Article

ISSN 0006-2979, Biochemistry (Moscow), 2024, Vol. 89, No. 8, pp. 1349-1361 © The Author(s) 2024. This article is an open access publication.

1349

MINI-REVIEW

Ultrafast Proteomics

Ivan I. Fedorov

1,2

, Sergey A. Protasov

1,2

, Irina A. Tarasova

and Mikhail V. Gorshkov

2,a

Moscow Institute of Physics and Technology (National University), 141700 Dolgoprudny, Moscow Region, Russia

V. L. Talrose Institute for Energy Problems of Chemical Physics,

N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia

e-mail: mike.gorshkov@gmail.com

Received May 23, 2024

Revised June 21, 2024

Accepted June 24, 2024

Abstract— Current stage of proteomic research in the field of biology, medicine, development of new drugs,

population screening, or personalized approaches to therapy dictates the need to analyze large sets of sam-

ples within the reasonable experimental time. Until recently, mass spectrometry measurements in proteomics

were characterized as unique in identifying and quantifying cellular protein composition, but low throughput,

requiring many hours to analyze a single sample. This was in conflict with the dynamics of changes in biological

systems at the whole cellular proteome level upon the influence of external and internal factors. Thus, low speed

of the whole proteome analysis has become the main factor limiting developments in functional proteomics,

where it is necessary to annotate intracellular processes not only in a wide range of conditions, but also over

along period of time. Enormous level of heterogeneity of tissue cells or tumors, even of the same type, dictates

the need to analyze biological systems at the level of individual cells. These studies involve obtaining molecular

characteristics for tens, if not hundreds of thousands of individual cells, including their whole proteome profiles.

Development of mass spectrometry technologies providing high resolution and mass measurement accuracy,

predictive chromatography, new methods for peptide separation by ion mobility and processing of proteomic

data based on artificial intelligence algorithms have opened a way for significant, if not radical, increase in the

throughput of whole proteome analysis and led to implementation of the novel concept of ultrafast proteomics.

Work done just in the last few years has demonstrated the proteome-wide analysis throughput of several hun-

dred samples per day at a depth of several thousand proteins, levels unimaginable three or four years ago.

The review examines background of these developments, as well as modern methods and approaches that imple-

ment ultrafast analysis of the entire proteome.

DOI: 10.1134/S0006297924080017

Keywords: proteomics, mass spectrometry, peptides, proteins, ultrafast analysis, quantitative proteomics

Abbreviations: AMT tags, accurate mass and time tags; DDA, data-dependent acquisition method for whole proteome

analysis; DIA, data-independent acquisition method for whole proteome analysis; DISPA, direct infusion shotgun pro-

teome analysis without the use of online liquid chromatography separation; DirectMS1,method of direct protein iden-

tification using liquid chromatography and mass spectrometry; FDR,false discovery rate; FT-ICR,Fourier-transform ion

cyclotron resonance mass spectrometry;HPLC,high performance liquid chromatography;MS1,precursor ion mass spectra;

MS/MS,tandem mass spectrometry;PMF,peptide mass fingerprint.

* To whom correspondence should be addressed.

INTRODUCTION

Currently, whole proteome analysis is widely

used in many areas of biological and medical research

[1,2]. The primary method for such analysis is mass

spectrometry, which provides quantitative informa-

tion on the changes in cell proteomes under differ-

ent conditions. One of the fundamental initial stages

in the development of quantitative proteomics are

implementations of the concepts of database and/or

spectral library search [3] and identification of pro-

teins using unique set of masses of their proteolytic

FEDOROV et al.1350

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

(typically tryptic) peptides, the latter known as Pep-

tide Mass Fingerprint (PMF) [4]. Regarding the types of

mass analyzers employed, the first proteomes of model

organisms were identified using radio-frequency quad-

rupole ion traps and time-of-flight mass spectrome-

ters[5]. First results were obtained without controlling

the false-discovery rate (FDR) [6, 7], a concept intro-

duced in proteomics in 2007 with the target-decoy ap-

proach [7]. Early on, starting with the works by Smith

et al., necessity of high-resolution mass spectrome-

try for comprehensive proteome analysis was recog-

nized, initially represented exclusively by the Fourier

transform ion cyclotron resonance mass spectrom-

etry (FT-ICR) in combination with nanoflow peptide

separation and ionization [8, 9]. Emergence of more

efficient high resolution mass analyzers compared to

FT-ICR, such as the Orbitrap, enabled identification of

up to 80% of the yeast proteome within the one-hour

single HPLC-MS/MS run by 2015 [10]. Currently, 5000

to 6000 proteins can be identified for human cell pro-

teomes in a two-hour single-shot experiment [11]. Fur-

ther increase in the depth of proteome-wide analysis

is achieved through additional sample fractionation

at the protein or peptide levels [12-14], by extending

the HPLC gradient duration to several hours, as well as

by employing long chromatographic columns [12, 15].

For instance, combining proteolytic mixture fraction-

ation, long-hour LC gradients, and more than 40-cm

columns enabled identification of more than half of

the human proteome [16]. While achieving such pro-

teome coverage is of great interest, total instrumental

time in the cited work amounted to 288 hours, making

such analysis unique but impractical for many appli-

cations involving routine characterization of hundreds

of samples per day, such as in chemical and popula-

tion proteomics, or clinical studies. Sample multiplex-

ing by labeling techniques [17, 18], currently imple-

mented using tandem mass tags (TMT) [19], partially

addresses the issue of instrumental costs of quantita-

tive proteome-wide analysis of a single sample. How-

ever, problems associated with the increased analytical

complexity of the samples and the need for fraction-

ation do not position the TMT approach as a method

of ultrafast proteomics, which can be defined as the

analysis of more than 200 samples per day. Indeed,

recent studies on glioblastoma cell lines treated with

interferon demonstrated that the 40-minute quantita-

tive analysis using 10-plex TMT (equivalent to about

200 proteome analyses per day) provides a rather poor

picture ofinterferon-regulated proteins [20].

After a significantly long time since the initial

demonstrations of quantitative proteome-wide analy-

sis based on Accurate Mass and Time tags (AMT)

within a minute range of gradient separations, inter-

est has recently renewed in this area, which could be

tentatively called “ultrafast proteomics”. A number of

methods were developed for its implementation based

on the novel high-resolution mass spectrometry instru-

ments combined with the ultra-short separations of

peptide mixtures (including peptide ion separations in

the gas phase), such as Data Independent Acquisition

(DIA) [13] and DirectMS1 [20]. These methods allow

semi-quantitative proteome analysis with a through-

put of over 200 samples per day.

This review discusses new approaches in ultrafast

proteomics developed in recent years and briefly ex-

plores their future prospects.

EARLY METHODS OF ULTRAFAST PROTEOMICS

One of the first implementations of the idea of ul-

trafast proteome analysis was the PMF approach [4,

21, 22]. This approach involves preliminary protein

separation using gel electrophoresis or liquid chroma-

tography, digestion of the protein fractions into proteo-

lytic peptides (typically, using trypsin), forming a set

of peptides with masses specific for each protein, and

measuring mass spectra of these peptide ions in the

fractions. For protein identification, the experimen-

tally obtained peptide ion masses are compared with

the theoretical ones derived from the available protein

sequence databases of the organism’s proteome under

study, as shown schematically in Fig.1 [23].

Matrix-assisted laser desorption/ionization mass

spectrometry (MALDI-MS) is the most commonly used

method for PMF implementation [4, 24]. Generally

speaking, this approach is not ultrafast, as the pro-

teome is divided into many fractions, usually using

sodium dodecyl sulfate-polyacrylamide gel electropho-

resis (SDS-PAGE) [21], each subjected to digestion and

analysis. It quickly became apparent that the PMF

method is ineffective for analyzing complex mixtures

[25], involving digests of dozens or even thousands

of proteins of a proteome. Currently, it is used almost

exclusively for the analysis and confirmation of indi-

vidual, usually pre-purified proteins.

With advancements in high resolution mass an-

alyzers, such as ion cyclotron resonance mass spec-

trometry, the idea of identifying proteins based on

measuring peptide ion mass spectra as a way of rapid

proteome-wide analysis was realized in the Accurate

Mass Tags (AMT) approach [26]. The method involves

generation of a list of peptides potentially present in

the analyzed samples based on preliminary MS/MS

analysis of a pool of samples under study. This is fol-

lowed by obtaining peptide ion mass spectra in the

individual samples of the pool and protein identifi-

cation based on matching the experimental masses

with this project-specific list of unique peptide mass-

es linked to the particular proteins. The basic idea of

the approach is that if the molecular mass of a peptide

ULTRAFAST PROTEOMICS 1351

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

Fig. 1. Diagram of the PMF method. Since peptide masses are not specific to amino acid sequence, a single measured mass

oreven a set of masses could correspond to multiple, and in some cases, dozens of possible proteins, complicating their iden-

tification in complex mixtures.

Fig. 2. Diagram of the AMT method based on accurate peptide ion masses and normalized elution times (NET). Normalization

is performed into the range[0,1]. Predicted NETs are calculated for the employed separation conditions using either simple

linear conversion, or neural networks [27].

could be measured with high enough accuracy so that

its mass was unique among all possible peptides pre-

dicted from the genome, it could then be used as an

“accurate mass tag” for protein identification. Accord-

ingly, generating a list of such AMTs allows analyzing

products of the whole proteome digest (e.g., obtained

by trypsin cleavage) at greater speed and sensitivity.

Moreover, the subsequent analysis of the individual

samples can be conducted without peptide fragmen-

tation stage, making the approach potentially MS/MS-

free and, thus, compatible with separations of proteo-

lytic mixtures using short gradients.

It quickly became evident that using additional

complementary data to the accurately measured mass-

es, such as peptide elution times, makes such combina-

tions unique for peptide amino acid sequences. There-

fore, the subsequent development of this approach

involved addition of normalized peptide elution times

(NETs), further transforming it into the Accurate Mass

and Time tags (AMT-tags) method [26]. Early applica-

tions demonstrated feasibility of using the AMT tags

method for proteome-wide analysis of relatively small

proteomes, particularly, Deinococcus radiodurans [27].

Moreover, since the method does not require peptide

FEDOROV et al.1352

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

fragmentation for identification (except at the stage of

the AMT tag list generation), such proteome-wide pro-

teome analysis was performed for the first time within

a minute-range time frame. Standard implementation

of the AMT method consists of two main stages (Fig.2):

(i) generation of peptide AMT tags for pooled control

and test sample groups using deep (typically, employ-

ing fractionation) proteome-wide LC-MS/MS analysis;

and (ii) rapid HPLC-MS1-based analysis with protein

identification based on the AMT tags database creat-

ed in the first stage. In the first stage, each identified

peptide is assigned its mass within measurement error

and NET. The next stage involves analysis of a large

cohort of unfractionated samples under study using

HPLC-MS1, resulting in a list of experimental peptide

masses and charge states, as well as elution times. The

latter are converted to a normalized time scale, most

simply by linear function. Peptide identification for

each analyzed sample is based on matching the ex-

perimental data with the AMT tags, followed by pro-

tein identification and quantitation. The latter is per-

formed for the identified proteins using the peptide

ion intensities in the mass spectra.

While the AMT method demonstrated the possi-

bility of ultrafast quantitative analysis of proteomes

of various organisms [28, 29], its broader acceptance

in proteomics is hindered by the lack of FDR control.

Also, there are issues with aligning peptide elution

times for calculating NETs across different experi-

ments and separation conditions, especially between

the ones used for generating the AMT tag database and

those for subsequent rapid proteome analysis [30].

Oneof the possible solutions of the latter problem was

the use of various peptide retention time prediction

models [31, 32] and generation of the standardized

and/or universal peptide elution time databases for

the AMT tags based on them [33].

As the proteome-wide analysis using the so-called

Data-Dependent Acquisition (DDA) approach with hy-

brid mass spectrometers featuring high-resolution

Orbitrap ion traps [34-36] became routine laboratory

practice, the AMT method ceased to be widely used.

However, the DDA approach itself, where the peptide

ions detected in MS1 spectra are, next, sequentially

isolated in the radio-frequency ion trap of a hybrid

mass spectrometer and accumulated to quantities suf-

ficient for obtaining high quality fragmentation spec-

tra, inherently involves using long HPLC gradients.

Even in the case of multi-hour separations of the pro-

teome digests, extending up to 10 h in some extreme

cases [37], only a small fraction of peptides detectable

in the MS1 spectra are identified [38-40]. Nevertheless,

DDA has become the method of choice for quantita-

tive proteome-wide analysis in recent years with an

achievable depth of 10,000 or more protein identifica-

tions in some studies [16, 37, 41, 42]. Despite the obvi-

ous importance of achieving as large as possible depth

of proteome analysis, there is also an evident issue:

the enormously high instrumental time for analyzing a

single sample, especially when extensive pre-fraction-

ation of the analyzed mixtures is employed [41, 43-47].

DATA INDEPENDENT ACQUISITION

FOR ULTRAFAST PROTEOMICS

One of the obvious methods for implementing ul-

trafast proteomics is DIA [13]. Unlike DDA, this meth-

od does not rely on sequential selection of the precur-

sor ions based on their accurately measured mass in

MS1 spectra for subsequent isolation, accumulation,

and fragmentation, which is a primary reason for us-

ing long separation gradients. Instead, in DIA, ion ac-

cumulation and fragmentation occur within a broad

mass window, with the sequential change in the op-

erating parameters of the accumulation device to an

adjacent window, and so on (Fig. 3). As a result, nearly

all precursor ions present in the MS1 mass spectra are

fragmented in a series of such windows (typically of

20-25Th size), covering the entire m/z range of peptide

ions. It is clear that the fragmentation spectra in such

windows are mixed (or, are said to be highly multi-

plexed) and contain fragments from dozens of peptide

ions simultaneously, which present another challenge

of their further interpretation (deconvolution). Each

series of such windows corresponds to the prelimi-

nary measured MS1 spectrum and elution time, the

latter being a key parameter for subsequent deconvo-

lution of the fragmentation spectra and peptide iden-

tification. Size of the windows and, thus, efficiency of

the fragmentation spectrum deconvolution are deter-

mined by characteristics of the mass analyzer. For in-

stance, combination of the Orbitrap mass analyzer

and the Astral (ASymmetric TRAck Lossless) analyzer

allowed reducing the fragmentation windows to 2 Th,

effectively erasing the boundary between the DIA and

DDA methods in proteome-wide analysis [48,49].

The above-described DIA scheme corresponds to

its most widely used implementation, called SWATH-

MS (Sequential Window Acquisition of All THeoretical

Mass Spectra) [50]. The main advantage of this meth-

od is overcoming the data stochasticity problem of the

DDA approach, related to choosing a limited number

of the most intense precursor ions in a given mass

spectrum for selective accumulation and fragmenta-

tion. The result is a significantly lower level of missing

values, making DIA an alternative to DDA in quanti-

tative proteomics [51]. Simultaneously, since in DIA

all precursor ions are fragmented in a limited number

of windows, this method allows working with shorter

HPLC gradients [52]. Further optimization of the isola-

tion windows increased the depth of proteome analysis

ULTRAFAST PROTEOMICS 1353

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

Fig. 3. Scheme of DIA method. Instead of isolation of individual precursor ions detected in MS1 mass spectra for subsequent

fragmentation, the whole mass range is divided into a number of windows, in which all present ions are accumulated and frag-

mented. Thus, DIA allows obtaining tandem mass spectra for all precursor ions potentially present in a sample.

using ultra-short gradients [53, 54]. Additional separa-

tion of peptide ions by ion mobility demonstrated the

possibility of identifying over 1000 proteins from 5 ng

of HeLa digest in the DIA mode with 5-minute LC gra-

dients [55].

