Enhancing gene set enrichment using networks [version 1; peer review: 1 approved, 2 approved with reservations]

Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines

Advanced Diffusion-Weighted Imaging Sequences for Breast MRI: Comprehensive Comparison of Improved Sequences and Ultra-High B-Values to Identify the Optimal Combination

This study investigated the image quality and choice of ultra-high b-value of two DWI breast-MRI research applications. The study cohort comprised 40 patients (20 malignant lesions). In addition to s-DWI with two m-b-values (b50 and b800) and three e-b-values (e-b1500, e-b2000, and e-b2500), z-DWI and IR m-b1500 DWI were applied. z-DWI was acquired with the same measured b-values and e-b-values as the standard sequence. For IR m-b1500 DWI, b50 and b1500 were measured, and e-b2000 and e-b2500 were mathematically extrapolated. Three readers used Likert scales to independently analyze all ultra-high b-values (b1500-b2500) for each DWI with regards to scan preference and image quality. ADC values were measured in all 20 lesions. z-DWI was the most preferred (54%), followed by IR m-b1500 DWI (46%). b1500 was significantly preferred over b2000 for z-DWI and IR m-b1500 DWI (p = 0.001 and p = 0.002, respectively). Lesion detection was not significantly different among sequences or b-values (p = 0.174). There were no significant differences in measured ADC values within lesions between s-DWI (ADC: 0.97 [±0.09] × 10$^{-3}$ mm$^{2}$/s) and z-DWI (ADC: 0.99 [±0.11] × 10$^{-3}$ mm$^{2}$/s; p = 1.000). However, there was a trend toward lower values in IR m-b1500 DWI (ADC: 0.80 [±0.06] × 10$^{-3}$ mm$^{2}$/s) than in s-DWI (p = 0.090) and z-DWI (p = 0.110). Overall, image quality was superior and there were fewer image artifacts when using the advanced sequences (z-DWI + IR m-b1500 DWI) compared with s-DWI. Considering scan preferences, we found that the optimal combination was z-DWI with a calculated b1500, especially regarding examination time

scAmpi—A versatile pipeline for single-cell RNA-seq analysis from basics to clinics

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study

scROSHI: robust supervised hierarchical identification of single cells

Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene lists and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large

White-to-brown metabolic conversion of human adipocytes by JAK inhibition

The rising incidence of obesity and related disorders such as diabetes and heart disease has focused considerable attention on the discovery of novel therapeutics. One promising approach has been to increase the number or activity of brown-like adipocytes in white adipose depots, as this has been shown to prevent diet-induced obesity and reduce the incidence and severity of type 2 diabetes. Thus, the conversion of fat-storing cells into metabolically active thermogenic cells has become an appealing therapeutic strategy to combat obesity. Here, we report a screening platform for the identification of small molecules capable of promoting a white-to-brown metabolic conversion in human adipocytes. We identified two inhibitors of Janus Kinase (JAK) activity with no precedent in adipose tissue biology that stably confer brown-like metabolic activity to white adipocytes. Importantly, these metabolically converted adipocytes exhibit elevated UCP1 expression and increased mitochondrial activity. We further found that repression of interferon signalling and activation of hedgehog signalling in JAK-inactivated adipocytes contributes to the metabolic conversion observed in these cells. Our findings highlight a novel role for the JAK/STAT pathway in the control of adipocyte function and establish a platform to identify compounds for the treatment of obesity

Method for testing a substance interacting with a target molecule

The present invention relates to a method for testing a substance interacting with a target molecule in a cell by fluorescence correlation spectroscopy (FCS) by adding a test substance to the cell and determining a distribution of diffusion coefficients for the fluorescently labelled target molecule. Preferably, the target molecule is a nuclear hormone receptor, e.g. the estrogen receptor

Three-dimensional optical polarization tomography of single molecules

We apply the concept of tomography to polarization-sensitive optical microscopy of single fluorophores to determine the three-dimensional orientation of molecular absorption dipoles with isotropic sensitivity. Wide-field microscopy provides the opportunity to monitor simultaneously three-dimensional rotation and two-dimensional translation of many molecules in parallel. For orientation determination the molecules are illuminated from different directions of incidence with linearly polarized light. In each exposure the excitation along a particular projection of the absorption dipole on the electric field leads to a distinct fluorescence intensity. Five exposures are sufficient to determine the full orientation of the fluorophores. To demonstrate the potential of the method we determine the orientation and position of individual immobilized lipid membrane markers. The shot-noise-limited isotropic angular resolution is 2°. For time-resolved studies the bandwidth can be expanded up to 200 Hz