1
|
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.
Collapse
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
| |
Collapse
|
2
|
Zhang D, Gao S, Liu ZP, Gao R. LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data. Brief Bioinform 2024; 25:bbae286. [PMID: 38886006 PMCID: PMC11182660 DOI: 10.1093/bib/bbae286] [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: 03/30/2024] [Revised: 05/09/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.
Collapse
Affiliation(s)
- Dezhen Zhang
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Shuhua Gao
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Zhi-Ping Liu
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Rui Gao
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| |
Collapse
|
3
|
Pan X, Zhang X. Studying temporal dynamics of single cells: expression, lineage and regulatory networks. Biophys Rev 2024; 16:57-67. [PMID: 38495440 PMCID: PMC10937865 DOI: 10.1007/s12551-023-01090-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
Collapse
Affiliation(s)
- Xinhai Pan
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| |
Collapse
|
4
|
Shin B, Zhou W, Wang J, Gao F, Rothenberg EV. Runx factors launch T cell and innate lymphoid programs via direct and gene network-based mechanisms. Nat Immunol 2023; 24:1458-1472. [PMID: 37563311 PMCID: PMC10673614 DOI: 10.1038/s41590-023-01585-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 07/12/2023] [Indexed: 08/12/2023]
Abstract
Runx factors are essential for lineage specification of various hematopoietic cells, including T lymphocytes. However, they regulate context-specific genes and occupy distinct genomic regions in different cell types. Here, we show that dynamic Runx binding shifts in mouse early T cell development are mostly not restricted by local chromatin state but regulated by Runx dosage and functional partners. Runx cofactors compete to recruit a limited pool of Runx factors in early T progenitor cells, and a modest increase in Runx protein availability at pre-commitment stages causes premature Runx occupancy at post-commitment binding sites. This increased Runx factor availability results in striking T cell lineage developmental acceleration by selectively activating T cell-identity and innate lymphoid cell programs. These programs are collectively regulated by Runx together with other, Runx-induced transcription factors that co-occupy Runx-target genes and propagate gene network changes.
Collapse
Affiliation(s)
- Boyoung Shin
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Wen Zhou
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Program in Biochemistry and Molecular Biophysics, California Institute of Technology, Pasadena, CA, USA
- BillionToOne, Menlo Park, CA, USA
| | - Jue Wang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Program in Biochemistry and Molecular Biophysics, California Institute of Technology, Pasadena, CA, USA
| | - Fan Gao
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Bioinformatics Resource Center, Beckman Institute of California Institute of Technology, Pasadena, CA, USA
- Lyterian Therapeutics, South San Francisco, CA, USA
| | - Ellen V Rothenberg
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| |
Collapse
|
5
|
Jassinskaja M, Gonka M, Kent DG. Resolving the hematopoietic stem cell state by linking functional and molecular assays. Blood 2023; 142:543-552. [PMID: 36735913 PMCID: PMC10644060 DOI: 10.1182/blood.2022017864] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/17/2023] [Accepted: 01/17/2023] [Indexed: 02/05/2023] Open
Abstract
One of the most challenging aspects of stem cell research is the reliance on retrospective assays for ascribing function. This is especially problematic for hematopoietic stem cell (HSC) research in which the current functional assay that formally establishes its HSC identity involves long-term serial transplantation assays that necessitate the destruction of the initial cell state many months before knowing that it was, in fact, an HSC. In combination with the explosion of equally destructive single-cell molecular assays, the paradox facing researchers is how to determine the molecular state of a functional HSC when you cannot concomitantly assess its functional and molecular properties. In this review, we will give a historical overview of the functional and molecular assays in the field, identify new tools that combine molecular and functional readouts in populations of HSCs, and imagine the next generation of computational and molecular profiling tools that may help us better link cell function with molecular state.
Collapse
Affiliation(s)
- Maria Jassinskaja
- Department of Biology, York Biomedical Research Institute, University of York, York, United Kingdom
| | - Monika Gonka
- Department of Biology, York Biomedical Research Institute, University of York, York, United Kingdom
| | - David G. Kent
- Department of Biology, York Biomedical Research Institute, University of York, York, United Kingdom
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Marku M, Pancaldi V. From time-series transcriptomics to gene regulatory networks: A review on inference methods. PLoS Comput Biol 2023; 19:e1011254. [PMID: 37561790 PMCID: PMC10414591 DOI: 10.1371/journal.pcbi.1011254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023] Open
Abstract
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.
Collapse
Affiliation(s)
- Malvina Marku
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
| | - Vera Pancaldi
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
| |
Collapse
|
8
|
Rivera M, Zhang H, Pham J, Isquith J, Zhou QJ, Sasik R, Mark A, Ma W, Holm F, Fisch KM, Kuo DJ, Jamieson C, Jiang Q. Malignant A-to-I RNA editing by ADAR1 drives T-cell acute lymphoblastic leukemia relapse via attenuating dsRNA sensing. RESEARCH SQUARE 2023:rs.3.rs-2444524. [PMID: 37398458 PMCID: PMC10312963 DOI: 10.21203/rs.3.rs-2444524/v2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Leukemia initiating cells (LICs) are regarded as the origin of leukemia relapse and therapeutic resistance. Identifying direct stemness determinants that fuel LIC self-renewal is critical for developing targeted approaches to eliminate LICs and prevent relapse. Here, we show that the RNA editing enzyme ADAR1 is a crucial stemness factor that promotes LIC self-renewal by attenuating aberrant double-stranded RNA (dsRNA) sensing. Elevated adenosine-to-inosine (A-to-I) editing is a common attribute of relapsed T-ALL regardless of molecular subtypes. Consequently, knockdown of ADAR1 severely inhibits LIC self-renewal capacity and prolongs survival in T-ALL PDX models. Mechanistically, ADAR1 directs hyper-editing of immunogenic dsRNA and retains unedited nuclear dsRNA to avoid detection by the innate immune sensor MDA5. Moreover, we uncovered that the cell intrinsic level of MDA5 dictates the dependency on ADAR1-MDA5 axis in T-ALL. Collectively, our results show that ADAR1 functions as a self-renewal factor that limits the sensing of endogenous dsRNA. Thus, targeting ADAR1 presents a safe and effective therapeutic strategy for eliminating T-ALL LICs.
Collapse
Affiliation(s)
- Maria Rivera
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, La Jolla, CA 92037, USA
| | - Haoran Zhang
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, La Jolla, CA 92037, USA
| | - Jessica Pham
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jane Isquith
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Qingchen Jenny Zhou
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, La Jolla, CA 92037, USA
| | - Roman Sasik
- Center for Computational Biology & Bioinformatics (CCBB), University of California, San Diego, La Jolla, 92093-0681
| | - Adam Mark
- Center for Computational Biology & Bioinformatics (CCBB), University of California, San Diego, La Jolla, 92093-0681
| | - Wenxue Ma
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Frida Holm
- Department of Women’s and Children’s Health, Division of Pediatric Oncology and Surgery, Karolinska Institutet, Sweden
| | - Kathleen M Fisch
- Center for Computational Biology & Bioinformatics (CCBB), University of California, San Diego, La Jolla, 92093-0681
- Department of Obstetrics, Gynecology & Reproductive Sciences, University of California, San Diego, La Jolla, CA
| | - Dennis John Kuo
- Moores Cancer Center, La Jolla, CA 92037, USA
- Division of Pediatric Hematology-Oncology, Rady Children’s Hospital San Diego, University of California, San Diego, CA
| | - Catriona Jamieson
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, La Jolla, CA 92037, USA
| | - Qingfei Jiang
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, La Jolla, CA 92037, USA
| |
Collapse
|
9
|
Mincarelli L, Uzun V, Wright D, Scoones A, Rushworth SA, Haerty W, Macaulay IC. Single-cell gene and isoform expression analysis reveals signatures of ageing in haematopoietic stem and progenitor cells. Commun Biol 2023; 6:558. [PMID: 37225862 PMCID: PMC10209181 DOI: 10.1038/s42003-023-04936-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 05/12/2023] [Indexed: 05/26/2023] Open
Abstract
Single-cell approaches have revealed that the haematopoietic hierarchy is a continuum of differentiation, from stem cell to committed progenitor, marked by changes in gene expression. However, many of these approaches neglect isoform-level information and thus do not capture the extent of alternative splicing within the system. Here, we present an integrated short- and long-read single-cell RNA-seq analysis of haematopoietic stem and progenitor cells. We demonstrate that over half of genes detected in standard short-read single-cell analyses are expressed as multiple, often functionally distinct, isoforms, including many transcription factors and key cytokine receptors. We observe global and HSC-specific changes in gene expression with ageing but limited impact of ageing on isoform usage. Integrating single-cell and cell-type-specific isoform landscape in haematopoiesis thus provides a new reference for comprehensive molecular profiling of heterogeneous tissues, as well as novel insights into transcriptional complexity, cell-type-specific splicing events and consequences of ageing.
