Cysteine-Directed
Isobaric Labeling Combined with
GeLC-FAIMS-MS for Quantitative Top-Down Proteomics
Numerous biological processes during the
transcription (e.g., mRNA-splicing)
and during or after translation can lead to the formation of manifold
protein molecules, namely proteoforms, of which the number exceeds that of the genes by far. The various
modifications lead to a steep increase in proteome complexity, with
the different proteoforms having different physicochemical properties
and biological functions. Thus, the identification and quantification
of proteoforms is an essential prerequisite to understanding biological
processes at the molecular level. Proteomics approaches can
be classified into two major strategies:
bottom-up (BUP) and top-down proteomics (TDP). In BUP, proteins are
digested into peptides, which are then analyzed, e.g., by liquid chromatography–mass
spectrometry (LC-MS), and assigned to protein groups by employing
protein inference algorithms. While BUP approaches are well-elaborated
and highly sensitive, they lead to a loss of proteoform information. , In contrast, TDP is based on intact proteoform analysis and, thus,
retains the entire proteoform information. However, a limitation of
TDP is the inherently lower sensitivity when analyzing large proteoforms
compared to small peptides identified by BUP. With the increasing
size, the number of isotopes and charge states increases, splitting
the overall signal intensity and, thus, decreasing the overall sensitivity. Besides that, problems of TDP are encountered,
e.g., by the chromatographic separation of large proteoforms and the
high spectral complexity at the MS1 and MS2 levels, resulting in overlapping
peaks. Due to these reasons, TDP currently faces an upper mass limit
of approximately 30–35 kDa. , In all
proteomics approaches, efficient separation of the analytes
prior to MS is a major step to cope with sample complexity. In this
respect, in the early days of proteomics, GeLC-MS had become a workhorse technology in BUP. This method combines
intact proteoform level separation by sodium dodecyl sulfate-polyacrylamide
gel electrophoresis (SDS-PAGE), followed by an in-gel digestion of
the proteins, peptide elution, and subsequent LC-MS-based analytics
at the peptide level. The application of GeLC-MS for TDP was,
for a long time, hampered
by low recovery rates of intact proteoforms from polyacrylamide gels.
The development of PEPPI-MS (Passively Eluting Proteins from Polyacrylamide
gels as Intact species for MS) helped
to overcome this hurdle. With its ability to prefractionate and isolate
proteoforms in different mass ranges, easily achieved by cutting the
gel in distinct mass zones prior to elution, the PEPPI approach showed
particularly beneficial in combination with FAIMS utilizing internal
compensation voltage stepping , − (GeLC-FAIMS-MS). For quantitative analysis of proteoforms,
label-free quantification
(LFQ) has been applied in numerous TDP approaches. − Limiting factors
are long instrument times due to the need to analyze a sufficient
number of replicates, and the combination with multidimensional separation
schemes is critical due to variations in sample preparation. Recently, a proteoform reaction monitoring approach
has been developed, enabling the targeted quantification of proteoforms. , Isobaric labeling enables multidimensional
separation and minimizes the instrument time required, however, is
still underrepresented in TDP due to several challenges. Amino-directed
isobaric labeling of intact proteins can
suffer from factors such as potential over- and under-labeling, leading
to a large number of species out of a given proteoform and is accompanied by a number of challenges
in data interpretation. Despite these
challenges, several recent studies employed amino-directed isobaric
labeling for TDP quantification. − An alternative
approach recently introduced as a viable alternative
for quantitative TDP is Cys-directed isobaric labeling using iodo-tandem
mass tags (iodoTMT). While iodoTMT inherently
excludes proteoforms not containing cysteine from the quantification,
current limitations in database search and labeling efficiency present
in aminoTMT are omitted with this approach. In this study, we explored the combination of cysteine-directed
isobaric labeling with the GeLC-FAIMS-MS approach . The quantitative accuracy
of the workflow was evaluated using a defined ratio of iodoTMT sixplex-labeled Escherichia coli proteome. We optimized the GeLC-FAIMS-MS/MS
workflow (e.g., fragmentation strategy, isolation width, and the application
of FAIMS with internal CV stepping) to increase the number of quantified
proteoforms and reduce ratio compression. A mass feature-based quantification
method was integrated into the FLASHDeconv deconvolution software, which allows standardized TMT reporter ion detection
for large data sets. With the optimized GeLC-FAIMS-MS workflow and
data analysis pipeline, the relative proteoform abundances in E. coli grown on either glucose or acetate as
the sole carbon source were analyzed. 

