Import and representation of MERFISH mouse colon IBD data
1 August 2024
1 Setup
After the installation, we proceed by loading the package and additional packages used in the vignette.
library(MerfishData)
library(ExperimentHub)
library(ggplot2)
library(scater)
library(terra)2 Data
Gut inflammation involves contributions from immune and non-immune cells, whose interactions are shaped by the spatial organization of the healthy gut and its remodeling during inflammation. The crosstalk between fibroblasts and immune cells is an important axis in this process, but our understanding has been challenged by incomplete cell-type definition and biogeography.
To address this challenge, Cadinu et al., 2024 used multiplexed error-robust fluorescence in situ hybridization (MERFISH) to profile the expression of 943 genes in 1.35 million cells imaged across the onset and recovery from a mouse colitis model. To assign RNAs to individual cells, they used Baysor. They identified diverse cell populations, charted their spatial organization, and revealed their polarization or recruitment in inflammation.
This vignette demonstrates how to obtain the MERFISH mouse IBD colon dataset from Cadinu et al., 2024 from Bioconductor’s ExperimentHub.
The data was obtained from the datadryad data publication.
eh <- ExperimentHub()
AnnotationHub::query(eh, c("MerfishData", "Cadinu2024"))
#> ExperimentHub with 8 records
#> # snapshotDate(): 2024-08-01
#> # $dataprovider: Boston Children's Hospital
#> # $species: Mus musculus
#> # $rdataclass: HDF5Matrix, character, list, data.frame
#> # additional mcols(): taxonomyid, genome, description,
#> # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> # rdatapath, sourceurl, sourcetype
#> # retrieve records with, e.g., 'object[["EH9546"]]'
#>
#> title
#> EH9546 | Cadinu2024_ibd_counts
#> EH9547 | Cadinu2024_ibd_logcounts
#> EH9548 | Cadinu2024_ibd_coldata
#> EH9549 | Cadinu2024_ibd_metadata
#> EH9550 | Cadinu2024_ibd_rownames
#> EH9551 | Cadinu2024_ibd_blanks_counts
#> EH9552 | Cadinu2024_ibd_blanks_logcounts
#> EH9553 | Cadinu2024_ibd_blanks_rownames2.1 Segmented data
It is also possible to obtain the data in a SpatialExperiment, which integrates experimental data and cell metadata, and provides designated accessors for the spatial coordinates.
spe <- MouseColonIbdCadinu2024()
spe
#> class: SpatialExperiment
#> dim: 943 1348582
#> metadata(3): colors_tier1 colors_tier2 colors_tier3
#> assays(2): counts logcounts
#> rownames(943): Ackr1 Ackr2 ... Zc3h12a Znrf3
#> rowData names(0):
#> colnames: NULL
#> colData names(12): cell_id sample_id ... tier3 leiden_neigh
#> reducedDimNames(5): PCA UMAP UMAP_Tier1 UMAP_Tier2 UMAP_Tier3
#> mainExpName: NULL
#> altExpNames(1): Blank
#> spatialCoords names(2) : x y
#> imgData names(0):Inspect the data components:
counts(spe)[1:5,1:5]
#> <5 x 5> DelayedMatrix object of type "double":
#> [,1] [,2] [,3] [,4] [,5]
#> Ackr1 0 0 0 0 0
#> Ackr2 0 0 0 0 0
#> Ackr3 0 0 0 0 0
#> Ackr4 0 0 0 0 0
#> Acod1 0 0 0 0 0
logcounts(spe)[1:5,1:5]
#> <5 x 5> DelayedMatrix object of type "double":
#> [,1] [,2] [,3] [,4] [,5]
#> Ackr1 0 0 0 0 0
#> Ackr2 0 0 0 0 0
#> Ackr3 0 0 0 0 0
#> Ackr4 0 0 0 0 0
#> Acod1 0 0 0 0 0
colData(spe)
#> DataFrame with 1348582 rows and 12 columns
#> cell_id sample_id sample_type mouse_id
#> <character> <character> <factor> <factor>
#> 1 921aabdd-8ec6 0 Healthy 062921_D0_m3a
#> 2 75a81f5a-8326 0 Healthy 062921_D0_m3a
#> 3 8b50251b-6188 0 Healthy 062921_D0_m3a
#> 4 238282d7-771d 0 Healthy 062921_D0_m3a
#> 5 3ab2f7b6-1f1c 0 Healthy 062921_D0_m3a
#> ... ... ... ... ...
