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Yao S, Yu X, Wang X. SmartImpute: A Targeted Imputation Framework for Single-cell Transcriptome Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.15.603649. [PMID: 39071378 PMCID: PMC11275709 DOI: 10.1101/2024.07.15.603649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and tissue transcriptomic complexity. However, the high frequency of dropout events in scRNA-seq data complicates downstream analyses such as cell type identification and trajectory inference. Existing imputation methods address the dropout problem but face limitations such as high computational cost and risk of over-imputation. We present SmartImpute, a novel computational framework designed for targeted imputation of scRNA-seq data. SmartImpute focuses on a predefined set of marker genes, enhancing the biological relevance and computational efficiency of the imputation process while minimizing the risk of model misspecification. Utilizing a modified Generative Adversarial Imputation Network architecture, SmartImpute accurately imputes the missing gene expression and distinguishes between true biological zeros and missing values, preventing overfitting and preserving biologically relevant zeros. To ensure reproducibility, we also provide a function based on the GPT4 model to create target gene panels depending on the tissue types and research context. Our results, based on scRNA-seq data from head and neck squamous cell carcinoma and human bone marrow, demonstrate that SmartImpute significantly enhances cell type annotation and clustering accuracy while reducing computational burden. Benchmarking against other imputation methods highlights SmartImpute's superior performance in terms of both accuracy and efficiency. Overall, SmartImpute provides a lightweight, efficient, and biologically relevant solution for addressing dropout events in scRNA-seq data, facilitating deeper insights into cellular heterogeneity and disease progression. Furthermore, SmartImpute's targeted approach can be extended to spatial omics data, which also contain many missing values.
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Affiliation(s)
- Sijie Yao
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institution, Tampa, Florida, 33612, USA
| | - Xiaoqing Yu
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institution, Tampa, Florida, 33612, USA
| | - Xuefeng Wang
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institution, Tampa, Florida, 33612, USA
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2
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Peng D, Cahan P. OneSC: A computational platform for recapitulating cell state transitions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.31.596831. [PMID: 38895453 PMCID: PMC11185539 DOI: 10.1101/2024.05.31.596831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.
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Affiliation(s)
- Da Peng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
- Institute for Cell Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, Maryland, 21205, USA
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3
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Xue G, Zhang X, Li W, Zhang L, Zhang Z, Zhou X, Zhang D, Zhang L, Li Z. A logic-incorporated gene regulatory network deciphers principles in cell fate decisions. eLife 2024; 12:RP88742. [PMID: 38652107 PMCID: PMC11037919 DOI: 10.7554/elife.88742] [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] [Indexed: 04/25/2024] Open
Abstract
Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.
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Affiliation(s)
- Gang Xue
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaoyi Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Wanqi Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Zongxu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaolin Zhou
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Di Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lei Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Beijing International Center for Mathematical Research, Center for Machine Learning Research, Peking UniversityBeijingChina
| | - Zhiyuan Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
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4
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Malekpour SA, Haghverdi L, Sadeghi M. Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms. Brief Bioinform 2024; 25:bbae180. [PMID: 38653489 PMCID: PMC11036345 DOI: 10.1093/bib/bbae180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/29/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.
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Affiliation(s)
- Seyed Amir Malekpour
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), 19395-5746, Tehran, Iran
| | - Laleh Haghverdi
- Berlin Institute for Medical Systems Biology, Max Delbrück Center (BIMSB-MDC) in the Helmholtz Association, Berlin, Germany
| | - Mehdi Sadeghi
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology, 1497716316, Tehran, Iran
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5
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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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Yu D, Li M, Linghu G, Hu Y, Hajdarovic KH, Wang A, Singh R, Webb AE. CellBiAge: Improved single-cell age classification using data binarization. Cell Rep 2023; 42:113500. [PMID: 38032797 PMCID: PMC10791072 DOI: 10.1016/j.celrep.2023.113500] [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: 05/04/2023] [Revised: 10/20/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expression values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Additionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods.
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Affiliation(s)
- Doudou Yu
- Molecular Biology, Cell Biology, and Biochemistry Graduate Program, Brown University, Providence, RI 02912, USA; Data Science Institute, Brown University, Providence, RI 02912, USA
| | - Manlin Li
- Data Science Institute, Brown University, Providence, RI 02912, USA
| | - Guanjie Linghu
- Data Science Institute, Brown University, Providence, RI 02912, USA
| | - Yihuan Hu
- Data Science Institute, Brown University, Providence, RI 02912, USA
| | | | - An Wang
- Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ritambhara Singh
- Department of Computer Science, Brown University, Providence, RI 02912, USA; Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.
| | - Ashley E Webb
- Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI 02912, USA; Center on the Biology of Aging, Brown University, Providence, RI 02912, USA; Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA; Center for Translational Neuroscience, Brown University, Providence, RI 02912, USA.
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7
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Zhao YX, Song JY, Bao XW, Zhang JL, Wu JC, Wang LY, He C, Shao W, Bai XL, Liang TB, Sheng JP. Single-cell RNA sequencing-guided fate-mapping toolkit delineates the contribution of yolk sac erythro-myeloid progenitors. Cell Rep 2023; 42:113364. [PMID: 37922312 DOI: 10.1016/j.celrep.2023.113364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 09/16/2023] [Accepted: 10/16/2023] [Indexed: 11/05/2023] Open
Abstract
Erythro-myeloid progenitors of the yolk sac that originates during early embryo development has been suggested to generate tissue-resident macrophage, mast cell, and even endothelial cell populations from fetal to adult stages. However, the heterogeneity of erythro-myeloid progenitors (EMPs) is not well characterized. Here, we adapt single-cell RNA sequencing to dissect the heterogeneity of EMPs and establish several fate-mapping tools for each EMP subset to trace the contributions of different EMP subsets. We identify two primitive and one definitive EMP subsets from the yolk sac. In addition, we find that primitive EMPs are decoupled from definitive EMPs. Furthermore, we confirm that primitive and definitive EMPs give rise to microglia and other tissue-resident macrophages, respectively. In contrast, only Kit+ Csf1r- primitive EMPs generate endothelial cells transiently during early embryo development. Overall, our results delineate the contribution of yolk sac EMPs more clearly based on the single-cell RNA sequencing (scRNA-seq)-guided fate-mapping toolkit.
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Affiliation(s)
- Y X Zhao
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang University Cancer Center, Zhejiang University, Hangzhou 310002, China
| | - J Y Song
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang University Cancer Center, Zhejiang University, Hangzhou 310002, China
| | - X W Bao
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China
| | - J L Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang University Cancer Center, Zhejiang University, Hangzhou 310002, China
| | - J C Wu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang University Cancer Center, Zhejiang University, Hangzhou 310002, China
| | - L Y Wang
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang, China
| | - C He
- Infinity Scope Biotechnology Co., Ltd., Hangzhou 311200, China
| | - W Shao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China.
| | - X L Bai
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang University Cancer Center, Zhejiang University, Hangzhou 310002, China.
| | - T B Liang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang University Cancer Center, Zhejiang University, Hangzhou 310002, China.
| | - J P Sheng
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China; Zhejiang University Cancer Center, Zhejiang University, Hangzhou 310002, China.
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8
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Velten B, Stegle O. Principles and challenges of modeling temporal and spatial omics data. Nat Methods 2023; 20:1462-1474. [PMID: 37710019 DOI: 10.1038/s41592-023-01992-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/31/2023] [Indexed: 09/16/2023]
Abstract
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.
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Affiliation(s)
- Britta Velten
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Centre for Organismal Studies (COS) and Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
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9
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Joo JI, Park H, Cho K. Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2207322. [PMID: 37269056 PMCID: PMC10460890 DOI: 10.1002/advs.202207322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/17/2023] [Indexed: 06/04/2023]
Abstract
Accumulated genetic alterations in cancer cells distort cellular stimulus-response (or input-output) relationships, resulting in uncontrolled proliferation. However, the complex molecular interaction network within a cell implicates a possibility of restoring such distorted input-output relationships by rewiring the signal flow through controlling hidden molecular switches. Here, a system framework of analyzing cellular input-output relationships in consideration of various genetic alterations and identifying possible molecular switches that can normalize the distorted relationships based on Boolean network modeling and dynamics analysis is presented. Such reversion is demonstrated by the analysis of a number of cancer molecular networks together with a focused case study on bladder cancer with in vitro experiments and patient survival data analysis. The origin of reversibility from an evolutionary point of view based on the redundancy and robustness intrinsically embedded in complex molecular regulatory networks is further discussed.
