An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Expanding the diversity of mycobacteriophages: insights into genome architecture and evolution.

Mycobacteriophages are viruses that infect mycobacterial hosts such as Mycobacterium smegmatis and Mycobacterium tuberculosis. All mycobacteriophages characterized to date are dsDNA tailed phages, and have either siphoviral or myoviral morphotypes. However, their genetic diversity is considerable, and although sixty-two genomes have been sequenced and comparatively analyzed, these likely represent only a small portion of the diversity of the mycobacteriophage population at large. Here we report the isolation, sequencing and comparative genomic analysis of 18 new mycobacteriophages isolated from geographically distinct locations within the United States. Although no clear correlation between location and genome type can be discerned, these genomes expand our knowledge of mycobacteriophage diversity and enhance our understanding of the roles of mobile elements in viral evolution. Expansion of the number of mycobacteriophages grouped within Cluster A provides insights into the basis of immune specificity in these temperate phages, and we also describe a novel example of apparent immunity theft. The isolation and genomic analysis of bacteriophages by freshman college students provides an example of an authentic research experience for novice scientists

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Genetic analysis of two species of Mnomen in the Kalamazoo Watershed reveal panmixia in Z. Aquatica, structure among Z. Palustris, and hybridization in areas of sympatry

Mnomen or wild rice of the genus Zizania is an important part of Native American culture, especially in Michigan for the Ojibwe nation. An oil spill in 2010 along the Kalamazoo River and the subsequent clean-up lead to renewed interest in management of Mnomen within the Kalamazoo watershed. The affected water bodies were surveyed for Zizania species to map existing populations, determine the existing genetic diversity and species present, and to identify potential hybridization. Using Traditional Ecological Knowledge of rice beds and opportunistic sampling of encountered plants, 28 rice beds were sampled. Two species of Zizania were identified Z. palustris and Z. aquatica. Genetic diversity was measured using 11 microsatellite loci and was moderately high for both species (Z. aquatica HE = 0.669, H0 = 0.672, n = 26 and Z. palustris HE = 0.697, H0 = 0.636, n = 57). No evidence of population bottle-necking was found. Z. palustris was found to have k = 3 populations on the landscape, while Z. aquatica was found to be a single panmictic population. Several individual hybrids were confirmed using genotyping and they were all found in areas where the two species co-occurred. Additionally, Z. aquatica was found to have expanded into areas historically with only Z. palustris downstream of the oil spill, potentially due to dredging and sediment relocation as part of the clean-up effort

HartungDanielPharmacyChangesLongActingBetaAgonistUtilization(Figure1).jpg

PURPOSE: In February 2010, the US Food and Drug Administration (FDA) issued new recommendations for the safe use of long-acting beta agonists (LABA) in those with asthma. The objective of this study was to determine the impact of the FDA’s 2010 LABA advisory on LABA utilization. METHODS: Using administrative data from the state of Oregon Medicaid program we performed an interrupted time series regression to evaluate changes in the trend in new LABA prescriptions before and after the FDA’s 2010 advisory. Trends in incident fills were examined among those with and without an asthma diagnosis code, prior respiratory controller medication use, and by age. FINDINGS: Of the 8646 study patients, 53% had a diagnosis of asthma, 21% of patients had no respiratory diagnosis, and 32% did not use a respiratory controller medication in the recent past. The trend in new LABA prescriptions declined by 0.09 new starts per 10,000 patients per month (95% confidence interval [CI] -0.19 to -0.01) following the FDA’s advisory. Among those with a diagnosis of asthma, there was an immediate drop of 0.48 (95% CI -0.93 to -0.03) and a 0.10 (95% CI -0.13 to -0.06) decline in the monthly rate of new starts per 10,000 patients. Immediately following the FDA’s advisory we observed a statistically significant 4.7% increase (95% CI 0.8% to 8.7%) in the proportion of new LABA starts with history of previous respiratory controller medication use. Utilization of LABAs did not change in those without a diagnosis of asthma. IMPLICATIONS: The FDA’s 2010 advisory was associated with modest reductions in LABA utilization overall and in ways highlighted in their recommendations.Keywords: United States Food and Drug Administration, Adrenergic beta2-agonists (adrenergic β₂-agonists), Utilization, Medicai

Informing Human Trafficking Clinical Care Through Two Systematic Reviews on Sexual Assault and Intimate Partner Violence

