Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging

By Y. Goltsev, N. Samusik, J. Kennedy‐Darling, S. Bhate, M. Hale, Gustavo Vazquez, Sarah Black, Garry P. Nolan. bioRxiv, 2017

1,330 citations recorded by the source corpus.

A cytometric imaging approach, called CO-Detection by indEXing (CODEX), that enables high parameter multiplexing of antibody-tagged target epitopes is used here to create high parameter imaging datasets of normal mouse and lupus (MRL/lpr) spleens. In this procedure, antibody binding events are rendered iteratively using DNA barcodes, fluorescent dNTP analogs, and an in-situ polymerization-based indexing procedure. Fluorescent signals from multiple rounds of indexing are computationally combined into a multi-channel image stack and subjected to image segmentation and quantification. A segmentation and linear model algorithm was developed to accurately quantify membrane antigen levels on dissociated cells as well as tissue sections. Leveraging the spatially resolved nature of CODEX multiplexed single-cell imaging data, quantitative de novo characterization of lymphoid tissue architecture was enabled and overlaid onto previously described morphological features. 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 imaging data analysis demonstrated here will allow deep proteomic analysis and systematic characterization of complex tissue architecture in normal and clinically aberrant samples.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/44d1fc901056855e4548c182eb8dbc1ec543f2d3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

In Molecular Biology

Citations in the corpus

Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes (2018, background): “159 Multiplexing antibodies and stains makes it possible to discriminate among proliferating, quiescent and dying 160 cells, identify tumor and stroma, and collect immuno-phenotypes.(19,27,28) Use of phospho-specific antibodies and 161”
Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022, positive): “For example, performing bridge integration on spatially resolved unimodal datasets (for example, CODEX 74 ) could help to better characterize the spatial localization of scRNA-seq-defined cell types in large tissue sections.”
An introduction to spatial transcriptomics for biomedical research (2022, methodology): “Immunofluorescence is limited by the number of fluorophores that can be distinguished, so recent techniques such as t-CycIF and CODEX have used sequential methods to read out a barcode for an antibody [24, 25]; CODEX reads out antibody-conjugated DNA barcodes with fluorescent, hybridizing nucleotide”
The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution (2020, neutral): “Recent advances in single-cell and multiplexed spatial analysis of tissue allow us to interrogate this complexity at unprecedented resolution (Angelo et al., 2014; Buenrostro et al., 2015; Chen et al., 2016a; Chen et al., 2016b; Du et al., 2019; Gaublomme et al., 2019; Gierahn et al., 2017; Giesen e”
Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front (2019, methodology): “After single-cell segmentation, marker quantification and spatial fluorescence compensation (Goltsev et al., 2018), manual cleanup gating for each individual TMA core was performed in CellEngine (www.”