One of the limiting factors in developing DIA as

a routinely used method for ultrafast proteome-wide

analysis was high level of the fragmentation mass

spectra multiplexing, requiring complex data process-

ing algorithms for deconvolution. Standard solution to

this problem was using spectral libraries for the pool

of analyzed samples. These libraries were generated

using deep proteome-wide analysis by standard DDA,

making DIA not entirely data-independent. Besides the

obvious instrumental time costs for obtaining such li-

braries that makes DIA a conditionally fast proteome

analysis method, the use of experimental libraries

significantly limited application of DIA in inter-labo-

ratory and clinical studies. Moreover, a fundamental

issue remains with this approach: inability to identify

peptides with fragmentation spectra not present in the

library. Progress in the development of machine learn-

ing algorithms for predicting fragmentation spectra

and peptide retention times insilico has solved the lat-

ter problem [56-58]. However, extremely high level of

multiplexing, resulting from the interference of frag-

mentation spectra originating from different simulta-

neously eluting precursor ions, is significantly exacer-

bated when short separation gradients are used. Until

recently, this made it impossible to extract any mean-

ingful number of identifications from such spectra and

limited the use of DIA in applications requiring large

number of analyses. These issues were addressed

in the recent development of the DIA-NN algorithm,

based on the use of neural networks to distinguish sig-

nals of fragment ion from noise in the mass spectra

and employing new strategies for extracting quantita-

tive information and chromatogram aligning based on

the identified peptides [59]. In the DIA-NN algorithm,

elution peak of each precursor ion is described by a set

of scores, and the best candidate for the elution peak

of a particular precursor ion is determined through

an iteration procedure based on the linear classifier.

A key step in the algorithm’s operation is using deep

neural networks for assigning statistical significance

(q-value) to the identified precursors, calculated for

target and false candidates based on characteristics

of the corresponding elution peaks. Capabilities of the

DIA-NN algorithm for ultrafast proteome-wide analy-

sis were fully demonstrated in implementation of the

Scanning SWATH method [60, 61]. In this method the

sequential selection of peptide isolation windows, in

which fragmentation occurs, is replaced with contin-

uous scanning of the first ion isolating RF quadru-

pole of the mass spectrometer by a broad m/z window

across the entire mass range, simultaneously frag-

menting incoming precursor ions in the collisional RF

quadrupole. This creates an additional dimension for

matching fragmentation spectra to precursor candi-

dates during the subsequent deconvolution of highly

FEDOROV et al.1354

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

interfering MS/MS spectra and peptide identification

by the DIA-NN algorithm. In the recent collaborative

study by the developers of the Scanning SWATH meth-

od and DIA-NN algorithm, the previously unattainable

performance with the depth reaching several thou-

sand proteins was demonstrated in the analysis of

human cell proteomes using ultrafast HPLC gradients

of 0.5 to 5 min [61]. It is important to note that one

of the conditions for the method’s operation in ultra-

fast gradient mode required to maintain the resolving

power of chromatographic separation of complex mix-

tures, is using high HPLC flow rates, around several

hundred µl/min, which, in turn, leads to significant

sample consumption (up to several µg of the human

cell line digest).

Implementation of the DIA method in ultra-short

separation mode looks straightforward for the time-of-

flight mass analyzers, currently capable of acquiring

mass spectra over a broad m/z range with a scanning

rate of about 100 Hz and peak resolution in the spec-

tra of 50,000 or higher. One example of applying high

resolution time-of-flight mass analyzer for ultrafast

proteome-wide analysis is combination of additional

precursor separation by ion mobility using ion hold-

ing in an electric field gradient against the moving

gas column TIMS (Trapped Ion Mobility Spectrome-

try) [62, 63] with parallel accumulation and sequential

fragmentation of peptide ions PASEF (Parallel Accu-

mulation SErial Fragmentation [64]). In TIMS, peptide

ions eluting from the HPLC column and ionized in the

ionization source enter a drift chamber where they

are held in the radial direction by a constant electric

field, compensating their drift in collisions with carri-

er gas molecules, and are, thus, separated by ion mo-

bility. Instead of selecting one precursor ion for frag-

mentation, changes in the RF quadrupole parameters

are synchronized with ion mobility chamber operation

to isolate and fragment ions within the designated m/z

range. One 50-ms step of changing the trapping electric

field in the TIMS chamber allows obtaining fragmen-

tation spectra of several peptide ions. PASEF signifi-

cantly increases the rate of fragmentation spectrum

acquisition without losing analysis sensitivity [65]. Im-

plementing the TIMS-TOP/PASEF combination in DIA

mode (dia-PASEF) and using the DIA-NN algorithm for

data processing demonstrated the possibility of pro-

teome-wide analysis with several thousand proteins

identified for human cell line at a rate of up to 400

samples per day (3-minute HPLC gradient) [66].

DIRECT INFUSION METHOD DISPA

A logical step in the development of ultrafast pro-

teomic methods and simplification of the instrumen-

tal component of analysis is elimination of the on-line

chromatographic separation of proteolytic mixtures.

This approach is not unique and was used in proteome

analysis more than fifteen years ago [67]. However, its

early implementations were based on mass analyzers

of low resolution and mass measurement accuracy,

there were no advanced search engines for identifica-

tion existing at the time, and no any capabilities for

additional ion separation, e.g., by ion mobility. Several

years ago, the concept of direct injection of proteolyt-

ic mixture into the ionization source without on-line

HPLC separation was renewed in the Direct Infusion

Shotgun Proteomic Analysis approach (DISPA) owing

to advancements in the mass spectrometry technolo-

gies, emergence of high resolution mass analyzers and

fast ion mobility separation methods [68]. Technically,

implementation of the DISPA method is quite simple:

the proteolytic mixture is injected in a nanoflow mode

directly from a syringe filled with a sample into the

mass spectrometer’s ionization source. Ion mobility

separation is used as an additional dimension. Analy-

sis is performed using DIA. It is clear that the multi-

plexing level of fragmentation spectra in this case is

more than an order of magnitude higher than in the

case of HPLC-based analysis, which, accordingly, lim-

its the achievable depth of proteome coverage. In the

cited work, a depth of about 500 proteins was demon-

strated for the human cell line proteome. However,

this depth was achieved within a few minutes of ex-

perimental time, allowing analysis of 132 samples in

4.5 h (3 min per sample) with quantitative identifica-

tion of over 300 proteins. Inability to “link” fragment

peaks to chromatographic times for effective decon-

volution of highly multiplexed tandem spectra signifi-

cantly limited capabilities of the DISPA method when

using the standard HPLC-based DIA data processing

algorithms. To overcome these limitations, software

based on the CsoDIAq algorithm (Cosine similarity op-

timization for DIA qualitative and quantitative analy-

sis [69]) was developed. Using this algorithm for the

DISPA data processing demonstrated a depth of hu-

man cell line proteome analysis (HeLa and 293T cell

lines) of about 2000 proteins in a single experimental

run with total analysis time of a few minutes [70]. This

work also demonstrated capabilities of the method

for quantitative analysis of large cohorts of samples.