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Affiliation(s)
- Jae Il Joo
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
- Present address:
biorevert IncDaejeon34051Republic of Korea
| | - Hwa‐Jeong Park
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
- Present address:
Promega Corporationan affiliate of PromegaSouth Korea
| | - Kwang‐Hyun Cho
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
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10
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Wang Y, He S. Inference on autoregulation in gene expression with variance-to-mean ratio. J Math Biol 2023; 86:87. [PMID: 37131095 PMCID: PMC10154285 DOI: 10.1007/s00285-023-01924-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/04/2023]
Abstract
Some genes can promote or repress their own expressions, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation from gene expression data. This method only needs to compare the mean and variance of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.
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Affiliation(s)
- Yue Wang
- Department of Computational Medicine, University of California, Los Angeles, CA, 90095, USA.
- Institut des Hautes Études Scientifiques (IHÉS), Bures-sur-Yvette, 91440, Essonne, France.
| | - Siqi He
- Simons Center for Geometry and Physics, Stony Brook University, Stony Brook, NY, 11794, USA
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11
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Subbaroyan A, Sil P, Martin OC, Samal A. Leveraging developmental landscapes for model selection in Boolean gene regulatory networks. Brief Bioinform 2023; 24:7145905. [PMID: 37114653 DOI: 10.1093/bib/bbad160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 03/26/2023] [Accepted: 04/03/2023] [Indexed: 04/29/2023] Open
Abstract
Boolean models are a well-established framework to model developmental gene regulatory networks (DGRNs) for acquisition of cellular identities. During the reconstruction of Boolean DGRNs, even if the network structure is given, there is generally a large number of combinations of Boolean functions that will reproduce the different cell fates (biological attractors). Here we leverage the developmental landscape to enable model selection on such ensembles using the relative stability of the attractors. First we show that previously proposed measures of relative stability are strongly correlated and we stress the usefulness of the one that captures best the cell state transitions via the mean first passage time (MFPT) as it also allows the construction of a cellular lineage tree. A property of great computational importance is the insensitivity of the different stability measures to changes in noise intensities. That allows us to use stochastic approaches to estimate the MFPT and thereby scale up the computations to large networks. Given this methodology, we revisit different Boolean models of Arabidopsis thaliana root development, showing that a most recent one does not respect the biologically expected hierarchy of cell states based on relative stabilities. We therefore developed an iterative greedy algorithm that searches for models which satisfy the expected hierarchy of cell states and found that its application to the root development model yields many models that meet this expectation. Our methodology thus provides new tools that can enable reconstruction of more realistic and accurate Boolean models of DGRNs.
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Affiliation(s)
- Ajay Subbaroyan
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Priyotosh Sil
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Olivier C Martin
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
- Université de Paris, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
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12
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Bouland GA, Mahfouz A, Reinders MJT. Consequences and opportunities arising due to sparser single-cell RNA-seq datasets. Genome Biol 2023; 24:86. [PMID: 37085823 PMCID: PMC10120229 DOI: 10.1186/s13059-023-02933-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 04/10/2023] [Indexed: 04/23/2023] Open
Abstract
With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes, we demonstrate here that downstream analyses on binary-based gene expression give similar results as count-based analyses. Moreover, a binary representation scales up to ~ 50-fold more cells that can be analyzed using the same computational resources. We also highlight the possibilities provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity.
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Affiliation(s)
- Gerard A Bouland
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands
| | - Ahmed Mahfouz
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.
- Department of Human Genetics, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands.
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands.
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.
- Department of Human Genetics, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands.
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands.
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13
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Howell R, Davies J, Clarke MA, Appios A, Mesquita I, Jayal Y, Ringham-Terry B, Boned Del Rio I, Fisher J, Bennett CL. Localized immune surveillance of primary melanoma in the skin deciphered through executable modeling. SCIENCE ADVANCES 2023; 9:eadd1992. [PMID: 37043573 PMCID: PMC10096595 DOI: 10.1126/sciadv.add1992] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
While skin is a site of active immune surveillance, primary melanomas often escape detection. Here, we have developed an in silico model to determine the local cross-talk between melanomas and Langerhans cells (LCs), the primary antigen-presenting cells at the site of melanoma development. The model predicts that melanomas fail to activate LC migration to lymph nodes until tumors reach a critical size, which is determined by a positive TNF-α feedback loop within melanomas, in line with our observations of murine tumors. In silico drug screening, supported by subsequent experimental testing, shows that treatment of primary tumors with MAPK pathway inhibitors may further prevent LC migration. In addition, our in silico model predicts treatment combinations that bypass LC dysfunction. In conclusion, our combined approach of in silico and in vivo studies suggests a molecular mechanism that explains how early melanomas develop under the radar of immune surveillance by LC.
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Affiliation(s)
| | | | - Matthew A. Clarke
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Anna Appios
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Inês Mesquita
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Yashoda Jayal
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Ben Ringham-Terry
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Isabel Boned Del Rio
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
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14
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Wang Y, He S. Using Fano factors to determine certain types of gene autoregulation. ARXIV 2023:arXiv:2301.06692v2. [PMID: 36713249 PMCID: PMC9882590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The expression of one gene might be regulated by its corresponding protein, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation in certain scenarios from gene expression data. This method only depends on the Fano factor, namely the ratio of variance and mean of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.
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Affiliation(s)
- Yue Wang
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
- Institut des Hautes Études Scientifiques, Bures-sur-Yvette, Essonne, France
| | - Siqi He
- Simons Center for Geometry and Physics, Stony Brook University, Stony Brook, New York, United States of America
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15
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Song Q, Ruffalo M, Bar-Joseph Z. Using single cell atlas data to reconstruct regulatory networks. Nucleic Acids Res 2023; 51:e38. [PMID: 36762475 PMCID: PMC10123116 DOI: 10.1093/nar/gkad053] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 12/16/2022] [Accepted: 01/19/2023] [Indexed: 02/11/2023] Open
Abstract
Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)-gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.
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Affiliation(s)
- Qi Song
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Matthew Ruffalo
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.,Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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16
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Malekpour SA, Shahdoust M, Aghdam R, Sadeghi M. wpLogicNet: logic gate and structure inference in gene regulatory networks. Bioinformatics 2023; 39:7039679. [PMID: 36790055 PMCID: PMC9936836 DOI: 10.1093/bioinformatics/btad072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 01/25/2023] [Accepted: 02/14/2023] [Indexed: 02/16/2023] Open
Abstract
MOTIVATION The gene regulatory process resembles a logic system in which a target gene is regulated by a logic gate among its regulators. While various computational techniques are developed for a gene regulatory network (GRN) reconstruction, the study of logical relationships has received little attention. Here, we propose a novel tool called wpLogicNet that simultaneously infers both the directed GRN structures and logic gates among genes or transcription factors (TFs) that regulate their target genes, based on continuous steady-state gene expressions. RESULTS wpLogicNet proposes a framework to infer the logic gates among any number of regulators, with a low time-complexity. This distinguishes wpLogicNet from the existing logic-based models that are limited to inferring the gate between two genes or TFs. Our method applies a Bayesian mixture model to estimate the likelihood of the target gene profile and to infer the logic gate a posteriori. Furthermore, in structure-aware mode, wpLogicNet reconstructs the logic gates in TF-gene or gene-gene interaction networks with known structures. The predicted logic gates are validated on simulated datasets of TF-gene interaction networks from Escherichia coli. For the directed-edge inference, the method is validated on datasets from E.coli and DREAM project. The results show that compared to other well-known methods, wpLogicNet is more precise in reconstructing the network and logical relationships among genes. AVAILABILITY AND IMPLEMENTATION The datasets and R package of wpLogicNet are available in the github repository, https://github.com/CompBioIPM/wpLogicNet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Seyed Amir Malekpour
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran 19395-5746, Iran
| | - Maryam Shahdoust
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran 19395-5746, Iran
| | - Rosa Aghdam
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran 19395-5746, Iran.,Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - Mehdi Sadeghi
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran 19395-5746, Iran
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17
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NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity. Genome Biol 2022; 23:270. [PMID: 36575445 PMCID: PMC9793520 DOI: 10.1186/s13059-022-02835-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022] Open
Abstract
A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.
