What is 4D Label-free Quantitative Proteomics?

Introduction to 4D Label-free Proteomics

Proteomics aims to catalog and quantify proteins in a complex mixture. Traditional proteomic workflows relied on two or three dimensions of separation. These dimensions included liquid chromatography and mass-to-charge (m/z) selection. Such approaches offered a reasonable depth of proteome coverage. Yet, they suffered from limitations in speed, sensitivity, and reproducibility. The emergence of ion mobility separation added a third dimension. This development enabled better discrimination of coeluting peptides. However, challenges remained. Overlapping ion signals could still mask low-abundance species.

4D label-free quantitative proteomics addresses these challenges. It integrates four orthogonal dimensions of separation and measurement, increasing the peak capacity for peptide detection and enhancing quantitative accuracy. The method does not require isotope or chemical labeling, thus reducing cost and complexity. It also mitigates artifacts introduced by chemical derivatization. Through increased scan speed and ion utilization, 4D label-free quantitation yields deeper proteome coverage.

Key Principles of 4D Label-free Quantitative Proteomics

Dimension 1: High-Resolution Liquid Chromatography Separation

Liquid chromatography (LC) is the first dimension of separation. Peptides are typically separated using reversed-phase columns based on hydrophobicity. This step minimizes co-elution and enhances ionization efficiency by removing salts, detergents, and other contaminants.

Gradient elution strategies are applied to achieve optimal peptide dispersion. Modern ultra-high-performance LC (UHPLC) systems support narrower peak widths and reduced sample loss. Improved chromatographic resolution leads directly to better feature detection during downstream MS acquisition.

Dimension 2: Ion Mobility Separation (TIMS Technology)

The second separation occurs via trapped ion mobility spectrometry (TIMS). This step differentiates peptides based on their collisional cross-section (CCS), which reflects their size, shape, and charge in the gas phase.

Unlike traditional drift tube ion mobility, TIMS captures ions within an electric field against a gas flow. Altering the electric field gradient sequentially releases peptides. This process enables high-resolution separation within milliseconds, independent of m/z or retention time.

TIMS offers several key benefits:

• Separates isobaric peptides with similar m/z
• Increases identification confidence by adding CCS as a unique identifier
• Enhances proteome coverage without extending LC gradients

Dimension 3: High-Accuracy Mass-to-Charge (m/z) Measurement

Mass spectrometry provides the third analytical dimension. Peptides are introduced into a quadrupole or time-of-flight (TOF) analyzer, where their m/z ratios are measured with high precision.

Modern instruments achieve sub-ppm mass accuracy and high resolving power. Accurate precursor ion selection enhances the quality of MS/MS spectra, directly impacting peptide identification rates.

In the 4D workflow, precursor selection is informed by intensity and ion mobility characteristics. This results in a richer and more diverse fragmented dataset.

Dimension 4: Precise Retention Time Alignment

The fourth dimension addresses temporal consistency across LC-MS runs. Retention time (RT) varies slightly due to column aging, temperature shifts, or gradient fluctuations. Precise RT alignment ensures that the same peptide features are matched across multiple runs.

RT correction algorithms like those implemented in MaxQuant or Spectronaut align retention times using internal standards or landmark peptides. This process is critical for consistent label-free quantification.

When integrated with m/z and CCS data, aligned RT allows for:

• Accurate peak matching in large cohorts
• Reliable comparison of protein abundance
• Reduced false discovery rates in differential analysis

Integrative Value of the Four Dimensions

The true strength of 4D label-free proteomics lies in the orthogonality of its components. Each dimension uniquely filters the data space:

• LC separates based on hydrophobicity
• TIMS separates by ion mobility (CCS)
• MS measures m/z values with high precision
• RT ensures temporal reproducibility across experiments

Figure 1. Schematic diagram of 4D label-free quantitative proteomics (Megger D A, et al. 2013).

Advanced Technologies for 4D Label-free Quantification

Trapped Ion Mobility Spectrometry (TIMS)

TIMS represents a significant improvement over conventional drift tube ion mobility spectrometry. This method separates ions based on their CCS—a biophysical property reflecting ion size, shape, and charge—within an electric field that opposes a constant neutral gas flow. TIMS devices capture and trap high-resolution ions before selectively releasing them for MS/MS analysis. This approach enables the resolution of isobaric and near-isobaric species that would otherwise be indistinguishable in standard m/z-based separation.

The benefits of TIMS include:

• Orthogonal separation that enhances peak capacity
• Increased dynamic range, enabling detection of low-abundance peptides
• Short analysis times without sacrificing resolution

Parallel Accumulation–Serial Fragmentation (PASEF)

PASEF is an acquisition strategy developed to complement TIMS. It capitalizes on TIMS's ability to accumulate ions while previous scans are being analyzed. Ions accumulate in parallel and are released rapidly in a time-synchronized manner. This duty cycle optimization ensures that ions are fragmented with exceptional speed and minimal loss.

Key advantages of PASEF include:

• Fragmentation rates exceeding 100 Hz
• Up to tenfold higher sequencing speed compared to conventional methods
• No compromise in sensitivity due to efficient ion utilization

Quadrupole Time-of-Flight (QTOF) and timsTOF Pro Platform

The timsTOF Pro instrument integrates QTOF mass analysis with TIMS-PASEF acquisition. The quadrupole filters precursor ions by m/z, while the TOF analyzer detects ions based on their flight time with high resolution and mass accuracy. This instrument is particularly suited to 4D label-free workflows due to its consistent performance, fast duty cycle, and compatibility with DDA and DIA acquisition schemes.

Distinct features of the timsTOF Pro system include:

• High resolution
• Sub-ppm mass accuracy across wide dynamic ranges
• Robust performance across replicates and instrument runs
• Compact ion mobility-enabled instrument footprint

Data-Independent Acquisition (DIA) vs. Data-Dependent Acquisition (DDA)

DIA and DDA are compatible with 4D platforms, but each serves different experimental goals.

• DDA selects high-intensity precursor ions for MS/MS fragmentation in real-time. While effective, it may miss low-abundance features and has limited reproducibility across runs.
• DIA systematically fragments all ions within predefined m/z windows, generating comprehensive spectral libraries. Including TIMS and PASEF enhances DIA by reducing spectral complexity and improving precursor-fragment pairing.

When used with 4D platforms, DDA excels in discovery-phase studies for identifying new proteins. Due to its reproducibility and lower missing value rates, DIA is preferred for quantitative profiling, particularly in clinical or biomarker-driven studies.

Workflow of 4D Label-free Quantitative Proteomics

Sample Preparation: Protein Extraction and Cleanup

Effective lysis and extraction protocols are critical. Detergents and salts are removed through filter-aided sample preparation (FASP) to ensure MS compatibility.

Enzymatic Digestion and Peptide Fractionation

Proteins are digested using trypsin. Optional high-pH reverse-phase fractionation may be applied to reduce sample complexity and enhance proteome depth.

Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) with Ion Mobility Separation

LC separates peptides, ionizes them, and introduces them into the TIMS cell. Ion mobility adds a second dimension of separation before peptides are analyzed in the QTOF analyzer.

Optimizing Data Acquisition Parameters for 4D Analysis

PASEF settings, LC gradients, ion mobility scan ranges, and collision energy parameters are adjusted for each sample type. This optimization ensures consistent and comprehensive quantitation.

Data Processing: Feature Detection, Alignment, and Quantitation

Proteins are quantified based on ion intensity. Software tools such as MaxQuant or DIA-NN align retention times, match features across runs, and perform statistical analyses to identify differentially expressed proteins.

Compare with 3D Label-free Quantitative Techniques

Applications of 4D Label-free Quantitative Proteomics

Biomarker Discovery in Disease Research

By offering enhanced sensitivity, 4D proteomics enables the detection of disease-associated biomarkers in complex biological matrices, such as plasma or tumor tissue.

Drug Target Validation and Mechanism of Action Studies

This platform identifies protein targets affected by drug compounds and maps their interaction networks, aiding in mechanism-of-action studies.

Cellular Pathway Profiling in Systems Biology

Quantitative proteomic data supports the construction of signaling and metabolic pathways, providing insights into cellular behavior under various stimuli.

Microbial and Plant Proteomics for Environmental Studies

Microbial communities and plant tissues often present complex proteomes. The multidimensional separation of 4D label-free proteomics facilitates high-resolution analysis of microbial and plant proteomics.

Case Studies

Identifying Differential Protein Expression in Cancer Subtypes

4D label-free quantitative proteomics has been instrumental in uncovering protein expression differences across cancer subtypes. In a study focusing on hepatocellular carcinoma (HCC), researchers used this technique to compare proteomic profiles between tumor and adjacent non-tumor tissues. The analysis identified differentially expressed proteins (DEPs) associated with hepatocarcinogenesis, notably ribosomal proteins and tRNA synthetases. RPL27, RPS16, and TARS2 were validated as potential prognostic biomarkers through Western blotting and parallel reaction monitoring (PRM). These findings suggest that such proteins could be indicators for tracking therapy responses in HCC patients (Suo L, et al., 2023).

