Spatial Proteomics Methods: A Guide to Imaging Mass Cytometry, CODEX, and Other Techniques

Spatial proteomics encompasses a family of technologies that measure the location and abundance of proteins directly within intact tissue sections, preserving the architectural context that bulk and single-cell suspension assays destroy. For researchers deciding among imaging mass cytometry (IMC), CODEX, multiplexed ion beam imaging (MIBI), and mass spectrometry-based approaches, the choice depends on multiplexing capacity, spatial resolution, sample compatibility, and the biological question at hand. This article provides a practical comparison of these methods, their workflow requirements, and decision criteria for selecting an appropriate platform.

The Rationale for Spatial Protein Measurement

Protein subcellular localization is tightly controlled and directly linked to protein function in health and disease. Capturing the spatial proteome, meaning the localizations of proteins and their dynamics at the subcellular level, is essential for a complete understanding of cell biology [6]. While single-cell technologies have provided deep insights into cellular heterogeneity, they fall short in explaining how cells form tissue structures, a crucial aspect for understanding the principles of complex tissues [19]. Most biological processes are controlled by proteins, and genomic-level methods provide indirect measurements of cellular states [19]. Spatial proteomics therefore has the potential to reveal how cells organize into functional niches within tissues.

The joint analysis of the genome, epigenome, transcriptome, proteome, and metabolome from single cells is transforming understanding of cell biology in health and disease [5]. Spatial proteomics adds a critical dimension to this multi-omic landscape by anchoring molecular measurements to tissue architecture. Recent advances in mass spectrometry have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues [7]. Simultaneously, improvements in throughput and robustness are facilitating clinical applications [7].

Categories of Spatial Proteomics Technologies

Spatial proteomics techniques fall into three main categories: antibody-based imaging methods, liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based approaches, and imaging mass spectrometry-based methods [19]. Each category has distinct strengths and weaknesses that determine its suitability for particular research questions.

Antibody-Based Multiplexed Imaging

Antibody-based methods use labeled antibodies to detect specific protein targets within tissue sections. These methods achieve high multiplexing through iterative staining cycles, spectral unmixing, or mass-labeled detection. The most widely used antibody-based platforms include imaging mass cytometry (IMC), CO-Detection by indEXing (CODEX), and multiplexed ion beam imaging (MIBI).

Imaging mass cytometry uses antibodies conjugated to rare earth metal isotopes. A laser ablates the tissue in a raster pattern, and the vaporized material is analyzed by time-of-flight mass spectrometry. This approach achieves high multiplexing capacity with minimal spectral overlap between channels. IMC provides spatial resolution in the micrometer range, suitable for cellular and some subcellular analysis.

CODEX uses antibodies conjugated to unique DNA oligonucleotide barcodes. Iterative rounds of hybridization, imaging, and probe removal build up a multiplexed image over multiple cycles. This method operates on standard fluorescence microscopes and can achieve high plex on formalin-fixed paraffin-embedded (FFPE) tissues.

MIBI uses antibodies conjugated to metal isotopes, similar to IMC, but employs an ion beam to desorb ions from the tissue surface instead of a laser. This approach offers high spatial resolution and sensitivity for detecting low-abundance proteins.

Mass Spectrometry-Based Spatial Proteomics

Mass spectrometry-based spatial proteomics does not require antibodies and can detect thousands of proteins in an unbiased manner. Laser microdissection isolates regions of interest from tissue sections, and the collected material undergoes LC-MS/MS analysis. This approach provides deep proteome coverage but has historically been limited by the sensitivity required for small regions.

Recent advances have enabled the proteomic analysis of ultra-low-input archival material. A robust and scalable workflow benchmarked in murine liver quantified up to 2,000 proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions [11]. Applied to human tonsil, this workflow profiled 146 microregions including T and B lymphocyte niches and quantified cell-type-specific markers, cytokines, and transcription factors [11].

Mass spectrometry-based proteomics has evolved into a powerful tool for comprehensively analyzing biological systems [7]. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics [8]. Combined with high-content imaging, artificial intelligence, and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level [8].

Imaging Mass Spectrometry

Imaging mass spectrometry techniques such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry directly analyze proteins, peptides, and metabolites across tissue sections. These methods require no antibodies and provide label-free detection of hundreds to thousands of molecular species. Spatial resolution varies by instrument configuration, with some systems achieving cellular or subcellular resolution.