Materials Unless stated otherwise, all chemicals and
solutions were purchased from Sigma-Aldrich (Steinheim, Germany).
Ultrapure water (18.2 MΩ/cm –1 ) was prepared
using the Arium VF 611 system (Sartorius, Göttingen, Germany). Escherichia coli Cultivation Escherichia coli K-12 strain MG1655
was cultured as described previously. Briefly, bacteria were cultured in M9 minimal medium supplemented with glucose (15 mM) or acetate
(45 mM) as the sole carbon source. Bacteria were inoculated at an
optical density at 600 nm (OD 600 ) of 0.1 and incubated
at 37 °C under constant shaking. The cells were harvested at
an OD 600 of approximately 1 by centrifugation for 3 min
at 3,000 rcf and room temperature. The cells were washed with ultrapure
water and stored at −80 °C prior to cell lysis. Cell Lysis Frozen aliquots of E. coli cells were resuspended in 1 mL of 8 M guanidine hydrochloride, 200
mM triethylammonium bicarbonate (TEAB, pH 8.5), 1× cOmplete EDTA-free
protease inhibitor (Roche, Basel, Swiss) and lysed using an ultrasonic
source (Bandelin electronics, Berlin, Germany). Samples were cooled
on ice during sonication in ten cycles in 30 s (Power 28%). Cellular
debris was removed by centrifugation at 21,100 rcf for 30 min at 4
°C, and the protein concentration was determined utilizing the
Pierce BCA Protein assay kit (Thermo Scientific, Waltham, MA). Intact Protein IodoTMT Labeling Cys-directed iodoTMT
labeling was conducted to label all proteoforms directly after cell
lysis, as described previously. In brief,
160 μg protein was transferred into a reaction tube for each
TMT channel, and the volume was filled up to 80 μL with lysis
buffer. For proteoform reduction, 1 μL 200 mM Tris(2-Carboxyethyl)-phosphine
(TCEP) was added and incubated for 60 min at 50 °C and 800 rpm
on a shaker. The iodoTMTsixplex reagents (Thermo Scientific) were
resuspended in 10 μL methanol and added to the samples, which
were then incubated for 50 min at 37 °C and 800 rpm in the dark.
The reaction was quenched by adding 4 μL of 200 mM DTT and incubation
for 15 min at room temperature. The samples were combined prior to
purification by methanol-chloroform water precipitation. Prior to
proteoform separation by SDS-PAGE, the protein pellets were resuspended
in Laemmli buffer and heated for 15 min
at 70 °C. For a proof of principle experiment, one biological E. coli replicate (grown under glucose condition)
was divided into six aliquots and labeled with iodoTMT sixplex using
a defined ratio of 8:4:1:1:4:8 (126:127:128:129:130:131) ( A). For the quantitative
proteoform analysis of E. coli grown under glucose or acetate as the sole carbon source, three
biological replicates each were labeled with different iodoTMT channels
(channels 126–128 for the glucose and channels 129–131
for the acetate samples) ( D-E). SDS-PAGE For gel electrophoretic separation, a 16%
separation gel (2.1 mL ultrapure water, 5.3 mL 30% acrylamide/Bis,
2.5 mL 1.5 M Tris-HCl pH 8.8, 100 μL 10% (w/v) SDS, 50 μL
10% (w/v) ammonium persulfate (APS), 10 μL tetramethylethylenediamine
(TEMED)) in combination with a 4% stacking gel (2.1 mL ultrapure water,
1.3 mL 30% acrylamide/Bis, 2.5 mL 0.5 M Tris-HCl pH 6.8, 100 μL
10% (w/v) SDS, 50 μL 10% (w/v) APS, 10 μL TEMED) was cast
using the PROTEAN 1 mm system (BioRad, Hercules, CA). Each sample
was loaded into two adjacent gel pockets, with 80 μg loaded
for the enrichment of proteoforms smaller than 40 kDa and 100 μg
for the fractionation approach. The proteoforms were separated using
an electric field at a constant voltage of 50 V, and after the running
front reached the separation gel, the voltage was increased to 90
V. The electrophoretic separation was monitored with the Pierce Prestained
Protein Ladder (Thermo Scientific). For enriching proteoforms smaller
than 40 kDa, the electrophoresis was terminated after the 40 kDa marker
band was separated from other marker bands ( B, D). In contrast, for fractionating the
proteoforms in 10 fractions, the electrophoresis was performed until
the running front reached the end of the gel ( E). After electrophoresis, the gels
were incubated for 20 min in fixing solution (40% (v/v) methanol,
5% (v/v) acetic acid), and the proteoforms were stained for 30 min
using Coomassie staining solution (0.2% (w/v) Coomassie R250, 0.05%
(w/v) Coomassie G250, 10% (v/v) ethanol, 40% (v/v) methanol, 2% (v/v)
glycerin). Fixation of the gel was conducted as it has been shown
to increase the recovery of small proteoforms. The gels were destained in a destaining solution (10% (v/v) ethanol,
5% (v/v) acetic acid) overnight. Passive Protein Extraction from the Gel The proteoforms
were extracted from the gel using the polyacrylamide-gel-based prefractionation
for analysis of intact proteoforms (PEPPI) protocol as previously
described. In brief, the gels were washed
with water before the gel regions of interest of two adjacent gel
lanes were excised with a scalpel and transferred into a 1.5 mL reaction
tube. The gel pieces were homogenized for 1 min using a BioMasher
plastic pestle (Nippi, Tokyo, Japan), and 300 μL of 0.1% (w/v)
SDS, 100 mM ammonium bicarbonate (ABC, ∼pH 9) were added, followed
by 30 s of further homogenization. Alternatively, for the fractionation
of the sample into ten fractions, an extrusion-tip protocol − was employed to crush the gel pieces and parallelize the extraction.
Extrusion-tips were manufactured by cutting the top of a GELoader
tip (Eppendorf, Hamburg, Germany), so that it could be inserted into
a 200 μL tip ( Suppl. Figure S1 ).
Ten fractions below 40 kDa were excised and subjected to the extrusion-tips
placed in 2 mL tubes. Extrusion was conducted by centrifugation for
4 min at 21,100 rcf. Then, 300 μL of 0.1% SDS (w/v), 100 mM
ABC (∼pH 9) were added. For passive elution of the proteoforms
from the gel matrix, the
samples were shaken for 30 min at 1,500 rpm and room temperature.
The gel pieces were removed using a Spin-X centrifugal filter (45
μm pore size, cellulose acetate, Corning, Corning, NY, USA)
by 3 min centrifugation at 3,000 rcf. The filtrate was subjected to
methanol-chloroform-water precipitation to purify the proteoforms. Methanol-Chloroform-Water Precipitation Methanol-chloroform-water
(MCW) precipitation was performed for proteoform purification as described
previously. In brief, the sample was transferred
to a 1.5 mL reaction tube, and if necessary, the volume was adjusted
to 300 μL with ultrapure water. Then, 600 μL of methanol,
150 μL chloroform, and 400 μL ultrapure water were added
with thorough vortexing between each solvent addition. The upper layer
was removed after centrifugation at 14,000 rcf for 10 min. Next, 600
μL of methanol was added, and the tube was gently inverted prior
to centrifugation (10 min, 14,000 rcf). The supernatant was removed,
and the pellet was washed twice with 600 μL methanol. After
the second washing, the pellet was dried for 30 min in a fume hood.
For MS analysis, the protein pellet was resuspended in 20 μL
3% acetonitrile (ACN), 0.05% trifluoroacetic acid (TFA), vortexed,
briefly sonicated, and centrifuged for 5 min at 21,100 rcf. The supernatant
was transferred into LC-MS vials with glass inserts and stored at
−80 °C until LC-MS analysis. LC-MS/MS Chromatographic separation of proteoforms
was conducted using a Dionex U3000 UHPLC system equipped with a C4
analytical column (50 cm × 75 μm, 2.6 μm, 150 Å,
Thermo Fisher) and a C4 precolumn (C4 PepMap300, 5 μm, 300 Å,
Thermo Fisher). The following 120 min gradient was employed using
eluent A (0.05% formic acid (FA)) and eluent B (80% ACN, 0.04% FA)
at 300 nL/min and 45 °C: 0–5 min 4% B, 5–7 min
4–15% B, 7–127 min 15–50% B, 127–129 min
50–90% B, 129–140 min 90% B, 140–150 min 90–4%
B. One μL of the sample were injected into the LC, corresponding
to approximately 0.5–1 μg of protein. The LC was
coupled online to a Fusion Lumos Tribrid mass spectrometer (Thermo
Scientific, Bremen, Germany) equipped with a FAIMS Pro interface (Thermo
Scientific). Each sample was injected twice, using two complementary
multi-CV-FAIMS-MS/MS methods to cover a wide proteoform mass range. For targeting proteoforms smaller than approximately
20 kDa, a high/high acquisition strategy and more negative CVs were
applied (hereinafter referred to as high/high method). In contrast,
a medium/high acquisition and more positive
CVs were applied to target larger proteoforms (medium/high method).
The MS settings were optimized for certain CVs based on the favored
proteoform mass range. Within a cycle
time of 3 s, the most intense precursors (charge states 4–50,
including undetermined charge states; dynamic exclusion was enabled
with n = 2, t = 60 s) were selected
for fragmentation. Two fragmentation strategies were investigated
in this study: (i) A stepped HCD fragmentation (30, 40, 50%), referred
to as the one-scan method, and (ii) two separate MS/MS events (CID
25%, HCD 80%) for proteoform identification and TMT quantification,
referred to as the two-scan method. All details of the MS settings
(resolutions, AGC target, ion injection
time) utilized for the different methods and FAIMS CVs are listed
in Supporting Table S1 . Deconvolution, Reporter Ion Extraction, and Database Search Raw files were converted to the mzML format using msConvert, with peak picking enabled for the high/high
data acquired with high MS1 resolution. Deconvolution and mass feature
TMT ratio extraction were performed with FLASHDeconv (version 3.1.0-pre-FDdevelop-2024–08–17). Briefly, while performing spectral deconvolution,
FLASHDeconv also measures reporter ion intensities using IsobaricQuantifier
library in OpenMS. Then FLASHDeconv aggregates
the reporter ion intensities in MS2 spectra from the same deconvolved
precursor masses (subject to input ppm mass tolerance) within 30 s
retention time window to increase the signal-to-noise ratio of the
quantification while reducing missing values. This aggregated quantification
information is hereafter referred to as “mass-feature-based
quantification”. Default deconvolution parameters were
used, except for the medium/high data (low MS1 resolution), where
the MS1 tolerance was increased to 50 ppm. The generated TopFD output
files were analyzed with TopPIC (v.1.6.5)
against a FASTA protein database containing proteins from E. coli K12 (taxon: 83333, UniProt release 2022_03).
TopPIC database search settings were set as default if not mentioned
otherwise. IodoTMT sixplex was set as a fixed modification (329.226595
Da, cysteine residues) and acetylation (42.0106 Da, lysine residues),
phosphorylation (79.9663 Da, serine, threonine, and tyrosine residues)
and oxidation (15.9949 Da, methionine, cysteine, lysine, aspartate,
arginine, asparagine, and tyrosine residues) were allowed as variable
PTMs with the maximum number of variable PTMs set to three. A maximum
number of two unexpected modifications within a mass shift of ±500
Da were allowed, enabling the identification of both previously known
and unknown modified proteoforms. For each quantification method,
the identification results from replicates were merged into a single
result, keeping an overall proteoform-level false discovery rate (FDR)
of 1%. To do so, we keep all the target and decoy identifications
in each TopPIC analysis, allowing FDR of 100%. When merging, the proteoforms
of the same sequence and precursor masses (within 10 ppm mass errors)
were merged into a single hit, with its E-value score being the lowest
(i.e., the best) E-value of the merged proteoforms. Then, FDR was
calculated with these merged proteoforms, and only the ones with FDR
less than 1% were retained. Quantitative Analysis Quantitative information from
the FLASHDeconv output was assigned to the identified proteoforms
via the scan numbers of the corresponding raw file. For classical
quantitative analysis, the quantitative TMT reporter ion information
on a single MS2 scan (“QuantForCh1–6” in FLASHDeconv
output) was matched to the associated proteoform based on the scan
number reported by TopPIC. Thus, each proteoform is only quantified
by the MS2 scan related to the identification of the proteoform. In contrast, for the mass feature-based quantification, the combined
quantitative information on several quantification scans originating
from the same mass feature (within a 10 ppm precursor mass and 30
s retention time tolerance) were combined (“MergedQuantForCh1–6”
in FLASHDeconv output) and matched to the TopPIC search results via
the scan number. This means that the mass feature-based quantification
uses the quantitative information from all MS2 spectra acquired for
a given mass feature, even if not all of them led to the identification
of the proteoform. Proteoforms without cysteines or lacking one of
the six TMT reporter ion intensities were removed prior to the quantitative
analysis. Statistical analysis was performed in Perseus (v.2.0.10.0). TMT reporter ion intensities were
median
normalized, log2 transformed, and assigned to their respective biological
condition. A two-sided paired t test (s0 = 0, permutation-based
FDR, cutoff displayed in plot, 250 randomizations) was performed to
evaluate differentially abundant proteoforms. Data Availability All raw data has been uploaded to
the ProteomeXchange consortium via the
PRIDE partner repository under the accession PXD055510. 

