Targeting low levels of MIF expression as a potential therapeutic strategy for ALS.

Mutations in SOD1 cause amyotrophic lateral sclerosis (ALS), a neurodegenerative disease characterized by motor neuron (MN) loss. We previously discovered that macrophage migration inhibitory factor (MIF), whose levels are extremely low in spinal MNs, inhibits mutant SOD1 misfolding and toxicity. In this study, we show that a single peripheral injection of adeno-associated virus (AAV) delivering MIF into adult SOD1G37R mice significantly improves their motor function, delays disease progression, and extends survival. Moreover, MIF treatment reduces neuroinflammation and misfolded SOD1 accumulation, rescues MNs, and corrects dysregulated pathways as observed by proteomics and transcriptomics. Furthermore, we reveal low MIF levels in human induced pluripotent stem cell-derived MNs from familial ALS patients with different genetic mutations, as well as in post mortem tissues of sporadic ALS patients. Our findings indicate that peripheral MIF administration may provide a potential therapeutic mechanism for modulating misfolded SOD1 in vivo and disease outcome in ALS patients.

Disease related changes in ATAC-seq of iPSC-derived motor neuron lines from ALS patients and controls.

Amyotrophic Lateral Sclerosis (ALS), like many other neurodegenerative diseases, is highly heritable, but with only a small fraction of cases explained by monogenic disease alleles. To better understand sporadic ALS, we report epigenomic profiles, as measured by ATAC-seq, of motor neuron cultures derived from a diverse group of 380 ALS patients and 80 healthy controls. We find that chromatin accessibility is heavily influenced by sex, the iPSC cell type of origin, ancestry, and the inherent variance arising from sequencing. Once these covariates are corrected for, we are able to identify ALS-specific signals in the data. Additionally, we find that the ATAC-seq data is able to predict ALS disease progression rates with similar accuracy to methods based on biomarkers and clinical status. These results suggest that iPSC-derived motor neurons recapitulate important disease-relevant epigenomic changes.

A systems-biology approach connects aging mechanisms with Alzheimer's disease pathogenesis.

Age is the strongest risk factor for developing Alzheimer's disease, the most common neurodegenerative disorder. However, the mechanisms connecting advancing age to neurodegeneration in Alzheimer's disease are incompletely understood. We conducted an unbiased, genome-scale, forward genetic screen for age-associated neurodegeneration in Drosophila to identify the underlying biological processes required for maintenance of aging neurons. To connect genetic screen hits to Alzheimer's disease pathways, we measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer's disease. We further identified Alzheimer's disease human genetic variants that modify expression in disease-vulnerable neurons. Through multi-omic, multi-species network integration of these data, we identified relationships between screen hits and tau-mediated neurotoxicity. Furthermore, we computationally and experimentally identified relationships between screen hits and DNA damage in Drosophila and human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to attenuate the effects of age on neurodegeneration and Alzheimer's disease.

Identification of gene fusions associated with amyotrophic lateral sclerosis.

INTRODUCTION/AIMS: Genetics is an important risk factor for amyotrophic lateral sclerosis (ALS), a neurodegenerative disease affecting motor neurons. Recent findings demonstrate that in addition to specific genetic mutations, structural variants caused by genetic instability can also play a causative role in ALS. Genomic instability can lead to deletions, duplications, insertions, inversions, and translocations in the genome, and these changes can sometimes lead to fusion of distinct genes into a single transcript. Gene fusion events have been studied extensively in cancer; however, they have not been thoroughly investigated in ALS. The aim of this study was to determine whether gene fusions are present in ALS. METHODS: Gene fusions were identified using STAR Fusion v1.10.0 software in bulk RNA-Seq data from human postmortem samples from publicly available data sets from Target ALS and the New York Genome Center ALS Consortium. RESULTS: We report the presence of gene fusion events in several brain regions as well as in spinal cord samples in ALS. Although most gene fusions were intra-chromosomal events between neighboring genes and present in both ALS and control samples, there was a significantly greater number of unique gene fusions in ALS compared to controls. Lastly, we identified specific gene fusions with a significant burden in ALS, that were absent from both control samples and known cancer gene fusion databases. DISCUSSION: Collectively, our findings reveal an enrichment of gene fusions in ALS and suggest that these events may be an additional genetic cause linked to ALS pathogenesis.

