scramblase_assay_input_template {flippant}

scramblase_assay_plot

Description

Functions for the presentation and evaluaton of dithionite scramblase assays

Usage

scramblase_assay_input_template(
  path = "scramblase_assay_input_template.txt",
  input_directory = NULL,
  overwrite = FALSE
)

scramblase_assay_plot(
  x,
  scale_to = c("model", "data"),
  ppr_scale_factor = 0.65,
  force_through_origin = TRUE,
  generation_of_algorithm = c(2, 1),
  split_by_experiment = TRUE,
  r_bar = 88,
  sigma_r_bar = 28
)

scramblase_assay_stats(
  x,
  scale_to = c("model", "data"),
  ppr_scale_factor = 0.65,
  force_through_origin = TRUE,
  generation_of_algorithm = c(2, 1),
  split_by_experiment = TRUE,
  r_bar = 88,
  sigma_r_bar = 28
)

scramblase_assay_traces(
  x,
  ppr_scale_factor = 0.65,
  time_min_sec = NA_real_,
  time_max_sec = NA_real_,
  adjust = TRUE,
  timepoint_of_measurement = 400,
  n_averaging = 10,
  annotate_traces = FALSE
)

Arguments

path

character object giving the path of an empty template for a spreadsheet that can provide x.

input_directory

if not NULL, character object giving the path to a directory where spectrometer output resides for the prepopulation of the template spreadsheet.

overwrite

logical object allowing to overwrite existing template paths.

x

data.frame or path to a tab delimited file representing it (see "Details").

scale_to

Defines the source of ymax, defaulting to model. See "Details".

ppr_scale_factor

numeric object providing a scale factor to adjust internally calculated Protein per Phospholipid (mg/mmol) ratios (PPR; see "Details").

force_through_origin

logical indicating whether to force the fitted curve(s) to penetrate the origin (defaulting to TRUE). See "Details".

generation_of_algorithm

Either 2 or 1 (numeric; defaulting to 2). See "Details".

split_by_experiment

A single logical, indicating whether or not calculations and plots will treat experimental series from different experiments separately (TRUE, default) or whether data from all experiments included is used for a single calculation/plot per experimental series (FALSE). While the former emphasizes reproducibility, the latter likely produces a more reliable fit.

r_bar

A numeric, representing the average radius of the liposomes used in the assay. Only used in generatio_of_algorithm = 2 and defaulting to 88 (see Ploier et al. 2016 for details).

sigma_r_bar

A numeric, representing the standard deviationaverage of the radius distribution of the liposomes used in the assay. Only used in generatio_of_algorithm = 2 and defaulting to 28 (see Ploier et al. 2016 for details).

time_min_sec

A single numeric. If given, scramblase_assay_traces produces a time/x axis trimmed to this value (in seconds).

time_max_sec

A single numeric. If given, scramblase_assay_traces produces a time/x axis trimmed to this value (in seconds).

adjust

A single logical, indicating whether (default) or not spectral traces to be plotted are algorithmically aligned at the time point of dithionite addition.

timepoint_of_measurement

A numeric indicating the time (in sec) at which fluorescence extrema are calculated (DEPENDENT ON adjust!).

n_averaging

A numeric indicating the number of data points used for extrema calculations.

annotate_traces

A logical idicating whether fluorescence traces should be annotated.

Details

The data.frame accepted by the majority of the functions a an R object or path to a corresponding file (x) must have the following mandatory columns:

Path:

Paths to existing and readable ASCII output files of a fluorimeter. See parse_fluorimeter_output for details and supported formats.

Protein Reconstituted (mg):

Self-explanatory. In the case of scramblase_assay_traces ONLY this may be abused by taking character values rather than the usually required numerics. Handy when e.g. plotting traces for "Liposomes" and "Proteoliposomes", rather than defined PPRs.

Further (facultative) columns are:

Fluorescence Assay Vol. w/o DT (ul):

Volume of the fluorescence assay prior to addition of ditihionite (defaulting to 2000).

Fluorescence Assay Vol. with DT (ul):

Volume of the fluorescence assay after the addition ditihionite (defaulting to 2040).

Lipid in Reconstitution (mmol):

Self-explanatory. For the standard phospholipid experiment defaulting to 0.0045 (1 ml of a 4.5 mM solution).

Timepoint of Measurement (s):

The time to determine terminal fluorescence, calculated from the point when dithionite is added, in seconds, defaulting to 400).

Experiment:

Identifier for any given experiment. Used for facet_wrap during generation of ggplot output. All data with one Experiment identifier ends up on one plot/facet.

Experimental Series:

Identifier for a given series/graph (e.g. Extract and Depleted Extract). Used by color during generation of ggplot output to differentiate lines in the same plot/facet.

Based on Goren et al. (2014) and Ploier et al. (2016) data is processed as follows (the majority of the processing is split off into the internal function scramblase_assay_calculations):

• Input is format checked and defaults are injected for facultative parameters/columns as appropriate (see input data.frame format above). The internal function scramblase_assay_input_validation supplies this functionality.

• Fluorescence spectra are parsed using parse_fluorimeter_output. This includes automated determination of when dithionite was added to the sample using pracma-supplied methodology and resetting the acquisition time accordingly (0 henceforth corresponds to the time of addition).

• Pre-dithionite-addition Baseline Fluorescence is determined for each spectrum by averaging (median) over the 10 values preceding dithionite addition.

• Post-dithinonite-addition Minimum Fluorescence is determined for each spectrum by averaging (median) over the last ten datapoints \leq 400\,\mbox{s} (or Timepoint of Measurement (s), see above).

