A dataset profiling the multiomic landscape of the prefrontal cortex in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease, which still lacks effective disease-modifying therapies. Similar to other neurodegenerative disorders, such as Alzheimer and Parkinson disease, ALS pathology is presumed to propagate over time, originating from the motor cortex and spreading to other cortical regions. Exploring early disease stages is crucial to understand the causative molecular changes underlying the pathology.

A comprehensive comparison of deep learning-based compound-target interaction prediction models to unveil guiding design principles.

The evaluation of compound-target interactions (CTIs) is at the heart of drug discovery efforts. Given the substantial time and monetary costs of classical experimental screening, significant efforts have been dedicated to develop deep learning-based models that can accurately predict CTIs. A comprehensive comparison of these models on a large, curated CTI dataset is, however, still lacking.

Robust, credible, and interpretable AI-based histopathological prostate cancer grading.

Background: Prostate cancer (PCa) is among the most common cancers in men and its diagnosis requires the histopathological evaluation of biopsies by human experts. While several recent artificial intelligence-based (AI) approaches have reached human expert-level PCa grading, they often display significantly reduced performance on external datasets. This reduced performance can be caused by variations in sample preparation, for instance the staining protocol, section thickness, or scanner used.

Targeting T cell plasticity in kidney and gut inflammation by pooled single-cell CRISPR screening.

Pro-inflammatory CD4 T cells are major drivers of autoimmune diseases, yet therapies modulating T cell phenotypes to promote an anti-inflammatory state are lacking. Here, we identify T helper 17 (T17) cell plasticity in the kidneys of patients with antineutrophil cytoplasmic antibody-associated glomerulonephritis on the basis of single-cell (sc) T cell receptor analysis and scRNA velocity. To uncover molecules driving T cell polarization and plasticity, we established an in vivo pooled scCRISPR droplet sequencing (iCROP-seq) screen and applied it to mouse models of glomerulonephritis and colitis.

Multiomic ALS signatures highlight subclusters and sex differences suggesting the MAPK pathway as therapeutic target.

Amyotrophic lateral sclerosis (ALS) is a debilitating motor neuron disease and lacks effective disease-modifying treatments. This study utilizes a comprehensive multiomic approach to investigate the early and sex-specific molecular mechanisms underlying ALS. By analyzing the prefrontal cortex of 51 patients with sporadic ALS and 50 control subjects, alongside four transgenic mouse models (C9orf72-, SOD1-, TDP-43-, and FUS-ALS), we have uncovered significant molecular alterations associated with the disease.

A prognostic neural epigenetic signature in high-grade glioma.

Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is limited. We present an epigenetically defined neural signature of glioblastoma that independently predicts patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors.

DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation.

Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data. An unmet challenge in cell deconvolution is the scarcity of realistic training data and the domain shift often observed in synthetic training data. Here, we show that two novel deep neural networks with simultaneous consistency regularization of the target and training domains significantly improve deconvolution performance.

Fetal lung growth predicts the risk for early-life respiratory infections and childhood asthma.

Background: Early-life respiratory infections and asthma are major health burdens during childhood. Markers predicting an increased risk for early-life respiratory diseases are sparse. Here, we identified the predictive value of ultrasound-monitored fetal lung growth for the risk of early-life respiratory infections and asthma.

Temporal change of DNA methylation subclasses between matched newly diagnosed and recurrent glioblastoma.

The longitudinal transition of phenotypes is pivotal in glioblastoma treatment resistance and DNA methylation emerged as an important tool for classifying glioblastoma phenotypes. We aimed to characterize DNA methylation subclass heterogeneity during progression and assess its clinical impact. Matched tissues from 47 glioblastoma patients were subjected to DNA methylation profiling, including CpG-site alterations, tissue and serum deconvolution, mass spectrometry, and immunoassay.

Testosterone affects type I/type II interferon response of neutrophils during hepatic amebiasis.

Differences in immune response between men and women may influence the outcome of infectious diseases. Intestinal infection with Entamoeba histolytica leads to hepatic amebiasis, which is more common in males. Previously, we reported that innate immune cells contribute to liver damage in males in the murine model for hepatic amebiasis.

A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes.

