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Bacon, W.A., Hamilton, R.S., Yu, Z., Kieckbusch, J., Hawkes, D., Krzak, A., Abell, C., Colucci, F. & Charnock-Jones, D.S. (2018) Single-Cell Analysis Identifies Thymic Maturation Delay in Growth-Restricted Neonatal Mice. Front. Immunol., 9:2523

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Single-Cell Analysis Identifies Thymic Maturation Delay in Growth-Restricted Neonatal Mice

Bacon, W.A.‡,§,1,2,3, Hamilton, R.S.‡,2,3, Yu, Z.4, Kieckbusch, J.1,2, Hawkes, D.1, Krzak, A.1, Abell, C.4, Colucci, F.1,2 & Charnock-Jones, D.S.§,1,2

Co-first authors, § Corresponding authors
1 Department of Obstetrics & Gynaecology, 2 Centre for Trophoblast Research, 3 Department of Physiology, Development, & Neuroscience, University of Cambridge, Downing Site, Cambridge, CB2 3DY, 4 Department of Chemistry


Bacon, W.A., Hamilton, R.S., Yu, Z., Kieckbusch, J., Hawkes, D., Krzak, A., Abell, C., Colucci, F. & Charnock-Jones, D.S. (2018) Single-Cell Analysis Identifies Thymic Maturation Delay in Growth-Restricted Neonatal Mice Front. Immunol., 9:2523 [DOI]

Preprint Version:
Bacon, W.A., Hamilton, R.S., Yu, Z., Kieckbusch, J., Hawkes, D., Krzak, A., Abell, C., Colucci, F. & Charnock-Jones, D.S. (2018) Single-Cell Analysis Identifies Thymic Maturation Delay in Growth-Restricted Neonatal Mice bioRxiv, 372862 [DOI]


If there are any questions or suggestions please submit an issue using the GitHub menu above otherwise contact rsh46 -at- for bioinformatics related queries


To be added on paper acceptance

Data Processing

All custom analysis scripts are freely available from including code to recreate the figures relating the single cell sequencing.

Note: The provided R scripts all assume the script is placed in a directory containing the DEMs and/or Robjects. The script can be run interactively in R-studio or as a batch using Rscript. Note that some of the figures in the manuscript have had some label positions moved manually to prevent overlaps. R package versions are listed in the table below.

Raw Fastq files are demultiplexed ( using the Nextera indices and then converted to uBAM using PicardTools:FastqToSam (v2.9.0). Quality control, alignment (STAR v020201) gene quantification and final matrix generation were performed using DropSeqTools (v1.12 Alignments were performed against the mouse reference genome (mm10 available from The resulting digital expression matrix (DEM) was imported into Seurat (Butler et al, 2018)(v2.3.0) for downstream analysis. Initial thresholds of a minimum 200 genes per cell and genes must be present in at least 3 cells were applied.

All downstream analysis is performed using Seurat and has been split into two scripts. The first, dropseq_seurat_splitDEMs.R, performs the more computationally intensive tasks intended to be run on high performance computers, the Seurat object is saves in Robj format to be imported in to the, the second script, dropseq_seurat_splitDEMs_Plots.R, for plotting and figure creation. This second script is intended for running on a laptop.

Seurat Pipeline (Part 1)

Here we outline the Seurat pipeline used for the more computationally demanding steps dropseq_seurat_splitDEMs.R.

Two separate DEMs were calculated for the WT and WT+P0 samples. The WT only samples were used to calculate variable genes (FindVariableGenes), which were then used as input to generate the PCA (RunPCA), find clusters (FindClusters) and produce a tSNE (t-distributed stochastic neighbor embedding) visualisation (RunTSNE) from the combined WT and P0 sample DEM. FindClusters is run across multiple resolutions (0.2, 0.4, 0.6 0.8 and 1.0), each stored on the Seurat Object. Normalisation (NormalizeData), UMI and MT regression (FilterCells) were performed using Seurat. Cell cycle assignments were performed using SCRAN (Lun et al, 2016)(v1.6.9) on the combined WT+P0 DEM, using an intermediate SingleCellExperiment (v1.0.0) data structure, and then added back to the Seurat Object. Cell cycle genes were regressed out using a subtraction of G2M from S cell cycle scores per cell. The resulting Seurat data object is saved as an RObj for input into the plotting and differential analysis part of the pipeline.

