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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
Spatial and Temporal Control of The Lung Development
I. Embryonic
E 9 - 12
II. Pseudoglandular
E 12 - 15
III. Canalicular
E 15 - 17
IV. Saccular
E 17 - Birth
V. Alveolar
Birth – PN20
Research Highlights
We have developed analytic pipelines utilizing functional
genomics and systems biology approaches to analyze largescale 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
convert RNA into a sequencing library using Fluidigm PN100-5989
RNA-Seq on 96 individual lung cells per run on Illumina 2500 Hi-Seq
RNA-seq data QC, alignment and analysis
Samples and Pipeline
• Mouse Lung E16.5
• FluidigmTM C1TM System
• Data for Pipeline Analysis
– 148 lung single cells
• S1: 86 cells
• S2: 62 cells
– 36188 Expression Profiles
• Expression Filter
– 𝛿𝑖𝑠 (𝜃) ≥ 𝑁
• cell specificity index 𝜏𝑖𝑠
–
𝑠
𝑥𝑖𝑗
–
𝑦𝑖𝑗𝑠
𝑠
𝐸𝑖𝑗
−min(𝐸𝑖𝑠 )
= m𝑎𝑥
𝐸𝑖𝑠 −min(𝐸𝑖𝑠 )
𝑠
𝑦𝑖𝑗
= max{𝑦𝑠 |𝑗=1,…,𝑁𝑠 }
– 𝜏𝑖𝑠 =
𝑖𝑗
𝑁𝑠
𝑠
𝑗=1(1−𝑦𝑖𝑗 )
𝑁𝑠 −1
[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 of epithelial sub-types
followed the order C9a (Sox9+ transient epithelial
progenitor) > C9d (Foxa2+ epithelial progenitor)
>C9b (pre-type II) > C9c (per-type I)
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).
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 (10446959,
10339573)
Epithelial TF-TG Network
A
B
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