#> 1348578 0da4af94-f339 19 DSS9 062921_D9_m5
#> 1348579 bb64edbf-7028 19 DSS9 062921_D9_m5
#> 1348580 0abd0ca1-db27 19 DSS9 062921_D9_m5
#> 1348581 a450d75d-0753 19 DSS9 062921_D9_m5
#> 1348582 cc87557d-eb57 19 DSS9 062921_D9_m5
#> technical_repeat_number slice_id fov n_genes tier1
#> <factor> <factor> <factor> <integer> <factor>
#> 1 1 2 104 67 Epithelial
#> 2 1 2 104 66 Epithelial
#> 3 1 2 104 42 Epithelial
#> 4 1 2 104 36 Epithelial
#> 5 1 2 104 118 Epithelial
#> ... ... ... ... ... ...
#> 1348578 2 1 397 12 Epithelial
#> 1348579 2 2 113 14 Immune
#> 1348580 2 1 402 12 Epithelial
#> 1348581 2 3 219 12 Smooth Muscle Cells
#> 1348582 2 1 468 12 Fibroblast
#> tier2 tier3 leiden_neigh
#> <factor> <factor> <factor>
#> 1 Colonocytes Colonocytes MU3
#> 2 Colonocytes Colonocytes MU3
#> 3 Colonocytes Colonocytes MU3
#> 4 Colonocytes Colonocytes MU3
#> 5 Colonocytes Colonocytes MU3
#> ... ... ... ...
#> 1348578 Stem cells Stem cells MU7
#> 1348579 Neutrophil 2 Neutrophil 2 SM2
#> 1348580 Stem cells Stem cells MU1
#> 1348581 IASMC 1 IASMC 1 MU8
#> 1348582 IAF 4 IAF 4 MU8
head(spatialCoords(spe))
#> x y
#> [1,] 238.4697 308.1568
#> [2,] 246.4109 303.1072
#> [3,] 249.0404 309.5399
#> [4,] 237.0386 322.2053
#> [5,] 244.5144 317.2789
#> [6,] 270.2699 297.4906The dataset includes cell type labels with three levels of granularity
(variables tier1, tier2, tier3 in the colData).
table(spe$tier1)
#>
#> Adipose Endothelial EntericNervous Epithelial
#> 760 59317 24396 381553
#> Fibroblast ICC Immune Smooth Muscle Cells
#> 298918 3109 256978 323551
table(spe$tier2)
#>
#> Adipose Arterial EC
#> 760 9592
#> B cell 1 B cell 2
#> 14392 36874
#> B cell (Aicda+) Capillarial EC
#> 2451 15523
#> Colonocytes DC 2
#> 75453 2614
#> DC (Fscn1+) EEC
#> 10166 1557
#> Epithelial (Clu+) FRC
#> 4560 5077
#> Fibro 1 Fibro 2
#> 31435 31368
#> Fibro 4 Fibro 5
#> 23221 22173
#> Fibro 6 Fibro 7
#> 19842 18080
#> Fibro 12 Fibro 13
#> 8982 7894
#> Fibro 15 Glia (Apod+)
#> 5960 3152
#> Glia (Gfap+) Glia (Gfra3+)
#> 4560 5207
#> Glia (Mpz+) Goblet 1
#> 643 23884
#> Goblet 2 IAE 1
#> 17891 32911
#> IAE 2 IAE 3
#> 39906 12986
#> IAF 1 IAF 2
#> 6147 49567
#> IAF 3 IAF 4
#> 29991 16431
#> IAF 5 IASMC 1
#> 3536 43792
#> IASMC 2 IASMC 3
#> 33657 9029
#> ICC 1 ICC 2
#> 2249 860
#> ILC2 Lymphatic EC
#> 776 15272
#> Lymphatic EC (Ccl21a+) M cells
#> 6980 287
#> Macrophage (Cd163+) Macrophage (Cxcl10+)
#> 8518 25140
#> Macrophage (Itgax+) Macrophage (Mrc1+)
#> 21571 18415
#> Mast cell Monocyte
#> 17 24292
#> Neuron (Chat+) Neuron (Nos1+)
#> 4831 6003
#> Neutrophil 1 Neutrophil 2
#> 28775 21292
#> Pericyte 1 Pericyte 2
#> 12952 6262
#> Plasma cell Repair associated (Arg1+)
#> 10424 16554
#> SMC 1 SMC 2
#> 171823 63129
#> SMC Mesentery Stem cells
#> 2121 64512
#> T TA
#> 18769 91052
#> Venous EC cDC1
#> 11950 12492
table(spe$tier3)
#>
#> Adipose Arterial EC
#> 760 9592
#> B cell 1 B cell 2
#> 14392 36874
#> B cell (Aicda+) Capillarial EC
#> 2451 15523
