Spatial Proteomics Platforms: A Comparison of Commercial and Open-Source Solutions

Spatial proteomics platforms differ substantially in multiplexing capacity, resolution, cost structure, and analytical complexity. This article compares commercial systems including Akoya CODEX, Fluidigm Hyperion, and NanoString GeoMx against open-source and academic approaches, providing a decision framework for researchers selecting a platform based on study objectives and budget constraints. The comparison covers data inputs, workflow choices, quality controls, reproducibility considerations, interpretation limits, and practical decision criteria relevant to students, researchers, analysts, and life-science professionals.

The Current Landscape of Spatial Proteomics Technologies

Spatial proteomics has emerged as a critical tool for understanding how proteins are organized within tissues and how that organization relates to biological function. Unlike bulk proteomics, which measures average protein abundance across a homogenized sample, spatial proteomics preserves the tissue architecture and reveals where specific proteins are located relative to cells, structures, and other proteins. This positional information is essential for understanding cell communication, tissue microenvironments, and disease mechanisms.

The field has expanded rapidly, with technologies now capable of measuring dozens to hundreds of protein markers simultaneously within a single tissue section. Highly multiplexed tissue imaging platforms leverage different antibody labeling strategies, including DNA oligonucleotide-tagged, fluorophore-tagged, and metal-tagged reagents, to capture these rich molecular readouts within the same specimen. These approaches generate spatially indexed datasets that can reveal cell neighborhoods, spatial programs, and interaction patterns across contexts such as cancer, embryonic development, and neuroscience.

The practical value of spatial proteomics lies in its ability to connect protein expression to tissue structure. For example, in oncology research, spatial proteomics can reveal how tumor cells interact with surrounding immune cells and stroma, information that is lost when tissues are dissociated for single-cell analysis. Studies of breast and gynecologic cancers have demonstrated that combined image-based and molecular approaches can predict treatment response and survival, with spatial transcriptomics and proteomics deepening understanding of tumor heterogeneity and the interactions between tumor cells, stroma, and immune cells that drive disease progression.

Platform Categories and Technical Foundations

Spatial proteomics technologies fall into three main categories based on their detection chemistry and readout method: antibody-based imaging, liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based approaches, and imaging mass spectrometry. Each category has distinct strengths and limitations that influence platform selection.

Antibody-based imaging methods use labeled antibodies to detect specific proteins in tissue sections. These methods include cyclic immunofluorescence, where antibodies are applied, imaged, and removed in successive rounds, and multiplexed ion beam imaging, where metal-tagged antibodies are detected by mass spectrometry. Antibody-based approaches offer high multiplexing capacity and single-cell resolution but require validated antibody panels and careful optimization of staining conditions.

LC-MS/MS-based spatial proteomics methods extract proteins from defined tissue regions and analyze them by mass spectrometry. These approaches provide deep proteome coverage without requiring antibodies, making them suitable for discovery studies. However, they typically have lower spatial resolution than imaging methods and require specialized sample preparation. A sparse sampling strategy for spatial proteomics using computationally assisted image reconstruction has demonstrated the potential to map more than 9000 proteins in the mouse brain, suggesting that deep coverage of centimeter-sized samples is achievable with reduced measurement time.

Imaging mass spectrometry methods, including matrix-assisted laser desorption/ionization (MALDI) imaging, directly analyze proteins and other molecules from tissue surfaces. These methods provide label-free detection and can measure hundreds of analytes simultaneously, but they face challenges in sensitivity and spatial resolution compared to antibody-based approaches.

Commercial Platform Comparison

Akoya CODEX

The Akoya CODEX platform uses DNA oligonucleotide-tagged antibodies that are sequentially visualized through complementary DNA probes. This cyclic approach allows high multiplexing capacity, typically measuring 40 to 60 protein markers in a single tissue section. The platform operates on a standard fluorescence microscope equipped with fluidics, making it accessible to many core facilities.

