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Deep Visual Proteomics

Deep Visual Proteomics Overview

Deep Visual Proteomics (DVP) is a cutting-edge approach that maps protein expression at the single-cell level while maintaining its spatial context within tissues[1]. It combines AI-driven image analysis, single-cell laser microdissection, and ultra-sensitive mass spectrometry to overcome the limitations of traditional proteomics methods. Developed to overcome the limitations of conventional imaging-based and mass spectrometry-based, it studies spatial proteomics by linking protein abundance to specific cell types and their micro-environments. DVP provides a powerful way to study cellular diversity, disease progression, and biomarker discovery, particularly in cancer research and precision medicine.

Background

Traditional proteomics relies on bulk mass spectrometry (MS), which measures protein abundance across large cell populations but lacks single-cell resolution[2]. While useful, obscures cellular heterogeneity, making it difficult to study variations in protein expression that drive processes like cancer progression. High-performance liquid chromatography (HPLC), often used in traditional MS workflows, provides protein separation before analysis, but it does not retain spatial information and still requires bulk cell samples[3].

Current spatial proteomics methods such as immunohistochemistry (IHC) and imaging mass spectrometry (IMS) allow researchers to visualize protein distribution within tissues[4][5]. However, these techniques have limitations: IHC can only detect a small set of predefined proteins, and IMS lacks the sensitivity and depth of MS-based approaches.

Deep Visual Proteomics (DVP) was developed to bridge this gap by combining AI-driven image analysis, single-cell laser microdissection, and ultra-sensitive MS[1][6]. This approach enables protein profiling at the single-cell level while preserving spatial relationships within tissues, making it possible to study cell-type-specific protein expression, disease progression, and biomarker discovery with high precision. This allows researchers to analyze individual cells within their native tissue environment, providing a clearer picture of how proteins vary between different cell types and disease states.

Improvements Over Traditional Proteomics

DVP offers two major advantages over traditional proteomics: single-cell resolution and spatial preservation. Bulk MS techniques require pooling many cells together, which averages out differences between them[7]. DVP, in contrast, isolates individual cells and retains information about their precise location within the tissue. This is crucial for understanding how proteins behave differently across various cell types, especially in diseases like cancer.

Compared to IHC and IMS, DVP allows for unbiased protein discovery—it doesn’t rely on predefined antibody targets, and it achieves far greater sensitivity and depth than imaging-based methods[1]. By integrating deep learning for image segmentation and machine learning for cell classification, DVP can automate and optimize single-cell selection before proteomic analysis, making the process more efficient and reproducible.

Additionally, AI-driven automation makes DVP more efficient and reproducible than manual cell selection methods. The BIAS software streamlines image processing, feature extraction, and cell classification, improving both throughput and accuracy. DVP provides a more detailed, scalable, and powerful tool for studying cell heterogeneity, disease mechanisms, and biomarker discovery, particularly in cancer and precision medicine.

Methods

Traditional proteomic approaches, while invaluable for profiling protein expression, often lack the spatial resolution necessary to understand the complex cellular heterogeneity and microenvironmental interactions within tissues. Conventional mass spectrometry-based proteomics typically analyzes bulk samples, averaging protein expression across large cell populations and thereby obscuring cell-specific differences and spatial context. Imaging-based techniques, though capable of high-resolution visualization, are generally limited to pre-defined biomarkers and cannot capture the full complexity of the proteome. Deep Visual Proteomics (DVP) addresses these limitations by integrating high-resolution imaging, artificial intelligence-driven image analysis, and ultra-sensitive mass spectrometry to profile proteomes at single-cell resolution while preserving spatial information[1]. By enabling the precise isolation and proteomic analysis of individual cells or subcellular compartments within their native tissue context, DVP uncovers cellular heterogeneity, phenotypic states, and microenvironmental influences that traditional proteomics cannot resolve. This approach is particularly transformative for studying complex biological processes such as cancer progression, immune responses, and tissue development, where spatial relationships and cellular diversity play crucial roles.

Sample Preparation

The first critical step in the Deep Visual Proteomics (DVP) workflow is the meticulous preparation of biological samples to ensure both structural integrity and compatibility with downstream high-resolution imaging and proteomic analysis. This process encompasses tissue selection, fixation, staining, and preparation for laser microdissection (LMD), all while maintaining the spatial context essential for single-cell proteomic profiling.