Inparticular, 96 human cell line samples treated with

a drug were analyzed within 8h with a depth of about

1000 quantitatively identified proteins. It is worth not-

ing that the DISPA method is an interesting alternative

to the standard HPLC-based ultrafast proteome-wide

analysis approaches, yet, it is at its early development

stage. Limitations of the method stem from the ex-

treme complexity of proteolytic mixtures, such as the

whole proteome digests, which can contain millions

of individual peptide sequences in a dynamic concen-

tration range reaching several orders of magnitude

ULTRAFAST PROTEOMICS 1355

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

(over 10 orders in the case of human blood plasma

proteome, for example). These limitations affect both

the number of identifications and accuracy of quanti-

tative analysis. High dynamic range leads to the strong

suppression of the low-concentration peptides ions in

both ionization source and accumulation ion trap of a

hybrid mass spectrometer. Limitations of the method

also include low sequence coverage of the identified

proteins due to the low efficiency of deconvolution of

the highly multiplexed tandem mass spectra, and dif-

ficulties in controlling the false discovery rate. Never-

theless, DISPA continues to demonstrate its potential-

ly high efficiency as an ultrafast proteome analysis

method. In the recent study, a throughput of 1000

samples per day (1.4min per sample) was demonstrat-

ed, profiling blood plasma proteins in the corona of

nanoparticles with identification of 280 proteins, 44 of

which were confirmed biomarkers of various patholo-

gies[71].

DIRECT PROTEIN IDENTIFICATION METHOD

As mentioned above, one of the reasons for long

duration of the standard proteome-wide analysis using

DDA is the need to obtain fragmentation spectra from

as many peptide ions eluting from the chromatograph-

ic column as possible. The dominant fragmentation

method is peptide backbone dissociation in collisions

with carrier gas molecules at -CO-NH- bonds, mostly

forming y- and b-series fragments. The peptide colli-

sion-activated dissociation process is ergodic, requir-

ing time for breaking bonds. Additionally, to enhance

information content of the fragmentation spectra and,

hence, increase accuracy of the corresponding pep-

tide sequence identification, it is desirable to obtain as

many fragment ions as possible, which also requires

time for accumulation of the isolated precursor ions

for this purpose. Thus, during the analysis of a pro-

teolytic mixture by DDA, for each MS1 spectrum ac-

quired in a wide m/z range and registering all peptide

ions eluting at a given time, the sequential selection of

a limited number of the most intense precursor ions,

their accumulation, and fragmentation is performed.

In the case of complex organism proteomes, the ana-

lyzed proteolytic mixture can contain millions of in-

dividual peptide sequences. Accordingly, to increase

the number of peptide ions selected for fragmentation

and, consequently, depth of the proteome coverage

by the analysis, and considering time constraints im-

posed by the ion accumulation rates and tandem mass

spectrum acquisition, the multi-hour HPLC gradients

are used. Thus, in order to increase efficiency of the

proteome-wide characterization, the concept of direct

mass spectrometric protein identification was sug-

gested, in which the peptide ion fragmentation step of

the analysis is omitted. Proteins are identified directly

from the MS1 spectra based on the accurately measur-

able masses of all ions in the spectra and taking into

account specificity of the protein digestion. At the

same time, intensity distribution of the registered pep-

tide ion peaks in the mass spectrum, corresponding

to different

C isotope content in the sequence (pep-

tide ion spectral profile), provides information specif-

ic to this peptide’s elemental composition [72]. Simul-

taneously, retention times are specific to the peptide

amino acid sequences [73-75], including peptides with

residue modifications [76,77]. Thus, MS1 spectra allow

extracting a set of complementary data about the pep-

tide sequence and elemental composition. Obviously,

omission of peptide fragmentation reduces capabil-

ities of their identification due to the significantly

lower specificity of chromatographic times to amino

acid sequences compared to the tandem mass spectra.

However, omission of the fragmentation stage allows

significant shortening the time of analysis by using

short HPLC gradients.

The concept of direct mass spectrometric pro-

tein identification was implemented in the DirectMS1

method, which in early works demonstrated depth

of the proteome-wide analysis of over 1000 proteins

using 5-minute separation gradients [78]. Schematic

representation of the method implementation is pre-

sented in Fig. 4. Mass analyzer operates in the mode

of continuous MS1 spectra acquisition throughout the

entire gradient elution time. Speed of the MS1 spectra

acquisition depends on the mass analyzer type and

requirements for the mass resolution and measure-

ment accuracy. These requirements are high: at least

100,000 mass resolving power and measurement ac-

curacy of less than 1 ppm. Another key factor affect-

ing efficiency of the method is scanning speed of the

mass analyzer and accuracy of the prediction of pep-

tide elution times, which are used in the DirectMS1

data processing algorithm to distinguish correct and

false identifications. Until recently, several models and

algorithms for the peptide retention time prediction

existed with prediction accuracy (correlation between

experimental and predicted times) of R

 ~ 0.96 for the

Pearson coefficient [32]. In recent years, with the de-

velopment of machine learning algorithms, peptide

retention time prediction models of significantly high-

er accuracy have emerged. Specifically, the DirectMS1

search algorithm uses the DeepLC prediction model,

which has substantially increased the proteome cov-

erage depth to over 2000 identified proteins using

5-minute HPLC gradients and 7.5 min total time per

experimental run [79]. Further increase in the num-

ber of identifiable proteins is achieved by adding pep-

tide ion mobility separation. The DirectMS1 method

implementation does not require significant chang-

es in the instrument, except for the need for higher

FEDOROV et al.1356

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

Fig. 4. Diagram of the DirectMS1 experiment. Key factor in the method’s efficiency is the use of machine learning algorithms

forpredicting peptide retention times and classifying correct and false identifications based on combination of the complemen-

tary data, such as the peptide ion mass spectral

C profile, retention times, ion mobility (when ion mobility separation is added

tothe workflow), and measured accurate peptide masses.

HPLC flow rates, up to 1 µl/min or more, in order to

maintain chromatographic resolution under ultra-

short gradient conditions. Software for processing of

the peptide ion mass spectra, ranking identifications,

correlating them with proteins in the corresponding

databases, and determining confidence levels is the

key part of the method. This task is performed using

the tools for determination of the peptide ion spec-

tral profiles in the MS1 spectra, such as Biosaur [80],

and protein identification ms1searchpy [81]; the lat-

ter is based on the machine learning algorithms and

integrated with the peptide retention time prediction

models. It should be noted that the drawback of the

DirectMS1 method is lack of the FDR control at the

peptide level. According to the authors of the method,

the level of false positive peptide identifications can

reach 30% [78]. Importantly, in the ultra-short gradi-

ent mode of separation, unlike in the MS/MS-based

approaches, the DirectMS1 method allows identifying

proteins with significantly (almost an order of mag-

nitude) greater sequence coverage. This, in turn, pro-

vides more accurate measurements of the changes in

protein concentrations. Notably, despite the lower pro-

teome coverage depth, the DirectMS1 method allows

protein quantitation in ultrafast analysis with efficien-

cy comparable to the long HPLC gradient DIA and DDA

methods [20], and it was successfully applied for iden-

tifying differentially expressed proteins in the cellu-

lar response to drug treatment [82].

CONCLUSIONS

Currently, we observe an active development of

the technologies for proteome-wide analysis based on

mass spectrometry and their application in various

fields of post-genomic research. However, throughput

of this analysis, which typically takes hours of experi-

mental time for quantitative profiling of a single pro-

teolytic mixture, is one of the main factors behind the

limited use of proteomics in many areas of biomedi-

cal studies. These areas include drug development and

repurposing, personalized medicine, population and

clinical proteomics, single-cell proteomics, and more.

Advancement of the high throughput high resolution

mass spectrometry technologies, as well as new data

processing methods based on machine learning algo-

rithms, increased the throughput to several hundred

whole proteome analyses per day. These capabilities

have been realized in proteomics in just the last few

years, and ultrafast proteomics methods are now rap-

idly evolving to become dominant approaches in ad-

dressing many of the above-mentioned problems and

emerging areas of post-genomic research. Methods

such as DIA, DirectMS1, and DISPA not only reduce

analysis time by more than an order of magnitude but

also increase its depth to the levels of 2000 to 5000

proteins identified within 3 to 5 min of total experi-

mental time, which was unimaginable a decade ago.

