Developmental scRNAseq Trajectories in Gene- and Cell-State Space—The Flatworm Example

Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental “vector fields” using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data

Structural magnetic resonance imaging in amyotrophic lateral sclerosis: cortical morphometry, diffusion properties and lesion detection as potential biomarkers for the state and progression of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting the motor system of the central nervous system. The aim of this thesis is to evaluate the potential of structural magnetic resonance imaging (MRI) biomarkers of the brain grey matter (GM) and white matter (WM) for the state and progression of ALS. The thesis has been conducted on behalf of a treatment program on a named patient basis at the University Hospital of Regensburg. 31 patients with written informed consent are compared to a control sample of 34 age-matched healthy participants. Routine MRI scans have been conducted approximately every 3 months and include T1-weighted imaging, diffusion weighted imaging (DWI), and fluid-attenuated inversion recovery (FLAIR) sequences at 1.5 Tesla. The subprojects of the thesis investigate precentral and postcentral cortical thinning (study 1), spread of alterations of fractional anisotropy (FA) across different WM types (study 2), and FLAIR lesion detection (study 3) in the same ALS cohort. Candidate MRI biomarkers are associated with neurophysiological and clinical biomarkers. Statistical analysis includes both cross-sectional and longitudinal analyses. Special focus is set on the individual patient. Cortical thinning is more pronounced in the precentral cortex than in the postcentral cortex. Combinatory biomarker use reveals evident differences in temporal dynamics of cortical thickness, clinical and neurophysiological biomarkers over time. Reduction of FA is consistently detected in the corticospinal tract (CST) and extra motor WM and most pronounced in the brainstem. Spread of FA alterations resembles both dying-forward and dying-back disease propagation and is not linked to patients’ clinical or demographic characteristics. WM lesions as detected by FLAIR hyperintensity are more frequent in ALS patients than in controls, most pronounced in the CST, and associated with an inferior survival. Together, the findings of this thesis suggest that MRI biomarkers may contribute to the diagnosis, prognosis and understanding of ALS disease and disease courses on an individual scope

REVISÃO DA VIDA TODA: NECESSÁRIA REFLEXÃO SOBRE REGRAS DE TRANSIÇÃO EM ÉPOCA DE REFORMAS

O presente artigo analisa a aplicação da regra de transição prevista na Lei 9.876/1999, que alterando as regras de cálculo de benefício previdenciário, limitou o período básico de cálculo dos segurados já filiados ao sistema às contribuições vertidas após julho de 1994. O objetivo é provocar a reflexão acerca do caráter protetivo das regras transitórias fixadas em momentos de rupturas abruptas das regras previdenciárias, tema que se mostra especialmente relevante em meio à atual discussão acerca da Reforma da Previdência. Deu-se especial ênfase aos princípios da segurança jurídica e da confiança legítima. O artigo também dialoga com a função do intérprete na aplicação da legislação previdenciária, sugerindo a prevalência da filtragem constitucional, que propõe a análise do texto normativo à luz do texto constitucional

Alternative forms of corporate communications : the use of social media demonstrated by three exemplary companies

Der Einsatz von Social Media im Unternehmen kann auf unterschiedliche Art und Weise vollzogen werden. Diese Arbeit beschäftigt sich mit den Social-Media-Aktivitäten der drei Unternehmen mymuesli, dm und Deutsche Bahn und deren Auftritt bei Facebook, Twitter und auf YouTube. Grundlage für die Beobachtung und Auswertung der Social-Media-Maßnahmen der drei Unternehmen, sind theoretische Ausführungen zum Thema Social Media in der Unternehmenskommunikation

Projection of High-Dimensional Genome-Wide Expression on SOM Transcriptome Landscapes

The self-organizing maps portraying has been proven to be a powerful approach for analysis of transcriptomic, genomic, epigenetic, single-cell, and pathway-level data as well as for “multi-omic” integrative analyses. However, the SOM method has a major disadvantage: it requires the retraining of the entire dataset once a new sample is added, which can be resource- and timedemanding. It also shifts the gene landscape, thus complicating the interpretation and comparison of results. To overcome this issue, we have developed two approaches of transfer learning that allow for extending SOM space with new samples, meanwhile preserving its intrinsic structure. The extension SOM (exSOM) approach is based on adding secondary data to the existing SOM space by “meta-gene adaptation”, while supervised SOM portrayal (supSOM) adds support vector machine regression model on top of the original SOM algorithm to “predict” the portrait of a new sample. Both methods have been shown to accurately combine existing and new data. With simulated data, exSOM outperforms supSOM for accuracy, while supSOM significantly reduces the computing time and outperforms exSOM for this parameter. Analysis of real datasets demonstrated the validity of the projection methods with independent datasets mapped on existing SOM space. Moreover, both methods well handle the projection of samples with new characteristics that were not present in training datasets

Melanoma Single-Cell Biology in Experimental and Clinical Settings

Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches

Integrated Multi-Omics Maps of Lower-Grade Gliomas

Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data. We present an intuitive method enabling the combined analysis of multi-omics data based on self-organizing maps machine learning. It “portrays” the expression, methylation and copy number variations (CNV) landscapes of each tumour using the same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the different omics layers on a personalized basis. We applied this combined molecular portrayal to lower grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes defined by genetic key lesions, which associate with large-scale effects on DNA methylation and gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-, astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of concerted changes of expression, methylation and CNV are governed by the degree of co-regulation within and between the omics layers. The method is not restricted to the triple-omics data used here. The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked differentiation in a subtype specific fashion. It can be extended to integrate other omics features such as genetic mutation, protein expression data as well as extracting prognostic markers

Impact of a diabetes disease management program on guideline-adherent care, hospitalization risk and health care costs: a propensity score matching study using real-world data.

OBJECTIVE To evaluate the impact of a DMP for patients with diabetes mellitus in a Swiss primary care setting. METHODS In a prospective observational study, we compared diabetes patients in a DMP (intervention group; N = 538) with diabetes patients receiving usual care (control group; N = 5050) using propensity score matching with entropy balancing. Using a difference-in-difference (DiD) approach, we compared changes in outcomes from baseline (2017) to 1-year (2017/18) and to 2-year follow-up (2017/19). Outcomes included four measures for guideline-adherent diabetes care, hospitalization risk, and health care costs. RESULTS We identified a positive impact of the DMP on the share of patients fulfilling all measures for guideline-adherent care [DiD 2017/18: 7.2 percentage-points, p < 0.01; 2017/19: 8.4 percentage-points, p < 0.001]. The hospitalization risk was lower in the intervention group in both years, but only statistically significant in the 1-year follow-up [DiD 2017/18: - 5.7 percentage-points, p < 0.05; 2017/19: - 3.9 percentage points, n.s.]. The increase in health care costs was smaller in the intervention than in the control group [DiD 2017/18: CHF - 852; 2017/19: CHF - 909], but this effect was not statistically significant. CONCLUSION The DMP under evaluation seems to exert a positive impact on the quality of diabetes care, reflected in the increase in the measures for guideline-adherent care and in a reduction of the hospitalization risk in the intervention group. It also might reduce health care costs, but only a longer follow-up will show whether the observed effect persists over time