Unless stated otherwise, all chemicals and
solutions were purchased from Sigma-Aldrich (Steinheim, Germany).
Ultrapure water (18.2 MΩ/cm –1 ) was prepared
using the Arium VF 611 system (Sartorius, Göttingen, Germany). 

Cultivation Escherichia coli K-12 strain MG1655
was cultured as described previously. Briefly, bacteria were cultured in M9 minimal medium supplemented with glucose (15 mM) or acetate
(45 mM) as the sole carbon source. Bacteria were inoculated at an
optical density at 600 nm (OD 600 ) of 0.1 and incubated
at 37 °C under constant shaking. The cells were harvested at
an OD 600 of approximately 1 by centrifugation for 3 min
at 3,000 rcf and room temperature. The cells were washed with ultrapure
water and stored at −80 °C prior to cell lysis. 

Frozen aliquots of E. coli cells were resuspended in 1 mL of 8 M guanidine hydrochloride, 200
mM triethylammonium bicarbonate (TEAB, pH 8.5), 1× cOmplete EDTA-free
protease inhibitor (Roche, Basel, Swiss) and lysed using an ultrasonic
source (Bandelin electronics, Berlin, Germany). Samples were cooled
on ice during sonication in ten cycles in 30 s (Power 28%). Cellular
debris was removed by centrifugation at 21,100 rcf for 30 min at 4
°C, and the protein concentration was determined utilizing the
Pierce BCA Protein assay kit (Thermo Scientific, Waltham, MA). 

Cys-directed iodoTMT
labeling was conducted to label all proteoforms directly after cell
lysis, as described previously. In brief,
160 μg protein was transferred into a reaction tube for each
TMT channel, and the volume was filled up to 80 μL with lysis
buffer. For proteoform reduction, 1 μL 200 mM Tris(2-Carboxyethyl)-phosphine
(TCEP) was added and incubated for 60 min at 50 °C and 800 rpm
on a shaker. The iodoTMTsixplex reagents (Thermo Scientific) were
resuspended in 10 μL methanol and added to the samples, which
were then incubated for 50 min at 37 °C and 800 rpm in the dark.
The reaction was quenched by adding 4 μL of 200 mM DTT and incubation
for 15 min at room temperature. The samples were combined prior to
purification by methanol-chloroform water precipitation. Prior to
proteoform separation by SDS-PAGE, the protein pellets were resuspended
in Laemmli buffer and heated for 15 min
at 70 °C. For a proof of principle experiment, one biological E. coli replicate (grown under glucose condition)
was divided into six aliquots and labeled with iodoTMT sixplex using
a defined ratio of 8:4:1:1:4:8 (126:127:128:129:130:131) ( A). For the quantitative
proteoform analysis of E. coli grown under glucose or acetate as the sole carbon source, three
biological replicates each were labeled with different iodoTMT channels
(channels 126–128 for the glucose and channels 129–131
for the acetate samples) ( D-E). 

For gel electrophoretic separation, a 16%
separation gel (2.1 mL ultrapure water, 5.3 mL 30% acrylamide/Bis,
2.5 mL 1.5 M Tris-HCl pH 8.8, 100 μL 10% (w/v) SDS, 50 μL
10% (w/v) ammonium persulfate (APS), 10 μL tetramethylethylenediamine
(TEMED)) in combination with a 4% stacking gel (2.1 mL ultrapure water,
1.3 mL 30% acrylamide/Bis, 2.5 mL 0.5 M Tris-HCl pH 6.8, 100 μL
10% (w/v) SDS, 50 μL 10% (w/v) APS, 10 μL TEMED) was cast
using the PROTEAN 1 mm system (BioRad, Hercules, CA). Each sample
was loaded into two adjacent gel pockets, with 80 μg loaded
for the enrichment of proteoforms smaller than 40 kDa and 100 μg
for the fractionation approach. The proteoforms were separated using
an electric field at a constant voltage of 50 V, and after the running
front reached the separation gel, the voltage was increased to 90
V. The electrophoretic separation was monitored with the Pierce Prestained
Protein Ladder (Thermo Scientific). For enriching proteoforms smaller
than 40 kDa, the electrophoresis was terminated after the 40 kDa marker
band was separated from other marker bands ( B, D). In contrast, for fractionating the
proteoforms in 10 fractions, the electrophoresis was performed until
the running front reached the end of the gel ( E). After electrophoresis, the gels
were incubated for 20 min in fixing solution (40% (v/v) methanol,
5% (v/v) acetic acid), and the proteoforms were stained for 30 min
using Coomassie staining solution (0.2% (w/v) Coomassie R250, 0.05%
(w/v) Coomassie G250, 10% (v/v) ethanol, 40% (v/v) methanol, 2% (v/v)
glycerin). Fixation of the gel was conducted as it has been shown
to increase the recovery of small proteoforms. The gels were destained in a destaining solution (10% (v/v) ethanol,
5% (v/v) acetic acid) overnight. 

The proteoforms
were extracted from the gel using the polyacrylamide-gel-based prefractionation
for analysis of intact proteoforms (PEPPI) protocol as previously
described. In brief, the gels were washed
with water before the gel regions of interest of two adjacent gel
lanes were excised with a scalpel and transferred into a 1.5 mL reaction
tube. The gel pieces were homogenized for 1 min using a BioMasher
plastic pestle (Nippi, Tokyo, Japan), and 300 μL of 0.1% (w/v)
SDS, 100 mM ammonium bicarbonate (ABC, ∼pH 9) were added, followed
by 30 s of further homogenization. Alternatively, for the fractionation
of the sample into ten fractions, an extrusion-tip protocol − was employed to crush the gel pieces and parallelize the extraction.
Extrusion-tips were manufactured by cutting the top of a GELoader
tip (Eppendorf, Hamburg, Germany), so that it could be inserted into
a 200 μL tip ( Suppl. Figure S1 ).
Ten fractions below 40 kDa were excised and subjected to the extrusion-tips
placed in 2 mL tubes. Extrusion was conducted by centrifugation for
4 min at 21,100 rcf. Then, 300 μL of 0.1% SDS (w/v), 100 mM
ABC (∼pH 9) were added. For passive elution of the proteoforms
from the gel matrix, the
samples were shaken for 30 min at 1,500 rpm and room temperature.
The gel pieces were removed using a Spin-X centrifugal filter (45
μm pore size, cellulose acetate, Corning, Corning, NY, USA)
by 3 min centrifugation at 3,000 rcf. The filtrate was subjected to
methanol-chloroform-water precipitation to purify the proteoforms. 

Methanol-chloroform-water
(MCW) precipitation was performed for proteoform purification as described
previously. In brief, the sample was transferred
to a 1.5 mL reaction tube, and if necessary, the volume was adjusted
to 300 μL with ultrapure water. Then, 600 μL of methanol,
150 μL chloroform, and 400 μL ultrapure water were added
with thorough vortexing between each solvent addition. The upper layer
was removed after centrifugation at 14,000 rcf for 10 min. Next, 600
μL of methanol was added, and the tube was gently inverted prior
to centrifugation (10 min, 14,000 rcf). The supernatant was removed,
and the pellet was washed twice with 600 μL methanol. After
the second washing, the pellet was dried for 30 min in a fume hood.
For MS analysis, the protein pellet was resuspended in 20 μL
3% acetonitrile (ACN), 0.05% trifluoroacetic acid (TFA), vortexed,
briefly sonicated, and centrifuged for 5 min at 21,100 rcf. The supernatant
was transferred into LC-MS vials with glass inserts and stored at
−80 °C until LC-MS analysis. 

Chromatographic separation of proteoforms
was conducted using a Dionex U3000 UHPLC system equipped with a C4
analytical column (50 cm × 75 μm, 2.6 μm, 150 Å,
Thermo Fisher) and a C4 precolumn (C4 PepMap300, 5 μm, 300 Å,
Thermo Fisher). The following 120 min gradient was employed using
eluent A (0.05% formic acid (FA)) and eluent B (80% ACN, 0.04% FA)
at 300 nL/min and 45 °C: 0–5 min 4% B, 5–7 min
4–15% B, 7–127 min 15–50% B, 127–129 min
50–90% B, 129–140 min 90% B, 140–150 min 90–4%
B. One μL of the sample were injected into the LC, corresponding
to approximately 0.5–1 μg of protein. The LC was
coupled online to a Fusion Lumos Tribrid mass spectrometer (Thermo
Scientific, Bremen, Germany) equipped with a FAIMS Pro interface (Thermo
Scientific). Each sample was injected twice, using two complementary
multi-CV-FAIMS-MS/MS methods to cover a wide proteoform mass range. For targeting proteoforms smaller than approximately
20 kDa, a high/high acquisition strategy and more negative CVs were
applied (hereinafter referred to as high/high method). In contrast,
a medium/high acquisition and more positive
CVs were applied to target larger proteoforms (medium/high method).
The MS settings were optimized for certain CVs based on the favored
proteoform mass range. Within a cycle
time of 3 s, the most intense precursors (charge states 4–50,
including undetermined charge states; dynamic exclusion was enabled
with n = 2, t = 60 s) were selected
for fragmentation. Two fragmentation strategies were investigated
in this study: (i) A stepped HCD fragmentation (30, 40, 50%), referred
to as the one-scan method, and (ii) two separate MS/MS events (CID
25%, HCD 80%) for proteoform identification and TMT quantification,
referred to as the two-scan method. All details of the MS settings
(resolutions, AGC target, ion injection
time) utilized for the different methods and FAIMS CVs are listed
in Supporting Table S1 . 

Raw files were converted to the mzML format using msConvert, with peak picking enabled for the high/high
data acquired with high MS1 resolution. Deconvolution and mass feature
TMT ratio extraction were performed with FLASHDeconv (version 3.1.0-pre-FDdevelop-2024–08–17). Briefly, while performing spectral deconvolution,
FLASHDeconv also measures reporter ion intensities using IsobaricQuantifier
library in OpenMS. Then FLASHDeconv aggregates
the reporter ion intensities in MS2 spectra from the same deconvolved
precursor masses (subject to input ppm mass tolerance) within 30 s
retention time window to increase the signal-to-noise ratio of the
quantification while reducing missing values. This aggregated quantification
information is hereafter referred to as “mass-feature-based
quantification”. Default deconvolution parameters were
used, except for the medium/high data (low MS1 resolution), where
the MS1 tolerance was increased to 50 ppm. The generated TopFD output
files were analyzed with TopPIC (v.1.6.5)
against a FASTA protein database containing proteins from E. coli K12 (taxon: 83333, UniProt release 2022_03).
TopPIC database search settings were set as default if not mentioned
otherwise. IodoTMT sixplex was set as a fixed modification (329.226595
Da, cysteine residues) and acetylation (42.0106 Da, lysine residues),
phosphorylation (79.9663 Da, serine, threonine, and tyrosine residues)
and oxidation (15.9949 Da, methionine, cysteine, lysine, aspartate,
arginine, asparagine, and tyrosine residues) were allowed as variable
PTMs with the maximum number of variable PTMs set to three. A maximum
number of two unexpected modifications within a mass shift of ±500
Da were allowed, enabling the identification of both previously known
and unknown modified proteoforms. For each quantification method,
the identification results from replicates were merged into a single
result, keeping an overall proteoform-level false discovery rate (FDR)
of 1%. To do so, we keep all the target and decoy identifications
in each TopPIC analysis, allowing FDR of 100%. When merging, the proteoforms
of the same sequence and precursor masses (within 10 ppm mass errors)
were merged into a single hit, with its E-value score being the lowest
(i.e., the best) E-value of the merged proteoforms. Then, FDR was
calculated with these merged proteoforms, and only the ones with FDR
less than 1% were retained. 

Quantitative information from
the FLASHDeconv output was assigned to the identified proteoforms
via the scan numbers of the corresponding raw file. For classical
quantitative analysis, the quantitative TMT reporter ion information
on a single MS2 scan (“QuantForCh1–6” in FLASHDeconv
output) was matched to the associated proteoform based on the scan
number reported by TopPIC. Thus, each proteoform is only quantified
by the MS2 scan related to the identification of the proteoform. In contrast, for the mass feature-based quantification, the combined
quantitative information on several quantification scans originating
from the same mass feature (within a 10 ppm precursor mass and 30
s retention time tolerance) were combined (“MergedQuantForCh1–6”
in FLASHDeconv output) and matched to the TopPIC search results via
the scan number. This means that the mass feature-based quantification
uses the quantitative information from all MS2 spectra acquired for
a given mass feature, even if not all of them led to the identification
of the proteoform. Proteoforms without cysteines or lacking one of
the six TMT reporter ion intensities were removed prior to the quantitative
analysis. Statistical analysis was performed in Perseus (v.2.0.10.0). TMT reporter ion intensities were
median
normalized, log2 transformed, and assigned to their respective biological
condition. A two-sided paired t test (s0 = 0, permutation-based
FDR, cutoff displayed in plot, 250 randomizations) was performed to
evaluate differentially abundant proteoforms. 

All raw data has been uploaded to
the ProteomeXchange consortium via the
PRIDE partner repository under the accession PXD055510. 

