scholarly journals Single‐cell transcriptomes of mouse bladder urothelium uncover novel cell type markers and urothelial differentiation characteristics

2021 ◽  
Vol 54 (4) ◽  
Author(s):  
Yan Li ◽  
Yaxiao Liu ◽  
Zhengdong Gao ◽  
Lekai Zhang ◽  
Lipeng Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Type ◽  
Bladder Urothelium

scholarly journals Single-Cell RNA Sequencing in Parkinson’s Disease

Biomedicines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 368
Author(s):  
Shi-Xun Ma ◽  
Su Bin Lim
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Single Cell ◽  
Rna Sequencing ◽  
Rna Degradation ◽  
Postmortem Brain ◽  
Cell Type ◽  
New Findings ◽  
Postmortem Brain Tissue ◽  
Technical Challenges

Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) technologies have enhanced the understanding of the molecular pathogenesis of neurodegenerative disorders, including Parkinson’s disease (PD). Nonetheless, their application in PD has been limited due mainly to the technical challenges resulting from the scarcity of postmortem brain tissue and low quality associated with RNA degradation. Despite such challenges, recent advances in animals and human in vitro models that recapitulate features of PD along with sequencing assays have fueled studies aiming to obtain an unbiased and global view of cellular composition and phenotype of PD at the single-cell resolution. Here, we reviewed recent sc/snRNA-seq efforts that have successfully characterized diverse cell-type populations and identified cell type-specific disease associations in PD. We also examined how these studies have employed computational and analytical tools to analyze and interpret the rich information derived from sc/snRNA-seq. Finally, we highlighted important limitations and emerging technologies for addressing key technical challenges currently limiting the integration of new findings into clinical practice.

scholarly journals Conditional out-of-distribution generation for unpaired data using transfer VAE

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i610-i617
Author(s):  
Mohammad Lotfollahi ◽  
Mohsen Naghipourfar ◽  
Fabian J Theis ◽  
F Alexander Wolf
Keyword(s):  
Single Cell ◽  
Generative Models ◽  
Response To Treatment ◽  
High Dimensional ◽  
Compact Representation ◽  
Hair Color ◽  
Great Success ◽  
Cell Type ◽  
Style Transfer ◽  
Cell Type Specific

Abstract Motivation While generative models have shown great success in sampling high-dimensional samples conditional on low-dimensional descriptors (stroke thickness in MNIST, hair color in CelebA, speaker identity in WaveNet), their generation out-of-distribution poses fundamental problems due to the difficulty of learning compact joint distribution across conditions. The canonical example of the conditional variational autoencoder (CVAE), for instance, does not explicitly relate conditions during training and, hence, has no explicit incentive of learning such a compact representation. Results We overcome the limitation of the CVAE by matching distributions across conditions using maximum mean discrepancy in the decoder layer that follows the bottleneck. This introduces a strong regularization both for reconstructing samples within the same condition and for transforming samples across conditions, resulting in much improved generalization. As this amount to solving a style-transfer problem, we refer to the model as transfer VAE (trVAE). Benchmarking trVAE on high-dimensional image and single-cell RNA-seq, we demonstrate higher robustness and higher accuracy than existing approaches. We also show qualitatively improved predictions by tackling previously problematic minority classes and multiple conditions in the context of cellular perturbation response to treatment and disease based on high-dimensional single-cell gene expression data. For generic tasks, we improve Pearson correlations of high-dimensional estimated means and variances with their ground truths from 0.89 to 0.97 and 0.75 to 0.87, respectively. We further demonstrate that trVAE learns cell-type-specific responses after perturbation and improves the prediction of most cell-type-specific genes by 65%. Availability and implementation The trVAE implementation is available via github.com/theislab/trvae. The results of this article can be reproduced via github.com/theislab/trvae_reproducibility.

scholarly journals Tumor to normal single-cell mRNA comparisons reveal a pan-neuroblastoma cancer cell

