The hypothalamic paraventricular nucleus (PVN) is a highly complex brain region that is crucial for homeostatic regulation through neuroendocrine signaling, outflow of the autonomic nervous system,... Show moreThe hypothalamic paraventricular nucleus (PVN) is a highly complex brain region that is crucial for homeostatic regulation through neuroendocrine signaling, outflow of the autonomic nervous system, and projections to other brain areas. In the past years, single-cell datasets of the hypothalamus have contributed immensely to the current understanding of the diverse hypothalamic cellular composition. While the PVN has been adequately classified functionally, its molecular classification is currently still insufficient. To address this, we created a detailed atlas of PVN transcriptomic cell types by integrating various PVN single-cell datasets into a recently published hypothalamus single-cell transcriptome atlas. Furthermore, we functionally profiled transcriptomic cell types, based on relevant literature, existing retrograde tracing data, and existing single-cell data of a PVN-projection target region. Finally, we validated our findings with immunofluorescent stainings. In our PVN atlas dataset, we identify the well-known different neuropeptide types, each composed of multiple novel subtypes. We identify Avp-Tac1, Avp-Th, Oxt-Foxp1, Crh-Nr3c1, and Trh-Nfib as the most important neuroendocrine subtypes based on markers described in literature. To characterize the preautonomic functional population, we integrated a single-cell retrograde tracing study of spinally projecting preautonomic neurons into our PVN atlas. We identify these (presympathetic) neurons to cocluster with the Adarb2(+) clusters in our dataset. Further, we identify the expression of receptors for Crh, Oxt, Penk, Sst, and Trh in the dorsal motor nucleus of the vagus, a key region that the pre-parasympathetic PVN neurons project to. Finally, we identify Trh-Ucn3 and Brs3-Adarb2 as some centrally projecting populations. In conclusion, our study presents a detailed overview of the transcriptomic cell types of the murine PVN and provides a first attempt to resolve functionality for the identified populations. Show less
Eltager, M.; Abdelaal, T.; Charrout, M.; Mahfouz, A.; Reinders, M.J.T.; Makrodimitris, S. 2023
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
Michielsen, L.; Lotfollahi, M.; Strobl, D.; Sikkema, L.; Reinders, M.J.T.; Theis, F.J.; Mahfouz, A. 2023
Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such large-scale... Show moreSingle-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such large-scale atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups specializing in different cell type compartments, often using distinct terminology. Understanding how these distinct sets of annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge in the field. Whereas recent computational techniques, referred to as 'reference mapping' methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e. queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present 'treeArches', a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases for treeArches, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases. Show less
Novella-Rausell, C.; Grudniewska, M.; Peters, D.J.M.; Mahfouz, A. 2023
The kidney's cellular diversity is on par with its physiological intricacy; yet identifying cell populations and their markers remains challenging. Here, we created a comprehensive atlas of the... Show moreThe kidney's cellular diversity is on par with its physiological intricacy; yet identifying cell populations and their markers remains challenging. Here, we created a comprehensive atlas of the healthy adult mouse kidney (MKA: Mouse Kidney Atlas) by integrating 140.000 cells and nuclei from 59 publicly available single-cell and single-nuclei RNA-sequencing datasets from eight independent studies. To harmonize annotations across datasets, we built a hierarchical model of the cell populations. Our model allows the incorporation of novel cell populations and the refinement of known profiles as more datasets become available. Using MKA and the learned model of cellular hierarchies, we predicted previously missing cell annotations from several studies. The MKA allowed us to identify reproducible markers across studies for poorly understood cell types and transitional states, which we verified using existing data from micro-dissected samples and spatial transcriptomics. Show less