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18
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Boe RH, Ayyappan V, Schuh L, Raj A. Allelic correlation is a marker of trade-offs between barriers to transmission of expression variability and signal responsiveness in genetic networks. Cell Syst 2022; 13:1016-1032.e6. [PMID: 36450286 PMCID: PMC9811561 DOI: 10.1016/j.cels.2022.10.008] [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: 12/23/2021] [Revised: 06/28/2022] [Accepted: 10/28/2022] [Indexed: 12/03/2022]
Abstract
Genetic networks should respond to signals but prevent the transmission of spontaneous fluctuations. Limited data from mammalian cells suggest that noise transmission is uncommon, but systematic claims about noise transmission have been limited by the inability to directly measure it. Here, we build a mathematical framework modeling allelic correlation and noise transmission, showing that allelic correlation and noise transmission correspond across model parameters and network architectures. Limiting noise transmission comes with the trade-off of being unresponsive to signals, and within responsive regimes, there is a further trade-off between response time and basal noise transmission. Analysis of allele-specific single-cell RNA-sequencing data revealed that genes encoding upstream factors in signaling pathways and cell-type-specific factors have higher allelic correlation than downstream factors, suggesting they are more subject to regulation. Overall, our findings suggest that some noise transmission must result from signal responsiveness, but it can be minimized by trading off for a slower response. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Ryan H Boe
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vinay Ayyappan
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Lea Schuh
- Institute of AI for Health, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Department of Mathematics, Technical University of Munich, Garching 85748, Germany
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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19
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Zeng L, Yang K, Zhang T, Zhu X, Hao W, Chen H, Ge J. Research progress of single-cell transcriptome sequencing in autoimmune diseases and autoinflammatory disease: A review. J Autoimmun 2022; 133:102919. [PMID: 36242821 DOI: 10.1016/j.jaut.2022.102919] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 12/07/2022]
Abstract
Autoimmunity refers to the phenomenon that the body's immune system produces antibodies or sensitized lymphocytes to its own tissues to cause an immune response. Immune disorders caused by autoimmunity can mediate autoimmune diseases. Autoimmune diseases have complicated pathogenesis due to the many types of cells involved, and the mechanism is still unclear. The emergence of single-cell research technology can solve the problem that ordinary transcriptome technology cannot be accurate to cell type. It provides unbiased results through independent analysis of cells in tissues and provides more mRNA information for identifying cell subpopulations, which provides a novel approach to study disruption of immune tolerance and disturbance of pro-inflammatory pathways on a cellular basis. It may fundamentally change the understanding of molecular pathways in the pathogenesis of autoimmune diseases and develop targeted drugs. Single-cell transcriptome sequencing (scRNA-seq) has been widely applied in autoimmune diseases, which provides a powerful tool for demonstrating the cellular heterogeneity of tissues involved in various immune inflammations, identifying pathogenic cell populations, and revealing the mechanism of disease occurrence and development. This review describes the principles of scRNA-seq, introduces common sequencing platforms and practical procedures, and focuses on the progress of scRNA-seq in 41 autoimmune diseases, which include 9 systemic autoimmune diseases and autoinflammatory diseases (rheumatoid arthritis, systemic lupus erythematosus, etc.) and 32 organ-specific autoimmune diseases (5 Skin diseases, 3 Nervous system diseases, 4 Eye diseases, 2 Respiratory system diseases, 2 Circulatory system diseases, 6 Liver, Gallbladder and Pancreas diseases, 2 Gastrointestinal system diseases, 3 Muscle, Bones and joint diseases, 3 Urinary system diseases, 2 Reproductive system diseases). This review also prospects the molecular mechanism targets of autoimmune diseases from the multi-molecular level and multi-dimensional analysis combined with single-cell multi-omics sequencing technology (such as scRNA-seq, Single cell ATAC-seq and single cell immune group library sequencing), which provides a reference for further exploring the pathogenesis and marker screening of autoimmune diseases and autoimmune inflammatory diseases in the future.
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Affiliation(s)
- Liuting Zeng
- Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China.
| | - Kailin Yang
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China.
| | - Tianqing Zhang
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China
| | - Xiaofei Zhu
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China.
| | - Wensa Hao
- Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Hua Chen
- Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China.
| | - Jinwen Ge
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China; Hunan Academy of Chinese Medicine, Changsha, China.
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20
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Hérault L, Poplineau M, Duprez E, Remy É. A novel Boolean network inference strategy to model early hematopoiesis aging. Comput Struct Biotechnol J 2022; 21:21-33. [PMID: 36514338 PMCID: PMC9719905 DOI: 10.1016/j.csbj.2022.10.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/24/2022] [Accepted: 10/24/2022] [Indexed: 11/07/2022] Open
Abstract
Hematopoietic stem cell (HSC) aging is a multifactorial event leading to changes in HSC properties and functions, which are intrinsically coordinated and affect the early hematopoiesis. To better understand the mechanisms and factors controlling these changes, we developed an original strategy to construct a Boolean model of HSC differentiation. Based on our previous scRNA-seq data, we exhaustively characterized active transcription modules or regulons along the differentiation trajectory and constructed an influence graph between 15 selected components involved in the dynamics of the process. Then we defined dynamical constraints between observed cellular states along the trajectory and using answer set programming with in silico perturbation analysis, we obtained a Boolean model explaining the early priming of HSCs. Finally, perturbations of the model based on age-related changes revealed important deregulations, such as the overactivation of Egr1 and Junb or the loss of Cebpa activation by Gata2. These new regulatory mechanisms were found to be relevant for the myeloid bias of aged HSC and explain the decreased transcriptional priming of HSCs to all mature cell types except megakaryocytes.
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Affiliation(s)
- Léonard Hérault
- Aix Marseille Université, CNRS, Marseille I2M, France,Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Mathilde Poplineau
- Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Estelle Duprez
- Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Élisabeth Remy
- Aix Marseille Université, CNRS, Marseille I2M, France,Corresponding author.
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21
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Cuevas-Diaz Duran R, González-Orozco JC, Velasco I, Wu JQ. Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases. Front Cell Dev Biol 2022; 10:884748. [PMID: 36353512 PMCID: PMC9637968 DOI: 10.3389/fcell.2022.884748] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 10/06/2022] [Indexed: 08/10/2023] Open
Abstract
Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.
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Affiliation(s)
| | | | - Iván Velasco
- Instituto de Fisiología Celular—Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Laboratorio de Reprogramación Celular, Instituto Nacional de Neurología y Neurocirugía “Manuel Velasco Suárez”, Mexico City, Mexico
| | - Jia Qian Wu
- The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Center for Stem Cell and Regenerative Medicine, UT Brown Foundation Institute of Molecular Medicine, Houston, TX, United States
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States
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22
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Hérault L, Poplineau M, Remy E, Duprez E. Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging. Cells 2022; 11:cells11193125. [PMID: 36231086 PMCID: PMC9563410 DOI: 10.3390/cells11193125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/15/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Single-cell transcriptomic technologies enable the uncovering and characterization of cellular heterogeneity and pave the way for studies aiming at understanding the origin and consequences of it. The hematopoietic system is in essence a very well adapted model system to benefit from this technological advance because it is characterized by different cellular states. Each cellular state, and its interconnection, may be defined by a specific location in the global transcriptional landscape sustained by a complex regulatory network. This transcriptomic signature is not fixed and evolved over time to give rise to less efficient hematopoietic stem cells (HSC), leading to a well-documented hematopoietic aging. Here, we review the advance of single-cell transcriptomic approaches for the understanding of HSC heterogeneity to grasp HSC deregulations upon aging. We also discuss the new bioinformatics tools developed for the analysis of the resulting large and complex datasets. Finally, since hematopoiesis is driven by fine-tuned and complex networks that must be interconnected to each other, we highlight how mathematical modeling is beneficial for doing such interconnection between multilayered information and to predict how HSC behave while aging.
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Affiliation(s)
- Léonard Hérault
- I2M, CNRS, Aix Marseille University, 13009 Marseille, France
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
| | - Mathilde Poplineau
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
- Equipe Labellisée Ligue Nationale Contre le Cancer, 75013 Paris, France
| | - Elisabeth Remy
- I2M, CNRS, Aix Marseille University, 13009 Marseille, France
| | - Estelle Duprez
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
- Equipe Labellisée Ligue Nationale Contre le Cancer, 75013 Paris, France
- Correspondence:
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23
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Pušnik Ž, Mraz M, Zimic N, Moškon M. Review and assessment of Boolean approaches for inference of gene regulatory networks. Heliyon 2022; 8:e10222. [PMID: 36033302 PMCID: PMC9403406 DOI: 10.1016/j.heliyon.2022.e10222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/22/2022] [Accepted: 08/03/2022] [Indexed: 10/25/2022] Open
Abstract
Boolean descriptions of gene regulatory networks can provide an insight into interactions between genes. Boolean networks hold predictive power, are easy to understand, and can be used to simulate the observed networks in different scenarios. We review fundamental and state-of-the-art methods for inference of Boolean networks. We introduce a methodology for a straightforward evaluation of Boolean inference approaches based on the generation of evaluation datasets, application of selected inference methods, and evaluation of performance measures to guide the selection of the best method for a given inference problem. We demonstrate this procedure on inference methods REVEAL (REVerse Engineering ALgorithm), Best-Fit Extension, MIBNI (Mutual Information-based Boolean Network Inference), GABNI (Genetic Algorithm-based Boolean Network Inference) and ATEN (AND/OR Tree ENsemble algorithm), which infers Boolean descriptions of gene regulatory networks from discretised time series data. Boolean inference approaches tend to perform better in terms of dynamic accuracy, and slightly worse in terms of structural correctness. We believe that the proposed methodology and provided guidelines will help researchers to develop Boolean inference approaches with a good predictive capability while maintaining structural correctness and biological relevance.
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Affiliation(s)
- Žiga Pušnik
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Miha Mraz
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Nikolaj Zimic
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Miha Moškon
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
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24
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Zhao X, Lan Y, Chen D. Exploring long non-coding RNA networks from single cell omics data. Comput Struct Biotechnol J 2022; 20:4381-4389. [PMID: 36051880 PMCID: PMC9403499 DOI: 10.1016/j.csbj.2022.08.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 08/01/2022] [Accepted: 08/01/2022] [Indexed: 11/03/2022] Open
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25
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Cheong A, Nagel ZD. Human Variation in DNA Repair, Immune Function, and Cancer Risk. Front Immunol 2022; 13:899574. [PMID: 35935942 PMCID: PMC9354717 DOI: 10.3389/fimmu.2022.899574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
DNA damage constantly threatens genome integrity, and DNA repair deficiency is associated with increased cancer risk. An intuitive and widely accepted explanation for this relationship is that unrepaired DNA damage leads to carcinogenesis due to the accumulation of mutations in somatic cells. But DNA repair also plays key roles in the function of immune cells, and immunodeficiency is an important risk factor for many cancers. Thus, it is possible that emerging links between inter-individual variation in DNA repair capacity and cancer risk are driven, at least in part, by variation in immune function, but this idea is underexplored. In this review we present an overview of the current understanding of the links between cancer risk and both inter-individual variation in DNA repair capacity and inter-individual variation in immune function. We discuss factors that play a role in both types of variability, including age, lifestyle, and environmental exposures. In conclusion, we propose a research paradigm that incorporates functional studies of both genome integrity and the immune system to predict cancer risk and lay the groundwork for personalized prevention.
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26
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Chen F, Li C. Inferring structural and dynamical properties of gene networks from data with deep learning. NAR Genom Bioinform 2022; 4:lqac068. [PMID: 36110897 PMCID: PMC9469930 DOI: 10.1093/nargab/lqac068] [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: 04/18/2022] [Revised: 07/22/2022] [Accepted: 08/24/2022] [Indexed: 11/29/2022] Open
Abstract
The reconstruction of gene regulatory networks (GRNs) from data is vital in systems biology. Although different approaches have been proposed to infer causality from data, some challenges remain, such as how to accurately infer the direction and type of interactions, how to deal with complex network involving multiple feedbacks, as well as how to infer causality between variables from real-world data, especially single cell data. Here, we tackle these problems by deep neural networks (DNNs). The underlying regulatory network for different systems (gene regulations, ecology, diseases, development) can be successfully reconstructed from trained DNN models. We show that DNN is superior to existing approaches including Boolean network, Random Forest and partial cross mapping for network inference. Further, by interrogating the ensemble DNN model trained from single cell data from dynamical system perspective, we are able to unravel complex cell fate dynamics during preimplantation development. We also propose a data-driven approach to quantify the energy landscape for gene regulatory systems, by combining DNN with the partial self-consistent mean field approximation (PSCA) approach. We anticipate the proposed method can be applied to other fields to decipher the underlying dynamical mechanisms of systems from data.
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Affiliation(s)
- Feng Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University , Shanghai 200433, China
- Shanghai Center for Mathematical Sciences, Fudan University , Shanghai 200433, China
| | - Chunhe Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University , Shanghai 200433, China
- Shanghai Center for Mathematical Sciences, Fudan University , Shanghai 200433, China
- School of Mathematical Sciences, Fudan University , Shanghai 200433, China
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27
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Gao S, Sun C, Xiang C, Qin K, Lee TH. Learning Asynchronous Boolean Networks From Single-Cell Data Using Multiobjective Cooperative Genetic Programming. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:2916-2930. [PMID: 33027020 DOI: 10.1109/tcyb.2020.3022430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Recent advances in high-throughput single-cell technologies provide new opportunities for computational modeling of gene regulatory networks (GRNs) with an unprecedented amount of gene expression data. Current studies on the Boolean network (BN) modeling of GRNs mostly depend on bulk time-series data and focus on the synchronous update scheme due to its computational simplicity and tractability. However, such synchrony is a strong and rarely biologically realistic assumption. In this study, we adopt the asynchronous update scheme instead and propose a novel framework called SgpNet to infer asynchronous BNs from single-cell data by formulating it into a multiobjective optimization problem. SgpNet aims to find BNs that can match the asynchronous state transition graph (STG) extracted from single-cell data and retain the sparsity of GRNs. To search the huge solution space efficiently, we encode each Boolean function as a tree in genetic programming and evolve all functions of a network simultaneously via cooperative coevolution. Besides, we develop a regulator preselection strategy in view of GRN sparsity to further enhance learning efficiency. An error threshold estimation heuristic is also proposed to ease tedious parameter tuning. SgpNet is compared with the state-of-the-art method on both synthetic data and experimental single-cell data. Results show that SgpNet achieves comparable inference accuracy, while it has far fewer parameters and eliminates artificial restrictions on the Boolean function structures. Furthermore, SgpNet can potentially scale to large networks via straightforward parallelization on multiple cores.
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Zhang H, Chen J, Tian T. Bayesian Inference of Stochastic Dynamic Models Using Early-Rejection Methods Based on Sequential Stochastic Simulations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1484-1494. [PMID: 33216717 DOI: 10.1109/tcbb.2020.3039490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Stochastic modelling is an important method to investigate the functions of noise in a wide range of biological systems. However, the parameter inference for stochastic models is still a challenging problem partially due to the large computing time required for stochastic simulations. To address this issue, we propose a novel early-rejection method by using sequential stochastic simulations. We first show that a large number of stochastic simulations are required to obtain reliable inference results. Instead of generating a large number of simulations for each parameter sample, we propose to generate these simulations in a number of stages. The simulation process will go to the next stage only if the accuracy of simulations at the current stage satisfies a given error criterion. We propose a formula to determine the error criterion and use a stochastic differential equation model to examine the effects of different criteria. Three biochemical network models are used to evaluate the efficiency and accuracy of the proposed method. Numerical results suggest the proposed early-rejection method achieves substantial improvement in the efficiency for the inference of stochastic models.
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Karaaslanli A, Saha S, Aviyente S, Maiti T. scSGL: Kernelized Signed Graph Learning for Single-Cell Gene Regulatory Network Inference. Bioinformatics 2022; 38:3011-3019. [PMID: 35451460 DOI: 10.1093/bioinformatics/btac288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 03/10/2022] [Accepted: 04/19/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Elucidating the topology of gene regulatory networks (GRNs) from large single-cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle heterogeneity and dropouts, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing (GSP) have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, a characteristic feature of GRNs, which are capable of accounting for both activating and inhibitory relationships in the gene network. They are also incapable of handling high proportion of zero values present in the single cell datasets. RESULTS To this end, we propose a novel signed GL approach, scSGL, that learns GRNs based on the assumption of smoothness and non-smoothness of gene expressions over activating and inhibitory edges, respectively. scSGL is then extended with kernels to account for non-linearity of co-expression and for effective handling of highly occurring zero values. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework. Performance assessment using simulated datasets demonstrates the superior performance of kernelized scSGL over existing state of the art methods in GRN recovery. The performance of scSGL is further investigated using human and mouse embryonic datasets. AVAILABILITY AND IMPLEMENTATION The scSGL code and analysis scripts are available on https://github.com/Single-Cell-Graph-Learning/scSGL. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Abdullah Karaaslanli
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, US
| | - Satabdi Saha
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, US
| | - Selin Aviyente
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, US
| | - Tapabrata Maiti
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, US
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Werle SD, Ikonomi N, Schwab JD, Kraus JM, Weidner FM, Lenhard Rudolph K, Pfister AS, Schuler R, Kühl M, Kestler HA. Identification of dynamic driver sets controlling phenotypical landscapes. Comput Struct Biotechnol J 2022; 20:1603-1617. [PMID: 35465155 PMCID: PMC9010550 DOI: 10.1016/j.csbj.2022.03.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 11/03/2022] Open
Abstract
Controlling phenotypical landscapes is of vital interest to modern biology. This task becomes highly demanding because cellular decisions involve complex networks engaging in crosstalk interactions. Previous work on control theory indicates that small sets of compounds can control single phenotypes. However, a dynamic approach is missing to determine the drivers of the whole network dynamics. By analyzing 35 biologically motivated Boolean networks, we developed a method to identify small sets of compounds sufficient to decide on the entire phenotypical landscape. These compounds do not strictly prefer highly related compounds and show a smaller impact on the stability of the attractor landscape. The dynamic driver sets include many intervention targets and cellular reprogramming drivers in human networks. Finally, by using a new comprehensive model of colorectal cancer, we provide a complete workflow on how to implement our approach to shift from in silico to in vitro guided experiments.
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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Heydari T, A. Langley M, Fisher CL, Aguilar-Hidalgo D, Shukla S, Yachie-Kinoshita A, Hughes M, M. McNagny K, Zandstra PW. IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data. PLoS Comput Biol 2022; 18:e1009907. [PMID: 35213533 PMCID: PMC8906617 DOI: 10.1371/journal.pcbi.1009907] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 03/09/2022] [Accepted: 02/08/2022] [Indexed: 01/03/2023] Open
Abstract
The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to scRNA-seq datasets from early mouse T-cell and red blood cell development, and show that the platform can infer overall over 74% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of known gene perturbations. Finally, we implement an IQCELL gene selection pipeline that allows us to identify candidate genes, without prior knowledge. We demonstrate that GRN simulations based on the inferred set yield results similar to the original curated lists. In summary, the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems. Executable GRNs provide an important strategy to model complex intracellular dynamics in development and disease. Here we introduce IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from single cell sequencing data. IQCELL is an integrative platform that includes modules for gene selection, building logical GRNs, and simulating developmental trajectories under normal and perturbed conditions. We demonstrate the utility of IQCELL by reconstructing GRNs for early mouse T-cell and red blood cell development. We show that IQCELL can “automatically” infer the vast majority of gene interactions previously reported from decades of experimental research. IQCELL also provides users with a platform to simulate the developmental trajectories of cells. We show that dynamic simulations of the inferred GRNs resemble experimentally observed gene expression dynamics and capture the effects of genetic perturbation studies. IQCELL offers a versatile tool to infer and simulate GRNs in dynamic biological systems.
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Affiliation(s)
- Tiam Heydari
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matthew A. Langley
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Cynthia L. Fisher
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Daniel Aguilar-Hidalgo
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Shreya Shukla
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Notch Therapeutics, Vancouver, British Columbia, Canada
| | - Ayako Yachie-Kinoshita
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Michael Hughes
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Kelly M. McNagny
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Peter W. Zandstra
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
- * E-mail:
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Jiang R, Sun T, Song D, Li JJ. Statistics or biology: the zero-inflation controversy about scRNA-seq data. Genome Biol 2022; 23:31. [PMID: 35063006 PMCID: PMC8783472 DOI: 10.1186/s13059-022-02601-5] [Citation(s) in RCA: 122] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/04/2022] [Indexed: 12/13/2022] Open
Abstract
Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.
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Affiliation(s)
- Ruochen Jiang
- Department of Statistics, University of California, Los Angeles, 90095-1554, CA, USA
| | - Tianyi Sun
- Department of Statistics, University of California, Los Angeles, 90095-1554, CA, USA
| | - Dongyuan Song
- Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, 90095-7246, CA, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, 90095-1554, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, 90095-7088, CA, USA.
- Department of Computational Medicine, University of California, Los Angeles, 90095-1766, CA, USA.
- Department of Biostatistics, University of California, Los Angeles, 90095-1772, CA, USA.
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Watson ER, Taherian Fard A, Mar JC. Computational Methods for Single-Cell Imaging and Omics Data Integration. Front Mol Biosci 2022; 8:768106. [PMID: 35111809 PMCID: PMC8801747 DOI: 10.3389/fmolb.2021.768106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.
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Affiliation(s)
| | - Atefeh Taherian Fard
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Jessica Cara Mar
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
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Ibañez-Solé O, Ascensión AM, Araúzo-Bravo MJ, Izeta A. Lack of evidence for increased transcriptional noise in aged tissues. eLife 2022; 11:80380. [PMID: 36576247 PMCID: PMC9934862 DOI: 10.7554/elife.80380] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022] Open
Abstract
Aging is often associated with a loss of cell type identity that results in an increase in transcriptional noise in aged tissues. If this phenomenon reflects a fundamental property of aging remains an open question. Transcriptional changes at the cellular level are best detected by single-cell RNA sequencing (scRNAseq). However, the diverse computational methods used for the quantification of age-related loss of cellular identity have prevented reaching meaningful conclusions by direct comparison of existing scRNAseq datasets. To address these issues we created Decibel, a Python toolkit that implements side-to-side four commonly used methods for the quantification of age-related transcriptional noise in scRNAseq data. Additionally, we developed Scallop, a novel computational method for the quantification of membership of single cells to their assigned cell type cluster. Cells with a greater Scallop membership score are transcriptionally more stable. Application of these computational tools to seven aging datasets showed large variability between tissues and datasets, suggesting that increased transcriptional noise is not a universal hallmark of aging. To understand the source of apparent loss of cell type identity associated with aging, we analyzed cell type-specific changes in transcriptional noise and the changes in cell type composition of the mammalian lung. No robust pattern of cell type-specific transcriptional noise alteration was found across aging lung datasets. In contrast, age-associated changes in cell type composition of the lung were consistently found, particularly of immune cells. These results suggest that claims of increased transcriptional noise of aged tissues should be reformulated.