Tissue Selection & Fixation

Biological samples, such as cell cultures or tissue sections, are selected based on the specific research objective. A key feature of DVP is its compatibility with formalin-fixed paraffin-embedded (FFPE) tissues, which are commonly archived in clinical biobanks. This expands the potential for retrospective studies on patient samples, facilitating disease progression studies and biomarker discovery. FFPE tissues retain structural morphology and protein stability, making them ideal for the imaging and proteomic requirements of DVP. In some cases, fresh-frozen tissues or cell culture monolayers may also be used, particularly when preservation of labile post-translational modifications is necessary.

Staining & Labeling

To facilitate high-resolution imaging and enable accurate cell phenotyping, tissue sections or cell cultures undergo specific staining protocols. Depending on the study design, either immunohistochemical (IHC) staining or immunofluorescence (IF) is applied. Fluorescent dyes such as DAPI are commonly used to visualize nuclei, while cytoplasmic stains or membrane markers help define cell boundaries. Antibodies targeting proteins of interest (e.g., EpCAM, SOX10, CD146) are used to label specific cell types or states, a step critical for distinguishing between normal and pathological tissues or identifying rare cell populations. In complex tissues, multiple biomarkers can be stained simultaneously using multiplexed immunofluorescence, where fluorophore-conjugated antibodies allow for multi-channel imaging, facilitating in-depth spatial analyses of cell populations. The staining process is carefully optimized to ensure that downstream laser microdissection and proteomic analysis are not hindered by excessive background or fluorescence quenching.

Slide Preparation

Following staining, samples are mounted onto specialized glass membrane slides, such as polyethylene naphthalate (PEN)-coated slides, designed for laser microdissection (LMD). These membrane slides provide the structural support necessary for precise laser-guided cell excision while preserving spatial fidelity. For FFPE samples, deparaffinization and rehydration are carried out through graded ethanol washes to restore tissue morphology before staining[1]. Heat-induced epitope retrieval is then performed to unmask protein antigens, enhancing antibody binding during IHC or IF staining. To ensure tissue sections remain firmly attached throughout the imaging and microdissection processes, slides are treated with adhesion-promoting agents, such as APES coating. Finally, the slides are dried under controlled conditions to minimize residual moisture, preventing laser scattering or tissue distortion during microdissection.

By the end of this preparation stage, the biological samples are fully optimized for high-resolution imaging, with intact spatial organization and clearly defined cellular structures[8]. This careful sample preparation is fundamental to achieving the high fidelity and resolution that distinguishes DVP from traditional bulk proteomics, enabling the precise profiling of individual cells within their native tissue microenvironment.

High Resolution Imaging

The high-resolution imaging step in the Deep Visual Proteomics (DVP) workflow is crucial for capturing the intricate structural and spatial details of biological samples, which serve as the foundation for downstream single-cell proteomic analysis. Using advanced microscopy techniques, such as confocal or wide-field fluorescence microscopy, entire tissue sections or cell cultures are scanned to generate comprehensive two-dimensional (2D) or three-dimensional (3D) images[9]. This whole-slide scanning approach ensures that every region of the sample is documented at high resolution, preserving critical spatial relationships between cells and their microenvironment. The imaging process captures fine structural details at subcellular resolution, allowing for the clear identification of individual cells, nuclei, and even specific organelles within their native tissue context. This level of detail is essential for accurately segmenting cells and linking their phenotypic characteristics to proteomic data[10]. The resulting high-parametric images not only facilitate precise single-cell isolation but also enable a deeper understanding of tissue organization, cellular heterogeneity, and dynamic biological processes. By integrating subcellular spatial information with proteomic analysis, DVP overcomes the limitations of conventional proteomics, which typically loses spatial context during sample preparation, thereby offering a more comprehensive view of cellular function and disease mechanisms.

