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. Here, we show that males exhibit more pronounced changes in molecular pathways compared to females. Our integrated analysis of transcriptomes, (phospho)proteomes, and miRNAomes also identified distinct ALS subclusters in humans, characterized by variations in immune response, extracellular matrix composition, mitochondrial function, and RNA processing. The molecular signatures of human subclusters were reflected in specific mouse models. Our study highlighted the mitogen-activated protein kinase (MAPK) pathway as an early disease mechanism. We further demonstrate that trametinib, a MAPK inhibitor, has potential therapeutic benefits in vitro and in vivo, particularly in females, suggesting a direction for developing targeted ALS treatments.

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 (TH17) 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. CRISPR-based gene targeting in TH17 cells could be ranked according to the resulting transcriptional perturbations, and polarization biases into T helper 1 (TH1) and regulatory T cells could be quantified. Furthermore, we show that iCROP-seq can facilitate the identification of therapeutic targets by efficient functional stratification of genes and pathways in a disease- and tissue-specific manner. These findings uncover TH17 to TH1 cell plasticity in the human kidney in the context of renal autoimmunity.

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. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals a high abundance of malignant stemcell-like cells in high-neural glioblastoma, primarily of the neural lineage. These cells are further classified as neural-progenitor-cell-like, astrocyte-like and oligodendrocyte-progenitor-like, alongside oligodendrocytes and excitatory neurons. In line with these findings, high-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature is associated with decreased overall and progression-free survival. High-neural tumors also exhibit increased functional connectivity in magnetencephalography and resting-state magnet resonance imaging and can be detected via DNA analytes and brain-derived neurotrophic factor in patients' plasma. The prognostic importance of the neural signature was further validated in patients diagnosed with diffuse midline glioma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant. High-neural gliomas likely require a maximized surgical resection approach for improved outcomes.

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. Our algorithm, DISSECT, outperforms competing algorithms in cell fraction and gene expression estimation by up to 14 percentage points. DISSECT can be easily adapted to other biomedical data types, as exemplified by our proteomic deconvolution experiments.

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. Effects of clinical characteristics on temporal changes and outcomes were studied. Among 47 patients, 8 (17.0%) had non-matching classifications at recurrence. In the remaining 39 cases, 28.2% showed dominant DNA methylation subclass transitions, with 72.7% being a mesenchymal subclass. In general, glioblastomas with a subclass transition showed upregulated metabolic processes. Newly diagnosed glioblastomas with mesenchymal transition displayed increased stem cell-like states and decreased immune components at diagnosis and exhibited elevated immune signatures and cytokine levels in serum. In contrast, tissue of recurrent glioblastomas with mesenchymal transition showed increased immune components but decreased stem cell-like states. Survival analyses revealed comparable outcomes for patients with and without subclass transitions. This study demonstrates a temporal heterogeneity of DNA methylation subclasses in 28.2% of glioblastomas, not impacting patient survival. Changes in cell state composition associated with subclass transition may be crucial for recurrent glioblastoma targeted therapies.

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. METHODS: Fetal lung size was serially assessed at standardized time points by transabdominal ultrasound in pregnant women participating in a pregnancy cohort. Correlations between fetal lung growth and respiratory infections in infancy or early-onset asthma at five years were examined. Machine-learning models relying on extreme gradient boosting regressor or classifier algorithms were developed to predict respiratory infection or asthma risk based on fetal lung growth. For model development and validation, study participants were randomly divided into a training and a testing group, respectively, by the employed algorithm. RESULTS: Enhanced fetal lung growth throughout pregnancy predicted a lower early-life respiratory infection risk. Male sex was associated with a higher risk for respiratory infections in infancy. Fetal lung growth could also predict the risk of asthma at five years of age. We designed three machine-learning models to predict the risk and number of infections in infancy as well as the risk of early-onset asthma. The models' R2 values were 0.92, 0.90 and 0.93, respectively, underscoring a high accuracy and agreement between the actual and predicted values. Influential variables included known risk factors and novel predictors, such as ultrasound-monitored fetal lung growth. CONCLUSION: Sonographic monitoring of fetal lung growth allows to predict the risk for early-life respiratory infections and asthma.

