Designing two-species subcommunities

Author

Shane Hogle

Published

July 4, 2025

Abstract

This notebook creates and lays out the steps for constructing the experimental species pairs for this experiment. It is inspired by the method outlined in this paper: Full factorial construction of synthetic microbial communities, eLife 13:RP101906. Note that we did not include competition between the evolved and ancestral form of each species because they would not be distinguishable using 16S rRNA amplicon sequencing. However, they could concievably be competed through CFU counting on a counter selective agar containing streptomycin.

1 Setup

1.1 Libraries

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library(here)
library(tidyverse)
library(ggplate)

1.2 Global variables

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data <- here::here("data", "communities", "experiment_design")

# make processed data directory if it doesn't exist
fs::dir_create(data)

1.3 Functions and vars

Species color vector

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my_colors <- c(
  "ANC_0403_10" = "#ffaaaa", "ANC_0403_70" = "#aa0000", "ANC_0403_80" = "#aa0000", "ANC_0403_90" = "#aa0000", 
  "ANC_1287_10" = "#ffeeaa", "ANC_1287_70" = "#d4aa00", "ANC_1287_80" = "#d4aa00", "ANC_1287_90" = "#d4aa00", 
  "ANC_1896_10" = "#ccffaa", "ANC_1896_70" = "#44aa00", "ANC_1896_80" = "#44aa00", "ANC_1896_90" = "#44aa00", 
  "ANC_1977_10" = "#aaccff", "ANC_1977_70" = "#0055d4", "ANC_1977_80" = "#0055d4", "ANC_1977_90" = "#0055d4",
  "EVO_0403_10" = "#ffaaee", "EVO_0403_70" = "#ff00cc", "EVO_0403_80" = "#ff00cc", "EVO_0403_90" = "#ff00cc", 
  "EVO_1287_10" = "#ffccaa", "EVO_1287_70" = "#ff6600", "EVO_1287_80" = "#ff6600", "EVO_1287_90" = "#ff6600", 
  "EVO_1896_10" = "#aaffee", "EVO_1896_70" = "#00ffcc", "EVO_1896_80" = "#00ffcc", "EVO_1896_90" = "#00ffcc", 
  "EVO_1977_10" = "#ccaaff", "EVO_1977_70" = "#7f2aff", "EVO_1977_80" = "#7f2aff", "EVO_1977_90" = "#7f2aff"
  )

For plotting plates

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plate_plot_steps <- function(df, well_colmun, val_column, step, colr_vec){
  ggplate::plate_plot(df,
  position = {{ well_colmun }},
  value = {{ val_column }},
  colour = colr_vec,
  plate_size = 96,
  plate_type = "round"
) + ggtitle(step)
}

2 Construct pair combinations

Get all possible pairs while excluding combinations of ANC/EVO of the same species

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combos02 <- t(combn(c("ANC_0403", "EVO_0403", "ANC_1287", "EVO_1287", "ANC_1896", "EVO_1896", "ANC_1977", "EVO_1977"),  2))
combos02 <- tibble("a" = combos02[, 1], "b" = combos02[, 2])
combos02_filt <- combos02 %>%
  filter(str_extract(a, "\\d+") != str_extract(b, "\\d+"))

Make plate layouts for the 2-species combos

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combos02_filt_well_left <- combos02_filt %>% 
  mutate(a = paste(a, 90, sep = "_"), 
         b = paste(b, 10, sep = "_")) %>% 
  add_count(a, name = "a_count") %>% 
  add_count(b, name = "b_count") %>% 
  arrange(desc(a_count), a) %>% 
  mutate(row = rep(LETTERS[1:n_distinct(a)], 
                   times = rep(c(n_distinct(a):2)[n_distinct(a):2%%2 == 0], each = 2))) %>% 
  group_by(row) %>% 
  arrange(desc(b_count), b) %>% 
  mutate(col = str_pad(1:length(row), 2, pad = "0")) %>% 
  ungroup() %>%
  mutate(well = paste0(row, col))

combos02_filt_well_right <- combos02_filt %>% 
  mutate(a = paste(a, 10, sep = "_"), 
         b = paste(b, 90, sep = "_")) %>% 
  add_count(a, name = "a_count") %>% 
  add_count(b, name = "b_count") %>% 
  arrange(desc(a_count), a) %>% 
  mutate(row = rep(LETTERS[1:n_distinct(a)], 
                   times = rep(c(n_distinct(a):2)[n_distinct(a):2%%2 == 0], each = 2))) %>% 
  group_by(row) %>% 
  arrange(desc(b_count), b) %>% 
  mutate(col = str_pad(1:length(row)+6, 2, pad = "0")) %>% 
  ungroup() %>%
  mutate(well = paste0(row, col)) %>% 
  rename(a = b, b = a )

combos02_filt_well <- bind_rows(combos02_filt_well_left, combos02_filt_well_right)

