Spatial proteomics is rapidly emerging as a transformative approach in biological research, offering a new way to reveal how protein expression and proteome organization vary across tissue architecture and cellular environments. Unlike traditional proteomic methods that dissociate cells or homogenize tissue, spatial proteomics preserves the spatial resolution and spatial distribution of proteins in situ, enabling scientists to map protein abundance, localization and molecular networks directly on tissue sections or single cells.
At the heart of this advancement is the integration of miniaturized fluidic control and microfluidic platforms, which enable precise sample preparation, reagent delivery and seamless workflow automation for multiplexed imaging, mass spectrometry and multi-omics profiling. By combining a microfluidic chip or microfluidic device with high-sensitivity mass spectrometry or imaging technologies, researchers can capture the molecular complexity of tissues at unprecedented depth and detail.
In applications ranging from breast cancer tumour microenvironment to developmental biology in the mouse brain, spatial proteomics offers unique insights into how cellular heterogeneity, microenvironment interactions and protein regulation govern disease, normal function and response to therapy.
Yet although the method holds great promise, the field also faces complex challenges in sample preparation, data acquisition, accuracy, reproducibility and workflow standardisation. This article explores what spatial proteomics is, how microfluidics enhances proteomic workflows, the key technologies in use, typical applications, sample preparation strategies, insights that can be gained, and finally the major challenges and future directions for the field.
Spatial proteomics is the study of protein expression and localization within intact biological tissues, preserving the spatial context of every cell. Instead of analysing homogenized extracts, this approach measures proteins in their native microenvironment, revealing spatial patterns, molecular gradients, and functional domains that shape tissue organization and disease mechanisms. By linking morphology with molecular identity, spatial proteomics provides a direct view of the proteome in situ, connecting structure to biological function.
The central principle is simple yet powerful: each protein molecule is detected and quantified while maintaining its position in the tissue. This spatially resolved information enables researchers to reconstruct protein maps across tissue sections, identify distinct cell types, and explore how protein abundance and interactions change within the microenvironment. It bridges the gap between traditional proteomics and imaging, combining high-resolution microscopy, antibody-based labeling, and mass spectrometry imaging into a unified workflow.
Schematic workflow for high-throughput spatially resolved proteomics showing tissue section preparation, automated droplet-based sample processing, LC–MS analysis, and spatial image reconstruction. From Piehowski P. et al., Nature Communications 11, 8 (2020), DOI 10.1038/s41467-020-14756-6
Typical spatial proteomic workflows begin with thin tissue sections, human, mouse, or other model organisms, mounted on a glass slide or microfluidic chip. The samples undergo fixation, permeabilization, and labeling steps where antibodies, fluorescent tags, or isotopic labels bind to specific protein targets. Enzymatic digestion may follow, producing peptides for mass spectrometry analysis. Each step must preserve spatial resolution and avoid signal loss, a challenge that microfluidic devices address by ensuring uniform reagent flow, minimal dead volume, and reproducible reaction conditions.
Different technological strategies have emerged to achieve this goal. Imaging-based proteomics, such as cyclic immunofluorescence, imaging mass cytometry (IMC), and multiplexed ion beam imaging (MIBI), use sequential staining and detection cycles to profile dozens to thousands of proteins per tissue section. Mass spectrometry-based approaches, including matrix-assisted laser desorption ionization (MALDI) and desorption electrospray ionization (DESI), rely on on-tissue digestion and precise ionization of peptides for spectral analysis. More recently, methods like laser capture microdissection and hydrogel-based tissue expansion enable single-cell or sub-cellular spatial resolution, connecting spatial proteomics to broader spatial omics and spatial transcriptomics frameworks.
Together, these technologies form an integrated analytical platform that captures the complexity of the proteome in its native context. They allow the mapping of cellular heterogeneity, molecular regulation, and protein function across tissues, whether in healthy organs, tumors, or developmental models, turning each experiment into a high-resolution atlas of molecular activity.
Microfluidics has become a key enabler for spatial proteomics, providing the precision, reproducibility, and scalability that conventional bench workflows often lack. In this field, every microliter matters. Reagents, antibodies, and enzymes must be delivered with perfect timing and minimal waste across delicate tissue sections, without disturbing cellular morphology or losing spatial resolution. Microfluidic devices and microfluidic chips achieve this by controlling flow in channels smaller than a human hair, creating stable laminar conditions that ensure uniform reagent distribution and predictable reaction kinetics.