Proof of Principle To demonstrate the compatibility
of iodoTMT labeling with the GeLC-FAIMS-MS workflow for the quantitative
analysis of proteoforms by TDP, an E. coli lysate was divided into six aliquots, labeled with iodoTMTsixplex
reagents and recombined at a defined ratio of 8:4:1:1:4:8 ( A). The sample was
separated by SDS-PAGE and a gel slice below approximately 40 kDa (i.e.,
the mass range currently accessible for in-depth TDP ) was excised in a single fraction and proteoforms extracted
using the PEPPI procedure. This approach leads to the depletion of
large proteoforms but is not considered a fractionation approach in
the traditional sense of a two-dimensional separation. The proteoforms
were purified and analyzed by LC-FAIMS-MS/MS. The labeling efficiency
was examined by deconvolution of the raw files and mass shift analysis by MSTopDiff ( Suppl. Figure S2 ). Besides
the typical high abundant mass shifts at 16.00 Da (oxidation) and
32.00 Da (dioxidation), no mass shift assignable to iodoTMT over-
or under-labeling was detected, demonstrating the high labeling efficiency
and overall quality of the data. For quantitative analysis,
the reporter ion intensities were determined
for each MS2 spectrum and assigned to the correlated proteoform by
the scan number (in the following referred to as the “classical
quantification approach”). Overall, 2,492 proteoforms were
identified, of which 631 (25.3%) could be quantified, i.e., these
proteoforms contain at least one cysteine in their sequence, and all
six TMT reporter ions were detected. In addition to this classical
quantification approach, a new tool
was implemented in the widely used deconvolution algorithm FLASHDeconv
(version 3.1.0-pre-FDdevelop-2024–08–17) that enables
quantification at the mass feature level (“mass feature-based
quantification”) by combining the quantitative information
(i.e., the intensities of the reporter ions) of MS2 spectra generated
by the same precursor masses (subject to a mass tolerance) within
a 30 s retention time window. Unlike the classical quantification,
the mass feature-based approach has the advantage of utilizing quantitative
information from MS2 spectra that may not necessarily result in the
identification of proteoforms but contain reporter TMT ions. Both
the classical and the mass feature-based approach resulted in a distribution
of the TMT reporter ions  that approximate the expected ratios (8:4:1:1:4:8).
However, the mass feature-based approach showed slightly narrower
distributions around the expected values ( Suppl. Figure S4 ). This observation can be explained by the high number
of quantitative data used for the mass feature-based approach (on
average, 2.5 quantitative fragment scans per precursor), resulting
in a robust proteoform quantification, which is, e.g., less susceptible
to effects such as coisolation and ratio compression. It is noteworthy that, in some cases, TMT reporter
ions were detected
from precursors assigned to proteoforms lacking a cysteine residue,
e.g., due to coisolation with cysteine-containing proteoforms or nonspecific
reporter ion assignment. In order to verify whether the quantification
algorithm assigns nonspecific reporter ions, we additionally analyzed
a sample without TMT-labeled proteoforms. No TMT ratios were reported
over the entire length of the gradient, demonstrating the accuracy
of the reporter ion assignment and rules out artifacts introduced
by the quantification algorithm. Thus, the occurrence of TMT reporter
ions from precursors assigned to proteoforms without a cysteine residue
can likely be explained by coisolation with cysteine-containing proteoforms. The mass distribution of the quantified proteoforms was shifted
toward higher molecular weights compared to that of all identified
proteoforms ( C). This might be due to the higher stochastic abundance of cysteine
residues with increasing proteoform sequence length, i.e., the longer
the protein sequence, the higher the probability that a cysteine residue
is present. MS Optimization A major challenge in isobaric labeling-based
approaches is that proteoform fragment generation and efficient TMT
reporter ion formation require different fragmentation energies. , Two main ion activation strategies have been utilized for labeling-based
quantitative TDP: ( i ) HCD fragmentation with stepped
collision energies (referred to as the one-scan method; here, we used
HCD fragmentation with normalized collision energies of 30, 40 and
50%), and ( ii ) the performance
of two separate fragmentation scans for identification and quantification,
utilizing optimized collision energies for the two tasks (two-scan
method; quantification scan with HCD and an NCE of 80% and identification
scan with CID and an NCE of 25%). In order to optimize the LC-FAIMS-MS/MS setup, the same sample used
in the proof of principle experiment was analyzed utilizing the two
different MS/MS acquisition approaches. The two-scan method significantly
outperformed the one-scan method in terms of the number of quantified
proteoforms (631 and 317 quantified proteoforms, respectively, A). This observation
can be explained by the efficient generation of TMT reporter ions
by the two-scan method at high collision energies, as revealed by
the ratio of quantified to identified cysteine-containing proteoforms
(58.3% for two-scan, and 26.7% for one-scan). However, more proteoforms
were identified using the one-scan (3,581) compared to the two-scan
method (2,492), as theoretically, every MS2 scan can result in the
identification of a proteoform, while for the two-scan method, only
every second spectrum can result in a proteoform identification. The distribution of the experimentally determined TMT ratios was
close to the expected values for both fragmentation methods ( B). Nevertheless,
the one-scan method showed slightly broader distributions of the reporter
ion ratios, which can probably be attributed to a lower number of
quantitative data points, i.e., the number of utilized scans with
detected reporter ions. Only 7.5% of MS2 spectra contained all TMT
reporter ions for the one-scan method (an average of 674 spectra per
raw file), while 39.5% of all HCD spectra contained all TMT reporter
ions for the two-scan method (an average of 2228 spectra per raw file).
Based on the presented results, the two-scan method was selected for
all future experiments. Another challenge in labeling-based
quantification is the cofragmentation
of multiple proteoforms caused by coeluting analytes, resulting in
ratio compression (i.e., an underestimation of the proteoform abundance). Note that in iodoTMT-based quantification experiments,
ratio compression is generally expected to be lower compared to aminoTMT-based
experiments since the number of labeled analytes is much lower, reducing
the likelihood of coisolation of labeled analytes and, thus, reducing
the overall ratio compression. One possibility to diminish the effect of ratio compression is
narrowing the isolation window to avoid cofragmentation of precursors
with closely spaced isotopic envelopes. However, this decreases the sensitivity of TDP analysis as the larger
isotopic distribution of proteoforms in comparison to peptides has
to be considered. The sweet spot of the
isolation window regarding the sensitivity and ion compression was
examined using a two-proteome interference model with E. coli (TMT ratios 8:4:1:1:4:8) and yeast (4:4:4:4:4:4)
proteoforms ( Suppl. Figure S4A ). The highest
number of quantified proteoforms was obtained with an isolation window
of 2 m / z ( n = 317 E. coli proteoforms, Suppl. Figure S4B ). In contrast, larger isolation windows resulted
in a lower number of identifications (3 m / z : n = 284), e.g., due to extensive coisolation
and the occurrence of chimeric spectra, hampering the subsequent data
analysis. In addition, also smaller isolation
windows resulted in a significantly lower number of quantified proteoforms
(1.2 m / z : n = 223),
likely due to the decreased sensitivity. The effect of ratio compression
was slightly reduced when narrowing the isolation window from 3 m / z to 2 m / z ; however, no further improvement was observed when using an isolation
window of 1.2 m / z ( Suppl. Figure S4C ). Controlling the isolation window width
dynamically based on signal-to-ratio within the window by advanced
real-time data acquisition control (e.g., by FLASHIda ) could help to further reduce the ratio compression. Another possibility to tackle the challenge of ratio compression
is the fractionation of proteoforms to reduce the sample complexity
prior to the MS analysis. In this study, we used the gas-phase fractionation
technique FAIMS utilizing internal compensation voltage (CV) stepping.
Notably, different CVs in FAIMS favor the identification of proteoforms
within specific mass ranges, which is
the base for a recently established workflow encompassing internal
CV stepping, allowing the identification
of proteoforms over a broad range of proteoform masses. Moreover,
this approach enables to adapt the MS1 and MS2 parameters to the needs
of specific proteoform mass ranges. , We employed
two complementary LC-multi-CV-FAIMS-MS methods to cover a broad proteoform
mass range. The first method aimed to target lower mass proteoforms
using more negative CVs and a high/high acquisition strategy, i.e.,
high-resolution MS1 and MS2 parameters, including adapted values for
the number of microscans and injection times. In contrast, the second
method aimed to target larger proteoforms by using more positive CVs
and a medium/high acquisition strategy, i.e., medium-resolution MS1 scans but an elevated number of microscans
and high-resolution MS2 parameters ( C). Compared to measurements without FAIMS,
the multi-CV-FAIMS approach substantially improved the number of quantified
proteoforms (317 vs 106 proteoforms) and reduced ratio compression,
demonstrating that gas-phase fractionation efficiently reduces spectral
complexity ( Suppl. Figure S4B,C ). Moreover, the complementarity in the number
of identified proteoforms also led to a higher number of quantified
proteoforms ( D). While a direct comparison with other TDP data sets is hampered
by the fact that different sample preparation protocols may lead to
different proteoform identifications and
depend strongly on factors such as the MS and data interpretation
algorithms utilized, the mass distribution of the proteoforms identified
in our study (10.35 (mean) ± 6.42 (standard deviation) kDa) was
comparable with that reported for E. coli (11.41 ± 6.77 kDa) and for a collection
of different bacteria (7.49 ± 5.11 kDa). In summary, the combination of iodoTMT-labeling, gel-based
separation,
PEPPI-based extraction of the proteoforms, and FAIMS with internal
CV stepping, as well as a two-scan method using optimized fragmentation
energies for the generation of proteoform fragments and reporter ions
and a new mass feature-based data analysis tool integrated in FLASHDeconv
enables an optimized in-depth quantitative analysis of proteoforms
by TDP. Quantitative Top-Down Analysis of E. coli Grown on Different Carbon Sources The optimized GeLC-FAIMS-MS/MS
workflow was applied to quantitatively analyze the proteome of E. coli cells cultivated in a minimal medium
supplemented with glucose or acetate as the sole carbon source, respectively. Three biological replicates for each condition
were labeled with different channels of the iodoTMT sixplex. The combined
samples were processed using ( i ) a single gel band
containing proteoforms smaller than 40 kDa ( D) and ( ii ) ten fractions
of proteoforms smaller than 40 kDa ( E). A protocol that combines the principles
of the ConGnaC-tip, a Gel Shredder and Syringe Maceration Extraction, was utilized to homogenize the gel pieces by
extruding them through a narrow-cavity pipet tip via centrifugation
in order to process multiple samples simultaneously ( Suppl. Figure S1 ). Analyzing three technical replicates
with the high/high and medium/high methods, 2,426 proteoforms were
identified in the single PEPPI fraction, of which 784 proteoforms
(32.1%) from 137 proteins could be quantified. From these, 324 proteoforms
showed a fold change of at least 1.4 and, thus, were classified as
significantly differentially abundant ( A). In contrast, the multidimensional separation using
ten gel fractions
led to a significantly increased number of identified (4,964 proteoforms)
and quantified (1,614 = 34.1%) proteoforms ( Suppl. Figure S5A ). However, it should be noted that the greater depth
of analysis required a significantly longer measurement time (112.5
instead of 15 h). With increasing molecular weight, the relative proportion
of quantified proteoforms increased ( Suppl. Figure S5B ). In total, 726 proteoforms from 140 proteins were significantly
differentially abundant (fold change ≥1.4). From these, 172
proteoforms (from 46 proteins) were significantly more abundant under
glucose conditions, and 554 proteoforms (from 95 proteins) under acetate
conditions ( , Suppl. Table S2–S25 ). A variety of proteoforms were quantified that are
involved in carbohydrate
metabolic processes, such as triosephosphate isomerase (P0A858), glyceraldehyde-3-phosphate
dehydrogenase A (P0A9B2), or fructose-bisphosphate aldolase class
A (P0AB71). Furthermore, various ribosomal proteoforms were quantified.
The overall highest fold change was observed for six C-terminally
truncated proteoforms from the autonomous glycyl radical cofactor
(P68066, log2 fold-change larger than 3.5), which were significantly
higher abundant in the glucose condition. Another example of a proteoform
that was significantly higher abundant under glucose growth conditions
(log2 fold-change larger than 3) was the putative selenoprotein YdfZ
(P64463) ( B–E). Multiple examples of proteins were detected, of which various proteoforms
were identified, but only specific proteoforms showed differing abundance,
including some with opposite abundance. For example, the Succiante-CoA
ligase (ADP-forming) subunit alpha (P0AGE9) was identified with 16
proteoforms. However, mainly modified full-length and C-terminally
truncated proteoforms were significantly higher abundant under acetate
growth conditions (log2 fold change −1.72), whereas the N-terminally
truncated proteoforms were not significantly differentially abundant,
indicating potentially different biological functions ( Suppl. Figure S6A, B ). The same was observed
for the Succiante-CoA ligase (ADP-forming) subunit beta (UniProt accession
P0A836), the other part of the functional unit in the TCA cycle. Two
N-terminally truncated proteoforms showed insignificant abundance
changes, while five other truncated proteoforms were significantly
higher abundant under acetate conditions ( Suppl. Figure S6C, D ). Quantification at the proteoform level
by top-down proteomics can
elucidate changes in the proteome that are difficult to detect with
classical BUP-based quantitative proteomics. Two quantified proteoforms of the selenide, water dikinase (P16456)
were significantly differentially abundant, one in each of the two
compared biological conditions ( Suppl. Figure S7A–G ). Both proteoforms cover the same canonical protein
sequence; however, the proteoform enriched under acetate-supplemented
growth conditions showed a mass shift of −281.243 Da, which
could be explained as a trioxidation instead of an iodoTMT tag on
Cys17 (theoretical mass shift of −281.2418 Da, proteoform Δ M = −0.24 ppm). The proteoforms may have different
biological functions as indicated by their different abundance and
are difficult to separate in a BUP experiment as most tryptic peptides
are identical between the two proteoforms. Indeed, in a BUP study
of the same biological conditions, the
protein was not significantly differentially abundant (log2 fold-change
of 0.36), highlighting the utility of quantitative TDP. 

