Skip to contents

fpcountr

Source: vignettes/fpcountr.Rmd
fpcountr.Rmd

This page includes a full overview of fpcountr starting from package installation, through all steps of an example calibration and experimental data analysis. For more detail on the steps of this workflow, see the Articles.


Installation

The package is written in R and can be installed straight from GitHub:

# install.packages("devtools") # run this if you don't have devtools installed already
devtools::install_github("ec363/fpcountr") # run this to install fpcountr

You should only need to run the above code once.


Example Scenario


Example Scenario: We wish to work out how many molecules of fluorescent protein are being produced from a vector expressing mTagBFP2.

What we have: experimental data of mTagBFP2 expression, as well as an mTagBFP2 calibration carried out using the FPCountR method.

What we want to do is:

  1. create conversion factors that describe how blue fluorescence from mTagBFP2 relates to its molecule number in our instrument.
  2. use these conversion factors to interpret our experiment file, to work out the number of proteins per cell expressed by our mTagBFP2 expression vectors across different conditions.

The package comes with example data of exactly this type, and example code that will allow you to calculate protein numbers.


Example Data


The following example data files are provided with this package:

file contents
example_absorbance.csv absorbance spectrum of a dilution series of mTagBFP2
example_absorbance_meta.csv metadata for the above
example_fluorescence.csv blue fluorescence data at a range of gains for the dilution series of mTagBFP2
example_fluorescence_meta.csv metadata for the above
example_experiment.csv plate reader experiment, using two mTagBFP2 vectors with different origins of replication, induced across a range of inducer concentrations and tracked for 16 hours
example_experiment_meta.csv metadata for the above

This example data will be used in the script below.

To try it yourself, create a new folder for running the script in this vignette (e.g. call it fpcountr_example). Open a new R script in RStudio (File > New File > R Script), save it to that folder, and navigate to its current working directory (Session > Set Working Directory > To Source File Location).

To find the example data, run:

system.file("extdata", "", package = "fpcountr", mustWork = TRUE)

This gives you the location of the files. Find this folder, copy all of the files listed here into a subfolder of the fpcountr_example called data.


Example Code


All the code from here to the end of this vignette can be copied into your own script and run line by line. Most of these functions will take existing data files, process them, and save new files (data as CSV files, and plots as PDF files). The data files will be required for subsequent functions, whereas the plots are intended to let you check the progress of the functions and identify if something has gone wrong.

Load the fpcountr package and verify that you are in the fpcountr_example directory.


Protein concentration determination with the ECmax assay


1. Parse absorbance spectrum data

The first thing we need to do is to process our absorbance spectrum data file, but the exported data file is not in the right format for our processing function. So before we can process the data, we need to extract the data from the exported data file, tidy it and join it to metadata. This process is called parsing.

Parsing: All raw data files exported from plate readers require ‘parsing’ before they can be processed by downstream functions. See vignette("data_parsing") for more details.

Metadata: Parsing requires that metadata be joined to the data. While it is largely up to you what aspects of your experiments you record as metadata, each data-processing function in FPCountR expects a a minimum amount of metadata that is required for downstream analysis. See vignette("data_parsing") for details on what these are.

If using a Tecan Spark plate reader running Magellan software: use parse_magellan_spectrum(). If using other plate readers: see vignette("data_parsing").

We used a Spark, so we’ll use parse_magellan_spectrum(): this takes the absorbance data file (example_absorbance.csv), in the export format provided by our plate reader, and a user-produced metadata file (example_absorbance_meta.csv), and parses it into the correct format.

parsed_data_spectrum <- parse_magellan_spectrum(
  data_csv = "data/example_absorbance.csv",
  metadata_csv = "data/example_absorbance_meta.csv",
  wellstart = "A1", wellend = "B12"
)
## 24 wells identified.

Arguments required:

  • data_csv - file path of the absorbance data file
  • metadata_csv - file path of the metadata file
  • wellstart = “A1”, wellend = “B12” - first and last wells of the data

Warnings expected:

  • You will likely get the warning NAs introduced by coercion. This just means that empty wells were filled in with NA values.

Outputs produced:

  • A processed data file of the name [raw data filename]_parsed.csv (here,example_absorbance_parsed_processed.csv), in the same location where the data was found.
  • A dataframe (which we called parsed_data_spectrum) of the parsed data is returned, which can be used to inspect the parsing.

View a fragment of the data frame to check.

