1
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Zhou P, Bocci F, Li T, Nie Q. Spatial transition tensor of single cells. Nat Methods 2024; 21:1053-1062. [PMID: 38755322 PMCID: PMC11166574 DOI: 10.1038/s41592-024-02266-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 04/02/2024] [Indexed: 05/18/2024]
Abstract
Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial-mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT's capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.
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Affiliation(s)
- Peijie Zhou
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- Center for Machine Learning Research, Peking University, Beijing, China
- AI for Science Institute, Beijing, China
- National Engineering Laboratory for Big Data Analysis and Applications, Beijing, China
| | - Federico Bocci
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
| | - Tiejun Li
- LMAM and School of Mathematical Sciences, Peking University, Beijing, China
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- Department of Cell and Developmental Biology, University of California, Irvine, Irvine, CA, USA.
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2
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Liu Y, Huang K, Chen W. Resolving cellular dynamics using single-cell temporal transcriptomics. Curr Opin Biotechnol 2024; 85:103060. [PMID: 38194753 DOI: 10.1016/j.copbio.2023.103060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 01/11/2024]
Abstract
Cellular dynamics, the transition of a cell from one state to another, is central to understanding developmental processes and disease progression. Single-cell transcriptomics has been pushing the frontiers of cellular dynamics studies into a genome-wide and single-cell level. While most single-cell RNA sequencing approaches are disruptive and only provide a snapshot of cell states, the dynamics of a cell could be reconstructed by either exploiting temporal information hiding in the transcriptomics data or integrating additional information. In this review, we describe these approaches, highlighting their underlying principles, key assumptions, and the rationality to interpret the results as models. We also discuss the recently emerging nondisruptive live-cell transcriptomics methods, which are highly complementary to the computational models for their assumption-free nature.
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Affiliation(s)
- Yifei Liu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Kai Huang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wanze Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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3
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Lederer AR, Leonardi M, Talamanca L, Herrera A, Droin C, Khven I, Carvalho HJF, Valente A, Mantes AD, Arabí PM, Pinello L, Naef F, Manno GL. Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576093. [PMID: 38328127 PMCID: PMC10849531 DOI: 10.1101/2024.01.18.576093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression. However, information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally-defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.
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Kim H, Choi H, Lee D, Kim J. A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing. Genes Genomics 2024; 46:1-11. [PMID: 38032470 DOI: 10.1007/s13258-023-01473-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND Understanding gene regulatory networks (GRNs) is essential for unraveling the molecular mechanisms governing cellular behavior. With the advent of high-throughput transcriptome measurement technology, researchers have aimed to reverse engineer the biological systems, extracting gene regulatory rules from their outputs, which represented by gene expression data. Bulk RNA sequencing, a widely used method for measuring gene expression, has been employed for GRN reconstruction. However, it falls short in capturing dynamic changes in gene expression at the level of individual cells since it averages gene expression across mixed cell populations. OBJECTIVE In this review, we provide an overview of 15 GRN reconstruction tools and discuss their respective strengths and limitations, particularly in the context of single cell RNA sequencing (scRNA-seq). METHODS Recent advancements in scRNA-seq break new ground of GRN reconstruction. They offer snapshots of the individual cell transcriptomes and capturing dynamic changes. We emphasize how these technological breakthroughs have enhanced GRN reconstruction. CONCLUSION GRN reconstructors can be classified based on their requirement for cellular trajectory, which represents a dynamical cellular process including differentiation, aging, or disease progression. Benchmarking studies support the superiority of GRN reconstructors that do not require trajectory analysis in identifying regulator-target relationships. However, methods equipped with trajectory analysis demonstrate better performance in identifying key regulatory factors. In conclusion, researchers should select a suitable GRN reconstructor based on their specific research objectives.
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Affiliation(s)
- Hyeonkyu Kim
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea
| | - Hwisoo Choi
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea
| | - Daewon Lee
- School of Art and Technology, Chung-Ang University, 4726 Seodong-Daero, Anseong-Si, Gyeonggi-Do, 17546, Republic of Korea.
| | - Junil Kim
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea.