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Affiliation(s)
- Olga Ibañez-Solé
- Biodonostia Health Research Institute, Computational Biology and Systems Biomedicine GroupDonostia-San SebastiánSpain,Biodonostia Health Research Institute, Tissue Engineering groupDonostia-San SebastiánSpain
| | - Alex M Ascensión
- Biodonostia Health Research Institute, Computational Biology and Systems Biomedicine GroupDonostia-San SebastiánSpain,Biodonostia Health Research Institute, Tissue Engineering groupDonostia-San SebastiánSpain
| | - Marcos J Araúzo-Bravo
- Biodonostia Health Research Institute, Computational Biology and Systems Biomedicine GroupDonostia-San SebastiánSpain,Biodonostia Health Research Institute, Computational Biomedicine Data Analysis PlatformDonostia-San SebastiánSpain,CIBER of Frailty and Healthy Aging (CIBERfes)MadridSpain,IKERBASQUE, Basque Foundation for ScienceBilbaoSpain
| | - Ander Izeta
- Biodonostia Health Research Institute, Tissue Engineering groupDonostia-San SebastiánSpain,Tecnun-University of NavarraDonostia-San SebastiánSpain
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Xu C, Cai L, Gao J. An efficient scRNA-seq dropout imputation method using graph attention network. BMC Bioinformatics 2021; 22:582. [PMID: 34876032 PMCID: PMC8650344 DOI: 10.1186/s12859-021-04493-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 11/25/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Single-cell sequencing technology can address the amount of single-cell library data at the same time and display the heterogeneity of different cells. However, analyzing single-cell data is a computationally challenging problem. Because there are low counts in the gene expression region, it has a high chance of recognizing the non-zero entity as zero, which are called dropout events. At present, the mainstream dropout imputation methods cannot effectively recover the true expression of cells from dropout noise such as DCA, MAGIC, scVI, scImpute and SAVER. RESULTS In this paper, we propose an autoencoder structure network, named GNNImpute. GNNImpute uses graph attention convolution to aggregate multi-level similar cell information and implements convolution operations on non-Euclidean space on scRNA-seq data. Distinct from current imputation tools, GNNImpute can accurately and effectively impute the dropout and reduce dropout noise. We use mean square error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC) and Cosine similarity (CS) to measure the performance of different methods with GNNImpute. We analyze four real datasets, and our results show that the GNNImpute achieves 3.0130 MSE, 0.6781 MAE, 0.9073 PCC and 0.9134 CS. Furthermore, we use Adjusted rand index (ARI) and Normalized mutual information (NMI) to measure the clustering effect. The GNNImpute achieves 0.8199 (ARI) and 0.8368 (NMI), respectively. CONCLUSIONS In this investigation, we propose a single-cell dropout imputation method (GNNImpute), which effectively utilizes shared information for imputing the dropout of scRNA-seq data. We test it with different real datasets and evaluate its effectiveness in MSE, MAE, PCC and CS. The results show that graph attention convolution and autoencoder structure have great potential in single-cell dropout imputation.
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Affiliation(s)
- Chenyang Xu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, People’s Republic of China
| | - Lei Cai
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, People’s Republic of China
| | - Jingyang Gao
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, People’s Republic of China
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Glauche I, Marr C. Mechanistic models of blood cell fate decisions in the era of single-cell data. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 28:None. [PMID: 34950807 PMCID: PMC8660645 DOI: 10.1016/j.coisb.2021.100355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Billions of functionally distinct blood cells emerge from a pool of hematopoietic stem cells in our bodies every day. This progressive differentiation process is hierarchically structured and remarkably robust. We provide an introductory review to mathematical approaches addressing the functional aspects of how lineage choice is potentially implemented on a molecular level. Emerging from studies on the mutual repression of key transcription factors, we illustrate how those simple concepts have been challenged in recent years and subsequently extended. Especially, the analysis of omics data on the single-cell level with computational tools provides descriptive insights on a yet unknown level, while their embedding into a consistent mechanistic and mathematical framework is still incomplete.
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Affiliation(s)
- Ingmar Glauche
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Carsten Marr
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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Weijts B, Yvernogeau L, Robin C. Recent Advances in Developmental Hematopoiesis: Diving Deeper With New Technologies. Front Immunol 2021; 12:790379. [PMID: 34899758 PMCID: PMC8652083 DOI: 10.3389/fimmu.2021.790379] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 10/28/2021] [Indexed: 12/15/2022] Open
Abstract
The journey of a hematopoietic stem cell (HSC) involves the passage through successive anatomical sites where HSCs are in direct contact with their surrounding microenvironment, also known as niche. These spatial and temporal cellular interactions throughout development are required for the acquisition of stem cell properties, and for maintaining the HSC pool through balancing self-renewal, quiescence and lineage commitment. Understanding the context and consequences of these interactions will be imperative for our understanding of HSC biology and will lead to the improvement of in vitro production of HSCs for clinical purposes. The aorta-gonad-mesonephros (AGM) region is in this light of particular interest since this is the cradle of HSC emergence during the embryonic development of all vertebrate species. In this review, we will focus on the developmental origin of HSCs and will discuss the novel technological approaches and recent progress made to identify the cellular composition of the HSC supportive niche and the underlying molecular events occurring in the AGM region.
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Affiliation(s)
- Bart Weijts
- Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) & University Medical Center Utrecht, Utrecht, Netherlands
| | - Laurent Yvernogeau
- Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) & University Medical Center Utrecht, Utrecht, Netherlands
| | - Catherine Robin
- Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) & University Medical Center Utrecht, Utrecht, Netherlands
- Regenerative Medicine Center, University Medical Center Utrecht, Utrecht, Netherlands
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39
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Hung RJ, Li JSS, Liu Y, Perrimon N. Defining cell types and lineage in the Drosophila midgut using single cell transcriptomics. CURRENT OPINION IN INSECT SCIENCE 2021; 47:12-17. [PMID: 33609768 DOI: 10.1016/j.cois.2021.02.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 02/05/2021] [Accepted: 02/08/2021] [Indexed: 06/12/2023]
Abstract
The Drosophila midgut has emerged in recent years as a model system to study stem cell renewal and differentiation and tissue homeostasis. Histological, genetic and gene expression studies have provided a wealth of information on gut cell types, regionalization, genes and pathways involved in cell proliferation and differentiation, stem cell renewal, and responses to changes in environmental factors such as the microbiota and nutrients. Here, we review the contribution of single cell transcriptomic methods to our understanding of gut cell type diversity, lineage and behavior.
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Affiliation(s)
- Ruei-Jiun Hung
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, United States
| | - Joshua Shing Shun Li
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, United States
| | - Yifang Liu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, United States
| | - Norbert Perrimon
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, United States; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, United States.
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40
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Saint-André V. Computational biology approaches for mapping transcriptional regulatory networks. Comput Struct Biotechnol J 2021; 19:4884-4895. [PMID: 34522292 PMCID: PMC8426465 DOI: 10.1016/j.csbj.2021.08.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 08/16/2021] [Accepted: 08/16/2021] [Indexed: 12/13/2022] Open
Abstract
Transcriptional Regulatory Networks (TRNs) are mainly responsible for the cell-type- or cell-state-specific expression of gene sets from the same DNA sequence. However, so far there are no precise maps of TRNs available for each cell-type or cell-state, and no ideal tool to map those networks clearly and in full from biological samples. In this review, major approaches and tools to map TRNs from high-throughput data are presented, depending on the type of methods or data used to infer them, and their advantages and limitations are discussed. After summarizing the main principles defining the topology and structure–function relationships in TRNs, an overview of the extensive work done to map TRNs from bulk transcriptomic data will be presented by type of methodological approach. Most recent modellings of TRNs using other types of molecular data or integrating different data types, including single-cell RNA-sequencing and chromatin information, will then be discussed, before briefly concluding with improvements expected to come in the field.
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Affiliation(s)
- Violaine Saint-André
- Hub de Bioinformatique et Biostatistique - Département Biologie Computationnelle, Institut Pasteur, Paris, France
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41
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Schwab JD, Ikonomi N, Werle SD, Weidner FM, Geiger H, Kestler HA. Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells. Comput Struct Biotechnol J 2021; 19:5321-5332. [PMID: 34630946 PMCID: PMC8487005 DOI: 10.1016/j.csbj.2021.09.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/20/2021] [Accepted: 09/12/2021] [Indexed: 01/08/2023] Open
Abstract
Regulatory dependencies in molecular networks are the basis of dynamic behaviors affecting the phenotypical landscape. With the advance of high throughput technologies, the detail of omics data has arrived at the single-cell level. Nevertheless, new strategies are required to reconstruct regulatory networks based on populations of single-cell data. Here, we present a new approach to generate populations of gene regulatory networks from single-cell RNA-sequencing (scRNA-seq) data. Our approach exploits the heterogeneity of single-cell populations to generate pseudo-timepoints. This allows for the first time to uncouple network reconstruction from a direct dependency on time series measurements. The generated time series are then fed to a combined reconstruction algorithm. The latter allows a fast and efficient reconstruction of ensembles of gene regulatory networks. Since this approach does not require knowledge on time-related trajectories, it allows us to model heterogeneous processes such as aging. Applying the approach to the aging-associated NF-κB signaling pathway-based scRNA-seq data of human hematopoietic stem cells (HSCs), we were able to reconstruct eight ensembles, and evaluate their dynamic behavior. Moreover, we propose a strategy to evaluate the resulting attractor patterns. Interaction graph-based features and dynamic investigations of our model ensembles provide a new perspective on the heterogeneity and mechanisms related to human HSCs aging.