At a Glance: Comparison of Spatial Proteomics Methods

The following table summarizes key features of major spatial proteomics platforms to guide method selection.

Workflow Considerations for Antibody-Based Methods

Antibody Validation and Panel Design

The quality of antibody-based spatial proteomics depends entirely on antibody specificity and performance in the chosen tissue type. Each antibody must be validated for the specific fixation, antigen retrieval, and detection conditions used in the workflow. Antibody panels should be designed with consideration of the biological question, expected cell types, and protein abundance ranges.

For IMC and MIBI, antibodies must be conjugated to metal isotopes without losing affinity or specificity. For CODEX, antibodies are conjugated to DNA barcodes, and each barcode must be verified to not cross-react with other probes in the panel. Validation should include testing on control tissues with known expression patterns and comparison with established immunohistochemistry results.

Tissue Preparation and Sectioning

Tissue preparation significantly affects the quality of spatial proteomics data. FFPE tissues are widely available and compatible with most antibody-based methods, but fixation can mask epitopes and require optimized antigen retrieval protocols. Fresh frozen tissues often preserve protein epitopes better but require more careful handling and storage.

Section thickness typically ranges from 4 to 10 micrometers depending on the method and tissue type. Thinner sections provide better spatial resolution but contain less material for detection. The choice of section thickness should balance resolution requirements with detection sensitivity.

Staining and Acquisition

The staining protocol varies by platform. IMC and MIBI require a single staining step with metal-conjugated antibodies followed by ablation and mass spectrometry detection. CODEX requires iterative cycles of probe hybridization, imaging, and probe removal, which extends acquisition time but allows very high multiplexing.

Acquisition time is an important practical consideration. IMC ablation of a 1 square millimeter tissue region at cellular resolution can take 30 to 60 minutes. CODEX acquisition time scales with the number of markers and the area imaged. MIBI acquisition is generally faster than IMC for equivalent areas but still requires specialized instrumentation.

Data Processing and Analysis

All antibody-based spatial proteomics methods generate multi-channel images that require computational processing. Key steps include image segmentation to identify individual cells, background subtraction, and normalization across samples or batches. Cell segmentation is particularly challenging in dense tissues where cell boundaries are difficult to distinguish.

Computational methods have evolved to manage the increasing dimensionality of spatial proteomics data [10]. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications [10]. The development of these fields demands diverse domain expertise, creating barriers to their integration and further application [10].

Mass Spectrometry-Based Spatial Proteomics Workflow

Sample Preparation and Laser Microdissection

Mass spectrometry-based spatial proteomics begins with the identification and isolation of regions of interest from tissue sections. Laser microdissection allows precise collection of specific cell populations or anatomical regions. The amount of material collected directly determines the depth of proteome coverage achievable.

Optimized workflows that preserve morphological information for phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected tissue are currently lacking [11]. However, recent advances have produced robust and scalable workflows for ultra-low-input archival material [11].

Protein Extraction and Digestion

Collected tissue regions undergo protein extraction, reduction, alkylation, and enzymatic digestion to produce peptides suitable for LC-MS/MS analysis. Miniaturized sample preparation methods minimize sample loss and improve sensitivity for small input amounts. The choice of digestion enzyme, typically trypsin, and the digestion protocol affect peptide recovery and coverage.

LC-MS/MS Analysis

Peptides are separated by liquid chromatography and analyzed by tandem mass spectrometry. Data-dependent acquisition (DDA) selects the most abundant precursor ions for fragmentation, while data-independent acquisition (DIA) systematically fragments all precursor ions in a defined mass range. DIA methods generally provide more consistent quantification across samples.

Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues [7]. Improvements in throughput and robustness are facilitating clinical applications [7]. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation [7].

Protein Identification and Quantification

Raw mass spectrometry data are processed through database searching or spectral library matching to identify peptides and proteins. Label-free quantification compares peptide intensities across samples, while isobaric labeling methods such as tandem mass tags (TMT) enable multiplexed quantification of multiple samples in a single analysis.

Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete [8]. This finding suggests that spatial proteomics can provide more direct measurements of cellular state than transcriptomics in some contexts.

Emerging Technologies and Innovations

Tissue Expansion Methods

Tissue expansion techniques physically enlarge tissue samples before imaging, effectively increasing spatial resolution. The iPEX method enables micrometer-resolution deep spatial proteomics via tissue expansion [22]. This approach allows conventional microscopes to resolve structures that would otherwise require super-resolution instrumentation.