#> Colonocytes DC (Ccl22+)
#> 75453 2614
#> DC (Fscn1+) EEC
#> 10166 1557
#> Epithelial (Clu+) FRC
#> 4560 5077
#> Fibro 1 Fibro 2
#> 31435 31368
#> Fibro 4 Fibro 5
#> 23221 22173
#> Fibro 6 Fibro 7
#> 19842 18080
#> Fibro 12 Fibro 13
#> 8982 7894
#> Fibro 15 Glia (Apod+)
#> 5960 3152
#> Glia (Gfap+) Glia (Gfra3+)
#> 4560 5207
#> Glia (Mpz+) Goblet 1
#> 643 23884
#> Goblet 2 IAE 1
#> 17891 32911
#> IAE 2 IAE 3
#> 39906 12986
#> IAF 1 IAF 2
#> 6147 49567
#> IAF 3 IAF 4
#> 29991 16431
#> IAF 5 IASMC 1
#> 3536 43792
#> IASMC 2 IASMC 3
#> 33657 9029
#> ICC 1 ICC 2
#> 2249 860
#> ILC2 ILC3-LTi-like
#> 776 1901
#> Lymphatic EC Lymphatic EC (Ccl21a+)
#> 15272 6980
#> M cells Macrophage (Cxcl10+)
#> 287 25140
#> Macrophage (Itgax+) Macrophage (Lyve1+)
#> 21571 8518
#> Macrophage (Mrc1+) Mast cell
#> 18415 17
#> Monocyte NK
#> 24292 2187
#> Neuron (Chat+) Neuron (Nos1+)
#> 4831 6003
#> Neutrophil 1 Neutrophil 2
#> 28775 21292
#> Pericyte 1 Pericyte 2
#> 12952 6262
#> Plasma cell Repair associated (Arg1+)
#> 10424 16554
#> SMC 1 SMC 2
#> 171823 63129
#> SMC Mesentery Stem cells
#> 2121 64512
#> T (Cd4+ Ccr7+) T (Cd8+)
#> 4379 627
#> T (Mki67+) TA
#> 2598 91052
#> Tfh Treg
#> 2935 4142
#> Venous EC cDC1
#> 11950 12492The data have been collected before the insurgence of colitis
(sample_type="Healthy") and after some time intervals (3 days, 9 days, 21 days).
table(spe$sample_type)
#>
#> DSS3 DSS9 DSS21 Healthy
#> 299274 660977 148881 2394503 Visualization
3.1 Reduced dimensions
Replicate Fig. 1C of the paper, to visualize cell types in the UMAP space:
plotReducedDim(spe, "UMAP", colour_by = "tier1",
scattermore = TRUE, rasterise = TRUE) +
scale_color_manual(values = metadata(spe)$colors_tier1) +
labs(color = "cell type") 
3.2 Spatial organization
We can also replicate Fig 1.D, to see the spatial distribution of cell types in one slide:
# Filter spatial coordinates for the selected slice ID
slice_coords <- spatialCoords(spe)[spe$mouse_id == "082421_D0_m6" &
spe$sample_type == "Healthy" &
spe$technical_repeat_number == "1" &
spe$slice_id == "2", ]
# Rotate coordinates to have them match the rotation in the paper
slice_coords_vec <- vect(slice_coords, type = "points")
slice_coords_r <- spin(slice_coords_vec, 180)
slice_c <- as.data.frame(slice_coords_r, geom = "XY")
slice_df <- data.frame(x = slice_c[,1],
y = slice_c[,2],
tier1 = spe$tier1[spe$mouse_id == "082421_D0_m6" &
spe$sample_type == "Healthy" &
spe$technical_repeat_number == "1" &
spe$slice_id == "2"])
# Plot
ggplot(data = slice_df, aes(x = x, y = y, color = tier1)) +
geom_point(shape = 19, size = 0.5) +
scale_color_manual(values = metadata(spe)$colors_tier1) +
guides(colour = guide_legend(override.aes = list(size = 2))) +
labs(color = "cell type") +
theme_bw(10)
We can compare the spatial organization of cells before the insurgence of colitis and at the considered timepoints (after 3, 9, and 21 days):
# Filter spatial coordinates for the selected slice ID
slice_coords <- spatialCoords(spe)[(spe$mouse_id == "062921_D0_m3a" &
