Integrating multiomics longitudinal data to reconstruct networks underlying lung development

The technological landscape and applications of single-cell multi-omics

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

Temporal modelling using single-cell transcriptomics

Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.

Proteomic Analysis of Human Lung Development

Rationale: The current understanding of human lung development derives mostly from animal studies. Although transcript-level studies have analyzed human donor tissue to identify genes expressed during normal human lung development, protein-level analysis that would enable the generation of new hypotheses on the processes involved in pulmonary development are lacking. Objectives: To define the temporal dynamic of protein expression during human lung development. Methods: We performed proteomics analysis of human lungs at 10 distinct times from birth to 8 years to identify the molecular networks mediating postnatal lung maturation. Measurements and Main Results: We identified 8,938 proteins providing a comprehensive view of the developing human lung proteome. The analysis of the data supports the existence of distinct molecular substages of alveolar development and predicted the age of independent human lung samples, and extensive remodeling of the lung proteome occurred during postnatal development. Evidence of post-transcriptional control was identified in early postnatal development. An extensive extracellular matrix remodeling was supported by changes in the proteome during alveologenesis. The concept of maturation of the immune system as an inherent part of normal lung development was substantiated by flow cytometry and transcriptomics. Conclusions: This study provides the first in-depth characterization of the human lung proteome during development, providing a unique proteomic resource freely accessible at Lungmap.net. The data support the extensive remodeling of the lung proteome during development, the existence of molecular substages of alveologenesis, and evidence of post-transcriptional control in early postnatal development.

Patho-mechanisms of the origins of bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) continues to be one of the most common complications of prematurity, despite significant advancement in neonatology over the last couple of decades. The new BPD is characterized histopathologically by impaired lung alveolarization and dysregulated vascularization. With the increased survival of extremely preterm infants, the risk for the development of BPD remains high, emphasizing the continued need to understand the patho-mechanisms that play a role in the development of this disease. This brief review summarizes recent advances in our understanding of the maldevelopment of the premature lung, highlighting recent research in pathways of oxidative stress-related lung injury, the role of placental insufficiency, growth factor signaling, the extracellular matrix, and microRNAs.

Single-cell transcriptomics reveals lasting changes in the lung cellular landscape into adulthood after neonatal hyperoxic exposure

Ventilatory support, such as supplemental oxygen, used to save premature infants impairs the growth of the pulmonary microvasculature and distal alveoli, leading to bronchopulmonary dysplasia (BPD). Although lung cellular composition changes with exposure to hyperoxia in neonatal mice, most human BPD survivors are weaned off oxygen within the first weeks to months of life, yet they may have persistent lung injury and pulmonary dysfunction as adults. We hypothesized that early-life hyperoxia alters the cellular landscape in later life and predicts long-term lung injury. Using single-cell RNA sequencing, we mapped lung cell subpopulations at postnatal day (pnd)7 and pnd60 in mice exposed to hyperoxia (95% O2) for 3 days as neonates. We interrogated over 10,000 cells and identified a total of 45 clusters within 32 cell states. Neonatal hyperoxia caused persistent compositional changes in later life (pnd60) in all five type II cell states with unique signatures and function. Premature infants requiring mechanical ventilation with different durations also showed similar alterations in these unique signatures of type II cell states. Pathologically, neonatal hyperoxic exposure caused alveolar simplification in adult mice. We conclude that neonatal hyperoxia alters the lung cellular landscape in later life, uncovering neonatal programing of adult lung dysfunction.

Intrauterine Hypoxia and Epigenetic Programming in Lung Development and Disease

Clinically, intrauterine hypoxia is the foremost cause of perinatal morbidity and developmental plasticity in the fetus and newborn infant. Under hypoxia, deviations occur in the lung cell epigenome. Epigenetic mechanisms (e.g., DNA methylation, histone modification, and miRNA expression) control phenotypic programming and are associated with physiological responses and the risk of developmental disorders, such as bronchopulmonary dysplasia. This developmental disorder is the most frequent chronic pulmonary complication in preterm labor. The pathogenesis of this disease involves many factors, including aberrant oxygen conditions and mechanical ventilation-mediated lung injury, infection/inflammation, and epigenetic/genetic risk factors. This review is focused on various aspects related to intrauterine hypoxia and epigenetic programming in lung development and disease, summarizes our current knowledge of hypoxia-induced epigenetic programming and discusses potential therapeutic interventions for lung disease.

