Hendrik Schäfer

Macrophages play a crucial role in the homeostasis of lymph nodes. Their phenotypes and functions are dictated by the molecular composition of the microenvironment. The presence of tumor cells can influence the microenvironment, leading to changes in the cellular functions of tumor-associated macrophages (TAMs). Cellular alterations often correlate with histomorphometric and distributional changes. This study aims to characterize these pathomic modifications resulting from tumor cell infiltration by comparing them to reactive conditions. We assessed CD68 ⁺ and CD163 ⁺ TAMs in 160 three-dimensional (3D) images of human lymphoid tissue sections derived from 82 cases comprising six different diagnoses. To investigate TAM profiles, we employed a hybrid approach involving computer vision and graph theoretical algorithms. For calculating the histomorphometric features of TAMs, we utilized an image analysis pipeline with IMARIS Advanced Tracking Software. The distributions of TAMs were characterized using cell graphs. The incorporation of 3D tissue analysis in targeted areas of thick tissue sections revealed specific patterns of histomorphometric and distributional changes in TAMs. In chronic lymphocytic leukemia and diffuse large B-cell lymphoma, these distinctive alteration patterns demonstrated entity specificity. Furthermore, pathomic alterations displayed possible correlations with established functional aberrations in lymphomas. These findings imply that the pathomic properties of TAMs mirror their functional aberrations. Through validation and generalization via molecular pathological examinations, this approach has the potential to unveil and understand functional aberrations in histopathological assessments.

This study deals with a 4D investigation of lymphocytes in human tissue under reactive and neoplastic conditions. The immune system’s response to pathogens highly depends on cell interaction and movement, which makes it essential to analyze these dynamics. To achieve this, we observed cells and their movement in 4D. Human lymphoid tissue was examined, including 8 cases with 23 movies of hyperplastic tissue from the pharyngeal tonsil and 12 cases with 35 movies of lymphadenitis. Additionally, there were 4 cases involving 16 movies of marginal zone lymphoma (MZL), 3 cases with 19 movies of nodular lymphocyte predominant Hodgkin lymphoma (NLPHL), 3 cases with 6 movies of follicular lymphoma grade 1/2 (FL), and 2 cases with 10 movies of diffuse large B-cell lymphoma (DLBCL). We tracked the movement, analyzed the activity of B cells and PD1-positive T cells under reactive conditions, and compared the results to different types of lymphomas. In this study, CD20-positive B cells were examined in the context of CD35 staining. CD35 stains follicular dendritic cells (FDC), an indication for a germinal center, so we primarily analyzed B cells in the germinal center areas and partially in the immediate periphery. We categorized cells by defining track types (Low motion cells, Moving and turning in place, Long distance movement) to describe their movement pattern and action types (Passive cells, Interactive cells, Active cells) to describe their behavior and interactions. In neoplastic tissue, slower cellular dynamics and fewer interactions were observed compared to reactive tissue. This indicates differences in cellular behavior between reactive and neoplastic tissues, with a more dynamic cellular environment observed in reactive tissue.

Nodal T-follicular helper cell lymphoma, angioimmunoblastic-type (AITL), is characterized by constitutional symptoms, advanced-stage disease, and generalized lymphadenopathy. A genetic hallmark of this lymphoma is the frequent occurrence of the RHOA mutation G17V in neoplastic cells, which is observed in around 60% of patients. Because RHOA is involved in both T-cell receptor downstream signalling and cell migration, we hypothesized that the characteristic presentation of AITL could be the result of enhanced tumor cell migration. Therefore, this study aimed to elucidate the impact of the RHOA variant G17V on the migration of neoplastic T cells. We transfected the T-cell lymphoma cell lines HH and HuT78 to stably express the RHOA-G17V variant. RHOA-G17V-expressing T cells did not exhibit enhanced motility compared to empty-vector-transfected cells in microchannels, a 3D collagen gel, or primary human lymphatic tissue. Cells of the HH cell line expressing RHOA-G17V had an increased number of cells with cleaved collagen compared with the empty-vector-transfected cells. Therefore, we hypothesized that the early spread of AITL tumor cells may be related to remodelling of the extracellular matrix. Accordingly, we observed a significant negative correlation between the relative area of collagen in histological sections from 18 primary AITL and the allele frequency of the RHOA-G17V mutation. In conclusion, our results suggest that the characteristic presentation of AITL with early, widespread dissemination of lymphoma cells is not the result of an enhanced migration capacity due to the RHOA-G17V mutation; instead, this feature may rather be related to extracellular matrix remodelling.

