Quantifying In Situ Adaptive Immunity in Human Tubulointerstitial Lupus Nephritis

Abstract

The specific goals of this grant are to use high-dimensional (16 primary antibodies or more) multicolorconfocal microscopy to describe the frequency and spatial distributions of the different types of immunecells infiltrating the lupus kidney (Aim 1). We will then use machine learning approaches, including CDM3,to quantify the true complexity of inflammation and identify functional relationships between different T cellpopulations and the cells that are presenting antigen, and therefore activating, pathogenic T cells (Aim 2).By understanding which cells, functional relationships and mechanisms are associated with progression torenal failure, we will both identify new therapeutic targets and the patients for which such therapies arelikely to be efficacious.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2022
Accession Number
AD1191042

Entities

People

  • Marcus R Clark

Organizations

  • University of Chicago

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Adaptive Immunity
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Autoimmune Diseases
  • Blood
  • Cells
  • Computational Science
  • Computer Vision
  • Confocal Microscopy
  • Convolutional Neural Networks
  • Data Mining
  • Dimensionality Reduction
  • Information Science
  • Kidney Diseases
  • Lymphocytes
  • Machine Learning
  • Medical Personnel
  • Neural Networks
  • Supervised Machine Learning

Fields of Study

  • Biology
  • Medicine

Readers

  • Computational Modeling and Simulation
  • Immunology
  • Molecular and Cellular Biology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference