scholarly journals Evolution of Synapses and Neurotransmitter Systems: The Divide-and-Conquer Model for Early Neural Cell-Type Evolution

2021 ◽  
Author(s):  
Pawel Burkhardt ◽  
Gáspár Jékely
Keyword(s):  
The Body ◽  
Divide And Conquer ◽  
Cambrian Explosion ◽  
Neuron Type ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Neuronal Genes ◽  
Voltage Gated Channels ◽  
Presynaptic Proteins

Nervous systems evolved around 560 million years ago to coordinate and empower animal bodies. Ctenophores – one of the earliest-branching lineages – are thought to share few neuronal genes with bilaterians and may have evolved neurons convergently. Here we review our current understanding of the evolution of neuronal molecules in non-bilaterians. We also reanalyse single-cell sequencing data in light of new cell-cluster identities from a ctenophore and uncover evidence supporting the homology of one ctenophore neuron-type with neurons in Bilateria. The specific coexpression of the presynaptic proteins Unc13 and RIM with voltage-gated channels, neuropeptides and homeobox genes pinpoint a spiking sensory-peptidergic cell in the ctenophore mouth. Similar Unc13-RIM neurons may have been present in the first eumetazoans to rise to dominance only in stem Bilateria. We hypothesize that the Unc13-RIM lineage ancestrally innervated the mouth and conquered other parts of the body with the rise of macrophagy and predation during the Cambrian explosion.

scholarly journals Cell-type composition analysis of scRNA-seq data with deep convolution neural network

2020 ◽  
Author(s):  
He Ma ◽  
Zhihao Fang ◽  
Zongbin Liu ◽  
Yan Chen
Keyword(s):  
Single Cell ◽  
Data Matrix ◽  
Composition Analysis ◽  
Cell Type ◽  
Sequencing Data ◽  
Gene Markers ◽  
Continuous Increase ◽  
Single Cell Sequencing ◽  
Cell Type Composition ◽  
Type Composition

Abstract BackgroundWith the rapid development of single-cell RNA sequencing (scRNA-seq), more large-scale single-cell sequencing data has been generated. Due to the continuous increase of single-cell sequencing data, the analysis of cell-type composition from single-cell transcriptomics has also to face huge challenges. Since the emergence of scRNA-seq technology, the size of sequencing datasets has grown more than 1 million times in just over a decade. Meanwhile, as more gene markers are discovered, the data dimension of single-cell sequencing becomes higher. All of these put forward more stringent requirements on data dimensionality reduction and clustering algorithms. Under the constraints of practical factors such as occurrence of noise and dropouts and the limitation of overhead, it is also required an effective and effcient method that can obtain accurate analysis results in a very short time, and has a competitive algorithm stability.ResultsWe present scCAE, an effective and effcient method based on convolution autoencoder that can accurately and rapidly analyze cell-type composition from single-cell transcriptomics datasets. Our method achieved the best results in the data sets that simulate the cell differentiation process among existing methods, which achieved the ARI of 69.64% and 68.83% at 10 and 25 clusters tasks. And, in the case of different dropouts, our method also works well. When the sparsity level of data metric is 71%, scCAE can achieved the ARI of 45.29%, which is the highest of the existing methods. In terms of algorithm overhead, our method has also achieved good results by comparing with several existing methods. It takes less time than most methods and takes up much less memory than other algorithms based neural networks.ConclusionsOur method, scCAE, has more accurate and reasonable results in the analysis of cell-types composition. And, because of the design of imputer, it can deal with a large number of dropouts in the data matrix. Because of the structure of convolution network, scCAE has less time and space overhead than other deep-learning-based methods. Thus, we demonstrate that scCAE is a competitive method for analysis of cell-type composition from scRNA-seq data. We expect that our study can be a stepping stone for further prosperity of single-cell transcriptomics analysis.

scholarly journals Cell-ID: gene signature extraction and cell identity recognition at individual cell level

2020 ◽  
Author(s):  
Cortal Akira ◽  
Martignetti Loredana ◽  
Six Emmanuelle ◽  
Rausell Antonio
Keyword(s):  
Single Cell ◽  
Gene Signature ◽  
The Body ◽  
Model Organisms ◽  
Cell Subpopulation ◽  
Sequencing Data ◽  
Multivariate Statistical ◽  
Cell Identity ◽  
Identity Recognition ◽  
Single Cell Sequencing

AbstractThe exhaustive exploration of human cell heterogeneity requires the unbiased identification of molecular signatures that can serve as unique cell identity cards for every cell in the body. However, the stochasticity associated with high-throughput single-cell sequencing has made it necessary to use clustering-based computational approaches in which the characterization of cell-type heterogeneity is performed at cell-subpopulation level rather than at full single-cell resolution. We present here Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. Cell-ID signatures allow unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics technologies. Cell-ID is distributed as an open-source R software package: https://github.com/RausellLab/CelliD.

