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15N Metabolic Labeling Quantification Workflow in Arabidopsis Using Protein Prospector

Metabolic labeling using stable isotopes is widely used for the relative quantification of proteins in proteomic studies. In plants, metabolic labeling using 15N has great potential, but the associated complexity of data analysis has limited its usage. Here, we present the 15N stable-isotope labeled protein quantification workflow utilizing open-access web-based software Protein Prospector. Further, we discuss several important features of 15N labeling required to make reliable and precise protein quantification. These features include ratio adjustment based on labeling efficiency, median and interquartile range for protein ratios, isotope cluster pattern matching to flag incorrect monoisotopic peak assignment, and caching of quantification results for fast retrieval.


INTRODUCTION
Accurate and high-throughput protein quantification is fundamental to proteomic studies (Aebersold and Mann, 2003). To provide the highest quantification accuracy when comparing samples one needs to minimize differences introduced in the processing of samples and acquiring the data. This can be best achieved through the introduction of stable isotopes into samples that allow samples to be mixed and then analyzed in the mass spectrometer. The application of metabolic labeling, which uses stable-isotope labeled amino acids in cell culture (SILAC) (Ong et al., 2002) or 15 N nitrogen-containing salts (Schulze and Usadel, 2010;Guo and Li, 2011) into the whole cell or organism in vivo, enables relative quantifications of proteins on a global scale. In such a quantitative experiment, one sample is labeled with the natural abundance (light), and the other with a stable isotope of low natural abundance (heavy) during growth. The samples are mixed, processed, and analyzed by the mass spectrometer. Chemically identical peptides from these light-and heavy-labeled mixed samples co-elute by chromatography into the mass spectrometer, which can distinguish between the light and heavy peptides based on their mass difference, and thereby quantify the difference in peptide, and hence protein abundance between the samples. An alternative stable isotope-based strategy is to chemically tag peptides after enzymatic digestion; the most popular reagents for this strategy are isobaric tagging reagents Tandem Mass Tags (TMT) (Pappireddi et al., 2019). The TMT isobaric tagging reagents allow comparison of a larger number of samples, but the labeling is done at the peptide level after sample digestion and then samples are mixed. In contrast, the metabolic labeling is introduced into the proteins during growth, thus samples can be combined at the beginning, minimizing variations introduced by sample processing that can compromise quantification accuracy (Piehowski et al., 2013).
Although SILAC has been widely used in animal cell lines and has been the gold standard for MS-based proteomics quantification (Schubert et al., 2017), 15 N-labeling based quantitative applications are still quite limited in plants despite it being cheaper (Arsova et al., 2012). This could be due to the complexity of the data analysis. SILAC pairs are easily identifiable because they have well-defined mass differences as typically only lysine and arginine are labeled. In contrast, in 15 N labeling, each amino acid in the expressed proteins is labeled, and therefore, the mass difference in 15 N pairs varies depending on the number of nitrogen atoms in their composition. Also, as more amino acids are being labeled, the effect of incomplete incorporation of the heavy isotope can be more pronounced under some conditions, such that isotope clusters of heavy labeled peptides in the survey scan MS 1 spectra are generally broader, making it harder to identify the monoisotopic peak. This can lead to significantly reduced identification of heavy labeled peptides.
There are very few freely available software tools with workflows that can analyze large-scale 15 N labeled samples. Such tools include MSQuant (Mortensen et al., 2010), pFIND (Li et al., 2005), and Protein Prospector (Chalkley et al., 2005;Li et al., 2005). The workflow using MSQuant normally requires manual inspection of the pairs of the light and heavy forms that both fit with expected isotope envelope distribution; those that don't fit the criteria will be omitted from further analysis (Kierszniowska et al., 2009). This makes it very time-consuming for a large dataset because of the manual inspection prerequisite. In addition, if both forms need to be present for quantification, then there will be a high false-negative rate for some of those highly biologically interesting proteins which only express in one of the two conditions, or from immunoprecipitated (IP) samples where those proteins will be only in the bait-IP but may be completely absent in the control IP.
Here, we present the 15 N quantification workflow based on the free web-based software Protein Prospector (Chalkley et al., 2005;Li et al., 2005). After data search with respective 14 N and 15 N search parameters, quantification between the light and heavy peptide pairs is done based on the identification of either the light or heavy peptide, or both. The calculated peptide ratio is then adjusted based on the labeling efficiency input. Peptide ratios are then compiled into protein-level statistics such as median and interquartile ranges. Additional features in Protein Prospector include a Cosine Similarity (CS) score which can be utilized to reduce manual checking of spectra and a cache function that enables efficient result retrieval through cached result storage. This workflow allows us to report quantifications of thousands of proteins and is applicable to the quantification of the total proteomes, sub-proteomes, and immunoprecipitated samples (Park et al., 2019;Garcia et al., 2020;Bi et al., 2021). It can be also applied to the quantification of post-translational modification with a slight modification.