In another study, label-free quantitative proteomics was used to analyze plasma samples from colorectal cancer (CRC) patients. The investigation revealed aberrant expression levels of several proteins, including leucine-rich alpha-2-glycoprotein (LRG), complement component 9 (C9), fibronectin (FN), alpha-1-antitrypsin (A1AT), and alpha-1-acid glycoprotein 1 (AGP1). These proteins exhibited significant differential expression between CRC patients and healthy controls, highlighting their potential as biomarkers for CRC diagnosis and prognosis (Verathamjamras, et al., 2023).

Figure 2. Label-free quantitative proteomics reveals aberrant expression in the plasma of patients with colorectal cancer (Verathamjamras, et al., 2023).

Elucidating Host-Pathogen Interactions in Infectious Disease Models

The application of 4D label-free quantitative proteomics extends to the study of host-pathogen interactions. In research investigating the response of macrophages to infection by Cryptococcus neoformans, a fungal pathogen, this proteomic approach enabled the comprehensive profiling of both host and pathogen proteins. The study provided insights into the dynamic changes in protein expression during infection, facilitating a deeper understanding of the molecular mechanisms underlying host defense and pathogen virulence (Ball B, et al., 2020).

Similarly, a study examining the ocular surface's response to Pseudomonas aeruginosa infection employed label-free quantitative proteomics to distinguish between general and site-specific host responses. The analysis identified significant changes in protein abundance, including upregulation of proteins such as S100-A8 and S100-A9, which are associated with immune response and inflammation. These findings underscore the technique's capability to dissect complex host–pathogen interactions at the proteomic level (Yeung J, et al., 2020).

Figure 3. Overview of bottom-up proteomics workflow for profiling murine eye wash and corneal samples (Yeung J, et al., 2020).

Microbial and Plant Proteomics Studies

4D label-free quantitative proteomics has proven valuable in elucidating stress response mechanisms in plant biology. A study on Brassica napus cultivars with varying susceptibility to Xanthomonas campestris pv. campestris infection utilized this approach to analyze proteomic changes. The resistant cultivar exhibited upregulation of redox-related proteins, such as 2-cys peroxiredoxin and thioredoxin, which are implicated in maintaining redox homeostasis and mitigating oxidative stress. These proteomic alterations contribute to the plant's defense against pathogen invasion (Islam M T, et al., 2021).

Another study focused on the response of oat (Avena sativa) seedling roots to salt stress. Through label-free quantitative proteomics, researchers identified changes in proteins involved in respiratory metabolism, including those related to the tricarboxylic acid (TCA) cycle and glycolysis. The findings provided insights into plants' metabolic adjustments under salinity stress, enhancing our understanding of plant stress physiology (Chen X, et al., 2025).

Figure 4. A total of 7174 proteins were identified in the oat roots using label-free proteomics (Chen X, et al., 2025).

References

• Megger D A, et al. Label-free quantification in clinical proteomics. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 2013, 1834(8): 1581-1590. DOI: 10.1016/j.bbapap.2013.04.001
• Suo L, et al. Potential prognostic biomarkers of hepatocellular carcinoma based on 4D label-free quantitative proteomics analysis pilot investigation. The International Journal of Biological Markers, 2024, 39(1): 59-69. DOI: 10.1177/03936155231212925
• Ball B, Sukumaran A, Geddes-McAlister J. Label-free quantitative proteomics workflow for discovery-driven host-pathogen interactions. Journal of Visualized Experiments (JoVE), 2020 (164): e61881. DOI: 10.3791/61881
• Verathamjamras, et al. Label-free quantitative proteomics reveals aberrant expression levels of LRG, C9, FN, A1AT and AGP1 in the plasma of patients with colorectal cancer. Clinical Proteomics, 2023, 20, 15. DOI: 10.1186/s12014-023-09407-y
• Yeung J, Gadjeva M, Geddes‐McAlister J. Label‐free quantitative proteomics distinguishes general and site‐specific host responses to Pseudomonas aeruginosa infection at the ocular surface. Proteomics, 2020, 20(2): 1900290. DOI: 10.1002/pmic.201900290
• Islam M T, et al. Label-free quantitative proteomics analysis in susceptible and resistant Brassica napus cultivars infected with Xanthomonas campestris pv. campestris. Microorganisms, 2021, 9(2): 253. DOI: 10.3390/microorganisms9020253
• Chen X, et al. Label-Free Proteomics Reveals the Response of Oat (Avena sativa L.) Seedling Root Respiratory Metabolism to Salt Stress. International Journal of Molecular Sciences, 2025, 26(6): 2630. DOI: 10.3390/ijms26062630