Selective CDK7 Inhibition Suppresses Cell Cycle Progression and MYC Signaling While Enhancing Apoptosis in Therapy-resistant Estrogen Receptor-positive Breast Cancer.

PURPOSE: Resistance to endocrine therapy (ET) and CDK4/6 inhibitors (CDK4/6i) is a clinical challenge in estrogen receptor (ER)-positive (ER+) breast cancer. Cyclin-dependent kinase 7 (CDK7) is a candidate target in endocrine-resistant ER+ breast cancer models and selective CDK7 inhibitors (CDK7i) are in clinical development for the treatment of ER+ breast cancer. Nonetheless, the precise mechanisms responsible for the activity of CDK7i in ER+ breast cancer remain elusive. Herein, we sought to unravel these mechanisms. EXPERIMENTAL DESIGN: We conducted multi-omic analyses in ER+ breast cancer models in vitro and in vivo, including models with different genetic backgrounds. We also performed genome-wide CRISPR/Cas9 knockout screens to identify potential therapeutic vulnerabilities in CDK4/6i-resistant models. RESULTS: We found that the on-target antitumor effects of CDK7 inhibition in ER+ breast cancer are in part p53 dependent, and involve cell cycle inhibition and suppression of c-Myc. Moreover, CDK7 inhibition exhibited cytotoxic effects, distinctive from the cytostatic nature of ET and CDK4/6i. CDK7 inhibition resulted in suppression of ER phosphorylation at S118; however, long-term CDK7 inhibition resulted in increased ER signaling, supporting the combination of ET with a CDK7i. Finally, genome-wide CRISPR/Cas9 knockout screens identified CDK7 and MYC signaling as putative vulnerabilities in CDK4/6i resistance, and CDK7 inhibition effectively inhibited CDK4/6i-resistant models. CONCLUSIONS: Taken together, these findings support the clinical investigation of selective CDK7 inhibition combined with ET to overcome treatment resistance in ER+ breast cancer. In addition, our study highlights the potential of increased c-Myc activity and intact p53 as predictors of sensitivity to CDK7i-based treatments.

Compartments in medulloblastoma with extensive nodularity are connected through differentiation along the granular precursor lineage.

Medulloblastomas with extensive nodularity are cerebellar tumors characterized by two distinct compartments and variable disease progression. The mechanisms governing the balance between proliferation and differentiation in MBEN remain poorly understood. Here, we employ a multi-modal single cell transcriptome analysis to dissect this process. In the internodular compartment, we identify proliferating cerebellar granular neuronal precursor-like malignant cells, along with stromal, vascular, and immune cells. In contrast, the nodular compartment comprises postmitotic, neuronally differentiated malignant cells. Both compartments are connected through an intermediate cell stage resembling actively migrating CGNPs. Notably, we also discover astrocytic-like malignant cells, found in proximity to migrating and differentiated cells at the transition zone between the two compartments. Our study sheds light on the spatial tissue organization and its link to the developmental trajectory, resulting in a more benign tumor phenotype. This integrative approach holds promise to explore intercompartmental interactions in other cancers with varying histology.

Developmental basis of SHH medulloblastoma heterogeneity.