MATERIALS AND EQUIPMENT
The materials and data acquisition for the first Col/acinus-2 pinin-1 dataset were detailed in Bi et al. (2021). Briefly, the wild type (Col) and acinus-2 pinin-1 plants were grown on Hoagland medium containing 14 N or 15 N (1.34 g/L Hogland's No. 2 salt mixture without nitrogen, 6 g/L Phytoblend, and 1 g/L KNO 3 or 1 g/L K 15 NO 3 (Cambridge Isotope Laboratories), pH 5.8) for 14 days under the constant light condition at 21-22 • C on vertical plates. Proteins were extracted from six samples (one 14 N-labeled Col, two of 15 N-labeled Col, two of 14 N-labeled acinus-2 pinin-1, and one 15 N-labeled acinus-2 pinin-1) individually using SDS sample buffer and mixed as the following: one forward sample F1 ( 14 N Col/ 15 N acinus-2 pinin-1) and two reverse samples R1 and R2 ( 14 N acinus-2 pinin-1/ 15 N Col) and separated by the SDS-PAGE gel with a very short run (∼3 cm). Two segments (upper part (U) ranging from the loading well to ∼50 KD; lower part (L) ranging from ∼50 KD to the dye front) were excised, trypsin digested, and analyzed by liquid chromatography-mass spectrometry (LC-MS) as described in Bi et al. (2021) on a Q-Exactive HF instrument using 50 cm column ES803.
For the second Col/sec-5 dataset, the WT and sec-5 plants were grown on Hoagland medium containing 14 N or 15 N (1.34 g/L Hogland's No. 2 salt mixture without nitrogen, 6 g/L Phytoblend, and 1 g/L KNO 3 or 1 g/L K 15 NO 3 (Cambridge Isotope Laboratories), pH 5.8, 1% Sucrose) for 14 days. Plates were placed vertically in a growth chamber under the constant light condition at 21-22 • C. Whole plant tissues were harvested in liquid nitrogen. Proteins were extracted from eight samples (two 14 N-labeled Col samples -1 and 5; two of 15 N-labeled Col samples -2 and 6; two of 14 N-labeled sec-5 samples -3 and 7; and two 15 N-labeled sec-5 samples -4 and 8) individually using 2X SDS sample buffer (plant tissue mass (mg): buffer (µL) ratio = 1:3) and mixed as the following: two forward samples F1 and F2 ( 14 N Col/ 15 N sec-5, Mix1+4; Mix 5+8) and two reverse samples R1 and R2 ( 14 N sec-5/ 15 N Col, Mix 2+3; Mix 6+7) and separated by the SDS-PAGE. Five segments were excised, and trypsin digested. The peptide mixtures were desalted using C18 ZipTips (Millipore) and analyzed by liquid chromatography-mass spectrometry (LC-MS) the same as above described in Bi et al. (2021) on a Q-Exactive HF instrument using 50 cm column ES803.
The following items are required for data analysis in this method.
(1) A personal computer with internet access and 8 GB or above of RAM.
(3) Protein Prospector 1 . We recommend using a local installation for quantification as it needs to access the raw data. A version for local installation is provided for free upon request through the above website. (4) The Thermo Raw FileReader package. (5) Peak list generation software (e.g., MSConvert, part of the Proteowizard package that can be downloaded for free). (6) Image J (can be download for free from). 2 FIGURE 1 | Workflow of 15 N metabolic labeling protein quantification used in Protein Prospector. There are two stages: Stage I-Identification of proteins; Stage II-Quantification of proteins/peptides. Labeling efficiency is determined by the user and then input as a parameter to correct ratios to generate adjusted peptide ratios. Based on the identification of peptides either from 14 N search [L] or 15 N search [H], the PP software finds the matching counterpart and retrieves isotope cluster intensities and/or areas. The ratios between the peptide pair are reported and compiled into protein-level statistics such as median and interquartile ranges. CS scores are calculated on the matched pairs based on the comparison of experimental vs. theoretical isotope distributions.