To demonstrate the compatibility
of iodoTMT labeling with the GeLC-FAIMS-MS workflow for the quantitative
analysis of proteoforms by TDP, an E. coli lysate was divided into six aliquots, labeled with iodoTMTsixplex
reagents and recombined at a defined ratio of 8:4:1:1:4:8 ( A). The sample was
separated by SDS-PAGE and a gel slice below approximately 40 kDa (i.e.,
the mass range currently accessible for in-depth TDP ) was excised in a single fraction and proteoforms extracted
using the PEPPI procedure. This approach leads to the depletion of
large proteoforms but is not considered a fractionation approach in
the traditional sense of a two-dimensional separation. The proteoforms
were purified and analyzed by LC-FAIMS-MS/MS. The labeling efficiency
was examined by deconvolution of the raw files and mass shift analysis by MSTopDiff ( Suppl. Figure S2 ). Besides
the typical high abundant mass shifts at 16.00 Da (oxidation) and
32.00 Da (dioxidation), no mass shift assignable to iodoTMT over-
or under-labeling was detected, demonstrating the high labeling efficiency
and overall quality of the data. For quantitative analysis,
the reporter ion intensities were determined
for each MS2 spectrum and assigned to the correlated proteoform by
the scan number (in the following referred to as the “classical
quantification approach”). Overall, 2,492 proteoforms were
identified, of which 631 (25.3%) could be quantified, i.e., these
proteoforms contain at least one cysteine in their sequence, and all
six TMT reporter ions were detected. In addition to this classical
quantification approach, a new tool
was implemented in the widely used deconvolution algorithm FLASHDeconv
(version 3.1.0-pre-FDdevelop-2024–08–17) that enables
quantification at the mass feature level (“mass feature-based
quantification”) by combining the quantitative information
(i.e., the intensities of the reporter ions) of MS2 spectra generated
by the same precursor masses (subject to a mass tolerance) within
a 30 s retention time window. Unlike the classical quantification,
the mass feature-based approach has the advantage of utilizing quantitative
information from MS2 spectra that may not necessarily result in the
identification of proteoforms but contain reporter TMT ions. Both
the classical and the mass feature-based approach resulted in a distribution
of the TMT reporter ions  that approximate the expected ratios (8:4:1:1:4:8).
However, the mass feature-based approach showed slightly narrower
distributions around the expected values ( Suppl. Figure S4 ). This observation can be explained by the high number
of quantitative data used for the mass feature-based approach (on
average, 2.5 quantitative fragment scans per precursor), resulting
in a robust proteoform quantification, which is, e.g., less susceptible
to effects such as coisolation and ratio compression. It is noteworthy that, in some cases, TMT reporter
ions were detected
from precursors assigned to proteoforms lacking a cysteine residue,
e.g., due to coisolation with cysteine-containing proteoforms or nonspecific
reporter ion assignment. In order to verify whether the quantification
algorithm assigns nonspecific reporter ions, we additionally analyzed
a sample without TMT-labeled proteoforms. No TMT ratios were reported
over the entire length of the gradient, demonstrating the accuracy
of the reporter ion assignment and rules out artifacts introduced
by the quantification algorithm. Thus, the occurrence of TMT reporter
ions from precursors assigned to proteoforms without a cysteine residue
can likely be explained by coisolation with cysteine-containing proteoforms. The mass distribution of the quantified proteoforms was shifted
toward higher molecular weights compared to that of all identified
proteoforms ( C). This might be due to the higher stochastic abundance of cysteine
residues with increasing proteoform sequence length, i.e., the longer
the protein sequence, the higher the probability that a cysteine residue
is present. 

