Graph-based description of tertiary lymphoid organs at single-cell level

Abstract

Our aim is to complement observer-dependent approaches of immune cell evaluation in microscopy images with reproducible measures for spatial composition of lymphocytic infiltrates. Analyzing such patterns of inflammation is becoming increasingly important for therapeutic decisions, for example in transplantation medicine or cancer immunology. We developed a graph-based assessment of lymphocyte clustering in full whole slide images. Based on cell coordinates detected in the full image, a Delaunay triangulation and distance criteria are used to build neighborhood graphs. The composition of nodes and edges are used for classification, e.g. using a support vector machine. We describe the variability of these infiltrates on CD3/CD20 duplex staining in renal biopsies of long-term functioning allografts, in breast cancer cases, and in lung tissue of cystic fibrosis patients. The assessment includes automated cell detection, identification of regions of interest, and classification of lymphocytic clusters according to their degree of organization. We propose a neighborhood feature which considers the occurrence of edges with a certain type in the graph to distinguish between phenotypically different immune infiltrates. Our work addresses a medical need and provides a scalable framework that can be easily adjusted to the requirements of different research questions.

Fig 1. General workflow.

CD3⁺ T- and CD20⁺ B-cells are identified in whole slide images (WSIs). In large WSIs, regions of interest (ROIs) could be selected based on lymphocyte density maps. In each ROI/small WSI, neighborhood graphs are built by detected cells, results visualized by concave hulls.

Fig 2. Graph workflow.

A: Blue and red dots represent cells of different phenotypes. B: Cells’ Delaunay graph. C: Neighborhood graphs G₁, G₂, where triangles with large circumcircle radius were excluded. Edges between cells of different phenotype are illustrated as dashed lines. D: Classification based on the composition of nodes and edges. E: Illustration of edge labels used in the feature κ(a), see Section Features of infiltrates; nodes of type A are represented in red. For the illustrated graph G₂ = (V, E), $\kappa \left(2\right)=\frac{|E_{{\gamma }_3}|+|E_{{\gamma }_4}|+|E_{{\gamma }_6}|}{\left|E\right|-|E_{\alpha }|}=0.4$ and $\kappa \left(5\right)=\frac{|E_{{\gamma }_6}|}{\left|E\right|-|E_{\alpha }|}=0.05$.

Fig 3. Examples of identified infiltrates.

Left: as graph representation. Right: as histological image. T-cells and their links in brown, B-cells in red, edges between T- and B-cells in blue.

Fig 4. Principal component analysis between components with highest variance (PC1, PC2) for 700 samples in total (number of each class and tissue type listed in legend as brackets) that were visually classified by a pathologist.

The number of nodes (|V|, V_B|/|V|), edges (|E|, $|E_{{\alpha }_B}|/|E|$), TLO-like organization (κ(2), κ(5)), homogeneity (H), clustering coefficient (C), average degree (〈K〉), and the average Euclidean distance between all nodes are used as features, see Section Features of infiltrates.

Fig 5. Composition of infiltrates: The absolute number of infiltrates and the estimated area considered to detect infiltrates (given in mm²).

Fig 6. Distribution of relative number of edges between B-cells (A) and organization (B): Gray border represents significant difference between corresponding class and all remaining classes of same tissue type by Mann–Whitney U test using a significance level of 0.001.

Each distribution is displayed as raw data (left) and a box plot (right) with mean (line), quartiles (color box), and standard deviation. The TLO-like organization κ measures the number of edges between T- and B-cells that are connected to a B-cell cluster, see Eq (1).