parsed_data_spectrum[1:24,c(1:100)] # view a fragment of the dataframe
instrument plate seal media calibrant protein replicate dilution volume well 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 1.000000000 225 A1 3.2934 3.2799 3.3390 3.3396 3.3812 3.3875 3.4014 3.3519 3.3743 3.3190 3.4634 3.3941 3.3317 3.3466 3.6045 3.3346 3.3385 3.3678 3.4163 3.4045 3.3693 3.3348 3.5089 3.3959 3.4013 3.4789 3.5905 3.7117 3.7253 3.7382 3.6253 3.4443 3.3550 3.1695 2.8252 2.4266 2.0124 1.6102 1.2596 0.9745 0.7787 0.6370 0.5589 0.5031 0.4586 0.4224 0.4064 0.4018 0.4022 0.4070 0.4187 0.4318 0.4473 0.4626 0.4797 0.5015 0.5268 0.5512 0.5748 0.5920 0.6062 0.6182 0.6289 0.6525 0.6715 0.6958 0.7082 0.7030 0.6658 0.6275 0.5976 0.5883 0.5898 0.5914 0.5731 0.5318 0.4451 0.3631 0.2895 0.2397 0.1733 0.1359 0.1166 0.1039 0.0951 0.0886 0.0841 0.0820 0.0806 0.0782
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 0.500000000 225 A2 3.2612 3.3153 3.3262 3.3553 3.4475 3.3497 3.3685 3.3631 3.3890 3.3843 3.3826 3.4219 3.3178 3.3496 3.3306 3.4271 3.3804 3.3450 3.3397 3.3883 3.3744 3.3622 3.5758 3.4990 3.4029 3.5028 3.6401 3.6825 3.9397 3.7502 3.6304 3.5345 3.3159 3.1504 2.7906 2.4168 2.0340 1.6039 1.2347 0.9573 0.7499 0.6123 0.5355 0.4833 0.4405 0.4033 0.3888 0.3859 0.3876 0.3936 0.4054 0.4200 0.4363 0.4533 0.4717 0.4947 0.5182 0.5455 0.5691 0.5896 0.6035 0.6147 0.6277 0.6436 0.6715 0.6964 0.7092 0.7038 0.6644 0.6260 0.5996 0.5877 0.5900 0.5912 0.5754 0.5299 0.4455 0.3667 0.2840 0.2191 0.1654 0.1314 0.1099 0.0981 0.0882 0.0813 0.0778 0.0765 0.0739 0.0714
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 0.250000000 225 A3 3.2943 3.3587 3.3668 3.3376 3.3925 3.3521 3.3763 3.3388 3.3662 3.3503 3.3968 3.3796 3.3023 3.3271 3.4081 3.3153 3.2824 3.3790 3.5070 3.3855 3.3766 3.3514 3.4241 3.4321 3.3681 3.4388 3.5811 3.8147 3.8244 3.6775 3.5795 3.5555 3.3279 3.0927 2.7866 2.4192 1.9948 1.5810 1.1987 0.9367 0.7338 0.5980 0.5226 0.4671 0.4252 0.3936 0.3782 0.3755 0.3770 0.3835 0.3958 0.4108 0.4282 0.4450 0.4646 0.4867 0.5119 0.5387 0.5649 0.5838 0.5972 0.6102 0.6216 0.6428 0.6663 0.6919 0.7047 0.7002 0.6616 0.6236 0.5943 0.5833 0.5858 0.5890 0.5723 0.5235 0.4426 0.3617 0.2852 0.2174 0.1596 0.1256 0.1051 0.0940 0.0844 0.0779 0.0752 0.0720 0.0709 0.0687
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 0.125000000 225 A4 3.2608 3.3900 3.3016 3.3737 3.4401 3.3511 3.4485 3.3411 3.5792 3.4078 3.4102 3.3064 3.8285 3.3917 3.2961 3.2814 3.8080 3.3650 3.2824 3.3944 3.4117 3.3063 3.3953 3.4396 3.4291 3.4860 3.5467 3.7367 3.9342 3.7427 3.6230 3.4140 3.3619 3.1178 2.7753 2.3828 1.9684 1.5515 1.2404 0.9234 0.7298 0.5899 0.5132 0.4657 0.4245 0.3868 0.3728 0.3699 0.3718 0.3793 0.3921 0.4073 0.4246 0.4395 0.4611 0.4835 0.5076 0.5378 0.5621 0.5821 0.5958 0.6071 0.6205 0.6389 0.6650 0.6903 0.7032 0.6985 0.6590 0.6188 0.5916 0.5822 0.5843 0.5864 0.5712 0.5252 0.4418 0.3550 0.2787 0.2143 0.1593 0.1272 0.1047 0.0905 0.0821 0.0761 0.0720 0.0706 0.0684 0.0662
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 0.062500000 225 A5 3.2948 3.3343 3.3076 3.5574 3.7229 3.8790 3.4160 3.3230 3.8098 3.3552 3.3509 3.6952 3.4518 3.3204 3.2846 3.5490 3.5252 3.3531 3.3292 3.5832 3.3807 3.3015 3.4221 3.5056 3.4159 3.4701 3.4884 3.6432 3.9620 3.6865 3.6345 3.4748 3.3218 3.1609 2.7651 2.3884 1.9838 1.5570 1.2196 0.9282 0.7199 0.5845 0.5100 0.4606 0.4194 0.3833 0.3695 0.3660 0.3690 0.3761 0.3896 0.4046 0.4223 0.4389 0.4581 0.4824 0.5056 0.5352 0.5614 0.5802 0.5943 0.6065 0.6195 0.6391 0.6637 0.6893 0.7022 0.6956 0.6598 0.6208 0.5922 0.5801 0.5835 0.5864 0.5704 0.5233 0.4363 0.3568 0.2754 0.2136 0.1568 0.1198 0.1011 0.0903 0.0810 0.0752 0.0698 0.0686 0.0679 0.0656
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 0.031250000 225 A6 3.2894 3.3136 3.3454 3.4588 3.3556 3.3564 3.3910 3.4368 3.2939 3.3150 3.4114 3.4220 3.2776 3.3441 3.7313 3.3467 3.3102 3.3344 3.5875 3.3132 3.3724 3.3894 3.4423 3.3552 3.4170 3.4920 3.5895 3.6536 3.7007 3.6798 3.6098 3.4690 3.2665 3.1443 2.7748 2.3897 1.9684 1.5552 1.1968 0.9244 0.7280 0.5852 0.5114 0.4653 0.4180 0.3853 0.3694 0.3672 0.3690 0.3768 0.3893 0.4050 0.4230 0.4392 0.4600 0.4830 0.5086 0.5360 0.5611 0.5821 0.5952 0.6074 0.6182 0.6423 0.6644 0.6903 0.7041 0.6947 0.6601 0.6205 0.5935 0.5815 0.5844 0.5861 0.5713 0.5281 0.4392 0.3565 0.2826 0.2217 0.1548 0.1222 0.1038 0.0918 0.0811 0.0755 0.0711 0.0698 0.0664 0.0645
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 0.015625000 225 A7 3.2930 3.3226 3.3383 3.3680 3.3786 3.4243 3.4188 3.2832 3.3297 3.3482 3.4077 3.1910 3.2515 3.3426 3.2928 3.2119 3.5754 3.3134 3.3321 3.3449 3.4932 3.3675 3.3166 3.3769 3.3969 3.4915 3.4730 3.6856 3.9582 3.6629 3.6329 3.4022 3.3242 3.1102 2.7530 2.3679 1.9701 1.5259 1.1940 0.9354 0.7292 0.5946 0.5090 0.4588 0.4169 0.3837 0.3693 0.3661 0.3683 0.3750 0.3888 0.4044 0.4210 0.4376 0.4577 0.4818 0.5064 0.5335 0.5582 0.5800 0.5944 0.6060 0.6192 0.6390 0.6635 0.6904 0.7033 0.6958 0.6567 0.6200 0.5912 0.5817 0.5826 0.5858 0.5685 0.5221 0.4389 0.3539 0.2804 0.2150 0.1568 0.1218 0.1037 0.0906 0.0802 0.0734 0.0707 0.0680 0.0672 0.0658
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 0.007812500 225 A8 3.2936 3.3589 3.3350 3.3625 3.4282 3.3435 3.3975 3.3195 3.4037 3.3506 3.4429 3.4356 3.2514 3.3542 3.3768 3.2911 3.3065 3.3331 3.3948 3.4013 3.3414 3.3657 3.4822 3.4367 3.4058 3.4393 3.5555 3.8083 3.9582 3.6781 3.6301 3.5082 3.3226 3.1112 2.7751 2.3880 1.9582 1.5468 1.1743 0.9332 0.7239 0.5811 0.5105 0.4567 0.4144 0.3808 0.3666 0.3638 0.3664 0.3737 0.3863 0.4020 0.4196 0.4357 0.4558 0.4791 0.5056 0.5329 0.5572 0.5755 0.5917 0.6031 0.6166 0.6322 0.6617 0.6866 0.6987 0.6945 0.6564 0.6157 0.5907 0.5797 0.5811 0.5841 0.5661 0.5249 0.4395 0.3510 0.2789 0.2101 0.1550 0.1239 0.1017 0.0905 0.0794 0.0725 0.0699 0.0680 0.0658 0.0639
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 0.003906250 225 A9 3.2909 3.2733 3.3648 3.3259 3.4149 3.3244 3.4223 3.3273 3.3610 3.3466 3.4147 3.2290 3.2530 3.2955 3.2818 3.2407 3.3298 3.2760 3.3033 3.2598 3.3459 3.3107 3.3930 3.3258 3.3735 3.4647 3.5510 3.6056 3.8181 3.6571 3.6330 3.5236 3.3329 3.1459 2.7684 2.4014 1.9585 1.5504 1.2033 0.9280 0.7252 0.5899 0.5081 0.4628 0.4157 0.3826 0.3698 0.3668 0.3696 0.3763 0.3897 0.4051 0.4225 0.4397 0.4593 0.4829 0.5110 0.5366 0.5626 0.5800 0.5964 0.6084 0.6219 0.6385 0.6692 0.6929 0.7060 0.7020 0.6609 0.6239 0.5949 0.5830 0.5861 0.5885 0.5710 0.5264 0.4487 0.3545 0.2783 0.2111 0.1568 0.1219 0.1015 0.0893 0.0806 0.0733 0.0706 0.0685 0.0666 0.0649
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 0.001953125 225 A10 3.2934 3.2913 3.3022 3.4590 3.4969 3.3739 3.3996 3.4085 3.3520 3.3454 3.5889 3.6304 3.4700 3.2959 3.6712 3.7322 3.3344 3.3244 3.8001 3.5168 3.3640 3.3831 3.6125 3.4212 3.3625 3.4817 3.6488 3.7420 3.8355 3.6123 3.6343 3.5446 3.3554 3.1459 2.7516 2.3730 1.9514 1.5284 1.1930 0.9165 0.7063 0.5809 0.5068 0.4559 0.4143 0.3815 0.3649 0.3626 0.3650 0.3717 0.3849 0.4007 0.4178 0.4339 0.4527 0.4764 0.5026 0.5306 0.5551 0.5744 0.5882 0.6002 0.6130 0.6295 0.6559 0.6821 0.6962 0.6911 0.6519 0.6162 0.5864 0.5756 0.5779 0.5799 0.5648 0.5195 0.4363 0.3490 0.2752 0.2122 0.1539 0.1232 0.1011 0.0886 0.0801 0.0753 0.0699 0.0683 0.0664 0.0667
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 1 0.000976563 225 A11 3.2608 3.2641 3.3324 3.3941 3.4011 3.3272 3.4094 3.3708 3.3890 3.3852 3.5078 3.6834 3.2936 3.2956 NA 3.5216 3.3097 3.2762 3.5876 3.4762 3.3349 3.3392 3.7558 3.4210 3.3445 3.4422 3.6096 3.7390 3.8251 3.6651 3.6157 3.4447 3.3665 3.1070 2.7651 2.3705 1.9636 1.5345 1.1645 0.9179 0.7197 0.5809 0.5052 0.4540 0.4128 0.3793 0.3652 0.3624 0.3653 0.3724 0.3849 0.4015 0.4184 0.4349 0.4551 0.4782 0.5036 0.5313 0.5545 0.5757 0.5891 0.6011 0.6120 0.6348 0.6574 0.6836 0.6972 0.6877 0.6551 0.6159 0.5874 0.5768 0.5790 0.5809 0.5670 0.5235 0.4407 0.3531 0.2765 0.2115 0.1571 0.1216 0.1015 0.0895 0.0797 0.0738 0.0703 0.0683 0.0660 0.0646
spark1 uvclear noseal T5N15_pi mTagBFP2 none 1 NA 225 A12 3.2570 3.3090 3.3415 3.3510 3.3476 3.3447 3.4181 3.3793 3.3385 3.3196 3.6337 3.5760 3.2549 3.3214 NA 3.5654 3.3158 3.3305 3.6567 3.4572 3.3458 3.3636 3.7070 3.4361 3.3620 3.4791 3.7243 3.7038 3.7232 3.6674 3.6298 3.4699 3.3074 3.1031 2.7870 2.3795 1.9806 1.5580 1.1744 0.9180 0.7191 0.5871 0.5112 0.4584 0.4152 0.3805 0.3665 0.3637 0.3659 0.3735 0.3868 0.4011 0.4188 0.4356 0.4558 0.4794 0.5062 0.5319 0.5592 0.5771 0.5914 0.6043 0.6167 0.6377 0.6608 0.6860 0.6994 0.6904 0.6572 0.6177 0.5904 0.5790 0.5812 0.5832 0.5671 0.5220 0.4419 0.3535 0.2769 0.2147 0.1543 0.1202 0.1024 0.0897 0.0806 0.0731 0.0709 0.0679 0.0665 0.0652
