zoomed in DimPlot
DimPlot(merfish, reduction = "spatial", raster = FALSE) + coord_cartesian(xlim = c(-5000, 500))
#subset object by spatial reduction coordinates
spatial_coordinates <- as.data.frame(merfish@reductions$spatial@cell.embeddings)
middle_heart_coordinates <- spatial_coordinates[spatial_coordinates$SPATIAL_1 > -5000 & spatial_coordinates$SPATIAL_1 < 500,]
cells_to_keep <- rownames(middle_heart_coordinates)
merfish_heart <- subset(merfish, cells = cells_to_keep)

We can visualize the expression of known marker genes to identify different cell types and tissue architecture. We will look ar some markers for heart tissue including cardiac fibroblasts, atrial cardiomyocytes and ventricular cardiomyocytes.

FeaturePlot(merfish_heart, features = c("MYH6", "MYH7"), reduction = "spatial", raster = FALSE) #MYH6: atrium, MYH7: ventricles
FeaturePlot(merfish_heart, features = c("PDGFRA","PDGFRB", "DCN", "LUM"), reduction = "spatial", raster = FALSE) #cardiac fibroblasts

To identify other markers for the different clusters, we can use the FindAllMarkers function on the existing cluster identities. This dataset already has metadata assigned, including Leiden clusters, communities and populations. First we will visualise each of these annotations.

DimPlot(merfish_heart, reduction = "spatial", raster = FALSE, group.by = "leiden") + ggtitle("Leiden Clusters")
DimPlot(merfish_heart, reduction = "spatial", raster = FALSE, group.by = "populations") + ggtitle("Populations")
DimPlot(merfish_heart, reduction = "spatial", raster = FALSE, group.by = "communities", label = TRUE) + ggtitle("Communities")

Of course we can decide to find all markers for any of these annotations, but for this example we will use the “communities” annotation and use the FindMarkers function to identify differentially expressed genes between two communities of interest. Particularly interesting cases here are comparing the left and right atrium and the left and right ventricles.

#Find markers for left atrium  vs right atrium 
markers_atria <- FindMarkers(merfish_heart, ident.1 = "Left Atria", ident.2 = "Right Atria", group.by = "communities", only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25, pvalue.cutoff = 0.05)

#View top 5 markers for left atrium
head(markers_atria[order(markers_atria$p_val_adj, decreasing = FALSE), ], 5)

Let’s look at one of the top genes more highly expressed in the left atrium, COL2A1, which is a collagen gene. It’s IIA isoform is transiently expressed in the developing heart.

FeaturePlot(merfish_heart, features = c("COL2A1"), reduction = "spatial", raster = FALSE)

We can do the same for the ventricles. They are divided into communities for the outer and innerventricular regions, so we will compare these two separately for left and right ventricles and also compare outter to inner regions.

#Find markers for outer left ventricle vs right ventricle
markers_outerVentricles <- FindMarkers(merfish_heart, ident.1 = "Outer-LV", ident.2 = "Outer-RV", group.by = "communities", only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25, pvalue.cutoff = 0.05)

#Find markers for inner left ventricle vs right ventricle
markers_innerVentricles <- FindMarkers(merfish_heart, ident.1 = "Inner-LV", ident.2 = "Inner-RV", group.by = "communities", only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25, pvalue.cutoff = 0.05)

#Find markers for outer left ventricle vs inner left ventricle
markers_leftVentricle <- FindMarkers(merfish_heart, ident.1 = "Outer-LV", ident.2 = "Inner-LV", group.by = "communities", only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25, pvalue.cutoff = 0.05)
#Find markers for outer right ventricle vs inner right ventricle
markers_rightVentricle <- FindMarkers(merfish_heart, ident.1 = "Outer-RV", ident.2 = "Inner-RV", group.by = "communities", only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25, pvalue.cutoff = 0.05)

From the top results we can try looking at the FeaturePlots to identify markers for left vs right ventricles and inner vs outer regions.

FeaturePlot(merfish_heart, features = c( "IRX4"), reduction = "spatial", raster = FALSE) #left vs right
FeaturePlot(merfish_heart, features = c( "HEY2"), reduction = "spatial", raster = FALSE) #outer vs inner

As this is not a proper spatial dataset (for Seurat), we cannot use spatially aware functions like FindSpatiallyVariableFeatures or packages like BANKSY here. However, we can use the spatial coordinates to visualise the expression of genes of interest in the tissue context and the full dataset of heart samples from differen developmental stages can be used to track the gene expression changes during development.

15.1 Conclusion

MERFISH is a powerful technique for spatially resolved transcriptomics, allowing for the visualization and analysis of gene expression patterns in tissue sections. In this example, we have explored the spatial distribution of different cell types in the human heart tissue using MERFISH data. We have also identified marker genes for different communities in the heart tissue, including atrial and ventricular regions. This analysis provides insights into the cellular composition and organization of the heart tissue, which can be further explored to understand the underlying biological processes and disease mechanisms.

15.2 Summary

TipKey Points
  • MERFISH data can be analyzed using Seurat functions for single-cell data, with spatial coordinates used for visualization.
  • Marker genes can be identified for different communities in the tissue, providing insights into cellular composition and organization.
  • Visualization of gene expression patterns in the tissue context can reveal spatial distribution of cell types and regions.
  • MERFISH can be applied in a larger scale to compare gene expression patterns across different developmental stages or disease conditions, as can be more affordable compared to other spatial transcriptomics technologies.