Rainbow-seq: combining cell lineage tracking with single-cell RNA sequencing in preimplantation embryos

SUMMARYSingle-cell RNA-seq experiments cannot record cell division history and therefore cannot directly connect intercellular differences at a later developmental stage to their progenitor cells. We developed Rainbow-seq to combine cell division lineage tracing with single-cell RNA-seq. With distinct fluorescent protein genes as lineage markers, Rainbow-seq enables each single-cell RNA-seq experiment to simultaneously read single-cell transcriptomes and decode the lineage marker genes. We traced the lineages deriving from each blastomere in two-cell mouse embryos and observed inequivalent contributions to the embryonic and abembryonic poles in 72% of the blastocysts evaluated. Rainbow-seq on four- and eight-cell embryos with lineage tracing triggered at two-cell stage exhibited remarkable transcriptome-wide differences between the two cell lineages at both stages, including genes involved in negative regulation of transcription and signaling. These data provide critical insights on cell fate choices in cleavage embryos. Rainbow-seq bridged a critical gap between cellular division history and single-cell RNA-seq assays.

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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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EPEN-05. MUTATIONAL ANALYSIS OF THE C11ORF95 DOMAIN AND SINGLE-CELL RNA-SEQ PROFILE OF A MOUSE MODEL OF SUPRATENTORIAL EPENDYMOMA

Neuro-Oncology ◽

10.1093/neuonc/noab090.055 ◽

2021 ◽

Vol 23 (Supplement_1) ◽

pp. i14-i14

Author(s):

Kevin Truong ◽

James He ◽

Gavin Birdsall ◽

Ericka Randazzo ◽

Jesse Dunnack ◽

...

Keyword(s):

Mouse Model ◽

Single Cell ◽

Tumor Cells ◽

Cell Fate ◽

Transcriptional Activation ◽

Mutational Analysis ◽

Cell Types ◽

Marker Genes ◽

Rna Seq ◽

Loss Of Function

Abstract We used a recently developed mouse model to better understand the cellular and molecular determinants of tumors driven by the oncogenic fusion protein C11orf95-RELA. Our approach makes use of in utero electroporation and a binary transposase system to introduce human C11orf95-RELA sequence, wild type and mutant forms, into neural progenitors. We used single cell RNA-seq to profile the cellular constituents within the resulting tumors in mice. We find that approximately 70% of the cells in the tumors do not express the oncogene C11orf95-RELA and these non-oncogene expressing cells are a combination of different non-tumor cell cell-types, including significant numbers of T-cells, and macrophages. The C11orf95-RELA expressing tumor cells have a unique transcriptomic profile that includes both astrocytic and neural progenitor marker genes, and is distinct from glioblastoma transcriptomic profiles. Since C11orf95-RELA is believed to function through a combination of both activation of NF-κB response genes by constitutive activation of RELA, and genes not activated by NF-κB, we assessed the expression of NF-κB response genes across the populations of cells in the tumor. Interestingly, when tumor cells highly expressing C11orf95-RELA were analyzed further, the subclusters identified were distinguished by upregulation of non-NF-kB pathways involved in cell proliferation, cell fate determination, and immune activation. We hypothesized that the C11orf95 domain may function to bring RELA transcriptional activation to inappropriate non-NF-κB targets, and we therefore performed a point mutation analysis of the C11orf95 domain. We found that mutations in either of the cysteines or histidines that make up a possible zinc finger domain in C11orf95 eliminate the ability of the fusion to induce tumors. In cell lines, these loss-of-function point mutants still trafficked to nuclei, and activated NF-κB pathways. We are currently using RNAseq and CRISPR loss-of function to identify genes downstream of C11orf95-RELA that are required for tumorigenesis.

