Single-cell RNA-Seq Profiling Identified Molecular Signatures And Transcriptional Networks Regulating Lung Maturation Yan Xu Sept, 8, 2014 Cincinnati Children’s Hospital Medical Center, University of Cincinnati, OH, 45229

I. Embryonic E II. Pseudoglandular E III. Canalicular E V. Alveolar Birth – PN20 IV. Saccular E 17 - Birth Spatial and Temporal Control of The Lung Development

Research Highlights We have developed analytic pipelines utilizing functional genomics and systems biology approaches to analyze large- scale mRNA expression data from lung specific gene deletion and mutation mouse models to reveal transcriptional regulatory networks controlling lung maturation and surfactant homeostasis.  TRN regulating lung surfactant homeostasis (Static model)  Lung normal maturation time course (Dynamic model)  Meta-analysis of expression profiles from mouse models sharing common respiratory distress phenotypes at birth  Single cell genomics to identify cell-specific gene signature and function in the developing lung

Study Lung Transcriptome at Single Cell Level Developmental events are largely operative at the level of individual cells. Individual cells can differ by cell state, size, protein isoforms and mRNA transcripts, even within a homogeneous cell population. Recent advances in microfluidics and next generation sequencing technologies provide the opportunity to begin measuring and understanding cellular heterogeneity in complex biological systems such as lung. Study lung transcriptome at single cell level will significantly improve the sensitivity and resolution of mRNA expression analysis and enable the development of high resolution TRNs that directly reflect the physiological and pathological phenotypes of the cell. No existing ready-to-go analytic pipeline available for the analysis of single cell RNA-Seq profiling from heterogeneous samples.

Single Cell RNA-Seq Approach isolate single cells from protease-dispersed lungs (E16.5-E18.5) separate them using the Fluidigm C1™ Single-Cell Auto Prep System RNA-Seq on 96 individual lung cells per run on Illumina 2500 Hi-Seq convert RNA into a sequencing library using Fluidigm PN RNA-seq data QC, alignment and analysis

Samples and Pipeline [Yanai et al, 2005]

Anatomic Location Of Distinct Epithelial Cell Subtypes And Representative Epithelial Signature Genes

Ontogenic Changes In RNAs Defining Distinct Lung Cell Clusters During Lung Maturation Proliferative RNA profiles followed the order C1 (proliferative mesenchymal progenitor) > C4 (undefined fibroblast) > C6 (intermediate fibroblast) > C2 (myofibroblast) > C3 (pericyte) > C7 (endothelial cells) > C5 (matrix fibroblast). Proliferative RNA profiles of epithelial sub-types followed the order C9a (Sox9+ transient epithelial progenitor) > C9d (Foxa2+ epithelial progenitor) >C9b (pre-type II) > C9c (per-type I)

Trapnell et al. Nature Biotechnology, 2014

Cross Talk Between Lung Epithelial Cells And Endothelial Cells Via Extracellular Matrix Protein-protein Interactions Jup (Junction Plakoglobin) Function: Common junctional plaque protein; plays a central role in the structure and function of submembranous plaques. Jup formed adhesion complexes with E-cadherin (Cdh1); Cdh1 transfection resulted in the up-regulation of Jup ( , )

BA Epithelial TF-TG Network C9 epithelial signature genes were used to generate the TF-TG network using conditional dependency algorithm. Network contains total of 782 nodes and 1137 edges. Orange: TF, Blue: SM, Green: TG.

Summary and Conclusions We have developed an analytic pipeline for the analysis of single-cell RNA-Seq data We identified major cell types in the fetal mouse lung, four sub-types of epithelial, four subtypes of fibroblast, endothelial, smooth muscle, pericyte and myeloid cells were classified by their expression & function similarity within cell groups. We identified cell-specific gene signatures, key regulators, surface markers, bioprocesses and functional profiles associated with each cell type The study provided the framework to delineate cell signaling and communications across cell types via paracrine and autocrine signaling as well as protein-protein interaction. Algorithms were developed to map cell type specific transcriptional regulatory networks and identify driving forces for individual cell types. The data provide a knowledge base facilitating the understanding of lung maturation at high resolution.

Acknowledgements Mingzhe Guo Yina, Du Hui Wang Liya Hu Ben Vidourek Jeffrey Whitsett Steve Potter Bruce Aronow Philip Dexheimer John Shannon Joe Kitzmiller NIH/NHLBI