Concepts and limitations for learning developmental trajectories from single cell genomics

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. Summary: This Review describes the concepts and use of computational approaches to infer cellular trajectories from single cell expression data and discusses the applications of these approaches to the study of development and disease.

[1]  Fabian J. Theis,et al.  A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst , 2012, Bioinform..

[2]  A. Regev,et al.  Spatial reconstruction of single-cell gene expression , 2015, Nature Biotechnology.

[3]  Claire S. Simon,et al.  Charting the emergent organotypic landscape of the mammalian gut endoderm at single-cell resolution , 2018, bioRxiv.

[4]  William McGinnis,et al.  Homeobox genes and axial patterning , 1992, Cell.

[5]  Thomas M. Norman,et al.  Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens , 2016, Cell.

[6]  J. Marioni,et al.  Using single‐cell genomics to understand developmental processes and cell fate decisions , 2018, Molecular systems biology.

[7]  K. Kaestner,et al.  Transcriptional regulation of α‐cell differentiation , 2011, Diabetes, obesity & metabolism.

[8]  Thomas M. Norman,et al.  A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response , 2016, Cell.

[9]  André F. Rendeiro,et al.  Pooled CRISPR screening with single-cell transcriptome read-out , 2017, Nature Methods.

[10]  Heiko Lickert,et al.  Cellular and molecular mechanisms coordinating pancreas development , 2017, Development.

[11]  David J. Jörg,et al.  Defining murine organogenesis at single cell resolution reveals a role for the leukotriene pathway in regulating blood progenitor formation , 2018, Nature Cell Biology.

[12]  Fabian J. Theis,et al.  Characterisation of transcriptional networks in blood stem and progenitor cells using high-throughput single cell gene expression analysis , 2013, Nature Cell Biology.

[13]  Tao Wang,et al.  Cell-Cycle Control of Developmentally Regulated Transcription Factors Accounts for Heterogeneity in Human Pluripotent Cells , 2013, Stem cell reports.

[14]  Fabian J Theis,et al.  Diffusion pseudotime robustly reconstructs lineage branching , 2016, Nature Methods.

[15]  James A. Gagnon,et al.  Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain , 2018, Nature Biotechnology.

[16]  Lai Guan Ng,et al.  Dimensionality reduction for visualizing single-cell data using UMAP , 2018, Nature Biotechnology.

[17]  James A. Gagnon,et al.  Recording development with single cell dynamic lineage tracing , 2019, Development.

[18]  Liu Yang,et al.  Deciphering Pancreatic Islet β Cell and α Cell Maturation Pathways and Characteristic Features at the Single-Cell Level. , 2017, Cell metabolism.

[19]  Patrik L. Ståhl,et al.  Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections , 2018, Nature Protocols.

[20]  Berthold Göttgens,et al.  Reconstructing blood stem cell regulatory network models from single-cell molecular profiles , 2017, Proceedings of the National Academy of Sciences.

[21]  Thomas M. Norman,et al.  Approaches to maximize sgRNA-barcode coupling in Perturb-seq screens , 2018, bioRxiv.

[22]  Cole Trapnell,et al.  The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.

[23]  Kirsten L. Frieda,et al.  Synthetic recording and in situ readout of lineage information in single cells , 2016, Nature.

[24]  Aviezer Lifshitz,et al.  MetaCell: analysis of single cell RNA-seq data using k-NN graph partitions , 2018, bioRxiv.

[25]  Jan Hasenauer,et al.  Inferring population dynamics from single-cell RNA-sequencing time series data , 2019, Nature Biotechnology.

[26]  Y. Saeys,et al.  SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development , 2016, bioRxiv.

[27]  Allon M. Klein,et al.  Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo , 2018, Science.

[28]  Lars E. Borm,et al.  The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing , 2017, Science.

[29]  Kerstin B. Meyer,et al.  Single-cell reconstruction of the early maternal–fetal interface in humans , 2018, Nature.

[30]  P. Rigollet,et al.  Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming , 2019, Cell.

[31]  Anne Condon,et al.  Interpretable dimensionality reduction of single cell transcriptome data with deep generative models , 2017, Nature Communications.

[32]  Fabian J Theis,et al.  Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics , 2018, Science.

