The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease.

Lung disease accounts for every sixth death globally. Profiling the molecular state of all lung cell types in health and disease is currently revolutionizing the identification of disease mechanisms and will aid the design of novel diagnostic and personalized therapeutic regimens. Recent progress in high-throughput techniques for single-cell genomic and transcriptomic analyses has opened up new possibilities to study individual cells within a tissue, classify these into cell types, and characterize variations in their molecular profiles as a function of genetics, environment, cell-cell interactions, developmental processes, aging, or disease. Integration of these cell state definitions with spatial information allows the in-depth molecular description of cellular neighborhoods and tissue microenvironments, including the tissue resident structural and immune cells, the tissue matrix, and the microbiome. The Human Cell Atlas consortium aims to characterize all cells in the healthy human body and has prioritized lung tissue as one of the flagship projects. Here, we present the rationale, the approach, and the expected impact of a Human Lung Cell Atlas.Supported by the Helmholtz Association and the German Center for Lung Research (DZL) (H.B.S.); the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement 753039 (L.M.S.); U.K. Medical Research Council grant G0900424 (E.L.R.); National Institutes of Health (NIH) grants ES013995, HL071643, and AG049665, and Veterans Administration grant BX000201 and Department of Defense grant PR141319 (G.R.S.B.); NIH grants HL135124 and AI135964 and Department of Defense grant PR141319 (A.V.M.); NIH grants R01HL141852, R01HL127349, UHHL3123886, U01HL122626, and UG3TR002445, and Department of Defence grant PR151124 (N.K.); and the Netherlands Lung Foundation grants 5.1.14.020 and 4.1.18.226 (M.C.N.)

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib.

IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines

Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’

Abstract The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for ‘reverse phenotyping’. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states

Self-ordering of nanoparticles in magneto-organic composite films

Theis-Broehl K, Wolff M, Ennen I, Dewhurst CD, Hütten A, Toperverg BP. Self-ordering of nanoparticles in magneto-organic composite films. PHYSICAL REVIEW B. 2008;78(13): 134426.A combination of polarized neutron reflectometry and grazing incidence small-angle neutron scattering has been employed to deduce the structural and magnetic parameters of cobalt-oleyl amine nanocomplexes in thin films. It is demonstrated that inside the film the nanoparticles are self-organized into a three-dimensional paracrystallinelike lattice with the positional order well defined over a few interparticle spacings. Joint evaluation of the data elucidates the size of the saturated Co core and the CoO shell of the nanoparticles

The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease.

Lung disease accounts for every sixth death globally. Profiling the molecular state of all lung cell types in health and disease is currently revolutionizing the identification of disease mechanisms and will aid the design of novel diagnostic and personalized therapeutic regimens. Recent progress in high-throughput techniques for single-cell genomic and transcriptomic analyses has opened up new possibilities to study individual cells within a tissue, classify these into cell types, and characterize variations in their molecular profiles as a function of genetics, environment, cell-cell interactions, developmental processes, aging, or disease. Integration of these cell state definitions with spatial information allows the in-depth molecular description of cellular neighborhoods and tissue microenvironments, including the tissue resident structural and immune cells, the tissue matrix, and the microbiome. The Human Cell Atlas consortium aims to characterize all cells in the healthy human body and has prioritized lung tissue as one of the flagship projects. Here, we present the rationale, the approach, and the expected impact of a Human Lung Cell Atlas.Supported by the Helmholtz Association and the German Center for Lung Research (DZL) (H.B.S.); the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement 753039 (L.M.S.); U.K. Medical Research Council grant G0900424 (E.L.R.); National Institutes of Health (NIH) grants ES013995, HL071643, and AG049665, and Veterans Administration grant BX000201 and Department of Defense grant PR141319 (G.R.S.B.); NIH grants HL135124 and AI135964 and Department of Defense grant PR141319 (A.V.M.); NIH grants R01HL141852, R01HL127349, UHHL3123886, U01HL122626, and UG3TR002445, and Department of Defence grant PR151124 (N.K.); and the Netherlands Lung Foundation grants 5.1.14.020 and 4.1.18.226 (M.C.N.)

Exchange-bias instability in a bilayer with an ion-beam imprinted stripe pattern of ferromagnetic/antiferromagnetic interfaces

Theis-Broehl K, Wolff M, Westphalen A, et al. Exchange-bias instability in a bilayer with an ion-beam imprinted stripe pattern of ferromagnetic/antiferromagnetic interfaces. PHYSICAL REVIEW B. 2006;73(17): 174408.We have investigated the magnetization arrangement in an in-plane stripe pattern with alternating exchange-bias domains. The stripe pattern was produced by ion bombardment induced magnetic patterning, which changed locally the exchange-bias direction at the ferromagnet/antiferromagnet interface, but not the magnetic or antiferromagnetic properties of the Co70Fe30 and Mn83Ir17 layers, respectively. For the analysis of the magnetic domain structure evolution along the hysteresis loop we used a combination of experimental techniques: magneto-optical Kerr effect, Kerr microscopy, polarized neutron reflectometry, and off-specular scattering of polarized neutrons with polarization analysis. Instead of a perfect antiparallel alignment we found that the magnetization in neighboring stripes is periodically canted with respect to the stripe axis so that the net magnetization of the ferromagnetic film turns almost perpendicular to the stripes. At the same time the projection of the magnetization vector onto the stripe axis has a periodically alternating sign. The experimental observations are explained and quantitatively described within the frame of a phenomenological model, taking into account interfacial exchange bias, intralayer exchange energy, and uniaxial anisotropy. The model defines conditions which can be used for tailoring nano- and micro-patterned exchange-bias systems with different types of magnetic order

An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics

Aging impacts lung functionality and makes it more susceptible to chronic diseases. Combining proteomics and single cell transcriptomics, the authors chart molecular and cellular changes in the aging mouse lung, discover aging hallmarks, and predict the cellular sources of regulated proteins