Basement membrane remodelling regulates mouse embryogenesis

Christos Kyprianou^1,4, Neophytos Christodoulou^1,4, Russell Hamilton³, Wallis Nahaboo², Diana Suarez Boomgaard², Gianluca Amadei¹, Isabelle Migeotte², and Magdalena Zernicka-Goetz^1,*

¹ Mammalian Embryo and Stem Cell Group, University of Cambridge, Department of Physiology, Development and Neuroscience; Downing Street, Cambridge, CB2 3DY, UK
² IRIBHM, Université Libre de Bruxelles, Bruxelles, Belgium
^3a Department of Genetics, University of Cambridge, Cambridge, UK
^3b Centre for Trophoblast Research, University of Cambridge, Cambridge, UK
⁴ equal contribution
^* corresponding author: mz205@cam.ac.uk

Publication

Kyprianou, C., Christodoulou, N., Hamilton, R.S., Nahaboo, W., Boomgaard, D., Amadei, G., Migeotte, I. & Zernicka-Goetz, M.i (2020) Basement membrane remodelling regulates mouse embryogenesis. [Nature] [DOI]

Code Release to accompany paper:

Investigating Mmp expression in scRNA-Seq

Step 1: Get expression for Mmp genes in published scRNA-seq data

• Cheng, S., Pei, Y., He, L., Peng, G., Reinius, B., Tam, P.P.L, Jing, N. and Deng, Q. (2019) Single-Cell RNA-Seq Reveals Cellular Heterogeneity of Pluripotency Transition and X Chromosome Dynamics during Early Mouse Development. Cell Reports. 26:10, 2593--2607.e3. [DOI]

Download supplemental files from GSE109071

• GSE109071_read.txt
• GSE109071_rpkm.txt
• GSE109071_series_matrix.txt

Methods: Single Cell

Re-analysis of the GSE109071(from 10.1016/j.celrep.2019.02.031) dataset was performed using R (v3.4.4) and Seurat (v3.0.1). The matrix of read counts was input into Seurat, normalised (log), and scaled. Three thousand variable genes were used to identify clusters. Dimensional reduction was performed using UMAP. Cell identifiers were parsed from the GEO entry and added to the Seurat Object using ( make_sample2Age_table.sh). All expression values are log(counts). Marker genes were used to identify the Epiblast (Pou5f1), ExE (Bmp4) and VE (Amn) cells in the UMAP. The matrix of RPKM values from GSE109071 was used to calculate gene expression correlation between pairs of selected genes. Epiblast cells at ages 6.25 and 6.5 were extracted from the matrix and Pearson's correlation coefficient (R) with p-value are given for each comparison. The points are coloured by density using a kde2d kernel.

Figure E4B
Download	[PDF]
Legend	Marker genes were used to identify the Epiblast (Pou5f1), ExE (Bmp4) and VE (Amn) cells in the UMAP.

Find cells in UMAP by Age

Use bash script make_sample2Age_table.sh to make a look up table of cell identifiers to cell ages sample2age.tab.csv

Number of cells by age

Age	Number of Cells
All	1724
5.25	331
5.5	269
6.25	321
6.5	803

Number of cells by cell type

CellType	Number of Cells
All	1724
EPI	775
ExE	283
VE	666

Number of cells by cell type and age

	EPI	ExE	VE
5.25	128	67	136
5.5	120	45	104
6.25	143	87	91
6.5	384	84	335

UMAP showing distribution of cells by age:

Figure E4A
Legend	UMAP showing distribution of cells by age
Download	[PDF]

Plot gene specific UMAPs

For all ages (5.25, 5.5, 6.25 and 6.5) unless stated otherwise.

Figure Number	Genes	UMAP	PDF	Legend
Fig E4C	All Mmp genes		[PDF]	Expression levels for all available Mmp genes are plotted on the UMAP for all cell types. All expression values are log(counts).
-	Mmp2, Mmp14, Mmp25		[PDF]	As above but just for Mmp2, Mmp14 and Mmp25
-	At 6.25 Only: Mmp2, Mmp14, Mmp25		[PDF]	As above but the UMAP has been zoomed in to only show the Epiblase cluster.
-	Marker Genes		[PDF]	As above but for known marker genes T, Otx2, Nodal and Fam25c.
Fig E5A (top)	Laminins		[PDF]	As above but for the Laminin genes (Lama1, Lama5, Lamb1, Lamc1)
Fig E5A (bottom)	Collagens		[PDF]	As above but for the Collagen genes (Col18a1, Col4a1, Col9a3)

