The discovAIR project: a roadmap towards the Human Lung Cell Atlas

STIGMA: Single-cell tissue-specific gene prioritization using machine learning

Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing single-cell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.

CILP is a potential pan-cancer marker: combined silico study and in vitro analyses

CILP (Cartilage intermediate layer protein), an ECM (extracellular matrix) glycoprotein, is found to be associated with intervertebral disc degeneration, chronic heart failure, obese and cardiac fibrosis. However, there are few reports on the role of CILP in tumors. Thus, in this study, we mainly explored the function of CILP in the occurrence and development of tumors and whether it could be a potential pan-cancer marker. Pan-cancer data in this study were obtained from UCSC Xena. Single-cell data were obtained from GSE152938. ROC (Receiver operating characteristic) curves were used to evaluate the accuracy of CILP in predicting the occurrence of different tumor types. The Kaplan-Meier plots were used to assess the relationship between CILP expression and survival prognosis in different tumor types by COX regression analysis. Pseudotime analysis and cell communication analysis were used to further explore the function of CILP at Single cell level. The human RCC (renal cell carcinoma) cell lines ACHN and 786-O were used for further experimental verification. Bulk RNA-seq showed differences in CILP expression in several tumors. ROC curves showed that 14 tumors have AUC > 0.7. Kaplan-Meier plots indicated that CILP is a risk factor for patients in 3 kinds of tumors. ScRNA-seq (Single cell RNA sequencing) suggested that CILP might influence tumors through fibroblasts and cell-cell communication. Finally, we verified the function of CILP at the cellular level by using RCC cell lines ACHN and 786-O and found that knockdown of CILP could significantly inhibit migration and invasion. This finding supports that CILP could be a risk factor as well as a pan-cancer predictor for patients.

Ex vivo tissue perturbations coupled to single-cell RNA-seq reveal multilineage cell circuit dynamics in human lung fibrogenesis

Pulmonary fibrosis develops as a consequence of failed regeneration after injury. Analyzing mechanisms of regeneration and fibrogenesis directly in human tissue has been hampered by the lack of organotypic models and analytical techniques. In this work, we coupled ex vivo cytokine and drug perturbations of human precision-cut lung slices (hPCLS) with single-cell RNA sequencing and induced a multilineage circuit of fibrogenic cell states in hPCLS. We showed that these cell states were highly similar to the in vivo cell circuit in a multicohort lung cell atlas from patients with pulmonary fibrosis. Using micro-CT-staged patient tissues, we characterized the appearance and interaction of myofibroblasts, an ectopic endothelial cell state, and basaloid epithelial cells in the thickened alveolar septum of early-stage lung fibrosis. Induction of these states in the hPCLS model provided evidence that the basaloid cell state was derived from alveolar type 2 cells, whereas the ectopic endothelial cell state emerged from capillary cell plasticity. Cell-cell communication routes in patients were largely conserved in hPCLS, and antifibrotic drug treatments showed highly cell type-specific effects. Our work provides an experimental framework for perturbational single-cell genomics directly in human lung tissue that enables analysis of tissue homeostasis, regeneration, and pathology. We further demonstrate that hPCLS offer an avenue for scalable, high-resolution drug testing to accelerate antifibrotic drug development and translation.

Chromatin accessibility profiling of targeted cell populations with laser capture microdissection coupled to ATAC-seq

Spatially resolved omics technologies reveal context-dependent cellular regulatory networks in tissues of interest. Beyond transcriptome analysis, information on epigenetic traits and chromatin accessibility can provide further insights on gene regulation in health and disease. Nevertheless, compared to the enormous advancements in spatial transcriptomics technologies, the field of spatial epigenomics is much younger and still underexplored. In this study, we report laser capture microdissection coupled to ATAC-seq (LCM-ATAC-seq) applied to fresh frozen samples for the spatial characterization of chromatin accessibility. We first demonstrate the efficient use of LCM coupled to in situ tagmentation and evaluate its performance as a function of cell number, microdissected areas, and tissue type. Further, we demonstrate its use for the targeted chromatin accessibility analysis of discrete contiguous or scattered cell populations in tissues via single-nuclei capture based on immunostaining for specific cellular markers.

