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Patients with cancer undergoing chemotherapy frequently experience a neurological condition known as chemotherapy-related cognitive impairment, or “chemobrain,” which can persist for the remainder of their lives. Despite the growing prevalence of chemobrain, both its underlying mechanisms and treatment strategies remain poorly understood. Recent findings suggest that chemobrain shares several characteristics with neurodegenerative diseases, including chronic neuroinflammation, DNA damage, and synaptic loss. We investigated whether a noninvasive sensory stimulation treatment we term gamma entrainment using sensory stimuli (GENUS), which has been shown to alleviate aberrant immune and synaptic pathologies in mouse models of neurodegeneration, could also mitigate chemobrain phenotypes in mice administered a chemotherapeutic drug. When administered concurrently with the chemotherapeutic agent cisplatin, GENUS alleviated cisplatin-induced brain pathology, promoted oligodendrocyte survival, and improved cognitive function in a mouse model of chemobrain. These effects persisted for up to 105 days after GENUS treatment, suggesting the potential for long-lasting benefits. However, when administered to mice 90 days after chemotherapy, GENUS treatment only provided limited benefits, indicating that it was most effective when used to prevent the progression of chemobrain pathology. Furthermore, we demonstrated that the effects of GENUS in mice were not limited to cisplatin-induced chemobrain but also extended to methotrexate-induced chemobrain. Collectively, these findings suggest that GENUS may represent a versatile approach for treating chemobrain induced by different chemotherapy agents.

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

© 2019 by The Keio Journal of Medicine. The class 2 CRISPR-Cas endonuclease Cas12a (previously known as Cpf1) offers several advantages over Cas9, including the ability to process its own array and the requirement for just a single RNA guide. These attributes make Cas12a promising for many genome engineering applications. To further expand the suite of Cas12a tools available, we tested 16 Cas12a orthologs for activity in eukaryotic cells. Four of these new enzymes demonstrated targeted activity, one of which, from Moraxella bovoculi AAX11_00205 (Mb3Cas12a), exhibited robust indel formation. We also showed that Mb3Cas12a displays some tolerance for a shortened PAM (TTN versus the canonical Cas12a PAM TTTV). The addition of these enzymes to the genome editing toolbox will further expand the utility of this powerful technology., National Institutes of Health (Grants 1R01-HG009761, 1R01-MH110049 and 1DP1-HL141201)

Nucleic acid detection is vital for agricultural applications including trait detection during breeding, pest surveillance, and pathogen identification. Here, we use a modified version of the CRISPR-based nucleic acid detection platform SHERLOCK to quantify levels of a glyphosate resistance gene in a mixture of soybeans and to detect multiple plant genes in a single reaction. SHERLOCK is rapid (∼15 min), quantitative, and portable, and can process crude soybean extracts as input material for minimal nucleic acid sample preparation. This field-ready SHERLOCK platform with color-based lateral flow readout can be applied for detection and quantitation of genes in a range of agricultural applications.

Transcription factors (TFs) are one of the most promising but underutilized classes of drug targets. The high degree of intrinsic disorder in both the structure and the interactions (i.e., “fuzziness”) of TFs is one of the most important challenges to be addressed in this context. Here, we discuss the impacts of fuzziness on transcription factor drug discovery, describing how disorder poses fundamental problems to the typical drug design, and screening approaches used for other classes of proteins such as receptors or enzymes. We then speculate on ways modern biophysical and chemical biology approaches could synergize to overcome many of these challenges by directly addressing the challenges imposed by TF disorder and fuzziness., National Institutes of Health (Grants U54CA231630-01A1, 1F32CA261012-01)

Background The human gut harbors trillions of microbes that play dynamic roles in health. While the microbiome contributes to many cardiometabolic traits by modulating host inflammation and metabolism, there is an incomplete understanding regarding the extent that and mechanisms by which individual microbes impact risk and development of cardiovascular disease (CVD). The Framingham Heart Study (FHS) is a multi-generational observational study following participants over decades to identify risk factors for CVD by correlating genetic and phenotypic factors with clinical outcomes. As a large-scale population-based cohort with extensive clinical phenotyping, FHS provides a rich landscape to explore the relationships between the gut microbiome and cardiometabolic traits. Methods We performed 16S rRNA gene sequencing on stool from 1423 participants of the FHS Generation 3, OMNI2, and New Offspring Spouse cohorts. Data processing and taxonomic assignment were performed with the 16S bioBakery workflow using the UPARSE pipeline. We conducted statistical analyses to investigate trends in overall microbiome composition and diversity in relation to disease states and systematically examined taxonomic associations with a variety of clinical traits, disease phenotypes, clinical blood markers, and medications. Results We demonstrate that overall microbial diversity decreases with increasing 10-year CVD risk and body mass index measures. We link lifestyle factors, especially diet and exercise, to microbial diversity. Our association analyses reveal both known and unreported microbial associations with CVD and diabetes, related prescription medications, as well as many anthropometric and blood test measurements. In particular, we observe a set of microbial species that demonstrate significant associations with CVD risk, metabolic syndrome, and type 2 diabetes as well as a number of shared associations between microbial species and cardiometabolic subphenotypes. Conclusions, National Institutes of Health (R01 HL131015, RC2 DK114784 and P30 DK043351)