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Affiliation(s)
- Julian D Schwab
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Silke D Werle
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Felix M Weidner
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Hartmut Geiger
- Institute of Molecular Medicine, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Hans A Kestler
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
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42
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Refahi Y, Zardilis A, Michelin G, Wightman R, Leggio B, Legrand J, Faure E, Vachez L, Armezzani A, Risson AE, Zhao F, Das P, Prunet N, Meyerowitz EM, Godin C, Malandain G, Jönsson H, Traas J. A multiscale analysis of early flower development in Arabidopsis provides an integrated view of molecular regulation and growth control. Dev Cell 2021; 56:540-556.e8. [PMID: 33621494 PMCID: PMC8519405 DOI: 10.1016/j.devcel.2021.01.019] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 12/17/2020] [Accepted: 01/25/2021] [Indexed: 12/31/2022]
Abstract
We have analyzed the link between the gene regulation and growth during the early stages of flower development in Arabidopsis. Starting from time-lapse images, we generated a 4D atlas of early flower development, including cell lineage, cellular growth rates, and the expression patterns of regulatory genes. This information was introduced in MorphoNet, a web-based platform. Using computational models, we found that the literature-based molecular network only explained a minority of the gene expression patterns. This was substantially improved by adding regulatory hypotheses for individual genes. Correlating growth with the combinatorial expression of multiple regulators led to a set of hypotheses for the action of individual genes in morphogenesis. This identified the central factor LEAFY as a potential regulator of heterogeneous growth, which was supported by quantifying growth patterns in a leafy mutant. By providing an integrated view, this atlas should represent a fundamental step toward mechanistic models of flower development.
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Affiliation(s)
- Yassin Refahi
- The Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK; Laboratoire RDP, Université de Lyon 1, ENS-Lyon, INRAE, CNRS, UCBL, 69364 Lyon, France; Université de Reims Champagne Ardenne, INRAE, FARE, UMR A 614, 51097 Reims, France.
| | - Argyris Zardilis
- The Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK
| | - Gaël Michelin
- Université Côte d'Azur, Inria, Sophia Antipolis, CNRS, I3S, France
| | - Raymond Wightman
- The Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK
| | - Bruno Leggio
- Laboratoire RDP, Université de Lyon 1, ENS-Lyon, INRAE, CNRS, UCBL, 69364 Lyon, France
| | - Jonathan Legrand
- Laboratoire RDP, Université de Lyon 1, ENS-Lyon, INRAE, CNRS, UCBL, 69364 Lyon, France
| | | | - Laetitia Vachez
- Laboratoire RDP, Université de Lyon 1, ENS-Lyon, INRAE, CNRS, UCBL, 69364 Lyon, France
| | - Alessia Armezzani
- Laboratoire RDP, Université de Lyon 1, ENS-Lyon, INRAE, CNRS, UCBL, 69364 Lyon, France
| | - Anne-Evodie Risson
- Laboratoire RDP, Université de Lyon 1, ENS-Lyon, INRAE, CNRS, UCBL, 69364 Lyon, France
| | - Feng Zhao
- Laboratoire RDP, Université de Lyon 1, ENS-Lyon, INRAE, CNRS, UCBL, 69364 Lyon, France
| | - Pradeep Das
- Laboratoire RDP, Université de Lyon 1, ENS-Lyon, INRAE, CNRS, UCBL, 69364 Lyon, France
| | - Nathanaël Prunet
- Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA
| | - Elliot M Meyerowitz
- The Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK; Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute and Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA 91125, USA
| | - Christophe Godin
- Laboratoire RDP, Université de Lyon 1, ENS-Lyon, INRAE, CNRS, UCBL, 69364 Lyon, France
| | | | - Henrik Jönsson
- The Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK; Computational Biology and Biological Physics, Lund University, Sölvegatan 14A, 223 62 Lund, Sweden; Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Cambridge, UK.
| | - Jan Traas
- Laboratoire RDP, Université de Lyon 1, ENS-Lyon, INRAE, CNRS, UCBL, 69364 Lyon, France.
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43
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Turati VA, Guerra-Assunção JA, Potter NE, Gupta R, Ecker S, Daneviciute A, Tarabichi M, Webster AP, Ding C, May G, James C, Brown J, Conde L, Russell LJ, Ancliff P, Inglott S, Cazzaniga G, Biondi A, Hall GW, Lynch M, Hubank M, Macaulay I, Beck S, Van Loo P, Jacobsen SE, Greaves M, Herrero J, Enver T. Chemotherapy induces canalization of cell state in childhood B-cell precursor acute lymphoblastic leukemia. NATURE CANCER 2021; 2:835-852. [PMID: 34734190 PMCID: PMC7611923 DOI: 10.1038/s43018-021-00219-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 05/11/2021] [Indexed: 05/01/2023]
Abstract
Comparison of intratumor genetic heterogeneity in cancer at diagnosis and relapse suggests that chemotherapy induces bottleneck selection of subclonal genotypes. However, evolutionary events subsequent to chemotherapy could also explain changes in clonal dominance seen at relapse. We, therefore, investigated the mechanisms of selection in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) during induction chemotherapy where maximal cytoreduction occurs. To distinguish stochastic versus deterministic events, individual leukemias were transplanted into multiple xenografts and chemotherapy administered. Analyses of the immediate post-treatment leukemic residuum at single-cell resolution revealed that chemotherapy has little impact on genetic heterogeneity. Rather, it acts on extensive, previously unappreciated, transcriptional and epigenetic heterogeneity in BCP-ALL, dramatically reducing the spectrum of cell states represented, leaving a genetically polyclonal but phenotypically uniform population with hallmark signatures relating to developmental stage, cell cycle and metabolism. Hence, canalization of cell state accounts for a significant component of bottleneck selection during induction chemotherapy.
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Affiliation(s)
| | | | | | - Rajeev Gupta
- UCL Cancer Institute, University College London, United Kingdom
| | - Simone Ecker
- UCL Cancer Institute, University College London, United Kingdom
| | | | | | - Amy P. Webster
- UCL Cancer Institute, University College London, United Kingdom
| | - Chuling Ding
- UCL Cancer Institute, University College London, United Kingdom
| | - Gillian May
- UCL Cancer Institute, University College London, United Kingdom
| | - Chela James
- UCL Cancer Institute, University College London, United Kingdom
| | - John Brown
- UCL Cancer Institute, University College London, United Kingdom
| | - Lucia Conde
- UCL Cancer Institute, University College London, United Kingdom
| | - Lisa J. Russell
- Translational and Clinical Research Institute, Newcastle University Centre for Cancer, Newcastle University, UK
| | - Phil Ancliff
- Great Ormond Street Hospital, London, United Kingdom
| | - Sarah Inglott
- Great Ormond Street Hospital, London, United Kingdom
| | - Giovanni Cazzaniga
- Centro Ricerca M. Tettamanti, University of Milano Bicocca, Monza, Italy
| | - Andrea Biondi
- University of Milano-Bicocca, Department of Pediatrics, Fondazione MBBM/Ospedale San Gerardo, Monza, Italy
| | | | - Mark Lynch
- Fluidigm Corporation, San Francisco, CA, USA
| | - Mike Hubank
- Institute of Cancer Research, Sutton, United Kingdom
- Royal Marsden Hospital, Sutton, United Kingdom
| | | | - Stephan Beck
- UCL Cancer Institute, University College London, United Kingdom
| | | | - Sten E. Jacobsen
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
- Center for Hematology and Regenerative Medicine, Department of Medicine and Department of Cell and Molecular Biology, Karolinska Institutet and Karolinska University Hospital Huddinge, Stockholm, Sweden
| | - Mel Greaves
- Institute of Cancer Research, Sutton, United Kingdom
| | - Javier Herrero
- UCL Cancer Institute, University College London, United Kingdom
| | - Tariq Enver
- UCL Cancer Institute, University College London, United Kingdom
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44
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Katebi A, Ramirez D, Lu M. Computational systems-biology approaches for modeling gene networks driving epithelial-mesenchymal transitions. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2021; 1:e1021. [PMID: 34164628 PMCID: PMC8219219 DOI: 10.1002/cso2.1021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Epithelial-mesenchymal transition (EMT) is an important biological process through which epithelial cells undergo phenotypic transitions to mesenchymal cells by losing cell-cell adhesion and gaining migratory properties that cells use in embryogenesis, wound healing, and cancer metastasis. An important research topic is to identify the underlying gene regulatory networks (GRNs) governing the decision making of EMT and develop predictive models based on the GRNs. The advent of recent genomic technology, such as single-cell RNA sequencing, has opened new opportunities to improve our understanding about the dynamical controls of EMT. In this article, we review three major types of computational and mathematical approaches and methods for inferring and modeling GRNs driving EMT. We emphasize (1) the bottom-up approaches, where GRNs are constructed through literature search; (2) the top-down approaches, where GRNs are derived from genome-wide sequencing data; (3) the combined top-down and bottom-up approaches, where EMT GRNs are constructed and simulated by integrating bioinformatics and mathematical modeling. We discuss the methodologies and applications of each approach and the available resources for these studies.
