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Kunz DJ, Gomes T, James KR. Immune Cell Dynamics Unfolded by Single-Cell Technologies. Front Immunol 2018; 9:1435. [PMID: 29997618 PMCID: PMC6028612 DOI: 10.3389/fimmu.2018.01435] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 06/11/2018] [Indexed: 12/26/2022] Open
Abstract
The single-cell revolution is paving the way towards the molecular characterisation of every cell type in the human body, revealing relationships between cell types and states at high resolution. Changes in cellular phenotypes are particularly prevalent in the immune system and can be observed in its continuous remodelling up to adulthood, response to disease and development of immunological memory. In this review, we delve into the world of cellular dynamics of the immune system. We discuss current single-cell experimental and computational approaches in this area, giving insights into plasticity and commitment of cell fates. Finally, we provide an outlook on upcoming technological developments and predict how these will improve our understanding of the immune system.
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Affiliation(s)
- Daniel J. Kunz
- Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, United Kingdom
- The Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Tomás Gomes
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Kylie R. James
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
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252
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Chen S, Mar JC. Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data. BMC Bioinformatics 2018; 19:232. [PMID: 29914350 PMCID: PMC6006753 DOI: 10.1186/s12859-018-2217-z] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2017] [Accepted: 05/24/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data. RESULTS Standard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or simulated single cell data, which demonstrates their lack of performance for this task. Using default settings, network methods were applied to the same datasets. Comparisons of the learned networks highlighted the uniqueness of some predicted edges for each method. The fact that different methods infer networks that vary substantially reflects the underlying mathematical rationale and assumptions that distinguish network methods from each other. CONCLUSIONS This study provides a comprehensive evaluation of network modeling algorithms applied to experimental single cell gene expression data and in silico simulated datasets where the network structure is known. Comparisons demonstrate that most of these assessed network methods are not able to predict network structures from single cell expression data accurately, even if they are specifically developed for single cell methods. Also, single cell methods, which usually depend on more elaborative algorithms, in general have less similarity to each other in the sets of edges detected. The results from this study emphasize the importance for developing more accurate optimized network modeling methods that are compatible for single cell data. Newly-developed single cell methods may uniquely capture particular features of potential gene-gene relationships, and caution should be taken when we interpret these results.
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Affiliation(s)
- Shuonan Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Jessica C Mar
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA. .,Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA. .,Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, QLD, Australia.
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253
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Huang X, Liu S, Wu L, Jiang M, Hou Y. High Throughput Single Cell RNA Sequencing, Bioinformatics Analysis and Applications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1068:33-43. [PMID: 29943294 DOI: 10.1007/978-981-13-0502-3_4] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Single cell sequencing (SCS) can be harnessed to acquire the genomes, transcriptomes and epigenomes from individual cells. Next generation sequencing (NGS) technology is the driving force for single cell sequencing. scRNA-seq requires a lengthy pipeline comprising of single cell sorting, RNA extraction, reverse transcription, amplification, library construction, sequencing and subsequent bioinformatic analysis. Computational algorithms are essential to fulfill many tasks of interest using scRNA-seq data. scRNA-seq has already enabled researchers to revisit long-standing questions in cancer biology, including cancer metastasis, heterogeneity and evolution. Circulating Tumor Cells (CTC) are not only an important mechanism for cancer metastasis, but also provide a possibility to diagnose and monitor cancer in a convenient way independent of surgical resection of the cancer.
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254
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Chen J, Rénia L, Ginhoux F. Constructing cell lineages from single-cell transcriptomes. Mol Aspects Med 2017; 59:95-113. [PMID: 29107741 DOI: 10.1016/j.mam.2017.10.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 10/23/2017] [Accepted: 10/25/2017] [Indexed: 12/25/2022]
Abstract
Advances in single-cell RNA-sequencing have helped reveal the previously underappreciated level of cellular heterogeneity present during cellular differentiation. A static snapshot of single-cell transcriptomes provides a good representation of the various stages of differentiation as differentiation is rarely synchronized between cells. Data from numerous single-cell analyses has suggested that cellular differentiation and development can be conceptualized as continuous processes. Consequently, computational algorithms have been developed to infer lineage relationships between cell types and construct developmental trajectories along which cells are re-ordered such that similarity between successive cell pairs is maximized. Here, we compare and contrast the existing computational methods, and illustrate how they may be applied to build mouse myeloid progenitor lineages from massively parallel RNA single-cell sequencing data.
