Multi-omic analysis of human kidney tissue identified medulla-specific gene expression patterns.

The kidney medulla is a specialized region with important homeostatic functions. It has been implicated in genetic and developmental disorders along with ischemic and drug-induced injuries. Despite its role in kidney function and disease, the medulla's baseline gene expression and epigenomic signatures have not been well described in the adult human kidney. Here we generated and analyzed gene expression (RNA-seq), chromatin accessibility (ATAC-seq), chromatin conformation (Hi-C) and spatial transcriptomic data from the adult human kidney cortex and medulla. Tissue samples were obtained from macroscopically dissected cortex and medulla of tumor-adjacent normal material in nephrectomy specimens from five male patients. We used these carefully annotated specimens to reassign incorrectly labeled samples in the larger public Genotype-Tissue Expression (GTEx) Project, and to extract meaningful medullary gene expression signatures. Using integrated analysis of gene expression, chromatin accessibility and conformation profiles, we found insights into medulla development and function and then validated this by spatial transcriptomics and immunohistochemistry. Thus, our datasets provide a valuable resource for functional annotation of variants from genome-wide association studies and are freely accessible through an epigenome browser portal.

Satisfaction with radiotherapy care among cancer patients treated in Germany-secondary analysis of a large multicenter study.

PURPOSE: Patient satisfaction with healthcare has been linked to clinical outcomes and regulatory agencies demand its regular assessment. Therefore, we aimed to investigate patient satisfaction with radiotherapy care and its determinants. METHODS: This is a secondary analysis of a multicenter prospective cross-sectional study. Eligible cancer patients anonymously completed questionnaires at the end of a course of radiotherapy. The outcome variable was overall patient satisfaction with radiotherapy care measured with a 10-point Likert scaled single-item. Given patient satisfaction was defined for patients scoring ≥ 8 points. Determinants of given patient satisfaction were assessed by univariable and multivariable analyses. A p-value < 0.05 was considered statistically significant. RESULTS: Out of 2341 eligible patients, 1075 participated (participation rate 46%). Data on patient satisfaction was provided by 1054 patients. There was a right-skewed distribution towards more patient satisfaction (mean = 8.8; SD = 1.68). Given patient satisfaction was reported by 85% (899/1054) of the patients. Univariable analyses revealed significant associations of lower patient satisfaction with tumor entity (rectal cancer), concomitant chemotherapy, inpatient care, treating center, lower income, higher costs, and lower quality of life. Rectal cancer as tumor entity, treating center, and higher quality of life remained significant determinants of patient satisfaction in a multivariable logistic regression. CONCLUSION: Overall patient satisfaction with radiotherapy care was high across 11 centers in Germany. Determinants of patient satisfaction were tumor entity, treating center, and quality of life. Although these data are exploratory, they may inform other centers and future efforts to maintain high levels of patient satisfaction with radiotherapy care.

DOT1L activity affects neural stem cell division mode and reduces differentiation and ASNS expression.

Cortical neurogenesis depends on the balance between self-renewal and differentiation of apical progenitors (APs). Here, we study the epigenetic control of AP's division mode by focusing on the enzymatic activity of the histone methyltransferase DOT1L. Combining lineage tracing with single-cell RNA sequencing of clonally related cells, we show at the cellular level that DOT1L inhibition increases neurogenesis driven by a shift of APs from asymmetric self-renewing to symmetric neurogenic consumptive divisions. At the molecular level, DOT1L activity prevents AP differentiation by promoting transcription of metabolic genes. Mechanistically, DOT1L inhibition reduces activity of an EZH2/PRC2 pathway, converging on increased expression of asparagine synthetase (ASNS), a microcephaly associated gene. Overexpression of ASNS in APs phenocopies DOT1L inhibition, and also increases neuronal differentiation of APs. Our data suggest that DOT1L activity/PRC2 crosstalk controls AP lineage progression by regulating asparagine metabolism.

Psychosocial distress in cancer patients undergoing radiotherapy: a prospective national cohort of 1042 patients in Germany.

PURPOSE: Psychosocial distress is common among cancer patients in general, but those undergoing radiotherapy may face specific challenges. Therefore, we investigated the prevalence and risk factors for distress in a large national cohort. METHODS: We performed a secondary analysis of a multicenter prospective cross-sectional study which surveyed cancer patients at the end of a course of radiotherapy using a patient-reported questionnaire. Distress was measured with the distress thermometer (DT), using a cut-off of ≥ 5 points for clinically significant distress. Univariate analyses and multivariate multiple regression were used to assess associations of distress with patient characteristics. A two-sided p-value < 0.05 was considered statistically significant. RESULTS: Out of 2341 potentially eligible patients, 1075 participated in the study, of which 1042 completed the DT. The median age was 65 years and 49% (511/1042) of patients were female. The mean DT score was 5.2 (SD = 2.6). Clinically significant distress was reported by 63% (766/1042) of patients. Of the patient characteristics that were significantly associated with distress in the univariate analysis, a lower level of education, a higher degree of income loss, lower global quality of life, and a longer duration of radiotherapy in days remained significantly associated with higher distress in the multivariate analysis. Yet effect sizes of these associations were small. CONCLUSION: Nearly two in three cancer patients undergoing radiotherapy reported clinically significant distress in a large multicenter cohort. While screening and interventions to reduce distress should be maintained and promoted, the identified risk factors may help to raise awareness in clinical practice. TRIAL REGISTRY IDENTIFIER: DRKS: German Clinical Trial Registry identifier: DRKS00028784.