Many genes that drive normal cellular development also contribute to oncogenesis. Medulloblastoma (MB) tumors likely arise from neuronal progenitors in the cerebellum, and we hypothesized that the heterogeneity observed in MBs with sonic hedgehog (SHH) activation could be due to differences in developmental pathways. To investigate this question, here we perform single-nucleus RNA sequencing on highly differentiated SHH MBs with extensively nodular histology and observed malignant cells resembling each stage of canonical granule neuron development. Through innovative computational approaches, we connect these results to published datasets and find that some established molecular subtypes of SHH MB appear arrested at different developmental stages. Additionally, using multiplexed proteomic imaging and MALDI imaging mass spectrometry, we identify distinct histological and metabolic profiles for highly differentiated tumors. Our approaches are applicable to understanding the interplay between heterogeneity and differentiation in other cancers and can provide important insights for the design of targeted therapies.

Dictionary of immune responses to cytokines at single-cell resolution.

Cytokines mediate cell-cell communication in the immune system and represent important therapeutic targets1-3. A myriad of studies have highlighted their central role in immune function4-13, yet we lack a global view of the cellular responses of each immune cell type to each cytokine. To address this gap, we created the Immune Dictionary, a compendium of single-cell transcriptomic profiles of more than 17 immune cell types in response to each of 86 cytokines (>1,400 cytokine-cell type combinations) in mouse lymph nodes in vivo. A cytokine-centric view of the dictionary revealed that most cytokines induce highly cell-type-specific responses. For example, the inflammatory cytokine interleukin-1ß induces distinct gene programmes in almost every cell type. A cell-type-centric view of the dictionary identified more than 66 cytokine-driven cellular polarization states across immune cell types, including previously uncharacterized states such as an interleukin-18-induced polyfunctional natural killer cell state. Based on this dictionary, we developed companion software, Immune Response Enrichment Analysis, for assessing cytokine activities and immune cell polarization from gene expression data, and applied it to reveal cytokine networks in tumours following immune checkpoint blockade therapy. Our dictionary generates new hypotheses for cytokine functions, illuminates pleiotropic effects of cytokines, expands our knowledge of activation states of each immune cell type, and provides a framework to deduce the roles of specific cytokines and cell-cell communication networks in any immune response.

Identifying amyotrophic lateral sclerosis through interactions with an internet search engine.

INTRODUCTION/AIMS: Amyotrophic lateral sclerosis (ALS), a motor neuron disease, remains a clinical diagnosis with an average time from onset of symptoms to diagnosis of about 1 year. Herein we examine the possibility that interactions with an internet search engine can identify people with ALS. METHODS: We identified 285 anonymous Bing users whose queries indicated that they had been diagnosed with ALS and matched them to: (1) 3276 control users; and (2) 1814 users whose searches indicated they had ALS disease mimics. We tested whether the ALS group could be distinguished from controls and disease mimics based on search engine query data. Finally, we conducted a prospective validation from participants who provided access to their Bing search data. RESULTS: The model distinguished between the ALS group and controls with an area under the curve (AUC) of 0.81. Model scores for the ALS group differed from the disease mimics group (rank sum test, p < .05 with Bonferroni correction). Mild cognitive impairment could not be distinguished from ALS (p > .05). In the prospective analysis, the model reached an AUC of 0.74. DISCUSSION: Our results suggest that interactions with search engines should be further studied to understand the potential to act as a tool to assist in screening for ALS and to reduce diagnostic delay.

Circular extrachromosomal DNA promotes tumor heterogeneity in high-risk medulloblastoma.

Circular extrachromosomal DNA (ecDNA) in patient tumors is an important driver of oncogenic gene expression, evolution of drug resistance and poor patient outcomes. Applying computational methods for the detection and reconstruction of ecDNA across a retrospective cohort of 481 medulloblastoma tumors from 465 patients, we identify circular ecDNA in 82 patients (18%). Patients with ecDNA-positive medulloblastoma were more than twice as likely to relapse and three times as likely to die within 5 years of diagnosis. A subset of tumors harbored multiple ecDNA lineages, each containing distinct amplified oncogenes. Multimodal sequencing, imaging and CRISPR inhibition experiments in medulloblastoma models reveal intratumoral heterogeneity of ecDNA copy number per cell and frequent putative 'enhancer rewiring' events on ecDNA. This study reveals the frequency and diversity of ecDNA in medulloblastoma, stratified into molecular subgroups, and suggests copy number heterogeneity and enhancer rewiring as oncogenic features of ecDNA.