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A single-cell analysis of the molecular lineage of chordate embryogenesis

10.1101/2020.03.02.966440 ◽

2020 ◽

Cited By ~ 1

Author(s):

Tengjiao Zhang ◽

Yichi Xu ◽

Kaoru Imai ◽

Teng Fei ◽

Guilin Wang ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Developmental Stages ◽

Expression Profiles ◽

Cell Types ◽

Mapk Signaling ◽

Modular Structure ◽

Marker Genes ◽

List Type ◽

Cell Stage

SummaryIn multicellular organisms, a single zygote develops along divergent lineages to produce distinct cell types. What governs these processes is central to the understanding of cell fate specification and stem cell engineering. Here we used the protochordate model Ciona savignyi to determine gene expression profiles of every cell of single embryos from fertilization through the onset of gastrulation and provided a comprehensive map of chordate early embryonic lineage specification. We identified 47 cell types across 8 developmental stages up to the 110-cell stage in wild type embryos and 8 fate transformations at the 64-cell stage upon FGF-MAPK inhibition. The identities of all cell types were evidenced by in situ expression pattern of marker genes and expected number of cells based on the invariant lineage. We found that, for the majority of asymmetrical cell divisions, the bipotent mother cell shows predominantly the gene signature of one of the daughter fates, with the other daughter being induced by subsequent signaling. Our data further indicated that the asymmetric segregation of mitochondria in some of these divisions does not depend on the concurrent fate inducing FGF-MAPK signaling. In the notochord, which is an evolutionary novelty of chordates, the convergence of cell fate from two disparate lineages revealed modular structure in the gene regulatory network beyond the known master regulator T/Brachyury. Comparison to single cell transcriptomes of the early mouse embryo showed a clear match of cell types at the tissue level and supported the hypothesis of developmental-genetic toolkit. This study provides a high-resolution single cell dataset to understand chordate embryogenesis and the relationship between fate trajectories and the cell lineage.HighlightsTranscriptome profiles of 47 cell types across 8 stages in early chordate embryoBipotent mother in asymmetric division shows the default daughter fateModular structure of the notochord GRN beyond the known function of TInvariant lineage and manual cell isolation provide truth to trajectory analysis

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Generative modeling of single-cell population time series for inferring cell differentiation landscapes

10.1101/2020.08.26.269332 ◽

2020 ◽

Author(s):

Grace H.T. Yeo ◽

Sachit D. Saksena ◽

David K. Gifford

Keyword(s):

Gene Expression ◽

Time Series ◽

Cell Differentiation ◽

Single Cell ◽

Cell Fate ◽

Lineage Tracing ◽

Model Framework ◽

Modeling Framework ◽

Generative Modeling ◽

Model Complex

SummaryExisting computational methods that use single-cell RNA-sequencing for cell fate prediction either summarize observations of cell states and their couplings without modeling the underlying differentiation process, or are limited in their capacity to model complex differentiation landscapes. Thus, contemporary methods cannot predict how cells evolve stochastically and in physical time from an arbitrary starting expression state, nor can they model the cell fate consequences of gene expression perturbations. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from single-cell time-series gene expression data. Our generative model framework provides insight into the process of differentiation and can simulate differentiation trajectories for arbitrary gene expression progenitor states. We validate our method on a recently published experimental lineage tracing dataset that provides observed trajectories. We show that this model is able to predict the fate biases of progenitor cells in neutrophil/macrophage lineages when accounting for cell proliferation, improving upon the best-performing existing method. We also show how a model can predict trajectories for cells not found in the model’s training set, including cells in which genes or sets of genes have been perturbed. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations. PRESCIENT models are able to recover the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation.

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Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

10.1101/553040 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ning Wang ◽

Andrew E. Teschendorff

Keyword(s):

Transcription Factors ◽

Single Cell ◽

Cell Fate ◽

Regulatory Networks ◽

Large Scale ◽

Single Cells ◽

Differential Expression Analysis ◽

Dropout Rate ◽

Rna Seq ◽

Regulatory Activity

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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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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Abstract 39: Fate Of Renin Cell Progenitors In The Developing Kidney Vasculature

Hypertension ◽

10.1161/hyp.78.suppl_1.39 ◽

2021 ◽

Vol 78 (Suppl_1) ◽

Author(s):

Alexandre Martini ◽

Ariel R Gomez ◽

Maria Luisa Sequeira Lopez

Keyword(s):

