SLICE: determining cell differentiation and lineage based on single cell entropy

Abstract A complex organ contains a variety of cell types, each with its own distinct lineage and function. Understanding the lineage and differentiation state of each cell is fundamentally important for the ultimate delineation of organ formation and function. We developed SLICE, a novel algorithm that utilizes single-cell RNA-seq (scRNA-seq) to quantitatively measure cellular differentiation states based on single cell entropy and predict cell differentiation lineages via the construction of entropy directed cell trajectories. We validated our approach using three independent data sets with known lineage and developmental time information from both Homo sapiens and Mus musculus. SLICE successfully measured the differentiation states of single cells and reconstructed cell differentiation trajectories that have been previously experimentally validated. We then applied SLICE to scRNA-seq of embryonic mouse lung at E16.5 to identify lung mesenchymal cell lineage relationships that currently remain poorly defined. A two-branched differentiation pathway of five fibroblastic subtypes was predicted using SLICE. The present study demonstrated the general applicability and high predictive accuracy of SLICE in determining cellular differentiation states and reconstructing cell differentiation lineages in scRNA-seq analysis.

[1]  James C. Bezdek,et al.  A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[3]  F. Valafar.  Pattern Recognition Techniques in Microarray Data Analysis : A Survey , 2002 .

[4]  F. Valafar.  Pattern Recognition Techniques in Microarray Data Analysis , 2002, Annals of the New York Academy of Sciences.

[5]  Matteo Fischetti,et al.  An Algorithmic Framework for the Exact Solution of the Prize-Collecting Steiner Tree Problem , 2006, Math. Program..

[6]  Beatriz de la Iglesia,et al.  Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms , 2006, J. Math. Model. Algorithms.

[7]  T. Hastie,et al.  Principal Curves , 2007 .

[8]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[9]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[10]  Jing Chen,et al.  ToppGene Suite for gene list enrichment analysis and candidate gene prioritization , 2009, Nucleic Acids Res..

[11]  Korbinian Strimmer,et al.  Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks , 2008, J. Mach. Learn. Res..

[12]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[13]  Hans Clevers,et al.  Coexistence of Quiescent and Active Adult Stem Cells in Mammals , 2010, Science.

[14]  Tobias Müller,et al.  Bioinformatics Applications Note Systems Biology Bionet: an R-package for the Functional Analysis of Biological Networks , 2022 .

[15]  S. Wood.  Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models , 2011 .

[16]  B. Hogan,et al.  Epithelial progenitor cells in lung development, maintenance, repair, and disease. , 2011, Annual review of cell and developmental biology.

[17]  Susan E. Wert,et al.  Transcriptional Programs Controlling Perinatal Lung Maturation , 2012, PloS one.

[18]  Jochen Guck,et al.  Quantifying cellular differentiation by physical phenotype using digital holographic microscopy. , 2012, Integrative biology : quantitative biosciences from nano to macro.

[19]  A. Feinberg,et al.  Regulated Noise in the Epigenetic Landscape of Development and Disease , 2012, Cell.

[20]  Herbert Schneckenburger,et al.  Tumor cell differentiation by label-free fluorescence microscopy , 2012, Journal of biomedical optics.

[21]  Ruiqiang Li,et al.  Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells , 2013, Nature Structural &Molecular Biology.

[22]  S. Severini,et al.  Cellular network entropy as the energy potential in Waddington's differentiation landscape , 2013, Scientific Reports.

[23]  Ben D. MacArthur,et al.  Statistical Mechanics of Pluripotency , 2013, Cell.

[24]  Sean C. Bendall,et al.  Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development , 2014, Cell.

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

[26]  N. Neff,et al.  Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq , 2014, Nature.

[27]  E. Marco,et al.  Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape , 2014, Proceedings of the National Academy of Sciences.

[28]  Shawn M. Gillespie,et al.  Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma , 2014, Science.

[29]  J. Whitsett,et al.  Alveolar development and disease. , 2015, American journal of respiratory cell and molecular biology.

[30]  Hans Clevers,et al.  Single-cell messenger RNA sequencing reveals rare intestinal cell types , 2015, Nature.

[31]  David W. Nauen,et al.  Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis. , 2015, Cell stem cell.

[32]  Hui Wang,et al.  SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis , 2015, PLoS Comput. Biol..

[33]  M. Guo,et al.  ‘LungGENS’: a web-based tool for mapping single-cell gene expression in the developing lung , 2015, Thorax.

[34]  Mauro J. Muraro,et al.  De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data , 2016, Cell stem cell.