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Experiments II: Endpoint Cellular Fluorescence

Source: vignettes/expts_ii_endpoint.Rmd
expts_ii_endpoint.Rmd

In addition to timecourse data, single timepoint fluorescence data can also be processed with FPCountR, using the same functions. Just apply the argument timecourse = FALSE.


Processing data from E. coli fluorescent protein expression experiments - single timepoint data

Example for single-timepoint data

Let’s consider an example in which cells expressing mTagBFP2 were grown in an incubator and transferred to the plate reader for a single timepoint scan. This data has already been parsed. With conversion factors for calibrating both mTagBFP2 fluorescence and cell number (OD) in hand, we are ready to process the experimental data.

parsed_data <- read.csv("data/example_experiment2_parsed.csv")
parsed_data[1:24,c(3,6:10)] # view a fragment of the dataframe
plasmid ara_pc volume well OD700 blue
NA A1 NA NA
pS361 0 200 A2 0.5914 438
pS361 0 200 A3 0.5961 439
pS361 0 200 A4 0.6014 452
pS361 0.3 200 A5 0.6003 442
pS361 0.3 200 A6 0.5996 443
pS361 0.3 200 A7 0.6000 442
pS361_ara_mTagBFP2 0 200 A8 0.6001 441
pS361_ara_mTagBFP2 0 200 A9 0.5928 437
pS361_ara_mTagBFP2 0 200 A10 0.5911 438
none none 200 A11 0.0876 483
NA A12 NA NA
NA B1 NA NA
pS361_ara_mTagBFP2 0.00003 200 B2 0.6107 1297
pS361_ara_mTagBFP2 0.00003 200 B3 0.6017 1316
pS361_ara_mTagBFP2 0.00003 200 B4 0.6078 1337
pS361_ara_mTagBFP2 0.0001 200 B5 0.6108 2474
pS361_ara_mTagBFP2 0.0001 200 B6 0.6051 2363
pS361_ara_mTagBFP2 0.0001 200 B7 0.6054 2471
pS361_ara_mTagBFP2 0.0003 200 B8 0.5846 3270
pS361_ara_mTagBFP2 0.0003 200 B9 0.5808 3546
pS361_ara_mTagBFP2 0.0003 200 B10 0.5729 3458
none none 200 B11 0.0856 480
NA B12 NA NA


Process data

Process the experimental data using process_plate().

processed_data <- process_plate(
  data_csv = "data/example_experiment2_parsed.csv",
  blank_well = c("A11", "B11", "C11", "D11", "E11", "F11", "G11", "H11"),
  
  # timecourse
  timecourse = FALSE, ### Note requirement to declare `timecourse = FALSE`
  
  # od
  od_name = "OD700",
  
  # fluorescence labels
  flu_channels = c("blue"),
  flu_channels_rename = c("blueblue"),
  
  # correction
  do_quench_correction = TRUE,
  od_type = "OD700",
  
  # calibrations
  do_calibrate = TRUE,
  instr = "spark1",
  flu_slugs = c("mTagBFP2"),
  flu_gains = c(60),
  flu_labels = c("mTagBFP2"),
  
  # conversion factors
  od_coeffs_csv = "conversion_factors/od_conversion_factors_assembled.csv",
  fluor_coeffs_csv = "conversion_factors/fp_conversion_factors_assembled.csv",
  
  # background autofluorescence subtraction
  af_model = NULL, ### Note this is a requirement for `timecourse = FALSE`
  
  outfolder = "experiment_analysis"
)

The arguments are described in the ‘Get Started’ vignette. The output CSV is similar.

The following differences apply for process_plate() where timecourse = FALSE:

  • No requirement for a column labelled time.
  • Plots are in heatmap format, rather than line plot format.
  • Autofluorescence correction cannot be carried out by any of the model options, such as spline or loess, because there isn’t enough data to do this. In practice, this means the af_model parameter is rewritten to NULL, and fluorescence normalisation takes place by simply subtracting the fluorescence of the blank wells.
processed_data[14:24,c(3,6,8,14:20)] # view a fragment of the dataframe
plasmid ara_pc well pathlength normalised_OD_cm1 normalised_blueblue flu_quench corrected_normalised_blueblue calibrated_OD calibrated_mTagBFP2
pS361_ara_mTagBFP2 0.00003 B2 0.6072014 0.863593213 818.25 0.7888225 1037.305602 527056389.0 1.143966e+13
pS361_ara_mTagBFP2 0.00003 B3 0.6072014 0.848771113 837.25 0.7908680 1058.646922 518010367.5 1.167502e+13
pS361_ara_mTagBFP2 0.00003 B4 0.6072014 0.858817203 858.25 0.7894782 1087.110453 524141559.8 1.198892e+13
pS361_ara_mTagBFP2 0.0001 B5 0.6072014 0.863757903 1995.25 0.7887999 2529.475317 527156900.3 2.789567e+13
pS361_ara_mTagBFP2 0.0001 B6 0.6072014 0.854370573 1884.25 0.7900916 2384.850104 521427753.4 2.630071e+13
pS361_ara_mTagBFP2 0.0001 B7 0.6072014 0.854864643 1992.25 0.7900233 2521.761093 521729287.4 2.781060e+13
pS361_ara_mTagBFP2 0.0003 B8 0.6072014 0.820609122 2791.25 0.7948423 3511.702973 500822926.8 3.872792e+13
pS361_ara_mTagBFP2 0.0003 B9 0.6072014 0.814350902 3067.25 0.7957414 3854.581279 497003495.5 4.250926e+13
pS361_ara_mTagBFP2 0.0003 B10 0.6072014 0.801340391 2979.25 0.7976297 3735.129191 489063098.9 4.119192e+13
none none B11 0.6072014 -0.001194003 1.25 0.9929566 1.258867 -728707.3 1.388309e+10
B12 NA NA NA NA NA NA NA


The plot formats are heatmap styled.

Raw:

Normalised:

Quench corrected:

Calibrated:


Calculate per cell values

calc_fppercell() can be used to estimate molecules per cell.

pc_data_mTagBFP2 <- calc_fppercell(
  data_csv = "experiment_analysis/example_experiment2_parsed_processed.csv",
  timecourse = FALSE,
  flu_channels = c("blueblue"),
  flu_labels = c("mTagBFP2"),
  remove_wells = c("A11", "B11", "C11", "D11", "E11", "F11", "G11", "H11", # media
                   "A1", "B1", "C1", "D1", "E1", "F1", "G1", "H1", 
                   "A12", "B12", "C12", "D12", "E12", "F12", "G12", "H12"), # empty wells
  get_rfu_od = FALSE,
  get_mol_cell = TRUE,
  outfolder = "experiment_analysis"
)

The following differences apply for calc_fppercell() and calc_fpconc() where timecourse = FALSE:

  • No requirement for a column labelled time.
  • Plots are in heatmap format, rather than line plot format.

View a fragment of the dataframe to check it:

data_to_display <- pc_data_mTagBFP2 |>
  dplyr::select(plasmid, ara_pc, OD700, calibrated_OD, blueblue, calibrated_mTagBFP2, calibratedmTagBFP2_perCell)
data_to_display[c(13:15,19:21,25:27,31:33),]
plasmid ara_pc OD700 calibrated_OD blueblue calibrated_mTagBFP2 calibratedmTagBFP2_perCell
pS361_ara_mTagBFP2 1e-04 0.6108 527156900 2474 2.789567e+13 52917.21
pS361_ara_mTagBFP2 1e-04 0.6051 521427753 2363 2.630071e+13 50439.79
pS361_ara_mTagBFP2 1e-04 0.6054 521729287 2471 2.781060e+13 53304.65
pS361_ara_mTagBFP2 1e-03 0.5315 447451400 4554 5.562491e+13 124314.97
pS361_ara_mTagBFP2 1e-03 0.5327 448657536 4719 5.789930e+13 129050.10
pS361_ara_mTagBFP2 1e-03 0.5358 451773388 4739 5.822995e+13 128891.95
pS361_ara_mTagBFP2 1e-02 0.5109 426746062 8936 1.146661e+14 268698.57
pS361_ara_mTagBFP2 1e-02 0.5010 416795439 9415 1.207623e+14 289739.89
pS361_ara_mTagBFP2 1e-02 0.5136 429459869 8579 1.099235e+14 255957.51
pS361_ara_mTagBFP2 1e-01 0.5126 428454755 10704 1.387149e+14 323756.21
pS361_ara_mTagBFP2 1e-01 0.5046 420413847 10718 1.385372e+14 329525.79
pS361_ara_mTagBFP2 1e-01 0.5056 421418961 10659 1.377848e+14 326954.44

Again, output plots are heatmaps.

The data can be plotted downstream as a bar chart or similar.