We developed an approach to simultaneously measure biochemical activities and mRNA abundance in single cells. We’ve modified the existing 10x Genomics 3´-gene expression kit (v2) to measure DNA repair activities in single cells by including DNA repair substrates in single-cell mRNA sequencing experiment.
Following GEM generation and reverse transcription, we isolate the DNA repair fragments from the mRNA by size separation. We then prepare a single-cell repair library that captures DNA repair intermediates and products.
Following sequencing, the mRNA fastq files are processed using the 10x Genomics cellranger pipeline.
transcriptome="path/to/refdata-cellranger-GRC38-3.0.0"
cellranger count \
--id="sample_mrna" \
--fastqs="path/to/fastqs/" \
--sample="sample" \
--localcores=16 \
--localmem=35 \
--transcriptome=$transcriptomeThe DNA repair libraries are processed using custom snakemake pipeline available here. This pipeline produces a tsv with the following columns:
- gene: concatenation of the substrate and the 5´ end alignment position
- cell: cell barcode sequence
- count: number of UMI counts for the gene and cell
This file contains all known 10x cell barcodes present in the fastq files. To make filtered matrices to use with sc-RNA seq R packages like Seurat, we used the following functions from our analysis package scrunchy.
#Convert UMI-tools output to matrix format
umitools_to_mtx(count_file = "path/to/count.tsv",
output_path = "path/to/mtx/")
# Filter repair matrix for cell barcodes from cellranger 3.0.0 output
# barcodes.tsv file is present the output from cellranger
# outs/filtered_feature_bc_matrix/
filter_matrix(matrix_path = "path/to/mtx",
barcodes_path = "path/to/cell/barcodes.tsv",
output_path = "path/to/filtered_mtx")The mRNA matrices from cellranger and the DNA repair matrices can be analyzed further using most multimodal sc-RNA-seq software.
The Seurat object used in these plots was created here
load("../data/barnyard/barnyard.seurat.object.Rdata")
#Getting hairpin info from seurat object
df <- rownames_to_column(as.data.frame(t(as.matrix(GetAssayData(barnyard_seurat, assay = 'repair', slot = 'counts')))), "cell_id") %>%
gather(hairpin_pos, count, -cell_id) %>%
separate(hairpin_pos, into = c("hairpin", "position")) %>%
mutate(position = as.double(position),
count = as.double(count))
# Add repair positions based on average signal
repair_position = data_frame(hairpin = c('Uracil2', 'riboG2'),
repair_position = c(45, 44))
df %>% left_join(repair_position) %>%
filter(position == repair_position) -> rt
# Get cutoffs to determine cell type by repair
# Cutoff is > 5% of the maximum signal for each substrate
rt %>% group_by(hairpin, position) %>%
summarize(max_count = max(count)) %>%
mutate(cut_off = round(max_count * .05)) -> cutoffs
# Define ribo cutoff and uracil cutoff
r = cutoffs$cut_off[1]
u = cutoffs$cut_off[2]
# Define cell types:
# UNGKO = >5% maximum singal on ribo substrate and < 5% of the max on uracil substrate
# RNASEH2CKO = >5% maximum singal on uracil substrate and < 5% of the max on ribo substrate
# Both = >5% maximum singal on ribo substrate and >5% of the max on uracil substrate
# Low signal = <5% maximum singal on ribo substrate and <5% of the max on uracil substrate
rt %>% select(-position, -repair_position) %>%
spread(hairpin, count) %>%
mutate(color = if_else(riboG2 >= r & Uracil2 <+ u, 'UNGKO',
if_else(Uracil2 >= u & riboG2 <= r, 'RNASEH2KO',