Collapse
Affiliation(s)
- Laura Mincarelli
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, United Kingdom.
| | - Vladimir Uzun
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, United Kingdom
| | - David Wright
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, United Kingdom
| | - Anita Scoones
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, United Kingdom
| | - Stuart A Rushworth
- Norwich Medical School, The University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Wilfried Haerty
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, United Kingdom.
| | - Iain C Macaulay
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, United Kingdom.
| |
Collapse
|
10
|
Argyris GA, Lluch Lafuente A, Tribastone M, Tschaikowski M, Vandin A. Reducing Boolean networks with backward equivalence. BMC Bioinformatics 2023; 24:212. [PMID: 37221494 DOI: 10.1186/s12859-023-05326-9] [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: 03/15/2022] [Accepted: 05/05/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Boolean Networks (BNs) are a popular dynamical model in biology where the state of each component is represented by a variable taking binary values that express, for instance, activation/deactivation or high/low concentrations. Unfortunately, these models suffer from the state space explosion, i.e., there are exponentially many states in the number of BN variables, which hampers their analysis. RESULTS We present Boolean Backward Equivalence (BBE), a novel reduction technique for BNs which collapses system variables that, if initialized with same value, maintain matching values in all states. A large-scale validation on 86 models from two online model repositories reveals that BBE is effective, since it is able to reduce more than 90% of the models. Furthermore, on such models we also show that BBE brings notable analysis speed-ups, both in terms of state space generation and steady-state analysis. In several cases, BBE allowed the analysis of models that were originally intractable due to the complexity. On two selected case studies, we show how one can tune the reduction power of BBE using model-specific information to preserve all dynamics of interest, and selectively exclude behavior that does not have biological relevance. CONCLUSIONS BBE complements existing reduction methods, preserving properties that other reduction methods fail to reproduce, and vice versa. BBE drops all and only the dynamics, including attractors, originating from states where BBE-equivalent variables have been initialized with different activation values The remaining part of the dynamics is preserved exactly, including the length of the preserved attractors, and their reachability from given initial conditions, without adding any spurious behaviours. Given that BBE is a model-to-model reduction technique, it can be combined with further reduction methods for BNs.
Collapse
Affiliation(s)
- Georgios A Argyris
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Alberto Lluch Lafuente
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | | | - Max Tschaikowski
- Department of Computer Science, University of Aalborg, Aalborg, Denmark
| | - Andrea Vandin
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.
- Department of Excellence EMbeDS and Institute of Economics, Sant'Anna School for Advanced Studies, Pisa, Italy.
| |
Collapse
|
11
|
Li L, Sun L, Chen G, Wong CW, Ching WK, Liu ZP. LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data. Bioinformatics 2023; 39:btad256. [PMID: 37079737 PMCID: PMC10172039 DOI: 10.1093/bioinformatics/btad256] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/25/2023] [Accepted: 04/13/2023] [Indexed: 04/22/2023] Open
Abstract
MOTIVATION From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. RESULTS In this article, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets, and three real single-cell RNA sequencing datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. AVAILABILITY AND IMPLEMENTATION The source data and code are available at https://github.com/zpliulab/LogBTF.
Collapse
Affiliation(s)
- Lingyu Li
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, China
- Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong, China
| | - Liangjie Sun
- Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong, China
| | - Guangyi Chen
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, China
| | - Chi-Wing Wong
- Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong, China
| | - Wai-Ki Ching
- Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, China
| |
Collapse
|
12
|
Mao G, Pang Z, Zuo K, Liu J. Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data. J Comput Biol 2023; 30:619-631. [PMID: 36877552 DOI: 10.1089/cmb.2022.0355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
In recent years, with the rapid development of single-cell sequencing technology, this brings new opportunities and challenges to reconstruct gene regulatory networks. On the one hand, scRNA-seq data reveal statistical information of gene expression at single-cell resolution, which is beneficial to construct gene expression regulatory networks. On the other hand, the noise and dropout of single-cell data bring great difficulties to the analysis of scRNA-seq data, resulting in lower accuracy of gene regulatory networks reconstructed by traditional methods. In this article, we propose a novel supervised convolutional neural network (CNNSE), which can extract gene expression information from 2D co-expression matrices of gene doublets and identify interactions between genes. Our method can avoid the loss of extreme point interference by constructing a 2D co-expression matrix of gene pairs and significantly improve the regulation precision between gene pairs. And the CNNSE model is able to obtain detailed and high-level semantic information from the 2D co-expression matrix. Our method achieves satisfactory results on simulated data [accuracy (ACC): 0.712, F1: 0.724]. On two real scRNA-seq datasets, our method exhibits higher stability and accuracy in inference tasks compared with other existing gene regulatory network inference algorithms.
Collapse
Affiliation(s)
- Guo Mao
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
| | - Zhengbin Pang
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
| | - Ke Zuo
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
| | - Jie Liu
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
- Laboratory of Software Engineering for Complex System, National University of Defense Technology, Changsha, China
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|
14
|
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.
Collapse
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.
| |
Collapse
|
15
|
Gao S, Chen Y, Wu Z, Kajigaya S, Wang X, Young NS. Time-Varying Gene Expression Network Analysis Reveals Conserved Transition States in Hematopoietic Differentiation between Human and Mouse. Genes (Basel) 2022; 13:genes13101890. [PMID: 36292775 PMCID: PMC9601530 DOI: 10.3390/genes13101890] [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: 09/03/2022] [Revised: 10/10/2022] [Accepted: 10/14/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: analyses of gene networks can elucidate hematopoietic differentiation from single-cell gene expression data, but most algorithms generate only a single, static network. Because gene interactions change over time, it is biologically meaningful to examine time-varying structures and to capture dynamic, even transient states, and cell-cell relationships. (2) Methods: a transcriptomic atlas of hematopoietic stem and progenitor cells was used for network analysis. After pseudo-time ordering with Monocle 2, LOGGLE was used to infer time-varying networks and to explore changes of differentiation gene networks over time. A range of network analysis tools were used to examine properties and genes in the inferred networks. (3) Results: shared characteristics of attributes during the evolution of differentiation gene networks showed a “U” shape of network density over time for all three branches for human and mouse. Differentiation appeared as a continuous process, originating from stem cells, through a brief transition state marked by fewer gene interactions, before stabilizing in a progenitor state. Human and mouse shared hub genes in evolutionary networks. (4) Conclusions: the conservation of network dynamics in the hematopoietic systems of mouse and human was reflected by shared hub genes and network topological changes during differentiation.
Collapse
Affiliation(s)
- Shouguo Gao
- Hematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Correspondence:
| | - Ye Chen
- Department of Mathematics and Statistics, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Zhijie Wu
- Hematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sachiko Kajigaya
- Hematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Xujing Wang
- Division of Diabetes, Endocrinology, and Metabolic Diseases (DEM), National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20817, USA
| | - Neal S. Young
- Hematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| |
Collapse
|
16
|
Hérault L, Poplineau M, Remy E, Duprez E. Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging. Cells 2022; 11:3125. [PMID: 36231086 PMCID: PMC9563410 DOI: 10.3390/cells11193125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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.
