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Publication

A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry.

Cell metabolism | 2019

Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing β cells. A comprehensive picture of the changes during T1D development is lacking due to limited sample availability, inability to sample longitudinally, and the paucity of technologies enabling comprehensive tissue profiling. Here, we analyzed 1,581 islets from 12 human donors, including eight with T1D, using imaging mass cytometry (IMC). IMC enabled simultaneous measurement of 35 biomarkers with single-cell and spatial resolution. We performed pseudotime analysis of islets through T1D progression from snapshot data to reconstruct the evolution of β cell loss and insulitis. Our analyses revealed that β cell destruction is preceded by a β cell marker loss and by recruitment of cytotoxic and helper T cells. The approaches described herein demonstrate the value of IMC for improving our understanding of T1D pathogenesis, and our data lay the foundation for hypothesis generation and follow-on experiments.

Pubmed ID: 30713109 RIS Download

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Associated grants

Agency: NIAID NIH HHS, United States
Id: P01 AI042288
Agency: NIDDK NIH HHS, United States
Id: UC4 DK108132

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This is a list of tools and resources that we have found mentioned in this publication.

Fiji (tool)

RRID:SCR_002285

Software package as distribution of ImageJ and ImageJ2 together with Java, Java3D and plugins organized into coherent menu structure. Used to assist research in life sciences.

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CellProfiler Image Analysis Software (tool)

RRID:SCR_007358

Software tool to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. It counts cells and also measures the size, shape, intensity and texture of every cell (and every labeled subcellular compartment) in every image. It was designed for high throughput screening but can perform automated image analysis for images from time-lapse movies and low-throughput experiments. CellProfiler has an increasing number of algorithms to identify and measure properties of neuronal cell types.

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Ilastik (tool)

RRID:SCR_015246

Tool for interactive image classification, segmentation and analysis, built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection.

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Monocle2 (tool)

RRID:SCR_016339

Software package for analyzing single cell gene expression, classifying and counting cells, performing differential expression analysis between subpopulations of cells, and reconstructing cellular trajcectories. Works well with very large single-cell RNA-Seq experiments containing tens of thousands of cells or more. Used in computational analysis of gene expression data in single cell gene expression studies to profile transcriptional regulation in complex biological processes and highly heterogeneous cell populations.

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imctools (tool)

RRID:SCR_017132

Software Python package that implements preprocessing pipeline for imaging mass cytometry data. Can convert IMC raw files to tiff files that are used as inputs into CellProfiller, Ilastik, Fiji etc.

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