Summarise Gene Level UMAPs

Figure 3C
Legend	Expression levels for Mmp genes (Mmp2, Mmp14 & Mmp25) and known marker genes (T, Nodal, Tdgf1) are plotted on the UMAP for just the Epiblast cell types identified in Figure 3C. All expression values are log(counts).
Download	[PDF]

Step 2: Analyse expression for Mmp genes in published scRNA-seq data

Expression Vs Age

Download Figure: [CTR_mz205_0007_mmp.age.pdf] [CTR_mz205_0007_mmp.age.png]

Table: Mean Expression of Mmp genes (Epiblast) The expression values are a mean of each Mmp RNA across all epiblast cells (at individual time points), this will include cells where no expression is detected.

Mmp	5.25	5.5	6.25	6.5
Mmp24	0.41	0.18	0.05	0.21
Mmp9	0.07	0.53	0.40	0.52
Mmp16	0.19	0.19	0.57	0.52
Mmp23	5.39	5.35	4.61	2.50
Mmp17	1.20	2.51	1.86	1.95
Mmp21	0.15	0.14	0.12	0.16
Mmp2	11.16	9.52	13.64	16.01
Mmp15	2.50	2.00	2.93	2.65
Mmp1a	0.00	1.33	0.11	0.01
Mmp1b	0.00	0.21	0.00	0.00
Mmp12	0.51	1.60	0.20	0.16
Mmp7	0.23	0.02	0.00	0.00
Mmp11	3.83	5.79	3.83	3.48
Mmp19	0.11	0.13	0.08	0.08
Mmp28	0.05	0.00	0.06	0.06
Mmp14	11.32	9.99	13.70	21.59
Mmp25	22.69	17.15	23.91	13.35

Mean Expression of Mmp genes (Epiblast) For cells with expression > 0 for each Mmp RNA

Mmp	5.25	5.5	6.25	6.5
Mmp24	4.36	1.37	0.74	2.42
Mmp9	2.32	4.55	3.60	5.55
Mmp16	1.45	1.22	3.52	3.17
Mmp23	12.10	11.46	15.34	9.07
Mmp17	4.78	6.15	6.20	6.18
Mmp21	2.40	2.39	3.41	3.49
Mmp2	14.28	13.60	18.58	18.63
Mmp15	3.41	3.20	4.66	4.17
Mmp1a	NaN	19.99	7.89	4.59
Mmp1b	NaN	3.21	0.38	0.36
Mmp12	4.67	7.13	4.72	3.46
Mmp7	15.03	2.42	NaN	NaN
Mmp11	9.25	10.37	9.60	8.41
Mmp19	1.68	1.58	1.84	1.45
Mmp28	6.47	0.47	3.97	2.72
Mmp14	13.54	12.89	17.04	24.24
Mmp25	23.05	18.38	27.13	17.27

Step 3: Analyse correlation of expression of Mmp genes with marker genes

Correlations to marker genes

After extraction of the Epiblast cells from the scRNA-seq the next step is to plot the Mmp expression values against a selection of markers: T, Otx2 and Nodal. r values are Pearson's correlations with p.value.

Gene	Mmp25	Mmp14	Mmp2
Figure Number	Fig E4F (left)	Fig E4F (center)	Fig E4F (right)
Download	[PDF]	[PDF]	[PDF]
Legend	The matrix of RPKM values from [GSE109071](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109071) was used to calculate gene expression correlation between pairs of genes. Epiblast cells at ages 6.25 and 6.5 were extracted from the matrix and Pearson's correlation coefficient (R) with p-value are given for each comparison. The points are coloured by density (kde2d)