New Challenges for Anatomists in the Era of Omics

Anatomic studies have traditionally relied on macroscopic, microscopic, and histological techniques to investigate the structure of tissues and organs. Anatomic studies are essential in many fields, including medicine, biology, and veterinary science. Advances in technology, such as imaging techniques and molecular biology, continue to provide new insights into the anatomy of living organisms. Therefore, anatomy remains an active and important area in the scientific field. The consolidation in recent years of some omics technologies such as genomics, transcriptomics, proteomics, and metabolomics allows for a more complete and detailed understanding of the structure and function of cells, tissues, and organs. These have been joined more recently by "omics" such as radiomics, pathomics, and connectomics, supported by computer-assisted technologies such as neural networks, 3D bioprinting, and artificial intelligence. All these new tools, although some are still in the early stages of development, have the potential to strongly contribute to the macroscopic and microscopic characterization in medicine. For anatomists, it is time to hitch a ride and get on board omics technologies to sail to new frontiers and to explore novel scenarios in anatomy.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing

Standalone and consortia-led single-cell atlases of healthy and diseased human airways generated with single-cell RNA-sequencing (scRNA-seq) have ushered in a new era in respiratory research. Numerous discoveries, including the pulmonary ionocyte, potentially novel cell fates, and a diversity of cell states among common and rare epithelial cell types have highlighted the extent of cellular heterogeneity and plasticity in the respiratory tract. scRNA-seq has also played a pivotal role in our understanding of host-virus interactions in coronavirus disease 2019 (COVID-19). However, as our ability to generate large quantities of scRNA-seq data increases, along with a growing number of scRNA-seq protocols and data analysis methods, new challenges related to the contextualisation and downstream applications of insights are arising. Here, we review the fundamental concept of cellular identity from the perspective of single-cell transcriptomics in the respiratory context, drawing attention to the need to generate reference annotations and to standardise the terminology used in literature. Findings about airway epithelial cell types, states and fates obtained from scRNA-seq experiments are compared and contrasted with information accumulated through the use of conventional methods. This review attempts to discuss major opportunities and to outline some of the key limitations of the modern-day scRNA-seq that need to be addressed to enable efficient and meaningful integration of scRNA-seq data from different platforms and studies, with each other as well as with data from other high-throughput sequencing-based genomic, transcriptomic and epigenetic analyses.

Systems Biology in Asthma

The application of mathematical and computational analysis, together with the modelling of biological and physiological processes, is transforming our understanding of the pathophysiology of complex diseases. This systems biology approach incorporates large amounts of genomic, transcriptomic, proteomic, metabolomic, breathomic, metagenomic and imaging data from disease sites together with deep clinical phenotyping, including patient-reported outcomes. Integration of these datasets will provide a greater understanding of the molecular pathways associated with severe asthma in each individual patient and determine their personalised treatment regime. This chapter describes some of the data integration methods used to combine data sets and gives examples of the results obtained using single datasets and merging of multiple datasets (data fusion and data combination) from several consortia including the severe asthma research programme (SARP) and the Unbiased Biomarkers Predictive of Respiratory Disease Outcomes (U-BIOPRED) consortia. These results highlight the involvement of several different immune and inflammatory pathways and factors in distinct subsets of patients with severe asthma. These pathways often overlap in patients with distinct clinical features of asthma, which may explain the incomplete or no response in patients undergoing specific targeted therapy. Collaboration between groups will improve the predictions obtained using a systems medicine approach in severe asthma.

Role of Respiratory Epithelial Cells in Allergic Diseases

The airway epithelium provides the first line of defense to the surrounding environment. However, dysfunctions of this physical barrier are frequently observed in allergic diseases, which are tightly connected with pro- or anti-inflammatory processes. When the epithelial cells are confronted with allergens or pathogens, specific response mechanisms are set in motion, which in homeostasis, lead to the elimination of the invaders and leave permanent traces on the respiratory epithelium. However, allergens can also cause damage in the sensitized organism, which can be ascribed to the excessive immune reactions. The tight interaction of epithelial cells of the upper and lower airways with local and systemic immune cells can leave an imprint that may mirror the pathophysiology. The interaction with effector T cells, along with the macrophages, play an important role in this response, as reflected in the gene expression profiles (transcriptomes) of the epithelial cells, as well as in the secretory pattern (secretomes). Further, the storage of information from past exposures as memories within discrete cell types may allow a tissue to inform and fundamentally alter its future responses. Recently, several lines of evidence have highlighted the contributions from myeloid cells, lymphoid cells, stromal cells, mast cells, and epithelial cells to the emerging concepts of inflammatory memory and trained immunity.