Key characteristics of the CODEX platform include its compatibility with formalin-fixed paraffin-embedded (FFPE) tissues, which are the standard clinical specimen type, and its ability to image large tissue areas at single-cell resolution. The platform requires a validated antibody panel, and panel design is a critical step that influences data quality and interpretation. The cyclic staining approach introduces potential issues with tissue stability over multiple rounds, and careful quality control is needed to ensure consistent signal across cycles.

Fluidigm Hyperion

The Fluidigm Hyperion system uses imaging mass cytometry, where antibodies are tagged with stable metal isotopes and detected by time-of-flight mass spectrometry. This approach eliminates issues with spectral overlap that limit fluorescence-based methods and allows measurement of 40 or more markers simultaneously. The platform provides high-dimensional single-cell data with spatial context, making it valuable for immune profiling and tumor microenvironment studies.

The Hyperion system requires specialized instrumentation and is typically housed in core facilities. The metal-tagged antibody panels must be carefully validated, and the laser ablation process that generates the imaging data requires optimization for each tissue type. The platform produces data in a format that integrates with existing single-cell analysis workflows, facilitating comparison with flow cytometry and mass cytometry data.

NanoString GeoMx

The NanoString GeoMx Digital Spatial Profiler uses a different approach, combining morphological imaging with oligonucleotide-tagged antibodies that are released from user-defined regions of interest and quantified by next-generation sequencing or nCounter readout. This platform allows researchers to select specific tissue regions based on morphology and measure protein expression within those regions, providing a bridge between bulk and single-cell analysis.

The GeoMx platform offers flexibility in region selection and can measure hundreds of proteins when using the sequencing readout. It is compatible with FFPE tissues and supports both protein and RNA analysis from the same sample. The spatial resolution is determined by the size of the regions of interest, which can range from small cellular clusters to larger tissue areas. This platform is well suited for studies where specific tissue structures or cell populations are the focus of investigation.

Open-Source and Academic Approaches

Open-source and academic spatial proteomics approaches offer alternatives to commercial platforms, with different tradeoffs in accessibility, cost, and analytical flexibility. These approaches include both laboratory methods and computational tools that can be applied to data from multiple platforms.

Antibody-Based Open Methods

Academic laboratories have developed several open antibody-based spatial proteomics methods that use standard laboratory equipment. These methods typically involve cyclic immunofluorescence or multiplexed immunohistochemistry with tyramide signal amplification. While these approaches may have lower multiplexing capacity than commercial platforms, they offer significant cost savings and can be implemented in laboratories with standard fluorescence microscopes.

The main challenge with open antibody-based methods is the need for extensive optimization and validation. Antibody panels must be tested for specificity, sensitivity, and compatibility with the chosen detection chemistry. Tissue autofluorescence and antibody cross-reactivity can introduce artifacts that require careful controls. These methods also require substantial expertise in immunohistochemistry and image analysis.

Mass Spectrometry-Based Open Methods

Open mass spectrometry approaches for spatial proteomics include both LC-MS/MS-based methods with region-specific sampling and imaging mass spectrometry. These methods provide unbiased protein detection without requiring antibodies, making them suitable for discovery studies where the proteins of interest are not known in advance.

The sparse sampling strategy for spatial proteomics represents an open approach that reduces the number of samples requiring mass spectrometry analysis while maintaining spatial coverage through computational image reconstruction. This method has generated large spatial proteome datasets and identified potential new regional or cell type markers, suggesting that deep coverage of whole tissues is achievable with reduced measurement time.

Computational Tools and Databases

Open-source computational tools are essential for analyzing spatial proteomics data regardless of the platform used for data generation. These tools address the major analytical bottlenecks in highly multiplexed tissue imaging data analysis, including preprocessing and normalization, denoising, spillover correction, cell segmentation, cell-type annotation, and tissue microarchitecture analysis.