2.1 Format and save

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combos02_filt_well %>%
  group_by(a, b) %>% 
  mutate(microcosm_id = cur_group_id()) %>% 
  ungroup() %>% 
  mutate(a_sp = paste0(str_split_i(a, "_", 2), stringr::str_extract(str_split_i(a, "_", 1), "^.{1}")),
         b_sp = paste0(str_split_i(b, "_", 2), stringr::str_extract(str_split_i(b, "_", 1), "^.{1}")),
         a_f = str_split_i(a, "_", 3),
         b_f = str_split_i(b, "_", 3)) %>% 
  arrange(well) %>% 
  dplyr::select(microcosm_id, well, a, a_sp, a_f, b, b_sp, b_f) %>% 
  readr::write_tsv(here::here(data, "pairs_sample_composition_wide.tsv"))

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combos02_filt_well %>% 
  group_by(a, b) %>% 
  mutate(microcosm_id = cur_group_id()) %>% 
  ungroup() %>% 
  dplyr::select(a:b, well, microcosm_id) %>% 
  tidyr::pivot_longer(c(-well, -microcosm_id)) %>% 
  tidyr::separate(value, c("evo_hist", "strainID", "target_f")) %>% 
  dplyr::mutate(evo_hist = stringr::str_to_lower(evo_hist),
                strainID = paste0("HAMBI_", strainID),
                target_f = as.numeric(target_f)/100,
                n_species = 2) %>% 
  dplyr::select(-name) %>% 
  dplyr::relocate(microcosm_id, n_species) %>% 
  readr::write_tsv(here::here(data, "pairs_sample_composition_long.tsv"))

3 Pipetting

Proceed in the order of steps below to construct master plates used to inoculate different conditions

3.1 First pipetting step

Figure 1: Layout for the first pipetting step for constructing pairs. Different colors show different species/evolution combinations. The darkness of the color indicates whether a species/evolution combo is added at 90% (e.g., `_90`) or 10% (e.g., `_10`).

3.2 Second pipetting step

Figure 2: Layout for the second pipetting step for constructing pairs. Different colors show different species/evolution combinations. The darkness of the color indicates whether a species/evolution combo is added at 90% (e.g., `_90`) or 10% (e.g., `_10`).

