Yanagida, A., Spindlow, D., Nichols, J., Dattani, A., Smith, A., and Guo, G. (2021). Naive stem cell blastocyst model captures human embryo lineage segregation. Cell Stem Cell 28, 1016–1022.e4.

BioProject Accession: PRJNA720968
GEO Accession: GSE171820

Load required packages.

library(tidyverse)
library(magrittr)
library(Matrix)
library(patchwork)
library(extrafont)

Sys.time()

## [1] "2021-07-18 00:10:33 CDT"

Data preparation

Functions loading

source(
    file = file.path(
        SCRIPT_DIR,
        "utilities.R"
    )
)

Data loading

PROJECT_DIR <- "/Users/jialei/Dropbox/Data/Projects/UTSW/Peri-implantation"

Matrix

ad <- reticulate::import(module = "anndata", convert = TRUE)
print(ad$`__version__`)

## [1] "0.7.5"

BACKED <- NULL
adata <- ad$read_h5ad(
    filename = file.path(
        PROJECT_DIR,
        "raw/public/PRJNA720968/",
        "matrix",
        "adata.h5ad"
    ),
    backed = BACKED
)

matrix_readcount_use <- adata |> convert_adata()
matrix_readcount_use |> dim()

## [1] 33538   495

Embedding

EMBEDDING_FILE <- "embedding_ncomponents12_ccc1_seed20200416.csv.gz"

embedding <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJNA720968/",
        "clustering/PRJNA720968/exploring/batch_1covariate/",
        EMBEDDING_FILE
    )
)

embedding |> head()

Metadata

cell_metadata_PRJNA720968 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJNA720968/",
        "raw",
        "GSE171820_Sample_Assignments.csv.gz"
    )
) |>
    dplyr::rename_all(tolower) |>
    dplyr::rename(cell_name = name) |>
    dplyr::left_join(
        vroom::vroom(
            file = file.path(
                PROJECT_DIR,
                "raw/public/PRJNA720968/",
                "clustering",
                "PRJNA720968",
                "cell_metadata_GEO.csv"
            ),
            col_name = c("cell", "cell_name")
        )
    ) |>
    dplyr::rename(
        developmental_stage = time
    ) |>
    dplyr::left_join(
        adata$obs |>
            tibble::rownames_to_column(var = "cell") |>
            dplyr::select(-batch)
    )


cell_metadata_PRJNA720968 |>
    dplyr::count(origin, name = "num_cells") |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median")

origin	num_cells
Blastocyst	228
Blastoid	267

purrr::walk(list(matrix_readcount_use, cell_metadata_PRJNA720968), function(x) {
    print(object.size(x), units = "auto", standard = "SI")
})

## 57.9 MB
## 105.2 kB

Single-cell transcriptome analysis

embedding <- embedding |>
    dplyr::left_join(
        cell_metadata_PRJNA720968,
        by = c("cell" = "cell")
    ) |>
    dplyr::mutate(
        annotated = paste(origin, lineage, sep = ": "),
        annotated = factor(
            annotated,
            levels = c(
                "Blastoid: Epiblast",
                "Blastoid: Hypoblast",
                "Blastoid: Transitioning",
                "Blastoid: Trophectoderm",
                "Blastocyst: Epiblast",
                "Blastocyst: Hypoblast",
                "Blastocyst: Inner Cell Mass",
                "Blastocyst: Inner Cell Mass-Trophectoderm Transition",
                "Blastocyst: Early Trophectoderm",
                "Blastocyst: Trophectoderm",
                "Blastocyst: Unknown"
            )
        )
    )

embedding |>
    dplyr::group_by(
        origin
    ) |>
    summarise(
        num_cells = n(),
        median_umis = median(num_umis),
        num_features = median(num_features),
        median_mt_percentage = median(mt_percentage)
    ) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median")

origin	num_cells	median_umis	num_features	median_mt_percentage
Blastocyst	228	7781726	9003	0.06319315
Blastoid	267	7579396	9576	0.03904364