Interactive tools that support cell type assignment across large consortia, platforms, and modalities have been developed to address the challenge of uniformly assigning cell types across datasets. These tools provide support for the different steps involved in the assignment and dataset comparison process, including how to combine complementary data types and how to analyze and visualize spatial data. Open-source tools have been used to annotate datasets from multi-omics single-cell sequencing and spatial proteomics studies.

Foundation models for spatial proteomics have been developed to address the challenge of analyzing data across heterogeneous marker panels and protocols. These models learn marker-aware, multi-scale representations of proteins, cells, niches, and tissues directly from multiplex imaging data, supporting marker reconstruction, cell typing, niche annotation, spatial biomarker discovery, and patient stratification. Such models enable zero-shot annotation across heterogeneous panels and datasets, addressing the limitation that most analytical methods are tailored to single cohorts.

At a Glance: Platform Comparison Table

Decision Matrix for Platform Selection

Selecting a spatial proteomics platform requires evaluating research needs against platform capabilities and constraints. The following decision matrix organizes the key considerations for platform selection.

Practical Workflow for Platform Implementation

Implementing a spatial proteomics study requires careful planning across multiple stages, from experimental design through data analysis and interpretation. The following workflow outlines the key steps and decisions involved.

Experimental Design and Sample Preparation

The first step in any spatial proteomics study is defining the biological question and determining whether spatial information is necessary to address it. Studies of tissue microenvironments, cell-cell interactions, and spatial organization of disease processes are well suited to spatial proteomics. Studies where the question is primarily about average protein abundance across a sample may be better served by bulk proteomics methods.

Sample preparation is a critical determinant of data quality. FFPE tissues are the most common clinical specimen type and are compatible with most antibody-based platforms. Fresh frozen tissues are required for some mass spectrometry-based approaches and may provide better preservation of protein epitopes. Tissue section thickness, fixation conditions, and antigen retrieval protocols must be optimized for each platform and tissue type.

Panel Design and Validation

For antibody-based platforms, panel design is the most important decision affecting data quality and interpretation. The panel must include antibodies that are validated for the specific platform chemistry, whether that involves DNA oligonucleotide tagging, metal tagging, or fluorophore conjugation. Each antibody must be tested for specificity, sensitivity, and compatibility with the detection chemistry.

Panel validation should include positive and negative control tissues, titration experiments to determine optimal antibody concentrations, and testing for cross-reactivity between antibodies. The number of markers that can be measured simultaneously is limited by the platform chemistry, so panels must be prioritized based on the biological question. Markers that define major cell types, functional states, and key signaling pathways are typically prioritized.

Data Acquisition and Quality Control

Data acquisition parameters must be optimized for each platform and tissue type. For imaging platforms, these parameters include exposure times, laser power, and scanning resolution. For mass spectrometry-based approaches, they include ionization parameters, mass range, and spatial sampling density.

Quality control during data acquisition is essential for identifying technical artifacts that could compromise downstream analysis. Common quality issues include tissue detachment during staining cycles, uneven antibody staining, background signal from tissue autofluorescence or nonspecific binding, and signal decay over multiple imaging cycles. Standardized operating procedures and quality metrics are being developed to enable evaluation of samples across technical metrics and comparison of data across sites.

Data Processing and Analysis

Spatial proteomics data require multiple processing steps before biological interpretation. These steps include image registration and alignment, background subtraction, spillover correction for multiplexed fluorescence data, cell segmentation to identify individual cells, and feature extraction to quantify protein expression per cell.

Cell segmentation is a particularly challenging step that significantly influences downstream results. Segmentation algorithms must accurately identify cell boundaries in tissues with varying cell density and morphology. Super-resolution approaches have been developed to improve cell segmentation and clustering in spatial proteomics imaging, addressing limitations of standard segmentation methods.