Background: There is a lack of evidence on the clinical management of patients who have suffered human trafficking. Synthesizing the evidence from similar patient populations may provide valuable insight. This review summarizes findings on therapeutic interventions for survivors of sexual assault and intimate partner violence (IPV). Method: We conducted two systematic reviews using the MEDLINE database. We included only randomized controlled trials of therapies with primary outcomes related to health for survivors of sexual assault and IPV. For the sexual assault review, there were 78 abstracts identified, 16 full-text articles reviewed, and 10 studies included. For the IPV review, there were 261 abstracts identified, 24 full-text articles reviewed, and 17 studies included. Analysis compared study size, intervention type, patient population, primary health outcomes, and treatment effect. Results: Although our search included physical and mental health outcomes, almost all the studies meeting inclusion and exclusion criteria focused on mental health. The interventions for sexual assault included spiritually focused group therapy, interference control training, image rehearsal therapy, sexual revictimization prevention, educational videos, cognitive behavioral therapy, and exposure therapy. The interventions in the IPV review included group social support therapy, exposure therapy, empowerment sessions, physician counseling, stress management programs, forgiveness therapy, motivational interviewing, and interpersonal psychotherapy. Conclusions: Insights from these reviews included the importance of culturally specific group therapy, the central role of survivor empowerment, and the overwhelming focus on mental health. These key features provide guidance for the development of interventions to improve the health of human trafficking survivors

Hair endocannabinoids predict physiological fear conditioning and salivary endocannabinoids predict subjective stress reactivity in humans

On the basis of substantial preclinical evidence, the endogenous cannabinoid system has been proposed to be closely involved in stress reactivity and extinction of fear. Existing human research supports this proposal to some extent, but existing studies have used only a narrow range of tools and biomatrices to measure endocannabinoids during stress and fear experiments. In the present study we collected hair and saliva samples from 99 healthy participants who completed a fear conditioning and intrusive memory task. Subjective, physiological and biological stress reactivity to a trauma film, which later served as unconditional stimulus during fear conditioning, was also measured. We found that salivary endocannabinoid concentrations predicted subjective responses to stress, but not cortisol stress reactivity, and replicated previous findings demonstrating a sex dimorphism in hair and salivary endocannabinoid levels. Hair 2-arachidonoyl glycerol levels were significantly associated with better retention of safety learning during extinction and renewal phases of fear conditioning, while hair concentrations of oleoylethanolamide and palmitoylethanolamide were associated with overall physiological arousal, but not conditional learning, during fear conditioning. This study is the first to test the relationship between hair and salivary endocannabinoids and these important psychological processes. Our results suggest that these measures may serve as biomarkers of dysregulation in human fear memory and stress.</p

An integrated cell atlas of the lung in health and disease.

