Below an overview of the discovAIR publications

Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers

Christoph H Mayr, Lukas M Simon, Gabriela Leuschner, Meshal Ansari, Janine Schniering, Philipp E Geyer, Ilias Angelidis, Maximilian Strunz, Pawandeep Singh, Nikolaus Kneidinger, Frank Reichenberger, Edith Silbernagel, Stephan Böhm, Heiko Adler, Michael Lindner, Britta Maurer, Anne Hilgendorff, Antje Prasse, Jürgen Behr, Matthias Mann, Oliver Eickelberg, Fabian J Theis, Herbert B Schiller

The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single‐cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single‐cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type‐2 epithelial cell health status in lavage fluid and plasma. Using cross‐modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes.

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Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics

Christoph Muus, Malte D. Luecken, Gökcen Eraslan, Lisa Sikkema, Avinash Waghray, Graham Heimberg, Yoshihiko Kobayashi, Eeshit Dhaval Vaishnav, Ayshwarya Subramanian, Christopher Smillie, Karthik A. Jagadeesh, Elizabeth Thu Duong, Evgenij Fiskin, Elena Torlai Triglia, Meshal Ansari, Peiwen Cai, Brian Lin, Justin Buchanan, Sijia Chen, Jian Shu, Adam L. Haber, Hattie Chung, Daniel T. Montoro, Taylor Adams, Hananeh Aliee, Samuel J. Allon, Zaneta Andrusivova, Ilias Angelidis, Orr Ashenberg, Kevin Bassler, Christophe Bécavin, Inbal Benhar, Joseph Bergenstråhle, Ludvig Bergenstråhle, Liam Bolt, Emelie Braun, Linh T. Bui, Steven Callori, Mark Chaffin, Evgeny Chichelnitskiy, Joshua Chiou, Thomas M. Conlon, Michael S. Cuoco, Anna S. E. Cuomo, Marie Deprez, Grant Duclos, Denise Fine, David S. Fischer, Shila Ghazanfar, Astrid Gillich, Bruno Giotti, Joshua Gould, Minzhe Guo, Austin J. Gutierrez, Arun C. Habermann, Tyler Harvey, Peng He, Xiaomeng Hou, Lijuan Hu, Yan Hu, Alok Jaiswal, Lu Ji, Peiyong Jiang, Theodoros S. Kapellos, Christin S. Kuo, Ludvig Larsson, Michael A. Leney-Greene, Kyungtae Lim, Monika Litviňuková, Leif S. Ludwig, Soeren Lukassen, Wendy Luo, Henrike Maatz, Elo Madissoon, Lira Mamanova, Kasidet Manakongtreecheep, Sylvie Leroy, Christoph H. Mayr, Ian M. Mbano, Alexi M. McAdams, Ahmad N. Nabhan, Sarah K. Nyquist, Lolita Penland, Olivier B. Poirion, Sergio Poli, CanCan Qi, Rachel Queen, Daniel Reichart, Ivan Rosas, Jonas C. Schupp, Conor V. Shea, Xingyi Shi, Rahul Sinha, Rene V. Sit, Kamil Slowikowski, Michal Slyper, Neal P. Smith, Alex Sountoulidis, Maximilian Strunz, Travis B. Sullivan, Dawei Sun, Carlos Talavera-López, Peng Tan, Jessica Tantivit, Kyle J. Travaglini, Nathan R. Tucker, Katherine A. Vernon, Marc H. Wadsworth, Julia Waldman, Xiuting Wang, Ke Xu, Wenjun Yan, William Zhao, Carly G. K. Ziegler, The NHLBI LungMap Consortium & The Human Cell Atlas Lung Biological Network

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial–macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.

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A Single-Cell Atlas of the Human Healthy Airways

Marie Deprez, Laure-Emmanuelle Zaragosi, Marin Truchi, Christophe Becavin, Sandra Ruiz García, Marie-Jeanne Arguel, Magali Plaisant, Virginie Magnone, Kevin Lebrigand, Sophie Abelanet, Frédéric Brau, Agnès Paquet, Dana Pe'er, Charles-Hugo Marquette, Sylvie Leroy, Pascal Barbry

Rationale: The respiratory tract constitutes an elaborate line of defense that is based on a unique cellular ecosystem.
Objectives: We aimed to investigate cell population distributions and transcriptional changes along the airways by using single-cell RNA profiling.
Methods: We have explored the cellular heterogeneity of the human airway epithelium in 10 healthy living volunteers by single-cell RNA profiling. A total of 77,969 cells were collected at 35 distinct locations, from the nose to the 12th division of the airway tree.
Measurements and Main Results: The resulting atlas is composed of a high percentage of epithelial cells (89.1%) but also immune (6.2%) and stromal (4.7%) cells with distinct cellular proportions in different regions of the airways. It reveals differential gene expression between identical cell types (suprabasal, secretory, and multiciliated cells) from the nose (MUC4, PI3, SIX3) and tracheobronchial (SCGB1A1, TFF3) airways. By contrast, cell-type-specific gene expression is stable across all tracheobronchial samples. Our atlas improves the description of ionocytes, pulmonary neuroendocrine cells, and brush cells and identifies a related population of NREP-positive cells. We also report the association of KRT13 with dividing cells that are reminiscent of previously described mouse "hillock" cells and with squamous cells expressing SCEL and SPRR1A/B.
Conclusions: Robust characterization of a single-cell cohort in healthy airways establishes a valuable resource for future investigations. The precise description of the continuum existing from the nasal epithelium to successive divisions of the airways and the stable gene expression profile of these regions better defines conditions under which relevant tracheobronchial proxies of human respiratory diseases can be developed.

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Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19

Joana P. Bernardes, Neha Mishra, Florian Tran, Thomas Bahmer, Lena Best, Johanna I. Blase, Dora Bordoni, Jeanette Franzenburg, Ulf Geisen, Jonathan Josephs-Spaulding, Philipp Köhler, Axel Künstner, Elisa Rosati, Anna C. Aschenbrenner, Petra Bacher, Nathan Baran, Teide Boysen, Burkhard Brandt, Niklas Bruse, Jonathan Dörr, Andreas Dräger, Gunnar Elke, David Ellinghaus, Julia Fischer, Michael Forster, Andre Franke, Sören Franzenburg, Norbert Frey, Anette Friedrichs, Janina Fuß, Andreas Glück, Jacob Hamm, Finn Hinrichsen, Marc P. Hoeppner, Simon Imm, Ralf Junker, Sina Kaiser, Ying H. Kan, Rainer Knoll, Christoph Lange, Georg Laue, Clemens Lier, Matthias Lindner, Georgios Marinos, Robert Markewitz, Jacob Nattermann, Rainer Noth, Peter Pickkers, Klaus F. Rabe, Alina Renz, Christoph Röcken, Jan Rupp, Annika Schaffarzyk, Alexander Scheffold, Jonas Schulte-Schrepping, Domagoj Schunk, Dirk Skowasch, Thomas Ulas, Klaus-Peter Wandinger, Michael Wittig, Johannes Zimmermann, Hauke Busch, Bimba F. Hoyer, Christoph Kaleta, Jan Heyckendorf, Matthijs Kox, Jan Rybniker, Stefan Schreiber, Joachim L. Schultze, and Philip Rosenstiel, HCA Lung Biological Network, and the Deutsche COVID-19 Omics Initiative (DeCOI)

Temporal resolution of cellular features associated with a severe COVID-19 disease trajectory is needed for understanding skewed immune responses and defining predictors of outcome. Here, we performed a longi- tudinal multi-omics study using a two-center cohort of 14 patients. We analyzed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. Validation was performed in two independent cohorts of COVID-19 patients. Severe COVID-19 was characterized by an increase of proliferating, metabolically hyper- active plasmablasts. Coinciding with critical illness, we also identified an expansion of interferon-activated circulating megakaryocytes and increased erythropoiesis with features of hypoxic signaling. Megakaryo- cyte- and erythroid-cell-derived co-expression modules were predictive of fatal disease outcome. The study demonstrates broad cellular effects of SARS-CoV-2 infection beyond adaptive immune cells and provides an entry point toward developing biomarkers and targeted treatments of patients with COVID-19.

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SCRINSHOT enables spatial mapping of cell states in tissue sections with single-cell resolution

Alexandros Sountoulidis, Andreas Liontos, Hong Phuong Nguyen, Alexandra B. Firsova, Athanasios Fysikopoulos, Xiaoyan Qian, Werner Seeger, Erik Sundström, Mats Nilsson, Christos Samakovlis 

Changes in cell identities and positions underlie tissue development and disease progression. Although single-cell mRNA sequencing (scRNA-Seq) methods rapidly generate extensive lists of cell states, spatially resolved single-cell mapping presents a challenging task. We developed SCRINSHOT (Single-Cell Resolution IN Situ Hybridization On Tissues), a sensitive, multiplex RNA mapping approach. Direct hybridization of padlock probes on mRNA is followed by circularization with SplintR ligase and rolling circle amplification (RCA) of the hybridized padlock probes. Sequential detection of RCA-products using fluorophore-labeled oligonucleotides profiles thousands of cells in tissue sections. We evaluated SCRINSHOT specificity and sensitivity on murine and human organs. SCRINSHOT quantification of marker gene expression shows high correlation with published scRNA-Seq data over a broad range of gene expression levels. We demonstrate the utility of SCRINSHOT by mapping the locations of abundant and rare cell types along the murine airways. The amenability, multiplexity, and quantitative qualities of SCRINSHOT facilitate single-cell mRNA profiling of cell-state alterations in tissues under a variety of native and experimental conditions.

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Determinants of SARS-CoV-2 receptor gene expression in upper and lower airways

H. Aliee, F. Massip, C.Qi, M. Stella de Biase, J. van Nijnatten, E.T.G. Kersten, N. Z. Kermani, B. Khuder, J.M. Vonk, R.C.H. Vermeulen, U-BIOPRED study group, Cambridge Lung Cancer Early Detection Programme, INER-Ciencias Mexican Lung Program, NHLBI LungMAP Consortium, M. Neighbors, G.W. Tew, M. Grimbaldeston, N.H.T. ten Hacken, S. Hu, Y. Guo, X. Zhang, K. Sun, P.S. Hiemstra, B.A. Ponder, M.J. Mäkelä, K. Malmström, R.C. Rintoul, P.A. Reyfman, F.J. Theis, C.A. Brandsma, I. M. Adcock, W. Timens, C.J. Xu, M. van den Berge, R.F. Schwarz, G.H. Koppelman, M.C. Nawijn, A. Faiz 

Background The recent outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has led to a worldwide pandemic. A subset of COVID-19 patients progresses to severe disease, with high mortality and limited treatment options. Detailed knowledge of the expression regulation of genes required for viral entry into respiratory epithelial cells is urgently needed.

Methods Here we assess the expression patterns of genes required for SARS-CoV-2 entry into cells, and their regulation by genetic, epigenetic and environmental factors, throughout the respiratory tract using samples collected from the upper (nasal) and lower airways (bronchi).

Findings Genes encoding viral receptors and activating protease are increased in the nose compared to the bronchi in matched samples and associated with the proportion of secretory epithelial cells in cellular deconvolution analyses. Current or ex-smoking was found to increase expression of these genes only in lower airways, which was associated with a significant increase in the predicted proportion of goblet cells. Both acute and second hand smoke exposure were found to increase ACE2 expression while inhaled corticosteroids decrease ACE2 expression in the lower airways. A strong association of DNA- methylation with ACE2 and TMPRSS2- mRNA expression was identified.

Interpretation Genes associated with SARS-CoV-2 viral entry into cells are high in upper airways, but strongly increased in lower airways by smoke exposure. In contrast, ICS decreases ACE2 expression, indicating that inhaled corticosteroids are unlikely to increase the risk for more severe COVID-19 disease.

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Alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease

Kevin Baßler, Wataru Fujii, Theodore S. Kapellos, Arik Horne, Benedikt Reiz, Erika Dudkin, Malte Lücken, Nico Reusch, Collins Osei-Sarpong, Stefanie Warnat-Herresthal, Allon Wagner, Lorenzo Bonaguro, Patrick Günther, Carmen Pizarro, Tina Schreiber, Matthias Becker, Kristian Händler, Christian T. Wohnhaas, Florian Baumgartner, Meike Köhler, Heidi Theis, Michael Kraut, Marc H. Wadsworth II, Travis K. Hughes, Humberto J. G. Ferreira, Jonas Schulte-Schrepping, Emily Hinkley, Ines H. Kaltheuner, Matthias Geyer, Christoph Thiele, Alex K. Shalek, Andreas Feißt, Daniel Thomas, Henning Dickten, Marc Beyer, Patrick Baum, Nir Yosef, Anna C. Aschenbrenner, Thomas Ulas, Jan Hasenauer, Fabian J. Theis, Dirk Skowasch, Joachim L. Schultze

Despite the epidemics of chronic obstructive pulmonary disease (COPD), the cellular and molecular mechanisms of this disease are far from being understood. Here, we characterize and classify the cellular composition within the alveolar space and peripheral blood of COPD patients and control donors using a clinically applicable single-cell RNA-seq technology corroborated by advanced computational approaches for: machine learning-based cell-type classification, identification of differentially expressed genes, prediction of metabolic changes, and modeling of cellular trajectories within a patient cohort. These high-resolution approaches revealed: massive transcriptional plasticity of macrophages in the alveolar space with increased levels of invading and proliferating cells, loss of MHC expression, reduced cellular motility, altered lipid metabolism, and a metabolic shift reminiscent of mitochondrial dysfunction in COPD patients. Collectively, single-cell omics of multi-tissue samples was used to build the first cellular and molecular framework for COPD pathophysiology as a prerequisite to develop molecular biomarkers and causal therapies against this deadly disease.

A spatial multi-omics atlas of the human lung reveals a novel immune cell survival niche

Elo Madissoon, Amanda J. Oliver, Vitalii Kleshchevnikov, Anna Wilbrey-Clark, Krzysztof Polanski, Ana Ribeiro Orsi, Lira Mamanova, Liam Bolt, Nathan Richoz, Rasa Elmentaite, J. Patrick Pett, Ni Huang, Peng He, Monika Dabrowska, Sophie Pritchard, Liz Tuck, Elena Prigmore, Andrew Knights, Agnes Oszlanczi, Adam Hunter, Sara F. Vieira, Minal Patel, Nikitas Georgakopoulos, Krishnaa Mahbubani, Kourosh Saeb-Parsy, Menna Clatworthy, Omer Ali Bayraktar, Oliver Stegle, Natsuhiko Kumasaka, Sarah A. Teichmann, Kerstin B. Meyer

Multiple distinct cell types of the human lung and airways have been defined by single cell RNA sequencing (scRNAseq). Here we present a multi-omics spatial lung atlas to define novel cell types which we map back into the macro- and micro-anatomical tissue context to define functional tissue microenvironments. Firstly, we have generated single cell and nuclei RNA sequencing, VDJ-sequencing and Visium Spatial Transcriptomics data sets from 5 different locations of the human lung and airways. Secondly, we define additional cell types/states, as well as spatially map novel and known human airway cell types, such as adult lung chondrocytes, submucosal gland (SMG) duct cells, distinct pericyte and smooth muscle subtypes, immune-recruiting fibroblasts, peribronchial and perichondrial fibroblasts, peripheral nerve associated fibroblasts and Schwann cells. Finally, we define a survival niche for IgA-secreting plasma cells at the SMG, comprising the newly defined epithelial SMG-Duct cells, and B and T lineage immune cells. Using our transcriptomic data for cell-cell interaction analysis, we propose a signalling circuit that establishes and supports this niche. Overall, we provide a transcriptional and spatial lung atlas with multiple novel cell types that allows for the study of specific tissue microenvironments such as the newly defined gland-associated lymphoid niche (GALN).

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Fully Automatic Cell Segmentation with Fourier Descriptors

Dominik Hirling, Peter Horvath

Cell segmentation is a fundamental problem in biology for which convolutional neural networks yield the best results nowadays. In this paper, we present FourierDist, a network, which is a modification of the popular StarDist and SplineDist architectures. While StarDist and SplineDist describe an object by the lengths of equiangular rays and control points respectively, our network utilizes Fourier descriptors, predicting a coefficient vector for every pixel on the image, which implicitly define the resulting segmentation. We evaluate our model on three different datasets, and show that Fourier descriptors can achieve a high level of accuracy with a small number of coefficients. FourierDist is also capable of accurately segmenting objects that are not star-shaped, a case where StarDist performs suboptimally according to our experiments.

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The spatial landscape of gene expression isoforms in tissue sections

Kevin Lebrigand, Joseph Bergenstråhle, Kim Thrane, Annelie Mollbrink, Konstantinos Meletis, Pascal Barbry, Rainer Waldmann, Joakim Lundeberg

In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and fulllength sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce Spatial Isoform Transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity. We show in mouse brain how SIT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and use of external single cell data allowed to nominate cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that we independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (, where isoform expression and RNA editing can be visualized in a spatial context.

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This project is funded by
Grant no. 874656

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