Overview of the Procedure
This section outlines the major steps of 15 N Identification and quantification (ID and Quan) analysis. A detailed step-by-step protocol is provided in the procedure below. The ID and Quan analysis involves four major steps as listed in Figure 1 workflow.
(1) Identify 14 N and 15 N labeled proteins by searching the data with corresponding parameters separately. (2) Determine the labeling efficiency or enrichment of 15 N labeled peptides by comparing the experimental to the theoretical peak isotope profile for peptides with different labeling efficiency. (3) Submit quantification in "Search Compare" with userspecified parameters to extract quantification information. (4) Retrieve the report for the quantification and (optional) determine the quality of quantification and follow up with informatics analysis.
Step 1: Identification: Search and Identify 14 N and 15 N Labeled Proteins The peak lists are first generated from the raw files using inhouse peaklist generator script PAVA (Guan et al., 2011) and deposited into the folder, which is a mirror of the raw file folder, and searched against the Arabidopsis database. A project name is created for the search. Files from the same experiment can be searched together under the same project name with separate search parameters for 14 N and 15 N peptides as listed in Table 1.

Note
(1) If the samples are enriched for post-translational modifications (PTMs), users need to include these in the variable parameters for both the 14 N search [L] and 15 N search [H] search. (2) We recommend both the MS 1 survey and MS 2 fragment scans be acquired with high resolution.
Step 2: Determine the Labeling Efficiency or Enrichment The 15 N labeling can often be incomplete, but typically constant across all proteins in a given experiment (Skirycz et al., 2011). However, the labeling efficiency between different experiments can range between 93-99% after 14 days of labeling on plates or in liquid culture for Arabidopsis plants, depending on the chemical used, labeling duration (the number of the plant cell doubling), and the availability of the nitrogen. If a sample has 95% labeling enrichment, it means the 15 N labeled peptide has 95% of 15 N and 5% standard 14 N. Because Protein Prospector uses only the monoisotopic peak (M) for quantification, the accuracy of quantification will be affected if the ratio is not corrected for the labeling efficiency. To do this, PP allows estimation of the labeling efficiency in  The above parameters apply to data acquired on high-resolution Orbitrap data for both MS 1 survey scan and MS 2 fragment scan and can be easily adjusted to data acquired on other instruments if necessary.
the "MS-Isotope" module. MS-Isotope allows plotting of the theoretical isotope distribution pattern for user-defined levels of heavy isotope incorporation (Supplementary Figure 1), and the user will manually compare these plots to observed distributions to determine the labeling efficiency. Figure 2 and Supplementary Figures 1, 2 show how labeling efficiency is determined in one experiment. Figure 2A shows theoretical pattern computed for a range (95-99%) of labeling efficiency, and notably, M-1/M ratio is most affected by different labeling efficiency (Schaff et al., 2008), and thus this ratio is used to calculate the actual incorporation rate. Peptides with small mass (m < 1500) are more preferred for calculation of incorporation, because the M peak of such peptides is the largest peak and can be set as 100% maximum for easy calculation, and M-1/M peak is reversely correlated with the labeling efficiency ( Figure 2B) (Schaff et al., 2008).
To calculate M-1/M ratio, one easy way is to copy the image of the experimental peaks from Protein Prospector to imageJ. The intensity of monoisotopic peak (M) is set as known distance of 1, then M-1 peak intensity is measured and the percentage ratio of M-1/M is calculated, which is used to compare to theoretical numbers using MS-Isotope (Supplementary Figure 1) so to determine the labeling efficiency. Multiple peptides from different abundant proteins should be examined. We found in general examining about 8-10 peptides from different proteins (Supplementary Figure 2) is sufficient because the labeling efficiency within an experiment is relatively constant (Schaff et al., 2008). Based on these data, the overall labeling efficiency for this experiment is averaged around 97.0% (Supplementary Figure 2). Such manual calculation could be automated by the PP in the future.
By providing this labeling efficiency as a parameter when calculating the quantification, PP determines what percentage of the isotope cluster for a given peptide should be present in the monoisotopic peak, and therefore, the abundance of the satellite peaks (before M peaks due to incomplete labeling) is added to the total peptide abundance for ratio calculation.
Step 3: Perform Quantification Identification of at least one version of peptide ( 14 N or 15 N) in the pair of the peptides is necessary for quantification. For instance, if the peptide sequence is identified in the 14 N search, Protein Prospector will compute the heavy-labeled peptide's expected sequence and composition and derive the mass and isotope distribution. To improve the quality of the quantification, Protein Prospector can average together scans over a time window around when the peptide was identified to improve the signal to noise. It can use peak intensities and/or peak areas for ratio calculation based on the monoisotopic peak. The ratio reported by Protein Prospector is in the format of light divided by heavy (L/H) no matter whether the peptide is identified from 14 N search or 15 N search. Figure 3 displays example Search Compare parameters for quantification. The parameters can vary depending on the experimental goals and are user-specifiable.