Collapse
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
| |
Collapse
|
17
|
Rix B, Maduro AH, Bridge KS, Grey W. Markers for human haematopoietic stem cells: The disconnect between an identification marker and its function. Front Physiol 2022; 13:1009160. [PMID: 36246104 PMCID: PMC9564379 DOI: 10.3389/fphys.2022.1009160] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
The haematopoietic system is a classical stem cell hierarchy that maintains all the blood cells in the body. Haematopoietic stem cells (HSCs) are rare, highly potent cells that reside at the apex of this hierarchy and are historically some of the most well studied stem cells in humans and laboratory models, with haematopoiesis being the original system to define functional cell types by cell surface markers. Whilst it is possible to isolate HSCs to near purity, we know very little about the functional activity of markers to purify HSCs. This review will focus on the historical efforts to purify HSCs in humans based on cell surface markers, their putative functions and recent advances in finding functional markers on HSCs.
Collapse
Affiliation(s)
| | | | | | - William Grey
- *Correspondence: Katherine S. Bridge, ; William Grey,
| |
Collapse
|
18
|
Zhong Y, Sang H, Cook SJ, Kellstedt PM. Sparse spatially clustered coefficient model via adaptive regularization. Comput Stat Data Anal 2022; 177:107581. [PMID: 35919543 PMCID: PMC9335734 DOI: 10.1016/j.csda.2022.107581] [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: 11/10/2021] [Revised: 07/18/2022] [Accepted: 07/22/2022] [Indexed: 11/30/2022]
Abstract
Large spatial datasets with many spatial covariates have become ubiquitous in many fields in recent years. A question of interest is to identify which covariates are likely to influence a spatial response, and whether and how the effects of these covariates vary across space, including potential abrupt changes from region to region. To solve this question, a new efficient regularized spatially clustered coefficient (RSCC) regression approach is proposed, which could achieve variable selection and identify latent spatially heterogeneous covariate effects with clustered patterns simultaneously. By carefully designing the regularization term of RSCC as a chain graph guided fusion penalty plus a group lasso penalty, the RSCC model is computationally efficient for large spatial datasets while still achieving the theoretical guarantees for estimation. RSCC also adopts the idea of adaptive learning to allow for adaptive weights and adaptive graphs in its regularization terms and further improves the estimation performance. RSCC is applied to study the acceptance of COVID-19 vaccines using county-level data in the United States and discover the determinants of vaccination acceptance with varying effects across counties, revealing important within-state and across-state spatially clustered patterns of covariates effects.
Collapse
|
19
|
Li Y, Wang F, Zheng Z. Adaptive Synchronization-Based Approach for Finite-Time Parameters Identification of Genetic Regulatory Networks. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10754-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
|
20
|
Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00469-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
21
|
Discrete Logic Modeling of Cell Signaling Pathways. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2488:159-181. [PMID: 35347689 DOI: 10.1007/978-1-0716-2277-3_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Cell signaling pathways often crosstalk generating complex biological behaviors observed in different cellular contexts. Frequently, laboratory experiments focus on a few putative regulators, alone unable to predict the molecular mechanisms behind the observed phenotypes. Here, systems biology complements these approaches by giving a holistic picture to complex signaling crosstalk. In particular, Boolean network models are a meaningful tool to study large network behaviors and can cope with incomplete kinetic information. By introducing a model describing pathways involved in hematopoietic stem cell maintenance, we present a general approach on how to model cell signaling pathways with Boolean network models.
Collapse
|
22
|
Osaki J, Yamazaki S, Hikita A, Hoshi K. Hematopoietic progenitor cells specifically induce a unique immune response in dental pulp under conditions of systemic inflammation. Heliyon 2022; 8:e08904. [PMID: 35198771 PMCID: PMC8842015 DOI: 10.1016/j.heliyon.2022.e08904] [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: 11/01/2021] [Revised: 12/15/2021] [Accepted: 02/02/2022] [Indexed: 11/08/2022] Open
Abstract
Teeth are exposed to various stimuli, including bacterial, thermal, and physical stimuli. Therefore, immune cells present in the normal dental pulp and the immune response to these stimuli have been studied. However, the relationship between systemic inflammation, such as that induced by viral infection, and changes occurring in dental pulp is not well known. This study aimed to investigate the immunological and hematological responses to systemic inflammation in dental pulp. Poly(I:C), a toll-like receptor 3 agonist, was injected into mice every two days to simulate a systemic inflammatory state in which type I interferon (IFN–I) was produced. The untreated normal state was defined as a steady state, and the states of acute and chronic inflammation were defined according to the period of administration. Changes in the abundance and dynamics of hematopoietic and immune cells in dental pulp, bone marrow and peripheral blood were quantitatively investigated in the steady state and under conditions of inflammation induced by IFN-l. We found that dental pulp in the steady state contained only a few hematopoietic cells, but a greater variety of immune cells than previously reported. B cells were also found in the steady state. An increase in multipotent progenitor cell levels was observed in the dental pulp during both acute and chronic inflammation. The increased multipotent progenitor cells in the dental pulp during acute inflammation tended to differentiate into the myeloid lineage. On the other hand, there was an influx of B cells into the dental pulp during chronic inflammation. These results revealed that a unique immune response is induced in the dental pulp by systemic inflammation, which would lead to a significant change in the perspective of dentists on the utility of dental pulp in the management of systemic diseases.
Collapse
Affiliation(s)
- Julia Osaki
- Department of Sensory and Motor System Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Satoshi Yamazaki
- Division of Stem Cell Biology, Center for Stem Cell Therapy, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan, 4-6-1 Shirokane-dai, Minato-ku, Tokyo, 108-8639, Japan.,Laboratory of Stem Cell Therapy, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
| | - Atsuhiko Hikita
- Department of Tissue Engineering, The University of Tokyo Hospital, Tokyo, Japan, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Kazuto Hoshi
- Department of Sensory and Motor System Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.,Department of Tissue Engineering, The University of Tokyo Hospital, Tokyo, Japan, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.,Department of Oral-maxillofacial Surgery, Dentistry and Orthodontics, The University of Tokyo Hospital, Tokyo, Japan, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| |
Collapse
|
23
|
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.
Collapse
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
| |
Collapse
|
24
|
Ding J, Sharon N, Bar-Joseph Z. Temporal modelling using single-cell transcriptomics. Nat Rev Genet 2022; 23:355-368. [PMID: 35102309 DOI: 10.1038/s41576-021-00444-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2021] [Indexed: 12/16/2022]
Abstract
Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.
Collapse
|
25
|
Shrivastava H, Zhang X, Song L, Aluru S. GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks. J Comput Biol 2022; 29:27-44. [PMID: 35050715 DOI: 10.1089/cmb.2021.0437] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive ability of neural networks to capture complex dependencies between transcription factors and the corresponding genes they regulate, by developing a multitask learning framework. Second, to capture sparsity of GRNs observed in the real world, we design an unrolled algorithm technique for our framework. Our deep architecture requires supervision for training, for which we repurpose existing synthetic data simulators that generate scRNA-Seq data guided by an underlying GRN. Experimental results demonstrate that GRNUlar outperforms state-of-the-art methods on both synthetic and real data sets. Our study also demonstrates the novel and successful use of expression data simulators for supervised learning of GRN inference.
Collapse
Affiliation(s)
- Harsh Shrivastava
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Xiuwei Zhang
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Le Song
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Srinivas Aluru
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| |
Collapse
|
26
|
Luo Q, Yu Y, Lan X. SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging. Brief Bioinform 2022; 23:bbab547. [PMID: 34962260 PMCID: PMC8769917 DOI: 10.1093/bib/bbab547] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/13/2021] [Accepted: 11/25/2021] [Indexed: 11/17/2022] Open
Abstract
High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using linear models challenging. We present SIGNET, a deep learning-based framework for capturing complex regulatory relationships between genes under the assumption that the expression levels of transcription factors participating in gene regulation are strong predictors of the expression of their target genes. Evaluations based on a variety of real and simulated scRNA-seq datasets showed that SIGNET is more sensitive to ChIP-seq validated regulatory interactions in different types of cells, particularly rare cells. Therefore, this process is more effective for various downstream analyses, such as cell clustering and gene regulatory network inference. We demonstrated that SIGNET is a useful tool for identifying important regulatory modules driving various biological processes.