AI-Driven Image Analysis

AI-driven image analysis is a critical component of the Deep Visual Proteomics (DVP) workflow, enabling precise cell segmentation, phenotypic classification, and data extraction from high-resolution images. The process begins with image preprocessing, where raw microscopy images undergo normalization, denoising, and contrast enhancement to improve image clarity and consistency. These steps are essential to minimize artifacts and ensure optimal conditions for downstream segmentation and analysis. Once preprocessed, the images are analyzed using the BIAS (Biology Image Analysis Software) platform, which leverages deep learning algorithms, such as nucleAIzer, to perform highly accurate cell segmentation[9]. BIAS identifies key cellular structures, including nuclei, cytoplasm, and subcellular compartments, by analyzing morphological features and staining patterns. To increase its adaptability across various tissue types and staining techniques, the software is trained using both real and synthetically generated microscopy images, employing image style transfer methods to create diverse and representative training datasets[9].

Following segmentation, the platform conducts phenotypic classification using machine learning (ML) models that analyze a range of morphological features, such as cell area, perimeter, and solidity, as well as biomarker expression levels. This enables the identification of distinct cell types, functional states, and rare phenotypic subpopulations that may play critical roles in health and disease. Data augmentation techniques further enhance the robustness of these ML models by generating synthetic data, which expands the training dataset and improves the models' accuracy and generalizability[10]. This AI-driven approach not only streamlines the complex process of image analysis but also ensures high precision in identifying cellular heterogeneity, ultimately facilitating the accurate mapping of spatial proteomic landscapes in biological samples. By integrating AI into the DVP workflow, researchers can uncover subtle cellular variations and spatial dynamics that would be difficult to detect using conventional image analysis methods.

Single-Cell Isolation via Laser Microdissection (LMD)

Automated Cell Selection

The single-cell isolation step in the Deep Visual Proteomics (DVP) workflow utilizes laser microdissection (LMD) to precisely excise individual cells or specific subcellular regions identified during AI-driven image analysis[1]. This process begins with automated cell selection, where the phenotypic data generated by the BIAS (Biology Image Analysis Software) platform guides the identification of cells or regions of interest for downstream proteomic analysis. The AI-driven phenotyping ensures that only the most relevant cells—whether defined by morphological features, biomarker expression, or spatial location—are targeted, enabling a high degree of specificity and accuracy in sample selection[11].

Laser Microdissection (LMD)

Laser microdissection is then performed using the BIAS platform’s seamless integration with specialized LMD microscopes, such as the Zeiss PALM MicroBeam or Leica LMD7. This integration allows for the precise transfer of cell contour data from the imaging stage to the microdissection system, ensuring that cells are excised with sub-micron accuracy[11]. The system maintains spatial resolution up to 200 nanometers, preserving the integrity of the isolated cells while retaining their spatial context within the tissue[12]. The laser excises the selected cells by following the exact contours identified during image analysis, enabling the collection of individual cells or specific subcellular compartments without contamination from neighboring cells.

To further optimize the dissection process, advanced path-finding algorithms are employed to minimize laser exposure and prevent potential laser-induced damage to the cells. These algorithms also reduce the risk of sample loss and contamination, ensuring that the isolated material remains intact and suitable for high-sensitivity proteomic analysis. The precise and automated nature of this single-cell isolation process significantly increases the throughput and reliability of DVP, enabling the analysis of rare cell populations and complex tissue architectures with high spatial fidelity. By coupling AI-guided cell selection with laser microdissection, DVP achieves unparalleled precision in isolating cells for proteomic profiling, setting it apart from traditional bulk proteomic methods that lack spatial resolution.

Proteomic Analysis

The proteomic analysis stage of the Deep Visual Proteomics (DVP) workflow focuses on the comprehensive identification and quantification of proteins from isolated single cells or specific subcellular compartments. Following laser microdissection, the isolated material undergoes optimized protein extraction protocols specifically designed for ultra-low input samples. These lysis methods are carefully tailored to ensure maximal protein recovery while preserving protein integrity, which is crucial for achieving high sensitivity and accuracy in downstream mass spectrometry (MS) analysis. Special attention is given to minimizing sample loss and degradation during extraction, as even minor inefficiencies can significantly impact data quality when working with limited cellular material.

Mass Spectrometry (MS)

Once proteins are extracted, mass spectrometry (MS) is employed to perform deep and quantitative proteomic profiling[13]. The DVP workflow utilizes Data-Independent Acquisition (DIA) techniques, particularly the diaPASEF (parallel accumulation–serial fragmentation combined with data-independent acquisition) method[14]. This approach maximizes proteomic depth by enabling the concurrent analysis of a broad range of peptides, significantly improving both sensitivity and quantification accuracy. The diaPASEF technique is particularly well-suited for single-cell proteomics, where the detection of low-abundance proteins is critical for revealing subtle biological differences and rare cellular phenotypes.