Acknowledgements: This publication is part of the Human Cell Atlas (www.humancellatlas.org/publications/). This work was supported by the Chan Zuckerberg Initiative (CZI; LLC Seed Network grant CZF2019-002438 (Lung Cell Atlas 1.0) to P.B., M.D.L., A.V.M., M.C.N., D.P.S., J.R., P.R.T., K.B.M., F.J.T. and H.B.S.); National Institutes of Health (NIH; R01HL145372) and Department of Defense (W81XWH-19-1-0416) (to J.A.K. and N.E.B.); Fondation pour la Recherche Médicale (DEQ20180339158), Conseil Départemental des Alpes Maritimes (2016-294DGADSH-CV), Inserm Cross-cutting Scientific Program HuDeCA 2018, Agence Nationale de la Recherche SAHARRA (ANR-19-CE14-0027), ANR-19-P3IA-0002-3IA, National Infrastructure France Génomique (ANR-10-INBS-09-03) and PPIA 4D-OMICS (21-ESRE-0052) (to P.B.); H2020-SC1-BHC-2018-2020 Discovair (grant agreement 874656) (to P.B., K.B.M., S.A.T., M.C.N., F.J.T., M.P., H.B.S. and J.L.); NIH 1U54HL145608-01 (to M.D.L., K.Z., X.S., J.S.H. and G.P.); Wellcome (WT211276/Z/18/Z) and Sanger core grant WT206194 (to K.B.M. and S.A.T.); ESPOD fellowship of the European Molecular Biology Laboratory European Bioinformatics Institute and Sanger Institute (to E.M.); R01 HL153312, U19 AI135964, P01 AG049665, R01 HL158139, R01 ES034350 and U54 AG079754 (to A.V.M.); Lung Foundation Netherlands project numbers 5.1.14.020 and 4.1.18.226 (to M.C.N.); NIH grants R01HL146557 and R01HL153375 (to P.R.T.); German Center for Lung Research and Helmholtz Association (to H.B.S.); Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI (ZT-I-PF-5-01) and Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association ForInter (Interaction of Human Brain Cells) (to F.J.T.); Doris Duke Charitable Foundation (to J.A.K.); Joachim Herz Foundation (to L.D.); Ministry of Economic Affairs and Climate Policy by means of the Public–Private Partnership (to T.M.K.); 3IA Cote d’Azur PhD program (to A.C.); R01 HL135156, R01 MD010443, R01 HL128439, P01 HL132821, P01 HL107202, R01 HL117004 and Department of Defense grant W81WH-16-2-0018 (to M.A.S.); HL142568 and HL14507 from the NHLBI (to D.S.); P50 AR060780-06A1 (to R.L. and T.T.); Medical Research Council Clinician Scientist Fellowship (MR/W00111X/1) (to M.Z.N.); Jikei University School of Medicine (to M.Y.); University College London Birkbeck Medical Research Council Doctoral Training Programme (to K.B.W.); CZI (to J.W., Y.X. and N.K.); 5U01HL148856 (to J.W. and Y.X.); R01 HL153045 (to Y.X.); R01HL127349, R01HL141852 and U01HL145567 (to N.K.); 2R01HL068702 (to D.P.S. and J.R.); 5R01HL14254903 and 4UH3CA25513503 (to T.J.D.); R21HL156124, R56HL157632 and R21HL161760 (to A.M.T.); NIH U54 AG075931 and 5R01 HL146519 (to O.E.); Swedish Research Council and Cancerfonden (to C.S.); CZI Deep Visual Proteomics (to P.H.); U01HL148861-03 (to G.P.); CZI 2021-237918 (to J.S.H., P.R.T., H.B.S. and F.J.T.); CZIF2022-007488 from the CZI Foundation (F.J.T., S.A.T., M.D.L. and K.B.M.); European Respiratory Society and European Union’s Horizon 2020 Research and Innovation Programme under Marie Sklodowska-Curie grant agreement number 847462 (to J.G.-S. and A.J.O.); and Fondation de l’Institut Universitaire de Cardiologie et de Pneumologie de Québec (to Y.B.). We thank E. Spiegel from the Core Facility Statistical Consulting at the Helmholtz Center Munich Institute of Computational Biology for statistical consulting.Funder: Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0” NIH 1U54HL145608-01 CZIF2022-007488 from the Chan Zuckerberg Initiative Foundation CZIF2022-007488 from the Chan Zuckerberg Initiative FoundationFunder: ESPOD fellowship of EMBL-EBI and Sanger InstituteFunder: 3IA Cote d’Azur PhD programFunder: The Ministry of Economic Affairs and Climate Policy by means of the PPPFunder: Joachim Herz Stiftung (Joachim Herz Foundation); doi: https://doi.org/10.13039/100008662Funder: P50 AR060780-06A1Funder: University College London, Birkbeck MRC Doctoral Training ProgrammeFunder: Jikei University School of Medicine (Jikei University); doi: https://doi.org/10.13039/501100007962Funder: 5R01HL14254903, 4UH3CA25513503Funder: R01HL127349, R01HL141852, U01HL145567 and CZIFunder: MRC Clinician Scientist Fellowship (MR/W00111X/1)Funder: Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0” 2R01HL068702Funder: R01 HL135156, R01 MD010443, R01 HL128439, P01 HL132821, P01 HL107202, R01 HL117004, and DOD Grant W81WH-16-2-0018Funder: HL142568 and HL14507 from the NHLBIFunder: Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0”, 2R01HL068702Funder: Wellcome (WT211276/Z/18/Z) Sanger core grant WT206194 CZIF2022-007488 from the Chan Zuckerberg Initiative FoundationFunder: R21HL156124, R56HL157632, and R21HL161760Funder: CZI, 5U01HL148856Funder: CZI, 5U01HL148856, R01 HL153045Funder: The National Institute of Health R01HL145372Funder: Inserm Cross-cutting Scientific Program HuDeCA 2018, ANR SAHARRA (ANR-19-CE14–0027), ANR-19-P3IA-0002–3IA, the National Infrastructure France Génomique (ANR-10-INBS-09-03), PPIA 4D-OMICS (21-ESRE-0052), and the Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0”.Funder: Sanger core grant WT206194 Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0” CZIF2022-007488 from the Chan Zuckerberg Initiative FoundationFunder: Doris Duke Charitable Foundation (DDCF); doi: https://doi.org/10.13039/100000862Funder: The National Institute of Health R01HL145372 Department of Defense W81XWH-19-1-0416Funder: The National Institute of Health R01HL146557 and R01HL153375 and funds from Chan Zuckerberg Initiative - Human Lung Cell Atlas-pilot awardFunder: 1U54HL145608-01Funder: CZI Deep Visual ProteomicsFunder: 1U54HL145608-01, U01HL148861-03Funder: 1) the Chan Zuckerberg Initiative, LLC Seed Network grant CZF2019-002438 “Lung Cell Atlas 1.0”; 2) R01 HL153312; 3) U19 AI135964; 4) P01 AG049665Funder: Netherlands Lung Foundation project nos. 5.1.14.020 and 4.1.18.226, LLC Seed Network grant CZF2019-002438 “Lung Cell Atlas 1.0”Funder: grant number 2019-002438 from the Chan Zuckerberg Foundation, by the Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI [ZT-I-PF-5-01] and by the Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association “ForInter” (Interaction of human brain cells)Funder: 1 U01 HL14555-01, R01 HL123766-04Funder: NIH U54 AG075931, 5R01 HL146519Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas