Deep profiling of mouse splenic architecture with CODEX multiplexed imaging

Abstract

SUMMARYA highly multiplexed cytometric imaging approach, termed CO-Detection by indEXing (CODEX), is used here to create multiplexed datasets of normal and lupus (MRL/lpr) murine spleens. CODEX iteratively visualizes antibody binding events using DNA barcodes, fluorescent dNTP analogs, and an insitu polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease (MRL/lpr), extensive and previously uncharacterized splenic cell interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.

CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues

“…histo-cytometry, CODEX, MIBI, STARMAP). 4,[10][11][12][13][14][15][16][17] These techniques generate panoptic datasets describing phenotypic, transcriptional, functional, and morphologic cellular properties, while retaining information on the precise 2-dimensional (2D) or 3D positioning of cells within tissues, thus potentiating a more complete understanding of cellular function directly in situ. Analysis of images generated by these methods requires multiple processing steps, including image preprocessing, cell segmentation, object classification, and spatial analysis.…”

“…18,21 For spatial analysis, existing advanced software or published code include statistical tools for defining cellular phenotypes, as well as use nearest-neighbor algorithms to identify preferential interactions among different cell types. 10,11,19,[22][23][24] Nevertheless, there is a lack of accessible and simple to use informatics tools for extracting quantitative and comprehensible information on the complex spatial relationships between different cell types and their microenvironments, as well as for revealing the fundamental organizational features of complex tissues, especially for large volumetric and whole slide datasets. This has hampered the ability of biologists with access to high-dimensional imaging technologies to obtain an in-depth understanding of the spatial relationships of cells and localized microenvironments within their datasets and limited the utility of these advanced imaging approaches.…”

Islet–immune interactions in type 1 diabetes: the nexus of beta cell destruction

“…Single cell transcriptomics/ proteomics Assess expression of candidate genes and their protein products for paired cell genotype/ phenotype data, as well as adaptive immune cell repertoire analysis [104] Single cell ATAC-sequencing Investigate the contribution of dysregulated gene networks, particularly in candidate loci, to immune or β-cell function [106,107] CyTOF Following labeling with metal tagged antibodies, deep phenotyping of single cell suspensions can be performed [110] IMC By applying metal tagged antibodies to fixed tissue sections, deep phenotyping of cells can be performed in situ [111,112] Laser capture microdissection Identify differentially expressed genes and proteins to develop disease-predictive biomarkers or targeted therapeutics [108] nanoPOTS Identify novel post-translational modifications within a single islet and the proteomic basis for islet heterogeneity [109] CODEX Examine disease-related tissue and cell subset reorganization, as well as perform multiplexed deep phenotyping [113] Tissue clearing (e.g. X-CLARITY, PARS) Visualization of intact morphology, vasculature, innervation, and extracellular matrix [114] RNA single-molecule FISH Identify the dynamics of candidate gene and checkpoint molecule expression in all subsets present in the tissue microenvironment, examine how they influence cell phenotype and function [115] Microphysiological Systems Introduce targeted therapeutics or innate and adaptive immune cells to assess their contribution to disease development/treatment [116] Investigate the underlying cause of the islet anti-viral immune signature Examine the role of hybrid insulin peptides (HIPs), defective ribosomal products (DRiPs) and neoantigens in β-cell stress or destruction Tissue Slice Profile live immune and endocrine cells across the pancreas in the native tissue environment [24] Assess the role of chemokines and adhesion molecules Determine which cells are directly pathogenic vs bystander or tissue resident cells *nanoPOTS = Nanodroplet processing in one pot for trace samples; PARS = Perfusion-assisted agent release in situ; ATAC = Assay for Transposase Accessible Chromatin; CyTOF = Cytometry time of flight; IMC = Imaging mass cytometry.…”

“…A novel method developed by Nolan and colleagues, co-detection by indexing (CODEX), enables multiplexed in situ deep phenotyping through the use of polymerasedriven iterative antibody visualization, with up to 66 antigens able to be codetected on a standard 3-4 color microscope by iterative hybridization and staining [113] ( Table 3). A comprehensive image is then constructed from the combination of images, allowing associations to be drawn between cell phenotype and spatial arrangement in the tissue microenvironment.…”

Single cell immune profiling in transplantation research

“…19,20 CO-Detection by indexing (CODEX) and cyclic immunofluorescence (CycIF) utilize staining with oligonucleotide-conjugated antibodies and an iterative process of sequential primer extension with fluorescently labeled nucleotides in order to image expression of two markers at a time, for as many iterations as necessary. 21,22 The result is multiplexed image data with gene expression for many more genes than previously possible. A second approach is multiplexed ion beam imaging (MIBI) which uses metal-conjugated antibodies and secondary ion mass spectroscopy to image tissue sections.…”