embedding |>
    dplyr::group_by(
        leiden
    ) |>
    summarise(
        num_cells = n(),
        median_umis = median(num_umis),
        num_features = median(num_features),
        median_mt_percentage = median(mt_percentage)
    ) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median")

leiden	num_cells	median_umis	num_features	median_mt_percentage
0	83	6676351	8999	0.06627523
1	72	8036980	9678	0.03425774
2	69	5637674	7416	0.04448353
3	53	8096014	10089	0.03892195
4	53	7969080	10078	0.05799040
5	51	7452917	9544	0.04538753
6	50	7609314	9391	0.05459019
7	41	8255229	8814	0.07119083
8	23	8341361	10273	0.05194548

Embedding visualization

x_column <- "x_umap_min_dist=0.1"
y_column <- "y_umap_min_dist=0.1"


GEOM_POINT_SIZE <- 1.25
EMBEDDING_TITLE_PREFIX <- "UMAP"
RASTERISED <- TRUE

Clustering & UMI & MT

p_embedding_leiden <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$leiden |> as.factor(),
    label = paste(EMBEDDING_TITLE_PREFIX, "Leiden", sep = "; "),
    label_position = NULL,
    show_color_value_labels = TRUE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = RASTERISED
) +
    theme_customized()

CB_POSITION <- c(0.8, 0.995)
p_embedding_UMI <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = log10(embedding$num_umis),
    label = paste(EMBEDDING_TITLE_PREFIX, "UMI", sep = "; "),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    shuffle_values = FALSE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        x = CB_POSITION[1],
        legend_key_size = 2,
        legend_text_size = 5
    )

p_embedding_MT <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$mt_percentage,
    label = paste(EMBEDDING_TITLE_PREFIX, "MT%", sep = "; "),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    shuffle_values = FALSE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        x = CB_POSITION[1],
        legend_key_size = 2,
        legend_text_size = 5
    )

Clustering of 496 Smart-seq2 libraries.

purrr::reduce(
    list(
        p_embedding_leiden,
        p_embedding_UMI,
        p_embedding_MT
    ),
    `+`
) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Lineage & origin & developmental stage

p_embedding_lineage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$annotated,
    label = paste(EMBEDDING_TITLE_PREFIX, "Lineage", sep = "; "),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        x = CB_POSITION[1] - 0.3,
        legend_key_size = 2,
        legend_text_size = 5
    )

p_embedding_origin <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$origin |> as.factor(),
    label = paste(EMBEDDING_TITLE_PREFIX, "Origin", sep = "; "),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        x = CB_POSITION[1] - 0.08,
        legend_key_size = 2,
        legend_text_size = 5
    )

p_embedding_developmental_stage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$developmental_stage |> as.factor(),
    label = paste(EMBEDDING_TITLE_PREFIX, "Developmental stage", sep = "; "),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        x = CB_POSITION[1],
        legend_key_size = 2,
        legend_text_size = 5
    )

purrr::reduce(
    list(
        p_embedding_lineage,
        p_embedding_origin,
        p_embedding_developmental_stage
    ),
    `+`
) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Developmental stage

embedding |>
    dplyr::group_by(
        developmental_stage
    ) |>
    summarise(
        num_cells = n(),
        median_umis = median(num_umis),
        num_features = median(num_features),
        median_mt_percentage = median(mt_percentage)
    ) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median")

developmental_stage	num_cells	median_umis	num_features	median_mt_percentage
Day3	159	7329571	9462.0	0.03540585
Day4	108	7685250	9759.0	0.04054943
Day5	68	7692114	8909.0	0.07377675
Day6	80	6475820	8393.5	0.05267877
Day7	80	8645422	9454.0	0.06118594

values <- embedding |>
    dplyr::mutate(
        value = paste(origin, developmental_stage, sep = ": ")
    ) |>
    dplyr::pull(value)

purrr::map(sort(unique(values)), function(x) {
    plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = as.numeric(
            values == x
        ) |> as.factor(),
        label = paste(EMBEDDING_TITLE_PREFIX, x, sep = "; "),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = FALSE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        rasterise = RASTERISED
    ) +
        theme_customized() +
        scale_color_manual(
            values = c("grey70", "salmon")
        )
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Lineage

embedding |>
    dplyr::group_by(
        lineage
    ) |>
    summarise(
        num_cells = n(),
        median_umis = median(num_umis),
        num_features = median(num_features),
        median_mt_percentage = median(mt_percentage)
    ) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median")

lineage	num_cells	median_umis	num_features	median_mt_percentage
Early Trophectoderm	18	8361592	8773.0	0.072238465
Epiblast	104	7924372	9801.0	0.039743530
Hypoblast	27	8720780	9573.0	0.036048865
Inner Cell Mass	22	7335337	9136.5	0.071956141
Inner Cell Mass-Trophectoderm Transition	23	8187758	8853.0	0.079507155
Transitioning	7	4276121	8903.0	0.001258322
Trophectoderm	291	7489586	9338.0	0.050840133
Unknown	3	4890522	10178.0	0.060837473

embedding |>
    dplyr::group_by(
        annotated
    ) |>
    summarise(
        num_cells = n(),
        median_umis = median(num_umis),
        num_features = median(num_features),
        median_mt_percentage = median(mt_percentage)
    ) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median")

annotated	num_cells	median_umis	num_features	median_mt_percentage
Blastoid: Epiblast	73	7916385	9707.0	0.034658941
Blastoid: Hypoblast	13	8166701	9573.0	0.009467274
Blastoid: Transitioning	7	4276121	8903.0	0.001258322
Blastoid: Trophectoderm	174	7467628	9562.5	0.043218885
Blastocyst: Epiblast	31	8126430	9951.0	0.052335040
Blastocyst: Hypoblast	14	9776420	9619.5	0.049162825
Blastocyst: Inner Cell Mass	22	7335337	9136.5	0.071956141
Blastocyst: Inner Cell Mass-Trophectoderm Transition	23	8187758	8853.0	0.079507155
Blastocyst: Early Trophectoderm	18	8361592	8773.0	0.072238465
Blastocyst: Trophectoderm	117	7541800	8597.0	0.060103082
Blastocyst: Unknown	3	4890522	10178.0	0.060837473

purrr::map(levels(embedding$annotated), function(x) {
    plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = as.numeric(embedding$annotated == x) |> as.factor(),
        label = paste(EMBEDDING_TITLE_PREFIX, x, sep = "; "),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = FALSE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = FALSE,
        rasterise = RASTERISED,
        legend_size = 2
    ) +
        theme_customized(
            x = CB_POSITION[1] - 0.3,
            legend_key_size = 2,
            legend_text_size = 5
        ) +
        scale_color_manual(
            values = c("grey70", "salmon")
        )
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Composition

p_barplot_composition_batch <- calc_group_composition(
    data = embedding,
    x = "leiden",
    group = "origin"
) |>
    dplyr::mutate(
        leiden = factor(leiden)
    ) |>
    plot_barplot(
        x = "leiden",
        y = "percentage",
        z = "origin",
        legend_ncol = 1
    )

p_barplot_composition_lineage <- calc_group_composition(
    data = embedding,
    x = "leiden",
    group = "lineage"
) |>
    dplyr::mutate(
        leiden = factor(leiden)
    ) |>
    plot_barplot(
        x = "leiden",
        y = "percentage",
        z = "lineage",
        legend_ncol = 1
    )

p_barplot_composition_developmental_stage <- calc_group_composition(
    data = embedding,
    x = "leiden",
    group = "developmental_stage"
) |>
    dplyr::mutate(
        leiden = factor(leiden)
    ) |>
    plot_barplot(
        x = "leiden",
        y = "percentage",
        z = "developmental_stage",
        legend_ncol = 1
    )


p_barplot_combined <- list(
    p_barplot_composition_batch,
    p_barplot_composition_lineage,
    p_barplot_composition_developmental_stage
) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(nrow = 3, guides = "collect") +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )
p_barplot_combined

Expression

Embedding

Genes used in Fig. 2C.

