Virus-Dependent Immune Conditioning of Tissue Microenvironments

Abstract

A thorough understanding of complex spatial host-disease interactions in situ is necessary in order to develop effective preventative measures and therapeutic strategies. Here, we developed Protein And Nucleic acid IN situ Imaging (PANINI) and coupled it with Multiplexed Ion Beam Imaging (MIBI) to sensitively and simultaneously quantify DNA, RNA, and protein levels within the microenvironments of tissue compartments. The PANINI-MIBI approach was used to measure over 30 parameters simultaneously across large sections of archival lymphoid tissues from non-human primates that were healthy or infected with simian immunodeficiency virus (SIV), a model that accurately recapitulates human immunodeficiency virus infection (HIV). This enabled multiplexed dissection of cellular phenotypes, functional markers, viral DNA integration events, and viral RNA transcripts as resulting from viral infection. The results demonstrated immune coordination from an unexpected upregulation of IL10 in B cells in response to SIV infection that correlated with macrophage M2 polarization, thus conditioning a potential immunosuppressive environment that allows for viral production. This multiplexed imaging strategy also allowed characterization of the coordinated microenvironment around latently or actively infected cells to provide mechanistic insights into the process of viral latency. The spatial multi-modal framework presented here is applicable to deciphering tissue responses in other infectious diseases and tumor biology.

Rhesus Macaque CODEX Multiplexed Immunohistochemistry Panel for Studying Immune Responses During Ebola Infection

“…The initial host antibody marker selection was based on prior knowledge and antibody clones described in previous studies (12)(13)(14)(15)(16)(17)(18)(19). Various factors, such as the time between tissue collection and fixation, duration of fixation, processing into paraffin blocks, and storage conditions can affect tissue integrity and immunohistochemical analysis (20).…”

Multicellular modules as clinical diagnostic and therapeutic targets

“…Thus, how does one address understanding how given arrangements of cells represent tissue function at the local and global scale? Multiplexed tissue imaging is advancing our understanding of spatial context in areas such as oncology [5][6][7][8][9][10][11][12], immunology [13,14], and microbiology [15,16]. An early focus of the field has been the tumor immune microenvironment and its implications for immunotherapy.…”

“…Computational and statistical techniques have been developed to analyze multiplexed imaging data to define multicellular structures. Multicellular modules can be defined from raw imaging data or from segmented single cell data as recurrent combinations of spatially restricted proteins or cell types (Figure 3A) [6,8,10,15,16]. One approach registers neighboring cells in a local group of cells by a sliding window across the tissue, indexing each cell and then recording to varying levels the context of neighboring cells (cell type, geographic relative position, shape, etc.).…”

“…The important role of the functional state of a neighborhood structure extends the concept of functional cellular states. Analyzing neighborhoods rather than only individual cell-cell interactions has also proven useful in the contexts of microbial infection [15,16] and tumor subclone patterns [10].…”

Editorial: Defining the Spatial Organization of Immune Responses to Cancer and Viruses In Situ

“…Orthogonal interrogation with synergistic tools, such as RNA quantification methods including spatial transcriptomics, single-cell RNA sequencing, and other advanced techniques are needed to precisely define the genetic and protein topographies of human tissues. The development of multi-omic measurements in situ is a key fundamental advancement in this regard, including the quantification of metabolic states ( 21 ), nucleic acids and proteins ( 12 , 23 , 25 ), clonality ( 26 ), and epigenetic states ( 27 ). The continued advancement of computational methods is also vital for multi-scalar inferential analysis across different measurement modalities ( 28 ).…”