Characterization of aortic aging using 3D multi-parametric MRI-long-term follow-up in a population study.

We comprehensively studied morphological and functional aortic aging in a population study using modern three-dimensional MR imaging to allow future comparison in patients with diseases of the aortic valve or aorta. We followed 80 of 126 subjects of a population study (20 to 80 years of age at baseline) using the identical methodology 6.0 ± 0.5 years later. All underwent 3 T MRI of the thoracic aorta including 3D T1 weighted MRI (spatial resolution 1 mm3) for measuring aortic diameter and plaque thickness and 4D flow MRI (spatial/temporal resolution = 2 mm3/20 ms) for calculating global and regional aortic pulse wave velocity (PWV) and helicity of aortic blood flow. Mean diameter of the ascending aorta (AAo) decreased and plaque thickness increased significantly in the aortic arch (AA) and descending aorta (DAo) in females. PWV of the thoracic aorta increased (6.4 ± 1.5 to 7.0 ± 1.7 m/s and 6.8 ± 1.5 to 7.3 ± 1.8 m/s in females and males, respectively) over time. Local normalized helicity volumes (LNHV) decreased significantly in the AAo and AA (0.33 to 0.31 and 0.34 to 0.32 in females and 0.34 to 0.32 and 0.32 to 0.28 in males). By contrast, helicity increased significantly in the DAo in both genders (0.28 to 0.29 and 0.29 to 0.30, respectively). 3D MRI was able to characterize changes in aortic diameter, plaque thickness, PWV and helicity during six years in our population. Aortic aging determined by 3D multi-parametric MRI is now available for future comparisons in patients with diseases of the aortic valve or aorta.

Financial toxicity in cancer patients undergoing radiotherapy in a universal health care system - A prospective multicenter study of 1075 patients.

PURPOSE: To establish and confirm prevalence as well as risk factors of financial toxicity in a large national cohort of cancer patients undergoing radiotherapy in a universal health care system. METHODS: We conducted a prospective cross-sectional study offering a patient-reported questionnaire to all eligible cancer patients treated with radiotherapy in 11 centers in Germany during 60 consecutive days. The four-point subjective financial distress question of the EORTC QLQ-C30 was used as a surrogate for financial toxicity. Confirmatory hypothesis testing evaluated the primary study outcomes: overall prevalence of financial toxicity and its association with predefined risk factors. P-values < 0.05 were considered statistically significant. RESULTS: Of 2341 eligible patients, 1075 (46%) participated. The prevalence of subjective financial distress (=any grade higher than not present) was 41% (438/1075) exceeding the hypothesized range of 26.04-36.31%. Subjective financial distress was felt "A little" by 26% (280/1075), "Quite a bit" by 11% (113/1075) and "Very much" by 4% (45/1075) of the patients. Lower household income, lower global health status/ quality of life, higher direct costs and higher loss of income significantly predicted higher subjective financial distress per ordinal regression and confirmed these risk factors. Higher psychosocial distress and lower patient satisfaction were significantly associated with higher subjective financial distress in an exploratory ordinal regression model. CONCLUSION: The overall prevalence of financial toxicity was higher than anticipated, although reported at low or moderate degrees by most affected patients. As we confirmed risk factors associated with financial toxicity, patients at risk should be addressed early for potential support.

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data.

Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns via a joint embedding. Yet, this also raises the question of sample size requirements for identifying such patterns from single-cell multi-omics data. Here, we empirically examine the quality of DGM-based integrations for varying sample sizes. We first review the existing literature and give a short overview of deep learning methods for multi-omics integration. Next, we consider eight popular tools in more detail and examine their robustness to different cell numbers, covering two of the most common multi-omics types currently favored. Specifically, we use data featuring simultaneous gene expression measurements at the RNA level and protein abundance measurements for cell surface proteins (CITE-seq), as well as data where chromatin accessibility and RNA expression are measured in thousands of cells (10x Multiome). We examine the ability of the methods to learn joint embeddings based on biological and technical metrics. Finally, we provide recommendations for the design of multi-omics experiments and discuss potential future developments.