A major challenge in isobaric labeling-based
approaches is that proteoform fragment generation and efficient TMT
reporter ion formation require different fragmentation energies. , Two main ion activation strategies have been utilized for labeling-based
quantitative TDP: ( i ) HCD fragmentation with stepped
collision energies (referred to as the one-scan method; here, we used
HCD fragmentation with normalized collision energies of 30, 40 and
50%), and ( ii ) the performance
of two separate fragmentation scans for identification and quantification,
utilizing optimized collision energies for the two tasks (two-scan
method; quantification scan with HCD and an NCE of 80% and identification
scan with CID and an NCE of 25%). In order to optimize the LC-FAIMS-MS/MS setup, the same sample used
in the proof of principle experiment was analyzed utilizing the two
different MS/MS acquisition approaches. The two-scan method significantly
outperformed the one-scan method in terms of the number of quantified
proteoforms (631 and 317 quantified proteoforms, respectively, A). This observation
can be explained by the efficient generation of TMT reporter ions
by the two-scan method at high collision energies, as revealed by
the ratio of quantified to identified cysteine-containing proteoforms
(58.3% for two-scan, and 26.7% for one-scan). However, more proteoforms
were identified using the one-scan (3,581) compared to the two-scan
method (2,492), as theoretically, every MS2 scan can result in the
identification of a proteoform, while for the two-scan method, only
every second spectrum can result in a proteoform identification. The distribution of the experimentally determined TMT ratios was
close to the expected values for both fragmentation methods ( B). Nevertheless,
the one-scan method showed slightly broader distributions of the reporter
ion ratios, which can probably be attributed to a lower number of
quantitative data points, i.e., the number of utilized scans with
detected reporter ions. Only 7.5% of MS2 spectra contained all TMT
reporter ions for the one-scan method (an average of 674 spectra per
raw file), while 39.5% of all HCD spectra contained all TMT reporter
ions for the two-scan method (an average of 2228 spectra per raw file).
Based on the presented results, the two-scan method was selected for
all future experiments. Another challenge in labeling-based
quantification is the cofragmentation
of multiple proteoforms caused by coeluting analytes, resulting in
ratio compression (i.e., an underestimation of the proteoform abundance). Note that in iodoTMT-based quantification experiments,
ratio compression is generally expected to be lower compared to aminoTMT-based
experiments since the number of labeled analytes is much lower, reducing
the likelihood of coisolation of labeled analytes and, thus, reducing
the overall ratio compression. One possibility to diminish the effect of ratio compression is
narrowing the isolation window to avoid cofragmentation of precursors
with closely spaced isotopic envelopes. However, this decreases the sensitivity of TDP analysis as the larger
isotopic distribution of proteoforms in comparison to peptides has
to be considered. The sweet spot of the
isolation window regarding the sensitivity and ion compression was
examined using a two-proteome interference model with E. coli (TMT ratios 8:4:1:1:4:8) and yeast (4:4:4:4:4:4)
proteoforms ( Suppl. Figure S4A ). The highest
number of quantified proteoforms was obtained with an isolation window
of 2 m / z ( n = 317 E. coli proteoforms, Suppl. Figure S4B ). In contrast, larger isolation windows resulted
in a lower number of identifications (3 m / z : n = 284), e.g., due to extensive coisolation
and the occurrence of chimeric spectra, hampering the subsequent data
analysis. In addition, also smaller isolation
windows resulted in a significantly lower number of quantified proteoforms
(1.2 m / z : n = 223),
likely due to the decreased sensitivity. The effect of ratio compression
was slightly reduced when narrowing the isolation window from 3 m / z to 2 m / z ; however, no further improvement was observed when using an isolation
window of 1.2 m / z ( Suppl. Figure S4C ). Controlling the isolation window width
dynamically based on signal-to-ratio within the window by advanced
real-time data acquisition control (e.g., by FLASHIda ) could help to further reduce the ratio compression. Another possibility to tackle the challenge of ratio compression
is the fractionation of proteoforms to reduce the sample complexity
prior to the MS analysis. In this study, we used the gas-phase fractionation
technique FAIMS utilizing internal compensation voltage (CV) stepping.
Notably, different CVs in FAIMS favor the identification of proteoforms
within specific mass ranges, which is
the base for a recently established workflow encompassing internal
CV stepping, allowing the identification
of proteoforms over a broad range of proteoform masses. Moreover,
this approach enables to adapt the MS1 and MS2 parameters to the needs
of specific proteoform mass ranges. , We employed
two complementary LC-multi-CV-FAIMS-MS methods to cover a broad proteoform
mass range. The first method aimed to target lower mass proteoforms
using more negative CVs and a high/high acquisition strategy, i.e.,
high-resolution MS1 and MS2 parameters, including adapted values for
the number of microscans and injection times. In contrast, the second
method aimed to target larger proteoforms by using more positive CVs
and a medium/high acquisition strategy, i.e., medium-resolution MS1 scans but an elevated number of microscans
and high-resolution MS2 parameters ( C). Compared to measurements without FAIMS,
the multi-CV-FAIMS approach substantially improved the number of quantified
proteoforms (317 vs 106 proteoforms) and reduced ratio compression,
demonstrating that gas-phase fractionation efficiently reduces spectral
complexity ( Suppl. Figure S4B,C ). Moreover, the complementarity in the number
of identified proteoforms also led to a higher number of quantified
proteoforms ( D). While a direct comparison with other TDP data sets is hampered
by the fact that different sample preparation protocols may lead to
different proteoform identifications and
depend strongly on factors such as the MS and data interpretation
algorithms utilized, the mass distribution of the proteoforms identified
in our study (10.35 (mean) ± 6.42 (standard deviation) kDa) was
comparable with that reported for E. coli (11.41 ± 6.77 kDa) and for a collection
of different bacteria (7.49 ± 5.11 kDa). In summary, the combination of iodoTMT-labeling, gel-based
separation,
PEPPI-based extraction of the proteoforms, and FAIMS with internal
CV stepping, as well as a two-scan method using optimized fragmentation
energies for the generation of proteoform fragments and reporter ions
and a new mass feature-based data analysis tool integrated in FLASHDeconv
enables an optimized in-depth quantitative analysis of proteoforms
by TDP. 