For all available Mmp Genes

Mmp	PNG	PDF
Mmp1a	CTR_mz205_0007_Mmp1a.png	CTR_mz205_0007_Mmp1a.pdf
Mmp1b	CTR_mz205_0007_Mmp1b.png	CTR_mz205_0007_Mmp1b.pdf
Mmp2	CTR_mz205_0007_Mmp2.png	CTR_mz205_0007_Mmp2.pdf
Mmp3	missing	missing
Mmp7	CTR_mz205_0007_Mmp7.png	CTR_mz205_0007_Mmp7.pdf
Mmp8	missing	missing
Mmp9	CTR_mz205_0007_Mmp9.png	CTR_mz205_0007_Mmp9.pdf
Mmp10	missing	missing
Mmp11	CTR_mz205_0007_Mmp11.png	CTR_mz205_0007_Mmp11.pdf
Mmp12	CTR_mz205_0007_Mmp12.png	CTR_mz205_0007_Mmp12.pdf
Mmp13	missing	missing
Mmp14	CTR_mz205_0007_Mmp14.png	CTR_mz205_0007_Mmp14.pdf
Mmp15	CTR_mz205_0007_Mmp15.png	CTR_mz205_0007_Mmp15.pdf
Mmp16	CTR_mz205_0007_Mmp16.png	CTR_mz205_0007_Mmp16.pdf
Mmp17	CTR_mz205_0007_Mmp17.png	CTR_mz205_0007_Mmp17.pdf
Mmp19	CTR_mz205_0007_Mmp19.png	CTR_mz205_0007_Mmp19.pdf
Mmp21	CTR_mz205_0007_Mmp21.png	CTR_mz205_0007_Mmp21.pdf
Mmp23	CTR_mz205_0007_Mmp23.png	CTR_mz205_0007_Mmp23.pdf
Mmp24	CTR_mz205_0007_Mmp24.png	CTR_mz205_0007_Mmp24.pdf
Mmp25	CTR_mz205_0007_Mmp25.png	CTR_mz205_0007_Mmp25.pdf
Mmp26	missing	missing
Mmp27	missing	missing
Mmp28	CTR_mz205_0007_Mmp28.png	CTR_mz205_0007_Mmp28.png

Missing Mmps, not present in the scRNA RPKM matrix.

Mmp	Reason missing
Mmp3	?
Mmp8	?
Mmp10	?
Mmp13	?
Mmp26	not found in mouse
Mmp27	?

Table of Pearson's Correlations of Mmp genes to T, Nodal, Tdgf1 and Otx2 marker genes.

