Import and representation of MERFISH mouse ileum data
2 May 2024
1 Setup
library(MerfishData)
library(ExperimentHub)
library(ggplot2)
library(grid)2 Data
Spatial transcriptomics protocols based on in situ sequencing or multiplexed RNA fluorescent hybridization can reveal detailed tissue organization. However, distinguishing the boundaries of individual cells in such data is challenging. Current segmentation methods typically approximate cells positions using nuclei stains.
Petukhov et al., 2021, describe Baysor, a segmentation method, which optimizes 2D or 3D cell boundaries considering joint likelihood of transcriptional composition and cell morphology. Baysor can also perform segmentation based on the detected transcripts alone.
Petukhov et al., 2021, compare the results of Baysor segmentation (mRNA-only) to the results of a deep learning-based segmentation method called Cellpose from Stringer et al., 2021. Cellpose applies a machine learning framework for the segmentation of cell bodies, membranes and nuclei from microscopy images.
Petukhov et al., 2021 apply Baysor and Cellpose to MERFISH data from cryosections of mouse ileum. The MERFISH encoding probe library was designed to target 241 genes, including previously defined markers for the majority of gut cell types.
Def. ileum: the final and longest segment of the small intestine.
Samples were also stained with anti-Na+/K+-ATPase primary antibodies, oligo-labeled secondary antibodies and DAPI. MERFISH measurements across multiple fields of view and nine z planes were performed to provide a volumetric reconstruction of the distribution of the targeted mRNAs, the cell boundaries marked by Na+/K+-ATPase IF and cell nuclei stained with DAPI.
The data was obtained from the datadryad data publication.
This vignette demonstrates how to obtain the MERFISH mouse ileum dataset from Petukhov et al., 2021 from Bioconductor’s ExperimentHub.
eh <- ExperimentHub()
query(eh, c("MerfishData", "ileum"))
#> ExperimentHub with 9 records
#> # snapshotDate(): 2024-04-29
#> # $dataprovider: Boston Children's Hospital
#> # $species: Mus musculus
#> # $rdataclass: data.frame, matrix, EBImage
#> # additional mcols(): taxonomyid, genome, description,
#> # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> # rdatapath, sourceurl, sourcetype
#> # retrieve records with, e.g., 'object[["EH7543"]]'
#>
#> title
#> EH7543 | Petukhov2021_ileum_molecules
#> EH7544 | Petukhov2021_ileum_dapi
#> EH7545 | Petukhov2021_ileum_membrane
#> EH7547 | Petukhov2021_ileum_baysor_segmentation
#> EH7548 | Petukhov2021_ileum_baysor_counts
#> EH7549 | Petukhov2021_ileum_baysor_coldata
#> EH7550 | Petukhov2021_ileum_baysor_polygons
#> EH7551 | Petukhov2021_ileum_cellpose_counts
#> EH7552 | Petukhov2021_ileum_cellpose_coldata2.1 Raw data
mRNA molecule data: 820k observations for 241 genes
mol.dat <- eh[["EH7543"]]
dim(mol.dat)
#> [1] 819665 12
head(mol.dat)
#> molecule_id gene x_pixel y_pixel z_pixel x_um y_um z_um area
#> 1 1 Maoa 1705 1271 0 -2935.386 -1218.580 2.5 4
#> 2 2 Maoa 1725 1922 0 -2933.229 -1147.614 2.5 4
#> 3 3 Maoa 1753 1863 0 -2930.104 -1154.062 2.5 5
#> 4 4 Maoa 1760 1865 0 -2929.339 -1153.784 2.5 7
#> 5 5 Maoa 1904 794 0 -2913.718 -1270.474 2.5 6
#> 6 6 Maoa 1915 1430 0 -2912.497 -1201.232 2.5 6
#> total_magnitude brightness qc_score
#> 1 420.1126 2.021306 0.9543635
#> 2 269.5874 1.828640 0.9082457
#> 3 501.4615 2.001268 0.9772191
#> 4 639.0364 1.960428 0.9913161
#> 5 519.3154 1.937280 0.9832103
#> 6 842.2258 2.147277 0.9925655
length(unique(mol.dat$gene))
#> [1] 241Image data:
- DAPI stain signal:
dapi.img <- eh[["EH7544"]]
dapi.img
#> Image
#> colorMode : Grayscale
#> storage.mode : double
#> dim : 5721 9392 9
#> frames.total : 9
#> frames.render: 9
#>
#> imageData(object)[1:5,1:6,1]
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0 0 0 0 0 0
#> [2,] 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0
#> [4,] 0 0 0 0 0 0
#> [5,] 0 0 0 0 0 0
plot(dapi.img, all = TRUE)
plot(dapi.img, frame = 1)
- Membrane Na+/K+ - ATPase immunofluorescence signal:
While total poly(A) and DAPI staining can provide feature-rich costains suitable for segmentation in cell-sparse tissues such as the brain, such stains are not as useful for segmentation in cellular-dense tissues. To address this challenge, Petukhov et al., 2021 developed protocols to combine immunofluorescence (IF) of a pan-cell-type cell surface marker, the Na+/K+-ATPase, with MERFISH.