Sparse Sampling Strategies

A sparse sampling strategy for spatial proteomics (S4P) uses computationally assisted image reconstruction methods to reduce the number of samples required for whole-tissue mapping [21]. This approach generated the largest spatial proteome to date, mapping more than 9,000 proteins in the mouse brain, and discovered potential new regional or cell type markers [21]. The strategy is potentially capable of reducing the number of samples by tens to thousands of times depending on the spatial resolution [21].

Low-Abundance Protein Detection

Detecting proteins expressed at low levels remains challenging in widely available FFPE specimens [13]. Many biologically important regulators, including senescence markers, transcription factors, and secreted proteins, are difficult to study in situ using existing high-plex methods [13]. The integrable Co-detection of Low-Abundant Proteins (iCLAP) method combines iterative signal amplification with efficient fluorophore inactivation, enabling repeated staining of the same tissue section and profiling of more than 40 markers [13].

Artificial Intelligence Integration

Artificial intelligence is increasingly integrated into spatial proteomics workflows. Virtual Tissues (VirTues), a general-purpose foundation model for spatial proteomics, learns marker-aware, multi-scale representations of proteins, cells, niches, and tissues directly from multiplex imaging data [20]. From a single pretrained backbone, VirTues supports marker reconstruction, cell typing and niche annotation, spatial biomarker discovery, and patient stratification, including zero-shot annotation across heterogeneous panels and datasets [20].

Spatial AI combines high-resolution spatial multi-omics with deep learning approaches, particularly graph neural networks (GNNs), to elucidate the topological mechanisms of immune evasion [18]. Spatial phenotypes associated with immune resistance, such as immune exclusion, dysfunctional inflamed regions, and maturation states of tertiary lymphoid structures, can be defined using these approaches [18].

Selecting a Spatial Proteomics Method

Define the Biological Question

The first step in method selection is to define the biological question precisely. Questions about cell type composition and organization in tissues can be answered with antibody-based methods targeting a panel of cell type markers. Questions about unbiased protein discovery require mass spectrometry-based approaches. Questions about protein localization at subcellular resolution may require expansion methods or super-resolution imaging.

Consider Sample Type and Availability

Sample type constrains method choice. FFPE tissues are compatible with most antibody-based methods and with optimized LC-MS/MS workflows. Fresh frozen tissues are preferred for MALDI imaging and provide better epitope preservation for some antibodies. The availability of clinical samples with associated outcome data may determine whether a method can be applied to a particular cohort.

Evaluate Multiplexing Requirements

The number of markers needed to answer the biological question determines the required multiplexing capacity. Studies requiring 10 to 20 markers can use established immunohistochemistry panels or lower-plex imaging methods. Studies requiring 40 or more markers need high-plex platforms such as IMC, CODEX, or MIBI. Unbiased discovery studies require mass spectrometry-based approaches that can detect thousands of proteins.

Assess Spatial Resolution Needs

Spatial resolution requirements depend on the structures being studied. Cellular resolution is sufficient for most tissue-level analyses. Subcellular resolution is needed to study protein localization within organelles or membrane domains. The choice of method should match the resolution required to answer the biological question.

Evaluate Throughput and Cost

Throughput and cost vary substantially across methods. Antibody-based imaging methods require significant upfront investment in antibody panels and instrumentation. Mass spectrometry-based methods require access to high-performance LC-MS/MS systems and specialized expertise. The number of samples to be analyzed and the available budget should inform method selection.

Common Failure Patterns and Troubleshooting

Antibody Failure

Antibodies that work well in immunohistochemistry may fail in multiplexed imaging due to conjugation damage, altered epitope accessibility, or cross-reactivity. Validation on control tissues is essential before committing to a large study. If an antibody fails, consider testing alternative clones, adjusting antigen retrieval conditions, or using a different detection chemistry.

Tissue Quality Issues

Poor tissue preservation leads to autofluorescence, high background, and unreliable measurements. Necrotic, hemorrhagic, and myelin-rich tissues present particular challenges for spatial omics [15]. Pre-analytical variation, sampling bias, and platform artifacts can dominate apparent biology in these tissues [15]. Careful tissue handling, standardized fixation, and quality control checks are essential.

