Cross-species integration of single-cell RNA-seq resolved alveolar-epithelial transitional states in idiopathic pulmonary fibrosis

Pulmonary Fibrosis ◽

Single Cell ◽

Transitional Cell ◽

Notch Pathway ◽

Alveolar Epithelium ◽

State Transitions ◽

Specific Cell ◽

Differentiation Process ◽

Single-cell transcriptomics analyses of the fibrotic lung uncovered two cell states critical to lung injury recovery in the alveolar epithelium- a reparative transitional cell state in the mouse and a disease-specific cell state (KRT5-/KRT17+) in human idiopathic pulmonary fibrosis (IPF). The murine transitional cell state lies between the differentiation from type 2 (AT2) to type 1 pneumocyte (AT1), and the human KRT5-/KRT17+ cell state may arise from the dysregulation of this differentiation process. We review major findings of single-cell transcriptomics analyses of the fibrotic lung and re-analyzed data from 7 single-cell RNA sequencing studies of human and murine models of IPF, focusing on the alveolar epithelium. Our comparative and cross-species single-cell transcriptomics analyses allowed us to further delineate the differentiation trajectories from AT2 to AT1 and AT2 to the KRT5-/KRT17+ cell state. We observed AT1 cells in human IPF retain the transcriptional signature of the murine transitional cell state. Using pseudotime analysis, we recapitulated the differentiation trajectories from AT2 to AT1 and from AT2 to KRT5-/KRT17+ cell state in multiple human IPF studies. We further delineated transcriptional programs underlying cell state transitions and determined the molecular phenotypes at terminal differentiation. We hypothesize that in addition to the reactivation of developmental programs (SOX4, SOX9), senescence (TP63, SOX4) and the Notch pathway (HES1) are predicted to steer intermediate progenitors to the KRT5-/KRT17+ cell state. Our analyses suggest that activation of SMAD3 later in the differentiation process may explain the fibrotic molecular phenotype typical of KRT5-/KRT17+ cells.

Single-Cell Genomics: Catalyst for Cell Fate Engineering

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.748942 ◽

2021 ◽

Vol 9 ◽

Author(s):

Boxun Li ◽

Gary C. Hon

Keyword(s):

Single Cell ◽

Cell Fate ◽

Molecular Mechanisms ◽

Cell Types ◽

Cellular Heterogeneity ◽

State Transitions ◽

Small Scale ◽

Specific Cell ◽

Direct Reprogramming ◽

As we near a complete catalog of mammalian cell types, the capability to engineer specific cell types on demand would transform biomedical research and regenerative medicine. However, the current pace of discovering new cell types far outstrips our ability to engineer them. One attractive strategy for cellular engineering is direct reprogramming, where induction of specific transcription factor (TF) cocktails orchestrates cell state transitions. Here, we review the foundational studies of TF-mediated reprogramming in the context of a general framework for cell fate engineering, which consists of: discovering new reprogramming cocktails, assessing engineered cells, and revealing molecular mechanisms. Traditional bulk reprogramming methods established a strong foundation for TF-mediated reprogramming, but were limited by their small scale and difficulty resolving cellular heterogeneity. Recently, single-cell technologies have overcome these challenges to rapidly accelerate progress in cell fate engineering. In the next decade, we anticipate that these tools will enable unprecedented control of cell state.

Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET

10.1101/2021.10.22.465455 ◽

2021 ◽

Author(s):

Kushagra Pandey ◽

Hamim Zafar

Keyword(s):

Single Cell ◽

Cell Fate ◽

Markov Chain Model ◽

Metric Learning ◽

Transitional Cell ◽

Cell Types ◽

State Transitions ◽

Chain Model ◽

Rna Seq ◽

Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing cell-state manifold and inferring trajectory and cell fate plasticity for complex topologies. We present MARGARET, a novel TI method that utilizes a deep unsupervised metric learning-based approach for inferring the cellular embeddings and employs a novel measure of connectivity between cell clusters and a graph-partitioning approach to reconstruct complex trajectory topologies. MARGARET utilizes the inferred trajectory for determining terminal states and inferring cell-fate plasticity using a scalable absorbing Markov Chain model. On a diverse simulated benchmark, MARGARET outperformed state-of-the-art methods in recovering global topology and cell pseudotime ordering. When applied to experimental datasets from hematopoiesis, embryogenesis, and colon differentiation, MARGARET reconstructed major lineages and associated gene expression trends, better characterized key branching events and transitional cell types, and identified novel cell types, and branching events that were previously uncharacterized.

Integrated single cell analysis of human lung fibrosis resolves cellular origins of predictive protein signatures in body fluids

10.1101/2020.01.21.20018358 ◽

2020 ◽

Cited By ~ 3

Author(s):

Christoph H. Mayr ◽

Lukas M. Simon ◽

Gabriela Leuschner ◽

Meshal Ansari ◽

Philipp E. Geyer ◽

...