2021 ◽  
Vol 7 (6) ◽  
pp. eabd3311
Author(s):  
Gerda Kildisiute ◽  
Waleed M. Kholosy ◽  
Matthew D. Young ◽  
Kenny Roberts ◽  
Rasa Elmentaite ◽  
...  
Keyword(s):  
Childhood Cancer ◽  
Single Cell ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Developmental Stages ◽  
Neuroblastoma Cells ◽  
Cell Type ◽  
Adrenal Cell ◽  
Universal Feature ◽  
Principal Finding

Neuroblastoma is a childhood cancer that resembles developmental stages of the neural crest. It is not established what developmental processes neuroblastoma cancer cells represent. Here, we sought to reveal the phenotype of neuroblastoma cancer cells by comparing cancer (n = 19,723) with normal fetal adrenal single-cell transcriptomes (n = 57,972). Our principal finding was that the neuroblastoma cancer cell resembled fetal sympathoblasts, but no other fetal adrenal cell type. The sympathoblastic state was a universal feature of neuroblastoma cells, transcending cell cluster diversity, individual patients, and clinical phenotypes. We substantiated our findings in 650 neuroblastoma bulk transcriptomes and by integrating canonical features of the neuroblastoma genome with transcriptional signals. Overall, our observations indicate that a pan-neuroblastoma cancer cell state exists, which may be attractive for novel immunotherapeutic and targeted avenues.

scholarly journals Comprehensive analysis of single cell ATAC-seq data with SnapATAC

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Open Chromatin ◽  
Cell Type ◽  
Process Data ◽  
Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

scholarly journals Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data

2020 ◽  
Author(s):  
Qianhui Huang ◽  
Yu Liu ◽  
Yuheng Du ◽  
Lana X. Garmire
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Cell Type ◽  
R Packages

scholarly journals Author Correction: iLoF: An intelligent Lab on Fiber Approach for Human Cancer Single-Cell Type Identification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Joana S. Paiva ◽  
Pedro A. S. Jorge ◽  
Rita S. R. Ribeiro ◽  
Meritxell Balmaña ◽  
Diana Campos ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Cancer ◽  
Cell Type ◽  
Single Cell Type

Cell type diversity in scallop adductor muscles revealed by single-cell RNA-Seq

Genomics ◽  
2021 ◽  
Vol 113 (6) ◽  
pp. 3582-3598
Author(s):  
Xiujun Sun ◽  
Li Li ◽  
Biao Wu ◽  
Jianlong Ge ◽  
Yanxin Zheng ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Adductor Muscles

Single-cell chromatin accessibility identifies pancreatic islet cell type– and state-specific regulatory programs of diabetes risk

2021 ◽  
Author(s):  
Joshua Chiou ◽  
Chun Zeng ◽  
Zhang Cheng ◽  
Jee Yun Han ◽  
Michael Schlichting ◽  
...  
Keyword(s):  
Single Cell ◽  
Pancreatic Islet ◽  
Islet Cell ◽  
Chromatin Accessibility ◽  
Diabetes Risk ◽  
Cell Type ◽  
Pancreatic Islet Cell

Differential calcium signalling by m2 and m3 muscarinic acetylcholine receptors in a single cell type

1995 ◽  
Vol 352 (5) ◽  
pp. 469-476 ◽  
Author(s):  
Martina Schmidt ◽  
Christine Bienek ◽  
Chris J. van Koppen ◽  
Martin C. Michel ◽  
Karl H. Jakobs
Keyword(s):  
Single Cell ◽  
Acetylcholine Receptors ◽  
Calcium Signalling ◽  
Muscarinic Acetylcholine Receptors ◽  
Cell Type ◽  
Muscarinic Acetylcholine ◽  
Single Cell Type

scholarly journals Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

2021 ◽  
Author(s):  
Yixuan Qiu ◽  
Jiebiao Wang ◽  
Jing Lei ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
R Package ◽  
Supplementary Information ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Correlation Pattern ◽  
Tissue Samples ◽  
Bulk Data

Abstract Motivation Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.

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