Understanding the molecular mechanisms underlying frontotemporal dementia (FTD) is essential for the development of successful therapies. Systematic studies on human post-mortem brain tissue of patients with genetic subtypes of FTD are currently lacking. The Risk and Modyfing Factors of Frontotemporal Dementia (RiMod-FTD) consortium therefore has generated a multi-omics dataset for genetic subtypes of FTD to identify common and distinct molecular mechanisms disturbed in disease.

Reduction of IFN-I responses by plasmacytoid dendritic cells in a longitudinal trans men cohort.

Type I interferons (IFN-I) are important mediators of antiviral immunity and autoimmune diseases. Female plasmacytoid dendritic cells (pDCs) exert an elevated capacity to produce IFN-I upon toll-like receptor 7 (TLR7) activation compared to male pDCs, and both sex hormones and X-encoded genes have been implicated in these sex-specific differences. Using longitudinal samples from a trans men cohort receiving gender-affirming hormone therapy (GAHT), the impact of testosterone injections on TLR7-mediated IFN-I production by pDCs was assessed.

DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.

Background: Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification.

Results: Here, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset.

Epigenetic profiling reveals a strong association between lack of 5-ALA fluorescence and amplification in -wildtype glioblastoma.

Background: 5-aminolevulinic acid (5-ALA) fluorescence-guided resection increases the percentage of complete CNS tumor resections and improves the progression-free survival of -wildtype glioblastoma patients. A small subset of -wildtype glioblastoma shows no 5-ALA fluorescence. An explanation for these cases is missing.

Epigenetic neural glioblastoma enhances synaptic integration and predicts therapeutic vulnerability.

Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is nascent. We present an epigenetically defined neural signature of glioblastoma that independently affects patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors.

Proteomic analysis of the human hippocampus identifies neuronal pentraxin 1 (NPTX1) as synapto-axonal target in late-stage Parkinson's disease.

Parkinson's disease (PD) affects a significant proportion of the population over the age of 60 years, and its prevalence is increasing. While symptomatic treatment is available for motor symptoms of PD, non-motor complications such as dementia result in diminished life quality for patients and are far more difficult to treat. In this study, we analyzed PD-associated alterations in the hippocampus of PD patients, since this brain region is strongly affected by PD dementia.

Loss of TDP-43 causes ectopic endothelial sprouting and migration defects through increased , and .

Aggregation of the Tar DNA-binding protein of 43 kDa (TDP-43) is a pathological hallmark of amyotrophic lateral sclerosis and frontotemporal dementia and likely contributes to disease by loss of nuclear function. Analysis of TDP-43 function in knockout zebrafish identified an endothelial directional migration and hypersprouting phenotype during development prior lethality. In human umbilical vein cells (HUVEC) the loss of TDP-43 leads to hyperbranching.

DeePSC: A Deep Learning Model for Automated Diagnosis of Primary Sclerosing Cholangitis at Two-dimensional MR Cholangiopancreatography.

Purpose: To develop, train, and validate a multiview deep convolutional neural network (DeePSC) for the automated diagnosis of primary sclerosing cholangitis (PSC) on two-dimensional MR cholangiopancreatography (MRCP) images.

Materials And Methods: This retrospective study included two-dimensional MRCP datasets of 342 patients (45 years ± 14 [SD]; 207 male patients) with confirmed diagnosis of PSC and 264 controls (51 years ± 16; 150 male patients). MRCP images were separated into 3-T ( = 361) and 1.

Performance comparison of TCR-pMHC prediction tools reveals a strong data dependency.

The interaction of T-cell receptors with peptide-major histocompatibility complex molecules (TCR-pMHC) plays a crucial role in adaptive immune responses. Currently there are various models aiming at predicting TCR-pMHC binding, while a standard dataset and procedure to compare the performance of these approaches is still missing. In this work we provide a general method for data collection, preprocessing, splitting and generation of negative examples, as well as comprehensive datasets to compare TCR-pMHC prediction models.

Expansion-enhanced super-resolution radial fluctuations enable nanoscale molecular profiling of pathology specimens.

Expansion microscopy physically enlarges biological specimens to achieve nanoscale resolution using diffraction-limited microscopy systems. However, optimal performance is usually reached using laser-based systems (for example, confocal microscopy), restricting its broad applicability in clinical pathology, as most centres have access only to light-emitting diode (LED)-based widefield systems. As a possible alternative, a computational method for image resolution enhancement, namely, super-resolution radial fluctuations (SRRF), has recently been developed.