spe$slice_id=="2") |
(spe$mouse_id == "092421_D3_m1" &
spe$slice_id=="2")|
(spe$mouse_id == "062221_D9_m3" &
spe$slice_id=="2") |
(spe$mouse_id == "082421_D21_m1" &
spe$slice_id=="2"), ]
slice_df <- data.frame(x = scale(slice_coords[, 1], scale = FALSE),
y = scale(slice_coords[, 2], scale = FALSE),
tier1 = spe$tier1[(spe$mouse_id == "062921_D0_m3a" &
spe$slice_id=="2") |
(spe$mouse_id == "092421_D3_m1" &
spe$slice_id=="2")|
(spe$mouse_id == "062221_D9_m3" &
spe$slice_id=="2") |
(spe$mouse_id == "082421_D21_m1" &
spe$slice_id=="2")],
day = spe$sample_type[(spe$mouse_id == "062921_D0_m3a" &
spe$slice_id=="2") |
(spe$mouse_id == "092421_D3_m1" &
spe$slice_id=="2")|
(spe$mouse_id == "062221_D9_m3" &
spe$slice_id=="2") |
(spe$mouse_id == "082421_D21_m1" &
spe$slice_id=="2")])
slice_df$day <- factor(slice_df$day, levels = c("Healthy", "DSS3", "DSS9", "DSS21"))
ggplot(data = slice_df, aes(x = x, y = y, color = tier1)) +
geom_point(shape = 19, size = 0.5) +
scale_color_manual(values = metadata(spe)$colors_tier1) +
theme_bw(10) + guides(colour = guide_legend(override.aes = list(size=2)))+
labs(color = "cell type") +
facet_wrap( ~ day, ncol = 2, nrow = 2, scales = "free")
We can also restrict the analysis to a specific macro-group to see the different distribution of Tier2 cell types. See an example the different composition of epithelial cells:
slice_df$tier2 <- spe$tier2[(spe$mouse_id == "062921_D0_m3a" &
spe$slice_id=="2") |
(spe$mouse_id == "092421_D3_m1" &
spe$slice_id=="2")|
(spe$mouse_id == "062221_D9_m3" &
spe$slice_id=="2") |
(spe$mouse_id == "082421_D21_m1" &
spe$slice_id=="2")]
slice_df$color <- ifelse(slice_df$tier1 == "Epithelial",
as.character(slice_df$tier2), "grey")
slice_df$color <- factor(slice_df$color)
colored_df <- slice_df[slice_df$tier1 == "Epithelial", ]
ggplot() +
geom_point(data = slice_df, aes(x = x, y = y), color = "grey",
shape = 19, size = 0.1) +
geom_point(data = colored_df, aes(x = x, y = y, color = tier2),
shape = 19, size = 0.1) +
scale_color_manual(values = metadata(spe)$colors_tier2) +
theme_bw(10) +
guides(colour = guide_legend(override.aes = list(size = 2))) +
labs(color = 'cell type') +
facet_wrap(~ day, ncol = 2, scales = "free")
slice_df$color <- ifelse(slice_df$tier1 == "Immune",
as.character(slice_df$tier2),
"grey")
slice_df$color <- factor(slice_df$color)
colored_df <- subset(slice_df, tier1 == "Immune")
p <- ggplot() +
geom_point(data = slice_df, aes(x = x, y = y), color = "grey",
shape = 19, size = 0.1) +
geom_point(data = colored_df, aes(x = x, y = y, color = tier2),
shape = 19, size = 0.1) +
scale_color_manual(values = metadata(spe)$colors_tier2) +
theme_bw(10) +
guides(colour = guide_legend(override.aes = list(size = 2))) +
labs(color = 'cell type') +
facet_wrap(~ day, ncol = 2, scales = "free")
p
4 Interactive exploration
The MERFISH mouse colon IBD dataset is part of the gallery of publicly available MERFISH datasets.
