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Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain 1 – 3 . With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions 4 . We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis. The authors describe an integrated atlas of the diverse cell types in the mouse primary motor cortex.

The gender gaps faced by women in our societies globally-in pay, education, and even health outcomes-are well known. Yet, we still seek commitment and workable policies to ensure that these gaps close. In the case of invention, a sizeable gap exists, as women patent significantly less than men, and this disparity is explored to some extent in this article. However, our main goal is to highlight women in the American Association for the Advancement of Science (AAAS)-Lemelson Invention Ambassadors program who have broken the barriers and become important inventors. By focusing on their achievements, we hope to inspire others around the world to ensure that more women have the opportunities necessary to increase their participation in solving global problems.

Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization 1 – 5 . First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties. The BRAIN Initiative Cell Census Network has constructed a multimodal cell census and atlas of the mammalian primary motor cortex in a landmark effort towards understanding brain cell-type diversity, neural circuit organization and brain function.

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties. Competing Interest Statement The competing interests are detailed in the Competing Interests section in the manuscript file.

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis. Footnotes * Updated Extended Data Figures 6, 7 and author order. * https://assets.nemoarchive.org/dat-ch1nqb7 * https://nemoanalytics.org/ * https://brainome.ucsd.edu/annoj/BICCN_MOp/

Maternally expressed gene 3 (MEG3, mouse homolog Gtl2) encodes a long noncoding RNA (lncRNA) that is expressed in many normal tissues, but is suppressed in various cancer cell lines and tumors, suggesting it plays a functional role as a tumor suppressor. Hypermethylation has been shown to contribute to this loss of expression. We now demonstrate that MEG3 expression is regulated by the retinoblastoma protein (Rb) pathway and correlates with a change in cell proliferation. Microarray analysis of mouse embryonic fibroblasts (MEFs) isolated from mice with genetic deletion of all three Rb family members (TKO) revealed a significant silencing of Gtl2/MEG3 expression compared to WT MEFs, and re-expression of Gtl2/MEG3 caused decrease in cell proliferation and increased apoptosis. MEG3 levels also were suppressed in A549 lung cancer cells compared with normal human bronchial epithelial (NHBE) cells, and, similar to the TKO cells, re-constitution of MEG3 led to a decrease in cell proliferation and elevated apoptosis. Activation of pRb by treatment of A549 and SK-MES-1 cells with palbociclib, a CDK4/6 inhibitor, increased the expression of MEG3 in a dose-dependent manner, while knockdown of pRb/p107 attenuated this effect. In addition, expression of phosphorylation-deficient mutant of pRb increased MEG3 levels in both lung cancer cell types. Treatment of these cells with palbociclib also decreased the expression of pRb-regulated DNA methyltransferase 1 (DNMT1), while conversely, knockdown of DNMT1 resulted in increased expression of MEG3. As gene methylation has been suggested for MEG3 regulation, we found that palbociclib resulted in decreased methylation of the MEG3 locus similar to that observed with 5-aza-deoxycytidine. Anti-sense oligonucleotide silencing of drug-induced MEG3 expression in A549 and SK-MES-1 cells partially rescued the palbociclib-mediated decrease in cell proliferation, while analysis of the TCGA database revealed decreased MEG3 expression in human lung tumors harboring a disrupted RB pathway. Together, these data suggest that disruption of the pRb-DNMT1 pathway leads to a decrease in MEG3 expression, thereby contributing to the pro-proliferative state of certain cancer cells.

Background and Aim This review investigates the role of gastrointestinal and hepatic manifestations in COVID‐19, particularly with regard to the prevalence of isolated gastrointestinal (GI) symptoms. Methods We searched PubMed, Embase, and Cochrane library for COVID‐19 publications from 1 December 2019 to 18 May 2020. We included any study that reported the presence of GI symptoms in a sample of >5 COVID‐19 patients. Data collection and risk of bias assessment were performed independently by two reviewers. Where ≥3 studies reported data sufficiently similar to allow calculation of a pooled prevalence, we performed random effects meta‐analysis. Results This review included 17 776 COVID‐19 patients from 108 studies. Isolated GI symptoms only occurred in 1% (95% confidence interval [CI] 0–6%) of patients. GI symptoms were reported in 20% (95% CI 15–24%) of patients. The most common were anorexia (21%, 95% CI 15–27%), diarrhea (13%, 95% CI 11–16%), nausea or vomiting (8%, 95% CI 6–11%), and abdominal pain (4%, 95% CI 2–6%). Transaminase elevations were present in 24% (95% CI 17–31%) of patients. Higher prevalence of GI symptoms were reported in studies published after 1st April, with prevalence of diarrhea 16% (95% CI 13–20), nausea or vomiting 12% (95% CI 8–16%), and any GI symptoms 24% (95% CI 18–34%). GI symptoms were associated with severe COVID‐19 disease (odds ratio [OR] 2.1, 95% CI 1.3–3.2), but not mortality (OR 0.90, 95% CI 0.52–1.54). Conclusions Patients with isolated GI symptoms may represent a small but significant portion of COVID‐19 cases. When testing resources are abundant, clinicians should still consider testing patients with isolated GI symptoms or unexplained transaminase elevations for COVID‐19. More recent studies estimate higher overall GI involvement in COVID‐19 than was previously recognized. This review is a systematic review and meta‐analysis to characterize the prevalence of gastrointestinal (GI) and hepatic involvement in COVID‐19 and specifically assess the prevalence of GI symptoms in isolation. This review included 17 776 COVID‐19 patients from 108 studies. Isolated GI symptoms occurred in 1% (95% confidence interval 0–6%) of patients.

The goal of this work is to evaluate the effectiveness of Plan‐Checker Tool (PCT) which was created to improve first‐time plan quality, reduce patient delays, increase the efficiency of our electronic workflow, and standardize and automate the physics plan review in the treatment planning system (TPS). PCT uses an application programming interface to check and compare data from the TPS and treatment management system (TMS). PCT includes a comprehensive checklist of automated and manual checks that are documented when performed by the user as part of a plan readiness check for treatment. Prior to and during PCT development, errors identified during the physics review and causes of patient treatment start delays were tracked to prioritize which checks should be automated. Nineteen of 33 checklist items were automated, with data extracted with PCT. There was a 60% reduction in the number of patient delays in the six months after PCT release. PCT was successfully implemented for use on all external beam treatment plans in our clinic. While the number of errors found during the physics check did not decrease, automation of checks increased visibility of errors during the physics check, which led to decreased patient delays. The methods used here can be applied to any TMS and TPS that allows queries of the database. PACS number(s): 87.55.‐x, 87.55.N‐, 87.55.Qr, 87.55.tm, 89.20.Bb

Background Development of cancer therapeutics partially depends upon selection of appropriate animal models. Therefore, improvements to model selection are beneficial. Results Forty-nine human tumor xenografts at in vivo passages 1, 4 and 10 were subjected to cDNA microarray analysis yielding a dataset of 823 Affymetrix HG-U133 Plus 2.0 arrays. To illustrate mining strategies supporting therapeutic studies, transcript expression was determined: 1) relative to other models, 2) with successive in vivo passage, and 3) during the in vitro to in vivo transition. Ranking models according to relative transcript expression in vivo has the potential to improve initial model selection. For example, combining p53 tumor expression data with mutational status could guide selection of tumors for therapeutic studies of agents where p53 status purportedly affects efficacy (e.g., MK-1775). The utility of monitoring changes in gene expression with extended in vivo tumor passages was illustrated by focused studies of drug resistance mediators and receptor tyrosine kinases. Noteworthy observations included a significant decline in HCT-15 colon xenograft ABCB1 transporter expression and increased expression of the kinase KIT in A549 with serial passage. These trends predict sensitivity to agents such as paclitaxel (ABCB1 substrate) and imatinib (c-KIT inhibitor) would be altered with extended passage. Given that gene expression results indicated some models undergo profound changes with in vivo passage, a general metric of stability was generated so models could be ranked accordingly. Lastly, changes occurring during transition from in vitro to in vivo growth may have important consequences for therapeutic studies since targets identified in vitro could be over- or under-represented when tumor cells adapt to in vivo growth. A comprehensive list of mouse transcripts capable of cross-hybridizing with human probe sets on the HG-U133 Plus 2.0 array was generated. Removal of the murine artifacts followed by pairwise analysis of in vitro cells with respective passage 1 xenografts and GO analysis illustrates the complex interplay that each model has with the host microenvironment. Conclusions This study provides strategies to aid selection of xenograft models for therapeutic studies. These data highlight the dynamic nature of xenograft models and emphasize the importance of maintaining passage consistency throughout experiments.