Disentangling nigral and putaminal contribution to motor impairment and levodopa response in Parkinson's disease.

The extent to which the degeneration of the substantia nigra (SN) and putamen each contribute to motor impairment in Parkinson's disease (PD) is unclear, as they are usually investigated using different imaging modalities. To examine the pathophysiological significance of the SN and putamen in both motor impairment and the levodopa response in PD using diffusion microstructure imaging (DMI). In this monocentric retrospective cross-sectional study, DMI parameters from 108 patients with PD and 35 healthy controls (HC) were analyzed using a voxel- and region-based approach. Linear models were applied to investigate the association between individual DMI parameters and Movement Disorder Society Unified Parkinson's Disease Rating Scale-Part 3 performance in ON- and OFF-states, as well as the levodopa response, controlling for age and sex. Voxel- and region-based group comparisons of DMI parameters between PD and HC revealed significant differences in the SN and putamen. In PD, a poorer MDS-UPDRS-III performance in the ON-state was associated with increased free fluid in the SN (b-weight = 65.79, p = 0.004) and putamen (b-weight = 86.00, p = 0.006), and contrariwise with the demise of cells in both structures. The levodopa response was inversely associated with free fluid both in the SN (b-weight = -83.61, p = 0.009) and putamen (b-weight = -176.56, p < 0.001). Interestingly, when the two structures were assessed together, the integrity of the putamen, but not the SN, served as a predictor for the levodopa response (b-weight = -158.03, p < 0.001). Structural alterations in the SN and putamen can be measured by diffusion microstructure imaging in PD. They are associated with poorer motor performance in the ON-state, as well as a reduced response to levodopa. While both nigral and putaminal integrity are required for good performance in the ON-state, it is putaminal integrity alone that determines the levodopa response. Therefore, the structural integrity of the putamen is crucial for the improvement of motor symptoms to dopaminergic medication, and might therefore serve as a promising biomarker for motor staging.

Interpretable generative deep learning: an illustration with single cell gene expression data.

Deep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an introduction as well as an overview of such techniques, specifically illustrating their use with single-cell gene expression data. For example, the low dimensional latent representations offered by various approaches, such as variational auto-encoders, are useful to get a better understanding of the relations between observed gene expressions and experimental factors or phenotypes. Furthermore, by providing a generative model for the latent and observed variables, deep generative models can generate synthetic observations, which allow us to assess the uncertainty in the learned representations. While deep generative models are useful to learn the structure of high-dimensional omics data by efficiently capturing non-linear dependencies between genes, they are sometimes difficult to interpret due to their neural network building blocks. More precisely, to understand the relationship between learned latent variables and observed variables, e.g., gene transcript abundances and external phenotypes, is difficult. Therefore, we also illustrate current approaches that allow us to infer the relationship between learned latent variables and observed variables as well as external phenotypes. Thereby, we render deep learning approaches more interpretable. In an application with single-cell gene expression data, we demonstrate the utility of the discussed methods.

Carotid Geometry and Wall Shear Stress Independently Predict Increased Wall Thickness-A Longitudinal 3D MRI Study in High-Risk Patients.

Introduction: Carotid geometry and wall shear stress (WSS) have been proposed as independent risk factors for the progression of carotid atherosclerosis, but this has not yet been demonstrated in larger longitudinal studies. Therefore, we investigated the impact of these biomarkers on carotid wall thickness in patients with high cardiovascular risk. Methods: Ninety-seven consecutive patients with hypertension, at least one additional cardiovascular risk factor and internal carotid artery (ICA) plaques (wall thickness ≥ 1.5 mm and degree of stenosis ≤ 50%) were prospectively included. They underwent high-resolution 3D multi-contrast and 4D flow MRI at 3 Tesla both at baseline and follow-up. Geometry (ICA/common carotid artery (CCA)-diameter ratio, bifurcation angle, tortuosity and wall thickness) and hemodynamics [WSS, oscillatory shear index (OSI)] of both carotid bifurcations were measured at baseline. Their predictive value for changes of wall thickness 12 months later was calculated using linear regression analysis for the entire study cohort (group 1, 97 patients) and after excluding patients with ICA stenosis ≥10% to rule out relevant inward remodeling (group 2, 61 patients). Results: In group 1, only tortuosity at baseline was independently associated with carotid wall thickness at follow-up (regression coefficient = -0.52, p < 0.001). However, after excluding patients with ICA stenosis ≥10% in group 2, both ICA/CCA-ratio (0.49, p < 0.001), bifurcation angle (0.04, p = 0.001), tortuosity (-0.30, p = 0.040), and WSS (-0.03, p = 0.010) at baseline were independently associated with changes of carotid wall thickness at follow-up. Conclusions: A large ICA bulb and bifurcation angle and low WSS seem to be independent risk factors for the progression of carotid atherosclerosis in the absence of ICA stenosis. By contrast, a high carotid tortuosity seems to be protective both in patients without and with ICA stenosis. These biomarkers may be helpful for the identification of patients who are at particular risk of wall thickness progression and who may benefit from intensified monitoring and treatment.