This gallery consists of dedicated iSEE and Vitessce instances, published on Posit Connect, that enable the interactive exploration of different segmentations, the expression of marker genes, and overlay of cell metadata on a spatial grid or a microscopy image.
5 SessionInfo
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] rhdf5_2.49.0 terra_1.7-78
#> [3] scater_1.33.4 scuttle_1.15.2
#> [5] ggplot2_3.5.1 ExperimentHub_2.13.1
#> [7] AnnotationHub_3.13.1 BiocFileCache_2.13.0
#> [9] dbplyr_2.5.0 MerfishData_1.7.1
#> [11] SpatialExperiment_1.15.1 SingleCellExperiment_1.27.2
#> [13] SummarizedExperiment_1.35.1 Biobase_2.65.0
#> [15] GenomicRanges_1.57.1 GenomeInfoDb_1.41.1
#> [17] IRanges_2.39.2 S4Vectors_0.43.2
#> [19] BiocGenerics_0.51.0 MatrixGenerics_1.17.0
#> [21] matrixStats_1.3.0 EBImage_4.47.0
#> [23] BiocStyle_2.33.1
#>
#> loaded via a namespace (and not attached):
#> [1] DBI_1.2.3 bitops_1.0-8 gridExtra_2.3
#> [4] rlang_1.1.4 magrittr_2.0.3 compiler_4.4.1
#> [7] RSQLite_2.3.7 png_0.1-8 fftwtools_0.9-11
#> [10] vctrs_0.6.5 pkgconfig_2.0.3 crayon_1.5.3
#> [13] fastmap_1.2.0 magick_2.8.4 XVector_0.45.0
#> [16] labeling_0.4.3 utf8_1.2.4 rmarkdown_2.27
#> [19] ggbeeswarm_0.7.2 UCSC.utils_1.1.0 purrr_1.0.2
#> [22] bit_4.0.5 xfun_0.46 zlibbioc_1.51.1
#> [25] cachem_1.1.0 beachmat_2.21.5 jsonlite_1.8.8
#> [28] blob_1.2.4 highr_0.11 rhdf5filters_1.17.0
#> [31] DelayedArray_0.31.10 Rhdf5lib_1.27.0 BiocParallel_1.39.0
#> [34] jpeg_0.1-10 tiff_0.1-12 irlba_2.3.5.1
#> [37] parallel_4.4.1 R6_2.5.1 bslib_0.8.0
#> [40] scattermore_1.2 jquerylib_0.1.4 Rcpp_1.0.13
#> [43] bookdown_0.40 knitr_1.48 Matrix_1.7-0
#> [46] tidyselect_1.2.1 viridis_0.6.5 abind_1.4-5
#> [49] yaml_2.3.10 codetools_0.2-20 curl_5.2.1
#> [52] lattice_0.22-6 tibble_3.2.1 withr_3.0.1
#> [55] KEGGREST_1.45.1 evaluate_0.24.0 Biostrings_2.73.1
#> [58] pillar_1.9.0 BiocManager_1.30.23 filelock_1.0.3
#> [61] generics_0.1.3 RCurl_1.98-1.16 BiocVersion_3.20.0
#> [64] munsell_0.5.1 scales_1.3.0 glue_1.7.0
#> [67] tools_4.4.1 BiocNeighbors_1.23.0 ScaledMatrix_1.13.0
#> [70] locfit_1.5-9.10 cowplot_1.1.3 grid_4.4.1
#> [73] AnnotationDbi_1.67.0 colorspace_2.1-1 GenomeInfoDbData_1.2.12
#> [76] beeswarm_0.4.0 BiocSingular_1.21.2 HDF5Array_1.33.5
#> [79] vipor_0.4.7 rsvd_1.0.5 cli_3.6.3
#> [82] rappdirs_0.3.3 fansi_1.0.6 viridisLite_0.4.2
#> [85] S4Arrays_1.5.5 dplyr_1.1.4 gtable_0.3.5
#> [88] sass_0.4.9 digest_0.6.36 ggrepel_0.9.5
#> [91] SparseArray_1.5.27 farver_2.1.2 rjson_0.2.21
#> [94] htmlwidgets_1.6.4 memoise_2.0.1 htmltools_0.5.8.1
#> [97] lifecycle_1.0.4 httr_1.4.7 mime_0.12
#> [100] bit64_4.0.5