if_else(riboG2 >= r & Uracil2 >= u, 'Both',
'Low signal')))) %>%
select(-riboG2, -Uracil2) -> color_df
df <- left_join(df, color_df) %>%
left_join(repair_position)
# count number of cells in each category
df %>% filter(hairpin == "Uracil2",
position == 1) %>%
group_by(color) %>%
summarise(total = n()) %>%
ungroup() %>%
rename('Cell Type' = color)-> table
# Make barnyard plot
df %>% filter(position == repair_position) %>%
select(cell_id, hairpin, color, count) %>%
spread(hairpin, count) %>%
ggplot(aes(x = Uracil2, y = riboG2, color = color)) +
geom_count(alpha = .7) +
theme_cowplot() +
theme(legend.position= "top",
legend.title = element_blank()) +
scale_color_manual(values = c(colors[1], "#999999", colors[2:3])) +
xlab("Counts at uracil repair site") +
ylab("Counts at ribonucleotide repair site") +
annotate(geom = "table", x = 75, y = 40, label = list(table),
vjust = .5, hjust = 0)
# Add celltype to seurat object
barnyard_seurat$cell_id_from_repair <- color_df$color
barnyard_seurat$celltype <- barnyard_seurat$cell_id_from_repair
# Get average hairpin coverage by cell type above defined by repair
bulk_df = get_hairpin_coverage(barnyard_seurat) %>%
mutate(count_1000 = count/1000)
# Make coverage plots
bulk_df %>% filter(hairpin == "Uracil2",
celltype %in% c("RNASEH2KO", "UNGKO"),
position > 33) %>%
haircut_plot(., x= "position", y = "count_1000", col = "celltype",
xlim = c(34,61), point = T,
y_lab = "Total counts (10\u00B3)",
pal = colors[2:3]) + theme_cowplot() +
theme(legend.position= "top",
legend.title = element_blank()) +
ggtitle("Uracil") -> p1
bulk_df %>% filter(hairpin == "riboG2",
celltype %in% c("RNASEH2KO", "UNGKO"),
position > 33) %>%
haircut_plot(., x= "position", y = "count_1000", col = "celltype",
xlim = c(34,61), point = T,
y_lab = "Total counts (10\u00B3)",
pal = colors[2:3]) + theme_cowplot() +
theme(legend.position= "top",
legend.title = element_blank()) +
ggtitle("Ribonucleotide") -> p2
plot_grid(p1, p2, nrow = 2)
# Plot mRNA and repair coverage on UMAP plot using seurat functions
FeaturePlot(object = barnyard_seurat, features = c("repair_Uracil2-45", "repair_riboG2-44",
"UNG", "RNASEH2C"),
reduction = 'umap', cols = loupe_palette, order = T)
The seurat object used below was created here.
# Load PBMC seurat object
load("../data/pbmc/seurat/pbmc1.seurat.Rdata")
# Filter out platelets from data
pbmc1 <- subset(pbmc1, subset = celltype != "Platelet")
# Plot UMAP colored by cell type
DimPlot(pbmc1, reduction = 'umap', group.by = 'celltype', cols = colors)
# Get hairpin coverage by cell type
df <- get_hairpin_coverage(pbmc1)
# add adduction position for plotting and filter out platelets
df %>% mutate(adduct_position1 = 44,
adduct_position2 = -1) %>%
filter(celltype != "Platelet") -> df
# Make coverage plots
df %>%
filter(hairpin == 'Uracil',
position > 34) %>%
haircut_plot(., x = "position", y = "avg_count", point = TRUE,
xlim = c(35, 55), pal = colors, col = 'celltype',
y_lab = "Average counts per cell") +
theme(legend.position = 'top') +
ggtitle("U:A repair")
df %>%
filter(hairpin == 'GU',
position > 34) %>%
haircut_plot(., x = "position", y = "avg_count", point = TRUE,
xlim = c(35, 55), pal = colors, col = 'celltype',
y_lab = "Average counts per cell") +
theme(legend.position = 'top') +
ggtitle("U:G repair")
df %>%
filter(hairpin == 'riboG',
position > 34) %>%
haircut_plot(., x = "position", y = "avg_count", point = TRUE,
xlim = c(35, 55), pal = colors, col = 'celltype',