Note
(1) In our data, peptide chromatographic peak widths are about 30 s, but using a narrow window (−5 to +15s) in general gives better quantification results as this nearly always includes the apex of elution but is less prone to co-eluting peptide interference. The peak width is dependent on the column and the gradient, and should be empirically examined.
(2) While our data is typically acquired at 120,000K resolution, we find a 240,000K resolution setting in PP gives us more consistent quantification data to results using Skyline for quantification (Schilling et al., 2012) (parameters: ± 5 ppm cut-off in centroid mode).

Large-Scale Data Analysis and Quantification
This workflow can quantify thousands of proteins simultaneously. We demonstrate its performance using three datasets listed in the Table 2 and four new datasets (Col/sec5). Previously, we had shown ACINUS and PININ genes regulate transcription and alternative splicing and we hypothesized some proteins have altered expression in the double mutant. Therefore, MS experiments were designed to identify these altered proteins on a global scale.
Only two organisms can have very good labeling efficiency within a short period (1-2 weeks): algae (Kim, unpublished) and Arabidopsis (Garcia et al., 2020), have so far been shown to have a good labeling efficiency with 98-99% efficiency being possible. In contrast, tomatoes can achieve 99% labeling efficiency after 2 months of growth (Schaff et al., 2008). However, while we consistently observe 98-99% labeling efficiency in algae (Kim, unpublished), we have observed that labeling in Arabidopsis plants is more variable, ranging from 93-99% depending on the experiment. Within one experiment, the labeling efficiency FIGURE 3 | Search Compare parameters suitable for 15 N quantification. The users can generate different formats of reports depending on the next step analysis. Users can choose a cache function in the drop-down, as highlighted in [1], which allows the users to store the analysis result and retrieve the analysis data quickly in the future. For protein quantification, we recommend checking median [2] and IQR [3] to report a median ratio of peptides for a protein and the interquartile range. CS score [4] is selected to check the quality of the match to the isotope cluster. Zero noise [5] should be checked for high-resolution MS 1 data so the PP will not impute numbers based on the local noise level. Retention (RT) interval between -5 to 15s [6] is the window before and after when the MS 2 scan was acquired to use for averaging MS1 spectra before calculating peak ratios. The intensity of the respective peptides is averaged across the window and the L/H ratio is reported. Resolution [7] and labeling efficiency [8] are manually filled based on the resolution setting for the MS 1 data in the instrument settings and the data labeling efficiency decided in the previous step of this workflow, respectively. Figure 2). With less complete labeling, the identification rate of heavy labeled peptides is significantly lower than light due to errors in monoisotopic peak assignment ( Table 2). If the labeling efficiency is achieved 98.5% or above, the identification rate between 14 N and 15 N search is similar in our experience.

in different proteins is relatively constant (Supplementary
High-level labeling depends on three factors: (1) 15 N containing salt needs to be over 99% purity; we find 15 N chemicals from Cambridge Isotope Laboratories are generally high-purity. (2) The labeling time. We recommend growing Arabidopsis for 14 days to achieve high labeling efficiency. If plants can only be labeled for a shorter time before harvesting, then it is recommended to label the plants for one generation using a hydroponic system and start the experiment using the labeled seeds. If the Arabidopsis plants are small after 14 days of growth, then the labeling efficiency will be lower, for instance, our acinus-2 pinin mutants are smaller than wild-type plants, therefore the labeling efficiency is lower than wild-type with the same duration of labeling. (3) The availability of the 15 N salt. Seeds should not be sown too many on solid-medium plates or in the liquid medium. We recommend the Arabidopsis plants are labeled 14 days or more to achieve high labeling efficiency and high identification rate, but users should be cautious not to stress plants by leaving them on medium for too long. Almost all proteins except seed storage proteins are labeled. These are not synthesized during the seedling stage and therefore they don't incorporate the 15 N labeling during growth and will remain unlabeled.