In traditional proteomic workflows, manual pipetting or large-volume incubation can introduce variability and sample loss. Microfluidic platforms solve these issues by miniaturizing the process and automating reagent exchange, temperature control, and mixing. This miniaturized environment reduces the total reagent volume by up to 90%, increasing sensitivity and improving detection of low-abundance proteins. Because flow is deterministic, it also enhances reproducibility, a persistent challenge in multi-step imaging and mass spectrometry workflows.
A typical microfluidic proteomics setup integrates several components: a microfluidic chip for tissue mounting, precise flow control systems such as syringe pumps or pressure controllers, and a network of valves and channels that guide reagents through the sample in parallel or sequentially. This architecture allows automated cycles of antibody labeling, enzymatic digestion, washing, and peptide extraction. When connected directly to mass spectrometry instruments, these systems enable continuous sample transfer and high-throughput acquisition without manual intervention.
Microfluidics also supports advanced sample preparation techniques essential for spatial proteomics. For instance, on-chip digestion using trypsin or other proteases can be precisely timed to control peptide size and yield, improving downstream mass spectrometry performance. Similarly, gradient-based flow can gently remove unbound antibodies or fluorescent tags, preserving tissue structure and improving signal-to-noise ratio in imaging-based proteomics.
By combining microfluidic precision with imaging and analytical technologies, researchers can achieve single-cell and even subcellular spatial resolution. Integration with mass spectrometry imaging, cyclic immunofluorescence, or multiplexed ion beam imaging enables spatially resolved proteome mapping across entire tissue sections. The result is a more accurate, quantitative, and high-throughput proteomic analysis that captures the full molecular complexity of biological systems. Ultimately, microfluidics transforms spatial proteomics from a complex manual process into a robust, automated workflow. It enables parallel processing of multiple tissue sections, supports multiplexed assays, and ensures reproducibility across experiments. These advances not only enhance data quality and throughput but also bring spatial proteomics closer to routine use in translational research, disease profiling, and clinical diagnostics.
Spatial proteomics brings together a diverse set of analytical technologies that combine imaging, biochemistry, and computational analysis to map the proteome in space. These technologies fall into two main categories: imaging-based approaches that visualize labeled proteins directly in tissue sections, and mass spectrometry-based approaches that identify and quantify peptides extracted from defined regions. Both rely increasingly on microfluidic precision to handle complex sample preparation and to maintain reproducibility across multiplexed cycles.
Imaging-based spatial proteomics uses antibodies or molecular probes to detect specific proteins in situ. Techniques such as cyclic immunofluorescence (CyCIF), CO-Detection by Indexing (CODEX), imaging mass cytometry (IMC), and commercial systems like Lunaphore’s COMET platform apply iterative cycles of staining, imaging, and signal removal, each revealing a subset of proteins.
The COMET system exemplifies how microfluidics enhances spatial proteomics in practice. Reagents are delivered through a microfluidic chip that precisely controls flow, temperature, and timing across the tissue section, ensuring highly reproducible staining and minimal carryover between cycles. This approach provides single-cell spatial resolution and multiplexed imaging across dozens of biomarkers while maintaining tissue morphology, a key advantage for both clinical and translational research.
Microfluidic devices make these workflows more reliable by automating reagent delivery, washing, and temperature control across multiple cycles. In CODEX, COMET, and CyCIF, laminar flow and low-volume reagent switching maintain consistent staining and signal uniformity across large tissue areas, ensuring accurate and quantitative spatial profiling.
Legend: Multiplex immunofluorescence workflow on a microfluidic chip using the COMET platform, showing sequential antibody staining, imaging and elution cycles in situ. From Rivest F. et al., Scientific Reports 13, 16994 (2023), DOI 10.1038/s41598-023-43435-w
Mass spectrometry (MS) remains the gold standard for unbiased proteome identification and quantification. In spatial proteomics, MALDI imaging, DESI imaging, and SIMS (secondary ion mass spectrometry) provide spatially resolved peptide maps without fluorescent labeling.