Cell-type annotation is another critical step that requires reference knowledge of expected cell populations. Interactive tools that support cell type assignment across different data types and platforms can improve the consistency and accuracy of annotation. These tools allow researchers to combine complementary data types and analyze spatial data within a unified framework.

Data Integration and Interpretation

Spatial proteomics data are most powerful when integrated with other data types, including genomics, transcriptomics, and clinical data. Integration approaches can reveal relationships between protein expression, gene expression, and clinical outcomes that are not apparent from any single data type.

The FAIR Guiding Principles provide a framework for making spatial proteomics data findable, accessible, interoperable, and reusable. Applying these principles to data management and sharing enables comparison across studies and platforms, which is essential for validating findings and building cumulative knowledge. Data repositories and web-based applications that enable analysis and comparison of user data against extensive collections of imaging-based datasets support this goal.

Records and Measurements for Reproducibility

Reproducibility is a major challenge in spatial proteomics due to the complexity of experimental workflows and the variety of platforms and protocols in use. Standardized metrics for assessment and reproducibility are being developed to address this challenge, including standardized operating procedures and open-source software for evaluating samples across technical metrics.

Documentation Requirements

Comprehensive documentation of experimental procedures is essential for reproducibility. Documentation should include detailed protocols for sample preparation, antibody validation, staining procedures, data acquisition parameters, and data processing steps. Version control for protocols and analysis code ensures that the exact procedures used for each dataset can be reconstructed.

Quality Metrics and Controls

Quality metrics should be recorded for each experiment to enable assessment of data quality and comparison across batches. These metrics include signal-to-noise ratios, detection sensitivity, false discovery rates, and congruence with single-cell profiling data where available. Positive and negative control samples should be included in each experimental batch to monitor technical performance.

Data Storage and Sharing

Spatial proteomics datasets are large and require substantial storage capacity. Data management plans should address raw data storage, processed data formats, and metadata documentation. Sharing data through public repositories enables validation and reuse by the research community, consistent with data sharing policies that require deposition of data generated with public funding.

Common Failure Patterns and Troubleshooting

Several failure patterns recur across spatial proteomics studies. Recognizing these patterns and understanding their causes can help researchers avoid common pitfalls and troubleshoot problems when they arise.

Antibody Validation Failures

The most common cause of poor spatial proteomics data is inadequate antibody validation. Antibodies that work well for Western blotting or flow cytometry may not perform adequately in tissue imaging due to differences in antigen accessibility, fixation effects, or detection chemistry. Failure to validate antibodies in the specific platform context leads to nonspecific staining, high background, and unreliable quantification.

Troubleshooting antibody failures requires systematic testing of each antibody in the target tissue type, comparison with known expression patterns, and optimization of antigen retrieval and staining conditions. Antibodies that fail validation should be replaced with alternatives that have demonstrated performance in spatial proteomics applications.

Tissue Quality Issues

Tissue quality significantly affects spatial proteomics data. Poor fixation, delayed tissue processing, and suboptimal storage conditions can degrade protein epitopes and increase background signal. FFPE tissues that have been stored for extended periods may show reduced immunoreactivity, particularly for phospho-epitopes and other labile modifications.

Quality assessment of tissue sections before staining can identify potential problems. Hematoxylin and eosin staining of adjacent sections provides information about tissue morphology and preservation. Assessment of tissue integrity during the staining process can identify detachment or damage that would compromise data quality.

Segmentation and Annotation Errors

Cell segmentation errors propagate through the entire analysis pipeline, affecting cell-type annotation, neighborhood analysis, and biomarker discovery. Over-segmentation splits single cells into multiple objects, while under-segmentation merges adjacent cells. Both types of errors distort the single-cell measurements that form the basis of downstream analysis.

Annotation errors occur when cell types are incorrectly assigned based on marker expression patterns. This is particularly challenging in tissues with heterogeneous cell populations or when marker panels lack sufficient discriminating power. Interactive annotation tools and comparison with reference datasets can improve annotation accuracy.