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Mao S, Liu J, Zhao W, Zhou X. LVPT: Lazy Velocity Pseudotime Inference Method. Biomolecules 2023; 13:1242. [PMID: 37627306 PMCID: PMC10452358 DOI: 10.3390/biom13081242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
The emergence of RNA velocity has enriched our understanding of the dynamic transcriptional landscape within individual cells. In light of this breakthrough, we embarked on integrating RNA velocity with cellular pseudotime inference, aiming to improve the prediction of cell orders along biological trajectories beyond existing methods. Here, we developed LVPT, a novel method for pseudotime and trajectory inference. LVPT introduces a lazy probability to indicate the probability that the cell stays in the original state and calculates the transition matrix based on RNA velocity to provide the probability and direction of cell differentiation. LVPT shows better and comparable performance of pseudotime inference compared with other existing methods on both simulated datasets with different structures and real datasets. The validation results were consistent with prior knowledge, indicating that LVPT is an accurate and efficient method for pseudotime inference.
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Affiliation(s)
- Shuainan Mao
- The Department of Biotherapy and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jiajia Liu
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Weiling Zhao
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA
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6
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Zhang Y, Tran D, Nguyen T, Dascalu SM, Harris FC. A robust and accurate single-cell data trajectory inference method using ensemble pseudotime. BMC Bioinformatics 2023; 24:55. [PMID: 36803767 PMCID: PMC9942315 DOI: 10.1186/s12859-023-05179-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/09/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND The advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in individual cell resolution. In recent years, many trajectory inference methods have been developed. They have focused on using the graph method to infer the trajectory using single-cell data, and then calculate the geodesic distance as the pseudotime. However, these methods are vulnerable to errors caused by the inferred trajectory. Therefore, the calculated pseudotime suffers from such errors. RESULTS We proposed a novel framework for trajectory inference called the single-cell data Trajectory inference method using Ensemble Pseudotime inference (scTEP). scTEP utilizes multiple clustering results to infer robust pseudotime and then uses the pseudotime to fine-tune the learned trajectory. We evaluated the scTEP using 41 real scRNA-seq data sets, all of which had the ground truth development trajectory. We compared the scTEP with state-of-the-art methods using the aforementioned data sets. Experiments on real linear and non-linear data sets demonstrate that our scTEP performed superior on more data sets than any other method. The scTEP also achieved a higher average and lower variance on most metrics than other state-of-the-art methods. In terms of trajectory inference capacity, the scTEP outperforms those methods. In addition, the scTEP is more robust to the unavoidable errors resulting from clustering and dimension reduction. CONCLUSION The scTEP demonstrates that utilizing multiple clustering results for the pseudotime inference procedure enhances its robustness. Furthermore, robust pseudotime strengthens the accuracy of trajectory inference, which is the most crucial component in the pipeline. scTEP is available at https://cran.r-project.org/package=scTEP .
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Affiliation(s)
- Yifan Zhang
- Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA.
| | - Duc Tran
- grid.266818.30000 0004 1936 914XDepartment of Computer Science and Engineering, University of Nevada, Reno, Reno, NV USA
| | - Tin Nguyen
- grid.266818.30000 0004 1936 914XDepartment of Computer Science and Engineering, University of Nevada, Reno, Reno, NV USA
| | - Sergiu M. Dascalu
- grid.266818.30000 0004 1936 914XDepartment of Computer Science and Engineering, University of Nevada, Reno, Reno, NV USA
| | - Frederick C. Harris
- grid.266818.30000 0004 1936 914XDepartment of Computer Science and Engineering, University of Nevada, Reno, Reno, NV USA
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He D, Soneson C, Patro R. Understanding and evaluating ambiguity in single-cell and single-nucleus RNA-sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.04.522742. [PMID: 36711921 PMCID: PMC9881993 DOI: 10.1101/2023.01.04.522742] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Recently, a new modification has been proposed by Hjörleifsson and Sullivan et al. to the model used to classify the splicing status of reads (as spliced (mature), unspliced (nascent), or ambiguous) in single-cell and single-nucleus RNA-seq data. Here, we evaluate both the theoretical basis and practical implementation of the proposed method. The proposed method is highly-conservative, and therefore, unlikely to mischaracterize reads as spliced (mature) or unspliced (nascent) when they are not. However, we find that it leaves a large fraction of reads classified as ambiguous, and, in practice, allocates these ambiguous reads in an all-or-nothing manner, and differently between single-cell and single-nucleus RNA-seq data. Further, as implemented in practice, the ambiguous classification is implicit and based on the index against which the reads are mapped, which leads to several drawbacks compared to methods that consider both spliced (mature) and unspliced (nascent) mapping targets simultaneously - for example, the ability to use confidently assigned reads to rescue ambiguous reads based on shared UMIs and gene targets. Nonetheless, we show that these conservative assignment rules can be obtained directly in existing approaches simply by altering the set of targets that are indexed. To this end, we introduce the spliceu reference and show that its use with alevin-fry recapitulates the more conservative proposed classification. We also observe that, on experimental data, and under the proposed allocation rules for ambiguous UMIs, the difference between the proposed classification scheme and existing conventions appears much smaller than previously reported. We demonstrate the use of the new piscem index for mapping simultaneously against spliced (mature) and unspliced (nascent) targets, allowing classification against the full nascent and mature transcriptome in human or mouse in <3GB of memory. Finally, we discuss the potential of incorporating probabilistic evidence into the inference of splicing status, and suggest that it may provide benefits beyond what can be obtained from discrete classification of UMIs as splicing-ambiguous.