Collapse
Affiliation(s)
- Qinhuan Luo
- School of Medicine, Tsinghua University, Beijing, China
| | - Yongzhen Yu
- School of Medicine, Tsinghua University, Beijing, China
| | - Xun Lan
- School of Medicine,and the Tsinghua-Peking Center for Life science, MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
| |
Collapse
|
27
|
Prugger M, Einkemmer L, Beik SP, Wasdin PT, Harris LA, Lopez CF. Unsupervised logic-based mechanism inference for network-driven biological processes. PLoS Comput Biol 2021; 17:e1009035. [PMID: 34077417 PMCID: PMC8202945 DOI: 10.1371/journal.pcbi.1009035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 06/14/2021] [Accepted: 05/03/2021] [Indexed: 01/21/2023] Open
Abstract
Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.
Collapse
Affiliation(s)
- Martina Prugger
- Department of Biochemistry, University of Innsbruck, Innsbruck, Austria
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Lukas Einkemmer
- Department of Mathematics, University of Innsbruck, Innsbruck, Austria
| | - Samantha P. Beik
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Perry T. Wasdin
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Leonard A. Harris
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas, United States of America
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, Arkansas, United States of America
- Cancer Biology Program, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Carlos F. Lopez
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| |
Collapse
|
28
|
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.
Collapse
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
| |
Collapse
|
29
|
Murakami K, Sasaki H, Nishiyama A, Kurotaki D, Kawase W, Ban T, Nakabayashi J, Kanzaki S, Sekita Y, Nakajima H, Ozato K, Kimura T, Tamura T. A RUNX-CBFβ-driven enhancer directs the Irf8 dose-dependent lineage choice between DCs and monocytes. Nat Immunol 2021; 22:301-311. [PMID: 33603226 DOI: 10.1038/s41590-021-00871-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 01/11/2021] [Indexed: 01/30/2023]
Abstract
The transcription factor IRF8 is essential for the development of monocytes and dendritic cells (DCs), whereas it inhibits neutrophilic differentiation. It is unclear how Irf8 expression is regulated and how this single transcription factor supports the generation of both monocytes and DCs. Here, we identified a RUNX-CBFβ-driven enhancer 56 kb downstream of the Irf8 transcription start site. Deletion of this enhancer in vivo significantly decreased Irf8 expression throughout the myeloid lineage from the progenitor stages, thus resulting in loss of common DC progenitors and overproduction of Ly6C+ monocytes. We demonstrated that high, low or null expression of IRF8 in hematopoietic progenitor cells promotes differentiation toward type 1 conventional DCs, Ly6C+ monocytes or neutrophils, respectively, via epigenetic regulation of distinct sets of enhancers in cooperation with other transcription factors. Our results illustrate the mechanism through which IRF8 controls the lineage choice in a dose-dependent manner within the myeloid cell system.
Collapse
Affiliation(s)
- Koichi Murakami
- Department of Immunology, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
- Advanced Medical Research Center, Yokohama City University, Kanagawa, Japan
| | - Haruka Sasaki
- Department of Immunology, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
| | - Akira Nishiyama
- Department of Immunology, Yokohama City University Graduate School of Medicine, Kanagawa, Japan.
| | - Daisuke Kurotaki
- Department of Immunology, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
| | - Wataru Kawase
- Department of Immunology, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
| | - Tatsuma Ban
- Department of Immunology, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
| | - Jun Nakabayashi
- Advanced Medical Research Center, Yokohama City University, Kanagawa, Japan
| | - Satoko Kanzaki
- Laboratory of Stem Cell Biology, Department of Biosciences, Kitasato University School of Science, Kanagawa, Japan
| | - Yoichi Sekita
- Laboratory of Stem Cell Biology, Department of Biosciences, Kitasato University School of Science, Kanagawa, Japan
| | - Hideaki Nakajima
- Department of Stem Cell and Immune Regulation, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
| | - Keiko Ozato
- Program in Genomics of Differentiation, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA
| | - Tohru Kimura
- Laboratory of Stem Cell Biology, Department of Biosciences, Kitasato University School of Science, Kanagawa, Japan
| | - Tomohiko Tamura
- Department of Immunology, Yokohama City University Graduate School of Medicine, Kanagawa, Japan.
- Advanced Medical Research Center, Yokohama City University, Kanagawa, Japan.
| |
Collapse
|
30
|
Zhang Y, Chang X, Liu X. Inference of gene regulatory networks using pseudo-time series data. Bioinformatics 2021; 37:2423-2431. [PMID: 33576787 DOI: 10.1093/bioinformatics/btab099] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/18/2021] [Accepted: 02/10/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific data set. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. RESULTS Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. AVAILABILITY The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Yuelei Zhang
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310012, China.,Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233030, China.,School of Mathematics and Statistics, Shandong University, Weihai, Shandong, 264209, China
| | - Xiao Chang
- Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233030, China
| | - Xiaoping Liu
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310012, China.,School of Mathematics and Statistics, Shandong University, Weihai, Shandong, 264209, China
| |
Collapse
|
31
|
Abstract
Diverse cellular phenotypes are determined by groups of transcription factors (TFs) and other regulators that influence each others' gene expression, forming transcriptional gene regulatory networks (GRNs). In many biological contexts, especially in development and associated diseases, the expression of the genes in GRNs is not static but evolves in time. Modeling the dynamics of GRN state is an important approach for understanding diverse cellular phenomena such as cell-fate specification, pluripotency and cell-fate reprogramming, oncogenesis, and tissue regeneration. In this protocol, we describe how to model GRNs using a data-driven dynamic modeling methodology, gene circuits. Gene circuits do not require knowledge of the GRN topology and connectivity but instead learn them from training data, making them very general and applicable to diverse biological contexts. We utilize the MATLAB-based gene circuit modeling software Fast Inference of Gene Regulation (FIGR) for training the model on quantitative gene expression data and simulating the GRN. We describe all the steps in the modeling life cycle, from formulating the model, training the model using FIGR, simulating the GRN, to analyzing and interpreting the model output. This protocol highlights these steps with the example of a dynamical model of the gap gene GRN involved in Drosophila segmentation and includes example MATLAB statements for each step.
Collapse
Affiliation(s)
- Joanna E Handzlik
- Department of Biology, University of North Dakota, Grand Forks, ND, 58202, USA
| | - Yen Lee Loh
- Department of Physics and Astrophysics, University of North Dakota, Grand Forks, ND, 58202, USA
| | - Manu
- Department of Biology, University of North Dakota, Grand Forks, ND, 58202, USA.
| |
Collapse
|
32
|
Gao S, Wu Z, Feng X, Kajigaya S, Wang X, Young NS. Comprehensive network modeling from single cell RNA sequencing of human and mouse reveals well conserved transcription regulation of hematopoiesis. BMC Genomics 2020; 21:849. [PMID: 33372598 PMCID: PMC7771096 DOI: 10.1186/s12864-020-07241-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 11/18/2020] [Indexed: 12/17/2022] Open
Abstract
Background Presently, there is no comprehensive analysis of the transcription regulation network in hematopoiesis. Comparison of networks arising from gene co-expression across species can facilitate an understanding of the conservation of functional gene modules in hematopoiesis. Results We used single-cell RNA sequencing to profile bone marrow from human and mouse, and inferred transcription regulatory networks in each species in order to characterize transcriptional programs governing hematopoietic stem cell differentiation. We designed an algorithm for network reconstruction to conduct comparative transcriptomic analysis of hematopoietic gene co-expression and transcription regulation in human and mouse bone marrow cells. Co-expression network connectivity of hematopoiesis-related genes was found to be well conserved between mouse and human. The co-expression network showed “small-world” and “scale-free” architecture. The gene regulatory network formed a hierarchical structure, and hematopoiesis transcription factors localized to the hierarchy’s middle level. Conclusions Transcriptional regulatory networks are well conserved between human and mouse. The hierarchical organization of transcription factors may provide insights into hematopoietic cell lineage commitment, and to signal processing, cell survival and disease initiation. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-020-07241-2.
Collapse
Affiliation(s)
- Shouguo Gao
- Hematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, NHLBI, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Zhijie Wu
- Hematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, NHLBI, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Xingmin Feng
- Hematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, NHLBI, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Sachiko Kajigaya
- Hematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, NHLBI, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Xujing Wang
- Division of Diabetes, Endocrinology, and Metabolic Diseases (DEM), NIDDK, National Institutes of Health, Bethesda, MD, 20817, USA
| | - Neal S Young
- Hematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, NHLBI, National Institutes of Health, Bethesda, MD, 20892, USA
| |
Collapse
|
33
|
Choo SM, Almomani LM, Cho KH. Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion. Front Physiol 2020; 11:594151. [PMID: 33335489 PMCID: PMC7736109 DOI: 10.3389/fphys.2020.594151] [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: 08/12/2020] [Accepted: 11/03/2020] [Indexed: 11/13/2022] Open
Abstract
The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, single-cell measurement data such as single-cell RNA-sequencing databases have recently provided a new insight into resolving this problem by ordering thousands of cells in pseudo-time according to their differential gene expressions. Neural network modeling can be employed by using temporal data as learning data. In contrast, Boolean network modeling of MRNs has a growing interest, as it is a parameter-free logical modeling and thereby robust to noisy data while still capturing essential dynamics of biological networks. In this study, we propose a Boolean feedforward neural network (FFN) modeling by combining neural network and Boolean network modeling approach to reconstruct a practical and useful MRN model from large temporal data. Furthermore, analyzing the reconstructed MRN model can enable us to identify control targets for potential cellular state conversion. Here, we show the usefulness of Boolean FFN modeling by demonstrating its applicability through a toy model and biological networks.
Collapse
Affiliation(s)
- Sang-Mok Choo
- Department of Mathematics, University of Ulsan, Ulsan, South Korea
| | | | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| |
Collapse
|
34
|
Single-cell network biology for resolving cellular heterogeneity in human diseases. Exp Mol Med 2020; 52:1798-1808. [PMID: 33244151 PMCID: PMC8080824 DOI: 10.1038/s12276-020-00528-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 08/26/2020] [Accepted: 08/31/2020] [Indexed: 01/10/2023] Open
Abstract
Understanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profiling using tissue samples provide only average signals of diverse cell types. Therefore, reconstructing gene regulatory networks for a particular cell type is not feasible with tissue-based transcriptome data. Recently, single-cell omics technology has emerged and enabled the capture of the transcriptomic landscape of every individual cell. Although single-cell gene expression studies have already opened up new avenues, network biology using single-cell transcriptome data will further accelerate our understanding of cellular heterogeneity. In this review, we provide an overview of single-cell network biology and summarize recent progress in method development for network inference from single-cell RNA sequencing (scRNA-seq) data. Then, we describe how cell-type-specific gene networks can be utilized to study regulatory programs specific to disease-associated cell types and cellular states. Moreover, with scRNA data, modeling personal or patient-specific gene networks is feasible. Therefore, we also introduce potential applications of single-cell network biology for precision medicine. We envision a rapid paradigm shift toward single-cell network analysis for systems biology in the near future. Gene regulatory networks reconstructed from single-cell RNA sequencing datasets are allowing researchers to better understand the molecular circuits and cell states that contribute to complex human disease. Junha Cha and Insuk Lee from Yonsei University in Seoul, South Korea, review the concept of ‘single-cell network biology’, which involves using computational algorithms on genetic expression data from thousands of cells to infer functional interactions in various biological contexts. This systems biology approach to analyzing the profiles of messenger RNA in single cells is helping researchers discover new signaling pathways that could serve as disease biomarkers or therapeutic targets. In the future, patient-specific models of personal gene networks could explain why certain genetic variants affect disease risk. This research could also eventually lead to new types of individualized medical treatments.
Collapse
|
35
|
Wu WE, Zhou X, Xu N, Huang JX, Liu L, Tan YX, Luo J, Qin JY, Yin CX, Zhou LL, Liu XL. [Targeted next-generation sequencing for the molecular diagnosis of patients with chronic myeloid leukemia with resistance or intolerance to tyrosine kinase inhibitor]. ZHONGHUA XUE YE XUE ZA ZHI = ZHONGHUA XUEYEXUE ZAZHI 2020; 41:848-852. [PMID: 33190443 PMCID: PMC7656074 DOI: 10.3760/cma.j.issn.0253-2727.2020.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Indexed: 11/17/2022]
MESH Headings
- Drug Resistance, Neoplasm/drug effects
- Drug Resistance, Neoplasm/genetics
- High-Throughput Nucleotide Sequencing
- Humans
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/diagnosis
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
- Mutation/drug effects
- Protein Kinase Inhibitors/pharmacology
Collapse
Affiliation(s)
- W E Wu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - X Zhou
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - N Xu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - J X Huang
- Department of Hematology, Yuebei People's Hospital, Shantou University, Shaoguan 512025, China
| | - L Liu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Y X Tan
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - J Luo
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - J Y Qin
- Annoroad Gene Technology Co. Ltd, Beijing 100176, China
| | - C X Yin
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - L L Zhou
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - X L Liu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| |
Collapse
|
36
|
Wu W, Xu N, Zhou X, Liu L, Tan Y, Luo J, Huang J, Qin J, Wang J, Li Z, Yin C, Zhou L, Liu X. Integrative Genomic Analysis Reveals Cancer-Associated Gene Mutations in Chronic Myeloid Leukemia Patients with Resistance or Intolerance to Tyrosine Kinase Inhibitor. Onco Targets Ther 2020; 13:8581-8591. [PMID: 32943879 PMCID: PMC7468532 DOI: 10.2147/ott.s257661] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 07/30/2020] [Indexed: 12/18/2022] Open
Abstract
Introduction While the acquisition of mutations in the ABL1 kinase domain (KD) has been identified as a common mechanism behind tyrosine kinase inhibitor (TKI) resistance, recent genetic studies have revealed that patients with TKI resistance or intolerance frequently harbor one or more genetic alterations implicated in myeloid malignancies. This suggests that additional mutations other than ABL1 KD mutations might contribute to disease progression. Methods We performed targeted-capture sequencing of 127 known and putative cancer-related genes of 63 patients with CML using next-generation sequencing (NGS), including 42 patients with TKI resistance and 21 with TKI intolerance. Results The differences in the number of mutations between groups had no statistical significance. This could be explained in part by not all of the patients having achieved major molecular remission in the early period as expected. Overall, 66 mutations were identified in 96.8% of the patients, most frequently in the KTM2C (31.82%), ABL1 (31.82%), FAT1 (25.76%), and ASXL1 (22.73%) genes. CUX1, KIT, and GATA2 were associated with TKI intolerance, and two of them (CUX1, GATA2) are transcription factors in which mutations were identified in 82.61% of patients with TKI intolerance. ASXL1 mutations were found more frequently in patients with ABL1 KD mutations (38.1% vs 15.21%, P=0.041). Although the number of mutations was low, pairwise interaction between mutated genes showed that ABL1 KD mutations cooccurred with SH2B3 mutations (P<0.05). In Kaplan-Meier analyses, only TET2 mutations were associated with shorter progression-free survival (P=0.026). Conclusion Our data suggested that the CUX1, KIT, and GATA2 genes may play important roles in TKI intolerance. ASXL1 and TET2 mutations may be associated with poor patient prognosis. NGS helps improving the clinical risk stratification, which enables the identification of patients with TKI resistance or intolerance in the era of TKI therapy.