The simulation of immune response is a challenging task because quantitative data are scarce. Quantitative theoretical models either focus on specific cell–cell interactions or have to make assumptions about parameters. The broad variation of, e.g., the dimensions and abundance between lymph nodes as well as between individual patients hampers conclusive quantitative modeling. No theoretical model has been established representing a consensus on the set of major cellular processes involved in the immune response. In this paper, we apply the Petri net formalism to construct a semi-quantitative mathematical model of the lymph nodes. The model covers the major cellular processes of immune response and fulfills the formal requirements of Petri net models. The intention is to develop a model taking into account the viewpoints of experienced pathologists and computer scientists in the field of systems biology. In order to verify formal requirements, we discuss invariant properties and apply the asynchronous firing rule of a place/transition net. Twenty-five transition invariants cover the model, and each is assigned to a functional mode of the immune response. In simulations, the Petri net model describes the dynamic modes of the immune response, its adaption to antigens, and its loss of memory.

Histological sections of the lymphatic system are usually the basis of static (2D) morphological investigations. Here, we performed a dynamic (4D) analysis of human reactive lymphoid tissue using confocal fluorescent laser microscopy in combination with machine learning. Based on tracks for T-cells (CD3), B-cells (CD20), follicular T-helper cells (PD1) and optical flow of follicular dendritic cells (CD35), we put forward the first quantitative analysis of movement-related and morphological parameters within human lymphoid tissue. We identified correlations of follicular dendritic cell movement and the behavior of lymphocytes in the microenvironment. In addition, we investigated the value of movement and/or morphological parameters for a precise definition of cell types (CD clusters). CD-clusters could be determined based on movement and/or morphology. Differentiating between CD3- and CD20 positive cells is most challenging and long term-movement characteristics are indispensable. We propose morphological and movement-related prototypes of cell entities applying machine learning models. Finally, we define beyond CD clusters new subgroups within lymphocyte entities based on long term movement characteristics. In conclusion, we showed that the combination of 4D imaging and machine learning is able to define characteristics of lymphocytes not visible in 2D histology.

Human lymph nodes play a central part of immune defense against infection agents and tumor cells. Lymphoid follicles are compartments of the lymph node which are spherical, mainly filled with B cells. B cells are cellular components of the adaptive immune systems. In the course of a specific immune response, lymphoid follicles pass different morphological differentiation stages. The morphology and the spatial distribution of lymphoid follicles can be sometimes associated to a particular causative agent and development stage of a disease. We report our new approach for the automatic detection of follicular regions in histological whole slide images of tissue sections immuno-stained with actin. The method is divided in two phases: (1) shock filter-based detection of transition points and (2) segmentation of follicular regions. Follicular regions in 10 whole slide images were manually annotated by visual inspection, and sample surveys were conducted by an expert pathologist. The results of our method were validated by comparing with the manual annotation. On average, we could achieve a Zijbendos similarity index of 0.71, with a standard deviation of 0.07.

In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significantly favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types. In pathology, histological diagnosis is still challenging, in particular, for tumor diseases. Pathologists diagnose the disease and its stage of development on the basis of evaluation and interpretation of images of tissue sections. The quantification of experimental data to support decisions of diagnosis and prognosis, applying bioinformatics methods, is an important issue. Here, we introduce a new, general approach to analyze tissue images of tumor and non-tumor patients and to evaluate the distribution of tumor cells in the tissue. Moreover, we consider neighborhood relations between immunostained cells of different cell morphology. We focus on a special type of lymph node tumor, the Hodgkin lymphoma, exploring the two main types of the classical Hodgkin lymphoma, the nodular sclerosis and the mixed cellularity, and the non-tumor case, the lymphadenitis, representing an inflammation of the lymph node. We considered more than 400, 000 cells immunohistochemically stained with CD30 in 35 whole slide images of tissue sections. We found that cells of specific morphology exhibited significant relations to cells of certain morphology as spatial nearest neighbor. We could show different neighborhood patterns of CD30-positive cells between tumor and non-tumor. The approach is general and can easily be applied to other tumor types.