Last updated: 2024-09-19

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Data from paper Forming nephrons promote nephron progenitor maintenance and branching morphogenesis via paracrine BMP4 signalling under the control of Wnt4 (Moreau et al. 2023)

This study included 10X chromium single cell RNAseq data from 4 conditions, with 3-4 E14.5 mice pooled per group.

  • Sample1 (Wnt4FloxKO): Wnt4Flox/Flox Six2-Conditional Wnt4 Knockout
  • Sample2 (Wnt4FloxHet): Wnt4Flox/+ Six2-Conditional Wnt4 Het
  • Sample3 (Wnt4Het): Wnt4 GCE/+ Control Wnt4 Het
  • Sample4 (Wnt4KO): Wnt4 GCE/GCE Knockout Wnt4

In that paper they explain that complete or conditional homozygous knockout of Wnt4 gene results in abnormal kidney development, and they use scRNAseq data to explore effects at cellular level. (Moreau et al. 2023)

Libraries

library(Seurat)
Loading required package: SeuratObject
Loading required package: sp

Attaching package: 'SeuratObject'
The following objects are masked from 'package:base':

    intersect, t
#library(SeuratObject)
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.0     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Data load

Loading data from a previously annotated object used in the analysis.

dataset_dir      <- '~/projects/spatialsnippets/datasets'
project_data_dir <- file.path(dataset_dir,'Wnt4KO_Moreau2023')


seurat_file_loaded <- file.path(project_data_dir, "Wnt4KOE14.5_10_withdeconclust.rds")
seurat_file_saved  <- file.path(project_data_dir,   "Wnt4KOE14.5_11_ss.rds")

Load and tidy data

so <- readRDS(seurat_file_loaded)

# There's alot of historical/working feilds in the metadata. 
# Slim down to just useful ones.
keep_cols <- c(
  "barcode","orig.ident","nCount_RNA","nFeature_RNA" ,"sample", # basic annotation/stats.
  "Genotype","GTeffect", "GTshort" , # same info, 3 ways
  "MT_pc", "cc" ,"G1" ,"S" ,"G2M", "doublet_score" , # was filtered on scrublet.
  # "cluster_content","cluster_lab"    # More detailed groupings used in working  
  "Celltype", "CelltypeCode",          # final types in paper
  "CelltypeBroad", "CelltypeBroadCode" # broader groupings
)

so@meta.data <- so@meta.data[,keep_cols]

Data tour

DimPlot(so, group.by="CelltypeCode",label = TRUE) + NoLegend()

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DimPlot(so, group.by="CelltypeBroad",label = TRUE) + NoLegend()

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DimPlot(so, group.by="GTshort")

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DimPlot(so, group.by="cc")

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FeaturePlot(so, c('nCount_RNA','MT_pc'))

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4 Samples, n=1 per group. Can use ‘GTshort’ to identify the sample.

select(so@meta.data, sample, Genotype, GTeffect, GTshort) %>% 
  as_tibble() %>% 
  group_by( sample, Genotype, GTeffect, GTshort) %>%
  summarise(num_cells=n(), .groups = 'drop') %>%
  DT::datatable()

Save it

saveRDS(so, seurat_file_saved)

References

Moreau, Julie L. M., Sarah Williams, Jihane Homman-Ludiye, Andrew J. Mallett, and Alexander N. Combes. 2023. “Forming Nephrons Promote Nephron Progenitor Maintenance and Branching Morphogenesis via Paracrine BMP4 Signalling Under the Control of Wnt4.” November 19, 2023. https://doi.org/10.1101/2023.11.19.567482. 

sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] lubridate_1.9.3    forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4       
 [5] purrr_1.0.2        readr_2.1.5        tidyr_1.3.1        tibble_3.2.1      
 [9] ggplot2_3.5.0      tidyverse_2.0.0    Seurat_5.1.0       SeuratObject_5.0.2
[13] sp_2.1-3           workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3     rstudioapi_0.16.0      jsonlite_1.8.8        
  [4] magrittr_2.0.3         spatstat.utils_3.0-4   farver_2.1.1          
  [7] rmarkdown_2.26         fs_1.6.3               vctrs_0.6.5           
 [10] ROCR_1.0-11            spatstat.explore_3.2-7 htmltools_0.5.8.1     
 [13] sass_0.4.9             sctransform_0.4.1      parallelly_1.37.1     
 [16] KernSmooth_2.23-22     bslib_0.7.0            htmlwidgets_1.6.4     
 [19] ica_1.0-3              plyr_1.8.9             plotly_4.10.4         
 [22] zoo_1.8-12             cachem_1.0.8           whisker_0.4.1         
 [25] igraph_2.0.3           mime_0.12              lifecycle_1.0.4       
 [28] pkgconfig_2.0.3        Matrix_1.6-5           R6_2.5.1              
 [31] fastmap_1.1.1          fitdistrplus_1.1-11    future_1.33.2         
 [34] shiny_1.8.1.1          digest_0.6.35          colorspace_2.1-0      
 [37] patchwork_1.2.0        ps_1.7.6               rprojroot_2.0.4       
 [40] tensor_1.5             RSpectra_0.16-1        irlba_2.3.5.1         
 [43] crosstalk_1.2.1        labeling_0.4.3         progressr_0.14.0      
 [46] timechange_0.3.0       fansi_1.0.6            spatstat.sparse_3.0-3 
 [49] httr_1.4.7             polyclip_1.10-6        abind_1.4-5           
 [52] compiler_4.3.2         withr_3.0.0            fastDummies_1.7.3     
 [55] highr_0.10             MASS_7.3-60.0.1        tools_4.3.2           
 [58] lmtest_0.9-40          httpuv_1.6.15          future.apply_1.11.2   
 [61] goftest_1.2-3          glue_1.7.0             callr_3.7.6           
 [64] nlme_3.1-164           promises_1.2.1         grid_4.3.2            
 [67] Rtsne_0.17             getPass_0.2-4          cluster_2.1.6         
 [70] reshape2_1.4.4         generics_0.1.3         gtable_0.3.4          
 [73] spatstat.data_3.0-4    tzdb_0.4.0             hms_1.1.3             
 [76] data.table_1.15.4      utf8_1.2.4             spatstat.geom_3.2-9   
 [79] RcppAnnoy_0.0.22       ggrepel_0.9.5          RANN_2.6.1            
 [82] pillar_1.9.0           spam_2.10-0            RcppHNSW_0.6.0        
 [85] later_1.3.2            splines_4.3.2          lattice_0.22-6        
 [88] renv_1.0.5             survival_3.5-8         deldir_2.0-4          
 [91] tidyselect_1.2.1       miniUI_0.1.1.1         pbapply_1.7-2         
 [94] knitr_1.45             git2r_0.33.0           gridExtra_2.3         
 [97] scattermore_1.2        xfun_0.43              matrixStats_1.2.0     
[100] DT_0.33                stringi_1.8.3          lazyeval_0.2.2        
[103] yaml_2.3.8             evaluate_0.23          codetools_0.2-20      
[106] BiocManager_1.30.22    cli_3.6.2              uwot_0.1.16           
[109] xtable_1.8-4           reticulate_1.35.0      munsell_0.5.1         
[112] processx_3.8.4         jquerylib_0.1.4        Rcpp_1.0.12           
[115] globals_0.16.3         spatstat.random_3.2-3  png_0.1-8             
[118] parallel_4.3.2         dotCall64_1.1-1        listenv_0.9.1         
[121] viridisLite_0.4.2      scales_1.3.0           ggridges_0.5.6        
[124] leiden_0.4.3.1         rlang_1.1.3            cowplot_1.1.3