FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

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Resolving Cell Fate Decisions during Somatic Cell Reprogramming by Single-Cell RNA-Seq

Molecular Cell ◽

10.1016/j.molcel.2019.01.042 ◽

2019 ◽

Vol 73 (4) ◽

pp. 815-829.e7 ◽

Cited By ~ 29

Author(s):

Lin Guo ◽

Lihui Lin ◽

Xiaoshan Wang ◽

Mingwei Gao ◽

Shangtao Cao ◽

...

Keyword(s):

Somatic Cell ◽

Single Cell ◽

Cell Fate ◽

Cell Reprogramming ◽

Rna Seq ◽

Cell Fate Decisions ◽

Somatic Cell Reprogramming

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Single Cell RNA seq for Analysis of Cell Fate Decisions

Blood ◽

10.1182/blood.v126.23.sci-20.sci-20 ◽

2015 ◽

Vol 126 (23) ◽

pp. SCI-20-SCI-20

Author(s):

H. Leighton Grimes ◽

Singh Harinder ◽

Andre Olsson ◽

Nathan Salomonis ◽

Bruce J. Aronow ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Clustering Analysis ◽

Conflicts Of Interest ◽

Regulatory Gene ◽

Rna Seq ◽

Regulatory State ◽

Cell Fate Decisions ◽

General Utility ◽

Developmental Hierarchy

Abstract Single-cell RNA-Seq has the potential to become a dominant approach in probing diverse and complex developmental compartments. Its unbiased and comprehensive nature could enable developmental ordering of cellular and regulatory gene hierarchies without prior knowledge. To test general utility we performed single-cell RNA-seq of murine hematopoietic progenitors focusing on the myeloid developmental hierarchy. Using novel unsupervised clustering analysis, ICDS, we correctly ordered known hierarchical states as well as revealed rare intermediates. Regulatory state analysis suggested that the transcription factors Gfi1 and Irf8 function antagonistically to control homeostatic neutrophil and macrophage production, respectively. This prediction was validated by complementary genetic and genomic experiments in granulocyte-macrophage progenitors. Using knock-in reporters for Gfi1 and Irf8 and clonogenic analyses coupled with single-cell RNA-seq we distinguished regulatory states of bi-potential progenitors from their lineage specifying or committed progeny. Thus single-cell RNA-Seq is a powerful developmental tool to characterize hierarchical and rare cellular states along with the regulators that control their dynamics. Disclosures No relevant conflicts of interest to declare.

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Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET

10.1101/2021.10.22.465455 ◽

2021 ◽

Author(s):

Kushagra Pandey ◽

Hamim Zafar

Keyword(s):

Single Cell ◽

Cell Fate ◽

Markov Chain Model ◽

Metric Learning ◽

Transitional Cell ◽

Cell Types ◽

State Transitions ◽

Chain Model ◽

Rna Seq ◽

Cell State

Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing cell-state manifold and inferring trajectory and cell fate plasticity for complex topologies. We present MARGARET, a novel TI method that utilizes a deep unsupervised metric learning-based approach for inferring the cellular embeddings and employs a novel measure of connectivity between cell clusters and a graph-partitioning approach to reconstruct complex trajectory topologies. MARGARET utilizes the inferred trajectory for determining terminal states and inferring cell-fate plasticity using a scalable absorbing Markov Chain model. On a diverse simulated benchmark, MARGARET outperformed state-of-the-art methods in recovering global topology and cell pseudotime ordering. When applied to experimental datasets from hematopoiesis, embryogenesis, and colon differentiation, MARGARET reconstructed major lineages and associated gene expression trends, better characterized key branching events and transitional cell types, and identified novel cell types, and branching events that were previously uncharacterized.