Collapse
Affiliation(s)
- Waner Wu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, People's Republic of China
| | - Na Xu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, People's Republic of China
| | - Xuan Zhou
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, People's Republic of China
| | - Liang Liu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, People's Republic of China
| | - Yaxian Tan
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, People's Republic of China
| | - Jie Luo
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, People's Republic of China
| | - Jixian Huang
- Department of Hematology, Yuebei People's Hospital, Shantou University, Shaoguan 512025, Guangdong, People's Republic of China
| | - Jiayue Qin
- Yiwu Cancer Research Center, Fudan University Shanghai Cancer Center, Yiwu, Zhejiang 322000, People's Republic of China
| | - Juan Wang
- Yiwu Cancer Research Center, Fudan University Shanghai Cancer Center, Yiwu, Zhejiang 322000, People's Republic of China
| | - Zhimin Li
- Yiwu Cancer Research Center, Fudan University Shanghai Cancer Center, Yiwu, Zhejiang 322000, People's Republic of China
| | - Changxin Yin
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, People's Republic of China
| | - Lingling Zhou
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, People's Republic of China
| | - Xiaoli Liu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, People's Republic of China
| |
Collapse
|
37
|
Ikonomi N, Kühlwein SD, Schwab JD, Kestler HA. Awakening the HSC: Dynamic Modeling of HSC Maintenance Unravels Regulation of the TP53 Pathway and Quiescence. Front Physiol 2020; 11:848. [PMID: 32848827 PMCID: PMC7411231 DOI: 10.3389/fphys.2020.00848] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/24/2020] [Indexed: 12/22/2022] Open
Abstract
Hematopoietic stem cells (HSCs) provide all types of blood cells during the entire life of the organism. HSCs are mainly quiescent and can eventually enter the cell cycle to differentiate. HSCs are maintained and tightly regulated in a particular environment. The stem cell niche regulates dormancy and awakening. Deregulations of this interplay can lead to hematopoietic failure and diseases. In this paper, we present a Boolean network model that recapitulates HSC regulation in virtue of external signals coming from the niche. This Boolean network integrates and summarizes the current knowledge of HSC regulation and is based on extensive literature research. Furthermore, dynamic simulations suggest a novel systemic regulation of TP53 in homeostasis. Thereby, our model indicates that TP53 activity is balanced depending on external stimulations, engaging a regulatory mechanism involving ROS regulators and RAS activated transcription factors. Finally, we investigated different mouse models and compared them to in silico knockout simulations. Here, the model could recapitulate in vivo observed behaviors and thus sustains our results.
Collapse
Affiliation(s)
- Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Silke D Kühlwein
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Julian D Schwab
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Hans A Kestler
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| |
Collapse
|
38
|
Zhang S, Zhao J, Lv X, Fan J, Lu Y, Zeng T, Wu H, Chen L, Zhao Y. Analysis on gene modular network reveals morphogen-directed development robustness in Drosophila. Cell Discov 2020; 6:43. [PMID: 32637151 PMCID: PMC7324402 DOI: 10.1038/s41421-020-0173-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 04/27/2020] [Indexed: 12/21/2022] Open
Abstract
Genetic robustness is an important characteristic to tolerate genetic or nongenetic perturbations and ensure phenotypic stability. Morphogens, a type of evolutionarily conserved diffusible molecules, govern tissue patterns in a direction-dependent or concentration-dependent manner by differentially regulating downstream gene expression. However, whether the morphogen-directed gene regulatory network possesses genetic robustness remains elusive. In the present study, we collected 4217 morphogen-responsive genes along A-P axis of Drosophila wing discs from the RNA-seq data, and clustered them into 12 modules. By applying mathematical model to the measured data, we constructed a gene modular network (GMN) to decipher the module regulatory interactions and robustness in morphogen-directed development. The computational analyses on asymptotical dynamics of this GMN demonstrated that this morphogen-directed GMN is robust to tolerate a majority of genetic perturbations, which has been further validated by biological experiments. Furthermore, besides the genetic alterations, we further demonstrated that this morphogen-directed GMN can well tolerate nongenetic perturbations (Hh production changes) via computational analyses and experimental validation. Therefore, these findings clearly indicate that the morphogen-directed GMN is robust in response to perturbations and is important for Drosophila to ensure the proper tissue patterning in wing disc.
Collapse
Affiliation(s)
- Shuo Zhang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031 Shanghai, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Juan Zhao
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031 Shanghai, China
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223 Yunnan China
| | - Xiangdong Lv
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031 Shanghai, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Jialin Fan
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031 Shanghai, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Yi Lu
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031 Shanghai, China
| | - Tao Zeng
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031 Shanghai, China
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223 Yunnan China
| | - Hailong Wu
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031 Shanghai, China
| | - Luonan Chen
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031 Shanghai, China
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223 Yunnan China
- School of Life Science and Technology, ShanghaiTech University, 201210 Shanghai, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024 Zhejiang China
| | - Yun Zhao
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031 Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, 201210 Shanghai, China
- School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024 Zhejiang China
| |
Collapse
|
39
|
Johnston G, Ramsey HE, Liu Q, Wang J, Stengel KR, Sampathi S, Acharya P, Arrate M, Stubbs MC, Burn T, Savona MR, Hiebert SW. Nascent transcript and single-cell RNA-seq analysis defines the mechanism of action of the LSD1 inhibitor INCB059872 in myeloid leukemia. Gene 2020; 752:144758. [PMID: 32422235 DOI: 10.1016/j.gene.2020.144758] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 05/06/2020] [Indexed: 12/15/2022]
Abstract
Drugs targeting chromatin-modifying enzymes have entered clinical trials for myeloid malignancies, including INCB059872, a selective irreversible inhibitor of Lysine-Specific Demethylase 1 (LSD1). While initial studies of LSD1 inhibitors suggested these compounds may be used to induce differentiation of acute myeloid leukemia (AML), the mechanisms underlying this effect and dose-limiting toxicities are not well understood. Here, we used precision nuclear run-on sequencing (PRO-seq) and ChIP-seq in AML cell lines to probe for the earliest regulatory events associated with INCB059872 treatment. The changes in nascent transcription could be traced back to a loss of CoREST activity and activation of GFI1-regulated genes. INCB059872 is in phase I clinical trials, and we evaluated a pre-treatment bone marrow sample of a patient who showed a clinical response to INCB059872 while being treated with azacitidine. We used single-cell RNA-sequencing (scRNA-seq) to show that INCB059872 caused a shift in gene expression that was again associated with GFI1/GFI1B regulation. Finally, we treated mice with INCB059872 and performed scRNA-seq of lineage-negative bone marrow cells, which showed that INCB059872 triggered accumulation of megakaryocyte early progenitor cells with gene expression hallmarks of stem cells. Accumulation of these stem/progenitor cells may contribute to the thrombocytopenia observed in patients treated with LSD1 inhibitors.
Collapse
Affiliation(s)
- Gretchen Johnston
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Haley E Ramsey
- Department of Medicine and Program in Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Qi Liu
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA; Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jing Wang
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA; Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kristy R Stengel
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Shilpa Sampathi
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Pankaj Acharya
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Maria Arrate
- Department of Medicine and Program in Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | | | | | - Michael R Savona
- Department of Medicine and Program in Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37203, USA; Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37027, USA
| | - Scott W Hiebert
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37027, USA.
| |
Collapse
|
40
|
Wu S, Cui T, Zhang X, Tian T. A non-linear reverse-engineering method for inferring genetic regulatory networks. PeerJ 2020; 8:e9065. [PMID: 32391205 PMCID: PMC7195839 DOI: 10.7717/peerj.9065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 04/05/2020] [Indexed: 12/19/2022] Open
Abstract
Hematopoiesis is a highly complex developmental process that produces various types of blood cells. This process is regulated by different genetic networks that control the proliferation, differentiation, and maturation of hematopoietic stem cells (HSCs). Although substantial progress has been made for understanding hematopoiesis, the detailed regulatory mechanisms for the fate determination of HSCs are still unraveled. In this study, we propose a novel approach to infer the detailed regulatory mechanisms. This work is designed to develop a mathematical framework that is able to realize nonlinear gene expression dynamics accurately. In particular, we intended to investigate the effect of possible protein heterodimers and/or synergistic effect in genetic regulation. This approach includes the Extended Forward Search Algorithm to infer network structure (top-down approach) and a non-linear mathematical model to infer dynamical property (bottom-up approach). Based on the published experimental data, we study two regulatory networks of 11 genes for regulating the erythrocyte differentiation pathway and the neutrophil differentiation pathway. The proposed algorithm is first applied to predict the network topologies among 11 genes and 55 non-linear terms which may be for heterodimers and/or synergistic effect. Then, the unknown model parameters are estimated by fitting simulations to the expression data of two different differentiation pathways. In addition, the edge deletion test is conducted to remove possible insignificant regulations from the inferred networks. Furthermore, the robustness property of the mathematical model is employed as an additional criterion to choose better network reconstruction results. Our simulation results successfully realized experimental data for two different differentiation pathways, which suggests that the proposed approach is an effective method to infer the topological structure and dynamic property of genetic regulations.
