Dataset Information

Multiplex analysis of mass imaging data: Application to the pathology of experimental myocardial infarction.

ABSTRACT:

Aim

Imaging mass cytometry (IMC) affords simultaneous immune-labelling/imaging of multiple antigens in the same tissue. Methods utilizing multiplex data beyond co-registration are lacking. This study developed and applied an innovative spatial analysis workflow for multiplex imaging data to IMC data determined from cardiac tissues and revealed the mechanism(s) of neutrophil-mediated post-myocardial-infarction damage.

Methods

IMC produced multiplex images with various redox/inflammatory markers. The cardiac peri-infarct zone (PIZ) was determined to be up to 240 µm from the infarct border based on the presence of neutrophils. The tissue region beyond the infarct was defined as the remote area (RA). ImageJ was used to quantify the immunoreactivity. Functional assessments included infarct size, cell necro/apoptosis, total thiol assay and echocardiogram.

Results

Expression of damage markers decreased in order from the infarct area to PIZ and then RA, reflecting the neutrophil density in the regions. Concentrically spaced "shoreline contour analysis" around the cardiac infarct extending into the PIZ showed that immunoreactivity for damage markers decreased linearly with increasing distance from the infarct, concomitant with a decreasing neutrophil-myeloperoxidase (MPO) gradient from the infarct to the PIZ. Stratifying by concentric bands around individual MPO⁺ -signal identified that the immunoreactivity of haem-oxygenase-1 (HO-1) and phosphorylated-p38 mitogen-activated protein kinase (pP38) peaked near neutrophils. Furthermore, spatial dependence between neutrophils and markers of cardiac cellular damage was confirmed by nearest-neighbour distance analysis. Post-infarction tissue exhibited declined functional parameters that were associated with neutrophil migration from the infarct to PIZ.

Conclusion

This image-based quantitative protocol revealed the spatial association and provided potential molecular pathways responsible for neutrophil-mediated damage post-infarction.

SUBMITTER: Shi H

PROVIDER: S-EPMC9286669 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image.