scholarly journals Functional profiling of COVID-19 respiratory tract microbiomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niina Haiminen ◽  
Filippo Utro ◽  
Ed Seabolt ◽  
Laxmi Parida
Keyword(s):  
Respiratory Tract ◽  
Carbohydrate Metabolism ◽  
Lower Respiratory Tract ◽  
Viral Infections ◽  
Lavage Fluid ◽  
Degradation Pathway ◽  
Community Acquired Pneumonia ◽  
The Body ◽  
Sequencing Data ◽  
Functional Profiling

AbstractIn response to the ongoing global pandemic, characterizing the molecular-level host interactions of the new coronavirus SARS-CoV-2 responsible for COVID-19 has been at the center of unprecedented scientific focus. However, when the virus enters the body it also interacts with the micro-organisms already inhabiting the host. Understanding the virus-host-microbiome interactions can yield additional insights into the biological processes perturbed by viral invasion. Alterations in the gut microbiome species and metabolites have been noted during respiratory viral infections, possibly impacting the lungs via gut-lung microbiome crosstalk. To better characterize microbial functions in the lower respiratory tract during COVID-19 infection, we carry out a functional analysis of previously published metatranscriptome sequencing data of bronchoalveolar lavage fluid from eight COVID-19 cases, twenty-five community-acquired pneumonia patients, and twenty healthy controls. The functional profiles resulting from comparing the sequences against annotated microbial protein domains clearly separate the cohorts. By examining the associated metabolic pathways, distinguishing functional signatures in COVID-19 respiratory tract microbiomes are identified, including decreased potential for lipid metabolism and glycan biosynthesis and metabolism pathways, and increased potential for carbohydrate metabolism pathways. The results include overlap between previous studies on COVID-19 microbiomes, including decrease in the glycosaminoglycan degradation pathway and increase in carbohydrate metabolism. The results also suggest novel connections to consider, possibly specific to the lower respiratory tract microbiome, calling for further research on microbial functions and host-microbiome interactions during SARS-CoV-2 infection.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

Critical downstream analysis steps for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chen Lin ◽  
Lingling Zhao ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Noisy Data ◽  
Single Cell Level ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Level ◽  
Bioinformatics Tool ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

scholarly journals Poorly Differentiated Small-Cell-Type Neuroendocrine Carcinoma of the Prostate: A Case Report and Literature Review

2018 ◽  
Vol 11 (3) ◽  
pp. 676-681 ◽  
Author(s):  
Kishore Kumar ◽  
Rafeeq Ahmed ◽  
Chime Chukwunonso ◽  
Hassan Tariq ◽  
Masooma Niazi ◽  
...  
Keyword(s):  
Neuroendocrine Tumors ◽  
Tumor Grade ◽  
Small Cell ◽  
The Body ◽  
Neuroendocrine Cells ◽  
Cell Type ◽  
Variable Response ◽  
Ki 67 ◽  
Platinum Based Chemotherapy ◽  
Poorly Differentiated

Neuroendocrine cells are widespread throughout the body and can give rise of neuroendocrine tumors due to abnormal growth of the chromaffin cells. Neuroendocrine tumors divide into many subtypes based on tumor grade (Ki-67 index and mitotic count) and differentiation. These tumors can be further divided into secretory and nonsecretory types based on the production of peptide hormone by tumor cells. Poorly differentiated small-cell-type neuroendocrine tumors are one of the subtypes of neuroendocrine tumors. These tumors are less common; however, they tend to be locally invasive and aggressive in behavior with poor overall median survival. Treatment of the nonsecretory small-cell type is modeled to small-cell lung cancer with a regimen consisting of platinum-based chemotherapy and etoposide with variable response. Here, we present a case of poorly differentiated small-cell neuroendocrine tumor originating from the prostate.

scholarly journals Th2 Cytokine Modulates Herpesvirus Reactivation in a Cell Type Specific Manner

2021 ◽  
Author(s):  
Guoxun Wang ◽  
Christina Zarek ◽  
Tyron Chang ◽  
Lili Tao ◽  
Alexandria Lowe ◽  
...  
Keyword(s):  
B Cells ◽  
Cell Types ◽  
The Body ◽  
Receptor Expression ◽  
Conditional Knockout ◽  
Interleukin 4 ◽  
Virus Reactivation ◽  
Cell Type ◽  
Murine Gammaherpesvirus ◽  
Cell Type Specific