With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes... Show moreWith the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes, we demonstrate here that downstream analyses on binary-based gene expression give similar results as count-based analyses. Moreover, a binary representation scales up to similar to 50-fold more cells that can be analyzed using the same computational resources. We also highlight the possibilities provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity. 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
The characteristic endogenous circadian rhythm of plasma glucocorticoid concentrations is made up from an underlying ultradian pulsatile secretory pattern. Recent evidence has indicated that this... Show moreThe characteristic endogenous circadian rhythm of plasma glucocorticoid concentrations is made up from an underlying ultradian pulsatile secretory pattern. Recent evidence has indicated that this ultradian cortisol pul-satility is crucial for normal emotional response in man. In this study, we investigate the anatomical tran-scriptional and cell type signature of brain regions sensitive to a loss of ultradian rhythmicity in the context of emotional processing. We combine human cell type and transcriptomic atlas data of high spatial resolution with functional magnetic resonance imaging (fMRI) data. We show that the loss of cortisol ultradian rhythm alters emotional processing response in cortical brain areas that are characterized by transcriptional and cellular profiles of GABAergic function. We find that two previously identified key components of rapid non-genomic GC signaling - the ANXA1 gene and retrograde endocannabinoid signaling - show most significant differential expression (q = 3.99e- 10) and enrichment (fold enrichment = 5.56, q = 9.09e-4). Our results further indicate that specific cell types, including a specific NPY-expressing GABAergic neuronal cell type, and specific G protein signaling cascades underly the cerebral effects of a loss of ultradian cortisol rhythm. Our results provide a biological mechanistic underpinning of our fMRI findings, indicating specific cell types and cascades as a target for manipulation in future experimental studies. Show less
Wang, G.Q.; Heijs, B.; Kostidis, S.; Rietjens, R.G.J.; Koning, M.; Yuan, L.S.; ... ; Rabelink, T.J. 2022
Accumulating evidence demonstrates important roles for metabolism in cell fate determination. However, it is a challenge to assess metabolism at a spatial resolution that acknowledges both... Show moreAccumulating evidence demonstrates important roles for metabolism in cell fate determination. However, it is a challenge to assess metabolism at a spatial resolution that acknowledges both heterogeneity and cellular dynamics in its tissue microenvironment. Using a multi-omics platform to study cell-type-specific dynamics in metabolism in complex tissues, we describe the metabolic trajectories during nephrogenesis in the devel-oping human kidney. Exploiting in situ analysis of isotopic labeling, a shift from glycolysis toward fatty acid b-oxidation was observed during the differentiation from the renal vesicle toward the S-shaped body and the proximal tubules. In addition, we show that hiPSC-derived kidney organoids are characterized by a metabolic immature phenotype that fails to use mitochondrial long-chain fatty acids for energy metabolism. Further-more, supplementation of butyrate enhances tubular epithelial differentiation and maturation in cultured kid-ney organoids. Our findings highlight the relevance of understanding metabolic trajectories to efficiently guide stem cell differentiation. Show less
Understanding dynamic metabolic changes in complex biological samples often overlooks heterogeneity in cell composition. Wang et al. combine mass spectrometry imaging, isotope tracing, and... Show moreUnderstanding dynamic metabolic changes in complex biological samples often overlooks heterogeneity in cell composition. Wang et al. combine mass spectrometry imaging, isotope tracing, and multiplexed immunofluorescence microscopy to study metabolic dynamics in the kidney upon ischemia-reperfusion.A common drawback of metabolic analyses of complex biological samples is the inability to consider cell-to-cell heterogeneity in the context of an organ or tissue. To overcome this limitation, we present an advanced high-spatial-resolution metabolomics approach using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) combined with isotope tracing. This method allows mapping of cell-type-specific dynamic changes in central carbon metabolism in the context of a complex heterogeneous tissue architecture, such as the kidney. Combined with multiplexed immunofluorescence staining, this method can detect metabolic changes and nutrient partitioning in targeted cell types, as demonstrated in a bilateral renal ischemia-reperfusion injury (bIRI) experimental model. Our approach enables us to identify region-specific metabolic perturbations associated with the lesion and throughout recovery, including unexpected metabolic anomalies in cells with an apparently normal phenotype in the recovery phase. These findings may be relevant to an understanding of the homeostatic capacity of the kidney microenvironment. In sum, this method allows us to achieve resolution at the single-cell level in situ and hence to interpret cell-type-specific metabolic dynamics in the context of structure and metabolism of neighboring cells. 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
