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

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.

scholarly journals Single-Cell Genomics: Catalyst for Cell Fate Engineering

2021 ◽  
Vol 9 ◽  
Author(s):  
Boxun Li ◽  
Gary C. Hon
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
State Transitions ◽  
Small Scale ◽  
Specific Cell ◽  
Direct Reprogramming ◽  
Cell State

As we near a complete catalog of mammalian cell types, the capability to engineer specific cell types on demand would transform biomedical research and regenerative medicine. However, the current pace of discovering new cell types far outstrips our ability to engineer them. One attractive strategy for cellular engineering is direct reprogramming, where induction of specific transcription factor (TF) cocktails orchestrates cell state transitions. Here, we review the foundational studies of TF-mediated reprogramming in the context of a general framework for cell fate engineering, which consists of: discovering new reprogramming cocktails, assessing engineered cells, and revealing molecular mechanisms. Traditional bulk reprogramming methods established a strong foundation for TF-mediated reprogramming, but were limited by their small scale and difficulty resolving cellular heterogeneity. Recently, single-cell technologies have overcome these challenges to rapidly accelerate progress in cell fate engineering. In the next decade, we anticipate that these tools will enable unprecedented control of cell state.

Cross-species integration of single-cell RNA-seq resolved alveolar-epithelial transitional states in idiopathic pulmonary fibrosis

2021 ◽  
Author(s):  
Kevin Y. Huang ◽  
Enrico Petretto
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Single Cell ◽  
Transitional Cell ◽  
Notch Pathway ◽  
Alveolar Epithelium ◽  
State Transitions ◽  
Specific Cell ◽  
Differentiation Process ◽  
Cell State

Single-cell transcriptomics analyses of the fibrotic lung uncovered two cell states critical to lung injury recovery in the alveolar epithelium- a reparative transitional cell state in the mouse and a disease-specific cell state (KRT5-/KRT17+) in human idiopathic pulmonary fibrosis (IPF). The murine transitional cell state lies between the differentiation from type 2 (AT2) to type 1 pneumocyte (AT1), and the human KRT5-/KRT17+ cell state may arise from the dysregulation of this differentiation process. We review major findings of single-cell transcriptomics analyses of the fibrotic lung and re-analyzed data from 7 single-cell RNA sequencing studies of human and murine models of IPF, focusing on the alveolar epithelium. Our comparative and cross-species single-cell transcriptomics analyses allowed us to further delineate the differentiation trajectories from AT2 to AT1 and AT2 to the KRT5-/KRT17+ cell state. We observed AT1 cells in human IPF retain the transcriptional signature of the murine transitional cell state. Using pseudotime analysis, we recapitulated the differentiation trajectories from AT2 to AT1 and from AT2 to KRT5-/KRT17+ cell state in multiple human IPF studies. We further delineated transcriptional programs underlying cell state transitions and determined the molecular phenotypes at terminal differentiation. We hypothesize that in addition to the reactivation of developmental programs (SOX4, SOX9), senescence (TP63, SOX4) and the Notch pathway (HES1) are predicted to steer intermediate progenitors to the KRT5-/KRT17+ cell state. Our analyses suggest that activation of SMAD3 later in the differentiation process may explain the fibrotic molecular phenotype typical of KRT5-/KRT17+ cells.

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

eLife ◽  
2020 ◽  
Vol 9 ◽  
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.

scholarly journals DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets

2018 ◽  
Author(s):  
Erica A.K. DePasquale ◽  
Daniel J. Schnell ◽  
Íñigo Valiente-Alandí ◽  
Burns C. Blaxall ◽  
H. Leighton Grimes ◽  
...  
Keyword(s):  
Single Cell ◽  
Individual Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell State ◽  
Detection Algorithms ◽  
Distinct Cell ◽  
Standard Quality ◽  
Control Step ◽  
Unique Cell

SUMMARYMethods for single-cell RNA sequencing (scRNA-Seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-Seq, these technologies readily produce technical artifacts, such as doublet-cell and multiplet-cell captures. Doublets occurring between distinct cell-types can appear as hybrid scRNA-Seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic and cell-hashing cell singlets and doublets from scRNA-Seq datasets of varying cellular complexity. DoubletDecon is able to account for cell-cycle effects and is compatible with diverse species and unsupervised population detection algorithms (e.g., ICGS, Seurat). We believe this approach has the potential to become a standard quality control step for the accurate delineation of cell states.