mem.img <- eh[["EH7545"]]
mem.img
#> Image
#> colorMode : Grayscale
#> storage.mode : double
#> dim : 5721 9392 9
#> frames.total : 9
#> frames.render: 9
#>
#> imageData(object)[1:5,1:6,1]
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> [2,] 0 0.007843137 0.007843137 0.007843137 0.007843137 0.007843137
#> [3,] 0 0.007843137 0.007843137 0.007843137 0.007843137 0.007843137
#> [4,] 0 0.007843137 0.007843137 0.007843137 0.007843137 0.007843137
#> [5,] 0 0.007843137 0.007843137 0.007843137 0.007843137 0.007843137
plot(mem.img, all = TRUE)
plot(mem.img, frame = 1)
2.2 Segmentation
It is also possible to obtain the data in a SpatialExperiment, which integrates the segmented experimental data and cell metadata, and provides designated accessors for the spatial coordinates and the image data.
2.2.1 Baysor
Obtain dataset segmented with Baysor:
spe.baysor <- MouseIleumPetukhov2021(segmentation = "baysor")
spe.baysor
#> class: SpatialExperiment
#> dim: 241 5800
#> metadata(1): polygons
#> assays(2): counts molecules
#> rownames(241): Acsl1 Acta2 ... Vcan Vim
#> rowData names(0):
#> colnames: NULL
#> colData names(7): n_transcripts density ... leiden_final sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : x y
#> imgData names(4): sample_id image_id data scaleFactorInspect dataset:
assay(spe.baysor, "counts")[1:5,1:5]
#> [,1] [,2] [,3] [,4] [,5]
#> Acsl1 0 0 0 0 2
#> Acta2 0 129 86 51 66
#> Ada 0 0 0 0 0
#> Adgrd1 0 2 3 3 0
#> Adgrf5 1 0 0 0 0
assay(spe.baysor, "molecules")["Acsl1",5]
#> SplitDataFrameList of length 1
#> $Acsl1
#> DataFrame with 2 rows and 3 columns
#> x y z
#> <numeric> <numeric> <numeric>
#> 1 2216 40 68.8410
#> 2 2218 39 82.6091
colData(spe.baysor)
#> DataFrame with 5800 rows and 7 columns
#> n_transcripts density elongation area avg_confidence leiden_final
#> <numeric> <numeric> <numeric> <numeric> <numeric> <character>
#> 1 39 0.02159 5.082 1806 0.8647 Endothelial
#> 2 165 0.02016 1.565 8186 0.9528 Smooth Muscle
#> 3 139 0.02279 1.820 6100 0.9762 Smooth Muscle
#> 4 80 0.01828 1.546 4376 0.9076 Smooth Muscle
#> 5 75 0.02479 3.475 3025 0.8952 Smooth Muscle
#> ... ... ... ... ... ... ...
#> 5796 1 NaN NaN NaN 1.0000 Removed
#> 5797 9 0.02397 2.587 375.5 0.8405 Removed
#> 5798 4 0.02204 10.760 181.5 0.9962 Removed
#> 5799 1 NaN NaN NaN 0.9454 Removed
#> 5800 4 0.03587 17.720 111.5 0.9897 Removed
#> sample_id
#> <character>
#> 1 ileum
#> 2 ileum
#> 3 ileum
#> 4 ileum
#> 5 ileum
#> ... ...