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 1.000000000 225 B1 3.2622 3.3121 3.3903 3.6836 3.4257 3.3808 3.4499 3.4804 3.4558 3.3016 3.4034 3.5316 3.2732 3.3386 NA 3.4427 3.3662 3.3979 3.6061 3.5345 3.3747 3.3674 3.6928 3.4750 3.3723 3.4984 3.6710 3.7514 3.8388 3.6858 3.6241 3.6030 3.3409 3.1953 2.8693 2.4776 2.0870 1.6365 1.2872 0.9977 0.7943 0.6602 0.5714 0.5174 0.4694 0.4336 0.4139 0.4095 0.4110 0.4161 0.4264 0.4407 0.4570 0.4717 0.4905 0.5126 0.5371 0.5635 0.5869 0.6042 0.6212 0.6314 0.6434 0.6604 0.6849 0.7117 0.7229 0.7164 0.6823 0.6406 0.6109 0.6011 0.6024 0.6041 0.5863 0.5390 0.4661 0.3721 0.2979 0.2300 0.1828 0.1388 0.1203 0.1063 0.0965 0.0881 0.0857 0.0838 0.0813 0.0797
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 0.500000000 225 B2 3.2615 3.2786 3.3289 3.5397 3.4848 3.3831 3.4237 3.4103 3.4671 3.3550 3.4263 3.4997 3.2650 3.2978 NA 3.4055 3.3194 3.3829 3.6666 3.3555 3.3627 3.3949 3.5660 3.3634 3.4315 3.4951 3.6460 3.6147 3.8329 3.7620 3.6915 3.5492 3.3540 3.1385 2.8210 2.4422 2.0157 1.5962 1.2314 0.9604 0.7623 0.6233 0.5419 0.4882 0.4462 0.4109 0.3952 0.3909 0.3933 0.3996 0.4119 0.4263 0.4425 0.4583 0.4777 0.5004 0.5245 0.5520 0.5774 0.5980 0.6104 0.6231 0.6342 0.6559 0.6785 0.7045 0.7172 0.7108 0.6748 0.6353 0.6067 0.5956 0.5966 0.5989 0.5825 0.5382 0.4491 0.3661 0.2920 0.2292 0.1662 0.1335 0.1127 0.1009 0.0907 0.0838 0.0784 0.0774 0.0765 0.0740
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 0.250000000 225 B3 3.2896 3.3469 3.3185 3.3641 3.3797 3.2989 3.3664 3.3426 3.3452 3.3142 3.4726 3.4556 3.2925 3.3562 3.3578 3.3473 3.3256 3.3683 3.4075 3.4520 3.3756 3.3612 3.5242 3.3710 3.3664 3.4251 3.6084 3.6015 3.7987 3.6853 3.6437 3.6292 3.3103 3.1232 2.8008 2.4194 2.0022 1.5963 1.2389 0.9533 0.7439 0.6102 0.5312 0.4738 0.4319 0.3964 0.3818 0.3790 0.3808 0.3873 0.4006 0.4160 0.4320 0.4485 0.4670 0.4917 0.5197 0.5445 0.5682 0.5878 0.6032 0.6153 0.6273 0.6509 0.6703 0.6974 0.7105 0.7052 0.6684 0.6282 0.5995 0.5890 0.5907 0.5928 0.5759 0.5297 0.4575 0.3670 0.2902 0.2208 0.1618 0.1281 0.1081 0.0955 0.0862 0.0790 0.0754 0.0736 0.0716 0.0707
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 0.125000000 225 B4 3.2380 3.3249 3.3002 3.3603 3.4617 3.2994 3.4053 3.3191 3.4669 3.3457 3.4417 3.4639 3.2711 3.3551 3.4307 3.3425 3.3107 3.3406 3.3742 3.4077 3.3843 3.3964 3.4978 3.3612 3.3891 3.4826 3.5438 3.6611 3.8364 3.6908 3.6356 3.5090 3.3281 3.0877 2.7813 2.3816 1.9817 1.5605 1.2022 0.9505 0.7413 0.5976 0.5184 0.4692 0.4243 0.3897 0.3747 0.3723 0.3735 0.3802 0.3933 0.4094 0.4257 0.4424 0.4628 0.4856 0.5106 0.5375 0.5651 0.5828 0.5967 0.6085 0.6201 0.6415 0.6630 0.6901 0.7037 0.6967 0.6640 0.6234 0.5951 0.5832 0.5853 0.5872 0.5716 0.5289 0.4503 0.3601 0.2817 0.2221 0.1604 0.1257 0.1064 0.0931 0.0839 0.0762 0.0740 0.0717 0.0695 0.0674
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 0.062500000 225 B5 3.2873 3.3641 3.3029 3.4145 3.4475 3.3742 3.4139 3.3274 NA 3.3509 3.3497 3.2534 3.3395 3.3225 3.3712 3.6045 3.4193 3.3694 3.3676 3.5362 3.4696 3.3681 3.4208 3.5131 3.4326 3.4816 3.5418 3.8254 3.8425 3.7735 3.5762 3.4543 3.3098 3.1514 2.7632 2.3860 1.9686 1.5974 1.2111 0.9264 0.7275 0.5901 0.5169 0.4640 0.4191 0.3874 0.3715 0.3697 0.3715 0.3785 0.3913 0.4075 0.4243 0.4414 0.4594 0.4839 0.5117 0.5377 0.5622 0.5816 0.5970 0.6095 0.6223 0.6379 0.6672 0.6927 0.7051 0.7017 0.6616 0.6261 0.5955 0.5837 0.5862 0.5878 0.5735 0.5278 0.4612 0.3522 0.2828 0.2130 0.1636 0.1253 0.1043 0.0925 0.0825 0.0756 0.0722 0.0695 0.0680 0.0667
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 0.031250000 225 B6 3.2632 3.3036 3.3578 3.3831 3.4197 3.4162 3.3828 3.3283 3.2946 3.3496 3.4568 3.1970 3.2558 3.3001 3.3495 3.1916 3.3262 3.3750 3.3093 3.2897 3.3383 3.3137 3.4054 3.3669 3.3515 3.4877 3.6001 3.6764 3.9521 3.6147 3.5558 3.3958 3.2553 3.1254 2.7601 2.3841 1.9674 1.5712 1.2082 0.9383 0.7246 0.5899 0.5147 0.4571 0.4147 0.3824 0.3676 0.3646 0.3676 0.3738 0.3879 0.4031 0.4200 0.4376 0.4565 0.4791 0.5061 0.5325 0.5589 0.5781 0.5926 0.6045 0.6176 0.6363 0.6621 0.6877 0.7000 0.6955 0.6585 0.6197 0.5915 0.5809 0.5817 0.5843 0.5708 0.5196 0.4443 0.3568 0.2798 0.2163 0.1598 0.1215 0.1016 0.0893 0.0801 0.0740 0.0706 0.0684 0.0666 0.0659
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 0.015625000 225 B7 3.2952 3.4111 3.3145 3.3268 3.3382 3.3269 3.4227 3.3356 3.3215 3.3224 3.4519 3.5604 3.3040 3.3482 NA 3.4690 3.3356 3.3616 3.5512 3.3491 3.3703 3.3644 3.5594 3.4133 3.4359 3.4816 3.6516 3.6721 3.7369 3.6739 3.6804 3.5281 3.3265 3.1215 2.7994 2.3906 1.9799 1.5551 1.1862 0.9269 0.7188 0.5893 0.5091 0.4595 0.4159 0.3827 0.3688 0.3666 0.3687 0.3760 0.3893 0.4040 0.4223 0.4392 0.4576 0.4825 0.5103 0.5353 0.5618 0.5805 0.5948 0.6087 0.6205 0.6427 0.6647 0.6920 0.7054 0.7002 0.6616 0.6218 0.5938 0.5834 0.5851 0.5879 0.5715 0.5228 0.4402 0.3597 0.2774 0.2131 0.1582 0.1211 0.1017 0.0903 0.0801 0.0737 0.0703 0.0681 0.0657 0.0649
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 0.007812500 225 B8 3.2878 3.2837 3.3270 3.4123 3.3724 3.3476 3.4497 3.3785 3.3534 3.3298 3.3444 3.2938 3.2592 3.3133 3.2903 3.2847 3.3355 3.3083 3.2873 3.3393 3.3493 3.3279 3.3943 3.4185 3.4271 3.4915 3.4802 3.6436 3.8302 3.6619 3.6181 3.4146 3.3125 3.1098 2.7534 2.3873 1.9699 1.5688 1.1989 0.9274 0.7210 0.5900 0.5167 0.4629 0.4172 0.3829 0.3691 0.3667 0.3687 0.3764 0.3895 0.4055 0.4225 0.4386 0.4588 0.4823 0.5066 0.5373 0.5604 0.5830 0.5945 0.6078 0.6202 0.6371 0.6652 0.6911 0.7028 0.6968 0.6602 0.6205 0.5975 0.5832 0.5846 0.5869 0.5717 0.5298 0.4402 0.3555 0.2765 0.2163 0.1572 0.1218 0.1013 0.0898 0.0808 0.0740 0.0705 0.0681 0.0669 0.0648
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 0.003906250 225 B9 3.2595 3.2763 3.2816 3.3963 3.4222 3.3570 3.4114 3.3277 3.3779 3.3119 3.3452 3.4820 3.2909 3.3331 3.2984 3.2943 3.3058 3.2933 3.4052 3.3600 3.3792 3.3733 3.4822 3.5098 3.4510 3.4586 3.5993 3.7363 3.8326 3.6718 3.6061 3.5391 3.3263 3.1078 2.7524 2.3925 1.9586 1.5415 1.2125 0.9173 0.7182 0.5852 0.5077 0.4538 0.4134 0.3806 0.3659 0.3640 0.3666 0.3725 0.3867 0.4020 0.4192 0.4355 0.4547 0.4787 0.5070 0.5321 0.5572 0.5745 0.5907 0.6024 0.6153 0.6305 0.6607 0.6855 0.6974 0.6925 0.6544 0.6153 0.5893 0.5785 0.5799 0.5821 0.5659 0.5191 0.4370 0.3506 0.2755 0.2117 0.1559 0.1212 0.1017 0.0896 0.0809 0.0729 0.0712 0.0694 0.0669 0.0665
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 0.001953125 225 B10 3.2873 3.2803 3.3300 3.3002 3.3858 3.3193 3.4543 3.3469 3.4151 3.3207 3.4526 3.2234 3.2341 3.4165 3.3579 3.1899 3.3497 3.3472 3.3006 3.2550 3.3566 3.3508 3.3954 3.3293 3.4346 3.4808 3.5534 3.6273 3.7185 3.7585 3.6383 3.4241 3.2968 3.0707 2.7316 2.3400 1.9381 1.5190 1.1614 0.9027 0.7086 0.5744 0.4996 0.4499 0.4086 0.3759 0.3621 0.3592 0.3611 0.3683 0.3810 0.3964 0.4134 0.4301 0.4489 0.4717 0.4964 0.5227 0.5520 0.5677 0.5809 0.5933 0.6069 0.6260 0.6486 0.6742 0.6871 0.6802 0.6490 0.6081 0.5801 0.5683 0.5712 0.5730 0.5597 0.5124 0.4371 0.3463 0.2739 0.2071 0.1521 0.1203 0.1015 0.0899 0.0800 0.0736 0.0701 0.0693 0.0668 0.0648
spark1 uvclear noseal T5N15_pi mTagBFP2 mTagBFP2 2 0.000976563 225 B11 3.2873 3.2698 3.3276 3.3491 3.4232 3.3238 3.3816 3.3112 3.3709 3.3376 3.4112 3.4409 3.2723 3.2945 3.4030 3.3373 3.3586 3.2629 3.4046 3.3877 3.3526 3.3806 3.4712 3.4327 3.4091 3.4510 3.6081 3.6794 3.7067 3.6702 3.5075 3.4401 3.3460 3.1226 2.7646 2.3759 1.9478 1.5455 1.1979 0.9149 0.7150 0.5876 0.5051 0.4549 0.4131 0.3823 0.3660 0.3631 0.3659 0.3726 0.3854 0.4011 0.4183 0.4345 0.4539 0.4782 0.5024 0.5294 0.5546 0.5780 0.5886 0.6014 0.6138 0.6314 0.6584 0.6836 0.6978 0.6909 0.6540 0.6170 0.5886 0.5768 0.5789 0.5802 0.5657 0.5224 0.4401 0.3504 0.2779 0.2088 0.1562 0.1228 0.1015 0.0891 0.0795 0.0736 0.0696 0.0691 0.0671 0.0653
spark1 uvclear noseal T5N15_pi mTagBFP2 none 2 NA 225 B12 3.2589 3.3362 3.3382 3.4187 3.4431 3.3702 3.3816 3.3774 3.4513 3.3236 3.3384 3.3047 3.2743 3.3238 3.3219 3.2346 3.3103 3.2979 3.3100 3.3378 3.3628 3.3484 3.3729 3.3852 3.3785 3.4300 3.5622 3.6803 3.8182 3.6186 3.6281 3.5383 3.2968 3.1263 2.7757 2.3849 1.9535 1.5513 1.2100 0.9199 0.7139 0.5836 0.5081 0.4572 0.4127 0.3797 0.3657 0.3631 0.3651 0.3721 0.3846 0.4003 0.4172 0.4336 0.4533 0.4763 0.5053 0.5307 0.5541 0.5734 0.5884 0.6009 0.6126 0.6355 0.6563 0.6826 0.6956 0.6901 0.6545 0.6141 0.5881 0.5760 0.5785 0.5829 0.5640 0.5187 0.4387 0.3521 0.2780 0.2119 0.1523 0.1214 0.1010 0.0882 0.0801 0.0733 0.0701 0.0690 0.0664 0.0647


As you can see, the data is now in tidy, with one column per variable, allowing automated processing in subsequent steps.


2. Process absorbance spectrum data

Parsed spectrum data can then be processed. This involves calculation of the path length of the samples, adjusting the raw values to a path length of 1cm, and normalising the data to the blanks.

Path lengths: Calculating path lengths accurately is an important part of this process. See vignette("path_lengths") for more information.

# Create folder to hold the FP quantification function output files
dir.create("fp_quantification")

# Process spectra
processed_data_spectrum <- process_absorbance_spectrum(
  
  # basics
  spectrum_csv = "data/example_absorbance_parsed.csv",
  subset_rows = TRUE, rows_to_keep = c("A","B"), columns_to_keep = c(1:12),
  xrange = c(250,1000),
  
  # path length calcs
  pl_method = "calc_blanks",
  buffer_used = "TBS", concentration_used = 0.005, temperature_used = 30,
  
  # saving
  outfolder = "fp_quantification"
)
## 
## Calculating k-factor for TBS at concentration 0.005 at temperature 30 oC.
## 
## Reference k-factor 0.172.
## 
## K-factors available for given buffer: 
##   buffer concentration units description kfactor fold_change
## 1    TBS          0.05     M    TBS_50mM   0.166   0.9595376
## 
## Values used for model (kfactor ~ concentration): 
##   buffer concentration units description kfactor fold_change
## 1  water          0.00  none       Water   0.173   1.0000000
## 2    TBS          0.05     M    TBS_50mM   0.166   0.9595376
## 
## Change in k-factor required for given buffer: 0.996.
## 
## Values used for model (fold_change ~ temperature): 
##   temperature kfactor fold_change
## 1          25   0.172       1.000
## 2          28   0.174       1.012
## 3          31   0.177       1.029
## 4          34   0.179       1.041
## 5          37   0.183       1.064
## 6          41   0.188       1.093
## 7          45   0.191       1.110
## 
## Change in k-factor required for given temperature: 1.024.
## 
## Overall k-factor: 0.175.
## 
## Calculating path lengths using chosen method: calc_blanks.
## Path length will calculated from the blanks data.

Arguments required:

  • spectrum_csv - location of the parsed spectrum data file
  • subset_rows, rows_to_keep… - whether you want the function to consider only certain rows/columns of data. useful if you have multiple calibrants per plate, as this function can only handle one calibrant at a time
  • xrange - the range of wavelengths you want the function to restrict analysis to. Input the wavelength range you used for the experiment. If this causes errors, it is often beneficial to filter on the UV end, e.g. use 350-1000 or 400-1000.
  • pl_method - method to use for path length calculations. Options: "calc_each" (calculate the path length of each well separately), "calc_blanks" (calculate the path length of the blanks and use that for all wells), "volume" (calculate path length from given volume in the volume column). See vignette("path_lengths").
  • buffer_used, concentration_used, temperature_used - these related to path length calculation. The buffer_used must come from the list in view_kfactors(), so use view_kfactors() to find the closest buffer. Similarly, concentration_used must use the same units as the buffer it refers to in the view_kfactors() table. See vignette("path_lengths").
  • outfolder - where to save the files.

Warnings expected:

  • You will likely get warnings about rows containing missing values. This is normal.