Imidazoquinoline derivatives (IMDs) and related compounds function as synthetic agonists of Toll-like receptors 7 and 8 (TLR7/8) and one is FDA approved for topical antiviral and skin cancer treatments. Nevertheless, these innate immune system-activating drugs have potentially much broader therapeutic utility; they have been pursued as antitumor immunomodulatory agents and more recently as candidate vaccine adjuvants for cancer and infectious disease. The broad expression profiles of TLR7/8, poor pharmacokinetic properties of IMDs, and toxicities associated with systemic administration, however, are formidable barriers to successful clinical translation. Herein, we review IMD formulations that have advanced to the clinic and discuss issues related to biodistribution and toxicity that have hampered the further development of these compounds. Recent strategies aimed at enhancing safety and efficacy, particularly through the use of bioconjugates and nanoparticle formulations that alter pharmacokinetics, biodistribution, and cellular targeting, are described. Finally, key aspects of the biology of TLR7 signaling, such as TLR7 tolerance, that may need to be considered in the development of new IMD therapeutics are discussed., NIH (UM1 AI144462), NIH (1R01CA22046801)

© 2020, The Author(s), under exclusive licence to Springer Nature Limited. Nucleic acid detection by isothermal amplification and the collateral cleavage of reporter molecules by CRISPR-associated enzymes is a promising alternative to quantitative PCR. Here, we report the clinical validation of the specific high-sensitivity enzymatic reporter unlocking (SHERLOCK) assay using the enzyme Cas13a from Leptotrichia wadei for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—the virus that causes coronavirus disease 2019 (COVID-19)—in 154 nasopharyngeal and throat swab samples collected at Siriraj Hospital, Thailand. Within a detection limit of 42 RNA copies per reaction, SHERLOCK was 100% specific and 100% sensitive with a fluorescence readout, and 100% specific and 97% sensitive with a lateral-flow readout. For the full range of viral load in the clinical samples, the fluorescence readout was 100% specific and 96% sensitive. For 380 SARS-CoV-2-negative pre-operative samples from patients undergoing surgery, SHERLOCK was in 100% agreement with quantitative PCR with reverse transcription. The assay, which we show is amenable to multiplexed detection in a single lateral-flow strip incorporating an internal control for ribonuclease contamination, should facilitate SARS-CoV-2 detection in settings with limited resources.

© 2020 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd One of the most fundamental aspects of ecological research and monitoring is accurate species identification, but cryptic speciation and observer error can confound phenotype-based identification. The CRISPR-Cas toolkit has facilitated remarkable advances in many scientific disciplines, but the fields of ecology and conservation biology have yet to fully embrace this powerful technology. The recently developed CRISPR-Cas13a platform SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing) enables highly accurate taxonomic identification and has all the characteristics needed to transition to ecological and environmental disciplines. Here we conducted a series of “proof of principle” experiments to characterize SHERLOCK’s ability to accurately, sensitively and rapidly distinguish three fish species of management interest co-occurring in the San Francisco Estuary that are easily misidentified in the field. We improved SHERLOCK’s ease of field deployment by combining the previously demonstrated rapid isothermal amplification and CRISPR genetic identification with a minimally invasive and extraction-free DNA collection protocol, as well as the option of instrument-free lateral flow detection. This approach opens the door for redefining how, where and by whom genetic identifications occur in the future., California Department of Water Resources (Contract no. 4600012328)

We call upon the research community to standardize efforts to use daily self-reported data about COVID-19 symptoms in the response to the pandemic and to form a collaborative consortium to maximize global gain while protecting participant privacy.

Mechanisms underlying severe coronavirus disease 2019 (COVID-19) disease remain poorly understood. We analyze several thousand plasma proteins longitudinally in 306 COVID-19 patients and 78 symptomatic controls, uncovering immune and non-immune proteins linked to COVID-19. Deconvolution of our plasma proteome data using published scRNA-seq datasets reveals contributions from circulating immune and tissue cells. Sixteen percent of patients display reduced inflammation yet comparably poor outcomes. Comparison of patients who died to severely ill survivors identifies dynamic immune-cell-derived and tissue-associated proteins associated with survival, including exocrine pancreatic proteases. Using derived tissue-specific and cell-type-specific intracellular death signatures, cellular angiotensin-converting enzyme 2 (ACE2) expression, and our data, we infer whether organ damage resulted from direct or indirect effects of infection. We propose a model in which interactions among myeloid, epithelial, and T cells drive tissue damage. These datasets provide important insights and a rich resource for analysis of mechanisms of severe COVID-19 disease., NIH/NIAID (Grant U19 AI082630)

Virological testing is central to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) containment, but many settings face severe limitations on testing. Group testing offers a way to increase throughput by testing pools of combined samples; however, most proposed designs have not yet addressed key concerns over sensitivity loss and implementation feasibility. Here, we combined a mathematical model of epidemic spread and empirically derived viral kinetics for SARS-CoV-2 infections to identify pooling designs that are robust to changes in prevalence and to ratify sensitivity losses against the time course of individual infections. We show that prevalence can be accurately estimated across a broad range, from 0.02 to 20%, using only a few dozen pooled tests and using up to 400 times fewer tests than would be needed for individual identification. We then exhaustively evaluated the ability of different pooling designs to maximize the number of detected infections under various resource constraints, finding that simple pooling designs can identify up to 20 times as many true positives as individual testing with a given budget. Crucially, we confirmed that our theoretical results can be translated into practice using pooled human nasopharyngeal specimens by accurately estimating a 1% prevalence among 2304 samples using only 48 tests and through pooled sample identification in a panel of 960 samples. Our results show that accounting for variation in sampled viral loads provides a nuanced picture of how pooling affects sensitivity to detect infections. Using simple, practical group testing designs can vastly increase surveillance capabilities in resource-limited settings., National Institute of General Medical Sciences (Grant U54GM088558)

© Copyright 2015, Mary Ann Liebert, Inc. 2015. The Genotype-Tissue Expression (GTEx) project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals. A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project. Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community., National Institutes of Health (Grant HHSN261200800001E)

Cerebrospinal fluid (CSF) provides vital support for the brain. Abnormal CSF accumulation, such as hydrocephalus, can negatively affect perinatal neurodevelopment. The mechanisms regulating CSF clearance during the postnatal critical period are unclear. Here, we show that CSF K+, accompanied by water, is cleared through the choroid plexus (ChP) during mouse early postnatal development. We report that, at this developmental stage, the ChP showed increased ATP production and increased expression of ATP-dependent K+ transporters, particularly the Na+, K+, Cl−, and water cotransporter NKCC1. Overexpression of NKCC1 in the ChP resulted in increased CSF K+ clearance, increased cerebral compliance, and reduced circulating CSF in the brain without changes in intracranial pressure in mice. Moreover, ChP-specific NKCC1 overexpression in an obstructive hydrocephalus mouse model resulted in reduced ventriculomegaly. Collectively, our results implicate NKCC1 in regulating CSF K+ clearance through the ChP in the critical period during postnatal neurodevelopment in mice., NIH (Grants R01-AI130591 and R35-HL145242)

Mesenchymal stem cells (MSCs) are promising candidates for the development of cell-based drug delivery systems for autoimmune inflammatory diseases, such as multiple sclerosis (MS). Here, we investigated the effect of Ro-31-8425, an ATP-competitive kinase inhibitor, on the therapeutic properties of MSCs. Upon a simple pretreatment procedure, MSCs spontaneously took up and then gradually released significant amounts of Ro-31-8425. Ro-31-8425 (free or released by MSCs) suppressed the proliferation of CD4+ T cells in vitro following polyclonal and antigen-specific stimulation. Systemic administration of Ro-31-8425-loaded MSCs ameliorated the clinical course of experimental autoimmune encephalomyelitis (EAE), a murine model of MS, displaying a stronger suppressive effect on EAE than control MSCs or free Ro-31-8425. Ro-31-8425-MSC administration resulted in sustained levels of Ro-31-8425 in the serum of EAE mice, modulating immune cell trafficking and the autoimmune response during EAE. Collectively, these results identify MSC-based drug delivery as a potential therapeutic strategy for the treatment of autoimmune diseases., National Institutes of Health (Grant HL095722)

The N-terminal domain (NTD) of the GluN1 subunit (GluN1-NTD) is important for NMDA receptor structure and function, but the interacting proteins of the GluN1-NTD are not well understood. Starting with an unbiased screen of ~ 1,500 transmembrane proteins using the purified GluN1-NTD protein as a bait, we identify Protocadherin 7 (PCDH7) as a potential interacting protein. PCDH7 is highly expressed in the brain and has been linked to CNS disorders, including epilepsy. Using primary neurons and brain slice cultures, we find that overexpression and knockdown of PCDH7 induce opposing morphological changes of dendritic structures. We also find that PCDH7 overexpression reduces synaptic NMDA receptor currents. These data show that PCDH7 can regulate dendritic spine morphology and synaptic function, possibly via interaction with the GluN1 subunit.

Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases1–4. Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer–gene connections across cell types5,6. We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test >3,500 potential enhancer–gene connections for 30 genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer–gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome., NHGRI (Grant 1K99HG009917-01), National Institute of General Medical Sciences (Grant T32GM007753), NSF (Award PHY1427654), Welch Foundation (Grant Q-1866), USDA (Grant 2017-05741), NIH (Grant U01HL130010 and Award UM1HG009375)

Background: Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. Methods: The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. Results: We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated thi

Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.