--- title: "Designing two-species subcommunities" author: "Shane Hogle" date: today link-citations: true abstract: "This notebook creates and lays out the steps for constructing the experimental species pairs for this experiment. It is inspired by the method outlined in this paper: [Full factorial construction of synthetic microbial communities, eLife 13:RP101906](https://doi.org/10.7554/eLife.101906.1). Note that we did not include competition between the evolved and ancestral form of each species because they would not be distinguishable using 16S rRNA amplicon sequencing. However, they could concievably be competed through CFU counting on a counter selective agar containing streptomycin." --- # Setup ## Libraries ```{r} #| output: false library(here) library(tidyverse) library(ggplate) ``` ## Global variables ```{r} data <- here::here("data", "communities", "experiment_design") # make processed data directory if it doesn't exist fs::dir_create(data) ``` ## Functions and vars Species color vector ```{r} my_colors <- c( "ANC_0403_10" = "#ffaaaa", "ANC_0403_70" = "#aa0000", "ANC_0403_80" = "#aa0000", "ANC_0403_90" = "#aa0000", "ANC_1287_10" = "#ffeeaa", "ANC_1287_70" = "#d4aa00", "ANC_1287_80" = "#d4aa00", "ANC_1287_90" = "#d4aa00", "ANC_1896_10" = "#ccffaa", "ANC_1896_70" = "#44aa00", "ANC_1896_80" = "#44aa00", "ANC_1896_90" = "#44aa00", "ANC_1977_10" = "#aaccff", "ANC_1977_70" = "#0055d4", "ANC_1977_80" = "#0055d4", "ANC_1977_90" = "#0055d4", "EVO_0403_10" = "#ffaaee", "EVO_0403_70" = "#ff00cc", "EVO_0403_80" = "#ff00cc", "EVO_0403_90" = "#ff00cc", "EVO_1287_10" = "#ffccaa", "EVO_1287_70" = "#ff6600", "EVO_1287_80" = "#ff6600", "EVO_1287_90" = "#ff6600", "EVO_1896_10" = "#aaffee", "EVO_1896_70" = "#00ffcc", "EVO_1896_80" = "#00ffcc", "EVO_1896_90" = "#00ffcc", "EVO_1977_10" = "#ccaaff", "EVO_1977_70" = "#7f2aff", "EVO_1977_80" = "#7f2aff", "EVO_1977_90" = "#7f2aff" ) ``` For plotting plates ```{r} plate_plot_steps <- function(df, well_colmun, val_column, step, colr_vec){ ggplate::plate_plot(df, position = {{ well_colmun }}, value = {{ val_column }}, colour = colr_vec, plate_size = 96, plate_type = "round" ) + ggtitle(step) } ``` # Construct pair combinations Get all possible pairs while excluding combinations of ANC/EVO of the same species ```{r} combos02 <- t(combn(c("ANC_0403", "EVO_0403", "ANC_1287", "EVO_1287", "ANC_1896", "EVO_1896", "ANC_1977", "EVO_1977"), 2)) combos02 <- tibble("a" = combos02[, 1], "b" = combos02[, 2]) combos02_filt <- combos02 %>% filter(str_extract(a, "\\d+") != str_extract(b, "\\d+")) ``` Make plate layouts for the 2-species combos ```{r} combos02_filt_well_left <- combos02_filt %>% mutate(a = paste(a, 90, sep = "_"), b = paste(b, 10, sep = "_")) %>% add_count(a, name = "a_count") %>% add_count(b, name = "b_count") %>% arrange(desc(a_count), a) %>% mutate(row = rep(LETTERS[1:n_distinct(a)], times = rep(c(n_distinct(a):2)[n_distinct(a):2%%2 == 0], each = 2))) %>% group_by(row) %>% arrange(desc(b_count), b) %>% mutate(col = str_pad(1:length(row), 2, pad = "0")) %>% ungroup() %>% mutate(well = paste0(row, col)) combos02_filt_well_right <- combos02_filt %>% mutate(a = paste(a, 10, sep = "_"), b = paste(b, 90, sep = "_")) %>% add_count(a, name = "a_count") %>% add_count(b, name = "b_count") %>% arrange(desc(a_count), a) %>% mutate(row = rep(LETTERS[1:n_distinct(a)], times = rep(c(n_distinct(a):2)[n_distinct(a):2%%2 == 0], each = 2))) %>% group_by(row) %>% arrange(desc(b_count), b) %>% mutate(col = str_pad(1:length(row)+6, 2, pad = "0")) %>% ungroup() %>% mutate(well = paste0(row, col)) %>% rename(a = b, b = a ) combos02_filt_well <- bind_rows(combos02_filt_well_left, combos02_filt_well_right) ``` ## Format and save ```{r} combos02_filt_well %>% group_by(a, b) %>% mutate(microcosm_id = cur_group_id()) %>% ungroup() %>% mutate(a_sp = paste0(str_split_i(a, "_", 2), stringr::str_extract(str_split_i(a, "_", 1), "^.{1}")), b_sp = paste0(str_split_i(b, "_", 2), stringr::str_extract(str_split_i(b, "_", 1), "^.{1}")), a_f = str_split_i(a, "_", 3), b_f = str_split_i(b, "_", 3)) %>% arrange(well) %>% dplyr::select(microcosm_id, well, a, a_sp, a_f, b, b_sp, b_f) %>% readr::write_tsv(here::here(data, "pairs_sample_composition_wide.tsv")) ``` ```{r} combos02_filt_well %>% group_by(a, b) %>% mutate(microcosm_id = cur_group_id()) %>% ungroup() %>% dplyr::select(a:b, well, microcosm_id) %>% tidyr::pivot_longer(c(-well, -microcosm_id)) %>% tidyr::separate(value, c("evo_hist", "strainID", "target_f")) %>% dplyr::mutate(evo_hist = stringr::str_to_lower(evo_hist), strainID = paste0("HAMBI_", strainID), target_f = as.numeric(target_f)/100, n_species = 2) %>% dplyr::select(-name) %>% dplyr::relocate(microcosm_id, n_species) %>% readr::write_tsv(here::here(data, "pairs_sample_composition_long.tsv")) ``` # Pipetting Proceed in the order of steps below to construct master plates used to inoculate different conditions ## First pipetting step ::: {#fig-01} ```{r} #| fig.width: 8 #| fig.height: 5 #| echo: false plate_plot_steps(combos02_filt_well, well, a, "Pairs Step 1", my_colors) ``` Layout for the first pipetting step for constructing pairs. Different colors show different species/evolution combinations. The darkness of the color indicates whether a species/evolution combo is added at 90% (e.g., `_90`) or 10% (e.g., `_10`). ::: ## Second pipetting step ::: {#fig-02} ```{r} #| fig.width: 8 #| fig.height: 5 #| echo: false plate_plot_steps(combos02_filt_well, well, b, "Pairs Step 2", my_colors) ``` Layout for the second pipetting step for constructing pairs. Different colors show different species/evolution combinations. The darkness of the color indicates whether a species/evolution combo is added at 90% (e.g., `_90`) or 10% (e.g., `_10`). :::