These instruments typically operate on tissue sections treated with proteolytic enzymes such as trypsin for on-slide digestion, followed by matrix deposition and ionization. Recent advances like microfluidic-assisted gel digestion (Zhu et al., Analytical Chemistry, 2022) and on-chip peptide extraction improve spatial precision and signal intensity.
Complementary tools such as laser capture microdissection (LCM) (Leica LMD7, Thermo Arcturus XT, Zeiss PALM MicroBeam) isolate selected regions or single cells for targeted LC-MS/MS analysis, while on-chip microreactors integrate digestion, desalting, and transfer to LC columns, minimizing analyte loss and improving reproducibility.
Legend: Workflow of MALDI mass spectrometry imaging for spatial proteomics, illustrating tissue sectioning, matrix application, laser desorption/ionization, and peptide map reconstruction. From Aichler M. and Walch A., Laboratory Investigation 95, 422–431 (2015), DOI 10.1038/labinvest.2014.156
The explosion of spatial omics data has made computational analysis an integral part of the field. Deep learning and AI-based image segmentation are now used to identify cell types, detect spatial patterns, and quantify protein expression across large datasets. Platforms such as QuPath, CellProfiler, Ilastik, and commercial suites like Visiopharm or HALO AI apply neural networks to analyze multiplexed tissue images.
For mass spectrometry data, bioinformatic pipelines such as MSIReader, SCiLS Lab (Bruker), and Spectronaut process millions of spectra to reconstruct peptide abundance maps. Integration with spatial transcriptomics tools, like 10x Genomics Visium or NanoString CosMx SMI, creates multi-omics datasets that correlate gene expression, protein abundance, and molecular interactions within the same tissue context.
These computational advances allow researchers to reconstruct cell states, infer signaling pathways, and model disease progression or tumor heterogeneity with spatial precision.
Legend: Computational pipeline for spatial proteomics integration, showing segmentation, cell-type classification, and multi-omics data alignment for protein and transcriptomic mapping. From Mund A. et al., Molecular Cell 82, 2335–2352 (2022), DOI 10.1016/j.molcel.2022.05.022
Whether based on imaging, mass spectrometry, or hybrid workflows, all modern spatial proteomic technologies share common goals: high sensitivity, reproducibility, and throughput. Microfluidics serves as the foundation that connects these systems, enabling precise reagent flow, stable sample processing, and automation across complex assays. Together, these instruments and methods form a powerful analytical framework capable of mapping thousands of proteins across tissue sections and revealing how molecular organization drives cellular behavior.
Successful spatial proteomics begins long before imaging or mass spectrometry. Every dataset depends on how carefully a sample is prepared, processed, and maintained throughout the workflow. Each step, from tissue fixation to peptide acquisition, must preserve both molecular integrity and spatial organization. Because tissues are heterogeneous and often fragile, preparation must ensure that proteins remain localized and accessible for labeling or digestion without introducing artefacts or diffusion.
The workflow typically starts with tissue sectioning and fixation. Fresh or formalin-fixed paraffin-embedded (FFPE) samples are cut into thin sections, often 5–10 µm thick, and mounted on a conductive glass slide or microfluidic chip. For single cell analysis or high-resolution mapping, some laboratories apply tissue expansion or hydrogel embedding to physically enlarge the sample, improving spatial resolution during imaging. Fixation and permeabilization steps follow, allowing antibodies or enzymes to penetrate while maintaining structural integrity.
Workflow of spatially resolved proteomics showing tissue sectioning, on-slide digestion, peptide extraction, and data acquisition for high-resolution protein mapping. From Liu S. et al., Nature Communications 13, 7697 (2022), DOI 10.1038/s41467-022-34824-2
In antibody-based spatial proteomics, such as cyclic immunofluorescence or imaging mass cytometry, antibody incubation and washing cycles are automated through microfluidic devices. These systems control flow rate, incubation time, and reagent exchange with exceptional precision, minimizing sample loss and maintaining signal uniformity. Each cycle labels a subset of proteins, captures their fluorescence or isotopic signal, and prepares the tissue for the next round.
In mass spectrometry-based approaches, on-tissue digestion plays a central role. Enzymes like trypsin or Lys-C break down proteins into peptides that can be analyzed by mass spectrometry imaging. Microfluidic channels or droplets allow digestion under controlled flow, temperature, and pH conditions, ensuring reproducible peptide size and improving detection sensitivity. The peptides are then extracted or ionized directly from the slide for MS acquisition, generating spatially resolved proteomic data.