To further enhance proteome coverage and reduce sample complexity, the workflow incorporates Ion Mobility Spectrometry, which introduces an additional dimension of separation based on the physical properties of peptide ions[15]. This added layer of separation improves the resolution of co-eluting peptides, allowing for the identification of a greater number of proteins, including those present in low quantities that might otherwise be missed in conventional MS analyses.

Quality control is an integral part of the proteomic analysis process to ensure data reliability and reproducibility. Internal standards are included in each run to monitor instrument performance and normalize data across samples[16]. Additionally, biological and technical replicates are analyzed to validate the consistency of protein quantification, with high correlations consistently observed across replicates (Pearson r > 0.95). These stringent quality control measures are essential for maintaining the accuracy and reproducibility of single-cell proteomic data, which can be particularly sensitive to variability due to the small amount of starting material.

Through the combination of optimized protein extraction, advanced mass spectrometry techniques, and rigorous quality control protocols, DVP enables deep and quantitative proteomic profiling at the single-cell level. This comprehensive approach provides a high-resolution view of cellular heterogeneity and functional states, offering unprecedented insights into complex biological systems and disease processes that traditional bulk proteomics cannot achieve.

Computational Biology

Deep-Learning for Cell Recognition

The first step in the DVP pipeline involves imaging and segmenting individual cells within a tissue or culture. High-resolution microscopy generates detailed images, which are then processed using deep learning-based segmentation models. The BIAS (Biology Image Analysis Software) platform plays a key role here, automatically detecting cell boundaries with high accuracy[1][9].

To improve segmentation performance, and avoid laborious collection of real images, DVP employs pre-trained deep neural networks trained on synthetic microscopy images, allowing the model to adapt to different tissue types and staining techniques[17]. The study compared BIAS against other segmentation tools like unet4nuclei, Cellpose, and CellProfiler, showing superior accuracy in distinguishing single-cell features[18][19][20]. This step is critical for ensuring that proteomic analysis is performed on well-defined, individual cells rather than mixed populations.

Machine-Learning for Feature Extraction

Once cells are segmented, machine learning (ML) algorithms classify them into distinct groups based on morphology, protein markers, and spatial context. This classification can be supervised, using predefined markers like FOXJ1 for ciliated cells, or unsupervised, where cells are clustered into novel subpopulations based on shared characteristics[21].

In addition to classification, ML is used for feature extraction, quantifying morphological traits such as cell size, shape, and neighborhood interactions[22]. These computational models allow DVP to pinpoint rare cell states that might be overlooked in traditional proteomics, making it especially useful for studying heterogeneous tissues like tumors.

Bioinformatics for Biological Interpretation

After single-cell laser microdissection, the selected cells undergo mass spectrometry (MS)-based proteomic analysis, generating large datasets that require extensive bioinformatics processing. This includes aligning protein features across samples to ensure comparability, normalizing data to correct for sample-to-sample variability, and applying data-independent acquisition (DIA) processing, specifically diaPASEF, which enhances the detection of low-abundance proteins[23].

Once proteins are identified, bioinformatics tools map them to biological pathways to interpret their function. In the study, DVP revealed mRNA splicing dysregulation in invasive melanoma and a decrease in interferon signaling as tumors progressed, highlighting its ability to track disease-related proteomic shifts[1].

By integrating spatial context with proteomic profiling, DVP provides a powerful tool for understanding cell heterogeneity, cancer progression, and biomarker discovery, offering a new way to study proteins within their native tissue environment.

Applications of Deep Visual Proteomics

Deep Visual Proteomics (DVP) has emerged as a transformative technology in biological research, clinical pathology, and personalized medicine by providing high-resolution, single-cell proteomic data while preserving spatial context. Its ability to integrate high-content imaging with ultra-sensitive proteomics has expanded its application across various scientific fields, offering insights into cellular heterogeneity, tissue architecture, and disease mechanisms that traditional proteomic approaches cannot resolve.