Collapse
Affiliation(s)
- Siyuan Wu
- School of Mathematics, Monash University, Clayton, VIC, Australia
| | - Tiangang Cui
- School of Mathematics, Monash University, Clayton, VIC, Australia
| | - Xinan Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, PR China
| | - Tianhai Tian
- School of Mathematics, Monash University, Clayton, VIC, Australia
| |
Collapse
|
41
|
Qiu X, Rahimzamani A, Wang L, Ren B, Mao Q, Durham T, McFaline-Figueroa JL, Saunders L, Trapnell C, Kannan S. Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe. Cell Syst 2020; 10:265-274.e11. [PMID: 32135093 PMCID: PMC7223477 DOI: 10.1016/j.cels.2020.02.003] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 06/08/2019] [Accepted: 02/05/2020] [Indexed: 01/13/2023]
Abstract
Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.
Collapse
Affiliation(s)
- Xiaojie Qiu
- Molecular & Cellular Biology Program, University of Washington, Seattle, WA, USA; Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Arman Rahimzamani
- Department of Electrical Engineering, University of Washington, Seattle, WA, USA
| | - Li Wang
- Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA
| | - Bingcheng Ren
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qi Mao
- HERE company, Chicago, IL 60606, USA
| | - Timothy Durham
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Lauren Saunders
- Molecular & Cellular Biology Program, University of Washington, Seattle, WA, USA; Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Cole Trapnell
- Molecular & Cellular Biology Program, University of Washington, Seattle, WA, USA; Department of Genome Sciences, University of Washington, Seattle, WA, USA; Brotman-Baty Institute for Precision Medicine, Seattle, WA, USA.
| | - Sreeram Kannan
- Department of Electrical Engineering, University of Washington, Seattle, WA, USA.
| |
Collapse
|
42
|
Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali TM. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat Methods 2020; 17:147-154. [PMID: 31907445 PMCID: PMC7098173 DOI: 10.1038/s41592-019-0690-6] [Citation(s) in RCA: 317] [Impact Index Per Article: 79.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 11/22/2019] [Indexed: 01/10/2023]
Abstract
We present a systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks from single-cell transcriptional data. As the ground truth for assessing accuracy, we use synthetic networks with predictable trajectories, literature-curated Boolean models and diverse transcriptional regulatory networks. We develop a strategy to simulate single-cell transcriptional data from synthetic and Boolean networks that avoids pitfalls of previously used methods. Furthermore, we collect networks from multiple experimental single-cell RNA-seq datasets. We develop an evaluation framework called BEELINE. We find that the area under the precision-recall curve and early precision of the algorithms are moderate. The methods are better in recovering interactions in synthetic networks than Boolean models. The algorithms with the best early precision values for Boolean models also perform well on experimental datasets. Techniques that do not require pseudotime-ordered cells are generally more accurate. Based on these results, we present recommendations to end users. BEELINE will aid the development of gene regulatory network inference algorithms.
Collapse
Affiliation(s)
- Aditya Pratapa
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - Amogh P Jalihal
- Genetics, Bioinformatics, and Computational Biology Ph.D. Program, Virginia Tech, Blacksburg, VA, USA
| | - Jeffrey N Law
- Genetics, Bioinformatics, and Computational Biology Ph.D. Program, Virginia Tech, Blacksburg, VA, USA
| | - Aditya Bharadwaj
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.
| |
Collapse
|
43
|
Pernes G, Flynn MC, Lancaster GI, Murphy AJ. Fat for fuel: lipid metabolism in haematopoiesis. Clin Transl Immunology 2019; 8:e1098. [PMID: 31890207 PMCID: PMC6928762 DOI: 10.1002/cti2.1098] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 12/02/2019] [Accepted: 12/03/2019] [Indexed: 12/14/2022] Open
Abstract
The importance of metabolic regulation in the immune system has launched back into the limelight in recent years. Various metabolic pathways have been examined in the context of their contribution to maintaining immune cell homeostasis and function. Moreover, this regulation is also important in the immune cell precursors, where metabolism controls their maintenance and cell fate. This review will discuss lipid metabolism in the context of haematopoiesis, that is blood cell development. We specifically focus on nonoxidative lipid metabolism which encapsulates the synthesis and degradation of the major lipid classes such as phospholipids, sphingolipids and sterols. We will also discuss how these metabolic processes are affected by haematological malignancies such as leukaemia and lymphoma, which are known to have altered metabolism, and how these different pathways contribute to the pathology.
Collapse
Affiliation(s)
- Gerard Pernes
- Haematopoiesis and Leukocyte Biology Baker Heart and Diabetes Institute Melbourne VIC Australia.,Department of Immunology Monash University Melbourne VIC Australia
| | - Michelle C Flynn
- Haematopoiesis and Leukocyte Biology Baker Heart and Diabetes Institute Melbourne VIC Australia.,Department of Immunology Monash University Melbourne VIC Australia
| | - Graeme I Lancaster
- Haematopoiesis and Leukocyte Biology Baker Heart and Diabetes Institute Melbourne VIC Australia.,Department of Immunology Monash University Melbourne VIC Australia
| | - Andrew J Murphy
- Haematopoiesis and Leukocyte Biology Baker Heart and Diabetes Institute Melbourne VIC Australia.,Department of Immunology Monash University Melbourne VIC Australia
| |
Collapse
|
44
|
Classification-Based Inference of Dynamical Models of Gene Regulatory Networks. G3-GENES GENOMES GENETICS 2019; 9:4183-4195. [PMID: 31624138 PMCID: PMC6893186 DOI: 10.1534/g3.119.400603] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Cell-fate decisions during development are controlled by densely interconnected gene regulatory networks (GRNs) consisting of many genes. Inferring and predictively modeling these GRNs is crucial for understanding development and other physiological processes. Gene circuits, coupled differential equations that represent gene product synthesis with a switch-like function, provide a biologically realistic framework for modeling the time evolution of gene expression. However, their use has been limited to smaller networks due to the computational expense of inferring model parameters from gene expression data using global non-linear optimization. Here we show that the switch-like nature of gene regulation can be exploited to break the gene circuit inference problem into two simpler optimization problems that are amenable to computationally efficient supervised learning techniques. We present FIGR (Fast Inference of Gene Regulation), a novel classification-based inference approach to determining gene circuit parameters. We demonstrate FIGR’s effectiveness on synthetic data generated from random gene circuits of up to 50 genes as well as experimental data from the gap gene system of Drosophila melanogaster, a benchmark for inferring dynamical GRN models. FIGR is faster than global non-linear optimization by a factor of 600 and its computational complexity scales much better with GRN size. On a practical level, FIGR can accurately infer the biologically realistic gap gene network in under a minute on desktop-class hardware instead of requiring hours of parallel computing. We anticipate that FIGR would enable the inference of much larger biologically realistic GRNs than was possible before.
Collapse
|
45
|
Fan X, Wu C, Truitt LL, Espinoza DA, Sellers S, Bonifacino A, Zhou Y, Cordes SF, Krouse A, Metzger M, Donahue RE, Lu R, Dunbar CE. Clonal tracking of erythropoiesis in rhesus macaques. Haematologica 2019; 105:1813-1824. [PMID: 31582555 PMCID: PMC7327626 DOI: 10.3324/haematol.2019.231811] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 10/03/2019] [Indexed: 12/25/2022] Open
Abstract
The classical model of hematopoietic hierarchies is being reconsidered on the basis of data from in vitro assays and single cell expression profiling. Recent experiments suggested that the erythroid lineage might differentiate directly from multipotent hematopoietic stem cells / progenitors or from a highly biased subpopulation of stem cells, rather than transiting through common myeloid progenitors or megakaryocyte-erythrocyte progenitors. We genetically barcoded autologous rhesus macaque stem and progenitor cells, allowing quantitative tracking of the in vivo clonal output of thousands of individual cells over time following transplantation. CD34+ cells were lentiviral-transduced with a high diversity barcode library, with the barcode in an expressed region of the provirus, allowing barcode retrieval from DNA or RNA, with each barcode representing an individual stem or progenitor cell clone. Barcode profiles from bone marrow CD45-CD71+ maturing nucleated red blood cells were compared with other lineages purified from the same bone marrow sample. There was very high correlation of barcode contributions between marrow nucleated red blood cells and other lineages, with the highest correlation between nucleated red blood cells and myeloid lineages, whether at earlier or later time points post transplantation, without obvious clonal contributions from highly erythroid-biased or restricted clones. A similar profile occurred even under stressors such as aging or erythropoietin stimulation. RNA barcode analysis on circulating mature red blood cells followed over long time periods demonstrated stable erythroid clonal contributions. Overall, in this nonhuman primate model with great relevance to human hematopoiesis, we documented continuous production of erythroid cells from multipotent, non-biased hematopoietic stem cell clones at steady-state or under stress.
