Single-cell transcriptomic analysis of 3D-cultured human pre-gastrulation embryos stem cells
JD
2020-08-01
Xiang, L., Yin, Y., Zheng, Y., Ma, Y., Li, Y., Zhao, Z., Guo, J., Ai, Z., Niu, Y., Duan, K., et al. (2020). A developmental landscape of 3D-cultured human pre-gastrulation embryos. Nature 577, 537–542.
- BioProject Accession: PRJNA562548
- GEO Accession: GSE136447
Load required packages.
library(tidyverse)
library(magrittr)
library(Matrix)
library(Seurat)
library(extrafont)
library(patchwork)
# library(tidylog)Sys.time()## [1] "2020-08-01 15:23:42 CDT"Data preparation
Functions loading
source(
file = file.path(
SCRIPT_DIR,
"utilities.R"
)
)Data loading
Load SRA run info.
cell_metadata <- read_delim(
file = "../SraRunTable.txt",
delim = ","
)## Parsed with column specification:
## cols(
## .default = col_character(),
## AvgSpotLen = col_double(),
## Bases = col_double(),
## Bytes = col_double(),
## ReleaseDate = col_datetime(format = "")
## )## See spec(...) for full column specifications.cell_metadata$Cell_type %>%
table()## .
## CTBs EPI EVTs Hypoblast ICM PSA-EPI STBs
## 159 126 40 25 52 44 109cell_metadata %>% head()Prepare metadata for single cells.
embedding <- read_csv(
file = "embedding_ncomponents31_seed20200317.csv"
) %>%
select(cell:y_tsne) %>%
left_join(
cell_metadata[, c("Run", "Age", "Cell_type", "Sample Name")],
by = c("cell" = "Run")
) %>%
rename(
developmental_stage = Age,
lineage = Cell_type,
sample_name = `Sample Name`
) %>%
mutate(
louvain = factor(louvain),
#
developmental_stage = str_replace(
string = developmental_stage,
pattern = "embryo invitro day ",
replacement = "E"
),
developmental_stage = factor(
developmental_stage,
levels = stringr::str_sort(
x = unique(developmental_stage),
numeric = TRUE
)
),
lineage = factor(
lineage,
levels = c(
"ICM",
"EPI",
"PSA-EPI",
"Hypoblast",
"CTBs",
"STBs",
"EVTs"
)
)
)## Parsed with column specification:
## cols(
## cell = col_character(),
## batch = col_character(),
## louvain = col_double(),
## x_tsne = col_double(),
## y_tsne = col_double(),
## x_umap = col_double(),
## y_umap = col_double(),
## x_fitsne = col_double(),
## y_fitsne = col_double(),
## x_phate = col_double(),
## y_phate = col_double(),
## `x_min_dist=0.1` = col_double(),
## `y_min_dist=0.1` = col_double(),
## x_multicoretsne = col_double(),
## y_multicoretsne = col_double()
## )embedding %>% head()reticulate::py_discover_config()## python: /Users/jialei/.pyenv/shims/python
## libpython: /Users/jialei/.pyenv/versions/3.8.2/lib/libpython3.8.dylib
## pythonhome: /Users/jialei/.pyenv/versions/3.8.2:/Users/jialei/.pyenv/versions/3.8.2
## version: 3.8.2 (default, May 23 2020, 03:35:41) [Clang 11.0.3 (clang-1103.0.32.62)]
## numpy: /Users/jialei/.pyenv/versions/3.8.2/lib/python3.8/site-packages/numpy
## numpy_version: 1.19.0
##
## NOTE: Python version was forced by RETICULATE_PYTHONnp <- reticulate::import("numpy", convert = TRUE)
scipy.sparse <- reticulate::import(module = "scipy.sparse", convert = TRUE)
matrix_readcount_use <- scipy.sparse$load_npz("../matrix_readcount.npz")
matrix_readcount_use_features <- np$load("../matrix_readcount_features.npy")
matrix_readcount_use_barcodes <- np$load("../matrix_readcount_barcodes.npy")
colnames(matrix_readcount_use) <- matrix_readcount_use_barcodes
# rownames(matrix_readcount_use) <- matrix_readcount_use_features
rownames(matrix_readcount_use) <- paste(
gene_symbo_info$X1,
gene_symbo_info$X2,
sep = "_"
)
matrix_readcount_use <- matrix_readcount_use[, embedding$cell]
# calculate CPM
matrix_cpm_use <- calc_cpm(matrix_readcount_use)
stopifnot(
dim(matrix_readcount_use) == dim(matrix_cpm_use)
)
print(dim(matrix_readcount_use))## [1] 33538 555walk(list(embedding, matrix_readcount_use, matrix_cpm_use), function(x) {
print(object.size(x), units = "auto", standard = "SI")
})## 139.8 kB
## 71.6 MB
## 71.6 MBCharacterization of clustering
Clusters
embedding %>%
mutate(
num_umis = colSums(matrix_readcount_use[, cell]),
num_genes = colSums(matrix_readcount_use[, cell] > 0)
) %>%
group_by(louvain) %>%
summarise(
num_cell = n(),
median_umis = median(num_umis),
median_genes = median(num_genes)
) %>%
gt::gt() %>%
gt::tab_options(table.font.size = "median")## `summarise()` ungrouping output (override with `.groups` argument)
| louvain | num_cell | median_umis | median_genes |
|---|---|---|---|
| 0 | 114 | 8237856 | 9930.5 |
| 1 | 87 | 11307773 | 9017.0 |
| 2 | 86 | 9414532 | 10279.5 |
| 3 | 72 | 12111266 | 10120.0 |
| 4 | 72 | 11915513 | 11100.0 |
| 5 | 46 | 9897404 | 11808.5 |
| 6 | 41 | 10522366 | 11805.0 |
| 7 | 37 | 9180087 | 9615.0 |
p_embedding_cluster <- plot_embedding(
embedding = embedding[, c("x_tsne", "y_tsne")],
color_values = embedding$louvain %>% as.factor(),
label = "Cluster",
# label_position = c(label_x, label_y),
show_color_value_labels = TRUE,
show_color_legend = FALSE,
geom_point_size = 1,
sort_values = FALSE
)p_embedding_cluster +
scale_color_manual(
values = gg_color_hue(n = length(unique(embedding$louvain)))
)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
Distribution of UMIs
# Embedding
plot_embedding_value(