High-Resolution Scans for Both MS 1 and MS 2 Are Key for High-Quality Data
Co-eluting peptides are common problems, especially in highly complex samples, and interfere with quantification. Highresolution scans in MS 1 reduce peak overlap, improving the accuracy of quantification (Mann and Kelleher, 2008), so we typically acquire our data at 120K resolution.
High mass accuracy in MS 2 helps to reduce the false discovery rate (FDR). Higher FDR was reported in the 15 N sequence assignments due to more isobaric amino acid forms present in 15 N labeling (Nelson et al., 2007) when the MS 2 fragmentation was done using a low-resolution and mass accuracy QTOF2. To check this possibility in MS 2 data acquired at high resolution, we compared the FDR in our seven labeled experiments listed in Table 2. After we combined peptides for 14 N and 15 N searches together with 1% FDR, we parsed the target and decoy 14 N and 15 N matches and calculated the FDR separately. We found the 15 N data results had a lower FDR than that of 14 N data search when MS 2 scans were done in the highresolution and high-mass accuracy Orbitrap mass spectrometer (Figure 4). This trend is more pronounced in the higher labeling efficiency datasets. If FDR was calculated based only on first three datasets (Col/acinus-2 pinin-1), there was no significant difference between 14 N and 15 N FDR, despite the average of FDR is slightly lower in 15 N search. When four more Col/sec-5 datasets were included for comparison, the 15 N FDR is significantly lower than 14 N one, indicating using high-resolution and high-accuracy measurement, the unique mass of 15 N modification to the amino acid may empower less random matches in data searches.

Protein Quantification Process and Result
After each peptide is quantified, they are compiled into protein groups in "Search Compare". The spread of ratios for peptides from the same proteins are measured using the interquartile range, and Q1 (the lower quartile), median, and Q3 (the upper quartile) are reported, as illustrated in Figures 5A,B and quantification of the pairs can be visualized as Figures 5C,D).
Here we include two biological experiments (including one forward and one reverse label of Col/acinus-2 pinin-1 datasets) as a demonstration.
To calculate the median and Q1, Q3, the log base 10 of all the ratios of the peptides from the same protein are first calculated in Protein Prospector to generate log base 10 of median and Q1, Q3, followed by converting these log values to normal values by raising 10 to the power.
To plot these quantification results, an R script is written as in Supplementary Data 1. In the R script, the log base 2 of all the ratios of the peptides are calculated and converted back to normal values by ratio 2 to the power. The plot is the same as displayed in Protein Prospector, no matter base 10 or 2 is used.
A single protein can often be quantified by multiple peptides. A median value is preferentially reported instead of a mean value, as outliers, which are not unusual, can significantly skew the mean ratio, whereas median values are more tolerant. In general, the more peptides quantified from a single protein, the more accurate the median number is to the actual ratio. If the Q1 and Q3 are quite tight, then the quantification results are quite reliable. If the reciprocal labeling gives similar results, such as SR45, then the quantification should be reliable, even if the total peptides from this protein are only a few. We recommend at least three to four biological experiments be done for quantification, including at least one reciprocal labeling experiment (Wang et al., 2002).

Evaluation of Quantification Result
After the quantification is done, the users can evaluate the quality of the quantification of each protein and peptide of interest. Protein Prospector provides interactive feedback during the quantification process to allow for manual validation of the quantification results or visual assessment of what went wrong in case the ratio is incorrect. For protein quantification, a tight range between Q1 and Q3 often indicates the quantification is reliable. In cases where the range is big and the protein itself is of interest, then users can use the Cosine Similarity (CS) score to determine the quality of the matches or manually check them. CS scores are set up to determine the quality of the matching (L+H) peaks. Once the peptide sequence is identified, the elemental composition of the peptide is generated based on the peptide sequence. The CS score, similar to the Isotope Dot Product "idotp" product used in Skyline (Schilling et al., 2012), automatically measures the similarity between the experimentally measured isotope pattern and the calculated pattern using the M, M+1, M+2 peaks, thus reducing manual checking time by  ACT2 auto-flagging the contaminated peaks (Figure 6). The CS score ranges between 1.0 (a perfect match) and 0.0 and can be determined by measured peak intensity or area. Figure 6A shows the pair have both good CS scores and the L/H ratio of this peptide is close to the median number of the protein. Figure 6B shows one peptide in the pair of another peptide from the same protein gives a lower CS score (the M peak is contaminated with a co-eluting peptide), and thus the L/H ratio of this peptide will produce an outlier ratio. Importantly, Protein Prospector takes account of labeling efficiency when calculating the CS score, as low labeling efficiency changes the isotope pattern quite dramatically. It should be noted that the CS score will be less accurate when the peak intensity of the peptide is very low.

Analysis Data Storage and Quick Data Retrieved Enabled by Cache Function
Users can create a cache file when submitting the quantification in Search Compare. This stores the data required to regenerate the Search Compare report in a JSON file. The cache function is quite useful for various reasons: (1) when the user needs to retrieve the data or manually check the data, there is no need to re-calculate the quantification, which can take many hours for a large dataset. With the cache file, the reports come up quickly for a few seconds rather than hours; (2) Often it is hard to display an HTML peptide report when many proteins or peptides are quantified. The cache function can allow visualization of such reports easily.

Applications and Limitations, Further Steps and Perspectives
This workflow allows users to report quantification of thousands of proteins and is applicable to the quantification of the total proteomes, sub-proteomes, and immunoprecipitated samples (Park et al., 2019;Garcia et al., 2020;Bi et al., 2021). During the extraction of elution profile of every peptide identified, Protein Prospector averages together scans over a time window but doesn't fit the peak shape to a Gaussian function. Therefore, each identified peptide/protein will be quantified and none gets discarded due to failing the scoring threshold for fitting the Gaussian function.
To get high quality data, we recommend to get 97% or above labeling efficiency to achieve higher ID rates in 15 N samples, so more proteins will be reproducibly identified and quantified between different replicates. Data acquired on high resolution and high accuracy instruments will also improve the quality of the dataset.
A systematic normalization is normally required before comparing results between different experiments (Ting et al., 2009), as the samples are rarely mixed at exactly 1:1. One choice is to use the median number of all the quantified proteins, or median number of top one hundred abundant proteins (Table 2). Alternatively, users can use housekeeping proteins that are assumed to not change for normalization.
Statistical analysis of quantification data on three or more replicates is advised. Users need to determine how to apply statistical analysis on the data using a separate tool. Benjamini-Hochberg (BH) multiple hypothesis test has been used to determine significant regulated PTM peptide groups in 15 N metabolic labeled samples (Wong and Li, 2021). A standardized statistic pipeline for protein quantification is still lacking, particularly a pipeline that can leverage quantification ratios of each peptide from a protein. Our current workflow uses a median value, which takes advantage of the quantification ratios of each peptide but is less affected by outliers than using the mean. However, the statistical power utilizing quantifications from these multiple peptides from single protein has not been explored and awaits development in the future.
A targeted quantification strategy is recommended for further analysis of proteins of interest because this provides more accurate quantification and is less likely to have missing values, particularly in the 15 N labeled samples (Bi et al., 2021;Reyes et al., 2021). In addition to targeted analysis, data-independent acquisition (DIA) (Zhang and Bensaddek, 2021) can also be utilized, which can be done in label free samples or combined with 15 N metabolic labeling in the future. DIA benefits from having few missing values, but more efforts will be needed to deconvolve the mixed MS 2 spectra in DIA datasets. 15 N metabolic labeling has been utilized in studies of analyzing protein synthesis and degradation (Guan et al., 2011). These studies are based on incomplete and often low incorporation rates which result in very broad satellite peak distributions and cause 15 N labeled peptide isotope clusters to overlap with 14 N labeled peptide clusters. As Protein Prospector doesn't deconvolve the 15 N distribution from the 14 N distributions (separate the 14 N and 15 N signal when peaks overlap), the presented workflow will not provide accurate quantification in this type of study. On the other hand, for chase studies that analyze the assembly kinetics in vitro (Bunner and Williamson, 2009), the presented workflow can be applied because the proteins involved have a very high labeling efficiency.
This workflow can be also applied to the quantification of post-translational modification with a slight modification. The users will include related PTM search parameters into data search. Instead of reporting median number at protein level, ratios from each peptide are reported and then compared across different replicates.

DATA AVAILABILITY STATEMENT
The mass spectrometry proteomics data for this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository. Col/acinus-2 pinin-1 and Col/sec-5 data are available via ProteomeXchange with identifier PXD030081 and PXD030575.

AUTHOR CONTRIBUTIONS
RS and S-LX designed the experiment. RS performed the experiment. RS, AVR, and S-LX analyzed the data sets and generated figures and wrote the manuscript. PRB and RJC provided technical support for Protein Prospector and revised the manuscript. Z-YW provided suggestions. All authors contributed to the article and approved the submitted version.

FUNDING
This research was funded by the NIH grant R01GM135706 to S-LX and Carnegie endowment fund to Carnegie mass spectrometry facility.