Quantitative single-cell interactomes in normal and virus-infected mouse lungs

Lung repair and regeneration: Advanced models and insights into human disease

The respiratory system acts as both the primary site of gas exchange and an important sensor and barrier to the external environment. The increase in incidences of respiratory disease over the past decades has highlighted the importance of developing improved therapeutic approaches. This review will summarize recent research on the cellular complexity of the mammalian respiratory system with a focus on gas exchange and immunological defense functions of the lung. Different models of repair and regeneration will be discussed to help interpret human and animal data and spur the investigation of models and assays for future drug development.

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data

MOTIVATION

Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity.

RESULTS

Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data.

AVAILABILITY AND IMPLEMENTATION

The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.

Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data

BACKGROUND

Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis.

METHODS

Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors.

RESULTS

The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells.

CONCLUSIONS

The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.

Use of intercellular proximity labeling to quantify and decipher cell-cell interactions directed by diversified molecular pairs

FucoID is an intercellular proximity labeling technique for studying cell-cell interactions (CCIs) via fucosyltransferase (FT)-meditated fucosyl-biotinylation, which has been applied to probe antigen-specific dendritic cell (DC)-T cell interactions. In this system, bait cells of interest with cell surface-anchored FT are used to capture the interacting prey cells by transferring a biotin-modified substrate to prey cells. Here, we leveraged FucoID to study CCIs directed by different molecular pairs, e.g., programmed cell death protein-1(PD-1)/programmed cell death protein-ligand-1 (PD-L1), and identify unknown or little studied CCIs, e.g., the interaction of DCs and B cells. To expand the application of FucoID to complex systems, we also synthesized site-specific antibody-based FT conjugate, which substantially improves the ability of FucoID to probe molecular signatures of specific CCI when cells of interest (bait cells) cannot be purified, e.g., in clinical samples. Collectively, these studies demonstrate the general applicability of FucoID to study unknown CCIs in complex systems at a molecular resolution.

Cellular Heterogeneity and Molecular Reprogramming of the Host Response during Influenza Acute Lung Injury

An exuberant host response contributes to influenza A virus (IAV) (or influenza)-mediated lung injury. However, despite significant information on the host response to IAV, the cellular framework and molecular interactions that dictate the development of acute injury in IAV-infected lungs remain incompletely understood. We performed an unbiased single-cell RNA sequencing (scRNAseq) analysis to examine the cellular heterogeneity and regulation of host responses in the IAV model of acute lung injury. At the cellular level, IAV infection promoted the overwhelming recruitment of monocytes that exhibited the cell differentiation trajectory to monocyte-derived macrophages. Together, monocytes and monocyte-derived myeloid cells constituted over 50% of the total immune cells in IAV-infected lungs. In contrast, IAV infection resulted in a significant loss of nonhematopoietic cells. Molecularly, our data show the multidimensional cell-cell communication dynamics of interferon and chemokine signaling between immune and nonimmune cells and the cell-specific molecular pathways regulating the host responses during IAV-induced lung injury. Our data provide a foundation for further exploring the mechanistic association of the IAV host response with acute lung injury. IMPORTANCE A dysregulated host response develops acute lung injury during IAV infection. However, the pathological immune mechanism(s) associated with acute lung injury during IAV infection is yet to be elucidated. In this study, we performed scRNAseq to examine the dynamics of host responses during the peak of IAV-mediated lung injury. At the cellular level, our data reveal significant myelopoiesis predominated by monocytes and macrophages and the simultaneous disruption of the nonhematopoietic cell framework, crucial for regulating inflammation and barrier integrity in IAV-infected lungs. Molecularly, we observed a complex cellular network involving cell-cell communications and a number of unique regulons dictating the outcome of interferon and chemokine responses during peak lung injury. Our data present a unique atlas of cellular changes and the regulation of global and cell-specific host responses during IAV infection. We expect that this information will open new avenues to identify targets for therapeutic intervention against IAV lung injury.

