Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently... Show moreDeep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtyping of cancer. However, these models are difficult to train due to the large number of hyperparameters that need to be tuned. To get a better understanding of the importance of the different hyperparameters, we examined six different VAE models when trained on TCGA transcriptomics data and evaluated on the downstream tasks of cluster agreement with cancer subtypes and survival analysis. We studied the effect of the latent space dimensionality, learning rate, optimizer, initialization and activation function on the quality of subsequent downstream tasks on the TCGA samples. We found beta-TCVAE and DIP-VAE to have a good performance, on average, despite being more sensitive to hyperparameters selection. Based on these experiments, we derived recommendations for selecting the different hyperparameters settings. To ensure generalization, we tested all hyperparameter configurations on the GTEx dataset. We found a significant correlation (rho = 0.7) between the hyperparameter effects on clustering performance in the TCGA and GTEx datasets. This highlights the robustness and generalizability of our recommendations. In addition, we examined whether the learned latent spaces capture biologically relevant information. Hereto, we measured the correlation and mutual information of the different representations with various data characteristics such as gender, age, days to metastasis, immune infiltration, and mutation signatures. We found that for all models the latent factors, in general, do not uniquely correlate with one of the data characteristics nor capture separable information in the latent factors even for models specifically designed for disentanglement. Show less
Heezen, L.G.M.; Abdelaal, T.; Putten, M. van; Aartsma-Rus, A.; Mahfouz, A.; Spitali, P. 2023
Duchenne muscular dystrophy is caused by mutations in the DMD gene, leading to lack of dystrophin. Chronic muscle damage eventually leads to histological alterations in skeletal muscles. The... Show moreDuchenne muscular dystrophy is caused by mutations in the DMD gene, leading to lack of dystrophin. Chronic muscle damage eventually leads to histological alterations in skeletal muscles. The identification of genes and cell types driving tissue remodeling is a key step to developing effective therapies. Here we use spatial transcriptomics in two Duchenne muscular dystrophy mouse models differing in disease severity to identify gene expression signatures underlying skeletal muscle pathology and to directly link gene expression to muscle histology. We perform deconvolution analysis to identify cell types contributing to histological alterations. We show increased expression of specific genes in areas of muscle regeneration (Myl4, Sparc, Hspg2), fibrosis (Vim, Fn1, Thbs4) and calcification (Bgn, Ctsk, Spp1). These findings are confirmed by smFISH. Finally, we use differentiation dynamic analysis in the D2-mdx muscle to identify muscle fibers in the present state that are predicted to become affected in the future state. Show less
With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and... Show moreWith the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer (https://viewer.cytosplore.org) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment. Show less
Koppejan, H.; Beyrend, G.; Hameetman, M.; Abdelaal, T.; Toes, R.E.M.; Gaalen, F.A. van 2023
Objective: Mass cytometry (MC) immunoprofiling allows high-parameter phenotyping of immune cells. We set to investigate the potential of MC immuno-monitoring of axial spondyloarthritis (axSpA)... Show moreObjective: Mass cytometry (MC) immunoprofiling allows high-parameter phenotyping of immune cells. We set to investigate the potential of MC immuno-monitoring of axial spondyloarthritis (axSpA) patients enrolled in the Tight Control SpondyloArthritis (TiCoSpA) trial. Methods: Fresh, longitudinal PBMCs samples (baseline, 24, and 48 weeks) from 9 early, untreated axSpA patients and 7 HLA-B27+ controls were analyzed using a 35-marker panel. Data were subjected to HSNE dimension reduction and Gaussian mean shift clustering (Cytosplore), followed by Cytofast analysis. Linear discriminant analyzer (LDA), based on initial HSNE clustering, was applied onto