Integrative analysis reveals a conserved role for the amyloid precursor protein in proteostasis during aging.

Aß peptides derived from the amyloid precursor protein (APP) have been strongly implicated in the pathogenesis of Alzheimer's disease. However, the normal function of APP and the importance of that role in neurodegenerative disease is less clear. We recover the Drosophila ortholog of APP, Appl, in an unbiased forward genetic screen for neurodegeneration mutants. We perform comprehensive single cell transcriptional and proteomic studies of Appl mutant flies to investigate Appl function in the aging brain. We find an unexpected role for Appl in control of multiple cellular pathways, including translation, mitochondrial function, nucleic acid and lipid metabolism, cellular signaling and proteostasis. We mechanistically define a role for Appl in regulating autophagy through TGFß signaling and document the broader relevance of our findings using mouse genetic, human iPSC and in vivo tauopathy models. Our results demonstrate a conserved role for APP in controlling age-dependent proteostasis with plausible relevance to Alzheimer's disease.

Intratumoral drug-releasing microdevices allow in situ high-throughput pharmaco phenotyping in patients with gliomas.

The lack of reliable predictive biomarkers to guide effective therapy is a major obstacle to the advancement of therapy for high-grade gliomas, particularly glioblastoma (GBM), one of the few cancers whose prognosis has not improved over the past several decades. With this pilot clinical trial (number NCT04135807), we provide first-in-human evidence that drug-releasing intratumoral microdevices (IMDs) can be safely and effectively used to obtain patient-specific, high-throughput molecular and histopathological drug response profiling. These data can complement other strategies to inform the selection of drugs based on their observed antitumor effect in situ. IMDs are integrated into surgical practice during tumor resection and remain in situ only for the duration of the otherwise standard operation (2 to 3 hours). None of the six enrolled patients experienced adverse events related to the IMD, and the exposed tissue was usable for downstream analysis for 11 out of 12 retrieved specimens. Analysis of the specimens provided preliminary evidence of the robustness of the readout, compatibility with a wide array of techniques for molecular tissue interrogation, and promising similarities with the available observed clinical-radiological responses to temozolomide. From an investigational aspect, the amount of information obtained with IMDs allows characterization of tissue effects of any drugs of interest, within the physiological context of the intact tumor, and without affecting the standard surgical workflow.

Transcriptomic changes in oligodendrocytes and precursor cells predicts clinical outcomes of Parkinson's disease.

Several prior studies have proposed the involvement of various brain regions and cell types in Parkinson's disease (PD) pathology. Here, we performed snRNA-seq on the prefrontal cortex and anterior cingulate regions from post-mortem control and PD brain tissue. We found a significant association of oligodendrocytes (ODCs) and oligodendrocyte precursor cells (OPCs) with PD-linked risk loci and report several dysregulated genes and pathways, including regulation of tau-protein kinase activity, regulation of inclusion body assembly and protein processing involved in protein targeting to mitochondria. In an independent PD cohort with clinical measures (681 cases and 549 controls), polygenic risk scores derived from the dysregulated genes significantly predicted Montreal Cognitive Assessment (MoCA)-, and Beck Depression Inventory-II (BDI-II)-scores but not motor impairment (UPDRS-III). We extended our analysis of clinical outcome prediction by incorporating three separate datasets that were previously published by different laboratories. In the first dataset from the anterior cingulate cortex, we identified a correlation between ODCs and BDI-II. In the second dataset obtained from the substantia nigra (SN), OPCs displayed notable predictive ability for UPDRS-III. In the third dataset from the SN region, a distinct subtype of OPCs, labeled OPC_ADM, exhibited predictive ability for UPDRS-III. Intriguingly, the OPC_ADM cluster also demonstrated a significant increase in PD samples. These results suggest that by expanding our focus to glial cells, we can uncover region-specific molecular pathways associated with PD symptoms.

