Spatial Protein Expression Analysis in Lungs Using Capillary-Based Immunoassay After Laser-Assisted Microdissection

Unravelling the cellular and molecular mechanisms underlying lung injury and repair requires precise spatial context. Profiling cell-to-cell transcriptional variability and spatial orientation has become increasingly sophisticated, but validating results at the protein level still remains challenging, particularly for low-expressed proteins or small-scale samples. Here, we present a workflow established by our group for spatial protein analysis in the lung by combining two commercially available platforms: (1) laser-assisted microdissection (LMD) with (2) a capillary electrophoretic-based immunoassay (CEI). Using this workflow, we demonstrate a simple, accessible, and sensitive method for spatially capturing regions of interest to investigate small-scale samples or low-expressed proteins. This workflow provides an additional option for orthogonal validation for researchers using omics-based approaches. Furthermore, we validated transcriptome analysis results at the protein level by applying this workflow to a pre-clinical model of cigarette smoke (CS)-induced lung injury. In line with the previous findings, the results showed a significant downregulation of the endothelial cell marker in LMD-enriched alveolar regions, suggesting spatial capillary rarefaction, and activation of the mitogen-activated protein kinase (MAPK) signalling pathway in pulmonary vasculature of CS-exposed mice. Our approach overcomes traditional challenges and provides new opportunities for understanding complex disease pathomechanisms and identifying potential therapeutic targets.

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Publication Details

Journal
Cells
Published
2026-04-21
DOI
https://doi.org/10.3390/cells15080737
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatial Protein Expression Analysis in Lungs Using Capillary-Based Immunoassay After Laser-Assisted Microdissection

Marija Gredic, Stefan Hadžić, Norbert Weißmann, Cheng‐Yu Wu et al.
Cells
Single-cell and spatial transcriptomics
article

Spatial Protein Expression Analysis in Lungs Using Capillary-Based Immunoassay After Laser-Assisted Microdissection

Marija Gredic, Stefan Hadžić, Norbert Weißmann, Cheng‐Yu Wu, Vanessa Nebel
article en

Abstract

Unravelling the cellular and molecular mechanisms underlying lung injury and repair requires precise spatial context. Profiling cell-to-cell transcriptional variability and spatial orientation has become increasingly sophisticated, but validating results at the protein level still remains challenging, particularly for low-expressed proteins or small-scale samples. Here, we present a workflow established by our group for spatial protein analysis in the lung by combining two commercially available platforms: (1) laser-assisted microdissection (LMD) with (2) a capillary electrophoretic-based immunoassay (CEI). Using this workflow, we demonstrate a simple, accessible, and sensitive method for spatially capturing regions of interest to investigate small-scale samples or low-expressed proteins. This workflow provides an additional option for orthogonal validation for researchers using omics-based approaches. Furthermore, we validated transcriptome analysis results at the protein level by applying this workflow to a pre-clinical model of cigarette smoke (CS)-induced lung injury. In line with the previous findings, the results showed a significant downregulation of the endothelial cell marker in LMD-enriched alveolar regions, suggesting spatial capillary rarefaction, and activation of the mitogen-activated protein kinase (MAPK) signalling pathway in pulmonary vasculature of CS-exposed mice. Our approach overcomes traditional challenges and provides new opportunities for understanding complex disease pathomechanisms and identifying potential therapeutic targets.

CellsVol. 15(8)
Cardio-Pulmonary Institute (DE)
Deutsche Forschungsgemeinschaft, Uniklinikum Giessen und Marburg
Good health and well-being
Openalex Percentile: Top 17%
Single-cell and spatial transcriptomics
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