DAT SPECT Predicts Survival in Patients Assessed for Differential Diagnosis of Dementia.

BACKGROUND: Dopamine transporter (DAT) SPECT is an established diagnostic procedure in dementia diagnostics, yet its prognostic value is currently unknown. OBJECTIVE: We evaluated the prognostic value of DAT SPECT in patients assessed for differential diagnosis of dementia. METHODS: We included all patients who had received DAT SPECT for differential diagnosis of dementia from 10/2008 to 06/2016 at our site and whose survival status could be obtained in 09/2019. Clinical SPECT reports, categorizing scans into positive or negative for nigrostriatal degeneration (NSD), were tested for their prognostic value (Cox regressions, adjusted for age and sex). In addition, an automated region-of-interest analysis (striatum, occipital cortex as reference) was performed. RESULTS: Median follow-up of 97 included patients was 6.6 years. Patients with NSD had a significantly higher mortality risk than those without NSD (HR = 3.6 [2.0-6.7], p < 0.001). Results were confirmed by region-of-interest analysis: higher mortality risk was associated with lower striatal DAT binding (HR = 1.8 per standard deviation loss). CONCLUSION: Beyond its established utility in dementia diagnostics, DAT SPECT also conveys important prognostic information.

Synthetic single cell RNA sequencing data from small pilot studies using deep generative models.

Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale experiment by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBMs). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the [Formula: see text] variant resulted in high variability, most likely due to amplifying artifacts of small datasets. All approaches showed mixed results for cell types with different abundance by overrepresenting highly abundant cell types and missing less abundant cell types. With increasing pilot dataset sizes, the proportions of the cells in each cluster became more similar to that of ground-truth data. We also showed that all approaches learn the univariate distribution of most genes, but problems occurred with bimodality. Across all analyses, in comparing 10[Formula: see text] Genomics and Smart-seq2 technologies, we could show that for 10[Formula: see text] datasets, which have higher sparsity, it is more challenging to make inference from small to larger datasets. Overall, the results show that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.

[123I]FP-CIT SPECT in Clinically Uncertain Parkinsonism Predicts Survival: A Data-Driven Analysis.

BACKGROUND: Dopamine transporter SPECT is an established method to investigate nigrostriatal integrity in case of clinically uncertain parkinsonism. OBJECTIVE: The present study explores whether a data-driven analysis of [123I]FP-CIT SPECT is able to stratify patients according to mortality after SPECT. METHODS: Patients from our clinical registry were included if they had received [123I]FP-CIT SPECT between 10/2008 and 06/2016 for diagnosis of parkinsonism and if their vital status could be determined in 07/2017. Specific binding ratios (SBR) of the whole striatum, its asymmetry (asymmetry index, AI; absolute value), and the rostrocaudal gradient of striatal binding (C/pP: caudate SBR divided by posterior putamen SBR) were used as input for hierarchical clustering of patients. We tested differences in survival between these groups (adjusted for age) with a Cox proportional hazards model. RESULTS: Data from 518 patients were analyzed. Median follow-up duration was 3.3 years [95% C.I. 3.1 to 3.7]. Three subgroups identified by hierarchical clustering were characterized by relatively low striatal SBR, high AI, and low C/pP (group 1), low striatal SBR, high AI, and high C/pP (group 2), and high striatal SBR, low AI, and low C/pP (group 3). Mortality was significantly higher in group 1 compared to each of the other two groups (p = 0.029 and p = 0.003, respectively). CONCLUSION: Data-driven analysis of [123I]FP-CIT SPECT identified a subgroup of patients with significantly increased mortality during follow-up. This suggests that [123I]-FP-CIT SPECT might not only serve as a diagnostic tool to verify nigrostriatal degeneration but also provide valuable prognostic information.

Modeling Activity Tracker Data Using Deep Boltzmann Machines.

Commercial activity trackers are set to become an essential tool in health research, due to increasing availability in the general population. The corresponding vast amounts of mostly unlabeled data pose a challenge to statistical modeling approaches. To investigate the feasibility of deep learning approaches for unsupervised learning with such data, we examine weekly usage patterns of Fitbit activity trackers with deep Boltzmann machines (DBMs). This method is particularly suitable for modeling complex joint distributions via latent variables. We also chose this specific procedure because it is a generative approach, i.e., artificial samples can be generated to explore the learned structure. We describe how the data can be preprocessed to be compatible with binary DBMs. The results reveal two distinct usage patterns in which one group frequently uses trackers on Mondays and Tuesdays, whereas the other uses trackers during the entire week. This exemplary result shows that DBMs are feasible and can be useful for modeling activity tracker data.