The molecular mechanisms underlying caudal-to-rostral progression of Lewy body pathology in Parkinson’s disease remain poorly understood. Here, we identified transcriptomic signatures across brain... Show moreThe molecular mechanisms underlying caudal-to-rostral progression of Lewy body pathology in Parkinson’s disease remain poorly understood. Here, we identified transcriptomic signatures across brain regions involved in Braak Lewy body stages in non-neurological adults from the Allen Human Brain Atlas. Among the genes that are indicative of regional vulnerability, we found known genetic risk factors for Parkinson’s disease: SCARB2, ELOVL7, SH3GL2, SNCA, BAP1, and ZNF184. Results were confirmed in two datasets of non-neurological subjects, while in two datasets of Parkinson’s disease patients we found altered expression patterns. Co-expression analysis across vulnerable regions identified a module enriched for genes associated with dopamine synthesis and microglia, and another module related to the immune system, blood-oxygen transport, and endothelial cells. Both were highly expressed in regions involved in the preclinical stages of the disease. Finally, alterations in genes underlying these region-specific functions may contribute to the selective regional vulnerability in Parkinson’s disease brains. Show less
CD4(+)CD25(+)FOXP3(+) regulatory T (Treg) cells control immunological tolerance. Treg cells are generated in the thymus (tTreg) or in the periphery. Their superior lineage fidelity makes tTregs the... Show moreCD4(+)CD25(+)FOXP3(+) regulatory T (Treg) cells control immunological tolerance. Treg cells are generated in the thymus (tTreg) or in the periphery. Their superior lineage fidelity makes tTregs the preferred cell type for adoptive cell therapy (ACT). How human tTreg cells develop is incompletely understood. By combining single-cell transcriptomics and flow cytometry, we in this study delineated three major Treg developmental stages in the human thymus. At the first stage, which we propose to name pre-Treg I, cells still express lineage-inappropriate genes and exhibit signs of TCR signaling, presumably reflecting recognition of self-antigen. The subsequent pre-Treg II stage is marked by the sharp appearance of transcription factor FOXO1 and features induction of KLF2 and CCR7, in apparent preparation for thymic exit. The pre-Treg II stage can further be refined based on the sequential acquisition of surface markers CD31 and GPA33. The expression of CD45RA, finally, completes the phenotype also found on mature recent thymic emigrant Treg cells. Remarkably, the thymus contains a substantial fraction of recirculating mature effector Treg cells, distinguishable by expression of inflammatory chemokine receptors and absence of CCR7. The developmental origin of these cells is unclear and warrants caution when using thymic tissue as a source of stable cells for ACT. We show that cells in the major developmental stages can be distinguished using the surface markers CD1a, CD27, CCR7, and CD39, allowing for their viable isolation. These insights help identify fully mature tTreg cells for ACT and can serve as a basis for further mechanistic studies into tTreg development. Show less
Several studies have analyzed gene expression profiles in the substantia nigra to better understand the pathological mechanisms causing Parkinson's disease (PD). However, the concordance between... Show moreSeveral studies have analyzed gene expression profiles in the substantia nigra to better understand the pathological mechanisms causing Parkinson's disease (PD). However, the concordance between the identified gene signatures in these individual studies was generally low. This might have been caused by a change in cell type composition as loss of dopaminergic neurons in the substantia nigra pars compacta is a hallmark of PD. Through an extensive meta-analysis of nine previously published microarray studies, we demonstrated that a big proportion