As the scale of genomic and health-related data explodes and our understanding of these data matures, the privacy of the individuals behind the data is increasingly at stake. Traditional approaches to protect privacy have fundamental limitations. Here we discuss emerging privacy-enhancing technologies that can enable broader data sharing and collaboration in genomics research., NIH (Grant R01GM01108348)

More than 3000 antimicrobial peptides (AMPs) have been discovered, seven of which have been approved by the U.S. Food and Drug Administration (FDA). Now commercialized, these seven peptides have mostly been utilized for topical medications, though some have been injected into the body to treat severe bacterial infections. To understand the translational potential for AMPs, we analyzed FDA-approved drugs in the FDA drug database. We examined their physicochemical properties, secondary structures, and mechanisms of action, and compared them with the peptides in the AMP database. All FDA-approved AMPs were discovered in Gram-positive soil bacteria, and 98% of known AMPs also come from natural sources (skin secretions of frogs and toxins from different species). However, AMPs can have undesirable properties as drugs, including instability and toxicity. Thus, the design and construction of effective AMPs require an understanding of the mechanisms of known peptides and their effects on the human body. This review provides an overview to guide the development of AMPs that can potentially be used as antimicrobial drugs. Keywords: antimicrobial peptides; antibiotics; antibiotic resistance; rational protein design; peptide therapeutics; FDA-approved peptides, Defense Threat Reduction Agency (grant no. HDTRA11810041), NIH (grant no. U19AI142780)

CRISPR (clustered regularly interspaced short palindromic repeats) genome-editing experiments offer enormous potential for the evaluation of genomic loci using arrayed single guide RNAs (sgRNAs) or pooled sgRNA libraries. Numerous computational tools are available to help design sgRNAs with optimal on-target efficiency and minimal off-target potential. In addition, computational tools have been developed to analyze deep-sequencing data resulting from genome-editing experiments. However, these tools are typically developed in isolation and oftentimes are not readily translatable into laboratory-based experiments. Here, we present a protocol that describes in detail both the computational and benchtop implementation of an arrayed and/or pooled CRISPR genome-editing experiment. This protocol provides instructions for sgRNA design with CRISPOR (computational tool for the design, evaluation, and cloning of sgRNA sequences), experimental implementation, and analysis of the resulting high-throughput sequencing data with CRISPResso (computational tool for analysis of genome-editing outcomes from deep-sequencing data). This protocol allows for design and execution of arrayed and pooled CRISPR experiments in 4-5 weeks by non-experts, as well as computational data analysis that can be performed in 1-2 d by both computational and noncomputational biologists alike using web-based and/or command-line versions., NHGRI Career Development Award (R00HG008399), Defense Advanced Research Projects Agency (HR0011-17-2-0042)

Pilocytic astrocytoma (PA), the most common childhood brain tumor, is a low-grade glioma with a single driver BRAF rearrangement. Here, we perform scRNAseq in six PAs using methods that enabled detection of the rearrangement. When compared to higher-grade gliomas, a strikingly higher proportion of the PA cancer cells exhibit a differentiated, astrocyte-like phenotype. A smaller proportion of cells exhibit a progenitor-like phenotype with evidence of proliferation. These express a mitogen-activated protein kinase (MAPK) programme that was absent from higher-grade gliomas. Immune cells, especially microglia, comprise 40% of all cells in the PAs and account for differences in bulk expression profiles between tumor locations and subtypes. These data indicate that MAPK signaling is restricted to relatively undifferentiated cancer cells in PA, with implications for investigational therapies directed at this pathway.

Dysregulation of the immune response to bacterial infection can lead to sepsis, a condition with high mortality. Multiple whole-blood gene-expression studies have defined sepsis-associated molecular signatures, but have not resolved changes in transcriptional states of specific cell types. Here, we used single-cell RNA-sequencing to profile the blood of people with sepsis (n = 29) across three clinical cohorts with corresponding controls (n = 36). We profiled total peripheral blood mononuclear cells (PBMCs, 106,545 cells) and dendritic cells (19,806 cells) across all subjects and, on the basis of clustering of their gene-expression profiles, defined 16 immune-cell states. We identified a unique CD14+ monocyte state that is expanded in people with sepsis and validated its power in distinguishing these individuals from controls using public transcriptomic data from subjects with different disease etiologies and from multiple geographic locations (18 cohorts, n = 1,467 subjects). We identified a panel of surface markers for isolation and quantification of the monocyte state and characterized its epigenomic and functional phenotypes, and propose a model for its induction from human bone marrow. This study demonstrates the utility of single-cell genomics in discovering disease-associated cytologic signatures and provides insight into the cellular basis of immune dysregulation in bacterial sepsis.