FEATURES_SELECTED <- c(
    "ENSG00000204531_POU5F1",
    "ENSG00000111704_NANOG",
    "ENSG00000171872_KLF17",
    "ENSG00000186103_ARGFX",
    #
    "ENSG00000164736_SOX17",
    "ENSG00000125798_FOXA2",
    "ENSG00000136574_GATA4",
    "ENSG00000134853_PDGFRA",
    #
    "ENSG00000179348_GATA2",
    "ENSG00000070915_SLC12A3",
    "ENSG00000165556_CDX2",
    "ENSG00000007866_TEAD3"
)

purrr::map(FEATURES_SELECTED, function(x) {
    selected_feature <- x

    cat(selected_feature, "\n")
    values <- log10(calc_cpm(matrix_readcount_use[, embedding$cell])[selected_feature, ] + 1)
    values[embedding$origin == "Blastocyst"] <- NA

    p1 <- plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = values,
        # label = paste(EMBEDDING_TITLE_PREFIX, selected_feature, sep = "; "),
        label = paste(
            EMBEDDING_TITLE_PREFIX,
            "Blastoid",
            selected_feature |> stringr::str_remove(pattern = "^E.+_"),
            sep = "; "
        ),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = TRUE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        shuffle_values = FALSE,
        rasterise = RASTERISED,
        legend_size = 2
    ) +
        scale_color_viridis_c(
            na.value = "grey80"
        ) +
        theme_customized(
            x = CB_POSITION[1] + 0.05,
            legend_key_size = 2,
            legend_text_size = 5
        )

    values <- log10(calc_cpm(matrix_readcount_use[, embedding$cell])[selected_feature, ] + 1)
    values[embedding$origin == "Blastoid"] <- NA

    p2 <- plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = values,
        label = paste(
            EMBEDDING_TITLE_PREFIX,
            "Blastocyst",
            selected_feature |> stringr::str_remove(pattern = "^E.+_"),
            sep = "; "
        ),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = TRUE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        shuffle_values = FALSE,
        rasterise = RASTERISED,
        legend_size = 2
    ) +
        scale_color_viridis_c(
            na.value = "grey80"
        ) +
        theme_customized(
            x = CB_POSITION[1] + 0.05,
            legend_key_size = 2,
            legend_text_size = 5
        )
    return(list(p1, p2))
}) |>
    unlist(recursive = FALSE) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 4) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

## ENSG00000204531_POU5F1 
## ENSG00000111704_NANOG 
## ENSG00000171872_KLF17 
## ENSG00000186103_ARGFX 
## ENSG00000164736_SOX17 
## ENSG00000125798_FOXA2 
## ENSG00000136574_GATA4 
## ENSG00000134853_PDGFRA 
## ENSG00000179348_GATA2 
## ENSG00000070915_SLC12A3 
## ENSG00000165556_CDX2 
## ENSG00000007866_TEAD3

R session info

devtools::session_info()$platform

##  setting  value                       
##  version  R version 4.1.0 (2021-05-18)
##  os       macOS Big Sur 10.16         
##  system   x86_64, darwin20.4.0        
##  ui       unknown                     
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/Chicago             
##  date     2021-07-18

devtools::session_info()$pack |>
    as_tibble() |>
    dplyr::select(
        package,
        loadedversion,
        date,
        `source`
    ) |>
    # print(n = nrow(.))
    gt::gt() |>
    gt::tab_options(table.font.size = "median")