[33]  Paul Hoffman,et al.  Integrating single-cell transcriptomic data across different conditions, technologies, and species , 2018, Nature Biotechnology.

[34]  J. Aerts,et al.  SCENIC: Single-cell regulatory network inference and clustering , 2017, Nature Methods.

[35]  Berthold Göttgens,et al.  A single-cell molecular map of mouse gastrulation and early organogenesis , 2019, Nature.

[36]  Hannah A. Pliner,et al.  Reversed graph embedding resolves complex single-cell trajectories , 2017, Nature Methods.

[37]  Sarah A. Teichmann,et al.  Faculty Opinions recommendation of histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. , 2017 .

[38]  Laleh Haghverdi,et al.  Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors , 2018, Nature Biotechnology.

[39]  Allon M. Klein,et al.  Lineage tracing on transcriptional landscapes links state to fate during differentiation , 2018, Science.

[40]  Fabian J. Theis,et al.  Statistical single cell multi-omics integration , 2018 .

[41]  Paul C. Blainey,et al.  Lentiviral co-packaging mitigates the effects of intermolecular recombination and multiple integrations in pooled genetic screens , 2018, bioRxiv.

[42]  Fabian J Theis,et al.  Single cells make big data: New challenges and opportunities in transcriptomics , 2017 .

[43]  Shaying Zhao,et al.  Cell-Cycle Control of Developmentally Regulated Transcription Factors Accounts for Heterogeneity in Human Pluripotent Cells , 2014, Stem cell reports.

[44]  Bernd Bodenmiller,et al.  miCAT: A toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data , 2017, Nature Methods.

[45]  G. Hon,et al.  Frequent sgRNA-barcode recombination in single-cell perturbation assays , 2018, bioRxiv.

[46]  A. van Oudenaarden,et al.  Single-Cell Transcriptomics Meets Lineage Tracing. , 2018, Cell stem cell.

[47]  Thalia E. Chan,et al.  Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures , 2016, bioRxiv.

[48]  Meena Subramaniam,et al.  Lineage dynamics of murine pancreatic development at single-cell resolution , 2018, Nature Communications.

[49]  Cole Trapnell,et al.  On the design of CRISPR-based single cell molecular screens , 2018, Nature Methods.

[50]  A. Regev,et al.  Scaling single-cell genomics from phenomenology to mechanism , 2017, Nature.

[51]  B. Göttgens,et al.  Single-cell transcriptional profiling: a window into embryonic cell-type specification , 2018, Nature Reviews Molecular Cell Biology.

[52]  Neil D. Lawrence,et al.  Single-cell RNA-seq and computational analysis using temporal mixture modeling resolves TH1/TFH fate bifurcation in malaria , 2017, Science Immunology.

[53]  I. Amit,et al.  Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq , 2016, Cell.

[54]  Cole Trapnell,et al.  Aligning Single-Cell Developmental and Reprogramming Trajectories Identifies Molecular Determinants of Myogenic Reprogramming Outcome. , 2018, Cell systems.

[55]  Fabian J Theis,et al.  cgCorrect: a method to correct for confounding cell–cell variation due to cell growth in single-cell transcriptomics , 2016, bioRxiv.

[56]  Caleb Weinreb,et al.  Fundamental limits on dynamic inference from single-cell snapshots , 2017, Proceedings of the National Academy of Sciences.

[57]  Prisca Liberali,et al.  Exploring single cells in space and time during tissue development, homeostasis and regeneration , 2019, Development.

[58]  Yvan Saeys,et al.  A comparison of single-cell trajectory inference methods , 2019, Nature Biotechnology.

[59]  Kieran R. Campbell,et al.  Bayesian Gaussian Process Latent Variable Models for pseudotime inference in single-cell RNA-seq data , 2015, bioRxiv.

[60]  J. Knoblich,et al.  Mechanisms of Asymmetric Stem Cell Division , 2008, Cell.

[61]  Joseph L. Herman,et al.  Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis , 2015, Nature Methods.

[62]  Shalev Itzkovitz,et al.  Spatial transcriptomics: paving the way for tissue-level systems biology. , 2017, Current opinion in biotechnology.

[63]  Chenghang Zong,et al.  Endocrine lineage biases arise in temporally distinct endocrine progenitors during pancreatic morphogenesis , 2018, Nature Communications.