Gammaherpesviruses, such as Epstein-Barr virus (EBV), Kaposi’s sarcoma associated virus (KSHV), and murine γ-herpesvirus 68 (MHV68), establish latent infection in B cells, macrophages, and non-lymphoid cells, and can induce both lymphoid and non-lymphoid cancers. Research on these viruses has relied heavily on immortalized B cell and endothelial cell lines. Therefore, we know very little about the cell type specific regulation of virus infection. We have previously shown that treatment of MHV68-infected macrophages with the cytokine interleukin-4 (IL-4) or challenge of MHV68-infected mice with an IL-4-inducing parasite leads to virus reactivation. However, we do not know if all latent reservoirs of the virus, including B cells, reactivate the virus in response to IL-4. Here we used an in vivo approach to address the question of whether all latently infected cell types reactivate MHV68 in response to a particular stimulus. We found that IL-4 receptor expression on macrophages was required for IL-4 to induce virus reactivation, but that it was dispensable on B cells. We further demonstrated that the transcription factor, STAT6, which is downstream of the IL-4 receptor and binds virus gene 50 N4/N5 promoter in macrophages, did not bind to the virus gene 50 N4/N5 promoter in B cells. These data suggest that stimuli that promote herpesvirus reactivation may only affect latent virus in particular cell types, but not in others. Importance Herpesviruses establish life-long quiescent infections in specific cells in the body, and only reactivate to produce infectious virus when precise signals induce them to do so. The signals that induce herpesvirus reactivation are often studied only in one particular cell type infected with the virus. However, herpesviruses establish latency in multiple cell types in their hosts. Using murine gammaherpesvirus-68 (MHV68) and conditional knockout mice, we examined the cell type specificity of a particular reactivation signal, interleukin-4 (IL-4). We found that IL-4 only induced herpesvirus reactivation from macrophages, but not from B cells. This work indicates that regulation of virus latency and reactivation is cell type specific. This has important implications for therapies aimed at either promoting or inhibiting reactivation for the control or elimination of chronic viral infections.

scholarly journals SARS-CoV-2 infection and persistence throughout the human body and brain

2021 ◽  
Author(s):  
Daniel Chertow ◽  
Sydney Stein ◽  
Sabrina Ramelli ◽  
Alison Grazioli ◽  
Joon-Yong Chung ◽  
...  
Keyword(s):  
Respiratory Tract ◽  
Human Body ◽  
Symptom Onset ◽  
Acute Infection ◽  
Systemic Infection ◽  
The Body ◽  
Viral Clearance ◽  
Cell Type ◽  
Cell Type Specificity ◽  
The Brain

Abstract COVID-19 is known to cause multi-organ dysfunction1-3 in acute infection, with prolonged symptoms experienced by some patients, termed Post-Acute Sequelae of SARS-CoV-2 (PASC)4-5. However, the burden of infection outside the respiratory tract and time to viral clearance is not well characterized, particularly in the brain3,6-14. We performed complete autopsies on 44 patients with COVID-19 to map and quantify SARS-CoV-2 distribution, replication, and cell-type specificity across the human body, including brain, from acute infection through over seven months following symptom onset. We show that SARS-CoV-2 is widely distributed, even among patients who died with asymptomatic to mild COVID-19, and that virus replication is present in multiple extrapulmonary tissues early in infection. Further, we detected SARS-CoV-2 RNA in multiple anatomic sites, including regions throughout the brain, for up to 230 days following symptom onset. Despite extensive distribution of SARS-CoV-2 in the body, we observed a paucity of inflammation or direct viral cytopathology outside of the lungs. Our data prove that SARS-CoV-2 causes systemic infection and can persist in the body for months.

scholarly journals Decreased MEF2A Expression Regulated by Its Enhancer Methylation Inhibits Autophagy and May Play an Important Role in the Progression of Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Hui Li ◽  
Feng Wang ◽  
Xuqi Guo ◽  
Yugang Jiang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transcription Factor ◽  
Methylation Level ◽  
Life Quality ◽  
Enrichment Analysis ◽  
Amyloid Plaques ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Enhancer Factor

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by amyloid plaques and neurofibrillary tangles which significantly affects people’s life quality. Recently, AD has been found to be closely related to autophagy. The aim of this study was to identify autophagy-related genes associated with the pathogenesis of AD from multiple types of microarray and sequencing datasets using bioinformatics methods and to investigate their role in the pathogenesis of AD in order to identify novel strategies to prevent and treat AD. Our results showed that the autophagy-related genes were significantly downregulated in AD and correlated with the pathological progression. Furthermore, enrichment analysis showed that these autophagy-related genes were regulated by the transcription factor myocyte enhancer factor 2A (MEF2A), which had been confirmed using si-MEF2A. Moreover, the single-cell sequencing data suggested that MEF2A was highly expressed in microglia. Methylation microarray analysis showed that the methylation level of the enhancer region of MEF2A in AD was significantly increased. In conclusion, our results suggest that AD related to the increased methylation level of MEF2A enhancer reduces the expression of MEF2A and downregulates the expression of autophagy-related genes which are closely associated with AD pathogenesis, thereby inhibiting autophagy.

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