Segmentation Errors

Cell segmentation is a common source of error in antibody-based spatial proteomics. Dense tissues with tightly packed cells are difficult to segment accurately. Errors in segmentation propagate to downstream analyses of cell type composition and spatial relationships. Multiple segmentation algorithms should be tested, and results should be visually inspected.

Batch Effects

Spatial proteomics experiments performed across multiple batches can introduce systematic variation that confounds biological differences. Batch effects can arise from differences in antibody lots, staining conditions, instrument calibration, and acquisition settings. Including reference samples in each batch and applying batch correction methods can mitigate these effects.

Data Analysis Pitfalls

Inappropriate spatial assumptions can dominate apparent biology in spatial omics studies [15]. Standard statistical methods that assume independence of observations may not be appropriate for spatially correlated data. Spatial statistics methods that account for the spatial structure of the data should be used when analyzing spatial relationships.

Quality Control and Reproducibility

Technical Replicates

Technical replicates, where the same tissue region is analyzed multiple times, assess the reproducibility of the measurement. For antibody-based methods, replicate staining of serial sections can assess antibody performance. For mass spectrometry-based methods, replicate analyses of the same sample assess instrument performance.

Reference Samples

Reference samples with known protein expression patterns should be included in each batch to monitor assay performance over time. These samples can be cell lines, control tissues, or commercially available reference standards. Changes in reference sample measurements can indicate drift in instrument performance or reagent quality.

Reporting Standards

Reporting standards for spatial omics studies are emerging. A glioma-tailored reporting checklist has been proposed to improve comparability across multicenter and clinical translation [15]. This checklist covers study design, platform selection, quality control, segmentation, deconvolution, multimodal integration, and spatial interaction modeling [15].

Data Sharing and Reproducibility

Data sharing policies and infrastructure support reproducibility in spatial proteomics. The National Institutes of Health Genomic Data Sharing Policy provides expectations for data sharing in NIH-funded research [3]. The FAIR Guiding Principles describe best practices for making data findable, accessible, interoperable, and reusable [4]. Researchers should deposit spatial proteomics data in appropriate repositories and document analysis workflows thoroughly.

Applications in Disease Research

Cancer Biology

Spatial omics transforms understanding of cancer by revealing how tumor cells and the microenvironment are organized, interact, and evolve within tissues [12]. Analytical breakthroughs, including multimodal integration and emerging spatial foundation models, resolve functional niches and spatial communities, converting spatial patterns into mechanistic insights [12]. Spatially organized features, from immune hubs to microbiota and neural interfaces, shape tumor evolution and clinical outcomes [12].

Spatial proteomics offers invaluable insights into various human diseases [10]. In immuno-oncology, computational methods and biomarker discovery strategies for spatial proteomics have advanced significantly [10]. The field is moving toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology [10].

Neurobiology

High-grade gliomas remain highly lethal despite extensive molecular profiling [15]. Treatment failure is driven in part by spatially organized heterogeneity, including hypercellular cores, infiltrative margins, perivascular niches, and hypoxic or perinecrotic zones that concentrate stress adaptation, immune suppression, and therapy tolerance [15]. Spatial omics can map these programs in situ [15].

Glioblastoma invasion into brain parenchyma presents significant challenges for treatment but remains poorly understood [17]. Combining single-cell RNA sequencing, spatial transcriptomics, and multiplexed imaging of orthotopic xenograft models has revealed that models with distal invasion potential are enriched with oligodendrocyte progenitor-like cells, while models with only local invasion potential are enriched with mesenchymal-like cells [17].

Immunology and Immunotherapy

Immune checkpoint blockade has transformed cancer therapy, achieving lasting responses in some patients, yet most still encounter primary or acquired resistance [18]. Resistance is driven also by intrinsic cellular features but also by the spatial organization of the tumor microenvironment, including physical barriers, localized immunosuppressive niches, and organized immune cell aggregates [18].

Spatial proteomics can identify biomarkers that predict immunotherapy response. In triple-negative breast cancer, VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response and stratify disease-free survival in an independent cohort [20]. These biomarkers outperformed state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes [20].

Ovarian Cancer

Patients with metastatic high-grade serous ovarian carcinoma are often unresponsive to immunotherapies [14]. Salt-inducible kinases (SIKs) have been identified as key drivers of immunosuppression in this disease [14]. Multi-omics analyses on SIK inhibitor therapy revealed reduced disease progression, increased T cell infiltration with enhanced cytotoxicity, and a shift from immunosuppressive to immunostimulatory cellular niche [14].