Precalculated DEMs

Description File Name
WT & KO SLX-7632.XXXXXXXXXX.dge.txt.gz
WT Only SLX-7632.XXXXXXXXXX.WT.dge.txt.gz
KO Only SLX-7632.XXXXXXXXXX.KO.dge.txt.gz

Seurat Pipeline (Part 2)

Here we outline the Seurat pipeline used for plotting and differential transcript identification dropseq_seurat_splitDEMs_Plots.R.

The RObj generated from the dropseq_seurat_splitDEMs.R is used to extract (e.g. with GetCellEmbeddings) the required data for each of the plots in the figure. Custom tSNE plots were generated using ggplot2. Transcript abundance dotplots were generated from AverageExpression extracted from the Seurat object and ggplot2. Cluster trees were generated using clustree (Zappia & Oshlack, 2018). Differential transcript analysis was performed by comparing each cluster (FindAllMarkers) and using an adjusted p value < 0.01. The heatmap (pHeatmap), used the same threshold, with just the top 20 genes for each cluster selected.

Figure Output Filename Description
2 T-Cell.Figure.2.pdf tSNE, classical cell markers, clustree and summary tSNE
3 T-Cell.Figure.3.pdf Heatmap unsupervised clustering of top 20 genes per cluster
4A T-Cell.Figure.4A.pdf tSNE with cells coloured by cell cycle assignments
5B T-Cell.Figure.5B.pdf tSNE with cells coloured by averaged ribosomal protein transcript levels
6A T-Cell.Figure.6A.pdf tSNE with cells coloured by genotype (WT and P0)
7A T-Cell.Figure.7A.pdf Differential transcript levels by cell type
7B T-Cell.Figure.7B.pdf Differential transcript levels WT/P0 per cell type cluster
7C T-Cell.Figure.7C.pdf Comparison of WT/P0 per cluster transcripts
Supp Fig 4 T-Cell.Figure.Supp4.pdf Sequencing coverage for the Igf2 genomic region
Supp Fig 5 T-Cell.Figure.Supp5.pdf Cluster Mapping with and without cell cycle regression
Supp Fig 6 T-Cell.Figure.Supp6.pdf Resolution Effects on Cluster Calling (tSNE & clustree)
Supp Fig 7 T-Cell.Figure.Supp7.pdf Differential transcript levels by cluster size
Supp Fig 8 T-Cell.Figure.Supp8.pdf Differential transcript levels WT/P0 per cluster (by size)
Supp Fig 9 T-Cell.Figure.Supp9.pdf Gender assignment
Supp Table 1 T-Cell.Table.Supp1.xlsx Table of Sequencing Metrics
Supp Table 2 Zip file of differential transcript level files
Supp Movie 1 T-Cell.RibosomeMovie.mpg Ribosome structure with identified genes mapped on surface

Ribosomal Protein Analysis

A custom tool was created to classify whether ribosomal proteins are exposed on the surface or are internal to the ribosome. See for more details. The output includes Pymol commands to render the structure showing the ribosomal proteins of interest.