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Affiliation(s)
- Jinmiao Chen
- Singapore Immunology Network (SIgN), A*STAR, 8A Biomedical Grove, Immunos Building, Level 4, Singapore 138648, Singapore.
| | - Laurent Rénia
- Singapore Immunology Network (SIgN), A*STAR, 8A Biomedical Grove, Immunos Building, Level 4, Singapore 138648, Singapore
| | - Florent Ginhoux
- Singapore Immunology Network (SIgN), A*STAR, 8A Biomedical Grove, Immunos Building, Level 4, Singapore 138648, Singapore
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255
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Herbach U, Bonnaffoux A, Espinasse T, Gandrillon O. Inferring gene regulatory networks from single-cell data: a mechanistic approach. BMC SYSTEMS BIOLOGY 2017; 11:105. [PMID: 29157246 PMCID: PMC5697158 DOI: 10.1186/s12918-017-0487-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 11/09/2017] [Indexed: 01/13/2023]
Abstract
Background The recent development of single-cell transcriptomics has enabled gene expression to be measured in individual cells instead of being population-averaged. Despite this considerable precision improvement, inferring regulatory networks remains challenging because stochasticity now proves to play a fundamental role in gene expression. In particular, mRNA synthesis is now acknowledged to occur in a highly bursty manner. Results We propose to view the inference problem as a fitting procedure for a mechanistic gene network model that is inherently stochastic and takes not only protein, but also mRNA levels into account. We first explain how to build and simulate this network model based upon the coupling of genes that are described as piecewise-deterministic Markov processes. Our model is modular and can be used to implement various biochemical hypotheses including causal interactions between genes. However, a naive fitting procedure would be intractable. By performing a relevant approximation of the stationary distribution, we derive a tractable procedure that corresponds to a statistical hidden Markov model with interpretable parameters. This approximation turns out to be extremely close to the theoretical distribution in the case of a simple toggle-switch, and we show that it can indeed fit real single-cell data. As a first step toward inference, our approach was applied to a number of simple two-gene networks simulated in silico from the mechanistic model and satisfactorily recovered the original networks. Conclusions Our results demonstrate that functional interactions between genes can be inferred from the distribution of a mechanistic, dynamical stochastic model that is able to describe gene expression in individual cells. This approach seems promising in relation to the current explosion of single-cell expression data. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0487-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ulysse Herbach
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, Lyon, F-69007, France.,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France.,Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, Villeurbanne Cedex, F-6962, France
| | - Arnaud Bonnaffoux
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, Lyon, F-69007, France.,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France.,The CoSMo company, 5 passage du Vercors, Lyon, 69007, France
| | - Thibault Espinasse
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, Villeurbanne Cedex, F-6962, France
| | - Olivier Gandrillon
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, Lyon, F-69007, France. .,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France.
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256
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257
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Papili Gao N, Ud-Dean SMM, Gandrillon O, Gunawan R. SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles. Bioinformatics 2017; 34:258-266. [PMID: 28968704 PMCID: PMC5860204 DOI: 10.1093/bioinformatics/btx575] [Citation(s) in RCA: 126] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 06/12/2017] [Accepted: 09/13/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired. Results We developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development. Availability and implementation MATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES. The single cell THP-1 and T2EC transcriptional profiles are available from the original publications (Kouno et al., 2013; Richard et al., 2016). The in silico single cell data are available on SINCERITIES websites. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nan Papili Gao
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - S M Minhaz Ud-Dean
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Olivier Gandrillon
- Laboratory of Biology and Modelling of the Cell, Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR, INSERM Lyon, France.,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Rhône-Alpes, France
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
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