Increasing Sensitivity and Coverage in Spatial Proteomics

Spatial proteomics integrates proteomics with spatial tissue information, aiming to generate high-resolution maps of protein expression and localization at the subcellular and sub-tissue scale. In research areas such as the cancer microenvironment, neurodegenerative diseases, and developmental biology, spatial proteomic data can uncover biological mechanisms that are often inaccessible to conventional bulk proteomics.

However, spatial proteomics currently faces two fundamental challenges:

Insufficient sensitivity: Certain key proteins in tissues are expressed at extremely low levels and may be missed.

Limited coverage: Small sample sizes result in incomplete protein profiles, thereby affecting biological interpretation.

Enhancing sensitivity and coverage is essential for ensuring the overall quality of spatial proteomics studies.

Optimization of Sample Preparation: Minimizing Protein Loss at the Source

Sample preparation is a major determinant of protein detection sensitivity. Spatial proteomics often relies on micro-scale samples (e.g., laser microdissection or FFPE tissue sections) with extremely limited protein content; thus, each step from sampling to lysis must be carefully controlled.

Key strategies:

• Low-adsorption consumables and reagents: Use EP tubes and pipette tips with low protein-binding properties to minimize adsorption-related loss.

• Optimized lysis buffer composition: For FFPE or frozen tissues, apply lysis buffers containing SDS, urea, or de-crosslinking agents to increase protein extraction efficiency.

• SP3 magnetic bead enrichment: The SP3 method has been validated to provide high recovery and strong reproducibility for trace protein samples, making it well suited for spatial proteomic workflows.

Mass Spectrometry Platform Upgrade: Selecting High-Resolution and High-Sensitivity Instruments

Spatial proteomics imposes stringent requirements on mass spectrometry platforms, mandating the detection of a larger number of proteins from limited sample input. Common approaches include deploying highly sensitive Orbitrap-based instruments in combination with advanced data acquisition strategies.

1. Recommended Instrument Platforms

• Orbitrap Eclipse Tribrid: Combines a triple mass analyzer architecture, enabling deep profiling of micro-scale samples.

• timsTOF SCP (Bruker): Designed for low sample input and equipped with PASEF acceleration, capable of identifying over 3000 proteins from <1 ng of protein.

• Exploris 480/240 + FAIMS Pro: FAIMS-based ion filtering enhances the detection probability of low-abundance proteins.

2. Optimization of Acquisition Modes

• DIA (Data-Independent Acquisition): Compared with DDA, DIA substantially increases protein identification while maintaining high reproducibility, particularly beneficial for limited samples.

• BoxCar or Boosting approaches: Improve signal intensity for enhanced detection of low-abundance proteins.

• Microfluidic LC-MS interface (nanoLC): Significantly enhances sample loading and ionization efficiency and is widely adopted in spatial proteomics workflows.

Integration of Spatial Localization Technologies: Enabling High-Resolution Spatial Proteomic Mapping

As traditional proteomics lacks spatial context, spatial proteomics is often combined with imaging-assisted labeling technologies, such as:

• Immunofluorescence-guided microdissection (IF-guided Microdissection)

• Imaging mass spectrometry (Imaging MS)

• Spatial multiplexed immunolabeling technologies including CODEX and ImmunoSABER

These approaches enable targeted proteomic analysis of specific tissue regions (e.g., tumor margins or immune cell clusters), thereby improving the spatial accuracy of downstream interpretation.

Notably, laser capture microdissection (LCM) combined with mass spectrometry has become a pivotal method in spatial proteomics. However, post-LCM samples are often extremely limited (<100 cells), necessitating the use of ultra-sensitive mass spectrometry platforms and efficient enrichment strategies.

Data Analysis and Database Support: Improving Identification Rates and Quantitative Accuracy

Even with high-quality mass spectrometry data, analytical strategies critically influence protein identification depth and quantitative reliability.

Recommended strategies:

• Construction of tissue-specific protein reference libraries: combining transcriptomic data and spectral libraries to enhance identification rates.

• Application of deep learning-based search algorithms: tools such as DIA-NN and MSFragger improve the detection of low-abundance proteins.

• Batch normalization of samples: preventing technical bias from obscuring true biological variations.

A Comprehensive Workflow for Enhancing Sensitivity and Coverage in Spatial Proteomics

 



 

Recent developments from mass spectrometry service providers, such as MtoZ Biolabs, have established spatial proteomics platforms covering sample processing, mass spectrometry detection, data analysis, and scientific support. These platforms support full-process workflows from mouse tissues to clinical samples and aim to provide enhanced sensitivity, broader coverage, and more accurate spatial information. Such solutions may serve as valuable resources for researchers exploring the potential of spatial proteomics.

MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.

Related Services

Spatial Proteomics Service