[64]  Connor H. Ludwig,et al.  Mapping chromatin modifications at the single cell level , 2019, Development.

[65]  Fabian J Theis,et al.  The Human Cell Atlas , 2017, bioRxiv.

[66]  Hisanori Kiryu,et al.  SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation , 2016, bioRxiv.

[67]  Nicholas M. Luscombe,et al.  Generative adversarial networks simulate gene expression and predict perturbations in single cells , 2018, bioRxiv.

[68]  J. Junker,et al.  Simultaneous lineage tracing and cell-type identification using CRISPR/Cas9-induced genetic scars , 2018, Nature Biotechnology.

[69]  Sarah A Teichmann,et al.  A test metric for assessing single-cell RNA-seq batch correction , 2018, Nature Methods.

[70]  C. Waddington,et al.  The strategy of the genes , 1957 .

[71]  R. Satija,et al.  Integrative single-cell analysis , 2019, Nature Reviews Genetics.

[72]  Rebecca D Hodge,et al.  A Single-Cell Roadmap of Lineage Bifurcation in Human ESC Models of Embryonic Brain Development. , 2017, Cell stem cell.

[73]  Andrew C. Adey,et al.  Joint profiling of chromatin accessibility and gene expression in thousands of single cells , 2018, Science.

[74]  Gavin Brooks,et al.  Cell Cycle Control , 2004, Methods in Molecular Biology™.

[75]  Alexis Boukouvalas,et al.  GrandPrix: scaling up the Bayesian GPLVM for single-cell data , 2017, bioRxiv.

[76]  T. K. Vintsyuk.  Speech discrimination by dynamic programming , 1968 .

[77]  Erik Sundström,et al.  RNA velocity of single cells , 2018, Nature.

[78]  Nicholas M. Luscombe,et al.  Generative adversarial networks simulate gene expression and predict perturbations in single cells , 2018, bioRxiv.

[79]  Fabian J Theis,et al.  Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.

[80]  Pablo Cordero,et al.  Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories , 2016, bioRxiv.

[81]  Nicola K. Wilson,et al.  Resolving Early Mesoderm Diversification through Single Cell Expression Profiling , 2016, Nature.

[82]  A. Oudenaarden,et al.  Whole-organism clone tracing using single-cell sequencing , 2018, Nature.

[83]  Friedrich Beermann,et al.  Temporal control of neurogenin3 activity in pancreas progenitors reveals competence windows for the generation of different endocrine cell types. , 2007, Developmental cell.

[84]  D. Pe’er,et al.  Characterization of cell fate probabilities in single-cell data with Palantir , 2019, Nature Biotechnology.

[85]  Andrew J. Hill,et al.  The single cell transcriptional landscape of mammalian organogenesis , 2019, Nature.

[86]  André F. Rendeiro,et al.  Pooled CRISPR screening with single-cell transcriptome read-out , 2016, bioRxiv.

[87]  Fabian J Theis,et al.  PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells , 2019, Genome Biology.

[88]  X. Zhuang,et al.  Spatially resolved, highly multiplexed RNA profiling in single cells , 2015, Science.

[89]  Russell B. Fletcher,et al.  Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics , 2017, BMC Genomics.

[90]  G. Sanguinetti,et al.  scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells , 2018, Nature Communications.

[91]  Jessica C. Mar,et al.  Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data , 2018, BMC Bioinformatics.

[92]  A. Regev,et al.  Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis , 2018, Science.

[93]  Sean C. Bendall,et al.  Wishbone identifies bifurcating developmental trajectories from single-cell data , 2016, Nature Biotechnology.

[94]  H. Swerdlow,et al.  Large-scale simultaneous measurement of epitopes and transcriptomes in single cells , 2017, Nature Methods.

[95]  Michael I. Jordan,et al.  Deep Generative Modeling for Single-cell Transcriptomics , 2018, Nature Methods.

[96]  Fabian J Theis,et al.  Single-cell RNA-seq denoising using a deep count autoencoder , 2019, Nature Communications.

[97]  Jason E. Waller,et al.  Division , 2018, Bad Arguments.

[98]  Mohammad Lotfollahi,et al.  Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species , 2018, bioRxiv.

[99]  S. Shen-Orr,et al.  Alignment of single-cell trajectories to compare cellular expression dynamics , 2018, Nature Methods.