Single Cell ◽

Cell Fate ◽

Fluorescent Protein ◽

Enrichment Analysis ◽

Cell Fate Decisions ◽

Mural Cells ◽

Developing Kidney ◽

Green Fluorescent ◽

Old Mice ◽

Accessible Chromatin

The unique spatial arregement of the kidney arterioles is an essential event for its development. However, the mechanisms that govern this process are still poorly understood. During nephrogenesis, a group of stromal cells expressing the Forkhead Box D1 ( FoxD1 ) transcription factor (TF) will give rise to the metanephric progenitors for the mural cells of the kidneys arteries and arterioles. We aim to identify the core TFs involved in the cell fate along the differentiaton pathways of the developing kidney vasculature. Therefore, we generated Foxd1-cre; mTmG mice, whose Foxd1 derivative cells are labeled with green fluorescent protein (GFP). GFP+ cells were isolated from 5 (P5) or 30 (P30) days old mice kidneys, and processed either for single-cell RNA-Seq (scRNA-Seq) or for single-cell Assay for Transposase-Accessible Chromatin (scATAC-Seq ). The top5 highly expressed TFs on scRNA-Seq at P5 are: Tcf21, Zeb2, Meis2, Cebpd and Nme3 (p_adjusted_value(padj)= 0, 3.8E-187, 3.9E-180, 4E-172, 4.1E-172 and 3.2E-154, respectively). They are involved in developmental processes and cell proliferation. At P30, the top5 highly expressed TFs are: Atf3, klf2, Fos, Nr4a2 and Junb (padj= 4.2E-294, 2.1E-200, 3.5E-182, 1.7E-52 and 0.2E-24, respectively). They are implicated with calcium-signaling pathway and inflammation. Additionally, scATAC-Seq identifies regions of accessible chromatin for pontential TFs binding, leading to changes in gene expression content and cell identity. At P30, scATAC-Seq showed differential accessible regions with subsequent putative motif enrichment analysis for the TF N4a2 (padj: 4E-297). This is in accordance with our scRNA-Seq results and might play a role in the Foxd1 progenitors cell fate decisions. Our results tracks the fate of the Foxd1+ cells during the kidney vasculature assembly and suggest a new transcription factors network that might play a role to orchestrate cell fate decisions during kidney vascular development.

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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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Gfi1aa and Gfi1b set the pace for primitive erythroblast differentiation from hemangioblasts in the zebrafish embryo

Blood Advances ◽

10.1182/bloodadvances.2018020156 ◽

2018 ◽

Vol 2 (20) ◽

pp. 2589-2606 ◽

Cited By ~ 1

Author(s):

Chris Moore ◽

Joanna L. Richens ◽

Yasmin Hough ◽

Deniz Ucanok ◽

Sunir Malla ◽

...

Keyword(s):

Cell Fate ◽

Fluorescent Protein ◽

Erythroid Differentiation ◽

Transcriptional Repressors ◽

Rna Seq ◽

Promote Cell Proliferation ◽

Cell Fate Decisions ◽

Maturation Defect ◽

Green Fluorescent

Abstract The transcriptional repressors Gfi1(a) and Gfi1b are epigenetic regulators with unique and overlapping roles in hematopoiesis. In different contexts, Gfi1 and Gfi1b restrict or promote cell proliferation, prevent apoptosis, influence cell fate decisions, and are essential for terminal differentiation. Here, we show in primitive red blood cells (prRBCs) that they can also set the pace for cellular differentiation. In zebrafish, prRBCs express 2 of 3 zebrafish Gfi1/1b paralogs, Gfi1aa and Gfi1b. The recently identified zebrafish gfi1aa gene trap allele qmc551 drives erythroid green fluorescent protein (GFP) instead of Gfi1aa expression, yet homozygous carriers have normal prRBCs. prRBCs display a maturation defect only after splice morpholino-mediated knockdown of Gfi1b in gfi1aaqmc551 homozygous embryos. To study the transcriptome of the Gfi1aa/1b double-depleted cells, we performed an RNA-Seq experiment on GFP-positive prRBCs sorted from 20-hour-old embryos that were heterozygous or homozygous for gfi1aaqmc551, as well as wt or morphant for gfi1b. We subsequently confirmed and extended these data in whole-mount in situ hybridization experiments on newly generated single- and double-mutant embryos. Combined, the data showed that in the absence of Gfi1aa, the synchronously developing prRBCs were delayed in activating late erythroid differentiation, as they struggled to suppress early erythroid and endothelial transcription programs. The latter highlighted the bipotent nature of the progenitors from which prRBCs arise. In the absence of Gfi1aa, Gfi1b promoted erythroid differentiation as stepwise loss of wt gfi1b copies progressively delayed Gfi1aa-depleted prRBCs even further, showing that Gfi1aa and Gfi1b together set the pace for prRBC differentiation from hemangioblasts.

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