Throughout the workflow, sample preparation and microfluidic handling are tightly coupled. Microfluidics enables precise reagent delivery, laminar flow during washing, and parallel processing of multiple tissue sections. It also prevents air bubble formation and uneven flow that can distort imaging or cause loss of spatial information. By miniaturizing reaction volumes, microfluidic systems significantly reduce reagent consumption, an important advantage given the high cost of antibodies, enzymes, and labeling agents.
Data acquisition and processing complete the workflow. Images or spectral maps are aligned to reconstruct the tissue architecture, linking protein expression patterns with cellular morphology and gene expression data from complementary spatial transcriptomics experiments. This integrated analysis produces a molecular atlas of the sample, capturing how proteins vary across tissues and between cell types.
Ultimately, efficient sample preparation defines the success of spatial proteomics. Microfluidic control ensures reproducibility, sensitivity, and throughput, transforming a complex manual protocol into a standardized analytical process. Whether performed on a single tissue section or across hundreds of samples, a well-optimized workflow guarantees accurate, spatially resolved proteomic data that reveal the hidden molecular structure of biological systems.
Spatial proteomics provides an unprecedented window into how proteins are distributed, regulated, and function within their native biological context. By preserving spatial information, this approach connects molecular activity to tissue structure, revealing the underlying organization of biological systems that conventional proteomics or genomics alone cannot capture. At the cellular level, spatial proteomics enables single cell analysis of protein abundance, localization, and post-translational modifications. By integrating imaging, microfluidic flow control, and mass spectrometry, researchers can now observe how distinct cell types interact within complex tissue microenvironments. These spatially resolved datasets expose local signaling events, feedback mechanisms, and gradients that define cellular identity and function.
In oncology, spatial proteomics has become a critical tool for mapping tumor heterogeneity and identifying biomarkers of therapeutic response. Studies in breast cancer and other solid tumors have shown how distinct protein expression patterns and immune cell distributions influence disease progression and treatment resistance. By profiling hundreds of molecular targets in parallel, researchers can reconstruct how tumor cells, immune infiltrates, and stromal components communicate within the same tissue section, revealing regulatory pathways that drive invasion or immune evasion.
In neuroscience, spatial proteomics is helping decode the protein architecture of the brain. Mass spectrometry imaging of mouse and human tissue sections reveals how neurotransmitter receptors, synaptic proteins, and cytoskeletal elements are distributed across functional regions. Combined with single cell RNA sequencing or spatial transcriptomics, these studies connect protein activity with gene expression and network regulation, contributing to a deeper understanding of cognition, neurodegeneration, and development.
In immunology and biomedical research, spatial proteomics allows researchers to visualize immune-cell activation, cytokine gradients, and protein-based signaling within inflamed or infected tissues. Such information is vital for understanding immune regulation and for developing spatial biomarkers to monitor treatment efficacy.
Beyond basic research, spatial proteomics is increasingly used in drug discovery and translational medicine. Pharmaceutical platforms employ spatial omics workflows to evaluate drug distribution, target engagement, and off-target effects at the tissue and single cell levels. The integration of proteomic data with transcriptomic and metabolomic datasets helps identify molecular signatures of efficacy and toxicity early in development.
Importantly, spatial proteomics provides quantitative and visual insight into biological complexity. It enables scientists to link function to form: mapping where proteins reside, how they interact, and how these patterns shift in response to disease or environmental cues. These molecular maps can reveal spatial regulation across entire tissues, whether in tumors, organs, or engineered biological systems, offering a complete, spatially resolved picture of life at the protein level.
By coupling high-resolution imaging, microfluidic precision, and multi-omics data analysis, spatial proteomics continues to bridge the gap between structure and function. It turns static tissue sections into dynamic molecular landscapes, transforming how we study biology, understand disease mechanisms, and design the next generation of diagnostic and therapeutic strategies.
Despite its rapid progress, spatial proteomics remains a technically demanding and complex field. Each experiment involves hundreds of reagents, intricate workflows, and sophisticated analytical platforms that must work in perfect harmony to produce reliable results. This complexity introduces challenges in data accuracy, reproducibility, and standardization that must be addressed as the field moves toward industrial and clinical applications.
One of the main challenges lies in sample preparation and reproducibility. Variability in tissue fixation, section thickness, and digestion efficiency can significantly affect protein detection and quantitative accuracy. Because spatial proteomics relies on localizing signals within micrometer-scale regions, even minor inconsistencies in flow rate, temperature, or incubation time may lead to differences in signal intensity or spatial resolution. Microfluidic systems have improved reproducibility by standardizing reagent flow and incubation conditions, but achieving consistent results across different instruments and laboratories remains a limitation.
Another key limitation concerns data complexity and throughput. A single tissue section can generate thousands of spectra, images, or data points, each representing different proteins, cell types, and molecular interactions. Integrating these layers of omics data, proteomic, transcriptomic, and imaging, requires advanced computational frameworks and deep learning algorithms capable of distinguishing meaningful biological signals from background noise. Building robust analytical pipelines and validated databases for cross-study comparison remains a priority for the field.
Sensitivity and detection depth also present challenges. While spatial proteomics can now achieve subcellular spatial resolution, detecting low-abundance proteins, post-translational modifications, or transient complexes still demands more sensitive reagents and instrumentation. Miniaturized and microfluidic-based sample processing is helping by concentrating analytes and reducing background, yet balancing sensitivity with throughput remains a delicate trade-off.
Validation is another essential step toward wider adoption. With hundreds of multiplexed cycles, antibody-based techniques must ensure that each labeling, washing, and imaging phase is chemically compatible and free of cross-reactivity. Mass spectrometry workflows, meanwhile, require standardized calibration, peptide identification accuracy, and reproducibility across runs. Without validated reference materials and harmonized protocols, translating discoveries into clinical diagnostics or regulatory settings will remain limited.
Looking ahead, the future of spatial proteomics lies in greater integration and automation. Combining microfluidic control, miniaturized imaging, and artificial intelligence will allow fully automated, high-throughput spatial omics platforms capable of analyzing thousands of tissue sections in parallel. Improved reagents, from highly specific antibodies to advanced chemical probes, will enhance signal fidelity and reduce noise. Standardized workflows and open data sharing will further strengthen reproducibility and transparency across the community.
Ultimately, the goal is to move spatial proteomics from a complex experimental technique to a routine analytical platform, one that offers both high sensitivity and reproducibility while maintaining spatial accuracy. Microfluidics, automation, and intelligent data processing will play a decisive role in achieving this transformation, ensuring that spatial proteomics continues to expand our understanding of biological systems, disease mechanisms, and the molecular architecture of life itself.
Behind every successful spatial proteomics platform lies a foundation of precise fluid control. Whether the goal is to stain a tissue section with hundreds of antibodies or perform on-slide enzymatic digestion for mass spectrometry, the reproducibility and accuracy of each reaction depend on stable, automated fluid handling. This is precisely where Advanced Microfluidics provides value, offering OEM-ready microfluidic components designed for high-performance spatial omics workflows.
In spatial proteomic assays, reagents such as antibodies, buffers, and enzymes must be exchanged repeatedly across tissue sections without introducing air bubbles, backflow, or contamination. Traditional solenoid valves and manual switching often create dead volumes and carryover, compromising sample integrity. AMF’s RVM Series – Industrial Microfluidic Rotary Valve eliminates these limitations with:
This precision enables clean, repeatable reagent switching across hundreds of staining or digestion cycles, crucial for techniques such as cyclic immunofluorescence, imaging mass cytometry, or multiplexed ion beam imaging.
Maintaining a uniform flow rate is essential for achieving consistent hybridization, digestion, and washing steps. Even small fluctuations in pressure or flow can disturb tissue morphology or lead to uneven staining. AMF’s SPM Series – Industrial Programmable Syringe Pumps deliver pulse-free, nanoliter-precise fluid movement, enabling stable reagent delivery from gentle perfusion to continuous microflow.
During proteomic imaging or on-tissue digestion, these pumps ensure homogeneous reagent coverage, enhancing signal uniformity and reducing variability between runs. Their programmable architecture allows precise control of plunger speed, volume, and timing, enabling reproducible spatial workflows and quantitative imaging.