Batch Effects and Cross-Platform Variability

Batch effects arise from technical variation between experimental runs, including differences in reagent lots, instrument calibration, and environmental conditions. These effects can obscure biological differences and lead to false conclusions if not properly controlled. Cross-platform variability is a related challenge when data from different platforms are compared or integrated.

Standardized operating procedures, inclusion of reference samples in each batch, and statistical methods for batch effect correction are essential for managing these sources of variation. The development of standardized metrics for evaluating imaging-based spatial technologies across sites is an active area of research.

Limitations and Interpretation Boundaries

Spatial proteomics technologies have inherent limitations that constrain their interpretation. Understanding these limitations is essential for drawing appropriate conclusions from spatial proteomics data.

Sensitivity and Dynamic Range

Antibody-based spatial proteomics methods have limited sensitivity compared to mass spectrometry-based approaches. Proteins expressed at low levels may fall below the detection threshold, leading to false-negative results. The dynamic range of detection is also limited, making it difficult to accurately quantify proteins with very high and very low expression levels in the same sample.

Mass spectrometry-based spatial methods have their own sensitivity limitations due to the non-amplifiable nature of proteins and the sensitivity limitations of mass spectrometry. Deep coverage of the proteome requires substantial measurement time, creating a tradeoff between spatial resolution and proteome depth.

Multiplexing Constraints

The number of proteins that can be measured simultaneously is limited by the platform chemistry. Antibody-based platforms typically measure tens to hundreds of markers, which represents a small fraction of the proteome. This limitation means that spatial proteomics studies are inherently targeted, requiring prior knowledge of the proteins of interest.

The limited multiplexing capacity also affects the ability to identify cell types and states. Cell-type annotation relies on the expression of marker proteins, and panels that lack sufficient discriminating markers may not resolve all cell populations present in a tissue.

Spatial Resolution Limits

The spatial resolution of spatial proteomics platforms varies from subcellular to regional, depending on the technology. Imaging platforms can achieve single-cell or subcellular resolution, while region-based approaches such as GeoMx measure protein expression in user-defined areas that may contain multiple cells. Mass spectrometry-based approaches have variable resolution depending on the sampling strategy.

The choice of spatial resolution should be guided by the biological question. Studies of cell-cell interactions require single-cell resolution, while studies of tissue-level heterogeneity may be adequately served by region-based approaches.

Quantification Challenges

Spatial proteomics data are semi-quantitative at best. Antibody-based methods measure relative protein abundance based on signal intensity, which is influenced by antibody affinity, staining conditions, and detection chemistry. Absolute quantification of protein concentration is not typically possible with these methods.

Mass spectrometry-based methods can provide more quantitative measurements but face challenges in converting peptide signals to protein abundance, particularly for proteins with limited proteotypic peptides. The lack of absolute quantification limits the comparability of measurements across studies and platforms.

Safety and Regulatory Context

Spatial proteomics research involving human tissues is subject to regulatory requirements that vary by jurisdiction and funding source. Researchers should be aware of these requirements when planning studies and handling data.

Human Subjects Protection

Studies using human tissues must comply with institutional review board requirements and applicable regulations for human subjects research. Consent processes must address the use of tissues for research purposes, including potential future analyses. De-identification of samples and data is required to protect participant privacy.

Data Sharing Policies

Data sharing policies require that data generated with public funding be deposited in accessible repositories. These policies apply to spatial proteomics data, including raw imaging data, processed data, and associated metadata. Data sharing plans should be developed at the study design stage to ensure that data can be shared in compliance with applicable policies.

Genomic Data Considerations

Spatial proteomics studies that are integrated with genomic data may be subject to additional requirements for genomic data sharing. These requirements address the sensitive nature of genomic information and the need for appropriate data use limitations. Researchers should consult institutional data governance resources when planning studies that combine spatial proteomics with genomic data.