Intermediary Role of Lung Alveolar Type 1 Cells in Epithelial Repair upon Sendai Virus Infection

The lung epithelium forms the first barrier against respiratory pathogens and noxious chemicals; however, little is known about how more than 90% of this barrier, made of AT1 (alveolar type 1) cells, responds to injury. Using the Sendai virus to model natural infection in mice, we find evidence that AT1 cells have an intermediary role by persisting in areas depleted of AT2 cells, upregulating IFN responsive genes, and receding from invading airway cells. Sendai virus infection mobilizes airway cells to form alveolar SOX2⁺ (Sry-box 2⁺) clusters without differentiating into AT1 or AT2 cells. Large AT2 cell-depleted areas remain covered by AT1 cells, which we name "AT2-less regions", and are replaced by SOX2⁺ clusters spreading both basally and luminally. AT2 cell proliferation and differentiation are largely confined to topologically distal regions and form de novo alveolar surface, with limited contribution to in situ repairs of AT2-less regions. Time-course single-cell RNA sequencing profiling and RNAscope validation suggest enhanced immune responses and altered growth signals in AT1 cells. Our comprehensive spatiotemporal and genomewide study highlights the hitherto unappreciated role of AT1 cells in lung injury-repair.

Three-axis classification of mouse lung mesenchymal cells reveals two populations of myofibroblasts

The mesenchyme consists of heterogeneous cell populations that support neighboring structures and are integral to intercellular signaling, but are poorly defined morphologically and molecularly. Leveraging single-cell RNA-sequencing, 3D imaging and lineage tracing, we classify the mouse lung mesenchyme into three proximal-distal axes that are associated with the endothelium, epithelium and interstitium, respectively. From proximal to distal: the vascular axis includes vascular smooth muscle cells and pericytes that transition as arterioles and venules ramify into capillaries; the epithelial axis includes airway smooth muscle cells and two populations of myofibroblasts - ductal myofibroblasts, surrounding alveolar ducts and marked by CDH4, HHIP and LGR6, which persist post-alveologenesis, and alveolar myofibroblasts, surrounding alveoli and marked by high expression of PDGFRA, which undergo developmental apoptosis; and the interstitial axis, residing between the epithelial and vascular trees and sharing the marker MEOX2, includes fibroblasts in the bronchovascular bundle and the alveolar interstitium, which are marked by IL33/DNER/PI16 and Wnt2, respectively. Single-cell imaging reveals a distinct morphology of mesenchymal cell populations. This classification provides a conceptual and experimental framework applicable to other organs.

Differential chromatin binding of the lung lineage transcription factor NKX2-1 resolves opposing murine alveolar cell fates in vivo

Differential transcription of identical DNA sequences leads to distinct tissue lineages and then multiple cell types within a lineage, an epigenetic process central to progenitor and stem cell biology. The associated genome-wide changes, especially in native tissues, remain insufficiently understood, and are hereby addressed in the mouse lung, where the same lineage transcription factor NKX2-1 promotes the diametrically opposed alveolar type 1 (AT1) and AT2 cell fates. Here, we report that the cell-type-specific function of NKX2-1 is attributed to its differential chromatin binding that is acquired or retained during development in coordination with partner transcriptional factors. Loss of YAP/TAZ redirects NKX2-1 from its AT1-specific to AT2-specific binding sites, leading to transcriptionally exaggerated AT2 cells when deleted in progenitors or AT1-to-AT2 conversion when deleted after fate commitment. Nkx2-1 mutant AT1 and AT2 cells gain distinct chromatin accessible sites, including those specific to the opposite fate while adopting a gastrointestinal fate, suggesting an epigenetic plasticity unexpected from transcriptional changes. Our genomic analysis of single or purified cells, coupled with precision genetics, provides an epigenetic basis for alveolar cell fate and potential, and introduces an experimental benchmark for deciphering the in vivo function of lineage transcription factors.

Breathing fresh air into respiratory research with single-cell RNA sequencing

The complex cellular heterogeneity of the lung poses a unique challenge to researchers in the field. While the use of bulk RNA sequencing has become a ubiquitous technology in systems biology, the technique necessarily averages out individual contributions to the overall transcriptional landscape of a tissue. Single-cell RNA sequencing (scRNA-seq) provides a robust, unbiased survey of the transcriptome comparable to bulk RNA sequencing while preserving information on cellular heterogeneity. In just a few years since this technology was developed, scRNA-seq has already been adopted widely in respiratory research and has contributed to impressive advancements such as the discoveries of the pulmonary ionocyte and of a profibrotic macrophage population in pulmonary fibrosis. In this review, we discuss general technical considerations when considering the use of scRNA-seq and examine how leading investigators have applied the technology to gain novel insights into respiratory biology, from development to disease. In addition, we discuss the evolution of single-cell technologies with a focus on spatial and multi-omics approaches that promise to drive continued innovation in respiratory research.