Multiomics in Spatial Biology and Disease Research

Developing an understanding of the molecular landscape of health and disease requires more than the cataloging of individual biomolecules. The key challenge lies in revealing the spatial organization of proteins, nucleic acids, lipids, and metabolites within the complex architecture of cells and tissues.

 

Accurate identification, mapping, and contextualization of these molecular networks is essential in deepening biological knowledge and uncovering mechanisms of disease, which ultimately support the development of innovative approaches to clinical research.   

The rise of MALDI imaging  

Since the late 1990s, when Richard Caprioli and colleagues first demonstrated matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MS imaging), researchers have been able to visualize untargeted distributions of small biomolecules in intact tissue.1

This capability has transformed studies of metabolites, lipids, glycans, extracellular matrix proteins, and even small drug compounds. However, despite its impact, methods for spatially resolving larger macromolecules such as intact proteins and nucleic acids in a targeted manner remain limited.

Light microscopy-based methods such as immunohistochemistry (IHC) and in situ hybridization (ISH) can provide targeted, high-resolution imaging of proteins and nucleic acids at cellular and subcellular levels, but these approaches can be constrained by low multiplexing capability and the need for iterative, time-intensive cycles that can damage the tissue.   

Mass tags and antibody labeling

A key breakthrough in spatial proteomics addressing the limitations of traditional imaging including low multiplexing has been the development of workflows that integrate antibody-based labeling with MALDI imaging.2,3

In these approaches, antibodies are conjugated with photocleavable mass tags (PC-MTs) that act as molecular barcodes, which when detected by a MALDI mass spectrometer, enable simultaneous imaging of hundreds of targeted intact proteins in a tissue.

 

PC-MTs are designed to be released from antibodies under controlled UV light exposure prior to MALDI analysis. Once detached, the mass tag ions are both reproducible and easily detectable due to their well-defined mass-to-charge ratios and absence of side products from the photocleavage reaction.

These sharp, non-overlapping signal peaks reduce ambiguity in spectral interpretation and ensure that each target protein can be imaged simultaneously with high specificity.

 

What makes this approach especially powerful is its compatibility with standard histological workflows. Tissue staining with antibody–probe conjugates can be performed using conventional manual or automated immunohistology protocols.

 

After staining, controlled photocleavage of the probes liberates the tagged peptides prior to matrix application and MALDI imaging. Each liberated peptide mass tag provides a discrete, quantifiable signal, which can then be reconstructed into a two-dimensional image showing the spatial distributions of multiple proteins.

 

By layering these targeted protein maps with untargeted imaging of small molecules such as lipids, glycans, and metabolites, researchers can generate richly detailed molecular atlases that capture both the diversity and the spatial context of biological systems.4 

Case example: insights into neurodegenerative disease

The potential of integrative imaging is illustrated in neurodegenerative disease studies. Alzheimer’s disease affects more than 55 million people globally,5 and recent research6,7 has shown promising results by combining MALDI imaging with PC-MT probes.

 

In pharmaceutical research and development, characterizing the diverse biomolecular classes and cell types within the amyloid-β plaque microenvironment, a key pathological feature of Alzheimer’s disease, remains a significant challenge.

 

Simultaneous analysis of the complex interactions between proteins, lipids, and metabolites is required, as studying any one of these biomolecule classes in isolation fails to capture their contextual interactions within the tissue.

Ideally, multiple biomolecular classes would all be analyzed from a single tissue section using a benchtop imaging platform, although, until recently, integrating lipid and protein imaging on the same tissue slide has posed a significant technical challenge. 

 

Recent advances in MALDI imaging are beginning to make this integration possible. An initial untargeted scan identifies label-free analytes such as lipids, metabolites, and glycans, while a subsequent run incorporates antibody-linked probes carrying PC-MTs to localize targeted proteins. This dual-layer strategy enables the co-localization of small molecules with specific proteins in a truly multiomic workflow.

 

Within the Alzheimer’s model, this means proteins associated with plaque formation can be mapped alongside surrounding lipids and metabolites to reveal how different biomolecular classes interact to drive pathology.

MALDI-IHC is also becoming an important tool for developing new drugs to combat other neurodegenerative diseases, such as Parkinson’s.8  

Working towards achieving true multiomics

Techniques such as MALDI imaging have demonstrated robust untargeted analysis of small molecules, while antibody-based mass-tag labeling now allows highly multiplexed protein detection on the same tissue sections.

Together, these innovations are breaking down longstanding barriers between metabolomics, lipidomics, and proteomics within spatial tissue. 

 

Although challenges such as preserving sample integrity, expanding coverage to nucleic acids, and ensuring reproducibility at scale persist, multiomics is evolving into a practical approach for investigating complex diseases. Recently novel PC-MTs for multiplexed and multiomic tissue imaging of targeted transcripts was reported.9

 

By integrating diverse molecular classes into unified spatial maps, researchers are gaining unprecedented insights into the interplay of cellular components in health and pathology. Whether in neurodegenerative disorders like Alzheimer’s or other multifactorial diseases, true multiomic workflows promise to transform how we study disease mechanisms and start to redefine the future of personalized therapies.