Outputs produced:

..in the designated outfolder (here, fp_quantification/):

  • A processed data file of the name [raw data filename]_parsed_processed.csv (here,example_absorbance_parsed_processed.csv).
  • A list of plots in designated outfolder:
    • plot1a_raw.pdf - raw data overview
    • plot1b_raw_blanks.pdf - raw absorbance of the blank wells
    • plot2a_a9001000.pdf - raw data at 900-1000nm (relevant for path length calculation)
    • plot2b_pathlengths.pdf - path length estimations by all three methods (regardless of which was chosen)
    • plot2c_rawcm1.pdf - raw data normalised to pathlength = 1cm
    • plot3_normcm1.pdf - data normalised to blank wells (where the fluorescent protein peaks usually become visible)
    • plot4_mean_normcm1.pdf - mean of the normalised data, across replicates

..in RStudio:

  • In the R console, messages will let you follow the progress of the data processing.
  • A dataframe (which we called processed_data_spectrum) of the processed data is returned, which can be used to inspect the processing.

Let’s have a look at a fragment of the processed data:

processed_data_spectrum[195:225,c(1:3,7,9,10,12,13)] # view a fragment of the dataframe
instrument plate seal dilution measure raw_value raw_cm1_value normalised_cm1_value
spark1 uvclear noseal 1 394 0.04625 0.08629766 0.013714331
spark1 uvclear noseal 1 395 0.04685 0.08741720 0.014460689
spark1 uvclear noseal 1 396 0.04655 0.08685743 0.014087510
spark1 uvclear noseal 1 397 0.04660 0.08695072 0.013527741
spark1 uvclear noseal 1 398 0.04650 0.08676413 0.014180805
spark1 uvclear noseal 1 399 0.04660 0.08695072 0.013527741
spark1 uvclear noseal 1 400 0.04710 0.08788367 0.014553984
spark1 uvclear noseal 1 401 0.04720 0.08807026 0.015113752
spark1 uvclear noseal 1 402 0.04650 0.08676413 0.014087510
spark1 uvclear noseal 1 403 0.04630 0.08639096 0.013807626
spark1 uvclear noseal 1 404 0.04705 0.08779038 0.015113752
spark1 uvclear noseal 1 405 0.04580 0.08545801 0.012688088
spark1 uvclear noseal 1 406 0.04615 0.08611107 0.012874678
spark1 uvclear noseal 1 407 0.04600 0.08583119 0.013994215
spark1 uvclear noseal 1 408 0.04600 0.08583119 0.012221615
spark1 uvclear noseal 1 409 0.04530 0.08452506 0.012314909
spark1 uvclear noseal 1 410 0.04530 0.08452506 0.012408204
spark1 uvclear noseal 1 411 0.04495 0.08387200 0.010822193
spark1 uvclear noseal 1 412 0.04455 0.08312564 0.010728898
spark1 uvclear noseal 1 413 0.04425 0.08256587 0.009516066
spark1 uvclear noseal 1 414 0.04405 0.08219269 0.010075835
spark1 uvclear noseal 1 415 0.04500 0.08396529 0.010169130
spark1 uvclear noseal 1 416 0.04420 0.08247257 0.009702656
spark1 uvclear noseal 1 417 0.04345 0.08107315 0.007836761
spark1 uvclear noseal 1 418 0.04335 0.08088656 0.008209940
spark1 uvclear noseal 1 419 0.04305 0.08032680 0.007836761
spark1 uvclear noseal 1 420 0.04330 0.08079327 0.006623929
spark1 uvclear noseal 1 421 0.04300 0.08023350 0.008769708
spark1 uvclear noseal 1 422 0.04280 0.07986032 0.007650171
spark1 uvclear noseal 1 423 0.04235 0.07902067 0.006717223
spark1 uvclear noseal 1 424 0.04200 0.07836761 0.005690981

Note the new columns in red. The normalised_cm1_value column will be used for calculating the concentration of the FP in the next step.

While the absolute numbers are low, we can verify that the peak of absorbance of mTagBFP2 is 401 nm.

This is more easily evident in the plots, where we can also verify that the blanks are not too noisy and that the replicates were similar:


3. Get concentration using ECmax assay

Using this processed data, we can now work out the concentration of the FP in each dilution using the get_conc_ecmax() function. This takes the EC and excitation maximum of the FP in question from FPbase, and calculates the concentration from the absorbance at that position. It also normalises carefully for background signal in one of 3 ways.

Code:

proteinconcs <- get_conc_ecmax(
  protein_slug = "mtagbfp2",
  protein_seq = "MVHHHHHHGSGVSKGEELIKENMHMKLYMEGTVDNHHFKCTSEGEGKPYEGTQTMRIKVVEGGPLPFAFDILATSFLYGSKTFINHTQGIPDFFKQSFPEGFTWERVTTYEDGGVLTATQDTSLQDGCLIYNVKIRGVNFTSNGPVMQKKTLGWEAFTETLYPADGGLEGRNDMALKLVGGSHLIANAKTTYRSKKPAKNLKMPGVYYVDYRLERIKEANNETYVEQHEVAVARYCDLPSKLGHKLN",
  processed_spectrum_csv = "fp_quantification/example_absorbance_parsed_processed.csv",
  # wells_to_remove = c(),
  corr_method = "scatter", wav_to_use1 = 500, wav_to_use2 = 315,
  outfolder = "fp_quantification/concentration",
)
## FP data retrieved from FPbase.

Arguments required:

  • protein_slug - the short / lower-case form name of your FP, used to search FPbase
  • protein_seq - the sequence of your protein, for molecular weight calculations
  • processed_spectrum_csv - location of the processed spectrum data file
  • wells_to_remove - list of wells you might want to remove (e.g. if identified as anomalous in previous step)
  • corr_method, wav_to_use1, wav_to_use2 - correction method for path length correction. choose from “none” (no normalisation - absorbance at excitation maximum is used directly), “background” (subtract absorbance at wav_to_use1 wavelength) or “scatter” (extrapolate, using wav_to_use2 wavelength, the precise background to subtract).
  • outfolder - where to save the files

Warnings expected:

  • You will likely get warnings about transformation : NaNs produced, log-10 transformation introduced infinite values and rows containing missing values. These are normal.

Outputs produced:

..in the designated outfolder (here, fp_quantification/concentration/):

  • A processed data file of the name [raw data filename]_parsed_processed_ecmax.csv (here,example_absorbance_parsed_processed_ecmax.csv).
  • A small table containing estimated concentrations using all 3 normalisation methods: ecmax_coeffs.csv.
  • A table of protein concentrations joined to metadata, protein_concs_ecmax.csv, required for combining with fluorescence data in the next step, for calculating conversion factors.
  • A list of plots:
    • plot1_abs_spectra_replicates.pdf - data normalised to blank wells (identical to plot3_normcm1.pdf from process_absorbance_spectrum())
    • plot3a_abs_spectra_geomsmooth.pdf - same data as in plot1 plotted with the geom_smooth plotting function and annotated with the excitation maximum wavelength from FPbase. this serves as a check in case model fitting fails in next steps.
    • plot3b_abs_spectra_modelcheck.pdf - the results of LOESS model fitting through the absorbance data and and annotated with the excitation maximum wavelength from FPbase. Good plot to check that the model fitting worked and the FPbase excitation maximum matches the observed excitation maximum.
    • plot5a_ecmax.pdf - illustrates steps of processing: raw data, normalised data (red) and fitted data (blue).
    • plot5b_ecmax_stdmethod.pdf - visualisation of the linear fitting of data normalised with correction method “none”
    • plot5c_ecmax_baselinenorm.pdf - visualisation of the linear fitting of data normalised with correction method “baseline”
    • plot5c_ecmax_baselinenorm_baselinecheck.pdf - visualisation of the normalisation procedure using scatter
    • plot5d_ecmax_scatternorm.pdf - visualisation of the linear fitting of data normalised with correction method “scatter”
    • plot5d_ecmax_scatternorm_scattercheck.pdf - visualisation of the normalisation procedure using scatter
    • plot6a_ecmax_models_all.pdf - comparison of concentration vs dilution relationships across all normalisation methods
    • plot6b_ecmax_models_all_logplot.pdf - comparison of concentration vs dilution relationships across all normalisation methods as a log plot. Best place to verify choice of normalisation method.

.. in RStudio:

  • A dataframe (which we called proteinconcs) of the protein concentrations in each well, which can be used to inspect the processing.
proteinconcs[1:12,] # view a fragment of the dataframe
media calibrant protein replicate dilution well mw_gmol1 concentration_ngul
T5N15_pi mTagBFP2 mTagBFP2 1 1.000000000 A1 27777.47 4.842327810
T5N15_pi mTagBFP2 mTagBFP2 1 0.500000000 A2 27777.47 2.421163905
T5N15_pi mTagBFP2 mTagBFP2 1 0.250000000 A3 27777.47 1.210581952
T5N15_pi mTagBFP2 mTagBFP2 1 0.125000000 A4 27777.47 0.605290976
T5N15_pi mTagBFP2 mTagBFP2 1 0.062500000 A5 27777.47 0.302645488
T5N15_pi mTagBFP2 mTagBFP2 1 0.031250000 A6 27777.47 0.151322744
T5N15_pi mTagBFP2 mTagBFP2 1 0.015625000 A7 27777.47 0.075661372
T5N15_pi mTagBFP2 mTagBFP2 1 0.007812500 A8 27777.47 0.037830686
T5N15_pi mTagBFP2 mTagBFP2 1 0.003906250 A9 27777.47 0.018915343
T5N15_pi mTagBFP2 mTagBFP2 1 0.001953125 A10 27777.47 0.009457672
T5N15_pi mTagBFP2 mTagBFP2 1 0.000976563 A11 27777.47 0.004728838
T5N15_pi mTagBFP2 none 1 0.000000000 A12 27777.47 0.000000000

The example data should give 4.8 ng/ul for the top concentration.