embedding = embedding[, c("x_tsne", "y_tsne")],
color_values = matrix_readcount_use[, embedding$cell] %>%
colSums(),
colorbar_position = c(0.86, 0.28),
label = "UMI distribution",
label_position = NULL,
# label_position = c(label_x, label_y),
geom_point_size = 1,
sort_values = FALSE,
FUN = function(x) log10(x)
)
Distribution of samples
Development stage
prepare_cluster_composition(
embedding = embedding,
x = louvain, group = developmental_stage
) %>%
select(-num_cells) %>%
pivot_wider(
names_from = developmental_stage,
values_from = percentage
) %>%
replace(is.na(.), 0) %>%
as_tibble() %>%
gt::gt() %>%
gt::fmt_percent(starts_with("E")) %>%
gt::tab_options(table.font.size = "median")## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'louvain' (override with `.groups` argument)
| louvain | E6 | E7 | E8 | E9 | E10 | E12 | E13.5 | E14 |
|---|---|---|---|---|---|---|---|---|
| 0 | 22.81% | 14.04% | 7.89% | 7.89% | 11.40% | 17.54% | 2.63% | 15.79% |
| 1 | 4.60% | 49.43% | 14.94% | 10.34% | 4.60% | 10.34% | 1.15% | 4.60% |
| 2 | 0.00% | 0.00% | 0.00% | 3.49% | 16.28% | 33.72% | 8.14% | 38.37% |
| 3 | 0.00% | 0.00% | 20.83% | 18.06% | 37.50% | 12.50% | 1.39% | 9.72% |
| 4 | 0.00% | 2.78% | 30.56% | 47.22% | 19.44% | 0.00% | 0.00% | 0.00% |
| 5 | 0.00% | 0.00% | 0.00% | 0.00% | 10.87% | 43.48% | 6.52% | 39.13% |
| 6 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.44% | 0.00% | 97.56% |
| 7 | 89.19% | 10.81% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
p_barplot_cluster_composition_developmental_stage <- prepare_cluster_composition(
embedding = embedding,
x = louvain, group = developmental_stage
) %>%
arrange(louvain) %>%
plot_barplot(x = louvain, y = percentage, z = developmental_stage) +
scale_fill_manual(
values = ggthemes::tableau_color_pal("Tableau 20")(
length(unique(embedding$developmental_stage))
)
)## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'louvain' (override with `.groups` argument)Lineage
embedding %<>%
mutate(
lineage_full = as.character(lineage),
lineage_full = case_when(
lineage_full == c("CTBs") ~ "Cytotrophoblasts",
lineage_full == c("STBs") ~ "Syncytiotrophoblasts",
lineage_full == c("EVTs") ~ "Extravillous cytotrophoblasts",
lineage_full == c("PSA-EPI") ~ "Primitive streak anlage - epiblast",
lineage_full == c("EPI") ~ "Epiblast",
lineage_full == c("ICM") ~ "Inner cell mass",
TRUE ~ .data$lineage_full
),
#
lineage2 = as.character(lineage),
lineage2 = case_when(
lineage2 == c("CTBs") ~ "TE",
lineage2 == c("STBs") ~ "TE",
lineage2 == c("EVTs") ~ "TE",
lineage2 == c("PSA-EPI") ~ "EPI",
lineage2 == c("Hypoblast") ~ "HYP",
TRUE ~ .data$lineage2
),
#
lineage_full = factor(
lineage_full,
levels = c(
"Inner cell mass",
"Epiblast",
"Primitive streak anlage - epiblast",
"Hypoblast",
"Cytotrophoblasts",
"Syncytiotrophoblasts",
"Extravillous cytotrophoblasts"
)
),
#
lineage2 = factor(
lineage2,
levels = c("ICM", "EPI", "HYP", "TE")
)
)embedding %>%
dplyr::count(lineage, lineage_full, name = "num_cells") %>%
arrange(lineage) %>%
gt::gt() %>%
gt::tab_options(table.font.size = "median")
| lineage | lineage_full | num_cells |
|---|---|---|
| ICM | Inner cell mass | 52 |
| EPI | Epiblast | 126 |
| PSA-EPI | Primitive streak anlage - epiblast | 44 |
| Hypoblast | Hypoblast | 25 |
| CTBs | Cytotrophoblasts | 159 |
| STBs | Syncytiotrophoblasts | 109 |
| EVTs | Extravillous cytotrophoblasts | 40 |
prepare_cluster_composition(
embedding = embedding,
x = louvain, group = lineage
) %>%
select(-num_cells) %>%
pivot_wider(
names_from = lineage,
values_from = percentage
) %>%
replace(is.na(.), 0) %>%
as_tibble() %>%
gt::gt() %>%
gt::fmt_percent(c(2:8)) %>%
gt::tab_options(table.font.size = "median")## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'louvain' (override with `.groups` argument)
| louvain | EPI | ICM | PSA-EPI | Hypoblast | CTBs | EVTs | STBs |
|---|---|---|---|---|---|---|---|
| 0 | 100.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
| 1 | 3.45% | 17.24% | 2.30% | 28.74% | 48.28% | 0.00% | 0.00% |
| 2 | 1.16% | 0.00% | 1.16% | 0.00% | 51.16% | 46.51% | 0.00% |
| 3 | 0.00% | 0.00% | 4.17% | 0.00% | 5.56% | 0.00% | 90.28% |
| 4 | 1.39% | 1.39% | 0.00% | 0.00% | 95.83% | 0.00% | 1.39% |
| 5 | 2.17% | 0.00% | 4.35% | 0.00% | 0.00% | 0.00% | 93.48% |
| 6 | 12.20% | 0.00% | 87.80% | 0.00% | 0.00% | 0.00% | 0.00% |
| 7 | 2.70% | 97.30% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
p_barplot_cluster_composition_lineage <- prepare_cluster_composition(
embedding = embedding,
x = louvain, group = lineage
) %>%
arrange(louvain) %>%
plot_barplot(x = louvain, y = percentage, z = lineage) +
scale_fill_manual(
values = ggthemes::tableau_color_pal("Tableau 10")(
length(unique(embedding$lineage))
)
)## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'louvain' (override with `.groups` argument)# combine plots
p_barplot_cluster_composition_developmental_stage +
p_barplot_cluster_composition_lineage +
plot_annotation(theme = theme(plot.margin = margin())) +
plot_layout(guides = "collect")
Lineage; merged
prepare_cluster_composition(
embedding = embedding,
x = louvain, group = lineage2
) %>%
select(-num_cells) %>%
pivot_wider(
names_from = lineage2,
values_from = percentage
) %>%
replace(is.na(.), 0) %>%
as_tibble() %>%
gt::gt() %>%
gt::fmt_percent(c(2:5)) %>%
gt::tab_options(table.font.size = "median")## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'louvain' (override with `.groups` argument)
| louvain | EPI | ICM | HYP | TE |
|---|---|---|---|---|
| 0 | 100.00% | 0.00% | 0.00% | 0.00% |
| 1 | 5.75% | 17.24% | 28.74% | 48.28% |
| 2 | 2.33% | 0.00% | 0.00% | 97.67% |
| 3 | 4.17% | 0.00% | 0.00% | 95.83% |
| 4 | 1.39% | 1.39% | 0.00% | 97.22% |
| 5 | 6.52% | 0.00% | 0.00% | 93.48% |
| 6 | 100.00% | 0.00% | 0.00% | 0.00% |
| 7 | 2.70% | 97.30% | 0.00% | 0.00% |
prepare_cluster_composition(
embedding = embedding,
x = louvain, group = lineage2
) %>%
arrange(louvain) %>%
plot_barplot(x = louvain, y = percentage, z = lineage2) +
scale_fill_manual(
values = as.character(yarrr::piratepal(palette = "google"))
)## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'louvain' (override with `.groups` argument)
Delineating lineage by transcriptome
Expression
t-SNE embedding
FEATURES_SELECTED <- c(
"NANOG",
"POU5F1",
"PDGFRA",
"GATA6",
"GATA3",
"GATA2"
)
FEATURES_SELECTED <- rownames(matrix_readcount_use)[
gene_symbo_info$X2 %in% FEATURES_SELECTED
]map(FEATURES_SELECTED, function(x) {
plot_embedding_value(
embedding = embedding[, c("x_tsne", "y_tsne")],
color_values = matrix_cpm_use[x, embedding$cell],
colorbar_position = c(0.86, 0.28),
label = str_c("t-SNE; ", x),
label_position = NULL,
# label_position = c(label_x, label_y),
geom_point_size = 1,
sort_values = TRUE,
FUN = NULL
)
}) %>%
purrr::reduce(`+`) +
plot_annotation(theme = theme(plot.margin = margin()))
Heatmap
FEATURES_SELECTED <- c(
"GAPDH",
"PPIA",
# "OCT4",
"POU5F1",
"NANOG",
"SOX2",
"PRDM14",
"DPPA3",
"GDF3",
"CDH1",
"CDH2",
"GATA6",
"PDGFRA",
"GATA4",
"SOX17",
"DUSP4",
"TFAP2C",
# "CK7",
"KRT7",
"GATA2",
"CDX2",
"TEAD4",
"EOMES",
"TCEAL4",
"CSH1",
"HLA-G",
"MMP2",
"TBXT"
)
FEATURES_SELECTED <- rownames(matrix_readcount_use)[
gene_symbo_info$X2 %in% FEATURES_SELECTED
]
length(FEATURES_SELECTED)## [1] 26matrix_heatmap <- calc_cpm(m = matrix_readcount_use)[FEATURES_SELECTED, ]
matrix_heatmap <- matrix_heatmap[rowSums(matrix_heatmap) != 0, ]
matrix_heatmap <- log10(matrix_heatmap + 1)
matrix_heatmap <- t(scale(t(matrix_heatmap)))
# rownames(matrix_heatmap) <- str_remove(
# string = rownames(matrix_heatmap),
# pattern = "^E.+_"
# )
(heatmap_limits <- quantile(matrix_heatmap, c(0.05, 0.95)))## 5% 95%
## -1.243100 1.825677matrix_heatmap[matrix_heatmap < heatmap_limits[1]] <- heatmap_limits[1]
matrix_heatmap[matrix_heatmap > heatmap_limits[2]] <- heatmap_limits[2]ha_columns <- ComplexHeatmap::HeatmapAnnotation(
lineage = ComplexHeatmap::anno_simple(
embedding[match(
x = colnames(matrix_heatmap),
table = embedding$cell
), "lineage"],
# pch = anno_labels_cluster,
col = setNames(
object = ggthemes::tableau_color_pal("Tableau 10")(
length(unique(embedding$lineage))
),
nm = sort(unique(embedding$lineage))
),
which = "column",
pt_size = unit(2, "mm"),
pt_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
simple_anno_size = unit(3, "mm")
),
#
lineage_merged = ComplexHeatmap::anno_simple(
embedding[match(
x = colnames(matrix_heatmap),
table = embedding$cell
), "lineage2"],
# pch = anno_labels_cluster,
col = setNames(
object = as.character(yarrr::piratepal(palette = "google")),
nm = sort(unique(embedding$lineage2))
),
which = "column",
pt_size = unit(2, "mm"),
pt_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
simple_anno_size = unit(3, "mm")
),
#
developmental_stage = ComplexHeatmap::anno_simple(
embedding[match(
x = colnames(matrix_heatmap),
table = embedding$cell
), "developmental_stage"],
# pch = anno_labels_lineage,
col = setNames(
object = ggthemes::tableau_color_pal("Tableau 20")(
length(unique(embedding$developmental_stage))
),
nm = levels(embedding$developmental_stage)
),
which = "column",
# pt_size = unit(2, "mm"),
pt_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
simple_anno_size = unit(3, "mm")
),
show_annotation_name = TRUE,
annotation_label = c(
"Lineage",
"Lineage, merged",
"Developmental stage"
),
annotation_name_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
annotation_name_side = "left"
)rownames(matrix_heatmap) <- str_remove(
string = rownames(matrix_heatmap),
pattern = "^E.+_"
)
ht <- ComplexHeatmap::Heatmap(
matrix = matrix_heatmap,
col = wesanderson::wes_palette("Zissou1", 50, type = "continuous"),
# col = viridis::plasma(n = 10),
#
cluster_rows = TRUE,
cluster_columns = TRUE,
#
show_row_names = TRUE,
show_column_names = FALSE,
row_names_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
#
show_row_dend = TRUE,
show_column_dend = TRUE,
column_dend_side = c("bottom"),
#
show_heatmap_legend = TRUE,
top_annotation = ha_columns,
#
heatmap_legend_param = list(
title = "Z score",
title_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
),
legend_direction = "vertical",
labels_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
),
legend_height = unit(25, "mm"),
legend_width = unit(10, "mm")