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

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.

scholarly journals SCENIC: Single-cell regulatory network inference and clustering

2017 ◽  
Author(s):  
Sara Aibar ◽  
Carmen Bravo González-Blas ◽  
Thomas Moerman ◽  
Jasper Wouters ◽  
Vân Anh Huynh-Thu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Regulatory Network ◽  
Network Inference ◽  
Target Genes ◽  
Single Cells ◽  
State Transitions ◽  
Rna Seq ◽  
Cell State ◽  
Gene Regulatory

AbstractSingle-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

scholarly journals Single-cell RNA-seq of human induced pluripotent stem cells reveals cellular heterogeneity and cell state transitions between subpopulations

2018 ◽  
Vol 28 (7) ◽  
pp. 1053-1066 ◽  
Author(s):  
Quan H. Nguyen ◽  
Samuel W. Lukowski ◽  
Han Sheng Chiu ◽  
Anne Senabouth ◽  
Timothy J.C. Bruxner ◽  
...  
Keyword(s):  
Stem Cells ◽  
Induced Pluripotent Stem Cells ◽  
Single Cell ◽  
Pluripotent Stem Cells ◽  
Cellular Heterogeneity ◽  
State Transitions ◽  
Rna Seq ◽  
Cell State ◽  
Induced Pluripotent

scholarly journals Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peijie Zhou ◽  
Shuxiong Wang ◽  
Tiejun Li ◽  
Qing Nie
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Stochastic Dynamics ◽  
Coarse Grained ◽  
State Transitions ◽  
Rate Theory ◽  
Transcriptome Data ◽  
Cell State ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractAdvances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.

scholarly journals Single cell RNA-seq identifies the origins of heterogeneity in efficient cell transdifferentiation and reprogramming

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Mirko Francesconi ◽  
Bruno Di Stefano ◽  
Clara Berenguer ◽  
Luisa de Andrés-Aguayo ◽  
Marcos Plana-Carmona ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Cell Types ◽  
Rna Seq ◽  
Wide Range ◽  
Cell Fate Conversion ◽  
Induced Pluripotent ◽  
Cell Expression ◽  
Computational Analyses ◽  
Transcription Factor Expression

Forced transcription factor expression can transdifferentiate somatic cells into other specialised cell types or reprogram them into induced pluripotent stem cells (iPSCs) with variable efficiency. To better understand the heterogeneity of these processes, we used single-cell RNA sequencing to follow the transdifferentation of murine pre-B cells into macrophages as well as their reprogramming into iPSCs. Even in these highly efficient systems, there was substantial variation in the speed and path of fate conversion. We predicted and validated that these differences are inversely coupled and arise in the starting cell population, with Mychigh large pre-BII cells transdifferentiating slowly but reprogramming efficiently and Myclow small pre-BII cells transdifferentiating rapidly but failing to reprogram. Strikingly, differences in Myc activity predict the efficiency of reprogramming across a wide range of somatic cell types. These results illustrate how single cell expression and computational analyses can identify the origins of heterogeneity in cell fate conversion processes.

scholarly journals treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ruizhu Huang ◽  
Charlotte Soneson ◽  
Pierre-Luc Germain ◽  
Thomas S.B. Schmidt ◽  
Christian Von Mering ◽  
...  
Keyword(s):  
Single Cell ◽  
Synthetic Data ◽  
Cell Types ◽  
Data Driven ◽  
Rna Seq ◽  
Hierarchical Trees

AbstracttreeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.

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