Professional Escalation Criteria

Researchers should recognize situations where specialized expertise is needed to address technical or analytical challenges. The following criteria indicate when to escalate to specialists.

When to Consult a Core Facility

Core facilities with spatial proteomics expertise should be consulted when selecting a platform, designing antibody panels, or troubleshooting technical issues. Core facility staff can provide guidance on platform capabilities, sample preparation requirements, and data acquisition parameters. Consultation is particularly important for researchers new to spatial proteomics or when working with challenging tissue types.

When to Engage a Bioinformatics Specialist

Bioinformatics specialists should be engaged for complex data processing and analysis tasks, including cell segmentation optimization, cell-type annotation, batch effect correction, and integration with other data types. The computational demands of spatial proteomics data analysis often exceed the expertise of individual research groups, and specialized support can improve the quality and interpretability of results.

When to Seek Statistical Consultation

Statistical consultation is recommended for studies involving multiple comparisons, integration of multiple data types, or biomarker discovery. The high-dimensional nature of spatial proteomics data creates statistical challenges, including multiple testing, spatial autocorrelation, and confounding by technical factors. Statistical expertise can help design appropriate analyses and avoid common pitfalls.

When to Escalate for Clinical Translation

Studies intended to support clinical translation should engage regulatory and clinical expertise early in the design process. The requirements for clinical validation, regulatory approval, and clinical implementation differ substantially from basic research requirements. Early engagement with these experts can help ensure that study designs generate the evidence needed for translation.

Frequently Asked Questions

What is the difference between spatial proteomics and spatial transcriptomics?

Spatial proteomics measures the location and abundance of proteins within tissue sections, while spatial transcriptomics measures the location and abundance of RNA transcripts. Proteins are the functional molecules that regulate most biological processes and constitute the majority of biomarkers and drug targets, but they cannot be amplified like nucleic acids, which creates different technical challenges. Spatial proteomics provides direct measurement of protein expression, while spatial transcriptomics provides indirect measurements of cellular states that may not correlate perfectly with protein abundance due to post-transcriptional regulation and protein turnover.

How many proteins can be measured with current spatial proteomics platforms?

The multiplexing capacity varies substantially by platform. Commercial antibody-based imaging platforms such as Akoya CODEX and Fluidigm Hyperion typically measure 40 to 60 protein markers, while NanoString GeoMx can measure hundreds of proteins when using sequencing-based readout. Mass spectrometry-based approaches can measure thousands of proteins but with lower spatial resolution and throughput. The choice of platform should be guided by the number of markers needed to address the biological question.

What is the cost difference between commercial and open-source spatial proteomics approaches?

Commercial platforms require substantial investment in instrumentation and reagents, with costs varying by platform and multiplexing capacity. Open-source antibody-based methods use standard laboratory equipment and have lower reagent costs but require more personnel time for optimization and validation. Mass spectrometry-based approaches require access to specialized instrumentation but avoid antibody development costs. The total cost of a study includes also instrument and reagent costs but also personnel time, computational resources, and data storage.

Which spatial proteomics platform is best for clinical samples?

The best platform for clinical samples depends on the specific application and sample type. FFPE tissues are the standard clinical specimen type and are compatible with most antibody-based platforms. NanoString GeoMx is well suited for clinical samples because it allows morphology-guided region selection and is compatible with FFPE tissues. Studies intended to support clinical translation should consider platform reproducibility, standardization, and documentation requirements in addition to technical capabilities.

How do I validate antibodies for spatial proteomics?

Antibody validation should be performed in the specific platform context, as antibodies that work well in other applications may not perform adequately in tissue imaging. Validation should include testing in positive and negative control tissues, titration experiments to determine optimal concentrations, and assessment of specificity and sensitivity. Comparison with known expression patterns and orthogonal validation methods can provide additional confidence in antibody performance.

What are the main challenges in spatial proteomics data analysis?