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SCPattern: A statistical approach to identify and classify expression changes in single cell RNA-seq experiments with ordered conditions

10.1101/046110 ◽

2016 ◽

Cited By ~ 1

Author(s):

Ning Leng ◽

Li-Fang Chu ◽

Jeea Choi ◽

Christina Kendziorski ◽

James A. Thomson ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Empirical Bayes ◽

Time Course ◽

Expression Patterns ◽

R Package ◽

Embryonic Cell ◽

Rna Seq ◽

Cell Fate Decisions ◽

Probability Estimates

AbstractMotivationWith the development of single cell RNA-seq (scRNA-seq) technology, scRNA-seq experiments with ordered conditions (e.g. time-course) are becoming common. Methods developed for analyzing ordered bulk RNA-seq experiments are not applicable to scRNA-seq, since their distributional assumptions are often violated by additional heterogeneities prevalent in scRNA-seq. Here we present SC-Pattern - an empirical Bayes model to characterize genes with expression changes in ordered scRNA-seq experiments. SCPattern utilizes the non-parametrical Kolmogorov-Smirnov statistic, thus it has the flexibility to identify genes with a wide variety of types of changes. Additionally, the Bayes framework allows SCPattern to classify genes into expression patterns with probability estimates.ResultsSimulation results show that SCPattern is well powered for identifying genes with expression changes while the false discovery rate is well controlled. SCPattern is also able to accurately classify these dynamic genes into directional expression patterns. Applied to a scRNA-seq time course dataset studying human embryonic cell differentiation, SCPattern detected a group of important genes that are involved in mesendoderm and definitive endoderm cell fate decisions, positional patterning, and cell cycle.Availability and ImplementationThe SCPattern is implemented as an R package along with a user-friendly graphical interface, which are available at:https://github.com/lengning/SCPatternContact:[email protected]

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Single Cell Sequencing Reveals Early PGC-like Intermediates During Mouse Primed to Naïve Transition

10.21203/rs.3.rs-151340/v1 ◽

2021 ◽

Author(s):

Jing Liu ◽

Shengyong Yu ◽

Chunhua Zhou ◽

Jiangping He ◽

Xingnan Huang ◽

...

Keyword(s):

Germ Cell ◽

Single Cell ◽

Cell Fate ◽

Single Cell Analysis ◽

Primordial Germ Cell ◽

Model System ◽

Cell Analysis ◽

Rna Seq ◽

Single Cell Sequencing

Abstract Single cell analysis provides clarity unattainable with bulk approaches. Here we apply single cell RNA-seq to a newly established BMP4 induced mouse primed to naive transition (Bi-PNT) system and show that the reset is not a direct reversal of cell fate but through developmental intermediates. We first show that mEpiSCs bifurcate into c-Kit+ naïve and c-Kit- placenta-like cells, among which, the naive branch undergoes further transition through a primordial germ cell-like cells (PGCLCs) intermediate capable of spermatogenesis in vivo. Indeed, deficiency of Prdm1/Blimp1, the key regulator for PGC specification, blocks the induction of PGCLCs and naïve cells. Instead, Gata2 knockout arrests placenta-like fate, but facilitates the generation of PGCLCs. Our results not only reveal a newly cell fate dynamics between primed and naive states at single-cell resolution, but also provide a model system to explore mechanisms involved in regaining germline competence from primed pluripotency.

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Chromatin accessibility dynamics and single cell RNA-Seq reveal new regulators of regeneration in neural progenitors

eLife ◽

10.7554/elife.52648 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 2

Author(s):

Anneke Dixie Kakebeen ◽

Alexander Daniel Chitsazan ◽

Madison Corinne Williams ◽

Lauren M Saunders ◽

Andrea Elizabeth Wills

Keyword(s):

Single Cell ◽

Cell Fate ◽

Neural Progenitor Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Rna Seq ◽

Tail Regeneration ◽

Cell Fate Decisions ◽

Transcriptional Regulatory ◽

Regulatory Dynamics

Vertebrate appendage regeneration requires precisely coordinated remodeling of the transcriptional landscape to enable the growth and differentiation of new tissue, a process executed over multiple days and across dozens of cell types. The heterogeneity of tissues and temporally-sensitive fate decisions involved has made it difficult to articulate the gene regulatory programs enabling regeneration of individual cell types. To better understand how a regenerative program is fulfilled by neural progenitor cells (NPCs) of the spinal cord, we analyzed pax6-expressing NPCs isolated from regenerating Xenopus tropicalis tails. By intersecting chromatin accessibility data with single-cell transcriptomics, we find that NPCs place an early priority on neuronal differentiation. Late in regeneration, the priority returns to proliferation. Our analyses identify Pbx3 and Meis1 as critical regulators of tail regeneration and axon organization. Overall, we use transcriptional regulatory dynamics to present a new model for cell fate decisions and their regulators in NPCs during regeneration.