Limitations and Challenges

Sensitivity Limitations

Mass spectrometry-based spatial proteomics faces sensitivity limitations due to the non-amplifiable nature of proteins [21]. Unlike nucleic acids, proteins cannot be amplified, so the amount of protein in a small tissue region limits detection. Recent advances have improved sensitivity, but detecting low-abundance proteins in small regions remains challenging.

Throughput Constraints

Spatial proteomics methods have lower throughput than bulk proteomics methods. Antibody-based imaging methods require significant acquisition time per sample. Mass spectrometry-based methods require extensive sample preparation and instrument time. The limited multiplexing capability of current proteomics methods means that whole-tissue slice mapping with high spatial resolution requires a formidable amount of mass spectrometry time [21].

Standardization Gaps

Persistent challenges in standardization and scalability limit the clinical translation of spatial proteomics [12]. Each study uses a different marker panel and protocol, and most methods are tailored to single cohorts, which limits knowledge transfer and robust biomarker discovery [20]. Data standardization, computational scalability, explainability, and regulatory approval remain key obstacles [18].

Computational Complexity

Spatial proteomics generates large volumes of imaging data with unparalleled spatial resolution [10]. Computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics [10]. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application [10].

Practical Decision Framework

Step 1: Define the Research Question

Write a clear statement of the biological question, including the tissue type, the proteins or pathways of interest, and the spatial scale of the structures being studied. This statement will guide all subsequent decisions.

Step 2: Inventory Available Resources

Assess available tissue samples, instrumentation, expertise, and budget. Consider whether the required antibodies are available and validated, whether the necessary mass spectrometry instruments are accessible, and whether the team has the computational expertise to analyze the resulting data.

Step 3: Compare Method Options

Use the comparison table in this article to evaluate candidate methods against the research question and available resources. Consider multiplexing capacity, spatial resolution, sample compatibility, throughput, and cost.

Step 4: Pilot and Validate

Conduct a pilot experiment with a small number of samples to validate the chosen method. Test antibody panels, optimize staining protocols, and assess data quality. Use the pilot results to refine the experimental design before scaling up.

Step 5: Plan for Quality Control

Build quality control into the experimental design from the start. Include reference samples, technical replicates, and appropriate controls. Document all protocols and parameters to support reproducibility.

Step 6: Prepare for Data Analysis

Develop a data analysis plan before collecting data. Identify the computational tools and expertise needed for image processing, segmentation, statistical analysis, and visualization. Consider how the data will be shared and archived.

Professional Escalation Criteria

Researchers should seek specialized expertise when encountering the following situations:

• When the biological question requires integrating spatial proteomics with other omics modalities, consult with bioinformatics specialists experienced in multimodal data integration [5].
• When working with challenging tissues such as necrotic, hemorrhagic, or myelin-rich samples, consult with pathologists and spatial omics specialists who understand tissue-specific failure modes [15].
• When planning clinical or translational studies, consult with regulatory experts about data sharing policies and reporting standards [3].
• When developing new computational methods or adapting existing methods to novel data types, consult with computational biologists and statisticians.
• When results are unexpected or inconsistent with established biology, seek independent validation with orthogonal methods before drawing conclusions.

Frequently Asked Questions

What is the difference between spatial proteomics and spatial transcriptomics?

Spatial proteomics measures proteins directly in tissue sections, while spatial transcriptomics measures RNA transcripts. Most biological processes are controlled by proteins, and genomic-level methods provide indirect measurements of cellular states [19]. Spatial proteomics provides a readout closer to the functional level [8]. The two approaches are complementary, and combining them can provide a more complete picture of cellular states and tissue organization [5].

How many proteins can be measured with each spatial proteomics method?

Antibody-based methods such as IMC, CODEX, and MIBI can measure 30 to 60 or more protein markers depending on the platform and panel design. Mass spectrometry-based methods can detect thousands of proteins in an unbiased manner. A workflow benchmarked in murine liver quantified up to 2,000 proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions [11]. A sparse sampling strategy mapped more than 9,000 proteins in the mouse brain [21].

What is the best spatial proteomics method for FFPE tissues?

