Keidar Haran, T. | bioRxiv Science

bioRxiv2024

A spatial atlas of human gastro-intestinal acute GVHD reveals epithelial and immune dynamics underlying disease pathophysiology

Acute graft-versus-host disease (aGVHD) is a significant complication of allogeneic hematopoietic stem cell transplantation (aHSCT), driven by alloreactive donor T cells in the gut. However, the roles of additional donor and host cells in this process are not fully understood. We conducted multiplexed imaging on 59 biopsies from patients with gastrointestinal GVHD and 10 healthy controls, revealing key pathological changes, including fibrosis, crypt alterations, loss of Paneth cells, accumulation of endocrine cells, and disrupted immune organization, particularly a reduction in IgA-secreting plasma cells. Interestingly, CD8T cells were enriched only in a subset of patients, while others exhibited non-canonical enrichments of macrophages and neutrophils. Post-transplantation time significantly influenced immune composition, with host cells dominating plasma and T cell compartments long after transplantation. This spatial atlas of healthy duodenum and GVHD uncovers non-canonical immune dynamics, offering insights into disease pathophysiology and potential clinical applications in GVHD and other inflammatory bowel diseases.

immunology↗

bioRxiv2023

Escalating High-dimensional Imaging using Combinatorial Channel Multiplexing and Deep Learning

Understanding tissue structure and function requires tools that quantify the expression of multiple proteins at single-cell resolution while preserving spatial information. Current imaging technologies use a separate channel for each individual protein, inherently limiting their throughput and scalability. Here, we present CombPlex (COMBinatorial multiPLEXing), a combinatorial staining platform coupled with an algorithmic framework to exponentially increase the number of proteins that can be measured from C up to 2c - 1. In CombPlex, every protein can be imaged in several channels, and every channel contains agglomerated images of several proteins. These combinatorically-compressed images are then decompressed to individual protein-images using deep learning. We achieve accurate reconstruction when compressing the stains of twenty-two proteins to five imaging channels and demonstrate that the approach works in both fluorescence microscopy and in mass-based imaging. Combinatorial staining coupled with deep-learning decompression can escalate the number of proteins measured using any imaging modality, without the need for specialized instrumentation. Coupling CombPlex with instruments for high-dimensional imaging could pave the way to image hundreds of proteins at single-cell resolution in intact tissue sections.

systems biology↗