Single‐cell RNA sequencing reveals a high‐resolution cell atlas of xylem in Populus

High−resolution phenotyping of spermatogenic defects using single−cell RNA sequencing

Fertility and Sterility ◽

10.1016/j.fertnstert.2017.07.035 ◽

2017 ◽

Vol 108 (3) ◽

pp. e6-e7

Author(s):

M. Jung ◽

J. Rusch ◽

A. Usmani ◽

S. Ahmad ◽

D. Conrad

Keyword(s):

High Resolution ◽

Single Cell ◽

Rna Sequencing ◽

Single Cell Rna Sequencing

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CellVGAE: An unsupervised scRNA-seq analysis workflow with graph attention networks

10.1101/2020.12.20.423645 ◽

2020 ◽

Author(s):

David Buterez ◽

Ioana Bica ◽

Ifrah Tariq ◽

Helena Andrés-Terré ◽

Pietro Liò

Keyword(s):

Neural Networks ◽

High Resolution ◽

Dimensionality Reduction ◽

Single Cell ◽

Rna Sequencing ◽

Feedforward Neural Networks ◽

Attention Networks ◽

Single Cell Rna Sequencing ◽

Analysis Workflow ◽

Graph Neural Networks

AbstractCurrently, single-cell RNA sequencing (scRNA-seq) allows high-resolution views of individual cells, for libraries of up to (tens of) thousands of samples. In this study, we introduce the use of graph neural networks (GNN) in the unsupervised study of scRNA-seq data, namely for dimensionality reduction and clustering. Motivated by the success of non-neural graph-based techniques in bioinformatics, as well as the now common feedforward neural networks being applied to scRNA-seq measurements, we develop an architecture based on a variational graph autoencoder with graph attention layers that works directly on the connectivity of cells. With the help of three case studies, we show that our model, named CellVGAE, can be effectively used for exploratory analysis, even on challenging datasets, by extracting meaningful features from the data and providing the means to visualise and interpret different aspects of the model. Furthermore, we evaluate the dimensionality reduction and clustering performance on 9 well-annotated datasets, where we compare with leading neural and non-neural techniques. CellVGAE outperforms competing methods in all 9 scenarios. Finally, we show that CellVGAE is more interpretable than existing architectures by analysing the graph attention coefficients. The software and code to generate all the figures are available at https://github.com/davidbuterez/CellVGAE.

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Collection of cells for single-cell RNA sequencing using high-resolution fluorescence microscopy

STAR Protocols ◽

10.1016/j.xpro.2021.100718 ◽

2021 ◽

Vol 2 (3) ◽

pp. 100718

Author(s):

Hendrika A. Segeren ◽

Kiki C. Andree ◽

Lisa Oomens ◽

Bart Westendorp

Keyword(s):

High Resolution ◽

Fluorescence Microscopy ◽

Single Cell ◽

Rna Sequencing ◽

Single Cell Rna Sequencing

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BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

Genome Biology ◽

10.1186/s13059-019-1764-6 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 25

Author(s):

Tongxin Wang ◽

Travis S. Johnson ◽

Wei Shao ◽

Zixiao Lu ◽

Bryan R. Helm ◽

...

Keyword(s):

High Resolution ◽

Single Cell ◽

Rna Sequencing ◽

Transfer Learning ◽

Learning Method ◽

Batch Correction ◽

Single Cell Rna Sequencing

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Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data

Nucleic Acids Research ◽

10.1093/nar/gkaa1157 ◽

2020 ◽

Author(s):

Jia Song ◽

Yao Liu ◽

Xuebing Zhang ◽

Qiuyue Wu ◽

Juan Gao ◽

...

Keyword(s):

High Resolution ◽

Noise Reduction ◽

Single Cell ◽

Rna Sequencing ◽

Clustering Algorithm ◽

Cellular Heterogeneity ◽

Cell Clustering ◽

Single Cell Rna Sequencing ◽

Subspace Separation ◽

Marker Identification

Abstract Single-cell RNA sequencing enables us to characterize the cellular heterogeneity in single cell resolution with the help of cell type identification algorithms. However, the noise inherent in single-cell RNA-sequencing data severely disturbs the accuracy of cell clustering, marker identification and visualization. We propose that clustering based on feature density profiles can distinguish informative features from noise. We named such strategy as ‘entropy subspace’ separation and designed a cell clustering algorithm called ENtropy subspace separation-based Clustering for nOise REduction (ENCORE) by integrating the ‘entropy subspace’ separation strategy with a consensus clustering method. We demonstrate that ENCORE performs superiorly on cell clustering and generates high-resolution visualization across 12 standard datasets. More importantly, ENCORE enables identification of group markers with biological significance from a hard-to-separate dataset. With the advantages of effective feature selection, improved clustering, accurate marker identification and high-resolution visualization, we present ENCORE to the community as an important tool for scRNA-seq data analysis to study cellular heterogeneity and discover group markers.

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Intra-lineage Plasticity and Functional Reprogramming Maintain Natural Killer Cell Repertoire Diversity

10.1101/514463 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aline Pfefferle ◽

Benedikt Jacobs ◽

Eivind Heggernes Ask ◽

Susanne Lorenz ◽

Trevor Clancy ◽

...

Keyword(s):

Flow Cytometry ◽

High Resolution ◽

Single Cell ◽

Natural Killer ◽

Rna Sequencing ◽

Nk Cell ◽

Homeostatic Proliferation ◽

Cell Repertoire ◽

Single Cell Rna Sequencing ◽

Repertoire Diversity

AbstractNatural killer (NK) cell repertoires are made up of a vast number of phenotypically distinct subsets with different functional properties. The molecular programs involved in maintaining NK cell repertoire diversity under homeostatic conditions remains elusive. Here we show that subset-specific NK cell proliferation kinetics correlate with mTOR activation, and that global repertoire diversity is maintained through a high degree of intra-lineage subset plasticity during IL-15-driven homeostatic proliferation in vitro. High-resolution flow cytometry and single cell RNA sequencing revealed that slowly cycling sorted KIR+CD56dim NK cells with an induced CD57 phenotype display increased functional potential associated with inhibitory MHC interactions and activating DAP12 signaling. In contrast, rapidly cycling cells upregulate NKG2A and display a general loss of functionality associated with a transcriptional increase in RNA-binding metabolic enzymes and cytokine signaling pathways. These results shed new light on the role of intra-lineage plasticity during NK cell homeostasis and suggest that the functional fate of the cell is tightly linked to the acquired phenotype and determined by transcriptional reprogramming.One Sentence Summary:High-resolution flow cytometry combined with single-cell RNA sequencing reveal a role for intra-lineage plasticity and functional reprogramming in maintaining phenotypically and functionally diverse NK cell repertoires during IL-15-driven homeostatic proliferation.

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