Sequencing metrics

Sample Table

ArrayExpress or GEO submission E-MTAB-6945

Seq ID Index Experiment #Cells
SLX-7632 N701 WT 1220
SLX-7632 N704 WT 1341
SLX-7632 N705 WT 813
SLX-7632 N706 WT 1305
SLX-7632 N702 P0 1300
SLX-7632 N703 P0 675
SLX-7632 N707 P0 610

Alignment Rates

Expt IDX Genotype Total Reads Unique (#) Unique (%) Multi (#) Multi (%) Many (#) Many (%) Unmapped (#) Unmapped (%)
SLX-7632 N701 WT 32468733 26055586 80.2 3351483 10.3 346732 1.1 2714932 8.4
SLX-7632 N702 PO 20962102 16847146 80.4 1817857 8.7 214847 1 2082252 9.9
SLX-7632 N703 PO 11749198 9081883 77.3 1357304 11.6 121328 1 1188683 10.1
SLX-7632 N704 WT 32297193 25720288 79.6 3029288 9.4 274716 0.9 3272901 10.1
SLX-7632 N705 WT 77791423 56701446 72.9 11964824 15.4 672228 0.9 8452925 10.9
SLX-7632 N706 WT 26006979 18412761 70.8 4057400 15.6 399552 1.5 3137266 12.1
SLX-7632 N707 PO 26807105 18534932 69.1 5136585 19.2 235983 0.9 2899605 10.8

Sample Correlation By Gene Coverage

Cluster Cell Numbers

Cluster By Size Cluster Paper Number WT P0 Cell Type
0 2 964 745 DP
1 3 730 743 DP
2 6 961 231 TMat
3 4 974 48 DP
4 1 448 456 DN
5 5 554 293 DP
6 8 28 57 RBC
7 7 20 12 Macrophage


Description URL
Publications bioRxiv
Frontiers In Immunology
Raw Data ArrayExpress EMBL-EBI E-MTAB-6945

Software Used

Resource URL
DropSeqTools Link
Mouse Genome Link
FastQC Link
BBMap Link
clustree Link GitHub


Butler A, Hoffman P, Smibert P, Papalexi E, & Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology 36, 411–420 DOI

Lun ATL, McCarthy DJ, Marioni JC (2016). A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res., 5, 2122. DOI

Zappia, L. & Oshlack, A. (2018) Clustering trees: a visualisation for evaluating clusterings at multiple resolutions. GigaScience, giy083, DOI

R SessionInfo()

Details for the R version and packages used to create all figures

> sessionInfo()
R version 3.4.4 (2018-03-15)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tidyr_0.8.0                pheatmap_1.0.8             clustree_0.1.2             viridis_0.5.1              viridisLite_0.3.0          scater_1.6.3               scran_1.6.9               
 [8] SingleCellExperiment_1.0.0 SummarizedExperiment_1.8.1 DelayedArray_0.4.1         matrixStats_0.53.1         Biobase_2.38.0             GenomicRanges_1.30.3       GenomeInfoDb_1.14.0       
[15] IRanges_2.12.0             S4Vectors_0.16.0           BiocGenerics_0.24.0        BiocParallel_1.12.0        biomaRt_2.34.2             reshape2_1.4.3             useful_1.2.3              
[22] Seurat_2.3.0               Matrix_1.2-14              cowplot_0.9.2              ggraph_1.0.1               ggplot2_2.2.1              dplyr_0.7.4               