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. Here, we present multi-omics datasets generated from the frontal lobe of post-mortem human brain tissue from patients with mutations in MAPT, GRN and C9orf72 and healthy controls. This data resource consists of four datasets generated with different technologies to capture the transcriptome by RNA-seq, small RNA-seq, CAGE-seq, and methylation profiling. We show concrete examples on how to use the resulting data and confirm current knowledge about FTD and identify new processes for further investigation. This extensive multi-omics dataset holds great value to reveal new research avenues for this devastating 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. Single-cell RNA analyses of pDCs showed downregulation of IFN-I-related gene expression signatures but also revealed transcriptional inter-donor heterogeneity. Longitudinal quantification showed continuous reduction of IFN-I protein production by pDCs and reduced expression of IFN-I-stimulated genes in peripheral blood mononuclear cells (PBMCs). These studies in trans men demonstrate that testosterone administration reduces IFN-I production by pDCs over time and provide insights into the immune-modulatory role of testosterone in sex-specific IFN-I-mediated immune responses.

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. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We show that DISCERN is robust against differences between batches and is able to keep biological differences between batches, which is a common problem for imputation and batch correction algorithms. We use DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+ and CD8+ Tc2 T helper cells, with a potential role in adverse disease outcome. We utilize T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 80% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single-cell sequencing workflow. CONCLUSIONS: Thus, DISCERN is a flexible tool for reconstructing missing single-cell gene expression using a reference dataset and can easily be applied to a variety of data sets yielding novel insights, e.g., into disease mechanisms.

Epigenetic profiling reveals a strong association between lack of 5-ALA fluorescence and EGFR amplification in IDH-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 IDH-wildtype glioblastoma patients. A small subset of IDH-wildtype glioblastoma shows no 5-ALA fluorescence. An explanation for these cases is missing. In this study, we used DNA methylation profiling to further characterize non-fluorescent glioblastomas. Methods: Patients with newly diagnosed and recurrent IDH-wildtype glioblastoma that underwent surgery were analyzed. The intensity of intraoperative 5-ALA fluorescence was categorized as non-visible or visible. DNA was extracted from tumors and genome-wide DNA methylation patterns were analyzed using Illumina EPIC (850k) arrays. Furthermore, 5-ALA intensity was measured by flow cytometry on human gliomasphere lines (BT112 and BT145). Results: Of 74 included patients, 12 (16.2%) patients had a non-fluorescent glioblastoma, which were compared to 62 glioblastomas with 5-ALA fluorescence. Clinical characteristics were equally distributed between both groups. We did not find significant differences between DNA methylation subclasses and 5-ALA fluorescence (P = .24). The distribution of cells of the tumor microenvironment was not significantly different between the non-fluorescent and fluorescent tumors. Copy number variations in EGFR and simultaneous EGFRvIII expression were strongly associated with 5-ALA fluorescence since all non-fluorescent glioblastomas were EGFR-amplified (P < .01). This finding was also demonstrated in recurrent tumors. Similarly, EGFR-amplified glioblastoma cell lines showed no 5-ALA fluorescence after 24 h of incubation. Conclusions: Our study demonstrates an association between non-fluorescent IDH-wildtype glioblastomas and EGFR gene amplification which should be taken into consideration for recurrent surgery and future studies investigating EGFR-amplified gliomas.

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. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals high abundance of stem cell-like malignant cells classified as oligodendrocyte precursor and neural precursor cell-like in high-neural glioblastoma. High-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature associates with decreased survival as well as increased functional connectivity and can be detected via DNA analytes and brain-derived neurotrophic factor in plasma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant.

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. We focused on synapses, analyzing the proteome of post-mortal hippocampal tissue from 16 PD cases and 14 control subjects by mass spectrometry. Whole tissue lysates and synaptosomal fractions were analyzed in parallel. Differential analysis combined with bioinformatic network analyses identified neuronal pentraxin 1 (NPTX1) to be significantly dysregulated in PD and interacting with proteins of the synaptic compartment. Modulation of NPTX1 protein levels in primary hippocampal neuron cultures validated its role in synapse morphology. Our analysis suggests that NPTX1 contributes to synaptic pathology in late-stage PD and represents a putative target for novel therapeutic strategies.

Loss of TDP-43 causes ectopic endothelial sprouting and migration defects through increased fibronectin, vcam 1 and integrin α4/ß1.

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. We identified elevated expression of FIBRONECTIN 1 (FN1), the VASCULAR CELL ADHESION MOLECULE 1 (VCAM1), as well as their receptor INTEGRIN α4ß1 (ITGA4B1) in HUVEC cells. Importantly, reducing the levels of ITGA4, FN1, and VCAM1 homologues in the TDP-43 loss-of-function zebrafish rescues the angiogenic defects indicating the conservation of human and zebrafish TDP-43 function during angiogenesis. Our study identifies a novel pathway regulated by TDP-43 important for angiogenesis during development.

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 (n = 361) and 1.5-T (n = 398) datasets, of which 39 samples each were randomly chosen as unseen test sets. Additionally, 37 MRCP images obtained with a 3-T MRI scanner from a different manufacturer were included for external testing. A multiview convolutional neural network was developed, specialized in simultaneously processing the seven images taken at different rotational angles per MRCP examination. The final model, DeePSC, derived its classification per patient from the instance expressing the highest confidence in an ensemble of 20 individually trained multiview convolutional neural networks. Predictive performance on both test sets was compared with that of four licensed radiologists using the Welch t test. Results: DeePSC achieved an accuracy of 80.5% ± 1.3 (sensitivity, 80.0% ± 1.9; specificity, 81.1% ± 2.7) on the 3-T and 82.6% ± 3.0 (sensitivity, 83.6% ± 1.8; specificity, 80.0% ± 8.9) on the 1.5-T test set and scored even higher on the external test set (accuracy, 92.4% ± 1.1; sensitivity, 100.0% ± 0.0; specificity, 83.5% ± 2.4). DeePSC outperformed radiologists in average prediction accuracy by 5.5 (P = .34, 3 T) and 10.1 (P = .13, 1.5 T) percentage points. Conclusion: Automated classification of PSC-compatible findings based on two-dimensional MRCP was achievable and demonstrated high accuracy on internal and external test sets.Keywords: Neural Networks, Deep Learning, Liver Disease, MRI, Primary Sclerosing Cholangitis, MR Cholangiopancreatography Supplemental material is available for this article. © RSNA, 2023.

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. We collected, harmonized, and merged all the major publicly available TCR-pMHC binding data and compared the performance of five state-of-the-art deep learning models (TITAN, NetTCR-2.0, ERGO, DLpTCR and ImRex) using this data. Our performance evaluation focuses on two scenarios: 1) different splitting methods for generating training and testing data to assess model generalization and 2) different data versions that vary in size and peptide imbalance to assess model robustness. Our results indicate that the five contemporary models do not generalize to peptides that have not been in the training set. We can also show that model performance is strongly dependent on the data balance and size, which indicates a relatively low model robustness. These results suggest that TCR-pMHC binding prediction remains highly challenging and requires further high quality data and novel algorithmic approaches.

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 systems1. 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)2,3, has recently been developed. However, this method has not been explored in pathology specimens to date, because on its own, it does not achieve sufficient resolution for routine clinical use. Here, we report expansion-enhanced super-resolution radial fluctuations (ExSRRF), a simple, robust, scalable and accessible workflow that provides a resolution of up to 25 nm using LED-based widefield microscopy. ExSRRF enables molecular profiling of subcellular structures from archival formalin-fixed paraffin-embedded tissues in complex clinical and experimental specimens, including ischaemic, degenerative, neoplastic, genetic and immune-mediated disorders. Furthermore, as examples of its potential application to experimental and clinical pathology, we show that ExSRRF can be used to identify and quantify classical features of endoplasmic reticulum stress in the murine ischaemic kidney and diagnostic ultrastructural features in human kidney biopsies.

Transcriptional and Clonal Characterization of Cytotoxic T Cells in Crescentic Glomerulonephritis.

SIGNIFICANCE STATEMENT: T-cell infiltration is a hallmark of crescentic GN (cGN), often caused by ANCA-associated vasculitis. Pathogenic T-cell subsets, their clonality, and downstream effector mechanisms leading to kidney injury remain to be fully elucidated. Single-cell RNA sequencing and T-cell receptor sequencing revealed activated, clonally expanded cytotoxic CD4 + and CD8 + T cells in kidneys from patients with ANCA-associated cGN. In experimental cGN, kidney-infiltrating CD8 + T cells expressed the cytotoxic molecule, granzyme B (GzmB), which induced apoptosis in renal tissue cells by activation of procaspase-3, and aggravated disease pathology. These findings describe a pathogenic function of (clonally expanded) cytotoxic T cells in cGN and identify GzmB as a mediator and potential therapeutic target in immune-mediated kidney disease. BACKGROUND: Crescentic GN (cGN) is an aggressive form of immune-mediated kidney disease that is an important cause of end stage renal failure. Antineutrophilic cytoplasmic antibody (ANCA)-associated vasculitis is a common cause. T cells infiltrate the kidney in cGN, but their precise role in autoimmunity is not known. METHODS: Combined single-cell RNA sequencing and single-cell T-cell receptor sequencing were conducted on CD3 + T cells isolated from renal biopsies and blood of patients with ANCA-associated cGN and from kidneys of mice with experimental cGN. Functional and histopathological analyses were performed with Cd8a-/- and GzmB-/- mice. RESULTS: Single-cell analyses identified activated, clonally expanded CD8 + and CD4 + T cells with a cytotoxic gene expression profile in the kidneys of patients with ANCA-associated cGN. Clonally expanded CD8 + T cells expressed the cytotoxic molecule, granzyme B (GzmB), in the mouse model of cGN. Deficiency of CD8 + T cells or GzmB ameliorated the course of cGN. CD8 + T cells promoted macrophage infiltration and GzmB activated procaspase-3 in renal tissue cells, thereby increasing kidney injury. CONCLUSIONS: Clonally expanded cytotoxic T cells have a pathogenic function in immune-mediated kidney disease.

CD4+ T cells produce GM-CSF and drive immune-mediated glomerular disease by licensing monocyte-derived cells to produce MMP12.

GM-CSF in glomerulonephritisDespite glomerulonephritis being an immune-mediated disease, the contributions of individual immune cell types are not clear. To address this gap in knowledge, Paust et al. characterized pathological immune cells in samples from patients with glomerulonephritis and in samples from mice with the disease. The authors found that CD4+ T cells producing granulocyte-macrophage colony-stimulating factor (GM-CSF) licensed monocytes to promote disease by producing matrix metalloproteinase 12 and disrupting the glomerular basement membrane. Targeting GM-CSF to inhibit this axis reduced disease severity in mice, implicating this cytokine as a potential therapeutic target for patients with glomerulonephritis. -CM.

Pathogen detection in RNA-seq data with Pathonoia.

BACKGROUND: Bacterial and viral infections may cause or exacerbate various human diseases and to detect microbes in tissue, one method of choice is RNA sequencing. The detection of specific microbes using RNA sequencing offers good sensitivity and specificity, but untargeted approaches suffer from high false positive rates and a lack of sensitivity for lowly abundant organisms. RESULTS: We introduce Pathonoia, an algorithm that detects viruses and bacteria in RNA sequencing data with high precision and recall. Pathonoia first applies an established k-mer based method for species identification and then aggregates this evidence over all reads in a sample. In addition, we provide an easy-to-use analysis framework that highlights potential microbe-host interactions by correlating the microbial to the host gene expression. Pathonoia outperforms state-of-the-art methods in microbial detection specificity, both on in silico and real datasets. CONCLUSION: Two case studies in human liver and brain show how Pathonoia can support novel hypotheses on microbial infection exacerbating disease. The Python package for Pathonoia sample analysis and a guided analysis Jupyter notebook for bulk RNAseq datasets are available on GitHub.