)
)lgd_lineage <- ComplexHeatmap::Legend(
title = "Lineage",
labels = sort(unique(embedding$lineage)),
legend_gp = grid::gpar(fill = ggthemes::tableau_color_pal("Tableau 10")(
length(unique(embedding$lineage))
)),
labels_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
),
title_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
)
)
lgd_lineage_merged <- ComplexHeatmap::Legend(
title = "Lineage, merged",
labels = sort(unique(embedding$lineage2)),
legend_gp = grid::gpar(
fill = as.character(yarrr::piratepal(palette = "google"))
),
labels_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
),
title_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
)
)
lgd_developmental_stage <- ComplexHeatmap::Legend(
title = "Developmental stage",
labels = levels(embedding$developmental_stage),
legend_gp = grid::gpar(fill = ggthemes::tableau_color_pal("Tableau 20")(
length(levels(embedding$developmental_stage))
)),
labels_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
),
title_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
)
)pd <- ComplexHeatmap::packLegend(
lgd_lineage,
lgd_lineage_merged,
gap = unit(8, "mm"),
direction = "horizontal"
)
ComplexHeatmap::draw(
ht,
heatmap_legend_list = list(pd, lgd_developmental_stage)
)
Lineages and developmental stages
customized_theme <- function(x) {
theme(
legend.text = element_text(family = "Arial", size = 6),
legend.key.size = unit(3, "mm"),
legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "mm"),
legend.background = element_blank()
)
}p_embedding_cluster_lineage <- plot_embedding(
embedding = embedding[, c("x_tsne", "y_tsne")],
color_values = embedding$lineage %>% factor(),
label = "t-SNE; Lineage",
# label_position = c(label_x, label_y),
show_color_value_labels = TRUE,
show_color_legend = TRUE,
geom_point_size = 1,
sort_values = FALSE
) +
scale_color_manual(
values = ggthemes::tableau_color_pal("Tableau 10")(
length(unique(embedding$lineage))
)
) +
labs(color = NULL) +
guides(colour = guide_legend(override.aes = list(size = 3))) +
customized_theme()
p_embedding_cluster_lineage2 <- plot_embedding(
embedding = embedding[, c("x_tsne", "y_tsne")],
color_values = embedding$lineage2 %>% factor(),
label = "t-SNE; Lineage, merged",
# label_position = c(label_x, label_y),
show_color_value_labels = TRUE,
show_color_legend = TRUE,
geom_point_size = 1,
sort_values = FALSE
) +
scale_color_manual(
values = as.character(yarrr::piratepal(palette = "google"))
) +
labs(color = NULL) +
guides(colour = guide_legend(override.aes = list(size = 3))) +
customized_theme()
p_embedding_cluster_developmental_stage <- plot_embedding(
embedding = embedding[, c("x_tsne", "y_tsne")],
color_values = embedding$developmental_stage %>% factor(),
label = "t-SNE; Developmental stage",
# label_position = c(label_x, label_y),
show_color_value_labels = FALSE,
show_color_legend = TRUE,
geom_point_size = 1,
sort_values = FALSE
) +
scale_color_manual(
values = ggthemes::tableau_color_pal("Tableau 20")(
length(unique(embedding$developmental_stage))
)
) +
labs(color = NULL) +
guides(colour = guide_legend(override.aes = list(size = 3))) +
customized_theme()
p_embedding_cluster2 <- plot_embedding(
embedding = embedding[, c("x_tsne", "y_tsne")],
color_values = embedding$louvain %>% factor(),
label = "t-SNE; Cluster",
# label_position = c(label_x, label_y),
show_color_value_labels = TRUE,
show_color_legend = TRUE,
geom_point_size = 1,
sort_values = FALSE
) +
scale_color_manual(
values = gg_color_hue(n = length(unique(embedding$louvain)))
) +
labs(color = NULL) +
guides(colour = guide_legend(override.aes = list(size = 3))) +
customized_theme()list(
p_embedding_cluster2,
p_embedding_cluster_lineage,
p_embedding_cluster_lineage2,
p_embedding_cluster_developmental_stage
) %>%
purrr::reduce(`+`) +
plot_annotation(theme = theme(plot.margin = margin())) +
plot_layout(guides = "collect")
AME–EPI separation
# match sample ids
# https://www.ncbi.nlm.nih.gov/geo/browse/?view=samples&series=136447
sample_info <- read_csv(
file = "sample.csv"
)## Parsed with column specification:
## cols(
## Accession = col_character(),
## Title = col_character(),
## `Sample Type` = col_character(),
## Taxonomy = col_character(),
## Channels = col_double(),
## Platform = col_character(),
## Series = col_character(),
## `Supplementary Types` = col_character(),
## `Supplementary Links` = col_character(),
## `SRA Accession` = col_character(),
## Contact = col_character(),
## `Release Date` = col_character()
## )sample_info %>% head()embedding %<>%
left_join(
sample_info[, c("Accession", "Title")],
by = c("sample_name" = "Accession")
) %>%
rename(sample_id = Title)
embedding %>% head()SUPPLEMENTARY_INFORMATION_DIR <- "../../../../docs/A_developmental_landscape_of_3D-cultured_human_pre-gastrulation_embryos"# Table S8.1 Basic information of 555 single cells. The Embryo ID column listed the embryos to which the cells belong. The number after D is day post-fertilization; the remaining part is the serial number of embryos on the same days. The group column listed the major lineages of the cells as defined in Fig 2b.