E. coli Grown on Different Carbon Sources The optimized GeLC-FAIMS-MS/MS
workflow was applied to quantitatively analyze the proteome of E. coli cells cultivated in a minimal medium
supplemented with glucose or acetate as the sole carbon source, respectively. Three biological replicates for each condition
were labeled with different channels of the iodoTMT sixplex. The combined
samples were processed using ( i ) a single gel band
containing proteoforms smaller than 40 kDa ( D) and ( ii ) ten fractions
of proteoforms smaller than 40 kDa ( E). A protocol that combines the principles
of the ConGnaC-tip, a Gel Shredder and Syringe Maceration Extraction, was utilized to homogenize the gel pieces by
extruding them through a narrow-cavity pipet tip via centrifugation
in order to process multiple samples simultaneously ( Suppl. Figure S1 ). Analyzing three technical replicates
with the high/high and medium/high methods, 2,426 proteoforms were
identified in the single PEPPI fraction, of which 784 proteoforms
(32.1%) from 137 proteins could be quantified. From these, 324 proteoforms
showed a fold change of at least 1.4 and, thus, were classified as
significantly differentially abundant ( A). In contrast, the multidimensional separation using
ten gel fractions
led to a significantly increased number of identified (4,964 proteoforms)
and quantified (1,614 = 34.1%) proteoforms ( Suppl. Figure S5A ). However, it should be noted that the greater depth
of analysis required a significantly longer measurement time (112.5
instead of 15 h). With increasing molecular weight, the relative proportion
of quantified proteoforms increased ( Suppl. Figure S5B ). In total, 726 proteoforms from 140 proteins were significantly
differentially abundant (fold change ≥1.4). From these, 172
proteoforms (from 46 proteins) were significantly more abundant under
glucose conditions, and 554 proteoforms (from 95 proteins) under acetate
conditions ( , Suppl. Table S2–S25 ). A variety of proteoforms were quantified that are
involved in carbohydrate
metabolic processes, such as triosephosphate isomerase (P0A858), glyceraldehyde-3-phosphate
dehydrogenase A (P0A9B2), or fructose-bisphosphate aldolase class
A (P0AB71). Furthermore, various ribosomal proteoforms were quantified.
The overall highest fold change was observed for six C-terminally
truncated proteoforms from the autonomous glycyl radical cofactor
(P68066, log2 fold-change larger than 3.5), which were significantly
higher abundant in the glucose condition. Another example of a proteoform
that was significantly higher abundant under glucose growth conditions
(log2 fold-change larger than 3) was the putative selenoprotein YdfZ
(P64463) ( B–E). Multiple examples of proteins were detected, of which various proteoforms
were identified, but only specific proteoforms showed differing abundance,
including some with opposite abundance. For example, the Succiante-CoA
ligase (ADP-forming) subunit alpha (P0AGE9) was identified with 16
proteoforms. However, mainly modified full-length and C-terminally
truncated proteoforms were significantly higher abundant under acetate
growth conditions (log2 fold change −1.72), whereas the N-terminally
truncated proteoforms were not significantly differentially abundant,
indicating potentially different biological functions ( Suppl. Figure S6A, B ). The same was observed
for the Succiante-CoA ligase (ADP-forming) subunit beta (UniProt accession
P0A836), the other part of the functional unit in the TCA cycle. Two
N-terminally truncated proteoforms showed insignificant abundance
changes, while five other truncated proteoforms were significantly
higher abundant under acetate conditions ( Suppl. Figure S6C, D ). Quantification at the proteoform level
by top-down proteomics can
elucidate changes in the proteome that are difficult to detect with
classical BUP-based quantitative proteomics. Two quantified proteoforms of the selenide, water dikinase (P16456)
were significantly differentially abundant, one in each of the two
compared biological conditions ( Suppl. Figure S7A–G ). Both proteoforms cover the same canonical protein
sequence; however, the proteoform enriched under acetate-supplemented
growth conditions showed a mass shift of −281.243 Da, which
could be explained as a trioxidation instead of an iodoTMT tag on
Cys17 (theoretical mass shift of −281.2418 Da, proteoform Δ M = −0.24 ppm). The proteoforms may have different
biological functions as indicated by their different abundance and
are difficult to separate in a BUP experiment as most tryptic peptides
are identical between the two proteoforms. Indeed, in a BUP study
of the same biological conditions, the
protein was not significantly differentially abundant (log2 fold-change
of 0.36), highlighting the utility of quantitative TDP. 

In this study, we developed a workflow for
quantitative in-depth
TDP encompassing cysteine-directed isobaric labeling combined with
GeLC-FAIMS-MS and subsequent data analysis using FLASHDeconv. We demonstrated
the general compatibility of the gel-based fractionation approach,
PEPPI, with iodoTMT quantification. The labeling-based approach allowed
for the multiplexing of the analysis of up to six samples in parallel.
Compared to other TDP sample preparation workflows for isolating proteoforms
such as filter- or depletion-based methods, PEPPI allows for fractionation, substantially improving the number
of quantified proteoforms. Note that we utilized MCW precipitation
for proteoform purification; however, other purification methods are
applicable based on the specific research question, e.g., to enhance
the recovery of small proteoforms. , , The MS settings were optimized to address the
most common challenges
in labeling-based quantitative proteomics: fragmentation of the precursors,
i.e., to ensure sufficient reporter ion generation and proteoform
fragment ions, and ratio compression caused by the cofragmentation
of multiple proteoforms. A method utilizing separate MS2 scans optimized
for quantification (i.e., applying high collision energy of 80% HCD)
and identification (25% CID) resulted in a significant increase in
the number of quantified proteoforms compared to a one-scan method
applying stepped HCD (30/40/50%) fragmentation. The ratio compression
was successfully reduced by narrowing the isolation width to 2 m / z , which is the sweet spot of diminishing
cofragmentation and maintaining sensitivity. Additionally, gas-phase
fractionation with FAIMS significantly improved the number of quantified
proteoforms and reduced ratio compression compared to measurement
without FAIMS due to the decreased spectral complexity. We used complementary
LC-FAIMS-MS methods utilizing FAIMS with internal CV stepping, targeting
the lower and higher mass proteoforms, significantly increasing the
number of identifications across a wide proteoform mass range. A newly implemented tool in the FLASHDeconv deconvolution software
enables mass feature-based quantification of isobaric labeling-based
TDP data. FLASHDeconv combines the quantitative information on multiple
MS2 spectra generated by the same precursors (within a certain mass
and retention time tolerance), resulting in a more robust quantification.
The mass feature-based quantification is independent of the proteoform
identifications since they are matched by scan number or mass. Thus,
this approach is compatible with all database search engines. Notably,
all common labeling strategies (iTRAQ4plex, iTRAQ8plex, TMT10plex,
TMT11plex, TMT16plex, TMT18plex, TMT6plex) are already integrated
into FLASHDeconv and can be analyzed, ensuring simple and reproducible
quantification and eliminating the need for in-house (often unpublished)
scripts. The optimized GeLC-FAIMS-MS workflow was utilized to
quantitatively
analyze the proteomes of E. coli cells grown under glucose or acetate as the sole carbon source.
Fractionation into 10 fractions with PEPPI resulted in the quantification
of 1,614 proteoforms (from 288 proteins), from which 726 proteoforms
were significantly differently abundant. Recently, an LFQ-based quantification
on a similar model system has been published, where proteoforms below
30 kDa were enriched with PEPPI and analyzed using LC-FAIMS-MS. Although more expensive and technically more
challenging, iodoTMT offers the advantage of multiplexing, resulting
in a reduced overall measurement time (i.e., higher throughput) and
a lower number of missing values compared to LFQ. Moreover, labeling
the sample directly after cell disruption and combining the labeled
samples in the early stage of sample preparation reduces the sampling
bias. As multidimensional fractionation schemes are essential for
in-depth TDP studies, the straightforward
combination with isobaric labeling enables comprehensive proteoform
identification. An inherent limitation of iodoTMT labeling is
that only cysteine-containing
proteoforms can be quantified. Approximately 84% of proteins in the E. coli proteome contain at least one cysteine; however, in our study, only about 1/3 of the
identified proteoforms could be quantified. We observed a larger proportion
of quantified proteoforms as their mass increased, likely due to the
increased stochastic probability of containing a cysteine with increasing
sequence length. Due to the advantages over aminoTMT, such as almost
no overlabeling observed, iodoTMT may be particularly suitable for
large proteoforms. However, should the present limitations still encountered
with aminoTMT labeling of intact proteins (under/and overlabeling,
and database search) be solved in the future, the presented workflow
here is certainly applicable, too. Currently, the multiplexing capability
of iodoTMT is limited to six channels; however, the commercial release
of higher multiplexed reagents could further expand the application
of this approach. Compared to a previously published quantitative
BUP study of E. coli with the
same growth conditions, the presented
TDP provided quantification information
on the proteoform level. We identified changes in the abundance of
proteoforms, which have been masked by the peptide-based protein quantification
using BUP due to the need for protein inference. Thus, TDP, even if
much less sensitive and hampered by the limitations regarding the
currently accessible mass range mentioned above, adds an important
layer of complexity to elucidate the processes in the cell on a molecular
level since different proteoforms of the same protein can have different
functions. In summary, we here presented a workflow for in-depth
quantitative
top-down proteomics, including cysteine-targeting iodoTMT labeling,
GeLC-FAIMS-MS analysis, and mass feature-based quantification. The
integration of the data analysis into the widely used deconvolution
software FLASHDeconv enables the easy evaluation of label-based TDP
experiments. Due to the flexibility of the PEPPI approach regarding
the number and complexity of the analyzed fractions and the specific
mass range, a suitable quantitative TDP approach can be designed based
on certain research questions. 