Conversion factor calculation


Create a folder for this calculation:

dir.create("conversion_factors")

Assemble the metadata file, using the original metadata file, and the protein concentrations file just obtained from the ECmax assay above.

# Join files
fluorescence_meta1 <- read.csv("data/example_fluorescence_meta.csv")
fluorescence_meta2 <- read.csv("fp_quantification/concentration/protein_concs_ecmax.csv")
fluorescence_meta_joined <- dplyr::left_join(x = fluorescence_meta1, y = fluorescence_meta2)
## Joining with `by = join_by(media, calibrant, protein, replicate, well)`
fluorescence_meta_joined <- cbind(fluorescence_meta_joined[ , !names(fluorescence_meta_joined) %in% c("well")], fluorescence_meta_joined[ , "well"]) # move well column to right hand side - important for `generate_cfs()`
names(fluorescence_meta_joined)[ncol(fluorescence_meta_joined)] <- "well" # rename last column
write.csv(x = fluorescence_meta_joined,
          file = "conversion_factors/example_fluorescence_meta_joined.csv", 
          row.names = FALSE)
fluorescence_meta_joined[1:12,c(1,4,7,8,10,12:15)]
instrument channel_name media calibrant replicate dilution mw_gmol1 concentration_ngul well
spark1 blueblue T5N15_pi mTagBFP2 1 1.000000000 27777.47 4.842327810 A1
spark1 blueblue T5N15_pi mTagBFP2 1 0.500000000 27777.47 2.421163905 A2
spark1 blueblue T5N15_pi mTagBFP2 1 0.250000000 27777.47 1.210581952 A3
spark1 blueblue T5N15_pi mTagBFP2 1 0.125000000 27777.47 0.605290976 A4
spark1 blueblue T5N15_pi mTagBFP2 1 0.062500000 27777.47 0.302645488 A5
spark1 blueblue T5N15_pi mTagBFP2 1 0.031250000 27777.47 0.151322744 A6
spark1 blueblue T5N15_pi mTagBFP2 1 0.015625000 27777.47 0.075661372 A7
spark1 blueblue T5N15_pi mTagBFP2 1 0.007812500 27777.47 0.037830686 A8
spark1 blueblue T5N15_pi mTagBFP2 1 0.003906250 27777.47 0.018915343 A9
spark1 blueblue T5N15_pi mTagBFP2 1 0.001953125 27777.47 0.009457672 A10
spark1 blueblue T5N15_pi mTagBFP2 1 0.000976563 27777.47 0.004728838 A11
spark1 blueblue T5N15_pi mTagBFP2 1 0.000000000 27777.47 0.000000000 A12

You should be able to see that the concentration values have been joined to the other metadata. This joined metadata file will be our metadata for the fluorescence assay.


1. Parse fluorescence data

Parse the data. Here we’re using a different parsing function that handles standard (endpoint) and timecourse (kinetic) data from Tecan Spark instruments running Magellan software. See Data Parsing article if you have a different setup.

parsed_data <- parse_magellan(data_csv = "data/example_fluorescence.csv",
                              metadata_csv = "conversion_factors/example_fluorescence_meta_joined.csv",
                              timeseries = FALSE
)
## 9 channel(s) identified.
## Channel 1: blueblue040.
## Channel 2: blueblue050.
## Channel 3: blueblue060.
## Channel 4: blueblue070.
## Channel 5: blueblue080.
## Channel 6: blueblue090.
## Channel 7: blueblue100.
## Channel 8: blueblue110.
## Channel 9: blueblue120.

Arguments required:

  • data_csv - file path of the absorbance data file
  • metadata_csv - file path of the metadata file
  • timeseries - whether or not the data is a timeseries/timecourse/kinetic.

Warnings expected:

  • You will likely get the warning NAs introduced by coercion. This just means that empty wells were filled in with NA values.

Outputs produced:

  • A processed data file of the name [raw data filename]_parsed.csv (here,example_fluorescence_parsed.csv), in the same location where the data was found.
  • A dataframe (which we called parsed_data) of the parsed data is returned, which can be used to inspect the parsing.

View a fragment of the data frame to check.

parsed_data[1:12,c(1,4,8,12,14:24)] # view a fragment of the dataframe
instrument channel_name calibrant dilution concentration_ngul well blueblue040 blueblue050 blueblue060 blueblue070 blueblue080 blueblue090 blueblue100 blueblue110 blueblue120
spark1 blueblue mTagBFP2 1.000000000 4.842327810 A1 133 737 2960 9097 22595 50522 NA NA NA
spark1 blueblue mTagBFP2 0.500000000 2.421163905 A2 68 394 1588 4875 12140 27526 57864 NA NA
spark1 blueblue mTagBFP2 0.250000000 1.210581952 A3 36 219 886 2739 6849 15485 32882 64351 NA
spark1 blueblue mTagBFP2 0.125000000 0.605290976 A4 19 131 540 1662 4154 9504 20037 39740 NA
spark1 blueblue mTagBFP2 0.062500000 0.302645488 A5 11 88 370 1153 2868 6530 13895 27655 51126
spark1 blueblue mTagBFP2 0.031250000 0.151322744 A6 7 65 278 861 2168 4945 10525 20781 38599
spark1 blueblue mTagBFP2 0.015625000 0.075661372 A7 4 54 233 730 1830 4166 8822 17547 32679
spark1 blueblue mTagBFP2 0.007812500 0.037830686 A8 4 49 213 663 1668 3787 8036 15969 29849
spark1 blueblue mTagBFP2 0.003906250 0.018915343 A9 3 46 200 631 1582 3604 7658 15187 28256
spark1 blueblue mTagBFP2 0.001953125 0.009457672 A10 3 45 198 625 1555 3547 7516 14953 27956
spark1 blueblue mTagBFP2 0.000976563 0.004728838 A11 3 45 194 608 1523 3502 7404 14721 27357
spark1 blueblue mTagBFP2 0.000000000 0.000000000 A12 3 43 191 595 1504 3400 7237 14436 26968

Note the fact that the data is now in Tidy Format, with each fluorescence reading (at gains of 40, 50, etc..) in its own column, ready for processing.


2. Generate conversion factors

Use generate_cfs() to generate conversion factors that relates fluorescence brightness to molecule number.

fp_conversion_factors <- generate_cfs(
  calibration_csv = "data/example_fluorescence_parsed.csv",
  subset_rows = TRUE, rows_to_keep = c("A","B"),
  outfolder = "conversion_factors"
)
## Joining with `by = join_by(instrument, plate, seal, channel_name, channel_ex,
## channel_em, media, calibrant, measure)`

Arguments required:

  • calibration_csv - location of the parsed fluorescent data file
  • subset_rows, rows_to_keep… - whether you want the function to consider only certain rows/columns of data. useful if you have multiple calibrants per plate, as this function can only handle one calibrant at a time
  • outfolder - where to save the files

Warnings expected:

  • You will likely get warnings about NaNs produced. These are normal.

Outputs produced:

..in the designated outfolder (here, conversion_factors/):

  • A processed data file of the name [raw data filename]_parsed_cfs.csv (here,example_fluorescence_parsed_cfs.csv).
  • Four plots showing showing the fitting between normalised fluorescence and molecule number, and a gain vs conversion factor plot (if you have tested multiple gains).

.. in RStudio:

  • A dataframe (which we called fp_conversion_factors) of the conversion factors at each concentration.

For Advanced options for this function, see ?generate_cfs().

Let’s check how the conversion factor table looks:

fp_conversion_factors[,c(1,4,8:13)] # view a fragment of the dataframe
instrument channel_name calibrant measure gain cf beta residuals
spark1 blueblue mTagBFP2 blueblue040 40 6.090183e-12 2.581501e-11 1.0000665
spark1 blueblue mTagBFP2 blueblue050 50 3.249867e-11 1.830351e-11 1.0000817
spark1 blueblue mTagBFP2 blueblue060 60 1.276882e-10 1.228548e-11 1.0000039
spark1 blueblue mTagBFP2 blueblue070 70 4.036917e-10 7.135236e-11 1.0000836
spark1 blueblue mTagBFP2 blueblue080 80 9.783338e-10 1.451862e-10 0.9999074
spark1 blueblue mTagBFP2 blueblue090 90 2.229853e-09 4.187956e-10 1.0007114
spark1 blueblue mTagBFP2 blueblue100 100 4.698227e-09 8.614008e-10 0.9998300
spark1 blueblue mTagBFP2 blueblue110 110 9.153889e-09 1.681051e-09 0.9999546
spark1 blueblue mTagBFP2 blueblue120 120 1.648569e-08 3.052487e-09 1.0001829

Here, cf is conversion factor and residuals allows you to check the quality of the fit. We can see the fits are good, which we can also see by looking at the plots:


Assemble conversion factors


FP conversion factors

If multiple FPs are being calibrated, assembly can be used to join various files together. Here we will just simplify the conversion factor file and move it into a conversion factor folder.

fp_conversion_factors <- read.csv("conversion_factors/example_fluorescence_parsed_cfs.csv")
fp_conversion_factors <- fp_conversion_factors |>
  dplyr::select(instrument, plate, channel_name, media, calibrant, measure, gain, cf, beta)
write.csv(fp_conversion_factors, "conversion_factors/fp_conversion_factors_assembled.csv", 
          row.names = FALSE)


OD conversion factors

These can be obtained according to protocols in the FlopR package/paper. An example calibration output is provided with the example data. Let’s move it to the conversion factors folder too.

od_conversion_factors <- read.csv("data/od_conversion_factors2.csv")
write.csv(od_conversion_factors, "conversion_factors/od_conversion_factors_assembled.csv",
          row.names = FALSE)
od_conversion_factors
instrument plate calibrant measure cf
spark1 clear microspheres OD600 1.303335e-09
spark1 clear microspheres OD700 9.949125e-10


Processing data from E. coli fluorescent protein expression experiments


With conversion factors for both mTagBFP2 fluorescence and cell number (OD) in hand, we are ready to process the experimental data.

1. Parse data

First, we parse, though this time we use timeseries = TRUE:

parsed_data <- parse_magellan(
  data_csv = "data/example_experiment.csv",
  metadata_csv = "data/example_experiment_meta.csv",
  timeseries = TRUE,
  metadata_above = 1, # Well Positions
  custom = TRUE, startcol = 3, endcol = 97, insert_wells_above = 0, insert_wells_below = 1
)
## 4 channel(s) identified.
## 96 timepoints identified.
## 96 timepoints of 10 minutes = 960 minute (16 hour) timecourse.
## Channel 1: OD600.
## Channel 2: OD700.
## Channel 3: blue.
## Channel 4: bluelow.

Arguments required:

  • data_csv - file path of the absorbance data file
  • metadata_csv - file path of the metadata file
  • timeseries - whether or not the data is a timeseries/timecourse/kinetic.
  • metadata_above - number of lines of plate reader-produced metadata that exists above the data
  • custom, startcol, insert_wells_above.. - use custom = TRUE where the data doesn’t occupy the default columns 2 to 97. The others specify where the data is located. See ?parse_magellan().

Warnings expected:

  • You will likely get the warning NAs introduced by coercion. This just means that empty wells were filled in with NA values.

Outputs produced:

  • A processed data file of the name [raw data filename]_parsed.csv (here,example_experiment_parsed.csv), in the same location where the data was found.
  • A dataframe (which we called parsed_data) of the parsed data is returned, which can be used to inspect the parsing.
parsed_data[1:24,c(3,6:13)] # view a fragment of the dataframe
plasmid ara_pc volume well time OD600 OD700 blue bluelow
NA A1 0 NA NA NA NA
pS361 0 200 A2 0 0.1255 0.1137 471 17
pS361 0 200 A3 0 0.1276 0.1121 472 17
pS361 0 200 A4 0 0.1323 0.1173 481 18
pS361 0.3 200 A5 0 0.1219 0.1088 479 17
pS361 0.3 200 A6 0 0.1294 0.1154 475 17
pS361 0.3 200 A7 0 0.1290 0.1152 472 17
pS361_ara_mTagBFP2 0 200 A8 0 0.1282 0.1139 468 17
pS361_ara_mTagBFP2 0 200 A9 0 0.1228 0.1090 466 17
pS361_ara_mTagBFP2 0 200 A10 0 0.1284 0.1144 469 17
none none 200 A11 0 0.0969 0.0910 473 17
NA A12 0 NA NA NA NA
NA B1 0 NA NA NA NA
pS361_ara_mTagBFP2 0.00003 200 B2 0 0.1283 0.1144 477 18
pS361_ara_mTagBFP2 0.00003 200 B3 0 0.1253 0.1123 477 17
pS361_ara_mTagBFP2 0.00003 200 B4 0 0.1263 0.1127 474 17
pS361_ara_mTagBFP2 0.0001 200 B5 0 0.1265 0.1122 474 17
pS361_ara_mTagBFP2 0.0001 200 B6 0 0.1283 0.1150 475 17
pS361_ara_mTagBFP2 0.0001 200 B7 0 0.1241 0.1113 471 17
pS361_ara_mTagBFP2 0.0003 200 B8 0 0.1280 0.1139 476 17
pS361_ara_mTagBFP2 0.0003 200 B9 0 0.1324 0.1179 477 18
pS361_ara_mTagBFP2 0.0003 200 B10 0 0.1228 0.1090 471 17
none none 200 B11 0 0.0903 0.0858 475 17
NA B12 0 NA NA NA NA

Note the data consists of two OD measurements and two fluorescence measurements at low and high gain.


2. Process data

Process the experimental data using process_plate().

processed_data <- process_plate(
  data_csv = "data/example_experiment_parsed.csv",
  blank_well = c("A11", "B11", "C11", "D11", "E11", "F11", "G11", "H11"),
  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 = "spline",
  neg_well = c("A2", "A3", "A4", "A5", "A6", "A7"),
  
  outfolder = "experiment_analysis"
)
## Calibrating OD700 channel with conversion factor 9.95e-10...
## Calibrating blueblue fluorescence channel with conversion factor 1.28e-10...

This is a fairly involved function.

Arguments required:

  • data_csv - file path of the absorbance data file
  • blank_well - location of wells containing only growth media
  • od_name - name of column containing OD values

Fluorescence channel names:

  • flu_channels - column names in your data that represent the fluorescence channel(s)
  • flu_channels_rename - what to rename flu_channels columns to, if anything. it can be useful to rename them here to make sure your experimental data columns match the entries in your conversion factor table for that fluorescence channel/filter set.

Quench correction:

  • do_quench_correction - should it calculate corrected fluorescence values based on cellular fluorescence quenching?
  • od_type - was the OD taken at 600 or 700 nm?

Calibration parameters:

  • do_calibrate - should it calibrate fluorescence and OD values?
  • instr - what is the instrument name used in this experiment (to be matched to calibrations)
  • flu_slugs - the short / lower-case form name of your FP (to be matched to calibrations)
  • flu_gains - gain used in experiment (to be matched to calibrations)
  • flu_labels - what to label fluorescence axes in plots

Conversion factor data:

  • od_coeffs_csv - file path of OD conversion factor file
  • fluor_coeffs_csv - file path of fluorescence conversion factor file

Background autofluorescence subtraction:

  • af_model - what sort of autofluorescence model to use. options include NULL, which doesn’t use an autofluorescence model but instead normalises to the fluorescence in blank wells by time point.
  • neg_well - wells with cells but no FP to use for autofluorescence subtraction

Saving:

  • outfolder - where to save the files

Warnings expected:

  • You will likely get the warning rows containing missing values or values outside the scale range. This is normal.

Outputs produced:

..in the designated outfolder (here, experiment_analysis/):

  • A processed data file of the name [raw data filename]_parsed_processed.csv (here,example_experiment_parsed_processed.csv).
  • A list of plots:
    • OD_1_raw_normalised - Raw and normalised OD data.
    • OD_2_pathlength-normalised - OD data normalised to 1 cm path length (in OD cm-1).
    • OD_3_calibrated - Calibrated OD data (in ‘particles’ or ‘cells’).
    • blueblue_autofluorescence-normalisation-curve.pdf - Autofluorescence normalisation curve: how background fluorescence in non-fluorescent cells relates to their OD.
    • mTagBFP2_1_raw_normalised - Raw and normalised fluorescence data (in ‘relative fluorescence units’).
    • mTagBFP2_2_quench-corrected - Fluorescence data corrected for cell-based quenching (in ‘relative fluorescence units’).
    • mTagBFP2_3_calibrated - Calibrated fluorescence data (in molecules).

.. in RStudio:

  • A dataframe (which we called processed_data) of the conversion factors at each concentration.

For Advanced options for this function, see ?process_plate().