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Affiliation(s)
- Dongze He
- Department of Cell Biology and Molecular Genetics and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
| | - Charlotte Soneson
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Rob Patro
- Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
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8
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Gupta R, Cerletti D, Gut G, Oxenius A, Claassen M. Simulation-based inference of differentiation trajectories from RNA velocity fields. CELL REPORTS METHODS 2022; 2:100359. [PMID: 36590685 PMCID: PMC9795361 DOI: 10.1016/j.crmeth.2022.100359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/02/2022] [Accepted: 11/11/2022] [Indexed: 12/23/2022]
Abstract
We report Cytopath, a method for trajectory inference that takes advantage of transcriptional activity information from the RNA velocity of single cells to perform trajectory inference. Cytopath performs this task by defining a Markov chain model, simulating an ensemble of possible differentiation trajectories, and constructing a consensus trajectory. We show that Cytopath can recapitulate the topological and molecular characteristics of the differentiation process under study. In our analysis, we include differentiation trajectories with varying bifurcated, circular, convergent, and mixed topologies studied in single-snapshot as well as time-series single-cell RNA sequencing experiments. We demonstrate the capability to reconstruct differentiation trajectories, assess the association of RNA velocity-based pseudotime with actually elapsed process time, and identify drawbacks in current state-of-the art trajectory inference approaches.
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Affiliation(s)
- Revant Gupta
- Internal Medicine I, University Hospital Tübingen, Faculty of Medicine, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Dario Cerletti
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Gilles Gut
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | | | - Manfred Claassen
- Internal Medicine I, University Hospital Tübingen, Faculty of Medicine, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
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9
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Ranek JS, Stanley N, Purvis JE. Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction. Genome Biol 2022; 23:186. [PMID: 36064614 PMCID: PMC9442962 DOI: 10.1186/s13059-022-02749-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/16/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics. RESULTS Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods. CONCLUSIONS This work illustrates how integrated temporal gene expression modalities may be leveraged for predicting cellular trajectories and sample-associated perturbation and disease phenotypes. Additionally, this study provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.
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Affiliation(s)
- Jolene S. Ranek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Natalie Stanley
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Jeremy E. Purvis
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, USA
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10
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Kim D, Kim J, Yu YS, Kim YR, Baek SH, Won KJ. Systemic approaches using single cell transcriptome reveal that C/EBPγ regulates autophagy under amino acid starved condition. Nucleic Acids Res 2022; 50:7298-7309. [PMID: 35801910 PMCID: PMC9303372 DOI: 10.1093/nar/gkac593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 06/17/2022] [Accepted: 06/24/2022] [Indexed: 11/14/2022] Open
Abstract
Autophagy, a catabolic process to remove unnecessary or dysfunctional organelles, is triggered by various signals including nutrient starvation. Depending on the types of the nutrient deficiency, diverse sensing mechanisms and signaling pathways orchestrate for transcriptional and epigenetic regulation of autophagy. However, our knowledge about nutrient type-specific transcriptional regulation during autophagy is limited. To understand nutrient type-dependent transcriptional mechanisms during autophagy, we performed single cell RNA sequencing (scRNAseq) in the mouse embryonic fibroblasts (MEFs) with or without glucose starvation (GS) as well as amino acid starvation (AAS). Trajectory analysis using scRNAseq identified sequential induction of potential transcriptional regulators for each condition. Gene regulatory rules inferred using TENET newly identified CCAAT/enhancer binding protein γ (C/EBPγ) as a regulator of autophagy in AAS, but not GS, condition, and knockdown experiment confirmed the TENET result. Cell biological and biochemical studies validated that activating transcription factor 4 (ATF4) is responsible for conferring specificity to C/EBPγ for the activation of autophagy genes under AAS, but not under GS condition. Together, our data identified C/EBPγ as a previously unidentified key regulator under AAS-induced autophagy.
