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Setup

Importing Seahorse rates data

The read_data function takes a list of Excel files. An easy way to get such a list is to put all your data in a directory and list its contents. Here we use the package’s internal datasets, but list.files will take a directory name as its first argument.

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
raw_data <- readxl::read_excel(rep_list[1], sheet = 2)
knitr::kable(head(raw_data))
Measurement Well Group Time OCR ECAR PER
1 A01 Background 1.304765 0.0000 0.00000 0.0000
1 A02 Group_1 MITO 1.304765 305.2426 30.64529 334.4771
1 A03 Group_1 MITO 1.304765 307.9862 33.27668 358.4754
1 A04 Group_2 MITO 1.304765 339.3399 49.17751 503.4910
1 A05 Group_2 MITO 1.304765 321.9398 47.94602 492.2597
1 A06 Group_2 MITO 1.304765 323.7962 46.84232 482.1940

The data requires the following columns: Measurement, Well, Group, Time, OCR, ECAR, PER. The Group column needs to be in the format biological_group<space>Assay_type as shown above. Upon reading with read_data, the Group column is split into two group and assay columns on the space. This output format can be set in the Seahorse machine before starting the experiment. If you already have the data, this column will have to be converted to this format to work with ceas.

seahorse_rates <- read_data(rep_list)
knitr::kable(head(seahorse_rates))
Measurement Well Time OCR ECAR PER exp_group assay_type replicate
1 A01 1.304765 0.0000 0.00000 0.0000 Background NA 1
1 A02 1.304765 305.2426 30.64529 334.4771 Group_1 MITO 1
1 A03 1.304765 307.9862 33.27668 358.4754 Group_1 MITO 1
1 A04 1.304765 339.3399 49.17751 503.4910 Group_2 MITO 1
1 A05 1.304765 321.9398 47.94602 492.2597 Group_2 MITO 1
1 A06 1.304765 323.7962 46.84232 482.1940 Group_2 MITO 1

Normalization

There are two types of normalization involved in Seahorse data analysis. One is background normalization done by the Wave software. ceas will produce a warning if it finds that the “Background” data are not 0 (see first row of the table above).

The other is biological normalization based on the cell count or the mass of protein. If the data is not already biologically normalized, you will need a csv file containing experimental groups and cell counts or μ\mug of protein in this format:

norm_csv <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "norm.csv", full.names = TRUE)
read.csv(norm_csv) |> knitr::kable()
exp_group measure
Group_1 30000
Group_2 30000
Group_3 5000
Group_4 5000

Your csv file’s full path may be passed into read_data() using the norm argument.

read_data(rep_list, norm = norm_csv) |> head() |> knitr::kable()
Measurement Well Time OCR ECAR PER exp_group assay_type replicate
1 A01 1.304765 0.00000 0.000000 0.00000 Background NA 1
1 A02 1.304765 50.87376 5.107549 55.74619 Group_1 MITO 1
1 A03 1.304765 51.33103 5.546114 59.74590 Group_1 MITO 1
1 A04 1.304765 56.55665 8.196252 83.91516 Group_2 MITO 1
1 A05 1.304765 53.65663 7.991003 82.04329 Group_2 MITO 1
1 A06 1.304765 53.96603 7.807053 80.36566 Group_2 MITO 1

Calculating energetics

Partitioning data

Note:
When we use the term ‘max’ in the package documentation we mean the maximal experimental OCR and ECAR values rather than absolute biological maximums.

The energetics calculation workflow involves partitioning the data into its time point and assay intervals.

partitioned_data <- partition_data(seahorse_rates)

Alternative data formats

While the default options are set for an experiment with both a mitochondrial and glycolysis assay, if you have only a mitochondrial assay or no glycolysis assay, the assay_types list parameter can be modified to account for that.

partitioned_data <- partition_data(
  seahorse_rates,
  assay_types = list(
    basal = "MITO",
    uncoupled = "MITO",
    maxresp = "MITO",
    nonmito = "MITO",
    no_glucose_glyc = "GLYCO",
    glucose_glyc = "GLYCO",
    max_glyc = "GLYCO"
  ),
  basal_tp = 3,
  uncoupled_tp = 6,
  maxresp_tp = 8,
  nonmito_tp = 12,
  no_glucose_glyc_tp = 3,
  glucose_glyc_tp = 6,
  max_glyc_tp = 8
)
partitioned_data <- partition_data(
  seahorse_rates,
  assay_types = list(
    basal = "RefAssay",
    uncoupled = "RefAssay",
    maxresp = NA,
    nonmito = "RefAssay",
    no_glucose_glyc = "RefAssay",
    glucose_glyc = "RefAssay",
    max_glyc = NA
  ),
  basal_tp = 5,
  uncoupled_tp = 10,
  nonmito_tp = 12,
  maxresp = NA,
  no_glucose_glyc_tp = 1,
  glucose_glyc_tp = 5,
  max_glyc = NA
)
partitioned_data <- partition_data(
  seahorse_rates,
  assay_types = list(
    basal = "MITO",
    uncoupled = "MITO",
    maxresp = "MITO",
    nonmito = "MITO",
    no_glucose_glyc = NA,
    glucose_glyc = "MITO",
    max_glyc = NA
  ),
  basal_tp = 3,
  uncoupled_tp = 6,
  maxresp_tp = 8,
  nonmito_tp = 12,
  no_glucose_glyc_tp = NA,
  glucose_glyc_tp = 3,
  max_glyc_tp = NA
)
partitioned_data <- partition_data(
  seahorse_rates,
  assay_types = list(
    basal = "RCR",
    uncoupled = "RCR",
    maxresp = "RCR,"
    nonmito = "RCR",
    no_glucose_glyc = NA,
    glucose_glyc = "GC",
    max_glyc = "GC"
  ),
  basal_tp = 3,
  uncoupled_tp = 6,
  maxresp_tp = 8,
  nonmito_tp = 12,
  no_glucose_glyc = NA,
  glucose_glyc_tp = 3,
  max_glyc_tp = 9
)

Note that the time point parameters (maxresp_tp and no_glucose_glyc_tp) also need to be changed accordingly.