Adaptive bias correction for improved subseasonal forecasting.

Subseasonal forecasting-predicting temperature and precipitation 2 to 6 weeks ahead-is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remain poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. Here, to counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We show that, when applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60-90% (over baseline skills of 0.18-0.25) and precipitation forecasting skill by 40-69% (over baseline skills of 0.11-0.15) in the contiguous U.S. We couple these performance improvements with a practical workflow to explain ABC skill gains and identify higher-skill windows of opportunity based on specific climate conditions.

Large-scale differentiation of iPSC-derived motor neurons from ALS and control subjects.

Using induced pluripotent stem cells (iPSCs) to understand the mechanisms of neurological disease holds great promise; however, there is a lack of well-curated lines from a large array of participants. Answer ALS has generated over 1,000 iPSC lines from control and amyotrophic lateral sclerosis (ALS) patients along with clinical and whole-genome sequencing data. The current report summarizes cell marker and gene expression in motor neuron cultures derived from 92 healthy control and 341 ALS participants using a 32-day differentiation protocol. This is the largest set of iPSCs to be differentiated into motor neurons, and characterization suggests that cell composition and sex are significant sources of variability that need to be carefully controlled for in future studies. These data are reported as a resource for the scientific community that will utilize Answer ALS data for disease modeling using a wider array of omics being made available for these samples.

NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency.

The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.

Huntington disease oligodendrocyte maturation deficits revealed by single-nucleus RNAseq are rescued by thiamine-biotin supplementation.

The complexity of affected brain regions and cell types is a challenge for Huntington's disease (HD) treatment. Here we use single nucleus RNA sequencing to investigate molecular pathology in the cortex and striatum from R6/2 mice and human HD post-mortem tissue. We identify cell type-specific and -agnostic signatures suggesting oligodendrocytes (OLs) and oligodendrocyte precursors (OPCs) are arrested in intermediate maturation states. OL-lineage regulators OLIG1 and OLIG2 are negatively correlated with CAG length in human OPCs, and ATACseq analysis of HD mouse NeuN-negative cells shows decreased accessibility regulated by OL maturation genes. The data implicates glucose and lipid metabolism in abnormal cell maturation and identify PRKCE and Thiamine Pyrophosphokinase 1 (TPK1) as central genes. Thiamine/biotin treatment of R6/1 HD mice to compensate for TPK1 dysregulation restores OL maturation and rescues neuronal pathology. Our insights into HD OL pathology spans multiple brain regions and link OL maturation deficits to abnormal thiamine metabolism.

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Single cell metabolism: current and future trends.

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Self-supervised learning of cell type specificity from immunohistochemical images.

MOTIVATION: Advances in bioimaging now permit in situ proteomic characterization of cell-cell interactions in complex tissues, with important applications across a spectrum of biological problems from development to disease. These methods depend on selection of antibodies targeting proteins that are expressed specifically in particular cell types. Candidate marker proteins are often identified from single-cell transcriptomic data, with variable rates of success, in part due to divergence between expression levels of proteins and the genes that encode them. In principle, marker identification could be improved by using existing databases of immunohistochemistry for thousands of antibodies in human tissue, such as the Human Protein Atlas. However, these data lack detailed annotations of the types of cells in each image. RESULTS: We develop a method to predict cell type specificity of protein markers from unlabeled images. We train a convolutional neural network with a self-supervised objective to generate embeddings of the images. Using non-linear dimensionality reduction, we observe that the model clusters images according to cell types and anatomical regions for which the stained proteins are specific. We then use estimates of cell type specificity derived from an independent single-cell transcriptomics dataset to train an image classifier, without requiring any human labelling of images. Our scheme demonstrates superior classification of known proteomic markers in kidney compared to selection via single-cell transcriptomics. AVAILABILITY AND IMPLEMENTATION: Code and trained model are available at www.github.com/murphy17/HPA-SimCLR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.