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Affiliation(s)
- Dongha Kim
- Creative Research Initiatives Center for Epigenetic Code and Diseases, School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea.,Department of Anatomy, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Junil Kim
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, 2200 Copenhagen, Denmark.,School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 06978, Republic of Korea
| | - Young Suk Yu
- Creative Research Initiatives Center for Epigenetic Code and Diseases, School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Yong Ryoul Kim
- Creative Research Initiatives Center for Epigenetic Code and Diseases, School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Sung Hee Baek
- Creative Research Initiatives Center for Epigenetic Code and Diseases, School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Kyoung-Jae Won
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, 2200 Copenhagen, Denmark
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Kalinina A, Lagace D. Single-Cell and Single-Nucleus RNAseq Analysis of Adult Neurogenesis. Cells 2022; 11:1633. [PMID: 35626670 PMCID: PMC9139993 DOI: 10.3390/cells11101633] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/02/2022] [Accepted: 05/07/2022] [Indexed: 02/04/2023] Open
Abstract
The complexity of adult neurogenesis is becoming increasingly apparent as we learn more about cellular heterogeneity and diversity of the neurogenic lineages and stem cell niches within the adult brain. This complexity has been unraveled in part due to single-cell and single-nucleus RNA sequencing (sc-RNAseq and sn-RNAseq) studies that have focused on adult neurogenesis. This review summarizes 33 published studies in the field of adult neurogenesis that have used sc- or sn-RNAseq methods to answer questions about the three main regions that host adult neural stem cells (NSCs): the subventricular zone (SVZ), the dentate gyrus (DG) of the hippocampus, and the hypothalamus. The review explores the similarities and differences in methodology between these studies and provides an overview of how these studies have advanced the field and expanded possibilities for the future.
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Affiliation(s)
| | - Diane Lagace
- Neuroscience Program, Department of Cellular and Molecular Medicine, Ottawa Hospital Research Institute, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1H 8M5, Canada;
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12
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Liu R, Pisco AO, Braun E, Linnarsson S, Zou J. Dynamical Systems Model of RNA Velocity Improves Inference of Single-cell Trajectory, Pseudo-time and Gene Regulation. J Mol Biol 2022; 434:167606. [PMID: 35489382 DOI: 10.1016/j.jmb.2022.167606] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 04/19/2022] [Accepted: 04/21/2022] [Indexed: 11/24/2022]
Abstract
Recent development in inferring RNA velocity from single-cell RNA-seq opens up exciting new vista into developmental lineage and cellular dynamics. However, the estimated velocity only gives a snapshot of how the transcriptome instantaneously changes in individual cells, and it does not provide quantitative predictions and insights about the whole system. In this work, we develop RNA-ODE, a principled computational framework that extends RNA velocity to quantify systems level dynamics and improve single-cell data analysis. We model the gene expression dynamics by an ordinary differential equation (ODE) based formalism. Given a snapshot of gene expression at one time, RNA-ODE is able to predict and extrapolate the expression trajectory of each cell by solving the dynamic equations. Systematic experiments on simulations and on new data from developing brain demonstrate that RNA-ODE substantially improves many aspects of standard single-cell analysis. By leveraging temporal dynamics, RNA-ODE more accurately estimates cell state lineage and pseudo-time compared to previous state-of-the-art methods. It also infers gene regulatory networks and identifies influential genes whose expression changes can decide cell fate. We expect RNA-ODE to be a Swiss army knife that aids many facets of single-cell RNA-seq analysis.
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Affiliation(s)
- Ruishan Liu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | | | | | | | - James Zou
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Chan-Zuckerberg Biohub, San Francisco, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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13
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Zhang Z, Zhang X. Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity. CELL REPORTS METHODS 2021; 1:100095. [PMID: 35474895 PMCID: PMC9017235 DOI: 10.1016/j.crmeth.2021.100095] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 08/29/2021] [Accepted: 09/20/2021] [Indexed: 12/19/2022]
Abstract
Trajectory inference (TI) methods infer cell developmental trajectory from single-cell RNA sequencing data. Current TI methods can be categorized into those using RNA velocity information and those using only single-cell gene expression data. The latter type of methods are restricted to certain trajectory structures, and cannot determine cell developmental direction. Recently proposed TI methods using RNA velocity information have limited accuracy. We present CellPath, a method that infers cell trajectories by integrating single-cell gene expression and RNA velocity information. CellPath overcomes the restrictions of TI methods that do not use RNA velocity information: it can find multiple high-resolution trajectories without constraints on the trajectory structure, and can automatically detect the direction of each trajectory path. We evaluate CellPath on both real and simulated datasets and show that CellPath finds more accurate and detailed trajectories than the state-of-the-art TI methods using or not using RNA velocity information.
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Affiliation(s)
- Ziqi Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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