package	loadedversion	date	source
assertthat	0.2.1	2019-03-21	CRAN (R 4.1.0)
backports	1.2.1	2020-12-09	CRAN (R 4.1.0)
beeswarm	0.4.0	2021-06-01	CRAN (R 4.1.0)
bit	4.0.4	2020-08-04	CRAN (R 4.1.0)
bit64	4.0.5	2020-08-30	CRAN (R 4.1.0)
broom	0.7.8	2021-06-24	CRAN (R 4.1.0)
bslib	0.2.5.1	2021-05-18	CRAN (R 4.1.0)
cachem	1.0.5	2021-05-15	CRAN (R 4.1.0)
callr	3.7.0	2021-04-20	CRAN (R 4.1.0)
cellranger	1.1.0	2016-07-27	CRAN (R 4.1.0)
checkmate	2.0.0	2020-02-06	CRAN (R 4.1.0)
cli	3.0.1	2021-07-17	CRAN (R 4.1.0)
colorspace	2.0-2	2021-06-24	CRAN (R 4.1.0)
crayon	1.4.1	2021-02-08	CRAN (R 4.1.0)
DBI	1.1.1	2021-01-15	CRAN (R 4.1.0)
dbplyr	2.1.1	2021-04-06	CRAN (R 4.1.0)
desc	1.3.0	2021-03-05	CRAN (R 4.1.0)
devtools	2.4.1.9000	2021-06-16	Github (r-lib/devtools@6d6ade5)
digest	0.6.27	2020-10-24	CRAN (R 4.1.0)
dplyr	1.0.7.9000	2021-06-29	Github (tidyverse/dplyr@ca55fce)
ellipsis	0.3.2	2021-04-29	CRAN (R 4.1.0)
evaluate	0.14	2019-05-28	CRAN (R 4.1.0)
extrafont	0.17	2014-12-08	CRAN (R 4.1.0)
extrafontdb	1.0	2012-06-11	CRAN (R 4.1.0)
fansi	0.5.0	2021-05-25	CRAN (R 4.1.0)
farver	2.1.0	2021-02-28	CRAN (R 4.1.0)
fastmap	1.1.0	2021-01-25	CRAN (R 4.1.0)
forcats	0.5.1.9000	2021-06-08	Github (tidyverse/forcats@b5fce89)
fs	1.5.0	2020-07-31	CRAN (R 4.1.0)
generics	0.1.0	2020-10-31	CRAN (R 4.1.0)
ggbeeswarm	0.6.0	2017-08-07	CRAN (R 4.1.0)
ggplot2	3.3.5.9000	2021-07-13	Github (tidyverse/ggplot2@13c0730)
ggrastr	0.2.3	2021-03-01	CRAN (R 4.1.0)
glue	1.4.2	2020-08-27	CRAN (R 4.1.0)
gt	0.3.0	2021-05-12	CRAN (R 4.1.0)
gtable	0.3.0	2019-03-25	CRAN (R 4.1.0)
haven	2.4.1	2021-04-23	CRAN (R 4.1.0)
highr	0.9	2021-04-16	CRAN (R 4.1.0)
hms	1.1.0	2021-05-17	CRAN (R 4.1.0)
htmltools	0.5.1.1	2021-01-22	CRAN (R 4.1.0)
httr	1.4.2	2020-07-20	CRAN (R 4.1.0)
jquerylib	0.1.4	2021-04-26	CRAN (R 4.1.0)
jsonlite	1.7.2	2020-12-09	CRAN (R 4.1.0)
knitr	1.33	2021-04-24	CRAN (R 4.1.0)
labeling	0.4.2	2020-10-20	CRAN (R 4.1.0)
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Single cell transcriptomes of human blastoid cells

JD

2021-07-18

Data preparation

Functions loading

Data loading

Matrix

Embedding

Metadata

Single-cell transcriptome analysis

Embedding visualization

Clustering & UMI & MT

Lineage & origin & developmental stage

Developmental stage

Lineage

Composition

Expression

Embedding

R session info