of the detected differentially expressed genes was indeed caused by cyto-architectural alterations due to the heterogeneity in the neurodegenerative stage and/or technical artefacts. After correcting for cell composition, we identified a common signature that deregulated the previously unreported ammonium transport, as well as known biological processes such as bioenergetic pathways, response to proteotoxic stress, and immune response. By integrating with protein interaction data, we shortlisted a set of key genes, such as LRRK2, PINK1, PRKN, and FBXO7, known to be related to PD, others with compelling evidence for their role in neurodegeneration, such as GSK3 beta, WWOX, and VPC, and novel potential players in the PD pathogenesis. Together, these data show the importance of accounting for cyto-architecture in these analyses and highlight the contribution of multiple cell types and novel processes to PD pathology, providing potential new targets for drug development. Show less
Dieckman, T.; Schreurs, M.; Mahfouz, A.; Kooy-Winkelaar, Y.; Neefjes-Borst, A.; Bouma, G.; Koning, F. 2022
BACKGROUND & AIMS: Refractory celiac disease type II (RCDII) is a rare indolent lymphoma in the small intestine characterized by a clonally expanded intraepithelial intracellular CD3 thorn... Show moreBACKGROUND & AIMS: Refractory celiac disease type II (RCDII) is a rare indolent lymphoma in the small intestine characterized by a clonally expanded intraepithelial intracellular CD3 thorn surfa- ceCD3-CD7 thorn CD56- aberrantcell population. However, RCDII pathogenesis is ill-defined. Here, we aimed at single-cell charac-terization of the innate and adaptive immune system in RCDII. METHODS: Paired small intestinal and blood samples from 12 RCDII patients and 6 healthy controls were assessed by single-cell mass cytometry with a 39-cell surface marker antibody panel, designed to capture heterogeneity of the innate and adaptive immune system. A second single-cell mass cytometry panel that included transcription factors and immune checkpoints was used for analysis of paired samples from 5 RCDII patients. Single-cell RNA sequencing analysis was performed on duodenal samples from 2 RCDII patients. Finally, we developed a 40-marker imaging mass cytometry antibody panel to evaluate cell-cell interactions in duodenal biopsy specimens of RCDII patients. RESULTS: We provide evidence for intertumoral and intra-tumoral cell heterogeneity within the duodenal and peripheral aberrant cell population present in RCDII. Phenotypic discrep-ancy was observed between peripheral and duodenal aberrant cells. In addition, we observed that part of the aberrant cell population proliferated and observed co-localization of aber-rant cells with CD163 thorn antigen-presenting cells (APCs) in situ. In addition, we observed phenotypic discrepancy between pe-ripheral and duodenal aberrant cells. CONCLUSIONS: Novel high-dimensional single-cell technolo-gies show substantial intertumoral and intratumoral hetero-geneity in the aberrant cell population in RCDII. This may underlie variability in refractory disease status between pa-tients and responsiveness to therapy, pointing to the need for personalized therapy in RCDII based on patient-specific im-mune profiles. Show less
Glucocorticoid stress hormones are powerful modulators of brain function and can affect mood and cognitive processes. The hippocampus is a prominent glucocorticoid target and expresses both the... Show moreGlucocorticoid stress hormones are powerful modulators of brain function and can affect mood and cognitive processes. The hippocampus is a prominent glucocorticoid target and expresses both the glucocorticoid receptor (GR: Nr3c1) and the mineralocorticoid receptor (MR: Nr3c2). These nuclear steroid receptors act as ligand-dependent transcription factors. Transcriptional effects of glucocorticoids have often been deduced from bulk mRNA measurements or spatially informed individual gene expression. However, only sparse data exists allowing insights on glucocorticoid-driven gene transcription at the cell type level. Here, we used publicly available single-cell RNA sequencing data to assess the cell-type specificity of GR and MR signaling in the adult mouse hippocampus. The data confirmed that Nr3c1 and Nr3c2 expression differs across neuronal and non-neuronal cell populations. We analyzed co-expression with sex hormones receptors, transcriptional coregulators, and receptors for neurotransmitters and neuropeptides. Our results provide insights in the cellular basis of previous bulk mRNA results and allow the formulation of more defined hypotheses on the effects of glucocorticoids on hippocampal function. Show less
Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We... Show moreSingle-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose to use binarized expression profiles to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that a binarized representation of single-cell expression data accurately represents biological variation and reveals the relative abundance of transcripts more robustly than counts. Show less
Groen, N.; Leenders, F.; Mahfouz, A.; Munoz-Garcia, A.; Muraro, M.J.; Graaf, N. de; ... ; Carlotti, F. 2021
The maintenance of pancreatic islet architecture is crucial for proper beta-cell function. We previously reported that disruption of human islet integrity could result in altered beta-cell identity... Show moreThe maintenance of pancreatic islet architecture is crucial for proper beta-cell function. We previously reported that disruption of human islet integrity could result in altered beta-cell identity. Here we combine beta-cell lineage tracing and single-cell transcriptomics to investigate the mechanisms underlying this process in primary human islet cells. Using drug-induced ER stress and cytoskeleton modification models, we demonstrate that altering the islet structure triggers an unfolding protein response that causes the downregulation of beta-cell maturity genes. Collectively, our findings illustrate the close relationship between endoplasmic reticulum homeostasis and beta-cell phenotype, and strengthen the concept of altered beta-cell identity as a mechanism underlying the loss of functional beta-cell mass. Show less
Groen, N.; Leenders, F.; Mahfouz, A.; Munoz-Garcia, A.; Muraro, M.J.; Graaf, N. de; ... ; Carlotti, F. 2021
The maintenance of pancreatic islet architecture is crucial for proper β-cell function. We previously reported that disruption of human islet integrity could result in altered β-cell identity. Here... Show moreThe maintenance of pancreatic islet architecture is crucial for proper β-cell function. We previously reported that disruption of human islet integrity could result in altered β-cell identity. Here we combine β-cell lineage tracing and single-cell transcriptomics to investigate the mechanisms underlying this process in primary human islet cells. Using drug-induced ER stress and cytoskeleton modification models, we demonstrate that altering the islet structure triggers an unfolding protein response that causes the downregulation of β-cell maturity genes. Collectively, our findings illustrate the close relationship between endoplasmic reticulum homeostasis and β-cell phenotype, and strengthen the concept of altered β-cell identity as a mechanism underlying the loss of functional β-cell mass. Show less
Keo, A.; Dzyubachyk, O.; Grond, J. van der; Hilten, J.J. van; Reinders, M.J.T.; Mahfouz, A. 2021
Cortical atrophy is a common manifestation in Parkinson's disease (PD), particularly in advanced stages of the disease. To elucidate the molecular underpinnings of cortical thickness changes in PD,... Show moreCortical atrophy is a common manifestation in Parkinson's disease (PD), particularly in advanced stages of the disease. To elucidate the molecular underpinnings of cortical thickness changes in PD, we performed an integrated analysis of brain-wide healthy transcriptomic data from the Allen Human Brain Atlas and patterns of cortical thickness based on T1-weighted anatomical MRI data of 149 PD patients and 369 controls. For this purpose, we used partial least squares regression to identify gene expression patterns correlated with cortical thickness changes. In addition, we identified gene expression patterns underlying the relationship between cortical thickness and clinical domains of PD. Our results show that genes whose expression in the healthy brain is associated with cortical thickness changes in PD are enriched in biological pathways related to sumoylation, regulation of mitotic cell cycle, mitochondrial translation, DNA damage responses, and ER-Golgi traffic. The associated pathways were highly related to each other and all belong to cellular maintenance mechanisms. The expression of genes within most pathways was negatively correlated with cortical thickness changes, showing higher expression in regions associated with decreased cortical thickness (atrophy). On the other hand, sumoylation pathways were positively correlated with cortical thickness changes, showing higher expression in regions with increased cortical thickness (hypertrophy). Our findings suggest that alterations in the balanced interplay of these mechanisms play a role in changes of cortical thickness in PD and possibly influence motor and cognitive functions. Show less