Type VI CRISPR-Cas systems contain programmable single-effector RNA-guided RNases, including Cas13b, one of the four known family members. Cas13b, which has been used for both RNA editing and nucleic acid detection, is unique among type VI CRISPR effectors in its linear domain architecture and CRISPR RNA (crRNA) structure. Here, we report the crystal structure of Prevotella buccae Cas13b (PbuCas13b) bound to crRNA at 1.65 Å resolution. This structure, combined with biochemical experiments assaying the stability, kinetics, and function of Cas13b, provides a mechanistic model for Cas13b target RNA recognition and identifies features responsible for target and cleavage specificity. Based on these observations, we generated Cas13b variants with altered cleavage preferences, which may expand the utility of nuclease-based RNA detection assays and other applications of Cas13b in mammalian cells. Slaymaker et al. present a high-resolution structure of Cas13b in combination with biochemical studies showing that Cas13b is a highly dynamic, multi-turnover enzyme distinct from other Cas13 family members. Additionally, the authors demonstrate Cas13b can be rationally engineered to change RNA cleavage specificity and create a minimal variant suitable for delivery with viral vectors., NIH (Grants F30-NRSA-1F30-CA210382, 1R01-HG009761, 1R01-MH110049 and 1DP1-HL141201)

National Institutes of Health (Grants 1F31-MH117886, 1R01-MH110049 and 1DP1-HL141201)

In organ transplantation, infection and rejection are major causes of graft loss. They are linked by the net state of immunosuppression. To diagnose and treat these conditions earlier, and to improve long-term patient outcomes, refined strategies for the monitoring of patients after graft transplantation are needed. Here, we show that a fast and inexpensive assay based on CRISPR–Cas13 accurately detects BK polyomavirus DNA and cytomegalovirus DNA from patient-derived blood and urine samples, as well as CXCL9 messenger RNA (a marker of graft rejection) at elevated levels in urine samples from patients experiencing acute kidney transplant rejection. The assay, which we adapted for lateral-flow readout, enables—via simple visualization—the post-transplantation monitoring of common opportunistic viral infections and of graft rejection, and should facilitate point-of-care post-transplantation monitoring., National Science Foundation (Award 1122374)

Non-coding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 day.

A screening approach that combines high-content imaging with in situ sequencing can identify genes that affect spatially and temporally defined phenotypes like morphology and subcellular localization, expanding the list of scientific questions that can be asked with genetic tools., National Human Genome Research Institute (Grants HG009283 and HG006193), NIH (Grants 1R01-HG009761, 1R01-MH110049, and 1DP1-HL141201)

Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks. IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.

BACKGROUND: Data for front-line health-care workers and risk of COVID-19 are limited. We sought to assess risk of COVID-19 among front-line health-care workers compared with the general community and the effect of personal protective equipment (PPE) on risk. METHODS: We did a prospective, observational cohort study in the UK and the USA of the general community, including front-line health-care workers, using self-reported data from the COVID Symptom Study smartphone application (app) from March 24 (UK) and March 29 (USA) to April 23, 2020. Participants were voluntary users of the app and at first use provided information on demographic factors (including age, sex, race or ethnic background, height and weight, and occupation) and medical history, and subsequently reported any COVID-19 symptoms. We used Cox proportional hazards modelling to estimate multivariate-adjusted hazard ratios (HRs) of our primary outcome, which was a positive COVID-19 test. The COVID Symptom Study app is registered with ClinicalTrials.gov, NCT04331509. FINDINGS: Among 2 035 395 community individuals and 99 795 front-line health-care workers, we recorded 5545 incident reports of a positive COVID-19 test over 34 435 272 person-days. Compared with the general community, front-line health-care workers were at increased risk for reporting a positive COVID-19 test (adjusted HR 11·61, 95% CI 10·93-12·33). To account for differences in testing frequency between front-line health-care workers and the general community and possible selection bias, an inverse probability-weighted model was used to adjust for the likelihood of receiving a COVID-19 test (adjusted HR 3·40, 95% CI 3·37-3·43). Secondary and post-hoc analyses suggested adequacy of PPE, clinical setting, and ethnic background were also important factors. INTERPRETATION: In the UK and the USA, risk of reporting a positive test for COVID-19 was increased among front-line health-care workers. Health-care systems should ensure adequate availabili

BACKGROUND: Amyotrophic lateral sclerosis [1] is a genetically heterogeneous neurodegenerative disorder, characterized by late-onset degeneration of motor neurons leading to progressive limb and bulbar weakness, as well as of the respiratory muscles, which is the primary cause of disease fatality. To date, over 25 genes have been implicated as causative in ALS with C9orf72, SOD1, FUS, and TARDBP accounting for the majority of genetically positive cases. RESULTS: We identified two patients of Italian and French ancestry with a clinical diagnosis of juvenile-onset ALS who were mutation-negative in any of the known ALS causative genes. Starting with the index case, a consanguineous family of Italian origin, we performed whole-exome sequencing and identified candidate pathogenic mutations in 35 genes, 27 of which were homozygous. We next parsed all candidates against a cohort of 3641 ALS cases; only ATP13A2 was found to harbor recessive changes, in a patient with juvenile-onset ALS, similar to the index case. In vivo complementation of ATP13A2 using a zebrafish surrogate model that focused on the assessment of motor neuron morphology and cerebellar integrity confirmed the role of this gene in central and peripheral nervous system maintenance and corroborated the damaging direction of effect of the change detected in the index case of this study. CONCLUSIONS: We here expand the phenotypic spectrum associated with genetic variants in ATP13A2 that previously comprised Kufor-Rakeb syndrome, spastic paraplegia 78, and neuronal ceroid lipofuscinosis type 12 (CLN12), to also include juvenile-onset ALS, as supported by both genetic and functional data. Our findings highlight the importance of establishing a complete genetic profile towards obtaining an accurate clinical diagnosis.

The adoption of CRISPR-Cas9 technology for functional genetic screens has been a transformative advance. Due to its modular nature, this technology can be customized to address a myriad of questions. To date, pooled, genome-scale studies have uncovered genes responsible for survival, proliferation, drug resistance, viral susceptibility, and many other functions. The technology has even been applied to the functional interrogation of the non-coding genome. However, applications of this technology to neurological diseases remain scarce. This shortfall motivated the assembly of a review that will hopefully help researchers moving in this direction find their footing. The emphasis here will be on design considerations and concepts underlying this methodology. We will highlight groundbreaking studies in the CRISPR-Cas9 functional genetics field and discuss strengths and limitations of this technology for neurological disease applications. Finally, we will provide practical guidance on navigating the many choices that need to be made when implementing a CRISPR-Cas9 functional genetic screen for the study of neurological diseases.

Huntington’s disease (HD) is an incurable neurodegenerative disorder caused by CAG trinucleotide expansions in the huntingtin gene. Markers of both systemic and CNS immune activation and inflammation have been widely noted in HD and mouse models of HD. In particular, elevation of the pro-inflammatory cytokine interleukin-6 (IL-6) is the earliest reported marker of immune activation in HD, and this elevation has been suggested to contribute to HD pathogenesis. To test the hypothesis that IL-6 deficiency would be protective against the effects of mutant huntingtin, we generated R6/2 HD model mice that lacked IL-6. Contrary to our prediction, IL-6 deficiency exacerbated HD-model associated behavioral phenotypes. Single nuclear RNA Sequencing (snRNA-seq) analysis of striatal cell types revealed that IL-6 deficiency led to the dysregulation of various genes associated with synaptic function, as well as the BDNF receptor Ntrk2. These data suggest that IL-6 deficiency exacerbates the effects of mutant huntingtin through dysregulation of genes of known relevance to HD pathobiology in striatal neurons, and further suggest that modulation of IL-6 to a level that promotes proper regulation of genes associated with synaptic function may hold promise as an HD therapeutic target., NIH/NINDS (Award 1 R01 NS085880), Brain Research Foundation Fay/Frank Seed Award, Jeptha H. and Emily V. Wade Award, NIH (Awards R01 AG058002, U01 NS110453, UG3 NS115064), NIH (5T32EB019940–05)

Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25 to 50%. We explore the utility of transcriptome sequencing [RNA sequencing (RNA-seq)] as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to more than 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI-like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of having collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches. 2017, National Institutes of Health (Grant GM096911), National Institute of General Medical Sciences (Grants F32GM115208 and 4T32GM007748)

The RNA-guided Cpf1 nuclease cleaves double-stranded DNA targets complementary to the CRISPR RNA (crRNA), and it has been harnessed for genome editing technologies. Recently, Acidaminococcus sp. BV3L6 (AsCpf1) was engineered to recognize altered DNA sequences as the protospacer adjacent motif (PAM), thereby expanding the target range of Cpf1-mediated genome editing. Whereas wild-type AsCpf1 recognizes the TTTV PAM, the RVR (S542R/K548V/N552R) and RR (S542R/K607R) variants can efficiently recognize the TATV and TYCV PAMs, respectively. However, their PAM recognition mechanisms remained unknown. Here we present the 2.0 Å resolution crystal structures of the RVR and RR variants bound to a crRNA and its target DNA. The structures revealed that the RVR and RR variants primarily recognize the PAM-complementary nucleotides via the substituted residues. Our high-resolution structures delineated the altered PAM recognition mechanisms of the AsCpf1 variants, providing a basis for the further engineering of CRISPR-Cpf1., NIMH (5DP1- MH100706 and 1R01-MH110049), PRESTO (JPMJPR13L8), JSPS KAKENHI (Grant nos. 26291010 and 15H01463)

Diamond-Blackfan anemia (DBA) is a rare bone marrow failure disorder that affects 7 out of 1,000,000 live births and has been associated with mutations in components of the ribosome. In order to characterize the genetic landscape of this heterogeneous disorder, we recruited a cohort of 472 individuals with a clinical diagnosis of DBA and performed whole-exome sequencing (WES). We identified relevant rare and predicted damaging mutations for 78% of individuals. The majority of mutations were singletons, absent from population databases, predicted to cause loss of function, and located in 1 of 19 previously reported ribosomal protein (RP)-encoding genes. Using exon coverage estimates, we identified and validated 31 deletions in RP genes. We also observed an enrichment for extended splice site mutations and validated their diverse effects using RNA sequencing in cell lines obtained from individuals with DBA. Leveraging the size of our cohort, we observed robust genotype-phenotype associations with congenital abnormalities and treatment outcomes. We further identified rare mutations in seven previously unreported RP genes that may cause DBA, as well as several distinct disorders that appear to phenocopy DBA, including nine individuals with biallelic CECR1 mutations that result in deficiency of ADA2. However, no new genes were identified at exome-wide significance, suggesting that there are no unidentified genes containing mutations readily identified by WES that explain >5% of DBA-affected case subjects. Overall, this report should inform not only clinical practice for DBA-affected individuals, but also the design and analysis of rare variant studies for heterogeneous Mendelian disorders.

Although the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs × 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the “connectivity” framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics. A large compendium of cellular responses to drugs as profiled through proteomic assays of phosphosignaling and histone modifications reveals cellular responses that transcend lineage, discovers unexpected associations between drugs, and recognizes therapeutic hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. Keywords: mass spectrometry; proteomics; drug discovery; signaling; epigenetics; mechanism of action; LINCS project; GCP; P100; L1000, NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program (Grant U54HG008097), NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program (Grant U54HG008699)

The RNA-guided Cpf1 (also known as Cas12a) nuclease associates with a CRISPR RNA (crRNA) and cleaves the double-stranded DNA target complementary to the crRNA guide. The two Cpf1 orthologs from Acidaminococcus sp. (AsCpf1) and Lachnospiraceae bacterium (LbCpf1) have been harnessed for eukaryotic genome editing. Cpf1 requires a specific nucleotide sequence, called a protospacer adjacent motif (PAM), for target recognition. Besides the canonical TTTV PAM, Cpf1 recognizes suboptimal C-containing PAMs. Here, we report four crystal structures of LbCpf1 in complex with the crRNA and its target DNA containing either TTTA, TCTA, TCCA, or CCCA as the PAM. These structures revealed that, depending on the PAM sequences, LbCpf1 undergoes conformational changes to form altered interactions with the PAM-containing DNA duplexes, thereby achieving the relaxed PAM recognition. Collectively, the present structures advance our mechanistic understanding of the PAM-dependent, crRNA-guided DNA cleavage by the Cpf1 family nucleases. Keywords: CRISPR-Cas; Cas12a; Cpf1; crystal structure; protospacer adjacent motif, NIMH (Grant 5DP1-MH100706), NIMH (Grant 1R01-MH110049), NIDDK (Grant 5R01DK097768-03)

BACKGROUND: Rapid and extensive testing of large parts of the population and specific subgroups is crucial for proper management of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and decision-making in times of a pandemic outbreak. However, point-of-care (POC) testing in places such as emergency units, outpatient clinics, airport security points or the entrance of any public building is a major challenge. The need for thermal cycling and nucleic acid isolation hampers the use of standard PCR-based methods for this purpose. METHODS: To avoid these obstacles, we tested PCR-independent methods for the detection of SARS-CoV-2 RNA from primary material (nasopharyngeal swabs) including reverse transcription loop-mediated isothermal amplification (RT-LAMP) and specific high-sensitivity enzymatic reporter unlocking (SHERLOCK). RESULTS: Whilst specificity of standard RT-LAMP assays appears to be satisfactory, sensitivity does not reach the current gold-standard quantitative real-time polymerase chain reaction (qPCR) assays yet. We describe a novel multiplexed RT-LAMP approach and validate its sensitivity on primary samples. This approach allows for fast and reliable identification of infected individuals. Primer optimization and multiplexing helps to increase sensitivity significantly. In addition, we directly compare and combine our novel RT-LAMP assays with SHERLOCK. CONCLUSION: In summary, this approach reveals one-step multiplexed RT-LAMP assays as a prime-option for the development of easy and cheap POC test kits.

Improved methods are needed to model CRISPR screen data for interrogation of genetic elements that alter reporter gene expression readout. We create MAUDE (Mean Alterations Using Discrete Expression) for quantifying the impact of guide RNAs on a target gene’s expression in a pooled, sorting-based expression screen. MAUDE quantifies guide-level effects by modeling the distribution of cells across sorting expression bins. It then combines guides to estimate the statistical significance and effect size of targeted genetic elements. We demonstrate that MAUDE outperforms previous approaches and provide experimental design guidelines to best leverage MAUDE, which is available on https://github.com/Carldeboer/MAUDE.