week 24 and 48 samples. Results: Unsupervised analysis yielded a clear separation of baseline patients and controls including a significant difference in 9 T cell, B cell, and monocyte clusters (cl), indicating disrupted immune homeostasis. Decrease in disease activity (ASDAS score; median 1.7, range 0.6-3.2) from baseline to week 48 matched significant changes over time in five clusters: cl10 CD4 Tnai cells median 4.7 to 0.02%, cl37 CD4 T-em cells median 0.13 to 8.28%, cl8 CD4 Tcm cells median 3.2 to 0.02%, cl39 B cells median 0.12 to 2.56%, and cl5 CD38+ B cells median 2.52 to 0.64% (all p<0.05). Conclusions: Our results showed that a decrease in disease activity in axSpA coincided with normalization of peripheral T- and B-cell frequency abnormalities. This proof of concept study shows the value of MC immuno-monitoring in clinical trials and longitudinal studies in axSpA. MC immunophenotyping on a larger, multi-center scale is likely to provide crucial new insights in the effect of anti-inflammatory treatment and thereby the pathogenesis of inflammatory rheumatic diseases. Show less
Koppejan, H.; Beyrend, G.; Hameetman, M.; Abdelaal, T.; Toes, R.E.M.; Gaalen, F.A. van 2023
ObjectiveMass cytometry (MC) immunoprofiling allows high-parameter phenotyping of immune cells. We set to investigate the potential of MC immuno-monitoring of axial spondyloarthritis (axSpA)... Show moreObjectiveMass cytometry (MC) immunoprofiling allows high-parameter phenotyping of immune cells. We set to investigate the potential of MC immuno-monitoring of axial spondyloarthritis (axSpA) patients enrolled in the Tight Control SpondyloArthritis (TiCoSpA) trial.MethodsFresh, longitudinal PBMCs samples (baseline, 24, and 48 weeks) from 9 early, untreated axSpA patients and 7 HLA-B27+ controls were analyzed using a 35-marker panel. Data were subjected to HSNE dimension reduction and Gaussian mean shift clustering (Cytosplore), followed by Cytofast analysis. Linear discriminant analyzer (LDA), based on initial HSNE clustering, was applied onto week 24 and 48 samples.ResultsUnsupervised analysis yielded a clear separation of baseline patients and controls including a significant difference in 9 T cell, B cell, and monocyte clusters (cl), indicating disrupted immune homeostasis. Decrease in disease activity (ASDAS score; median 1.7, range 0.6–3.2) from baseline to week 48 matched significant changes over time in five clusters: cl10 CD4 Tnai cells median 4.7 to 0.02%, cl37 CD4 Tem cells median 0.13 to 8.28%, cl8 CD4 Tcm cells median 3.2 to 0.02%, cl39 B cells median 0.12 to 2.56%, and cl5 CD38+ B cells median 2.52 to 0.64% (all p<0.05).ConclusionsOur results showed that a decrease in disease activity in axSpA coincided with normalization of peripheral T- and B-cell frequency abnormalities. This proof of concept study shows the value of MC immuno-monitoring in clinical trials and longitudinal studies in axSpA. MC immunophenotyping on a larger, multi-center scale is likely to provide crucial new insights in the effect of anti-inflammatory treatment and thereby the pathogenesis of inflammatory rheumatic diseases. Show less
Sluis, T.C. van der; Beyrend, G.; Gracht, E.T.I.V.; Abdelaal, T.; Jochems, S.P.; Belderbos, R.A.; ... ; Arens, R. 2023
Immune checkpoint therapy (ICT) has the power to eradicate cancer, but the mechanisms that determine effective therapy-induced immune responses are not fully understood. Here, using high... Show moreImmune checkpoint therapy (ICT) has the power to eradicate cancer, but the mechanisms that determine effective therapy-induced immune responses are not fully understood. Here, using high-dimensional sin-gle-cell profiling, we interrogate whether the landscape of T cell states in the peripheral blood predict re-sponses to combinatorial targeting of the OX40 costimulatory and PD-1 inhibitory pathways. Single-cell RNA sequencing and mass cytometry expose systemic and dynamic activation states of therapy-responsive CD4+ and CD8+ T cells in tumor-bearing mice with expression of distinct natural killer (NK) cell receptors, granzymes, and chemokines/chemokine receptors. Moreover, similar NK cell receptor-expressing CD8+ T cells are also detected in the blood of immunotherapy-responsive cancer patients. Targeting the NK cell and chemokine receptors in tumor-bearing mice shows the functional importance of these receptors for ther-apy-induced anti-tumor immunity. These findings provide a better understanding of ICT and highlight the use and targeting of dynamic biomarkers on T cells to improve cancer immunotherapy. Show less
Motivation Single-cell technologies allow deep characterization of different molecular aspects of cells. Integrating these modalities provides a comprehensive view of cellular identity. Current... Show moreMotivation Single-cell technologies allow deep characterization of different molecular aspects of cells. Integrating these modalities provides a comprehensive view of cellular identity. Current integration methods rely on overlapping features or cells to link datasets measuring different modalities, limiting their application to experiments where different molecular layers are profiled in different subsets of cells.Results We present scTopoGAN, a method for unsupervised manifold alignment of single-cell datasets with non-overlapping cells or features. We use topological autoencoders (topoAE) to obtain latent representations of each modality separately. A topology-guided Generative Adversarial Network then aligns these latent representations into a common space. We show that scTopoGAN outperforms state-of-the-art manifold alignment methods in complete unsupervised settings. Interestingly, the topoAE for individual modalities also showed better performance in preserving the original structure of the data in the low-dimensional representations when compared to other manifold projection methods. Taken together, we show that the concept of topology preservation might be a powerful tool to align multiple single modality datasets, unleashing the potential of multi-omic interpretations of cells.Availability and implementation Implementation available on GitHub (https://github.com/AkashCiel/scTopoGAN). All datasets used in this study are publicly available. Show less
Pardieck, I.N.; Sluis, T.C. van der; Gracht, E.T.I. van der; Veerkamp, D.M.B.; Behr, F.M.; Duikeren, S. van; ... ; Arens, R. 2022
Vaccination regimens and the number of doses required for optimal immunity and protection are critical factors in the translation of vaccines. Here the authors show administration of a three dose... Show moreVaccination regimens and the number of doses required for optimal immunity and protection are critical factors in the translation of vaccines. Here the authors show administration of a three dose protocol of a single T cell epitope to the SARS-CoV-2 spike protein induces a robust CD8(+) T cell response and confers protection in a lethal murine challenge model of infection.Understanding the mechanisms and impact of booster vaccinations are essential in the design and delivery of vaccination programs. Here we show that a three dose regimen of a synthetic peptide vaccine elicits an accruing CD8(+) T cell response against one SARS-CoV-2 Spike epitope. We see protection against lethal SARS-CoV-2 infection in the K18-hACE2 transgenic mouse model in the absence of neutralizing antibodies, but two dose approaches are insufficient to confer protection. The third vaccine dose of the single T cell epitope peptide results in superior generation of effector-memory T cells and tissue-resident memory T cells, and these tertiary vaccine-specific CD8(+) T cells are characterized by enhanced polyfunctional cytokine production. Moreover, fate mapping shows that a substantial fraction of the tertiary CD8(+) effector-memory T cells develop from re-migrated tissue-resident memory T cells. Thus, repeated booster vaccinations quantitatively and qualitatively improve the CD8(+) T cell response leading to protection against otherwise lethal SARS-CoV-2 infection. Show less
Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy in need of effective (immuno)therapeutic treatment strategies. For the optimal application and development of... Show moreBackground: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy in need of effective (immuno)therapeutic treatment strategies. For the optimal application and development of cancer immunotherapies, a comprehensive understanding of local and systemic immune profiles in patients with PDAC is required. Here, our goal was to decipher the interplay between local and systemic immune profiles in treatment-naive patients with PDAC. Methods: The immune composition of PDAC, matched non-malignant pancreatic tissue, regional lymph nodes, spleen, portal vein blood, and peripheral blood samples (collected before and after surgery) from 11 patients with PDAC was assessed by measuring 41 immune cell markers by single-cell mass cytometry. Furthermore, the activation potential of tumor-infiltrating lymphocytes as determined by their ability to produce cytokines was investigated by flow cytometry. In addition, the spatial localization of tumor-infiltrating innate lymphocytes in the tumor microenvironment was confirmed by multispectral immunofluorescence. Results: We found that CD103(+)CD8(+) T cells with cytotoxic potential are infrequent in the PDAC immune microenvironment and lack the expression of activation markers and checkpoint blockade molecule programmed cell death protein-1 (PD-1). In contrast, PDAC tissues showed a remarkable increased relative frequency of B cells and regulatory T cells as compared with non-malignant pancreatic tissues. Besides, a previously unappreciated innate lymphocyte cell (ILC) population (CD127(-)CD103(+)CD39(+)CD45RO(+) ILC1-like) was discovered in PDAC tissues. Strikingly, the increased relative frequency of B cells and regulatory T cells in pancreatic cancer samples was reflected in matched portal vein blood samples but not in peripheral blood, suggesting a regional enrichment of immune cells that infiltrate the PDAC microenvironment. After surgery, decreased frequencies of myeloid dendritic cells were found in peripheral blood. Conclusions: Our work demonstrates an immunosuppressive landscape in PDAC tissues, generally deprived of cytotoxic T cells and enriched in regulatory T cells and B cells. The antitumor potential of ILC1-like cells in PDAC may be exploited in a therapeutic setting. Importantly, immune profiles detected in blood isolated from the portal vein reflected the immune cell composition of the PDAC microenvironment, suggesting that this anatomical location could be a source of tumor-associated immune cell subsets. Show less
Unen, V. van; Ouboter, L.F.; Li, N.; Schreurs, M.; Abdelaal, T.; Kooy-Winkelaar, Y.; ... ; Koning, F. 2022
Chronic intestinal inflammation underlies inflammatory bowel disease (IBD). Previous studies indicated alterations in the cellular immune system; however, it has been challenging to interrogate the... Show moreChronic intestinal inflammation underlies inflammatory bowel disease (IBD). Previous studies indicated alterations in the cellular immune system; however, it has been challenging to interrogate the role of all immune cell subsets simultaneously. Therefore, we aimed to identify immune cell types associated with inflammation in IBD using high-dimensional mass cytometry. We analyzed 188 intestinal biopsies and paired blood samples of newly-diagnosed, treatment-naive patients (n=42) and controls (n=26) in two independent cohorts. We applied mass cytometry (36-antibody panel) to resolve single cells and analyzed the data with unbiased Hierarchical-SNE. In addition, imaging-mass cytometry (IMC) was performed to reveal the spatial distribution of the immune subsets in the tissue. We identified 44 distinct immune subsets. Correlation network analysis identified a network of inflammation-associated subsets, including HLA-DR(+)CD38(+) EM CD4(+) T cells, T regulatory-like cells, PD1(+) EM CD8(+) T cells, neutrophils, CD27(+) TCR gamma delta cells and NK cells. All disease-associated subsets were validated in a second cohort. This network was abundant in a subset of patients, independent of IBD subtype, severity or intestinal location. Putative disease-associated CD4(+) T cells were detectable in blood. Finally, imaging-mass cytometry revealed the spatial colocalization of neutrophils, memory CD4(+) T cells and myeloid cells in the inflamed intestine. Our study indicates that a cellular network of both innate and adaptive immune cells colocalizes in inflamed biopsies from a subset of patients. These results contribute to dissecting disease heterogeneity and may guide the development of targeted therapeutics in IBD. Show less
Gracht, E.T.I. van der; Beyrend, G.; Abdelaal, T.; Pardieck, I.N.; Wesselink, T.H.; Haften, F.J. van; ... ; Arens, R. 2021