The get_energetics function requires pH, pKa_a and buffer values.

energetics <- get_energetics(partitioned_data, ph = 7.4, pka = 6.093, buffer = 0.10)

For more information on the calculations see the article on ATP calculations.

Plotting

Bioenergetic scope plot

The bioscope_plot function plots a 2D representation of group “bioenergetic scope.” Bioenergetic scope describes the theoretical energetic space in which a matrix operates. The bioenergetic scope coordinates are JATP from OXPHOS on the y-axis and JATP from glycolysis on the x-axis. The points represent mean basal and/or max JATP from OXPHOS and glycolysis and the vertical and horizontal lines represent the standard deviation or confidence interval of JATP from OXPHOS or glycolysis, respectively.

(bioscope <- bioscope_plot(energetics))

Rate plots

The rate_plot function provides an overview of OCR or ECAR for each assay type over time, which enables cross-group energetic comparisons before and after the addition of energetic-modulating compounds. The rate_plot line represents mean group OCR or ECAR over the sequential measurements (x-axis) and the shaded variance region represents standard deviation or specified confidence interval.

(ocr <- rate_plot(seahorse_rates, measure = "OCR", assay = "MITO"))

(ecar <- rate_plot(seahorse_rates, measure = "ECAR", assay = "GLYCO"))

ATP plots

The atp_plot function plots group JATP values, which enables cross-group OXPHOS and glycolytic JATP comparisons at basal and max conditions. The atp_plot symbols represent the mean basal or max JATP from OXPHOS or glycolysis, and the crossbar boundaries represent the standard deviation or confidence interval JATP variance.

(basal_glyc <- atp_plot(energetics, basal_vs_max = "basal", glyc_vs_resp = "glyc"))

(basal_resp <- atp_plot(energetics, basal_vs_max = "basal", glyc_vs_resp = "resp"))

(max_glyc <- atp_plot(energetics, basal_vs_max = "max", glyc_vs_resp = "glyc"))

(max_resp <- atp_plot(energetics, basal_vs_max = "max", glyc_vs_resp = "resp"))

Customizing plots

CEAS is designed to work with existing ggplot2 customization functionality and doesn’t include more than shape and size options for its plots.

For example, to change the colors used in the plot, simply make the plot and add the custom colors you’d like:

Colors

custom_colors <- c("#e36500", "#b52356", "#3cb62d", "#328fe1")
bioscope +
ggplot2::scale_color_manual(
  values = custom_colors
)

ocr +
ggplot2::scale_color_manual(
  values = custom_colors
)

Labels

ecar +
    ggplot2::labs(x = "Time points")

basal_glyc +
    ggplot2::theme(axis.text = ggplot2::element_text(size = 20))

Editing functions

We are working on making the plots as customizable as possible. However, if there are options that cannot be set in the calls to the plotting functions or with ggplot2 functions, you can get the code used to make the plots by running the function name without parenthesis and modify it. Further, since every step in the ceas workflow provides a dataset, you can run the modified function or your own custom plotting functions on those datasets.

rate_plot
function (seahorse_rates, measure = "OCR", assay = "MITO", error_bar = "ci", 
    conf_int = 0.95, group_label = "Experimental group") 
{
    stopifnot(`'measure' should be 'OCR' or 'ECAR'` = measure %in% 
        c("OCR", "ECAR"))
    stopifnot(`'error_bar' should be 'sd' or 'ci'` = error_bar %in% 
        c("sd", "ci"))
    stopifnot(`'conf_int' should be between 0 and 1` = conf_int > 
        0 && conf_int < 1)
    data_cols <- c("Measurement", "Well", "OCR", "ECAR", "PER", 
        "exp_group", "assay_type", "replicate")
    missing_cols <- setdiff(data_cols, colnames(seahorse_rates))
    if (length(missing_cols) != 0) {
        stop(paste0("'", missing_cols, "'", " column was not found in input data\n"))
    }
    Measurement <- NULL
    exp_group <- NULL
    lower_bound <- NULL
    upper_bound <- NULL
    plot_data <- get_rate_summary(seahorse_rates, measure, assay, 
        error_bar, conf_int)
    y_labels <- list(OCR = paste0(assay, " OCR (pmol/min)"), 
        ECAR = paste0(assay, " ECAR (mpH/min)"))
    ggplot(plot_data, aes(x = Measurement, y = mean, color = exp_group, 
        group = exp_group, fill = exp_group)) + geom_line(size = 2) + 
        geom_ribbon(aes(ymin = lower_bound, ymax = upper_bound), 
            alpha = 0.2, color = NA) + scale_x_continuous(breaks = seq(1, 
        12, by = 1)) + xlab("Measurement") + ylab(y_labels[measure]) + 
        labs(color = group_label, fill = group_label) + theme_bw()
}

In RStudio, you can run utils::edit to modify a function.

edit(rate_plot)

References

Mookerjee, Shona A., Akos A. Gerencser, David G. Nicholls, and Martin D. Brand. 2017. “Quantifying Intracellular Rates of Glycolytic and Oxidative ATP Production and Consumption Using Extracellular Flux Measurements.” Journal of Biological Chemistry 292 (17): 7189–7207. https://doi.org/10.1074/jbc.M116.774471.