Spatial proteomics often involves managing 10 to 50 reagents per experiment, from labeling antibodies to digestion buffers and wash solutions. Manual intervention introduces variability and limits throughput. AMF’s Distribution and Switch Valves, derived from the RVM architecture, allow fully automated reagent switching across up to 24 ports.
These compact, scalable valves support both parallel and sequential reagent routing. Their optimized rotor–stator geometry prevents cross-contamination, making them ideal for integrated OEM systems and robotic fluidic modules. In high-plex proteomic workflows, such as multiplexed antibody imaging or on-chip enzymatic processing, AMF valves provide deterministic reagent delivery that guarantees consistent results across hundreds of cycles.
Spatial proteomics reagents are often complex mixtures of fluorophores, detergents, peptides, and enzymes that can easily adsorb to metal or polymer surfaces. AMF components are engineered from high-purity materials such as PTFE, PCTFE, and PEEK, which ensure:
This design preserves reagent stability and prevents unwanted reactions or contamination during long experimental runs.
AMF’s components are designed for seamless integration into analytical instruments, imaging platforms, and automated proteomics workstations. Communication protocols such as I²C, RS-232, or TTL make synchronization with microscope control software or image acquisition systems straightforward. The compact form factor and modular configuration allow easy adaptation to existing architectures, from benchtop prototypes to industrial-scale systems.
By combining microfluidic rotary valves and programmable syringe pumps, AMF provides a complete foundation for automated reagent handling in spatial proteomics. These components deliver the reproducibility, throughput, and long-term reliability required for high-performance biological and biomedical applications.
Spatial proteomics is inherently complex, combining delicate biology with advanced instrumentation. AMF’s technology simplifies this complexity by ensuring every step, from antibody staining to peptide digestion, is executed with precision and repeatability. The result is consistent flow profiles, reduced sample loss, and stable assay performance, enabling researchers and instrument developers to focus on discovery rather than maintenance.
As spatial proteomics evolves into an industrialized platform for molecular profiling, AMF’s microfluidic expertise continues to support this transformation. Through reliable components, customizable designs, and deep engineering collaboration, AMF helps laboratories and OEM partners build the next generation of spatial omics systems, automated, sensitive, and reproducible by design.
The RVM is an industrial-grade rotary valve optimized for high-precision fluid handling. Designed for continuous operation, it ensures low internal volume, minimal dead volume, and high durability, making it ideal for automated liquid handling and complex microfluidic systems.
The SPM integrates a programmable syringe pump with rotary valve control, enabling precise dosing, sample preparation, and reagent mixing. This system is perfect for applications requiring controlled fluid movement, high accuracy, and automation compatibility.
Need a tailored microfluidic valve solution? AMF specializes in custom designs to meet specific application needs. Whether you require a unique valve configuration, specialized materials, or enhanced automation features, our engineering team works closely with you to create a solution perfectly suited to your microfluidic system.
Spatial proteomics marks a decisive step forward in understanding how proteins shape the function and structure of biological systems. By combining imaging, mass spectrometry, and molecular analysis, it bridges the microscopic world of single cells with the macroscopic organization of tissues and organs. Yet the full potential of this approach depends on one essential factor: precise and reproducible fluidic control.
Microfluidics provides the foundation that makes this precision possible. Through stable flow, controlled reagent switching, and automated sample handling, microfluidic systems transform complex laboratory workflows into standardized analytical platforms. They minimize variability, reduce reagent consumption, and enable high-throughput processing across hundreds of tissue sections.
As spatial biology moves toward integrated spatial omics, combining proteomic, transcriptomic, and metabolomic data within a single experimental framework, microfluidics will continue to play a central role. It connects different molecular layers, RNA, protein, and metabolite, into a unified workflow that captures the full molecular landscape of life at cellular resolution. This convergence of biology and engineering is shaping a new era of quantitative, reproducible, and scalable molecular analysis. At AMF, we design and manufacture the microfluidic components that make this integration possible. Our rotary valves, programmable syringe pumps, and distribution manifolds provide the stability and flexibility needed to build next-generation spatial omics instruments that are both precise and industrially reliable.
If you are developing a spatial proteomics, imaging, or multi-omics platform and need to integrate high-performance microfluidic solutions, we would be glad to collaborate.
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