#> 5796 ileum
#> 5797 ileum
#> 5798 ileum
#> 5799 ileum
#> 5800 ileum
head(spatialCoords(spe.baysor))
#> x y
#> [1,] 2072.205 16.12821
#> [2,] 2150.691 41.67879
#> [3,] 2079.842 76.07194
#> [4,] 2092.325 165.76250
#> [5,] 2242.400 18.28000
#> [6,] 2236.168 87.64671
imgData(spe.baysor)
#> DataFrame with 2 rows and 4 columns
#> sample_id image_id data scaleFactor
#> <character> <character> <list> <numeric>
#> 1 ileum dapi #### NA
#> 2 ileum membrane #### NA2.2.2 Cellpose
Obtain dataset segmented with Cellpose:
spe.cellpose <- MouseIleumPetukhov2021(segmentation = "cellpose",
use.images = FALSE)
spe.cellpose
#> class: SpatialExperiment
#> dim: 241 8439
#> metadata(0):
#> assays(1): counts
#> rownames(241): Acsl1 Acta2 ... Vcan Vim
#> rowData names(0):
#> colnames: NULL
#> colData names(2): leiden_final sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(3) : x y z
#> imgData names(0):Inspect dataset:
assay(spe.cellpose, "counts")[1:5,1:5]
#> [,1] [,2] [,3] [,4] [,5]
#> Acsl1 0 1 0 0 0
#> Acta2 0 0 0 8 1
#> Ada 0 0 0 0 0
#> Adgrd1 0 0 0 0 0
#> Adgrf5 0 0 0 0 0
colData(spe.cellpose)
#> DataFrame with 8439 rows and 2 columns
#> leiden_final sample_id
#> <character> <character>
#> 1 Removed ileum
#> 2 Stem + TA ileum
#> 3 Removed ileum
#> 4 Stromal ileum
#> 5 Enterocyte (Mid Vill.. ileum
#> ... ... ...
#> 8435 Removed ileum
#> 8436 Enterocyte (Mid Vill.. ileum
#> 8437 Enterocyte (Mid Vill.. ileum
#> 8438 Goblet ileum
#> 8439 Removed ileum
head(spatialCoords(spe.cellpose))
#> x y z
#> [1,] 4333.000 10.66667 64.25156
#> [2,] 3622.941 22.35294 50.21340
#> [3,] 5267.000 18.50000 75.72505
#> [4,] 2819.275 44.34783 51.88014
#> [5,] 5678.636 41.22727 43.80788
#> [6,] 4611.056 40.44444 56.602562.2.3 Segmentation cell counts (by cell type):
Here we inspect the difference in cell counts for the both segmentation methods, stratified by cell type label obtained from leiden clustering and annotation by marker gene expression:
seg <- rep(c("baysor", "cellpose"), c(ncol(spe.baysor), ncol(spe.cellpose)))
ns <- table(seg, c(spe.baysor$leiden_final, spe.cellpose$leiden_final))
df <- as.data.frame(ns, responseName = "n_cells")
colnames(df)[2] <- "leiden_final"
ggplot(df, aes(
reorder(leiden_final, n_cells), n_cells, fill = seg)) +
geom_bar(stat = "identity", position = "dodge") +
xlab("") +
ylab("Number of cells") +
theme_bw() +
theme(
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
3 Visualization
For visualization purposes, we focus in the following on the first z-plane of the membrane staining image.
mem.img <- imgRaster(spe.baysor, image_id = "membrane")3.1 Cell metadata
Overlay cell type annotation as in Figure 6 of the publication.