Keyword(s):

Single Cell ◽

Body Fluid ◽

Single Cell Analysis ◽

Alveolar Epithelium ◽

Specific Cell ◽

Altered Expression ◽

Cell State ◽

Protein Signatures ◽

State Changes ◽

Disease Specific

AbstractSingle cell genomics enables characterization of disease specific cell states, while improvements in mass spectrometry workflows bring the clinical use of body fluid proteomics within reach. The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we leverage single cell RNA-seq and proteomic analysis of large pulmonary fibrosis patient cohorts to identify disease specific changes on the cellular level and their corresponding reflection in body fluid proteomes. We discovered and validated transcriptional changes in 45 cell types across three patient cohorts that translated into distinct changes in the bronchoalveolar lavage fluid and plasma proteome. These protein signatures correlated with diagnosis, lung function, smoking and injury status. Specifically, the altered expression of a novel marker of lung health, CRTAC1, in alveolar epithelium is robustly reflected in patient plasma. Our findings have direct implications for future non-invasive prediction and monitoring of pathological cell state changes in patient organs.Abstract Figure

Insights Into Development and Progression of Idiopathic Pulmonary Fibrosis From Single Cell RNA Studies

Frontiers in Medicine ◽

10.3389/fmed.2020.611728 ◽

2020 ◽

Vol 7 ◽

Author(s):

Julia Nemeth ◽

Annika Schundner ◽

Manfred Frick

Keyword(s):

Pulmonary Fibrosis ◽

Single Cell ◽

Large Scale ◽

Current Knowledge ◽

Current Model ◽

Alveolar Epithelium ◽

Mesenchymal Cell ◽

Alveolar Type ◽

Sequencing Studies

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease with limited therapeutic options. The current model suggests that chronic or repetitive “micro-injuries” of the alveolar epithelium lead to activation and proliferation of fibroblasts and excessive extracellular matrix (ECM) deposition. Disruption of alveolar type II (ATII) epithelial cell homeostasis and the characteristics of mesenchymal cell populations in IPF have received particular attention in recent years. Emerging data from single cell RNA sequencing (scRNAseq) analysis shed novel light on alterations in ATII cell progenitor dysfunction and the diversity of mesenchymal cells within the fibrotic lung. Within this minireview, we summarize the data from most recent human scRNAseq studies. We aim to collate the current knowledge on cellular plasticity and heterogeneity in the development and progression of IPF, effects of drug treatment on transcriptional changes. Finally, we provide a brief outlook on future challenges and promises for large scale sequencing studies in the development of novel therapeutics for IPF.

Defining the Cell Types That Drive Idiopathic Pulmonary Fibrosis Using Single-Cell RNA Sequencing

American Journal of Respiratory and Critical Care Medicine ◽

10.1164/rccm.201901-0197ed ◽

2019 ◽

Vol 199 (12) ◽

pp. 1454-1456 ◽

Cited By ~ 1

Author(s):

Joanna M. Poczobutt ◽

Oliver Eickelberg

Keyword(s):

Pulmonary Fibrosis ◽

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Single Cell Rna Sequencing

scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

Bioinformatics ◽

10.1093/bioinformatics/bty058 ◽

2018 ◽

Vol 34 (12) ◽

pp. 2077-2086 ◽

Cited By ~ 33

Author(s):

Suoqin Jin ◽

Adam L MacLean ◽

Tao Peng ◽

Qing Nie

Keyword(s):

Single Cell ◽

Transition Probabilities ◽

Energy Landscape ◽

Control Cell ◽

Robust Inference ◽

Supplementary Information ◽

Myoblast Differentiation ◽

State Transitions ◽

Marker Genes ◽

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data. Results Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using ‘single-cell energy’ and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are—in combination—more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates. Availability and implementation A MATLAB package of scEpath is available at https://github.com/sqjin/scEpath. Supplementary information Supplementary data are available at Bioinformatics online.

Epithelial–Mesenchymal Transition in the Pathogenesis of Idiopathic Pulmonary Fibrosis

Medicina ◽

10.3390/medicina55040083 ◽

2019 ◽

Vol 55 (4) ◽

pp. 83 ◽

Cited By ~ 23

Author(s):

Francesco Salton ◽

Maria Volpe ◽

Marco Confalonieri

Keyword(s):

Epithelial Cells ◽

Pulmonary Fibrosis ◽

Epithelial Mesenchymal Transition ◽

Treatment Options ◽

Alveolar Epithelium ◽

Alveolar Epithelial Cells ◽

Mesenchymal Transition ◽

Alveolar Epithelial ◽

Serious Disease

Idiopathic pulmonary fibrosis (IPF) is a serious disease of the lung, which leads to extensive parenchymal scarring and death from respiratory failure. The most accepted hypothesis for IPF pathogenesis relies on the inability of the alveolar epithelium to regenerate after injury. Alveolar epithelial cells become apoptotic and rare, fibroblasts/myofibroblasts accumulate and extracellular matrix (ECM) is deposited in response to the aberrant activation of several pathways that are physiologically implicated in alveologenesis and repair but also favor the creation of excessive fibrosis via different mechanisms, including epithelial–mesenchymal transition (EMT). EMT is a pathophysiological process in which epithelial cells lose part of their characteristics and markers, while gaining mesenchymal ones. A role for EMT in the pathogenesis of IPF has been widely hypothesized and indirectly demonstrated; however, precise definition of its mechanisms and relevance has been hindered by the lack of a reliable animal model and needs further studies. The overall available evidence conceptualizes EMT as an alternative cell and tissue normal regeneration, which could open the way to novel diagnostic and prognostic biomarkers, as well as to more effective treatment options.