Further development of the technologies and methods

ULTRAFAST PROTEOMICS 1357

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

for ultrafast proteome-wide analysis will allow large-

scale studies on large sample cohorts with less time,

enabling more efficient determination of protein in-

teraction mechanisms and cellular changes at the pro-

teome level resulting from pathological processes, or

under the influence of chemotherapeutic and external

factors.

Contributions. I.I.F. and S.A.P. literature review

and analysis, manuscript writing; I.A.T. discussion of

the content and structure of the review; M.V.G. super-

vision of the work, manuscript writing and editing.

Funding. This work was financially supported by

the Russian Science Foundation, grant no. 20-14-00229.

Ethics declarations. This work does not contain

any studies involving human and animal subjects per-

formed by any of the authors. The authors of this work

declare that they have no conflicts of interest.

Open access. This article is licensed under a Cre-

ative Commons Attribution 4.0 International License,

which permits use, sharing, adaptation, distribution,

and reproduction in any medium or format, as long

as you give appropriate credit to the original author(s)

and the source, provide a link to the Creative Commons

license, and indicate if changes were made. Theimages

or other third-party material in this article are includ-

ed in the article’s Creative Commons license, unless

indicated otherwise in a credit line to the material.

Ifmaterial is not included in the article’s Creative Com-

mons license and your intended use is not permitted

by statutory regulation or exceeds the permitted use,

you will need to obtain permission directly from the

http://creativecommons.org/licenses/by/4.0/.

REFERENCES

1. Wang, H., Dey, K.K., Chen, P. C., Li, Y., Niu, M., Cho,

J.H., Wang,X., Bai,B., Jiao,Y., Chepyala, S.R., Harou-

tunian,V., Zhang,B., Beach, T.G., and Peng,J. (2020)

Integrated analysis of ultra-deep proteomes in cortex,

cerebrospinal fluid and serum reveals a mitochondri-

al signature in Alzheimer’s disease, Mol. Neurodege-

ner., 15, 43, https://doi.org/10.1186/s13024-020-00384-6.

2. Radovanovic,M., Day, R.O., Jones, G.D.R., Galettis,P.,

and Norris, R.L. G. (2022) LC-MS/MS method for si-

multaneous quantification of ten antibiotics in hu-

man plasma for routine therapeutic drug monitoring,

J. Mass Spectrom. Adv. Clin. Lab., 26, 48-59, https://

doi.org/10.1016/j.jmsacl.2022.11.001.

3. Desiere, F., Deutsch, E. W., King, N. L., Nesvizhskii,

A. I., Mallick, P., Eng, J., Chen, S., Eddes, J., Loeve-

nich, S.N., and Aebersold,R. (2006) The PeptideAtlas

project, Nucleic Acids Res., 34, D655-D658, https://

doi.org/10.1093/nar/gkj040.

4. Henzel, W. J., Watanabe, C., and Stults, J. T. (2003)

Protein identification: the origins of peptide mass fin-

gerprinting, J.Am. Soc. Mass Spectrom., 14, 931-942,

https://doi.org/10.1016/s1044-0305(03)00214-9.

5. Lay, J. O., Jr. (2001) MALDI-TOF mass spectrometry

of bacteria, Mass Spectrom. Rev., 20, 172-194, https://

doi.org/10.1002/mas.10003.

6. Aggarwal, S., and Yadav, A. K. (2016) False Discovery

Rate Estimation in Proteomics, in Statistical Analysis

in Proteomics (Jung, K., ed.) Springer, New York, NY,

pp.119-128.

7. Elias, J.E., and Gygi, S.P. (2007) Target-decoy search

strategy for increased confidence in large-scale pro-

tein identifications by mass spectrometry, Nat. Meth-

ods, 4, 207-214, https://doi.org/10.1038/nmeth1019.

8. Shen,Y., Tolić,N., Masselon,C., Pasa-Tolić,L., Camp,

D. G., 2nd, Hixson, K. K., Zhao, R., Anderson, G. A.,

and Smith, R.D. (2004) Ultrasensitive proteomics us-

ing high-efficiency on-line micro-SPE-nanoLC-nanoE-

SI MS and MS/MS, Anal. Chem., 76, 144-154, https://

doi.org/10.1021/ac030096q.

9. Shen, Y., Zhao, R., Berger, S. J., Anderson, G. A., Ro-

driguez, N., and Smith, R. D. (2002) High-efficiency

nanoscale liquid chromatography coupled on-line

with mass spectrometry using nanoelectron spray

ionization for proteomics, Anal. Chem., 74, 4235-4249,

https://doi.org/10.1021/ac0202280.

10. Richards, A.L., Hebert, A.S., Ulbrich,A., Bailey, D.J.,

Coughlin, E.E., Westphall, M.S., and Coon, J.J. (2015)

One-hour proteome analysis in yeast, Nat. Protoc.,

10, 701-714, https://doi.org/10.1038/nprot.2015.040.

11. Hebert, A.S., Prasad,S., Belford, M.W., Bailey, D.J.,

McAlister, G.C., Abbatiello, S.E., Huguet,R., Wouters,

E.R., Dunyach, J.J., Brademan, D.R., Westphall, M.S.,

and Coon, J. J. (2018) Comprehensive single-shot

proteomics with FAIMS on a hybrid orbitrap mass

spectrometer, Anal. Chem., 90, 9529-9537, https://

doi.org/10.1021/acs.analchem.8b02233.

12. Zubarev, R.A. (2013) The challenge of the proteome

dynamic range and its implications for in-depth

proteomics, Proteomics, 13, 723-726, https://doi.org/

10.1002/pmic.201200451.

13. Doerr,A. (2015) DIA mass spectrometry, Nat. Methods,

12, 35-35, https://doi.org/10.1038/nmeth.3234.

14. Geyer, P.E., Kulak, N.A., Pichler,G., Holdt, L.M., Te-

upser,D., and Mann,M. (2016) Plasma proteome pro-

filing to assess human health and disease, Cell Syst.,

2, 185-195, https://doi.org/10.1016/j.cels.2016.02.015.

15. Pirmoradian, M., Budamgunta, H., Chingin, K.,

Zhang,B., Astorga-Wells,J., and Zubarev, R.A. (2013)

Rapid and deep human proteome analysis by sin-

gle-dimension shotgun proteomics, Mol. Cell Pro-

teomics, 12, 3330-3338, https://doi.org/10.1074/mcp.

o113.028787.

16. Nagaraj, N., Wisniewski, J. R., Geiger, T., Cox, J.,

Kircher,M., Kelso,J., Pääbo,S., and Mann,M. (2011)

FEDOROV et al.1358

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

Deep proteome and transcriptome mapping of a hu-

man cancer cell line, Mol. Syst. Biol., 7, 548, https://

doi.org/10.1038/msb.2011.81.

17. Masselon, C., Anderson, G. A., Harkewicz,R., Bruce,

J.E., Pasa-Tolic,L., and Smith, R. D. (2000) Accurate

mass multiplexed tandem mass spectrometry for

high-throughput polypeptide identification from

mixtures, Anal. Chem., 72, 1918-1924, https://doi.org/

10.1021/ac991133+.

18. Chapman, J. D., Goodlett, D.R., and Masselon, C. D.

(2014) Multiplexed and data-independent tandem

mass spectrometry for global proteome profiling,

Mass Spectrom. Rev., 33, 452-470, https://doi.org/

10.1002/mas.21400.

19. Thompson, A., Schäfer, J., Kuhn, K., Kienle, S.,

Schwarz,J., Schmidt, G., Neumann,T., Johnstone,R.,

Mohammed, A. K., and Hamon, C. (2003) Tandem

mass tags: a novel quantification strategy for compar-

ative analysis of complex protein mixtures by MS/MS,

Anal. Chem., 75, 1895-1904, https://doi.org/10.1021/

ac0262560.

20. Ivanov, M.V., Bubis, J.A., Gorshkov,V., Tarasova, I.A.,

Levitsky, L.I., Solovyeva, E.M., Lipatova, A.V., Kjeld-

sen,F., and Gorshkov, M.V. (2022) DirectMS1Quant:

ultrafast quantitative proteomics with MS/MS-free

mass spectrometry, Anal. Chem., 94, 13068-13075,

https://doi.org/10.1021/acs.analchem.2c02255.

21. Aebersold,R., and Goodlett, D.R. (2001) Mass spec-

trometry in proteomics, Chem. Rev., 101, 269-295,

https://doi.org/10.1021/cr990076h.

22. Pappin, D.J., Hojrup,P., and Bleasby, A.J. (1993) Rap-

id identification of proteins by peptide-mass finger-

printing, Curr. Biol., 3, 327-332, https://doi.org/10.1016/

0960-9822(93)90195-t.

23. Aebersold,R., and Mann, M. (2003) Mass spectrom-

etry-based proteomics, Nature, 422, 198-207, https://

doi.org/10.1038/nature01511.

24. Gevaert, K., and Vandekerckhove, J. (2000) Protein

identification methods in proteomics, Electrophore-

sis, 21, 1145-1154, https://doi.org/10.1002/(sici)1522-

2683(20000401)21:6<1145::aid-elps1145>3.0.co;2-z.

25. He,Z., Yang,C., Yang,C., Qi, R.Z., Tam, J.P., and Yu,W.

(2010) Optimization-based peptide mass fingerprint-

ing for protein mixture identification, J.Comput. Biol.,

17, 221-235, https://doi.org/10.1089/cmb.2009.0160.

26. Conrads, T. P., Anderson, G. A., Veenstra, T. D., Pa-

sa-Tolić,L., and Smith, R.D. (2000) Utility of accurate

mass tags for proteome-wide protein identification,

Anal. Chem., 72, 3349-3354, https://doi.org/10.1021/

ac0002386.

27. Norbeck, A.D., Monroe, M.E., Adkins, J.N., Anderson,

K. K., Daly, D.S., and Smith, R. D. (2005) The utility

of accurate mass and LC elution time information in

the analysis of complex proteomes, J.Am. Soc. Mass

Spectrom., 16, 1239-1249, https://doi.org/10.1016/

j.jasms.2005.05.009.

28. Shen, Y., Strittmatter, E. F., Zhang, R., Metz, T. O.,

Moore, R.J., Li,F., Udseth, H.R., Smith, R.D., Unger,

K.K., Kumar,D., and Lubda,D. (2005) Making broad

proteome protein measurements in 1-5 min using

high-speed RPLC separations and high-accuracy mass

measurements, Anal. Chem., 77, 7763-7773, https://

doi.org/10.1021/ac051257o.

29. Shen,Y., Smith, R.D., Unger, K.K., Kumar,D., and Lub-

da,D. (2005) Ultrahigh-throughput proteomics using

fast RPLC separations with ESI-MS/MS, Anal. Chem.,

77, 6692-6701, https://doi.org/10.1021/ac050876u.

30. Lange,E., Tautenhahn,R., Neumann,S., and Gröpl,C.

(2008) Critical assessment of alignment procedures

for LC-MS proteomics and metabolomics measure-

ments, BMC Bioinformatics, 9, 375, https://doi.org/

10.1186/1471-2105-9-375.

31. Petritis,K., Kangas, L.J., Yan,B., Monroe, M.E., Strit-

tmatter, E. F., Qian, W. J., Adkins, J. N., Moore, R. J.,

Xu, Y., Lipton, M. S., Camp, D. G., 2nd, and Smith,

R. D. (2006) Improved peptide elution time predic-

tion for reversed-phase liquid chromatography-MS

by incorporating peptide sequence information,

Anal. Chem., 78, 5026-5039, https://doi.org/10.1021/

ac060143p.

32. Tarasova, I.A., Masselon, C. D., Gorshkov, A.V., and

Gorshkov, M.V. (2016) Predictive chromatography of

peptides and proteins as a complementary tool for

proteomics, Analyst, 141, 4816-4832, https://doi.org/

10.1039/c6an00919k.

33. Tarasova, I.A., Guryca,V., Pridatchenko, M.L., Gors-

hkov, A.V., Kieffer-Jaquinod,S., Evreinov, V.V., Mas-

selon, C.D., and Gorshkov, M.V. (2009) Standardiza-

tion of retention time data for AMT tag proteomics

database generation, J. Chromatogr.B, 877, 433-440,

https://doi.org/10.1016/j.jchromb.2008.12.047.

34. Han,X., Aslanian,A., and Yates, J.R. (2008) Mass spec-

trometry for proteomics, Curr. Opin. Chem. Biol., 12,

483-490, https://doi.org/10.1016/j.cbpa.2008.07.024.

35. Zhang, Y., Fonslow, B. R., Shan, B., Baek, M. C., and

Yates, J. R., 3rd (2013) Protein analysis by shotgun/

bottom-up proteomics, Chem. Rev., 113, 2343-2394,

https://doi.org/10.1021/cr3003533.

36. Miller, R. M., and Smith, L. M. (2023) Overview and

considerations in bottom-up proteomics, Analyst,

148, 475-486, https://doi.org/10.1039/d2an01246d.

37. Wang, H., Yang, Y., Li, Y., Bai, B., Wang, X., Tan, H.,

Liu, T., Beach, T. G., Peng, J., and Wu, Z. (2015) Sys-

tematic optimization of long gradient chromatog-

raphy mass spectrometry for deep analysis of brain

proteome, J. Proteome Res., 14, 829-838, https://

doi.org/10.1021/pr500882h.

38. Michalski,A., Cox,J., and Mann,M. (2011) More than

100,000 detectable peptide species elute in single shot-

gun proteomics runs but the majority is inaccessible

to data-dependent LC-MS/MS, J. Proteome Res., 10,

1785-1793, https://doi.org/10.1021/pr101060v.

ULTRAFAST PROTEOMICS 1359

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

39. Kreimer, S., Belov, M.E., Danielson, W. F., Levitsky,

L.I., Gorshkov, M.V., Karger, B.L., and Ivanov, A.R.

(2016) Advanced precursor ion selection algorithms

for increased depth of bottom-up proteomic profiling,

J.Proteome Res., 15, 3563-3573, https://doi.org/10.1021/

acs.jproteome.6b00312.

40. Hebert, A.S., Thöing,C., Riley, N.M., Kwiecien, N.W.,

Shiskova, E., Huguet, R., Cardasis, H. L., Kuehn, A.,

Eliuk,S., Zabrouskov,V., Westphall, M. S., McAlister,

G.C., and Coon, J.J. (2018) Improved precursor char-

acterization for data-dependent mass spectrometry,

Anal. Chem., 90, 2333-2340, https://doi.org/10.1021/

acs.analchem.7b04808.

41. Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N., and

Mann, M. (2014) Minimal, encapsulated proteom-

ic-sample processing applied to copy-number esti-

mation in eukaryotic cells, Nat. Methods, 11, 319-324,

https://doi.org/10.1038/nmeth.2834.

42. Zhang, B., Pirmoradian, M., Chernobrovkin, A., and

Zubarev, R. A. (2014) DeMix workflow for efficient

identification of cofragmented peptides in high res-

olution data-dependent tandem mass spectrom-

etry, Mol. Cell Proteomics, 13, 3211-3223, https://

doi.org/10.1074/mcp.o114.038877.

43. Angel, T.E., Aryal, U.K., Hengel, S.M., Baker, E.S., Kel-

ly, R.T., Robinson, E.W., and Smith, R.D. (2012) Mass

spectrometry-based proteomics: existing capabilities

and future directions, Chem. Soc. Rev., 41, 3912-3928,

https://doi.org/10.1039/c2cs15331a.