Visibility graph based temporal community detection with applications in biological time series

Temporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) Detect characteristic dynamic patterns or events as network temporal communities. General community detection methods use metrics comparing the connectivity within a community to random models, or are based on the betweenness centrality of edges or nodes. However, such methods were not designed for network representations of time series. We introduce a visibility-graph-based method to build networks from time series and detect temporal communities within these networks. To characterize unevenly sampled time series (typical of biological experiments), and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG). To detect temporal communities in individual signals, we first find the shortest path of the network between start and end nodes, identifying high intensity nodes as the main stem of our community detection algorithm that act as hubs for each community. Then, we aggregate nodes outside the shortest path to the closest nodes found on the main stem based on the closest path length, thereby assigning every node to a temporal community based on proximity to the stem nodes/hubs. We demonstrate the validity and effectiveness of our method through simulation and biological applications.

Childhood rare lung disease in the 21st century: "-omics" technology advances accelerating discovery

Childhood rare lung diseases comprise a large number of heterogeneous respiratory disorders that are individually rare but are collectively associated with substantial morbidity, mortality, and healthcare resource utilization. Although the genetic mechanisms for several of these disorders have been elucidated, the pathogenesis mechanisms for others remain poorly understood and treatment options remain limited. Childhood rare lung diseases are enriched for genetic etiologies; identification of the disease mechanisms underlying these rare disorders can inform the biology of normal human lung development and has implications for the treatment of more common respiratory diseases in children and adults. Advances in "-omics" technology, such as genomic sequencing, clinical phenotyping, biomarker discovery, genome editing, in vitro and model organism disease modeling, single-cell analyses, cellular imaging, and high-throughput drug screening have enabled significant progress for diagnosis and treatment of rare childhood lung diseases. The most striking example of this progress has been realized for patients with cystic fibrosis for whom effective, personalized therapies based on CFTR genotype are now available. In this chapter, we focus on recent technology advances in childhood rare lung diseases, acknowledge persistent challenges, and identify promising new technologies that will impact not only biological discovery, but also improve diagnosis, therapies, and survival for children with these rare disorders.

Transcriptional regulatory model of fibrosis progression in the human lung

To develop a systems biology model of fibrosis progression within the human lung we performed RNA sequencing and microRNA analysis on 95 samples obtained from 10 idiopathic pulmonary fibrosis (IPF) and 6 control lungs. Extent of fibrosis in each sample was assessed by microCT-measured alveolar surface density (ASD) and confirmed by histology. Regulatory gene expression networks were identified using linear mixed-effect models and dynamic regulatory events miner (DREM). Differential gene expression analysis identified a core set of genes increased or decreased before fibrosis was histologically evident that continued to change with advanced fibrosis. DREM generated a systems biology model (www.sb.cs.cmu.edu/IPFReg) that identified progressively divergent gene expression tracks with microRNAs and transcription factors that specifically regulate mild or advanced fibrosis. We confirmed model predictions by demonstrating that expression of POU2AF1, previously unassociated with lung fibrosis but proposed by the model as regulator, is increased in B lymphocytes in IPF lungs and that POU2AF1-knockout mice were protected from bleomycin-induced lung fibrosis. Our results reveal distinct regulation of gene expression changes in IPF tissue that remained structurally normal compared with moderate or advanced fibrosis and suggest distinct regulatory mechanisms for each stage.

Recent advances in our understanding of the mechanisms of lung alveolarization and bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) is the most common cause of morbidity and mortality in preterm infants. A key histopathological feature of BPD is stunted late lung development, where the process of alveolarization-the generation of alveolar gas exchange units-is impeded, through mechanisms that remain largely unclear. As such, there is interest in the clarification both of the pathomechanisms at play in affected lungs, and the mechanisms of de novo alveoli generation in healthy, developing lungs. A better understanding of normal and pathological alveolarization might reveal opportunities for improved medical management of affected infants. Furthermore, disturbances to the alveolar architecture are a key histopathological feature of several adult chronic lung diseases, including emphysema and fibrosis, and it is envisaged that knowledge about the mechanisms of alveologenesis might facilitate regeneration of healthy lung parenchyma in affected patients. To this end, recent efforts have interrogated clinical data, developed new-and refined existing-in vivo and in vitro models of BPD, have applied new microscopic and radiographic approaches, and have developed advanced cell-culture approaches, including organoid generation. Advances have also been made in the development of other methodologies, including single-cell analysis, metabolomics, lipidomics, and proteomics, as well as the generation and use of complex mouse genetics tools. The objective of this review is to present advances made in our understanding of the mechanisms of lung alveolarization and BPD over the period 1 January 2017-30 June 2019, a period that spans the 50th anniversary of the original clinical description of BPD in preterm infants.