• The Minimum Fluorescence is volume-corrected based on Reaction Volume w/o DT (ul) and Reaction Volume with DT (ul) (see above).

• For each spectrum/datapoint a measured Fluorescence Reduction is calculated as

  1 - \left(\frac{\mbox{\small Minimum Fluorescence}}{\mbox{\small Baseline Fluorescence}}\right)

• A Relative Fluorescence Reduction is calculated in comparison to the liposomes-only/no-protein control).

• A Protein per Phospholipid (mg/mmol) ratio (PPR) is calculated. If ppr_scale_factor is not NULL, the value is scaled (divided) by that value to account for liposomes that remain inaccessible to reconstitution with scramblase molecules.

• Depending on split_by_experiment, data are split for parallel treatment using either Experimental Series (split_by_experiment = TRUE) or a combined Experimental Series/Experiment (split_by_experiment = FALSE) identifier (see above).

• A probability for a liposome holding \geq 1 scramblase molecules is calculated using

  \frac{y-y_0}{y_{\mbox{\scriptsize max}}-y_0}

  where y is the Relative Fluorescence Reduction and y_0 is the Relative Fluorescence Reduction in an experiment without addition of protein extract. Depending on the scale_to parameter, y_{\mbox{\scriptsize max}} is either the maximal Relative Fluorescence Reduction in the series (scale_to = "data") or derived from a mono-exponential fit to the data (scale_to = "model"). The latter (default) is a precaution for the case where the protein/phospholipid titration did not reach the plateau of the saturation curve.

• A monoexponential curve is fitted using nlsLM.

  If generation_of_algorithm is 1, the underlying formula is derived from Goren et al. (2014) and data is fitted to either

  p(\geq 1)=b\cdot(1-e^{-\frac{\mbox{\tiny PPR}}{a}})

  (if force_through_origin = TRUE; default) or

  p(\geq 1)=b-c\cdot e^{-\frac{\mbox{\tiny PPR}}{a}}

  (if force_through_origin = FALSE). The latter implies more degrees of freedom and occasionaly results in better fits to experimental data. Mechanistic implication, however, are unclear.

  If generation_of_algorithm is 2 (default), the more elaborate model put forth in Ploier et al. (2016) is employed, using either

  p(\geq 1)=b\cdot(\frac{1}{\sqrt{1+\sigma^2\cdot a \cdot x}})\cdot exp(\frac{-\bar{r}^2\cdot a \cdot x}{1+\sigma^2\cdot a\cdot x})

  (if force_through_origin = TRUE; default) or

  p(\geq 1)=b-c\cdot(\frac{1}{\sqrt{1+\sigma^2\cdot a \cdot x}})\cdot exp(\frac{-\bar{r}^2\cdot a \cdot x}{1+\sigma^2\cdot a\cdot x})

  (if force_through_origin = FALSE).

• Data split apart above are recombined and a ggplot object is assembled with the following layers:

  • Lines (geom_line) representing the monoexponential fit(s). color is used to differentiate Experimental Series.

  • If generation_of_algorithm is 1, segments (geom_segment) representing the PPR at which the fit constant a is equal to PPR. This \tau value has the implication that at this PPR all vesicles on average have one scramblase and 63% have one or more (i.e. are active). color is used to differentiate Experimental Series. Where generation_of_algorithm is 2, interpretation of a is less obvious and this layer is thus ommited in the plot.

  • Points (geom_point) representing the corresponding datapoints. color is used to differentiate Experimental Series.

  • Plots are finally facet_wraped by Experiment (if split_by_experiment = TRUE) and labels adjusted cosmetically.

Value

scramblase_assay_traces and scramblase_assay_plot return ggplot objects representing the raw fluorescence traces and a complete PPR plot, respectively. scramblase_assay_input_template generates a tab-delimited ASCII file in the file system and invisibly returns the path name. scramblase_assay_stats assembles (and prints) assay statistics as a data.frame.

Author(s)

Johannes Graumann

References

Menon, I., Huber, T., Sanyal, S., Banerjee, S., Barre, P., Canis, S., Warren, J.D., Hwa, J., Sakmar, T.P., and Menon, A.K. (2011) <DOI:10.1016/j.cub.2010.12.031>

Goren, M.A., Morizumi, T., Menon, I., Joseph, J.S., Dittman, J.S., Cherezov, V., Stevens, R.C., Ernst, O.P., and Menon, A.K. (2014) <DOI:10.1038/ncomms6115>

Ploier, B., Caro, L.N., Morizumi, T., Pandey, K., Pearring, J.N., Goren, M.A., Finnemann, S.C., Graumann, J., Arshavsky, V.Y., Dittman, J.S., Ernst, O.P., Menon, A.K. (2016). <DOI:10.1038/ncomms12832>

See Also

parse_fluorimeter_output nlsLM

Examples

library(magrittr)
library(ggplot2)
# Extract example data
analysis_dir <- file.path(tempdir(), "flippant-case-study")
extract_case_study_data(analysis_dir)
template_file <- file.path(analysis_dir, "inputTable.txt")
# Plot the spectral traces
scramblase_assay_traces(
  template_file,
  time_max_sec = 350,
  timepoint_of_measurement = 350)
# Plot the PPR plot(s) faceting by experiment
scramblase_assay_plot(template_file)
# Generate tabular results
scramblase_assay_stats(template_file)
# Plot the PPR plot(s) forgoing faceting by experiment
scramblase_assay_plot(template_file, split_by_experiment = FALSE)
# Generate tabular results
scramblase_assay_stats(template_file, split_by_experiment = FALSE)