44. Stanley, J.R., Adkins, J.N., Slysz, G.W., Monroe, M.E.,

Purvine, S. O., Karpievitch, Y. V., Anderson, G. A.,

Smith, R. D., and Dabney, A. R. (2011) A statistical

method for assessing peptide identification confi-

dence in accurate mass and time tag proteomics,

Anal. Chem., 83, 6135-6140, https://doi.org/10.1021/

ac2009806.

45. Zimmer, J.S.D., Monroe, M.E., Qian, W.-J., and Smith,

R. D. (2006) Advances in proteomics data analysis

and display using an accurate mass and time tag

approach, Mass Spectrom. Rev., 25, 450-482, https://

doi.org/10.1002/mas.20071.

46. Dos Santos,A., Court,M., Thiers,V., Sar,S., Guettier,C.,

Samuel, D., Bréchot, C., Garin, J., Demaugre, F., and

Masselon, C.D. (2010) Identification of cellular targets

in human intrahepatic cholangiocarcinoma using la-

ser microdissection and accurate mass and time tag

proteomics, Mol. Cell Proteomics, 9, 1991-2004, https://

doi.org/10.1074/mcp.m110.000026.

47. Agron, I.A., Avtonomov, D.M., Kononikhin, A.S., Pop-

ov, I.A., Moshkovskii, S.A., and Nikolaev, E.N. (2010)

Accurate mass tag retention time database for urine

proteome analysis by chromatography-mass spec-

trometry, Biochemistry (Moscow), 75, 636-641, https://

doi.org/10.1134/s0006297910050147.

48. Heil, L.R., Damoc,E., Arrey, T.N., Pashkova,A., Den-

isov,E., Petzoldt,J., Peterson, A.C., Hsu,C., Searle, B.C.,

Shulman,N., Riffle,M., Connolly,B., MacLean, B.X.,

Remes, P. M., Senko, M. W., Stewart, H. I., Hock, C.,

Makarov, A.A., Hermanson,D., Zabrouskov, V., Wu,

C. C., and MacCoss, M. J. (2023) Evaluating the per-

formance of the astral mass analyzer for quantita-

tive proteomics using data-independent acquisition,

J.Proteome Res., 22, 3290-3300, https://doi.org/10.1021/

acs.jproteome.3c00357.

49. Guzman, U. H., Martinez-Val, A., Ye, Z., Damoc, E.,

Arrey, T.N., Pashkova,A., Renuse,S., Denisov,E., Pet-

zoldt, J., Peterson, A. C., Harking, F., Østergaard, O.,

Rydbirk, R., Aznar, S., Stewart, H., Xuan, Y., Her-

manson, D., Horning, S., Hock, C., Makarov, A.,

Zabrouskov,V., and Olsen, J.V. (2024) Ultra-fast label-

free quantification and comprehensive proteome cov-

erage with narrow-window data-independent acqui-

sition, Nat. Biotechnol., https://doi.org/10.1038/s41587-

023-02099-7.

50. Gillet, L.C., Navarro,P., Tate,S., Röst,H., Selevsek,N.,

Reiter,L., Bonner,R., and Aebersold,R. (2012) Target-

ed data extraction of the MS/MS spectra generated

by data-independent acquisition: a new concept for

consistent and accurate proteome analysis, Mol. Cell

Proteomics, 11, O111.016717, https://doi.org/10.1074/

mcp.o111.016717.

51. Bruderer,R., Bernhardt, O.M., Gandhi,T., Miladinović,

S.M., Cheng, L.Y., Messner,S., Ehrenberger,T., Zano-

telli,V., Butscheid,Y., Escher,C., Vitek,O., Rinner,O.,

and Reiter, L. (2015) Extending the limits of quanti-

tative proteome profiling with data-independent ac-

quisition and application to acetaminophen-treat-

ed three-dimensional liver microtissues, Mol. Cell

Proteomics, 14, 1400-1410, https://doi.org/10.1074/

mcp.m114.044305.

52. Kelstrup, C. D., Bekker-Jensen, D. B., Arrey, T. N.,

Hogrebe, A., Harder, A., and Olsen, J. V. (2018) Per-

formance evaluation of the Q Exactive HF-X for shot-

gun proteomics, J.Proteome Res., 17, 727-738, https://

doi.org/10.1021/acs.jproteome.7b00602.

53. Doellinger, J., Blumenscheit, C., Schneider, A., and

Lasch, P. (2020) Isolation window optimization of

data-independent acquisition using predicted li-

braries for deep and accurate proteome profiling,

Anal. Chem., 92, 12185-12192, https://doi.org/10.1021/

acs.analchem.0c00994.

54. Doellinger, J., Blumenscheit, C., Schneider, A., and

Lasch, P. (2023) Increasing proteome depth while

maintaining quantitative precision in short-gradi-

ent data-independent acquisition proteomics, J. Pro-

teome Res., 22, 2131-2140, https://doi.org/10.1021/acs.

jproteome.3c00078.

55. Bekker-Jensen, D.B., Martínez-Val,A., Steigerwald,S.,

Rüther, P., Fort, K. L., Arrey, T. N., Harder, A., Ma-

karov, A., and Olsen, J. V. (2020) A compact quad-

rupole-orbitrap mass spectrometer with FAIMS in-

terface improves proteome coverage in short LC

FEDOROV et al.1360

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

gradients, Mol. Cell Proteomics, 19, 716-729, https://

doi.org/10.1074/mcp.tir119.001906.

56. Yang,Y., Liu,X., Shen,C., Lin,Y., Yang,P., and Qiao,L.

(2020) In silico spectral libraries by deep learning

facilitate data-independent acquisition proteom-

ics, Nat. Commun., 11, 146, https://doi.org/10.1038/

s41467-019-13866-z.

57. Lou,R., Liu,W., Li,R., Li,S., He,X., and Shui,W. (2021)

DeepPhospho accelerates DIA phosphoproteome pro-

filing through in silico library generation, Nat. Com-

mun., 12, 6685, https://doi.org/10.1038/s41467-021-

26979-1.

58. Van Puyvelde,B., Willems,S., Gabriels,R., Daled,S., De

Clerck,L., Vande Casteele,S., Staes,A., Impens,F., De-

force,D., Martens,L., Degroeve,S., and Dhaenens,M.

(2020) Removing the hidden data dependency of

DIA with predicted spectral libraries, Proteomics,

20, e1900306, https://doi.org/10.1002/pmic.201900306.

59. Demichev, V., Messner, C. B., Vernardis, S. I., Lilley,

K.S., and Ralser,M. (2020) DIA-NN: neural networks

and interference correction enable deep proteome

coverage in high throughput, Nat. Methods, 17, 41-44,

https://doi.org/10.1038/s41592-019-0638-x.

60. Moseley, M. A., Hughes, C. J., Juvvadi, P. R., Soderb-

lom, E.J., Lennon,S., Perkins, S.R., Thompson, J.W.,

Steinbach, W.J., Geromanos, S.J., Wildgoose,J., Lan-

gridge, J.I., Richardson,K., and Vissers, J.P.C. (2018)

Scanning quadrupole data-independent acquisition,

partA: qualitative and quantitative characterization,

J.Proteome Res., 17, 770-779, https://doi.org/10.1021/

acs.jproteome.7b00464.

61. Messner, C. B., Demichev, V., Bloomfield, N., Yu,

J.S.L., White,M., Kreidl,M., Egger, A.S., Freiwald,A.,

Ivosev, G., Wasim, F., Zelezniak, A., Jürgens, L., Sut-

torp,N., Sander, L.E., Kurth,F., Lilley, K.S., Mülled-

er, M., Tate, S., and Ralser, M. (2021) Ultra-fast pro-

teomics with Scanning SWATH, Nat. Biotechnol., 39,

846-854, https://doi.org/10.1038/s41587-021-00860-4.

62. Fernandez-Lima, F., Kaplan, D. A., Suetering, J., and

Park, M. A. (2011) Gas-phase separation using a

trapped ion mobility spectrometer, Int.J. Ion Mobil.

