Spatially Resolved Immunometabolism to Understand Infectious Disease Progression.

Roel Tans,Paul M Kaye,Peter O’Toole,Nidhi Sharma Dey,Ron M A Heeren,Grant Calder,Shoumit Dey

doi:10.3389/fmicb.2021.709728

Abstract

Infectious diseases, including those of viral, bacterial, fungal, and parasitic origin are often characterized by focal inflammation occurring in one or more distinct tissues. Tissue-specific outcomes of infection are also evident in many infectious diseases, suggesting that the local microenvironment may instruct complex and diverse innate and adaptive cellular responses resulting in locally distinct molecular signatures. In turn, these molecular signatures may both drive and be responsive to local metabolic changes in immune as well as non-immune cells, ultimately shaping the outcome of infection. Given the spatial complexity of immune and inflammatory responses during infection, it is evident that understanding the spatial organization of transcripts, proteins, lipids, and metabolites is pivotal to delineating the underlying regulation of local immunity. Molecular imaging techniques like mass spectrometry imaging and spatially resolved, highly multiplexed immunohistochemistry and transcriptomics can define detailed metabolic signatures at the microenvironmental level. Moreover, a successful complementation of these two imaging techniques would allow multi-omics analyses of inflammatory microenvironments to facilitate understanding of disease pathogenesis and identify novel targets for therapeutic intervention. Here, we describe strategies for downstream data analysis of spatially resolved multi-omics data and, using leishmaniasis as an exemplar, describe how such analysis can be applied in a disease-specific context.

Highlights

Metabolism reflects an intrinsically complex interplay between multiple molecular and cellular networks
We provide a perspective on how multi-modal labelfree molecular imaging techniques such as mass spectrometry imaging (MSI) combined with spatially resolved highly multiplexed protein and transcriptome mapping can be used in concert to understand the progression of infectious diseases and delineate inflammatory microenvironments
In line with our proposed workflow depicted in Figure 1, we show preliminary results on aberrant spatially resolved protein and lipid profiles from granulomas in L. donovani-infected mice using Digital Spatial Profiler (DSP) and MSI (Figure 2)

Summary

INTRODUCTION

Metabolism reflects an intrinsically complex interplay between multiple molecular and cellular networks. Probabilistic methods of learning temporal trajectory have been described for single cell RNA data that can be applied for multi-modal ROI analysis (Campbell and Yau, 2016a) especially those that can incorporate prior knowledge in trajectory learning (Campbell and Yau, 2016b) At this stage, clustering information of lipid profiles (based on MSI) may be associated [co-analysis (Buescher and Driggers, 2016)] of exact serial sections) with the granuloma progression trajectory as inferred via protein expression data (DSP) to link metabolic profiles to granuloma progression (Figure 1.3). These can be used as a powerful research or diagnostic tool (Figure 1.4b)

CONCLUSION

DATA AVAILABILITY STATEMENT