file_name <- "41586_2019_1875_MOESM10_ESM.xlsx"
table_s8_sheet1 <- readxl::read_excel(
path = file.path(SUPPLEMENTARY_INFORMATION_DIR, file_name),
sheet = "S8.1",
col_names = FALSE
)## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4table_s8_sheet1 %>% head()# clean messy spreadsheet
table_s8_sheet1 %<>%
slice(-c(1:3)) %>%
`colnames<-`(c("sample_id", "day", "embryo_id", "group"))
table_s8_sheet1Add embryo ids.
embedding %<>%
mutate(
sample_id = str_remove(
string = sample_id,
pattern = "Embryo_"
)
) %>%
left_join(table_s8_sheet1[, c("sample_id", "embryo_id")])## Joining, by = "sample_id"# Table S8.3 Group information of single cells in extended data figure 4o.
file_name <- "41586_2019_1875_MOESM10_ESM.xlsx"
table_s8_sheet3 <- readxl::read_excel(
path = file.path(SUPPLEMENTARY_INFORMATION_DIR, file_name),
sheet = "S8.3",
col_names = FALSE
)## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3table_s8_sheet3 %>% head()# clean messy spreadsheet
table_s8_sheet3 %<>%
slice(-c(1:3)) %>%
`colnames<-`(c("sample_id", "day", "group"))
table_s8_sheet3# "D12A3B14" %in% embedding$sample_id
# "D14_3S10" %in% embedding$sample_id
# "D14A1S12" %in% embedding$sample_id
embedding2 <- embedding %>%
left_join(
table_s8_sheet3[, c("sample_id", "group")],
by = "sample_id"
)
color_value_factors <- embedding2$group
color_value_factors[is.na(embedding2$group)] <- "N/A"# Extended data figure 4o
plot_embedding(
embedding = embedding2[, c("x_tsne", "y_tsne")],
color_values = color_value_factors,
label = "t-SNE; Cell type",
# label_position = c(label_x, label_y),
show_color_value_labels = TRUE,
show_color_legend = TRUE,
geom_point_size = 1,
sort_values = FALSE
) +
scale_color_manual(
values = c(
RColorBrewer::brewer.pal(
n = length(unique(color_value_factors)) - 1,
name = "Dark2"
),
"grey70"
)
) +
labs(color = NULL) +
theme(
legend.text = element_text(family = "Arial", size = 6),
legend.key.size = unit(3, "mm"),
legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "mm"),
legend.background = element_blank()
) +
guides(
colour = guide_legend(override.aes = list(size = 3))
)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
HESX1-T+ / HESX1+T- EPI
# Table S8.4 Cells used in extended data figure 5i.
embedding2 <- embedding %>%
mutate(
group = case_when(
sample_id %in% c(
"D14A1S6",
"D14A1S28",
"D14A1S30",
"D14A1S7",
"D14A1S31",
"D14A1S13",
"D14A1S58",
"D14A1S47",
"D14A1S64",
"D14A3S20",
"D14A1S21",
"D14A1B27"
) ~ "HESX1-T+ EPI",
sample_id %in% c(
"D14A1S46",
"D14A1S51",
"D14A1B2",
"D14A1S12",
"D14A1S14",
"D14A1S29",
"D14A1S33",
"D14A1S56",
"D14_3S10",
"D14_3S15"
)
~ "HESX1+T- EPI"
),
group = ifelse(is.na(group), "N/A", group)
)embedding2 %>%
dplyr::count(group) %>%
gt::gt() %>%
gt::cols_label(n = "num_cell") %>%
gt::tab_options(table.font.size = "median")| group | num_cell |
|---|---|
| HESX1-T+ EPI | 12 |
| HESX1+T- EPI | 10 |
| N/A | 533 |
t-SNE embedding
# Extended data figure 4o
p_embedding_HESX1_p1 <- plot_embedding(
embedding = embedding2[, c("x_tsne", "y_tsne")],
color_values = embedding2$group %>% factor(),
label = "t-SNE; Extended Data Fig. 4o",
# label_position = c(label_x, label_y),
show_color_value_labels = FALSE,
show_color_legend = TRUE,
geom_point_size = 1,
sort_values = FALSE
) +
scale_color_manual(
values = c(
ggsci::pal_npg("nrc", alpha = 1)(
embedding2$group %>% unique() %>% length() - 1
),
"grey70"
)
) +
labs(color = NULL) +
theme(
legend.text = element_text(family = "Arial", size = 6),
legend.key.size = unit(3, "mm"),
legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "mm"),
legend.background = element_blank(),
#
legend.justification = c(0, 1),
legend.position = c(0.075, 0.975)
) +
guides(
colour = guide_legend(override.aes = list(size = 3))
)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.(rownames(matrix_readcount_use)[gene_symbo_info$X2 %in% "HESX1"])## [1] "ENSG00000163666_HESX1"p_embedding_HESX1_p2 <- plot_embedding_value(
embedding = embedding[, c("x_tsne", "y_tsne")],
color_values = matrix_cpm_use["ENSG00000163666_HESX1", embedding$cell],