Spectrom., 14, 93-98, https://doi.org/10.1007/s12127-

011-0067-8.

63. Ridgeway, M.E., Lubeck,M., Jordens,J., Mattias, M.,

and Melvin, A. P. (2018) Trapped ion mobility spec-

trometry: a short review, Int. J. Mass Spectrom.,

425, 22-35, https://doi.org/10.1016/j.ijms.2018.01.006.

64. Meier,F., Beck,S., Grassl,N., Lubeck,M., Park, M.A.,

Raether, O., and Mann, M. (2015) Parallel Accu-

mulation-Serial Fragmentation (PASEF): multiply-

ing sequencing speed and sensitivity by synchro-

nized scans in a trapped ion mobility device, J. Pro-

teome Res., 14, 5378-5387, https://doi.org/10.1021/

acs.jproteome.5b00932.

65. Meier,F., Brunner, A.D., Koch,S., Koch,H., Lubeck,M.,

Krause,M., Goedecke,N., Decker,J., Kosinski,T., Park,

M.A., Bache,N., Hoerning,O., Cox,J., Räther,O., and

Mann,M. (2018) Online parallel accumulation-serial

fragmentation (PASEF) with a novel trapped ion mo-

bility mass spectrometer, Mol. Cell Proteomics, 17,

2534-2545, https://doi.org/10.1074/mcp.tir118.000900.

66. Szyrwiel,L., Gille,C., Mülleder,M., Demichev,V., and

Ralser,M. (2024) Fast proteomics with dia-PASEF and

analytical flow-rate chromatography, Proteomics, 24,

e2300100, https://doi.org/10.1002/pmic.202300100.

67. Chen,S. (2006) Rapid protein identification using di-

rect infusion nanoelectrospray ionization mass spec-

trometry, Proteomics, 6, 16-25, https://doi.org/10.1002/

pmic.200500043.

68. Meyer, J.G., Niemi, N.M., Pagliarini, D.J., and Coon,

J.J. (2020) Quantitative shotgun proteome analysis by

direct infusion, Nat. Methods, 17, 1222-1228, https://

doi.org/10.1038/s41592-020-00999-z.

69. Cranney, C.W., and Meyer, J.G. (2021) CsoDIAq soft-

ware for direct infusion shotgun proteome analysis,

Anal. Chem., 93, 12312-12319, https://doi.org/10.1021/

acs.analchem.1c02021.

70. Jiang,Y., Hutton, A., Cranney, C.W., and Meyer, J.G.

(2023) Label-free quantification from Direct Infu-

sion Shotgun Proteome Analysis (DISPA-LFQ) with

CsoDIAq software, Anal. Chem., 95, 677-685, https://

doi.org/10.1021/acs.analchem.2c02249.

71. Jiang,Y., and Meyer, J.G. (2024) 1.4 min plasma pro-

teome profiling via nanoparticle protein corona and

direct infusion mass spectrometry, bioRxiv, https://

doi.org/10.1101/2024.02.06.579213.

72. Miladinović, S. M., Kozhinov, A.N., Gorshkov, M. V.,

and Tsybin, Y.O. (2012) On the utility of isotopic fine

structure mass spectrometry in protein identification,

Anal. Chem., 84, 4042-4051, https://doi.org/10.1021/

ac2034584.

73. Krokhin, O.V. (2006) Sequence-specific retention cal-

culator. Algorithm for peptide retention prediction in

ion-pair RP-HPLC: application to 300- and 100-A pore

size C18 sorbents, Anal. Chem., 78, 7785-7795, https://

doi.org/10.1021/ac060777w.

74. Gorshkov, A. V., Tarasova, I. A., Evreinov, V. V., Sav-

itski, M.M., Nielsen, M.L., Zubarev, R.A., and Gors-

hkov, M.V. (2006) Liquid chromatography at critical

conditions: comprehensive approach to sequence-

dependent retention time prediction, Anal. Chem., 78,

7770-7777, https://doi.org/10.1021/ac060913x.

75. Pridatchenko, M.L., Perlova, T.Y., Ben Hamidane,H.,

Goloborodko, A. A., Tarasova, I. A., Gorshkov, A. V.,

Evreinov, V. V., Tsybin, Y. O., and Gorshkov, M. V.

(2012) On the utility of predictive chromatography to

complement mass spectrometry based intact protein

identification, Anal. Bioanal. Chem., 402, 2521-2529,

https://doi.org/10.1007/s00216-011-5350-3.

76. Perlova, T. Y., Goloborodko, A. A., Margolin, Y., Pri-

datchenko, M. L., Tarasova, I. A., Gorshkov, A. V.,

Moskovets,E., Ivanov, A.R., and Gorshkov, M.V. (2010)

ULTRAFAST PROTEOMICS 1361

BIOCHEMISTRY (Moscow) Vol. 89 No. 8 2024

Retention time prediction using the model of liquid

chromatography of biomacromolecules at critical con-

ditions in LC-MS phosphopeptide analysis, Proteomics,

10, 3458-3468, https://doi.org/10.1002/pmic.200900837.

77. Sargaeva, N. P., Goloborodko, A. A., O’Connor, P. B.,

Moskovets,E., and Gorshkov, M.V. (2011) Sequence-

specific predictive chromatography to assist mass

spectrometric analysis of asparagine deamidation and

aspartate isomerization in peptides, Electrophoresis,

32, 1962-1969, https://doi.org/10.1002/elps.201000507.

78. Ivanov, M. V., Bubis, J. A., Gorshkov, V., Tarasova,

I.A., Levitsky, L.I., Lobas, A.A., Solovyeva, E.M., Pri-

datchenko, M. L., Kjeldsen, F., and Gorshkov, M. V.

(2020) DirectMS1: MS/MS-Free identification of 1000

proteins of cellular proteomes in 5 minutes, Anal.

Chem., 92, 4326-4333, https://doi.org/10.1021/acs.

analchem.9b05095.

79. Ivanov, M. V., Bubis, J. A., Gorshkov, V., Abdrakh-

imov, D.A., Kjeldsen,F., and Gorshkov, M. V. (2021)

Boosting MS1-only proteomics with machine learning

allows 2000 protein identifications in single-shot hu-

man proteome analysis using 5 min HPLC gradient,

J.Proteome Res., 20, 1864-1873, https://doi.org/10.1021/

acs.jproteome.0c00863.

80. Abdrakhimov, D.A., Bubis, J.A., Gorshkov, V., Kjeld-

sen,F., Gorshkov, M.V., and Ivanov, M.V. (2021) Bio-

saur: an open-source Python software for liquid chro-

matography-mass spectrometry peptide feature detec-

tion with ion mobility support, Rapid Commun. Mass

Spectrom., https://doi.org/10.1002/rcm.9045.

81. Ivanov, M.V., Tarasova, I.A., Levitsky, L.I., Solovye-

va, E. M., Pridatchenko, M. L., Lobas, A. A., Bubis,

J.A., and Gorshkov, M.V. (2017) MS/MS-free protein

identification in complex mixtures using multiple

enzymes with complementary specificity, J. Pro-

teome Res., 16, 3989-3999, https://doi.org/10.1021/

acs.jproteome.7b00365.

82. Solovyeva, E. M., Bubis, J. A., Tarasova, I. A., Lobas,

A.A., Ivanov, M.V., Nazarov, A.A., Shutkov, I.A., and

Gorshkov, M.V. (2022) On the feasibility of using an

ultra-fast DirectMS1 method of proteome-wide analy-

sis for searching drug targets in chemical proteom-

ics, Biochemistry (Moscow), 87, 1342-1353, https://

doi.org/10.1134/s000629792211013x.

Publisher’s Note. Pleiades Publishing remains

neutral with regard to jurisdictional claims in pub-

lished maps and institutional affiliations.