spe.list <- list(Baysor = spe.baysor, Cellpose = spe.cellpose)
plotTabset(spe.list, mem.img)3.1.1 Baysor
n_transcripts
density
elongation
area
avg_confidence
leiden_final
3.1.2 Cellpose
leiden_final
z
3.2 Marker gene expression
We can also overlay the individual molecules of selected marker genes such as the different cluster of differentiation genes assayed in the experiment:
gs <- grep("^Cd", unique(mol.dat$gene), value = TRUE)
ind <- mol.dat$gene %in% gs
rel.cols <- c("gene", "x_pixel", "y_pixel")
sub.mol.dat <- mol.dat[ind, rel.cols]
colnames(sub.mol.dat)[2:3] <- sub("_pixel$", "", colnames(sub.mol.dat)[2:3])
plotXY(sub.mol.dat, "gene", mem.img)
3.3 Segmentation cell borders
Here, we illustrate segmentation borders for the first z-plane:
poly <- metadata(spe.baysor)$polygons
poly <- as.data.frame(poly)
poly.z1 <- subset(poly, z == 1)We add holes to the cell polygons:
poly.z1 <- addHolesToPolygons(poly.z1)Plot over membrane image:
p <- plotRasterImage(mem.img)
p <- p + geom_polygon(
data = poly.z1,
aes(x = x, y = y, group = cell, subgroup = subid),
fill = "lightblue")
p + theme_void()
4 Interactive exploration
The MERFISH mouse ileum 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 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-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] grid stats4 stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] ggpubr_0.6.0 ggplot2_3.5.1
#> [3] ExperimentHub_2.12.0 AnnotationHub_3.12.0
#> [5] BiocFileCache_2.12.0 dbplyr_2.5.0
#> [7] MerfishData_1.6.0 SpatialExperiment_1.14.0
#> [9] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
#> [11] Biobase_2.64.0 GenomicRanges_1.56.0
#> [13] GenomeInfoDb_1.40.0 IRanges_2.38.0
#> [15] S4Vectors_0.42.0 BiocGenerics_0.50.0
#> [17] MatrixGenerics_1.16.0 matrixStats_1.3.0
#> [19] EBImage_4.46.0 BiocStyle_2.32.0
#>
#> loaded via a namespace (and not attached):
#> [1] DBI_1.2.2 bitops_1.0-7 rlang_1.1.3
#> [4] magrittr_2.0.3 compiler_4.4.0 RSQLite_2.3.6
#> [7] png_0.1-8 fftwtools_0.9-11 vctrs_0.6.5
#> [10] pkgconfig_2.0.3 crayon_1.5.2 fastmap_1.1.1
#> [13] backports_1.4.1 magick_2.8.3 XVector_0.44.0
#> [16] labeling_0.4.3 utf8_1.2.4 rmarkdown_2.26
#> [19] UCSC.utils_1.0.0 purrr_1.0.2 bit_4.0.5
#> [22] xfun_0.43 zlibbioc_1.50.0 cachem_1.0.8
#> [25] jsonlite_1.8.8 blob_1.2.4 highr_0.10
#> [28] DelayedArray_0.30.0 jpeg_0.1-10 tiff_0.1-12
#> [31] broom_1.0.5 R6_2.5.1 bslib_0.7.0
#> [34] car_3.1-2 jquerylib_0.1.4 Rcpp_1.0.12
#> [37] bookdown_0.39 knitr_1.46 Matrix_1.7-0
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#> [43] curl_5.2.1 lattice_0.22-6 tibble_3.2.1
#> [46] withr_3.0.0 KEGGREST_1.44.0 BumpyMatrix_1.12.0
#> [49] evaluate_0.23 Biostrings_2.72.0 pillar_1.9.0
#> [52] BiocManager_1.30.22 filelock_1.0.3 carData_3.0-5
#> [55] generics_0.1.3 RCurl_1.98-1.14 BiocVersion_3.19.1
#> [58] munsell_0.5.1 scales_1.3.0 glue_1.7.0
#> [61] tools_4.4.0 locfit_1.5-9.9 ggsignif_0.6.4
#> [64] tidyr_1.3.1 AnnotationDbi_1.66.0 colorspace_2.1-0
#> [67] GenomeInfoDbData_1.2.12 cli_3.6.2 rappdirs_0.3.3
#> [70] fansi_1.0.6 viridisLite_0.4.2 S4Arrays_1.4.0
#> [73] dplyr_1.1.4 gtable_0.3.5 ggsci_3.0.3
#> [76] rstatix_0.7.2 sass_0.4.9 digest_0.6.35
#> [79] SparseArray_1.4.0 farver_2.1.1 rjson_0.2.21
#> [82] htmlwidgets_1.6.4 memoise_2.0.1 htmltools_0.5.8.1
#> [85] lifecycle_1.0.4 httr_1.4.7 mime_0.12
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