Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

Wang, Jia Xu; Jenjaroenpun, Piroon; Bhinge, Akshay; Angarica, Vladimir Espinosa; Sol, Antonio del; Nookaew, Intawat; Kuznetsov, Vladimir A.; Stanton, Lawrence W.

doi:10.1101/gr.223313.117

Cited by 43 publications

(46 citation statements)

References 95 publications

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“…In our analysis of ~19,000 features across 42 developmental processes and nearly 150,000 single cells, we found that gene counts, or the number of detectably expressed genes per cell, powerfully associates with transcriptional diversity and differentiation status. Although anecdotally observed in specific experimental systems (mouse alveolar epithelial development, zebrafish thrombopoiesis, and neuron differentiation from hESCs [26][27][28] ), we demonstrate for the first time that this association (1) outperforms most stemness inference tools and pre-defined molecular signatures from a compendium of nearly 19,000 RNA-based features, (2) is generally independent of species, platform, and tissue type, and (3) is broadly applicable throughout cellular ontogenesis.…”

Section: Discussionmentioning

confidence: 76%

“…To assess these features, we compiled a training cohort consisting of nine gold standard scRNA-seq datasets with experimentally-confirmed differentiation trajectories. These datasets were selected to prioritize commonly used benchmarking datasets from prior studies 12,[18][19][20]26,29 and to ensure a broad sampling of unique developmental states from the mammalian zygote to terminally differentiated cells 26,30 . Overall, the training cohort encompassed 3,174 single cells spanning 49 phenotypes, six tissue types, and three scRNA-seq platforms ( Fig.…”

Section: Rna-based Correlates Of Single-cell Differentiation Statesmentioning

confidence: 99%

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Single-cell transcriptional diversity is a hallmark of developmental potential

Gulati

Sikandar

Wesche

et al. 2019

Preprint

137

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Single-cell RNA-sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation without prior knowledge has remained challenging. Here we describe a simple yet robust determinant of developmental potential-the number of detectably expressed genes per celland leverage this measure of transcriptional diversity to develop a new framework for predicting ordered differentiation states from scRNA-seq data. When evaluated on ~150,000 single-cell transcriptomes spanning 53 lineages and five species, our approach, called CytoTRACE, outperformed previous methods and ~19,000 molecular signatures for resolving experimentallyconfirmed developmental trajectories. In addition, it enabled unbiased identification of tissueresident stem cells, including cells with long-term regenerative potential. When used to analyze human breast tumors, we discovered candidate genes associated with less-differentiated luminal progenitor cells and validated GULP1 as a novel gene involved in tumorigenesis. Our study establishes a key RNA-based correlate of developmental potential and provides a new platform for robust delineation of cellular hierarchies (https://cytotrace.stanford.edu).

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Section: Discussionmentioning

confidence: 76%

Section: Rna-based Correlates Of Single-cell Differentiation Statesmentioning

confidence: 99%

Single-cell transcriptional diversity is a hallmark of developmental potential

Gulati

Sikandar

Wesche

et al. 2019

Preprint

137

245

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“…In this paper, we apply CCSN to Wang dataset [39], which comes from a study of neural progenitor cells (NPCs) that differentiate into mature neurons. The dataset contains six time points over a 30-day period.…”