FFPE tissues are compatible with most antibody-based methods, including IMC, CODEX, and MIBI. Optimized LC-MS/MS workflows can also analyze FFPE tissues, though fresh frozen tissues generally provide better proteome coverage. The iCLAP method enables sensitive and highly multiplexed protein detection within FFPE tissue sections, profiling more than 40 markers [13]. The choice of method depends on the number of markers needed and whether unbiased discovery is required.

How long does a spatial proteomics experiment take?

The time required varies substantially by method. Antibody-based imaging methods require antibody validation, staining, and acquisition, which can take days to weeks for a batch of samples. Mass spectrometry-based methods require sample preparation, LC-MS/MS analysis, and data processing, which can take weeks to months for large studies. Acquisition time for imaging methods scales with the area imaged and the number of markers.

What are the main sources of error in spatial proteomics?

Common sources of error include antibody failure or cross-reactivity, tissue quality issues, segmentation errors, batch effects, and inappropriate spatial assumptions in data analysis [15]. Pre-analytical variation, sampling bias, and platform artifacts can dominate apparent biology, especially in challenging tissues [15]. Careful validation, quality control, and appropriate statistical methods can mitigate these errors.

Can spatial proteomics be combined with other omics methods?

Yes, spatial proteomics can be combined with spatial transcriptomics, genomics, and other modalities. The joint analysis of the genome, epigenome, transcriptome, proteome, and metabolome from single cells is transforming understanding of cell biology [5]. Computational strategies are needed to integrate information across these molecular layers [5]. Combining spatial proteomics with ultra-sensitive proteomics and high-content imaging provides an unbiased molecular readout close to the functional level [8].

What computational skills are needed for spatial proteomics data analysis?

Spatial proteomics data analysis requires skills in image processing, cell segmentation, statistical analysis, and data visualization. Computational methods have evolved to manage the increasing dimensionality of spatial proteomics data [10]. Best practices for single-cell analysis across modalities have been summarized to help analysts derive biological insights [9]. Researchers without these skills should collaborate with bioinformatics specialists.

How should spatial proteomics data be shared and reported?

Spatial proteomics data should be deposited in appropriate repositories and documented thoroughly to support reproducibility. The FAIR Guiding Principles describe best practices for making data findable, accessible, interoperable, and reusable [4]. NIH-funded researchers should follow the Genomic Data Sharing Policy expectations for data sharing [3]. Reporting checklists specific to spatial omics studies can improve comparability across multicenter studies [15].

Related Bioinformatics Guides

• Epigenetics and Computational DNA Methylation Analysis: Mechanisms, Methods, and Veterinary Applications
• Modern Transcriptomics: From Bulk RNA-Seq to Single-Cell and Spatial Resolution
• The Rise of Omics: Genomics, Proteomics, and Metabolomics
• Spatial Transcriptomics: Mapping the Cellular Atlas
• Computational Strategies in Structure Based Drug Design

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.
• Methods and applications for single-cell and spatial multi-omics.. Nature reviews. Genetics, 2023.
• Spatial proteomics: a powerful discovery tool for cell biology.. Nature reviews. Molecular cell biology, 2019.
• Mass-spectrometry-based proteomics: from single cells to clinical applications.. Nature, 2025.
• Unbiased spatial proteomics with single-cell resolution in tissues.. Molecular cell, 2022.
• Best practices for single-cell analysis across modalities.. Nature reviews. Genetics, 2023.
• Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.. Briefings in bioinformatics, 2024.
• A framework for ultra-low-input spatial tissue proteomics.. Cell systems, 2023.
• Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications.. Cancer cell, 2026.
• iCLAP: an innovative method for integrable co-detection of low-abundance antigens with high-plex immunostaining.. 2026.
• Inhibition of salt-inducible kinases reprograms T cells and antitumor immunity in ovarian cancer.. 2026.
• Spatial omics in high-grade glioma: study design, analytical pitfalls, and standards for reproducible neuro-oncology.. 2026.
• Modeling immunotherapies in live 3D human cancer tissue bioreactors.. 2026.
• A blueprint for local and distal invasion programs in glioblastoma.. 2026.
• Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.. 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.
• Spatial proteomics: unveiling the multidimensional landscape of protein localization in human diseases. Proteome Science, 2024.
• New Technologies for Mass Spectrometry-based Single-cell Resolved Spatial Proteomics Research. Gaodeng Xuexiao Huaxue Xuebao Chemical Journal of Chinese Universities, 2024.

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