CCPE: Cell Cycle Pseudotime Estimation for Single Cell RNA-seq Data

AbstractThe rapid development of single-cell RNA-sequencing (scRNA-seq) technologies makes it possible to characterize cellular heterogeneity by detecting and quantifying transcriptional changes at the single-cell level. Pseudotime analysis enables to characterize the continuous progression of various biological processes, such as cell cycle. Cell cycle plays an important regulatory role in cell fate decisions and differentiation and is also often regarded as a confounder in scRNA-seq data analysis when analyzing the role of other factors on transcriptional regulation. Therefore, accurate prediction of cell cycle pseudotime and identify cell stages are important steps for characterizing the development-related biological processes, identifying important regulatory molecules and promoting the analysis of transcriptional heterogeneity. Here, we develop CCPE, a novel cell cycle pseudotime estimation method to characterize cell cycle timing and determine cell cycle phases from single-cell RNA-seq data. CCPE uses a discriminative helix to characterize the circular process and estimates pseudotime in the cell cycle. We evaluated the model performance based on a variety of simulated and real scRNA-seq datasets. Our results indicate that CCPE is an effective method for cell cycle estimation and competitive in various downstream analyses compared with other existing methods. CCPE successfully identified cell cycle marker genes and is robust to dropout events in scRNA-seq data. CCPE also has excellent performance on small datasets with fewer genes or cells. Accurate prediction of the cell cycle in CCPE effectively contributes to cell cycle effect removal across cell types or conditions.

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Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies.

10.1101/2021.11.03.467049 ◽

2021 ◽

Author(s):

Lorenzo Martini ◽

Roberta Bardini ◽

Stefano Di Carlo

Keyword(s):

Single Cell ◽

Meta Analysis ◽

Cell Types ◽

Cellular Heterogeneity ◽

Marker Genes ◽

Inhibitory Interneurons ◽

Rna Seq ◽

Circuit Function ◽

The Brain

The mammalian cortex contains a great variety of neuronal cells. In particular, GABAergic interneurons, which play a major role in neuronal circuit function, exhibit an extraordinary diversity of cell types. In this regard, single-cell RNA-seq analysis is crucial to study cellular heterogeneity. To identify and analyze rare cell types, it is necessary to reliably label cells through known markers. In this way, all the related studies are dependent on the quality of the employed marker genes. Therefore, in this work, we investigate how a set of chosen inhibitory interneurons markers perform. The gene set consists of both immunohistochemistry-derived genes and single-cell RNA-seq taxonomy ones. We employed various human and mouse datasets of the brain cortex, consequently processed with the Monocle3 pipeline. We defined metrics based on the relations between unsupervised cluster results and the marker expression. Specifically, we calculated the specificity, the fraction of cells expressing, and some metrics derived from decision tree analysis like entropy gain and impurity reduction. The results highlighted the strong reliability of some markers but also the low quality of others. More interestingly, though, a correlation emerges between the general performances of the genes set and the experimental quality of the datasets. Therefore, the proposed method allows evaluating the quality of a dataset in relation to its reliability regarding the inhibitory interneurons cellular heterogeneity study.

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Revealing cell fate decisions during reprogramming by scRNA-seq

E3S Web of Conferences ◽

10.1051/e3sconf/202014501033 ◽

2020 ◽

Vol 145 ◽

pp. 01033

Author(s):

Yu Liang

Keyword(s):

Small Molecules ◽

Single Cell ◽

Cell Fate ◽

Pluripotent Stem Cells ◽

Rapid Development ◽

Underlying Mechanism ◽

Cellular Heterogeneity ◽

Cell Level ◽

Cell Fate Decisions ◽

Cell Fate Conversion

Single-cell RNA sequencing (scRNA-seq) technologies serve as powerful tools to dissect cellular heterogeneity comprehensively. With the rapid development of scRNA-seq, many previously unsolved questions were answered by using scRNA-seq. Cell reprogramming allows to reprogram the somatic cell into pluripotent stem cells by specific transcription factors or small molecules. However, the underlying mechanism for the reprogramming progress remains unclear in some aspects for it is a highly heterogeneous process. By using scRNA-seq, it is of great value for better understanding the mechanism of reprogramming process by analyzing cell fate conversion at single-cell level. In this review, we will introduce the methods of scRNA-seq and generation of iPSCs by reprogramming, and summarize the main researches that revealing reprogramming mechanism with the use scRNA-seq.

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AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

10.1101/2020.02.21.940650 ◽

2020 ◽

Cited By ~ 5

Author(s):

Hananeh Aliee ◽

Fabian Theis

Keyword(s):

Single Cell ◽

Prior Knowledge ◽

Gene Selection ◽

Ground Truth ◽

Cell Types ◽

Cellular Heterogeneity ◽

Marker Genes ◽

Rna Seq ◽

Cell Type ◽

The Impact

AbstractTissues are complex systems of interacting cell types. Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. When measuring such responses using RNA-seq, bulk RNA-seq masks cellular heterogeneity. Hence, several computational methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles highly depends on the set of genes undergoing deconvolution. These genes are often selected based on prior knowledge or a single-criterion test that might not be useful to dissect closely correlated cell types. In this work, we introduce AutoGeneS, a tool that automatically extracts informative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. It can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Results from human samples of peripheral blood illustrate that AutoGeneS outperforms other methods. Our results also highlight the impact of our approach on analyzing bulk RNA samples with noisy single-cell reference profiles and closely correlated cell types. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of the predictions of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).

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Single-embryo and single-blastomere immunoblotting reports protein expression heterogeneity in early-stage preimplantation embryos

10.1101/357731 ◽

2018 ◽

Author(s):

Elisabet Rosàs-Canyelles ◽

Andrew J. Modzelewski ◽

Lin He ◽

Amy E. Herr

Keyword(s):

Protein Expression ◽

Single Cell ◽

High Specificity ◽

Preimplantation Development ◽

Cellular Heterogeneity ◽

Preimplantation Embryos ◽

Rna Seq ◽

Cell Fate Decisions ◽

Single Blastomere ◽

Β Tubulin

AbstractUnderstanding how a zygote develops from a single cell into a multicellular organism has benefitted from single-cell tools, including RNA sequencing (RNA-Seq) and immunofluorescence (IF). However, scrutinizing inter- and intra-embryonic phenotypic variation is hindered by two fundamental limitations; the loose correlation between transcription and translation and the cross-reactivity of immunoreagents. To address these challenges, we describe a high-specificity microfluidic immunoblot optimized to quantify protein expression from all stages of mouse preimplantation development. Despite limited availability of isoform-specific immunoreagents, the immunoblot resolves inter-embryonic heterogeneity of embryo-specific isoforms (i.e., DICER-1). We observed significantly higher DICER-1 isoform expression in oocytes when compared to two-cell embryos, and further find that protein expression levels follow the same trend as mRNA for both the full-length and truncated DICER-1 isoforms. At the morula stage, we assayed both whole and disaggregated embryos for loading controls (β-tubulin, GAPDH) and markers that regulate cell fate decisions (CDX-2, SOX-2). In disaggregated morula, we found that cell volume showed positive, linear correlation with expression of β-tubulin and SOX-2. In dissociated two-cell and four-cell blastomeres, we detect significant inter-blastomeric variation in GADD45a expression, corroborating suspected cellular heterogeneity even in the earliest multicellular stage of preimplantation embryos. As RNA-Seq and other transcript-centric approaches continue to further probe preimplantation development, the demand for companion protein-based techniques rises. The reported microfluidic immunoblot serves as an essential tool for understanding mammalian development by providing high-specificity and direct measurements of protein targets at single-embryo and single-blastomere resolution.

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Single-Cell Rna-Seq Reveals Cellular Heterogeneity of Pluripotency Transition and X-Chromosome Dynamics During Early Postimplantation Mouse Development

SSRN Electronic Journal ◽

10.2139/ssrn.3231846 ◽

2018 ◽

Author(s):

Shangli Cheng ◽

Yu Pei ◽

Liqun He ◽

Guangdun Peng ◽

Björn Reinius ◽

...

Keyword(s):

Single Cell ◽

X Chromosome ◽

Cellular Heterogeneity ◽

Rna Seq ◽

Mouse Development ◽

Chromosome Dynamics

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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

10.1101/2020.10.06.327601 ◽

2020 ◽

Author(s):

Mohit Goyal ◽

Guillermo Serrano ◽

Ilan Shomorony ◽

Mikel Hernaez ◽

Idoia Ochoa

Keyword(s):

Single Cell ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Rna Seq ◽

Batch Effects ◽

Cell Type ◽

Latent Space ◽

Cell Type Specific ◽

Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

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Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

Nature Communications ◽

10.1038/s41467-019-12917-9 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 16

Author(s):

Qingnan Liang ◽

Rachayata Dharmat ◽

Leah Owen ◽

Akbar Shakoor ◽

Yumei Li ◽

...

Keyword(s):

Single Cell ◽

Transcriptome Profiling ◽

Cell Types ◽

Retinal Cell ◽

Peripheral Retina ◽

Marker Genes ◽

Rna Seq ◽

Cell Type ◽

Retinal Tissue ◽

The Individual

AbstractSingle-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.

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scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btz701 ◽

2019 ◽

Cited By ~ 2

Author(s):

Congting Ye ◽

Qian Zhou ◽

Xiaohui Wu ◽

Chen Yu ◽

Guoli Ji ◽

...

Keyword(s):

Single Cell ◽

Alternative Polyadenylation ◽

Cellular Heterogeneity ◽

Supplementary Information ◽

Rna Seq ◽

Computational Tool ◽

Cell Level ◽

Wilcoxon Rank Sum Test ◽

Transcriptional Regulatory ◽

Cell Groups

Abstract Motivation Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 3′ enriched strategy in library construction, the most commonly used scRNA-seq protocol—10× Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data. Availability and implementation The scDAPA package is implemented in Shell and R, and is freely available at https://scdapa.sourceforge.io. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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