Section: Resultsmentioning

confidence: 99%

CCSN: Single Cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

Dai

Fang

et al. 2020

Preprint

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24The rapid advancement of single cell technologies has shed new light on the complex 25 mechanisms of cellular heterogeneity. However, compared with bulk RNA sequencing 26 (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower 27 coverage, which brings new computational difficulties. Based on statistical 28 independence, cell-specific network (CSN) is able to quantify the overall associations 29 between genes for each cell, yet suffering from a problem of overestimation related to 30 indirect effects. To overcome this problem, we propose the "conditional cell-specific 31 network" (CCSN) method, which can measure the direct associations between genes 32 by eliminating the indirect associations. CCSN can be used for cell clustering and 33 dimension reduction on a network basis of single cells. Intuitively, each CCSN can be 34 viewed as the transformation from less "reliable" gene expression to more "reliable" 35 gene-gene associations in a cell. Based on CCSN, we further design network flow 36 entropy (NFE) to estimate the differentiation potency of a single cell. A number of 37 scRNA-seq datasets were used to demonstrate the advantages of our approach: (1) one 38 direct association network for one cell; (2) most existing scRNA-seq methods designed 39 for gene expression matrices are also applicable to CCSN-transformed degree matrices; 40 (3) CCSN-based NFE helps resolving the direction of differentiation trajectories by 41 quantifying the potency of each cell. CCSN is publicly available at 42 http://sysbio.sibcb.ac.cn/cb/chenlab/soft/CCSN.zip. 43 44 45 KEYWORDS: Single cell analysis; Network flow entropy; Cell-specific network; 46 Single cell network; Direct association; Conditional independence 47 49 With the development of high-throughput single-cell RNA sequencing (scRNA-seq), 50 novel cell populations in complex tissues [1-5] can be identified and the differentiation 51 trajectory of cell states [6-8] can be obtained, which opens a new way to understand the 52 heterogeneity and transition of cells [9-11]. However, compared to traditional bulk 53RNA-seq data, the prevalence of high technical noise and dropout events is a major 54 problem in scRNA-seq [12][13][14][15][16][17], which raises substantial challenges for data analysis. 55Many computational methods were proposed to improve the identification of new cell 56 types [18][19][20][21]. Meanwhile, imputation is an effective strategy to transform the dropouts 57 to the substituted values [22][23][24][25][26]. However, most of these methods mainly analyze 58 mRNA expression/concentrations, while the information of gene-gene interactions (or 59 their network) is ignored. 60Recently, a network-based method, cell-specific network (CSN), was proposed to 61 perform network analysis for scRNA-seq data [27], which elegantly infers a network 62 for each cell and successfully transforms the noisy and "unreliable" gene expression 63 data to the more "reliable" gene association data. The network degree matrix (NDM) 64 derived from CSN can be furt...

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“…Such domains include surveying cell heterogeneity among embryonic stem cells and tumors (Azizi et al 2018;Rosenberg et al 2018), identifying genetic markers of specific cell types (Fan et al 2018), and investigating cell fate commitment and lineage trajectories (Wang et al 2017).…”

Section: Introductionmentioning

confidence: 99%

RISC: robust integration of single-cell RNA-seq datasets with different extents of cell cluster overlap

Liu

Wang²,

Zheng

2018

Preprint

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A running title: scRNA-seq data integration Abstract Single cell RNA-seq (scRNA-seq) has remarkably advanced our understanding of cellular heterogeneity and dynamics in tissue development, diseases, and cancers. Integrated data analysis can often uncover molecular and cellular links among individual datasets and thus provide new biological insights, such as developmental relationship. Due to differences in experimental platforms and biological sample batches, the integration of multiple scRNA-seq datasets is challenging. To address this, we developed a novel computational method for robust integration of scRNA-seq (RISC) datasets using principal component regression (PCR). Because of the natural compatibility of eigenvectors between PCR model and dimension reduction, RISC can accurately integrate scRNA-seq datasets and avoid over-integration. Compared to existing software, RISC shows particular improvement in integrating datasets that contain cells of the same types (more accurately clusters) but at distinct functional states. To demonstrate the value of RISC in finding small groups of cells common between otherwise heterogenous datasets, we applied it to scRNA-seq datasets of normal and malignant cells and successfully identified small clusters of cells in healthy kidney tissues that may be related to the origin of renal tumors.

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Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

Cited by 43 publications

References 95 publications

Single-cell transcriptional diversity is a hallmark of developmental potential

Single-cell transcriptional diversity is a hallmark of developmental potential

CCSN: Single Cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

RISC: robust integration of single-cell RNA-seq datasets with different extents of cell cluster overlap

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