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Affiliation(s)
- Ataur Katebi
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, Massachusetts, USA
| | - Daniel Ramirez
- Center for Theoretical Biological Physics, Northeastern University, Boston, Massachusetts, USA
- College of Health Solutions, Arizona State University, Tempe, Arizona, USA
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, Massachusetts, USA
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45
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Stein-O'Brien GL, Ainsile MC, Fertig EJ. Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 26:24-32. [PMID: 34660940 PMCID: PMC8516130 DOI: 10.1016/j.coisb.2021.03.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
As the single cell field races to characterize each cell type, state, and behavior, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multi-omics analysis. Merged with mathematical models, these algorithms will be able to forecast future states of biological systems, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps analogous to weather systems. Thus, systems biology for forecasting biological system dynamics from multi-omic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.
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Affiliation(s)
- Genevieve L Stein-O'Brien
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD
- Convergence Institute, Johns Hopkins University, Baltimore, MD
| | - Michaela C Ainsile
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Elana J Fertig
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
- Convergence Institute, Johns Hopkins University, Baltimore, MD
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
- Department of Applied Mathematics & Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD
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46
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Unexpected redundancy of Gpr56 and Gpr97 during hematopoietic cell development and differentiation. Blood Adv 2021; 5:829-842. [PMID: 33560396 DOI: 10.1182/bloodadvances.2020003693] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 12/01/2020] [Indexed: 02/06/2023] Open
Abstract
Integrated molecular signals regulate cell fate decisions in the embryonic aortic endothelium to drive hematopoietic stem cell (HSC) generation during development. The G-protein-coupled receptor 56 (Gpr56, also called Adgrg1) is the most highly upregulated receptor gene in cells that take on hematopoietic fate and is expressed by adult bone marrow HSCs. Despite the requirement for Gpr56 in hematopoietic stem/progenitor cell (HS/PC) generation in zebrafish embryos and the highly upregulated expression of GPR56 in treatment-resistant leukemic patients, its function in normal mammalian hematopoiesis remains unclear. Here, we examine the role of Gpr56 in HS/PC development in Gpr56 conditional knockout (cKO) mouse embryos and Gpr knockout (KO) embryonic stem cell (ESC) hematopoietic differentiation cultures. Our results show a bias toward myeloid differentiation of Gpr56 cKO fetal liver HSCs and an increased definitive myeloid progenitor cell frequency in Gpr56KO ESC differentiation cultures. Surprisingly, we find that mouse Gpr97 can rescue Gpr56 morphant zebrafish hematopoietic generation, and that Gpr97 expression is upregulated in mouse Gpr56 deletion models. When both Gpr56 and Gpr97 are deleted in ESCs, no or few hematopoietic PCs (HPCs) are generated upon ESC differentiation. Together, our results reveal novel and redundant functions for these 2 G-protein coupled receptors in normal mammalian hematopoietic cell development and differentiation.
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47
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Nguyen H, Tran D, Tran B, Pehlivan B, Nguyen T. A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data. Brief Bioinform 2021; 22:bbaa190. [PMID: 34020546 PMCID: PMC8138892 DOI: 10.1093/bib/bbaa190] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 06/19/2020] [Accepted: 07/24/2020] [Indexed: 12/13/2022] Open
Abstract
Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods' performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.
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Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Duc Tran
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Bang Tran
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Bahadir Pehlivan
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
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48
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Grieshaber-Bouyer R, Radtke FA, Cunin P, Stifano G, Levescot A, Vijaykumar B, Nelson-Maney N, Blaustein RB, Monach PA, Nigrovic PA. The neutrotime transcriptional signature defines a single continuum of neutrophils across biological compartments. Nat Commun 2021; 12:2856. [PMID: 34001893 PMCID: PMC8129206 DOI: 10.1038/s41467-021-22973-9] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 04/01/2021] [Indexed: 02/05/2023] Open
Abstract
Neutrophils are implicated in multiple homeostatic and pathological processes, but whether functional diversity requires discrete neutrophil subsets is not known. Here, we apply single-cell RNA sequencing to neutrophils from normal and inflamed mouse tissues. Whereas conventional clustering yields multiple alternative organizational structures, diffusion mapping plus RNA velocity discloses a single developmental spectrum, ordered chronologically. Termed here neutrotime, this spectrum extends from immature pre-neutrophils, largely in bone marrow, to mature neutrophils predominantly in blood and spleen. The sharpest increments in neutrotime occur during the transitions from pre-neutrophils to immature neutrophils and from mature marrow neutrophils to those in blood. Human neutrophils exhibit a similar transcriptomic pattern. Neutrophils migrating into inflamed mouse lung, peritoneum and joint maintain the core mature neutrotime signature together with new transcriptional activity that varies with site and stimulus. Together, these data identify a single developmental spectrum as the dominant organizational theme of neutrophil heterogeneity.
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Affiliation(s)
- Ricardo Grieshaber-Bouyer
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine V, Hematology, Oncology and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany
| | - Felix A Radtke
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine V, Hematology, Oncology and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany
| | - Pierre Cunin
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Giuseppina Stifano
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anaïs Levescot
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Brinda Vijaykumar
- Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA
| | - Nathan Nelson-Maney
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rachel B Blaustein
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul A Monach
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Rheumatology Section, VA Boston Healthcare System, Boston, MA, USA
| | - Peter A Nigrovic
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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49
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Karlsson G, Sommarin MNE, Böiers C. Defining the Emerging Blood System During Development at Single-Cell Resolution. Front Cell Dev Biol 2021; 9:660350. [PMID: 34055791 PMCID: PMC8158578 DOI: 10.3389/fcell.2021.660350] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/26/2021] [Indexed: 12/20/2022] Open
Abstract
Developmental hematopoiesis differs from adult and is far less described. In the developing embryo, waves of lineage-restricted blood precede the ultimate emergence of definitive hematopoietic stem cells (dHSCs) capable of maintaining hematopoiesis throughout life. During the last two decades, the advent of single-cell genomics has provided tools to circumvent previously impeding characteristics of embryonic hematopoiesis, such as cell heterogeneity and rare cell states, allowing for definition of lineage trajectories, cellular hierarchies, and cell-type specification. The field has rapidly advanced from microfluidic platforms and targeted gene expression analysis, to high throughput unbiased single-cell transcriptomic profiling, single-cell chromatin analysis, and cell tracing-offering a plethora of tools to resolve important questions within hematopoietic development. Here, we describe how these technologies have been implemented to address a wide range of aspects of embryonic hematopoiesis ranging from the gene regulatory network of dHSC formation via endothelial to hematopoietic transition (EHT) and how EHT can be recapitulated in vitro, to hematopoietic trajectories and cell fate decisions. Together, these studies have important relevance for regenerative medicine and for our understanding of genetic blood disorders and childhood leukemias.
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Affiliation(s)
| | | | - Charlotta Böiers
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden
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50
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Teschendorff AE, Feinberg AP. Statistical mechanics meets single-cell biology. Nat Rev Genet 2021; 22:459-476. [PMID: 33875884 PMCID: PMC10152720 DOI: 10.1038/s41576-021-00341-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,UCL Cancer Institute, University College London, London, UK.
| | - Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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