Collapse
Affiliation(s)
- Xing Fan
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA
| | - Chuanfeng Wu
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA
| | - Lauren L Truitt
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA
| | - Diego A Espinoza
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA.,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephanie Sellers
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA
| | - Aylin Bonifacino
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA
| | - Yifan Zhou
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA.,Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Stefan F Cordes
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA
| | - Allen Krouse
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA
| | - Mark Metzger
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA
| | - Robert E Donahue
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA
| | - Rong Lu
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Cynthia E Dunbar
- Translational Stem Cell Biology Branch, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, MA, USA
| |
Collapse
|
46
|
Conte F, Fiscon G, Licursi V, Bizzarri D, D'Antò T, Farina L, Paci P. A paradigm shift in medicine: A comprehensive review of network-based approaches. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194416. [PMID: 31382052 DOI: 10.1016/j.bbagrm.2019.194416] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 07/19/2019] [Accepted: 07/28/2019] [Indexed: 02/01/2023]
Abstract
Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
Collapse
Affiliation(s)
- Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.
| | - Valerio Licursi
- Biology and Biotechnology Department "Charles Darwin" (BBCD), Sapienza University of Rome, Rome, Italy
| | - Daniele Bizzarri
- Department of Internal Medicine and Medical Specialties, Sapienza University of Rome, Rome, Italy
| | - Tommaso D'Antò
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Paola Paci
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| |
Collapse
|
47
|
Tritschler S, Büttner M, Fischer DS, Lange M, Bergen V, Lickert H, Theis FJ. Concepts and limitations for learning developmental trajectories from single cell genomics. Development 2019; 146. [DOI: 10.1242/dev.170506] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Abstract
ABSTRACT
Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.
Collapse
Affiliation(s)
- Sophie Tritschler
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85353 Freising, Germany
| | - Maren Büttner
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| | - David S. Fischer
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85353 Freising, Germany
| | - Marius Lange
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Volker Bergen
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- German Center for Diabetes Research, 85764 Neuherberg, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| |
Collapse
|
48
|
Iacono G, Massoni-Badosa R, Heyn H. Single-cell transcriptomics unveils gene regulatory network plasticity. Genome Biol 2019; 20:110. [PMID: 31159854 PMCID: PMC6547541 DOI: 10.1186/s13059-019-1713-4] [Citation(s) in RCA: 128] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 05/08/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies. RESULTS We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using tools from graph theory, we compute an unbiased quantification of a gene's biological relevance and accurately pinpoint key players in organ function and drivers of diseases. CONCLUSIONS Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology.
Collapse
Affiliation(s)
- Giovanni Iacono
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, 08028, Barcelona, Spain.
| | - Ramon Massoni-Badosa
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, 08028, Barcelona, Spain
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, 08028, Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
| |
Collapse
|
49
|
Fernandez-Valverde SL, Aguilera F, Ramos-Díaz RA. Inference of Developmental Gene Regulatory Networks Beyond Classical Model Systems: New Approaches in the Post-genomic Era. Integr Comp Biol 2019; 58:640-653. [PMID: 29917089 DOI: 10.1093/icb/icy061] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The advent of high-throughput sequencing (HTS) technologies has revolutionized the way we understand the transformation of genetic information into morphological traits. Elucidating the network of interactions between genes that govern cell differentiation through development is one of the core challenges in genome research. These networks are known as developmental gene regulatory networks (dGRNs) and consist largely of the functional linkage between developmental control genes, cis-regulatory modules, and differentiation genes, which generate spatially and temporally refined patterns of gene expression. Over the last 20 years, great advances have been made in determining these gene interactions mainly in classical model systems, including human, mouse, sea urchin, fruit fly, and worm. This has brought about a radical transformation in the fields of developmental biology and evolutionary biology, allowing the generation of high-resolution gene regulatory maps to analyze cell differentiation during animal development. Such maps have enabled the identification of gene regulatory circuits and have led to the development of network inference methods that can recapitulate the differentiation of specific cell-types or developmental stages. In contrast, dGRN research in non-classical model systems has been limited to the identification of developmental control genes via the candidate gene approach and the characterization of their spatiotemporal expression patterns, as well as to the discovery of cis-regulatory modules via patterns of sequence conservation and/or predicted transcription-factor binding sites. However, thanks to the continuous advances in HTS technologies, this scenario is rapidly changing. Here, we give a historical overview on the architecture and elucidation of the dGRNs. Subsequently, we summarize the approaches available to unravel these regulatory networks, highlighting the vast range of possibilities of integrating multiple technical advances and theoretical approaches to expand our understanding on the global gene regulation during animal development in non-classical model systems. Such new knowledge will not only lead to greater insights into the evolution of molecular mechanisms underlying cell identity and animal body plans, but also into the evolution of morphological key innovations in animals.
Collapse
Affiliation(s)
- Selene L Fernandez-Valverde
- CONACYT, Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN, Irapuato, Guanajuato, Mexico
| | - Felipe Aguilera
- Departamento de Bioquímica y Biología Molecular, Facultad de Ciencias Biológicas, Universidad de Concepción, Chile
| | - René Alexander Ramos-Díaz
- CONACYT, Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN, Irapuato, Guanajuato, Mexico
| |
Collapse
|
50
|
Psaila B, Mead AJ. Single-cell approaches reveal novel cellular pathways for megakaryocyte and erythroid differentiation. Blood 2019; 133:1427-1435. [PMID: 30728145 PMCID: PMC6443046 DOI: 10.1182/blood-2018-11-835371] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 01/07/2019] [Indexed: 12/18/2022] Open
Abstract
The classical model of hematopoiesis proposes a hierarchy in which a small number of multipotent hematopoietic stem cells (HSCs) maintain all blood lineages by giving rise to progeny that pass through discrete progenitor stages. At each stage, lineage differentiation potential is restricted, coupled with the loss of ability to self-renew. Recently, single-cell approaches have been used to test certain assumptions made by this model, in particular relating to megakaryocyte (Mk) and erythroid (E) development. An alternative model has emerged in which substantial heterogeneity and lineage-priming exists within the HSC compartment, including the existence of multipotent but megakaryocyte/platelet-biased HSCs. Hematopoietic differentiation follows a hierarchical continuum, passing through cellular nodes and branch points. Megakaryocytes are produced via a shared pathway with the erythroid lineage, also shared in its early stages with mast cells, eosinophils, and basophils, but separate from other myeloid and lymphoid lineages. In addition, distinct pathways for direct differentiation of Mk from HSCs may coexist and could be important in situations of increased physiological requirements or in malignancies. Further work at single-cell resolution using multiomic approaches and examining Mk-E biased subsets within their physiological context will undoubtedly improve our understanding of normal hematopoiesis and ability to manipulate this in pathology.
Collapse
Affiliation(s)
- Bethan Psaila
- Haematopoietic Stem Cell Biology Laboratory, MRC Weatherall Institute of Molecular Medicine (WIMM). University of Oxford, Oxford, OX3 9DS, UK
- Medical Research Council Molecular Haematology Unit, WIMM, University of Oxford, Oxford, OX3 9DS, UK
- NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Adam J Mead
- Haematopoietic Stem Cell Biology Laboratory, MRC Weatherall Institute of Molecular Medicine (WIMM). University of Oxford, Oxford, OX3 9DS, UK
- Medical Research Council Molecular Haematology Unit, WIMM, University of Oxford, Oxford, OX3 9DS, UK
- NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| |
Collapse
|