y_lab = "Average counts per cell") +
theme(legend.position = 'top') +
ggtitle("Ribonucleotide repair")
df %>%
filter(hairpin == 'Abasic',
position > 34) %>%
haircut_plot(., x = "position", y = "avg_count", point = TRUE,
xlim = c(35, 55), pal = colors, col = 'celltype',
y_lab = "Average counts per cell") +
theme(legend.position = 'top') +
ggtitle("Abasic repair")
df %>%
filter(hairpin == 'Normal',
position > 34) %>%
haircut_plot(., x = "position", y = "avg_count", point = TRUE,
xlim = c(35, 55), pal = colors, col = 'celltype',
y_lab = "Average counts per cell") +
theme(legend.position = 'top') +
ggtitle("Unmodified substrate")
# Get dataframe of repair position activity values, cell ids,
# and cell types from seurat object
repair.positions = c("Uracil-45",
"riboG-44",
"GU-45",
"Abasic-46",
"Abasic-45",
"Normal-45")
df <- get_single_cell_df(pbmc1, feat = c(repair.positions, "celltype"))
#Make tidy data
df %>% gather(repair, activity, -celltype, -cell_id) -> df
#Add labels for plotting
repair_labels = tribble(~repair, ~label,
"Uracil_45", "U:A repair",
"GU_45", "U:G repair",
"riboG_44", "Ribonucelotide repair",
"Abasic_46", "Abasic repair long-patch",
"Abasic_45", "Abasic repair short-patch",
"Normal_45", "Unmodified substrate"
)
# Put samples in correct order
df %>% full_join(repair_labels) %>%
mutate(label = fct_relevel(label, "U:A repair",
"U:G repair",
"Ribonucelotide repair",
"Abasic repair long-patch",
"Abasic repair short-patch",
"Unmodified substrate")) -> df
# Plot activities
df %>% filter(repair %in% c("Uracil_45", "GU_45", "riboG_44", "Normal_45")) %>%
activity_plot() +
facet_wrap(~label, ncol = 1, strip.position = "left") 
df %>% filter(repair %in% c("Abasic_45", "Abasic_46")) %>%
activity_plot(lab = label) 
- Typical install time for all packages: ~30 minutes
- Expected run time for full data fastq files -> expression matrices: ~6-24 hours
- Expected run time for full data expression matrices -> plots: ~10 minutes
- Demo expression matrices found in scrunchy R package.
- No required non-standard hardware but platform LSF significantly reduces run time.
Software tested only on R version 3.5 using the following packages.
devtools::session_info()## ─ Session info ──────────────────────────────────────────────────────────
## setting value
## version R version 3.5.1 (2018-07-02)
## os macOS High Sierra 10.13.6
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Denver
## date 2019-11-12
##
## ─ Packages ──────────────────────────────────────────────────────────────
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## Github (hesselberthlab/scrunchy@b2f8cea)
## Github (ChristophH/sctransform@8e48f49)
## CRAN (R 3.5.2)
## CRAN (R 3.5.0)
## Github (satijalab/seurat@9a9f439)
## CRAN (R 3.5.0)
## Bioconductor
## CRAN (R 3.5.2)
## CRAN (R 3.5.2)
## CRAN (R 3.5.2)
## Bioconductor
## CRAN (R 3.5.2)
## CRAN (R 3.5.2)
## CRAN (R 3.5.2)
## CRAN (R 3.5.2)
## CRAN (R 3.5.2)
## CRAN (R 3.5.0)
## CRAN (R 3.5.0)
## CRAN (R 3.5.0)
## CRAN (R 3.5.1)
## CRAN (R 3.5.2)
## CRAN (R 3.5.0)
## CRAN (R 3.5.0)
## CRAN (R 3.5.0)
## CRAN (R 3.5.0)
## CRAN (R 3.5.2)
## CRAN (R 3.5.0)
## Bioconductor
## CRAN (R 3.5.0)
## Bioconductor
## CRAN (R 3.5.2)
##
## [1] /Library/Frameworks/R.framework/Versions/3.5/Resources/library
Additional software dependencies:
- Python v3
- Snakemake
- cellranger v3.0.2
- bedtools v2.26
- samtools v1.9
- bowtie2 v 2.3.2
- umi_tools from CGAT
- cutadapt v1.16