loaded via a namespace (and not attached):
  [1] shinydashboard_0.7.0   R.utils_2.6.0          tidyselect_0.2.4       RSQLite_2.1.0          AnnotationDbi_1.40.0   htmlwidgets_1.2        grid_3.4.4             trimcluster_0.1-2     
  [9] ranger_0.9.0           Rtsne_0.13             munsell_0.4.3          codetools_0.2-15       ica_1.0-1              units_0.5-1            DT_0.4                 statmod_1.4.30        
 [17] withr_2.1.2            colorspace_1.3-2       knitr_1.20             rstudioapi_0.7         geometry_0.3-6         ROCR_1.0-7             robustbase_0.92-8      dtw_1.18-1            
 [25] dimRed_0.1.0           lars_1.2               tximport_1.6.0         GenomeInfoDbData_1.0.0 mnormt_1.5-5           bit64_0.9-7            rhdf5_2.22.0           ipred_0.9-6           
 [33] diptest_0.75-7         R6_2.2.2               ggbeeswarm_0.6.0       VGAM_1.0-5             locfit_1.5-9.1         flexmix_2.3-14         DRR_0.0.3              bitops_1.0-6          
 [41] assertthat_0.2.0       promises_1.0.1         SDMTools_1.1-221       scales_0.5.0           nnet_7.3-12            beeswarm_0.2.3         gtable_0.2.0           ddalpha_1.3.2         
 [49] timeDate_3043.102      rlang_0.2.0            CVST_0.2-1             scatterplot3d_0.3-41   RcppRoll_0.2.2         splines_3.4.4          lazyeval_0.2.1         ModelMetrics_1.1.0    
 [57] acepack_1.4.1          broom_0.4.4            checkmate_1.8.5        yaml_2.1.18            abind_1.4-5            backports_1.1.2        httpuv_1.4.0           Hmisc_4.1-1           
 [65] caret_6.0-79           tools_3.4.4            lava_1.6.1             psych_1.8.3.3          gplots_3.0.1           RColorBrewer_1.1-2     proxy_0.4-22           dynamicTreeCut_1.63-1
 [73] ggridges_0.5.0         Rcpp_0.12.16           plyr_1.8.4             base64enc_0.1-3        progress_1.1.2         zlibbioc_1.24.0        purrr_0.2.4            RCurl_1.95-4.10       
 [81] prettyunits_1.0.2      rpart_4.1-13           pbapply_1.3-4          zoo_1.8-1              sfsmisc_1.1-2          ggrepel_0.7.0          cluster_2.0.7-1        magrittr_1.5          
 [89] data.table_1.10.4-3    lmtest_0.9-36          RANN_2.5.1             mvtnorm_1.0-7          fitdistrplus_1.0-9     xtable_1.8-2           mime_0.5               XML_3.98-1.11         
 [97] mclust_5.4             gridExtra_2.3          compiler_3.4.4         tibble_1.4.2           KernSmooth_2.23-15     R.oo_1.21.0            htmltools_0.3.6        later_0.7.1           
[105] segmented_0.5-3.0      Formula_1.2-2          snow_0.4-2             udunits2_0.13          tclust_1.3-1           lubridate_1.7.4        DBI_0.8                diffusionMap_1.1-0    
[113] tweenr_0.1.5           magic_1.5-8            MASS_7.3-49            fpc_2.1-11             R.methodsS3_1.7.1      gdata_2.18.0           metap_0.8              bindr_0.1.1           
[121] gower_0.1.2            igraph_1.2.1           pkgconfig_2.0.1        sn_1.5-1               numDeriv_2016.8-1      foreign_0.8-69         recipes_0.1.2          foreach_1.4.4         
[129] vipor_0.4.5            XVector_0.18.0         prodlim_2018.04.18     stringr_1.3.0          digest_0.6.15          tsne_0.1-3             htmlTable_1.11.2       edgeR_3.20.9          
[137] kernlab_0.9-25         shiny_1.0.5            gtools_3.5.0           modeltools_0.2-21      rjson_0.2.15           nlme_3.1-137           bindrcpp_0.2.2         limma_3.34.9          
[145] pillar_1.2.1           lattice_0.20-35        httr_1.3.1             DEoptimR_1.0-8         survival_2.42-3        glue_1.2.0             FNN_1.1                png_0.1-7             
[153] prabclus_2.2-6         iterators_1.0.9        bit_1.1-12             ggforce_0.1.1          class_7.3-14           stringi_1.1.7          mixtools_1.1.0         blob_1.1.1            
[161] doSNOW_1.0.16          latticeExtra_0.6-28    caTools_1.17.1         memoise_1.1.0          irlba_2.3.2            ape_5.1


Bacon, W.A., Hamilton, R.S., Yu, Z., Kieckbusch, J., Hawkes, D., Krzak, A., Abell, C., Colucci, F. & Charnock-Jones, D.S. (2018) Single-Cell Analysis Identifies Thymic Maturation Delay in Growth-Restricted Neonatal Mice. Front. Immunol., 9:2523





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