colorbar_position = c(0.86, 0.28),
label = str_c("t-SNE; ", "ENSG00000163666_HESX1"),
label_position = NULL,
# label_position = c(label_x, label_y),
geom_point_size = 1,
sort_values = TRUE,
FUN = NULL
)
color_values <- matrix_cpm_use["ENSG00000163666_HESX1", embedding$cell]
color_values[!embedding$lineage %in% c("EPI")] <- NA
p_embedding_HESX1_p3 <- plot_embedding_value(
embedding = embedding[, c("x_tsne", "y_tsne")],
color_values = color_values,
colorbar_position = c(0.86, 0.28),
label = str_c("t-SNE; ", "ENSG00000163666_HESX1", "; EPI"),
label_position = NULL,
# label_position = c(label_x, label_y),
geom_point_size = 1,
sort_values = TRUE,
FUN = NULL
)
color_values <- matrix_cpm_use["ENSG00000163666_HESX1", embedding$cell]
color_values[!embedding$lineage %in% c("PSA-EPI")] <- NA
p_embedding_HESX1_p4 <- plot_embedding_value(
embedding = embedding[, c("x_tsne", "y_tsne")],
color_values = color_values,
colorbar_position = c(0.86, 0.28),
label = str_c("t-SNE; ", "ENSG00000163666_HESX1", "; PSA-EPI"),
label_position = NULL,
# label_position = c(label_x, label_y),
geom_point_size = 1,
sort_values = TRUE,
FUN = NULL
)p_embedding_HESX1_p1 +
p_embedding_HESX1_p2 +
p_embedding_HESX1_p3 +
p_embedding_HESX1_p4 +
plot_annotation(theme = theme(plot.margin = margin())) +
plot_layout(guides = "keep")
Barplot
FEATURES_SELECTED <- c(
"ENSG00000163666_HESX1",
"ENSG00000164458_TBXT"
)
map(FEATURES_SELECTED, function(x) {
embedding %>%
mutate(feature_value = matrix_cpm_use[x, cell]) %>%
group_by(developmental_stage, lineage) %>%
summarise(mean_expr = mean(feature_value + 1)) %>%
plot_barplot_feature(
x = developmental_stage,
y = mean_expr,
z = "lineage",
title = x
)
}) %>%
purrr::reduce(`+`) +
plot_layout(ncol = 1) +
plot_annotation(
tag_levels = c("A"),
theme = theme(plot.margin = margin())
) &
theme(
plot.tag.position = c(0, 1),
plot.tag = element_text(
family = "Arial",
size = 7,
hjust = 0,
vjust = 0
)
)## `summarise()` regrouping output by 'developmental_stage' (override with `.groups` argument)
## `summarise()` regrouping output by 'developmental_stage' (override with `.groups` argument)
Trophoblast lineage development
# Table S8.5 Group information of 352 TrBs in extended data figure 8j_o.
file_name <- "41586_2019_1875_MOESM10_ESM.xlsx"
table_s8_sheet5 <- readxl::read_excel(
path = file.path(SUPPLEMENTARY_INFORMATION_DIR, file_name),
sheet = "S8.5",
col_names = FALSE
)## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3table_s8_sheet5 %>% head()# clean messy spreadsheet, again * 3
table_s8_sheet5 %<>%
slice(-c(1:3)) %>%
`colnames<-`(c("sample_id", "day", "group"))
table_s8_sheet5embedding2 <- embedding %>%
left_join(
table_s8_sheet5[, c("sample_id", "group")],
by = "sample_id"
)
color_value_factors <- embedding2 %>%
mutate(
group = ifelse(is.na(group), "N/A", group),
group = factor(
group,
levels = c(
"Pre-CTB",
"Post-CTB",
"Early-EVT",
"EVT",
"Early-STB",
"STB",
"N/A"
)
)
) %>%
pull(group)plot_embedding(
embedding = embedding2[, c("x_tsne", "y_tsne")],
color_values = color_value_factors,
label = "t-SNE; Extended Data Fig. 8j",
# label_position = c(label_x, label_y),
show_color_value_labels = TRUE,
show_color_legend = TRUE,
geom_point_size = 1,
sort_values = FALSE
) +
scale_color_manual(
values = c(
ggsci::pal_npg("nrc", alpha = 1)(
embedding2$group %>% unique() %>% length() - 1
),
"grey70"
)
) +
labs(color = NULL) +
theme(
legend.text = element_text(family = "Arial", size = 6),
legend.key.size = unit(3, "mm"),
legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "mm"),
legend.background = element_blank() # ,
#
# legend.justification = c(0, 1),
# legend.position = c(0.075, 0.975)
) +
guides(
colour = guide_legend(override.aes = list(size = 3))
) +
p_embedding_cluster_lineage +
plot_annotation(theme = theme(plot.margin = margin())) +
plot_layout(guides = "keep")## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
Epiblast development and transition
EPI transcriptome dynamics
Four main clusters: ICM, pre-implantation EPI (pre-EPI), post-EPI and PSA-EPI.