If we view a fragment of the dataframe to check it:

processed_data[14:24,c(3,6,8,9,17:23)] # view a fragment of the dataframe
plasmid ara_pc well time pathlength normalised_OD_cm1 normalised_blueblue flu_quench corrected_normalised_blueblue calibrated_OD calibrated_mTagBFP2
pS361_ara_mTagBFP2 0.00003 B2 0 0.6072014 0.041645985 12.814271 0.9765740 13.121659 25416808 102763267870
pS361_ara_mTagBFP2 0.00003 B3 0 0.6072014 0.038187495 12.155686 0.9778592 12.430916 23306069 97353662950
pS361_ara_mTagBFP2 0.00003 B4 0 0.6072014 0.038846255 9.281153 0.9776139 9.493679 23708115 74350465588
pS361_ara_mTagBFP2 0.0001 B5 0 0.6072014 0.038022805 9.124318 0.9779206 9.330326 23205558 73071159744
pS361_ara_mTagBFP2 0.0001 B6 0 0.6072014 0.042634125 11.002368 0.9762080 11.270517 26019876 88265905472
pS361_ara_mTagBFP2 0.0001 B7 0 0.6072014 0.036540595 5.841993 0.9784735 5.970517 22300956 46758555980
pS361_ara_mTagBFP2 0.0003 B8 0 0.6072014 0.040822535 11.657495 0.9768795 11.933402 24914251 93457344678
pS361_ara_mTagBFP2 0.0003 B9 0 0.6072014 0.047410135 13.910645 0.9744462 14.275437 28934705 111799169337
pS361_ara_mTagBFP2 0.0003 B10 0 0.6072014 0.032752725 5.120418 0.9798918 5.225493 19989195 40923843769
none none B11 0 0.6072014 -0.005455356 1.841709 0.9946429 1.851629 -3329438 14501172270
B12 0 NA NA NA NA NA NA NA

Note the many new columns created by process_plate().


Normalisation (black=raw, red=normalised):

Quench correction (black=normalised, red=corrected):

Calibration (red=calibrated):


3. Calculate per cell values

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

pc_data_mTagBFP2 <- calc_fppercell(
  data_csv = "experiment_analysis/example_experiment_parsed_processed.csv",
  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"
)

Arguments required:

  • data_csv - file path of the absorbance data file
  • flu_channels - column names in your data that represent the fluorescence channel(s)
  • flu_labels - what to label fluorescence axes in plots
  • remove_wells - list of wells to leave out of analysis, e.g. if they contained media or were empty
  • get_rfu_od - calculate relative fluorescence units per OD? (for non calibrated data)
  • get_mol_cell - calculate molecules per cell? (for calibrated data)
  • outfolder - where to save the files

Outputs produced:

..in the designated outfolder (here, experiment_analysis/percell_data/):

  • A processed data file of the name [raw data filename]_parsed_processed_pc.csv (here,example_experiment_parsed_processed_pc.csv).
  • A plot calibratedmTagBFP2_perCell.pdf to summarise the molecules per cell values.

.. in RStudio:

  • A dataframe (which we called pc_data_mTagBFP2) of the ‘per cell’ data.

View a fragment of the dataframe to check it:

data_to_display <- pc_data_mTagBFP2 |>
  dplyr::filter(time == max(pc_data_mTagBFP2$time)) |>
  dplyr::select(plasmid, ara_pc, time, OD600, calibrated_OD, blueblue, calibrated_mTagBFP2, calibratedmTagBFP2_perCell)
data_to_display[c(13:15,19:21,25:27,31:33),]
plasmid ara_pc OD600 calibrated_OD blueblue calibrated_mTagBFP2 calibratedmTagBFP2_perCell
950
pS361_ara_mTagBFP2 0.0001 0.7735 527156900 2474 2.015395e+13 38231.41
pS361_ara_mTagBFP2 0.0001 0.7651 521427753 2363 1.903409e+13 36503.79
pS361_ara_mTagBFP2 0.0001 0.7629 521729287 2471 2.010565e+13 38536.55
pS361_ara_mTagBFP2 0.001 0.6752 447451400 4554 3.997663e+13 89342.95
pS361_ara_mTagBFP2 0.001 0.6741 448657536 4719 4.159119e+13 92701.42
pS361_ara_mTagBFP2 0.001 0.6788 451773388 4739 4.182433e+13 92578.11
pS361_ara_mTagBFP2 0.01 0.6442 426746062 8936 8.190800e+13 191936.15
pS361_ara_mTagBFP2 0.01 0.6386 416795439 9415 8.623642e+13 206903.47
pS361_ara_mTagBFP2 0.01 0.6510 429459869 8579 7.854009e+13 182881.09
pS361_ara_mTagBFP2 0.1 0.6541 428454755 10704 9.898598e+13 231030.19
pS361_ara_mTagBFP2 0.1 0.6397 420413847 10718 9.885947e+13 235147.99
pS361_ara_mTagBFP2 0.1 0.6393 421418961 10659 9.832525e+13 233319.47

At the final timepoint, we can see that the abundance of mTagBFP2 in these samples was in the range of 40,000 to over 200,000 molecules per cell.



4. Calculate cellular concentration

calc_fpconc() can be used to estimate molecular concentration. This is somewhat similar in structure to the above.

molar_data_mTagBFP2 <- calc_fpconc(
  data_csv = "experiment_analysis/example_experiment_parsed_processed.csv",
  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_vol = FALSE,
  get_mol_vol = TRUE,
  
  od_specific_total_volume = 3.6,
  odmeasure = "OD700",
  odmeasure_conversion = 0.79,
  
  outfolder = "experiment_analysis"
)
## Note that the default 'OD-specific total volume' is 3.6 ul per (OD600 cm-1) - and requires measurement in OD600 or a conversion to estimate OD600 from the OD used.
## Using OD-specific total cell volume: 3.6ul per (OD600 cm-1).
## The empirical ratio between E. coli absorbance at OD700/OD600 is typically 0.79.
## Using OD: OD700.
## Using conversion: 0.79.

Arguments required:

  • data_csv - file path of the absorbance data file
  • flu_channels - column names in your data that represent the fluorescence channel(s)
  • flu_labels - what to label fluorescence axes in plots
  • remove_wells - list of wells to leave out of analysis, e.g. if they contained media or were empty
  • get_rfu_vol - calculate relative fluorescence units per volume? (for non calibrated data)
  • get_mol_vol - calculate molecules per volume (i.e. molar concentration)? (for calibrated data)
  • od_specific_total_volume - OD600-specific total cellular volume in ul x OD-1 x cm, i.e. the total cellular volume represented by 1 OD600 unit (in 1 cm path length). Recommended value is 3.6.
  • odmeasure - which OD measurement is being used in the data? e.g. “OD600” or “OD700”.
  • odmeasure_conversion - how to convert the measurement specified by odmeasure to OD600? i.e. OD600 = OD used / x. Use ‘1’ for OD600 (no conversion) and 0.79 for OD700.
  • outfolder - where to save the files

Outputs produced:

..in the designated outfolder (here, experiment_analysis/molar_data/):

  • A processed data file of the name [raw data filename]_parsed_processed_conc.csv (here,example_experiment_parsed_processed_con.csv).
  • A plot calibrated_mTagBFP2_concentration.pdf to summarise the protein concentration values.

.. in RStudio:

  • A dataframe (which we called molar_data_mTagBFP2) of the ‘per cell’ data.

View a fragment of the dataframe to check it:

data_to_display <- molar_data_mTagBFP2 |>
  dplyr::filter(time == max(molar_data_mTagBFP2$time)) |>
  dplyr::select(plasmid, ara_pc, time, OD700, calibrated_OD, blueblue, calibrated_mTagBFP2, calibrated_mTagBFP2_Molar)
data_to_display[c(13:15,19:21,25:27,31:33),]
plasmid ara_pc OD700 calibrated_OD blueblue calibrated_mTagBFP2 calibrated_mTagBFP2_Molar
950
pS361_ara_mTagBFP2 0.0001 0.6108 527156900 2474 2.015395e+13 8.502606e-06
pS361_ara_mTagBFP2 0.0001 0.6051 521427753 2363 1.903409e+13 8.118386e-06
pS361_ara_mTagBFP2 0.0001 0.6054 521729287 2471 2.010565e+13 8.570469e-06
pS361_ara_mTagBFP2 0.001 0.5315 447451400 4554 3.997663e+13 1.986973e-05
pS361_ara_mTagBFP2 0.001 0.5327 448657536 4719 4.159119e+13 2.061665e-05
pS361_ara_mTagBFP2 0.001 0.5358 451773388 4739 4.182433e+13 2.058923e-05
pS361_ara_mTagBFP2 0.01 0.5109 426746062 8936 8.190800e+13 4.268630e-05
pS361_ara_mTagBFP2 0.01 0.5010 416795439 9415 8.623642e+13 4.601500e-05
pS361_ara_mTagBFP2 0.01 0.5136 429459869 8579 7.854009e+13 4.067247e-05
pS361_ara_mTagBFP2 0.1 0.5126 428454755 10704 9.898598e+13 5.138075e-05
pS361_ara_mTagBFP2 0.1 0.5046 420413847 10718 9.885947e+13 5.229654e-05
pS361_ara_mTagBFP2 0.1 0.5056 421418961 10659 9.832525e+13 5.188988e-05

Concentration values are estimated at 8.5 - 52 uM.



These files can then be used to analyse data in absolute quantities…


Further information


Our paper covers the purpose and structure of these functions in more detail.