Factors that govern the complex formation of memory T cells are not completely understood. A better understanding of the development of memory T cell heterogeneity is however required to enhance... Show moreFactors that govern the complex formation of memory T cells are not completely understood. A better understanding of the development of memory T cell heterogeneity is however required to enhance vaccination and immunotherapy approaches. Here we examined the impact of pathogen- and tissue-specific cues on memory CD8(+) T cell heterogeneity using high-dimensional single-cell mass cytometry and a tailored bioinformatics pipeline. We identified distinct populations of pathogen-specific CD8(+) T cells that uniquely connected to a specific pathogen or associated tomultiple types of acute and persistent infections. In addition, the tissue environment shaped the memory CD8(+) T cell heterogeneity, albeit to a lesser extent than infection. The programming of memory CD8(+) T cell differentiation during acute infection is eventually superseded by persistent infection. Thus, the plethora of distinct memory CD8(+) T cell subsets that arise upon infection is dominantly sculpted by the pathogen-specific cues and further shaped by the tissue environment. Show less
Abdelaal, T.; Raadt, P. de; Lelieveldt, B.P.F.; Reinders, M.J.T.; Mahfouz, A. 2020
Motivation: Single cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further... Show moreMotivation: Single cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets.Results: We developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable time frames. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST. Show less
Single-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression... Show moreSingle-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but some methods can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently using in situ hybridization data from the Allen Mouse Brain Atlas. Show less
Vries, N.L. de; Unen, V. van; Ijsselsteijn, M.E.; Abdelaal, T.; Breggen, R. van der; Sarasqueta, A.F.; ... ; Miranda, N.F.C.C. de 2020
Objective A comprehensive understanding of anticancer immune responses is paramount for the optimal application and development of cancer immunotherapies. We unravelled local and systemic immune... Show moreObjective A comprehensive understanding of anticancer immune responses is paramount for the optimal application and development of cancer immunotherapies. We unravelled local and systemic immune profiles in patients with colorectal cancer (CRC) by high-dimensional analysis to provide an unbiased characterisation of the immune contexture of CRC.Design Thirty-six immune cell markers were simultaneously assessed at the single-cell level by mass cytometry in 35 CRC tissues, 26 tumour-associated lymph nodes, 17 colorectal healthy mucosa and 19 peripheral blood samples from 31 patients with CRC. Additionally, functional, transcriptional and spatial analyses of tumour-infiltrating lymphocytes were performed by flow cytometry, single-cell RNA-sequencing and multispectral immunofluorescence.Results We discovered that a previously unappreciated innate lymphocyte population (Lin(-)CD7+(C)D127(-)CD56(+)CD45RO(+)) was enriched in CRC tissues and displayed cytotoxic activity. This subset demonstrated a tissue-resident (CD103(+)CD69(+)) phenotype and was most abundant in immunogenic mismatch repair (MMR)-deficient CRCs. Their presence in tumours was correlated with the infiltration of tumour-resident cytotoxic, helper and gamma delta T cells with highly similar activated (HLA-DR(+)CD38(+)PD(-)1(+)) phenotypes. Remarkably, activated gamma delta T cells were almost exclusively found in MMR-deficient cancers. Non-activated counterparts of tumour-resident cytotoxic and gamma delta T cells were present in CRC and healthy mucosa tissues, but not in lymph nodes, with the exception of tumour-positive lymph nodes.Conclusion This work provides a blueprint for the understanding of the heterogeneous and intricate immune landscape of CRC, including the identification of previously unappreciated immune cell subsets. The concomitant presence of tumour-resident innate and adaptive immune cell populations suggests a multitargeted exploitation of their antitumour properties in a therapeutic setting. Show less
Abdelaal, T.; Hollt, T.; Unen, V. van; Lelieveldt, B.P.F.; Koning, F.; Reinders, M.J.T.; Mahfouz, A. 2019
