Download Supplementary Figure 1 Details of PIQED Automated Qualitative

Supplementary Figure 1
Details of PIQED Automated Qualitative and Quantitative Post-Translational Modification Analysis Workflow.
Modified peptides enriched from biological samples or peptides from proteome digestion without modification enrichment
are analyzed by data-independent acquisition. PIQED supports data from SCIEX or Thermo instruments. PIQED
automates all data analysis steps st arting from instrument .wiff or .raw files, including: (1) file conversion and pseudoMS/MS spectra generation using DIA-Umpire, (2) database searching by MS-GF+, X! Tandem, and/or Comet followed by
results refinement and combination using PeptideProphet/iProphet/PTMProphet, (3) automated spectral library generation
and fragment area extraction using SkylineRunner, and finally, (4) Skyline report generation, filtering for only localized
PTMs with certain iProphet and PTMProphet scores, option protein level correction, and table reformatting for significance
testing with mapDIA. All files produced along the analysis are output by the pipeline, including the raw search outputs, the
various refined pep.xml files, the Skyline file containing extracted peaks for manual review, and the final output from
mapDIA, which includes site-level fold change for all desired comparisons.
Supplementary Figure 2
Screenshot of the open-source PIQED GUI written in Java, windows batch, python, and R.
The software has several optional modules that can be run, where each subsequent module can automatically use the
output from the previous module. These modules are broadly divided into three sections: (1) file conversions and DIAUmpire signal extraction, (2) database searches, and (3) post processing, quantification and significance testing. The
leftmost section is used to specify parameters for file conversions and DIA-Umpire signal extraction module that
generates pseudo-MS/MS spectra for database searches. The middle section is used to set the database search
parameters for MS-GF+, X!Tandem, and Comet. The right section contains input for PeptideProphet, iProphet,
PTMProphet, Skyline peak area report generation, and mapDIA significance testing. The program user can save and
load up to four default parameter sets.
Supplementary Figure 3
Example of quantitative difference discovered using PIQED for the discovery of phosphorylation sites and changes using
previously published Q-Exactive DIA data collected from urine samples without phospho enrichment.
(A) Annotated spectra for the peptide TCVADEpSAENCDK, which contains phosphoserine pSer-83 from human serum
albumin (HSA). (B) Example of extracted ion chromatograms from the precursor (MS1) and fragment (MS2) ions showing
excellent coelution and high mass accuracy. (C) Plot of total MS1 and MS2 peak areas for all replicates measured
among the three diagnosis groups: undiagnosed pain (pain, n=12), diagnosed urinary tract infections (UTI, n=11), or
diagnosed ovarian cyst (OC, n=11). Group average of (D) raw site-level signal, (E) HSA protein-level, and (F) site-level
corrected by protein level and local TIC. Error bars are standard error. Although the protein level of HSA is not
significantly different between the diagnosis groups, we observed a statistically significant increase in abundance of pSer83 comparing the UTI group with the undiagnosed pain group with and without the normalization options available in
PIQED.
Supplementary Figure 4
Comparison of quantitative results for HSA and HSA pSer-83 using various normalization options in PIQED.
(A) Plot showing individual total site-level peak areas for the peptide TCVADEpSAENCDK containing phosphoserine
pSer-83. (B) Protein-level areas computed with mapDIA using unmodified HSA peptides. (C) Site-level areas (from A) for
the peptide TCVADEpSAENCDK containing phosphoserine pSer-83 corrected by the observed protein-level areas from
(B). (D) Site-level areas for phosphoserine pSer-83 from the peptide in (A) corrected by local total ion chromatogram
signal (TIC). (E) Site-level areas for phosphoserine pSer-83 from the peptide in (A) corrected by local total ion
chromatogram signal (TIC) and by the protein-level area from (B). In all cases with or without normalizations, this
phosphorylation site is determined to be statistically increased in urine from children diagnosed with UTI compared to the
undiagnosed pain group.
Supplementary Figure 5
Examples of high-quality annotated pseudo-MS/MS spectra showing 4/34 phosphorylation sites in Osteopontin identified
using PIQED. Osteopontin is overexpressed in a variety of cancers.
(A) Annotated spectra for pSer-219 showing the presence of phosphate neutral loss on all y-ions and a prominent
fragment ion corresponding to fragmentation n-terminal of proline. (B) Annotated spectra for a peptide containing pSer310 from the Osteopontin C-terminal, which has a nearly-complete b-ion series. (C) Annotated spectra for a peptide
containing pSer-308 and pSer-310 from the Osteopontin c-terminal, which also contains a nearly complete b-ion series
and neutral losses of one or two phosphoric acids starting at b 7 and b9, respectively. The spectra in (B) and (C) also
contain unlabeled fragment ions that correspond to a neutral loss of water from one of the unmodified serine residues.