Synthetic methylotrophy, the modification of organisms such as E. coli to grow on methanol, is a longstanding goal of metabolic engineering and synthetic biology. The poor kinetic properties of NAD-dependent methanol dehydrogenase, the first enzyme in most methanol assimilation pathways, limit pathway flux and present a formidable challenge to synthetic methylotrophy. To address this bottleneck, we used a formaldehyde biosensor to develop a phage-assisted noncontinuous evolution (PANCE) selection for variants of Bacillus methanolicus methanol dehydrogenase 2 (Bm Mdh2). Using this selection, we evolved Mdh2 variants with up to 3.5-fold improved Vmax. The mutations responsible for enhanced activity map to the predicted active site region homologous to that of type III iron-dependent alcohol dehydrogenases, suggesting a new critical region for future methanol dehydrogenase engineering strategies. Evolved Mdh2 variants enable twice as much 13C-methanol assimilation into central metabolites than previously reported state-of-the-art methanol dehydrogenases. This work provides improved Mdh2 variants and establishes a laboratory evolution approach for metabolic pathways in bacterial cells. ©2019 American Chemical Society., US NIH NIBIB R01 EB022376, NIGMS R35 GM118062, US DOE DE-AR0000433, NSF GRFP (award No. 1144152), NSF GRFP (award No. 1122374)

How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation. ©2019, The Author(s), under exclusive licence to Springer Nature America, Inc., NIH (grant no. K99-HG009920-01), Fellowship from the Canadian Institutes for Health Research, MIT Presidential Fellowship

The RNA-guided endonuclease Cas9 cleaves its target DNA and is a powerful genome-editing tool. However, the widely used Streptococcus pyogenes Cas9 enzyme (SpCas9) requires an NGG protospacer adjacent motif (PAM) for target recognition, thereby restricting the targetable genomic loci. Here, we report a rationally engineered SpCas9 variant (SpCas9-NG) that can recognize relaxed NG PAMs. The crystal structure revealed that the loss of the base-specific interaction with the third G is compensated by newly introduced non-base-specific interactions, enabling the NG PAM recognition. We showed that SpCas9-NG induces indels at endogenous target sites bearing NG PAMs in human cells. Furthermore, we found that the fusion of SpCas9-NG and the activation-induced cytidine deaminase (AID) mediates the C-to-T conversion at target sites with NG PAMs in human cells., NICHD (Grants P01HD087157, R01HD088412), NIMH (Grants 5DP1-MH100706,1R01-MH110049), NIDDK (Grant 5R01DK097768-03)

Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, " motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ∼90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

A major challenge for stem cell engineering is achieving a holistic understanding of the molecular networks and biological processes governing cell differentiation. To address this challenge, we describe a computational approach that combines gene expression analysis, previous knowledge from proteomic pathway informatics and cell signaling models to delineate key transitional states of differentiating cells at high resolution. Our network models connect sparse gene signatures with corresponding, yet disparate, biological processes to uncover molecular mechanisms governing cell fate transitions. This approach builds on our earlier CellNet and recent trajectory-defining algorithms, as illustrated by our analysis of hematopoietic specification along the erythroid lineage, which reveals a role for the EGF receptor family member, ErbB4, as an important mediator of blood development. We experimentally validate this prediction and perturb the pathway to improve erythroid maturation from human pluripotent stem cells. These results exploit an integrative systems perspective to identify new regulatory processes and nodes useful in cell engineering., National Institute of General Medical Sciences (NIGMS) (Grant R01-GM081336), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (Grant R24-DK092760), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (R24-DK49216)

The human genome contains thousands of long non-coding RNAs1, but specific biological functions and biochemical mechanisms have been discovered for only about a dozen2–7. A specific long non-coding RNA—non-coding RNA activated by DNA damage (NORAD)—has recently been shown to be required for maintaining genomic stability8, but its molecular mechanism is unknown. Here we combine RNA antisense purification and quantitative mass spectrometry to identify proteins that directly interact with NORAD in living cells. We show that NORAD interacts with proteins involved in DNA replication and repair in steady-state cells and localizes to the nucleus upon stimulation with replication stress or DNA damage. In particular, NORAD interacts with RBMX, a component of the DNA-damage response, and contains the strongest RBMX-binding site in the transcriptome. We demonstrate that NORAD controls the ability of RBMX to assemble a ribonucleoprotein complex—which we term NORAD-activated ribonucleoprotein complex 1 (NARC1)—that contains the known suppressors of genomic instability topoisomerase I (TOP1), ALYREF and the PRPF19–CDC5L complex. Cells depleted for NORAD or RBMX display an increased frequency of chromosome segregation defects, reduced replication-fork velocity and altered cell-cycle progression—which represent phenotypes that are mechanistically linked to TOP1 and PRPF19–CDC5L function. Expression of NORAD in trans can rescue defects caused by NORAD depletion, but rescue is significantly impaired when the RBMX-binding site in NORAD is deleted. Our results demonstrate that the interaction between NORAD and RBMX is important for NORAD function, and that NORAD is required for the assembly of the previously unknown topoisomerase complex NARC1, which contributes to maintaining genomic stability. In addition, we uncover a previously unknown function for long non-coding RNAs in modulating the ability of an RNA-binding protein to assemble a higher-order ribonucleoprotein complex.

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand–receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets (accessible online at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain) provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.