# Table S8.6 Group information of EPIs.
# Group information of 222 cells in fig4 (also used in extended data fig 9k-n and 10a-c)
file_name <- "41586_2019_1875_MOESM10_ESM.xlsx"
table_s8_sheet6 <- readxl::read_excel(
path = file.path(SUPPLEMENTARY_INFORMATION_DIR, file_name),
sheet = "S8.6",
col_names = FALSE
)## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * ...table_s8_sheet6 %>% head()# clean messy spreadsheet
table_s8_sheet6 %<>%
select(1:3) %>%
slice(-c(1:3)) %>%
`colnames<-`(c("sample_id", "day", "group"))
table_s8_sheet6embedding2 <- embedding %>%
left_join(
table_s8_sheet6[, c("sample_id", "group")],
by = "sample_id"
)
color_value_factors <- embedding2$group
color_value_factors[is.na(embedding2$group)] <- "N/A"
color_value_factors <- color_value_factors %>%
factor(., levels = c("ICM", "PSA_EPI", "PreEPI", "PostEPI", "N/A"))
# %>%
# forcats::fct_relevel("N/A", after = Inf)plot_embedding(
embedding = embedding2[, c("x_tsne", "y_tsne")],
color_values = color_value_factors,
label = "t-SNE; Fig. 4a",
# label_position = c(label_x, label_y),
show_color_value_labels = TRUE,
show_color_legend = TRUE,
geom_point_size = 1,
sort_values = FALSE
) +
scale_color_manual(
values = c(
ggsci::pal_npg("nrc", alpha = 1)(
embedding2$group %>% unique() %>% length() - 1
),
"grey70"
)
) +
labs(color = NULL) +
theme(
legend.text = element_text(family = "Arial", size = 6),
legend.key.size = unit(3, "mm"),
legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "mm"),
legend.background = element_blank() # ,
#
# legend.justification = c(0, 1),
# legend.position = c(0.075, 0.975)
) +
guides(
colour = guide_legend(override.aes = list(size = 3))
) +
p_embedding_cluster_lineage +
plot_annotation(theme = theme(plot.margin = margin())) +
plot_layout(guides = "keep")## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
EPI pluripotent state transition
# Table S8.6 Group information of EPIs.
# Group information of 136 cells in extended data fig9 e-j
file_name <- "41586_2019_1875_MOESM10_ESM.xlsx"
table_s8_sheet6 <- readxl::read_excel(
path = file.path(SUPPLEMENTARY_INFORMATION_DIR, file_name),
sheet = "S8.6",
col_names = FALSE
)## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * ...table_s8_sheet6 %>% head()# clean messy spreadsheet
table_s8_sheet6 %<>%
select(10:12) %>%
slice(-c(1:4)) %>%
`colnames<-`(c("sample_id", "day", "group"))embedding2 <- embedding %>%
left_join(
table_s8_sheet6[, c("sample_id", "group")],
by = "sample_id"
)
color_value_factors <- embedding2$group
color_value_factors[is.na(embedding2$group)] <- "N/A"
color_value_factors <- color_value_factors %>%
factor(., levels = c("ICM", "PreEPI", "PostEPI", "PSA_EPI", "N/A"))
# %>%
# forcats::fct_relevel("N/A", after = Inf)plot_embedding(
embedding = embedding2[, c("x_tsne", "y_tsne")],
color_values = color_value_factors,
label = "t-SNE; Extended Data Fig. 9e",
# label_position = c(label_x, label_y),
show_color_value_labels = TRUE,
show_color_legend = TRUE,
geom_point_size = 1,
sort_values = FALSE
) +
scale_color_manual(
values = c(
ggsci::pal_npg("nrc", alpha = 1)(
embedding2$group %>% unique() %>% length() - 1
),
"grey70"
)
) +
labs(color = NULL) +
theme(
legend.text = element_text(family = "Arial", size = 6),
legend.key.size = unit(3, "mm"),
legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "mm"),
legend.background = element_blank() # ,
#
# legend.justification = c(0, 1),
# legend.position = c(0.075, 0.975)
) +
guides(
colour = guide_legend(override.aes = list(size = 3))
) +
p_embedding_cluster_lineage +
plot_annotation(theme = theme(plot.margin = margin())) +
plot_layout(guides = "keep")## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
R session info
devtools::session_info()$platform## setting value
## version R version 4.0.2 (2020-06-22)
## os macOS Catalina 10.15.6
## system x86_64, darwin19.5.0
## ui unknown
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Chicago
## date 2020-08-01devtools::session_info()$pack %>%
as_tibble() %>%
select(
package,
loadedversion,
date,
`source`
) %>%
print(n = nrow(.))## # A tibble: 158 x 4