Mmp	Marker	5.25 (R)	5.25 (p.val)	5.5 (R)	5.5 (p.val)	6.25 (R)	6.25 (p.val)	6.5 (R)	6.5 (p.val)
Mmp24	T	-0.02	0.82	-0.027	0.77	-0.025	0.77	-0.038	0.46
	Otx2	-0.2	0.022	0.032	0.73	0.053	0.53	0.015	0.77
	Nodal	-0.077	0.38	-0.065	0.48	-0.069	0.41	0.019	0.7
	Tdgf1	-0.015	0.87	0.0059	0.95	-0.056	0.5	-0.037	0.47
Mmp9	T	0.034	0.7	-0.047	0.61	0.22	0.0085	0.09	0.077
	Otx2	-0.11	0.22	-0.04	0.66	-0.13	0.11	-0.11	0.03
	Nodal	0.015	0.87	0.027	0.77	0.039	0.64	-0.0087	0.86
	Tdgf1	-0.029	0.75	0.15	0.11	0.13	0.11	0.087	0.089
Mmp16	T	-0.037	0.67	0.077	0.4	-0.063	0.46	-0.043	0.4
	Otx2	0.02	0.83	0.14	0.14	-0.036	0.67	0.022	0.67
	Nodal	0.17	0.059	0.15	0.1	0.022	0.79	0.12	0.021
	Tdgf1	0.099	0.27	0.0076	0.93	0.024	0.78	0.0025	0.96
Mmp23	T	-0.1	0.26	-0.097	0.29	-0.14	0.092	-0.095	0.064
	Otx2	-0.072	0.42	-0.068	0.46	0.16	0.053	0.033	0.51
	Nodal	-0.18	0.043	-0.17	0.063	0.017	0.84	0.077	0.13
	Tdgf1	-0.049	0.58	-0.16	0.084	-0.23	0.0068	-0.063	0.22
Mmp17	T	0.084	0.35	-0.12	0.18	-0.13	0.12	-0.099	0.053
	Otx2	0.02	0.82	-0.11	0.24	0.0024	0.98	0.083	0.11
	Nodal	0.057	0.53	-0.045	0.63	-0.13	0.12	0.059	0.25
	Tdgf1	-0.12	0.19	-0.12	0.19	-0.098	0.25	-0.14	0.006
Mmp21	T	-0.026	0.77	0.15	0.11	0.025	0.76	-0.00083	0.99
	Otx2	0.14	0.11	0.075	0.42	-0.016	0.85	-0.024	0.63
	Nodal	0.06	0.5	0.11	0.21	-0.11	0.17	0.036	0.48
	Tdgf1	-0.056	0.53	0.11	0.25	-0.11	0.19	-0.0077	0.88
Mmp2	T	0.017	0.85	0.059	0.52	0.26	0.002	0.05	0.32
	Otx2	0.12	0.19	0.084	0.36	-0.11	0.18	-0.17	0.00074
	Nodal	0.033	0.71	0.32	0.00042	-0.093	0.27	0.0084	0.87
	Tdgf1	0.0049	0.96	0.2	0.03	0.2	0.017	0.15	0.0034
Mmp15	T	-0.052	0.56	-0.02	0.83	-0.059	0.48	-0.21	2.6e-05
	Otx2	-0.039	0.66	-0.062	0.5	-0.0014	0.99	0.17	0.00062
	Nodal	-0.025	0.78	-0.039	0.67	-0.063	0.45	0.028	0.58
	Tdgf1	0.005	0.96	-0.09	0.33	-0.008	0.92	-0.15	0.0034
Mmp1a	T	NA	NA	-0.05	0.58	-0.024	0.78	0.014	0.79
	Otx2	NA	NA	-0.079	0.39	0.0038	0.96	0.11	0.029
	Nodal	NA	NA	0.013	0.89	-0.041	0.63	-0.00038	0.99
	Tdgf1	NA	NA	0.16	0.08	0.082	0.33	0.1	0.045
Mmp1b	T	NA	NA	-0.048	0.6	-0.0058	0.94	0.051	0.32
	Otx2	NA	NA	-0.074	0.42	-0.075	0.37	0.049	0.34
	Nodal	NA	NA	0.023	0.8	0.012	0.89	-0.038	0.46
	Tdgf1	NA	NA	0.12	0.18	0.034	0.68	0.054	0.29
Mmp12	T	-0.068	0.45	-0.093	0.31	-0.057	0.5	-0.066	0.2
	Otx2	-0.19	0.036	0.15	0.11	-0.14	0.097	-0.12	0.023
	Nodal	-0.079	0.37	0.007	0.94	-0.0023	0.98	-0.026	0.61
	Tdgf1	-0.08	0.37	0.29	0.0011	-0.11	0.19	-0.035	0.5
Mmp7	T	-0.025	0.78	-0.028	0.76	NA	NA	NA	NA
	Otx2	0.052	0.56	0.023	0.8	NA	NA	NA	NA
	Nodal	-0.042	0.64	-0.095	0.3	NA	NA	NA	NA
	Tdgf1	0.061	0.49	-0.0022	0.98	NA	NA	NA	NA
Mmp11	T	-0.052	0.56	-0.13	0.16	-0.11	0.2	-0.09	0.079
	Otx2	0.12	0.19	0.13	0.16	0.19	0.023	0.044	0.39
	Nodal	0.15	0.095	0.044	0.63	-0.011	0.89	0.052	0.31
	Tdgf1	0.17	0.052	0.03	0.74	-0.15	0.066	-0.1	0.045
Mmp19	T	-0.034	0.7	-0.032	0.73	0.14	0.1	0.012	0.81
	Otx2	-0.1	0.25	-0.062	0.5	-0.0018	0.98	-0.095	0.064
	Nodal	0.074	0.4	-0.085	0.35	-0.066	0.44	-0.017	0.74
	Tdgf1	0.058	0.52	-0.056	0.54	-0.065	0.44	0.019	0.71
Mmp28	T	-0.025	0.78	-0.021	0.82	-0.037	0.66	-0.0059	0.91
	Otx2	-0.1	0.26	0.096	0.3	0.048	0.57	-0.088	0.085
	Nodal	-0.02	0.82	-0.034	0.72	0.18	0.033	0.017	0.74
	Tdgf1	-0.1	0.24	-0.064	0.49	0.0088	0.92	0.038	0.45
Mmp14	T	0.072	0.42	0.092	0.32	0.4	6.9e-07	0.29	1.1e-08
	Otx2	-0.033	0.71	-0.039	0.67	-0.16	0.054	-0.18	0.00044
	Nodal	-0.008	0.93	-0.087	0.34	-0.067	0.43	-0.072	0.16
	Tdgf1	-0.026	0.77	0.088	0.34	0.26	0.0014	0.34	1.1e-11
Mmp25	T	-0.26	0.0029	-0.16	0.074	-0.24	0.0036	-0.33	4.2e-11
	Otx2	0.072	0.42	0.15	0.099	0.13	0.12	0.3	2.3e-09
	Nodal	-9e-04	0.99	-0.014	0.88	-0.071	0.4	0.076	0.14
	Tdgf1	0.18	0.043	0.0023	0.98	-0.27	0.001	-0.36	1.6e-13

Investigating Mmp ChIP-Seq associations

ChIP Data generated in this paper:

• Wang, Q., Zou, Y., Nowotschin, S., Kim, S.Y., Li, Q.V. Soh, C., Su, J. Zhang, C., Shu, W. Xi, Q., Huangfu, D., Hadjantonakis, A. and Massague, J. (2017) The p53 Family Coordinates Wnt and Nodal Inputs in Mesendodermal Differentiation of Embryonic Stem Cells. Cell Stem Cell, 20(11), 70-86. [DOI]

And used in:

• Senft, A.D., Costello, I., King, H.W., Mould, A.W., Bikoff, E.K. and Robertson, E.J. (2018) Combinatorial Smad2/3 Activities Downstream of Nodal Signaling Maintain Embryonic/Extra-Embryonic Cell Identities during Lineage Priming. Cell Reports, 24(8), 1977--1985.e7. [DOI]

Accession for data:

• GSE70486 TDF files

Four tracks were chosen

• GSM1782914_Smad2_3_D3EB_AC_E14
• GSM1782915_Smad2_3_D3EB_SB_E14
• GSM1782928_Smad2_3_D3EB_AC_p53WT
• GSM1782929_Smad2_3_D3EB_SB_p53WT

The tracks are in IGVs tdf format, but to be compatible with karyoploteR they must first be converted to bedgraph, and then finally to bigwig using convert_tdf2bedgraph2bigwig.sh (which itself calls bdg2bw.sh taken from this Gist)

For the paper ChIP figure Mmp2 and Mmp14, IGV was used so the TDF format tracks could be normalised to give RPM (reads per million) i.e. signal intensities were normalised by 1x10^6/total reads.

Methods: ChIP-Seq

ChIP tracks for Smad2_3 from GSE70486(from 10.1016/j.stem.2016.10.002) in TDF (tiled data file) format were visualised in IGV (Integrative Genomics Viewer, 10.1038/nbt.1754) and signal intensities were normalised by 1x10^6/total reads to give RPM (Reads per million). Scripts to recreate ChIP-Seq analysis and figures can be found at github.com/darogan/CTR_mz205_0007. Analysis was performed using R (v3.4.4).

Mmp	ChIP Track Figure	Paper Figure	PDF/SVG
Mmp2		Fig E5F (top)	[PDF] [SVG]
Mmp14		Fig E5F (bottom)	[PDF] [SVG]

Figure E5, Legend: ChIP tracks for Smad2_3 from GSE70486 in TDF format were visualied in IGV (Integrative Genomics Viewer, 10.1038/nbt.1754) and signal intensities were normalised by 1x10^6/total reads to give RPM (Reads per million).

KaryoploteR versions of the ChIP plots. Note these have not been normalised to RPM values.

Mmp	ChIP Track Figure
Mmp11	CTR_mz205_0007.ChIP.Mmp11.pdf
Mmp12	CTR_mz205_0007.ChIP.Mmp12.pdf
Mmp14	CTR_mz205_0007.ChIP.Mmp14.pdf
Mmp15	CTR_mz205_0007.ChIP.Mmp15.pdf
Mmp16	CTR_mz205_0007.ChIP.Mmp16.pdf
Mmp17	CTR_mz205_0007.ChIP.Mmp17.pdf
Mmp19	CTR_mz205_0007.ChIP.Mmp19.pdf
Mmp1a	CTR_mz205_0007.ChIP.Mmp1a.pdf
Mmp1b	CTR_mz205_0007.ChIP.Mmp1b.pdf
Mmp2	CTR_mz205_0007.ChIP.Mmp2.pdf
Mmp21	CTR_mz205_0007.ChIP.Mmp21.pdf
Mmp23	CTR_mz205_0007.ChIP.Mmp23.pdf
Mmp24	CTR_mz205_0007.ChIP.Mmp24.pdf
Mmp25	CTR_mz205_0007.ChIP.Mmp25.pdf
Mmp28	CTR_mz205_0007.ChIP.Mmp28.pdf
Mmp7	CTR_mz205_0007.ChIP.Mmp7.pdf
Mmp9	CTR_mz205_0007.ChIP.Mmp9.pdf

Software Versions & Methods

R version 3.4.4 (2018-03-15)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.4

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

locale:
[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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] matrixStats_0.54.0 umap_0.2.2.0       viridis_0.5.1      viridisLite_0.3.0  cowplot_0.9.4      reshape2_1.4.3     dplyr_0.8.1       
 [8] Seurat_3.0.1       useful_1.2.6       ggplot2_3.1.1      Rtsne_0.15        

Contact

Contact Russell S. Hamilton (rsh46 -at- cam.ac.uk)