Cancer Research

DVP has significantly advanced cancer research by enabling the precise profiling of tumor heterogeneity, tracking cancer progression, and identifying spatially distinct tumor microenvironments. In melanoma research, DVP has been instrumental in revealing proteomic shifts associated with immune evasion and metastatic potential. By analyzing spatially distinct regions within tumor tissues, DVP uncovered key alterations in pathways such as mRNA splicing dysregulation and immune signaling suppression, which are linked to cancer progression and metastasis. Specifically, in studies of primary melanoma tissue, DVP identified the downregulation of interferon signaling and antigen presentation in invasive melanoma regions, highlighting mechanisms of immune escape and potential therapeutic targets​. Additionally, DVP has been applied to rare cancers, such as salivary gland acinic cell carcinoma, revealing disease-specific protein signatures and microenvironmental changes that were previously undetectable using conventional methods​.

Developmental Biology

In developmental biology, DVP has facilitated the mapping of developmental trajectories at single-cell resolution, providing a deeper understanding of the molecular programs guiding tissue formation and organogenesis. By retaining spatial information, DVP allows researchers to study how specific cell types emerge, differentiate, and interact within their native microenvironments. This has been particularly valuable in examining the development of complex tissues, such as epithelial layers and neural networks, where spatial cues are critical for proper tissue organization and function. The ability to link protein expression patterns with morphological changes enables the identification of key regulatory pathways driving development and offers new perspectives on how developmental disorders may arise from disruptions in these processes​.

Clinical Pathology

DVP holds significant potential in clinical pathology, particularly in the re-analysis of archived formalin-fixed paraffin-embedded (FFPE) tissue samples, which are routinely collected and stored in hospital biobanks. Traditional proteomic techniques often struggle with FFPE samples due to cross-linking and degradation; however, DVP overcomes these limitations, enabling retrospective studies on a wide range of clinical specimens. This capability has profound implications for biomarker discovery and personalized medicine, as historical patient samples can be revisited to identify new diagnostic markers, prognostic indicators, or therapeutic targets. For example, DVP has been used to analyze FFPE samples from melanoma and salivary gland carcinoma, revealing novel insights into disease progression and treatment resistance that were not apparent from conventional histopathology alone​. Its ability to extract spatially resolved proteomic data from archived samples opens new avenues for longitudinal studies and the development of precision oncology strategies.

Neuroscience & Immunology

The complex architecture of neural and immune tissues makes them particularly challenging to study using traditional bulk proteomics, which often obscures the contributions of individual cell types. DVP addresses this challenge by retaining spatial context during proteomic analysis, allowing for the detailed study of cell-cell interactions and microenvironmental influences in these intricate systems. In neuroscience, DVP enables the mapping of protein expression at single-cell resolution within brain tissue, shedding light on cellular heterogeneity, synaptic networks, and the molecular basis of neurological disorders. Similarly, in immunology, DVP has been used to study immune cell infiltration in tumor microenvironments and to explore the spatial dynamics of immune responses within lymphoid tissues. By linking proteomic data with cellular morphology and spatial localization, DVP provides a powerful tool for investigating the molecular underpinnings of neurodegenerative diseases, immune evasion in cancer, and inflammatory processes​.

Limitations & Future Directions

Despite its advancements, Deep Visual Proteomics (DVP) has several limitations. One major challenge is the sample preparation process, particularly for formalin-fixed paraffin-embedded (FFPE) tissues, where protein recovery can be inconsistent[24]. While DVP offers high spatial resolution, analyzing individual cells remains more time-consuming compared to bulk proteomics. Additionally, AI-driven image segmentation and classification may introduce biases if models are not well-trained on diverse tissue types. While DVP provides deep proteome coverage, low-abundance proteins may still be difficult to detect due to mass spectrometry limitations[25].

Future advancements integrating DVP with multi-omics approaches like single-cell transcriptomics and spatial metabolomics could provide a more complete view of cellular mechanisms. As the technology evolves, DVP could play a role in clinical diagnostics and precision medicine, driving the discovery of novel biomarkers and therapeutic targets.

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  25. ^ Kim, Min‐Sik; Zhong, Jun; Pandey, Akhilesh (2016-03). "Common errors in mass spectrometry‐based analysis of post‐translational modifications". PROTEOMICS. 16 (5): 700–714. doi:10.1002/pmic.201500355. ISSN 1615-9853. PMC 5548100. PMID 26667783. {{cite journal}}: Check date values in: |date= (help)CS1 maint: PMC format (link)
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