The main analytical challenges include preprocessing and normalization, denoising, spillover correction, cell segmentation, cell-type annotation, and tissue microarchitecture analysis. Cell segmentation is particularly challenging because errors propagate through the entire analysis pipeline. Cell-type annotation requires reference knowledge of expected cell populations and can be complicated by heterogeneous tissues and limited marker panels. Integration of spatial proteomics data with other data types adds additional complexity.

Can spatial proteomics data be integrated with single-cell RNA sequencing data?

Integration of spatial proteomics data with single-cell RNA sequencing data is an active area of development. The two data types provide complementary information, with spatial proteomics providing protein expression in tissue context and single-cell RNA sequencing providing deep transcriptome coverage. Integration approaches can transfer cell-type annotations between data types and reveal relationships between protein and RNA expression. However, discrepancies between proteome and transcriptome measurements suggest altered protein turnover, and integration requires careful attention to technical differences between platforms.

What are the emerging trends in spatial proteomics technology development?

Emerging trends include three-dimensional spatial proteomics, integration with spatial multi-omics, and the application of artificial intelligence for data analysis. Foundation models that learn marker-aware representations of proteins, cells, niches, and tissues from multiplex imaging data are enabling analysis across heterogeneous panels and datasets. Super-resolution approaches are improving cell segmentation and clustering. These developments are expanding the applications of spatial proteomics in basic research and clinical translation.

Related Bioinformatics Guides

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References and Further Reading

• EMBL-EBI Training. European Bioinformatics Institute.
• NCBI Data Resources. National Center for Biotechnology Information.
• Genomic Data Sharing Policy. National Institutes of Health.
• The FAIR Guiding Principles. Scientific Data.
• Proteomic landscape of Alzheimer's disease: emerging technologies, advances and insights (2021 - 2025).. Molecular neurodegeneration, 2025.
• Computational pathology in ovarian cancer.. Frontiers in oncology, 2022.
• Standardized metrics for assessment and reproducibility of imaging-based spatial transcriptomics datasets.. Nature biotechnology, 2026.
• Interactive single-cell data analysis using Cellar.. Nature communications, 2022.
• Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016.. Human genomics, 2016.
• Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.. Journal of translational medicine, 2026.
• Multi-omics profiling of chronic immune-mediated skin diseases: SKINERGY protocol and strategic evaluation.. Journal of the European Academy of Dermatology and Venereology : JEADV, 2026.
• Improved cell-type identification and comprehensive mapping of regulatory features with spatial epigenomics 96-channel microfluidic platform.. GEN biotechnology, 2023.
• What Are the Practical Applications of Single-Cell Proteomics?. 2026.
• Spatial Biology Evolution: Past, Present and Future of Mapping Life in Context.. 2026.
• Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.. 2026.
• Bridging the Precision Gap in Rheumatoid Arthritis: Spatial Transcriptomics, Spatial Proteomics, and Artificial Intelligence in Precision Health.. 2026.
• The evolving landscape of spatial proteomics technologies in the AI age. Fundamental Research, 2024.
• AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery. arXiv.org, 2025.
• In-depth and high-throughput spatial proteomics for whole-tissue slice profiling by deep learning-facilitated sparse sampling strategy. Cell Discovery, 2025.
• iPEX enables micrometre-resolution deep spatial proteomics via tissue expansion. Nature, 2025.
• Spatial proteomics for investigating solid tumor resistance mechanisms. Cancer Metastasis Review, 2025.
• SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging. Nature Communications, 2024.
• Mapping biology in space: from spatial transcriptomics platforms to analytical tools and databases. Science Bulletin, 2026.
• Spatial proteomics of breast ductal carcinoma in situ reveal distinct regional differences. Scientific Reports, 2026.
• Effects of electroacupuncture Baihui and Shenting acupoints on proteomics of hippocampal ubiquitination in rats with vascular cognitive impairment. Chinese Journal of Rehabilitation Medicine, 2023.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.