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Single Cell Transcriptome-Based Dissection of Lineage Fate Decisions in Myelopoiesis

Blood ◽

10.1182/blood.v124.21.1395.1395 ◽

2014 ◽

Vol 124 (21) ◽

pp. 1395-1395

Author(s):

Andre Olsson ◽

H. Leighton Grimes ◽

Virendra K Chaudhri ◽

Philip Dexheimer ◽

Bruce J Aronow ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Molecular Mechanisms ◽

Conflicts Of Interest ◽

Expression Patterns ◽

Rna Seq ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Cell Data ◽

Insight Into

Abstract In spite of tremendous advances in the analysis of hematopoietic progenitors and transcription factors that give rise to different lineages, molecular insight into the mechanisms that underlie cell fate choice at the level of individual cells is lacking. We utilized single-cell RNA sequencing of murine granulocyte-monocyte progenitors (GMPs) to analyze the molecular basis of cell fate choice. Over 200 libraries were generated with average read depths of 4 million per library and an expressed gene call of over 3,800 genes with FPKM >3. Our data reveal a varied but coherent spectrum of gene expression patterns in individual murine GMPs. The majority of cells could be clustered into ones expressing either granulocytic or monocytic genes, suggesting that they were primed for lineage determination. A minority of GMPs expressed a mixed-lineage pattern of genes. The single-cell data suggested an antagonistic transcription factor circuit involving Gfi1 and IRF8 that was validated with both loss- and gain-of-function experiments in GMPs. Our data highlight the utility of single cell RNA-Seq analysis to reveal molecular mechanisms controlling lineage fate decisions in hematopoiesis. Disclosures No relevant conflicts of interest to declare.

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Single-cell transcriptomics of the mouse gonadal soma reveals the establishment of sexual dimorphism in distinct cell lineages

10.1101/410407 ◽

2018 ◽

Cited By ~ 2

Author(s):

Isabelle Stévant ◽

Françoise Kühne ◽

Andy Greenfield ◽

Marie-Christine Chaboissier ◽

Emmanouil T. Dermitzakis ◽

...

Keyword(s):

Time Series ◽

Sex Determination ◽

Single Cell ◽

Cell Fate ◽

Somatic Cells ◽

Supporting Cells ◽

Single Population ◽

Cell Lineages ◽

Multipotent Progenitors ◽

Distinct Cell

SummarySex determination is a unique process that allows the study of multipotent progenitors and their acquisition of sex-specific fates during differentiation of the gonad into a testis or an ovary. Using time-series single-cell RNA sequencing (scRNA-seq) on ovarian Nr5a1-GFP+ somatic cells during sex determination, we identified a single population of early progenitors giving rise to both pre-granulosa cells and potential steroidogenic precursor cells. By comparing time-series scRNA-seq of XX and XY somatic cells, we demonstrate that the supporting cells emerge from the early progenitors with a non-sex-specific transcriptomic program, before pre-granulosa and Sertoli cells acquire their sex-specific identity. In XX and XY steroidogenic precursors similar transcriptomic profiles underlie the acquisition of cell fate, but with a delay in XX cells. Our data provide a novel framework, at single-cell resolution, for further interrogation of the molecular and cellular basis of mammalian sex determination.

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SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species

10.1101/508085 ◽

2018 ◽

Cited By ~ 8

Author(s):

Yuqi Tan ◽

Patrick Cahan

Keyword(s):

Single Cell ◽

Cell Fate ◽

Marker Genes ◽

Rna Seq ◽

Cell Type ◽

Computational Tool ◽

Cell Type Composition ◽

Type Composition ◽

Cell Type Specific

Single cell RNA-Seq has emerged as a powerful tool in diverse applications, ranging from determining the cell-type composition of tissues to uncovering the regulators of developmental programs. A near-universal step in the analysis of single cell RNA-Seq data is to hypothesize the identity of each cell. Often, this is achieved by finding cells that express combinations of marker genes that had previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single cell RNA-Seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single cell RNA-Seq data in comparison to reference single cell RNA-Seq data. SingleCellNet compares favorably to other methods, and it is notably able to make sensitive and accurate classifications across platforms and species. We demonstrate how SingleCellNet can be used to classify previously undetermined cells, and how it can be used to assess the outcome of cell fate engineering experiments.

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