## package loadedversion date source
## <chr> <chr> <chr> <chr>
## 1 abind 1.4-5 2016-07-21 CRAN (R 4.0.0)
## 2 ape 5.4 2020-06-03 CRAN (R 4.0.2)
## 3 assertthat 0.2.1 2019-03-21 CRAN (R 4.0.0)
## 4 backports 1.1.8 2020-06-17 CRAN (R 4.0.1)
## 5 BayesFactor 0.9.12-4.2 2018-05-19 CRAN (R 4.0.2)
## 6 blob 1.2.1 2020-01-20 CRAN (R 4.0.0)
## 7 broom 0.7.0.9001 2020-07-28 Github (tidymodels/broom@762e3ad)
## 8 callr 3.4.3.9000 2020-07-31 Github (r-lib/callr@b96da8f)
## 9 cellranger 1.1.0 2016-07-27 CRAN (R 4.0.0)
## 10 checkmate 2.0.0 2020-02-06 CRAN (R 4.0.0)
## 11 circlize 0.4.10 2020-06-15 CRAN (R 4.0.1)
## 12 cli 2.0.2 2020-02-28 CRAN (R 4.0.0)
## 13 clue 0.3-57 2019-02-25 CRAN (R 4.0.0)
## 14 cluster 2.1.0 2019-06-19 CRAN (R 4.0.2)
## 15 coda 0.19-3 2019-07-05 CRAN (R 4.0.0)
## 16 codetools 0.2-16 2018-12-24 CRAN (R 4.0.2)
## 17 colorspace 1.4-1 2019-03-18 CRAN (R 4.0.0)
## 18 ComplexHeatmap 2.4.3 2020-07-25 Bioconductor
## 19 cowplot 1.0.0 2019-07-11 CRAN (R 4.0.0)
## 20 crayon 1.3.4 2017-09-16 CRAN (R 4.0.0)
## 21 data.table 1.13.0 2020-07-24 CRAN (R 4.0.2)
## 22 DBI 1.1.0 2019-12-15 CRAN (R 4.0.0)
## 23 dbplyr 1.4.4.9000 2020-07-28 Github (tidyverse/dbplyr@a6ed629)
## 24 deldir 0.1-28 2020-07-15 CRAN (R 4.0.2)
## 25 desc 1.2.0 2018-05-01 CRAN (R 4.0.0)
## 26 devtools 2.3.1.9000 2020-07-31 Github (r-lib/devtools@df619ce)
## 27 digest 0.6.25 2020-02-23 CRAN (R 4.0.0)
## 28 dplyr 1.0.1.9000 2020-07-31 Github (tidyverse/dplyr@98fd0f6)
## 29 ellipsis 0.3.1.9000 2020-07-18 Github (r-lib/ellipsis@57a5071)
## 30 evaluate 0.14 2019-05-28 CRAN (R 4.0.0)
## 31 extrafont 0.17 2014-12-08 CRAN (R 4.0.2)
## 32 extrafontdb 1.0 2012-06-11 CRAN (R 4.0.0)
## 33 fansi 0.4.1 2020-01-08 CRAN (R 4.0.0)
## 34 farver 2.0.3 2020-01-16 CRAN (R 4.0.0)
## 35 fastmap 1.0.1 2019-10-08 CRAN (R 4.0.0)
## 36 fitdistrplus 1.1-1 2020-05-19 CRAN (R 4.0.0)
## 37 forcats 0.5.0.9000 2020-05-28 Github (tidyverse/forcats@ab81d1b)
## 38 fs 1.5.0 2020-08-01 Github (r-lib/fs@1399626)
## 39 future 1.18.0 2020-07-09 CRAN (R 4.0.2)
## 40 future.apply 1.6.0 2020-07-01 CRAN (R 4.0.2)
## 41 generics 0.0.2 2018-11-29 CRAN (R 4.0.0)
## 42 GetoptLong 1.0.2 2020-07-06 CRAN (R 4.0.2)
## 43 ggplot2 3.3.2.9000 2020-07-31 Github (tidyverse/ggplot2@5999889)
## 44 ggrepel 0.9.0 2020-07-24 Github (slowkow/ggrepel@4d0ef50)
## 45 ggridges 0.5.2 2020-01-12 CRAN (R 4.0.0)
## 46 ggsci 2.9 2020-06-04 Github (nanxstats/ggsci@589fe17)
## 47 ggthemes 4.2.0 2019-05-13 CRAN (R 4.0.0)
## 48 GlobalOptions 0.1.2 2020-06-10 CRAN (R 4.0.0)
## 49 globals 0.12.5 2019-12-07 CRAN (R 4.0.0)
## 50 glue 1.4.1.9000 2020-07-07 Github (tidyverse/glue@205f18b)
## 51 goftest 1.2-2 2019-12-02 CRAN (R 4.0.2)
## 52 gridExtra 2.3 2017-09-09 CRAN (R 4.0.0)
## 53 gt 0.2.1 2020-08-01 Github (rstudio/gt@6058358)
## 54 gtable 0.3.0 2019-03-25 CRAN (R 4.0.0)
## 55 gtools 3.8.2 2020-03-31 CRAN (R 4.0.0)
## 56 haven 2.3.1 2020-06-01 CRAN (R 4.0.0)
## 57 hms 0.5.3 2020-01-08 CRAN (R 4.0.0)
## 58 htmltools 0.5.0 2020-06-16 CRAN (R 4.0.1)
## 59 htmlwidgets 1.5.1 2019-10-08 CRAN (R 4.0.0)
## 60 httpuv 1.5.4 2020-06-06 CRAN (R 4.0.0)
## 61 httr 1.4.2 2020-07-20 CRAN (R 4.0.2)
## 62 ica 1.0-2 2018-05-24 CRAN (R 4.0.0)
## 63 igraph 1.2.5 2020-03-19 CRAN (R 4.0.2)
## 64 irlba 2.3.3 2019-02-05 CRAN (R 4.0.2)
## 65 jpeg 0.1-8.1 2019-10-24 CRAN (R 4.0.0)
## 66 jsonlite 1.7.0 2020-06-25 CRAN (R 4.0.2)
## 67 KernSmooth 2.23-17 2020-04-26 CRAN (R 4.0.2)
## 68 knitr 1.29 2020-06-23 CRAN (R 4.0.2)
## 69 labeling 0.3 2014-08-23 CRAN (R 4.0.0)
## 70 later 1.1.0.1 2020-06-05 CRAN (R 4.0.0)
## 71 lattice 0.20-41 2020-04-02 CRAN (R 4.0.2)
## 72 lazyeval 0.2.2 2019-03-15 CRAN (R 4.0.0)
## 73 leiden 0.3.3 2020-02-04 CRAN (R 4.0.0)
## 74 lifecycle 0.2.0 2020-03-06 CRAN (R 4.0.0)
## 75 listenv 0.8.0 2019-12-05 CRAN (R 4.0.0)
## 76 lmtest 0.9-37 2019-04-30 CRAN (R 4.0.2)
## 77 lubridate 1.7.9 2020-07-11 Github (tidyverse/lubridate@de2ee09)
## 78 magrittr 1.5.0.9000 2020-07-27 Github (tidyverse/magrittr@0d14075)
## 79 MASS 7.3-51.6 2020-04-26 CRAN (R 4.0.2)
## 80 Matrix 1.2-18 2019-11-27 CRAN (R 4.0.2)
## 81 MatrixModels 0.4-1 2015-08-22 CRAN (R 4.0.0)
## 82 memoise 1.1.0 2017-04-21 CRAN (R 4.0.0)
## 83 mgcv 1.8-31 2019-11-09 CRAN (R 4.0.2)
## 84 mime 0.9 2020-02-04 CRAN (R 4.0.0)
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## 86 modelr 0.1.8.9000 2020-05-19 Github (tidyverse/modelr@16168e0)
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## 141 tensor 1.5 2012-05-05 CRAN (R 4.0.2)
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