HORMONAL-INFLAMMATORY INTERFERENCE IN THE CONTROL OF DE NOVO GLUCOSE PRODUCTION BY THE LIVER

Abstract In mammals the maintenance of an appropriate glucose level in the blood is a prerequisite of good health and survival. The blockade of stress compensatory de novo glucose production by the liver, gluconeogenesis, during acute inflammation is one of the major characteristics of the aberrant metabolic state and a reason of death in septic patients. Interference with hormone signaling and the subsequent suppression of key rate limiting gluconeogenic enzyme phosphoenolpyruvate carboxykinase (PEPCK) through pro-inflammatory mediators importantly contribute to severe hypoglycemia in sepsis. However, the molecular mechanisms of aberrant PEPCK gene regulation under these conditions in vivo remain largely unknown. In the present study we report the generation of a liver-specific adenoviral reporter system for the identification of dysfunctional gene cis-regulatory promoter elements under pathological conditions in mice. By employing in vivo promoter reporter technology, the glucocorticoid response unit (GRU) and the cAMP-response element (CRE) of the PEPCK gene were identified as critical promoter target sites of pro-inflammatory signaling. The disruption of the synergistic function of these two promoter elements was found to mediate PEPCK gene inhibition under septic conditions. Furthermore, the expression of nuclear receptor co-factor PGC-1α, the molecular mediator of GRU/CRE synergism on the PEPCK promoter, was found to be specifically repressed in septic liver. The depletion of endogenous PGC-1α with RNAi blunts the inflammatory suppressive effect on the PEPCK gene in cytokine-exposed primary hepatocytes while PGC-1α over-expression restores PEPCK expression under the same conditions. These results provide an in vivo mechanism involved in the suppression of the key gluconeogenic gene PEPCK in septic mouse. The maintenance of PGC-1α activity might represent an attractive therapeutic defense for the rescue of gluconeogenic program repression and hypoglycemia in septic patients

Donor NK and T Cells in the Periphery of Lung Transplant Recipients Contain High Frequencies of Killer Cell Immunoglobulin-Like Receptor-Positive Subsets

Introduction For end-stage lung diseases, double lung transplantation (DLTx) is the ultimate curative treatment option. However, acute and chronic rejection and chronic dysfunction are major limitations in thoracic transplantation medicine. Thus, a better understanding of the contribution of immune responses early after DLTx is urgently needed. Passenger cells, derived from donor lungs and migrating into the recipient periphery, are comprised primarily by NK and T cells. Here, we aimed at characterizing the expression of killer cell immunoglobulin-like receptors (KIR) on donor and recipient NK and T cells in recipient blood after DLTx. Furthermore, we investigated the functional status and capacity of donor vs . recipient NK cells. Methods Peripheral blood samples of 51 DLTx recipients were analyzed pre Tx and at T0, T24 and 3wk post Tx for the presence of HLA-mismatched donor NK and T cells, their KIR repertoire as well as activation status using flow cytometry. Results Within the first 3 weeks after DLTx, donor NK and T cells were detected in all patients with a peak at T0. An increase of the KIR2DL/S1-positive subset was found within the donor NK cell repertoire. Moreover, donor NK cells showed significantly higher frequencies of KIR2DL/S1-positive cells (p<0.01) 3wk post DLTx compared to recipient NK cells. This effect was also observed in donor KIR + T cells 3wk after DLTx with higher proportions of KIR2DL/S1 (p<0.05) and KIR3DL/S1 (p<0.01) positive T cells. Higher activation levels of donor NK and T cells (p<0.001) were detected compared to recipient cells via CD25 expression as well as a higher degranulation capacity upon activation by K562 target cells. Conclusion Higher frequencies of donor NK and T cells expressing KIR compared to recipient NK and T cells argue for their origin in the lung as a part of a highly specialized immunocompetent compartment. Despite KIR expression, higher activation levels of donor NK and T cells in the periphery of recipients suggest their pre-activation during the ex situ phase. Taken together, donor NK and T cells are likely to have a regulatory effect in the balance between tolerance and rejection and, hence, graft survival after DLTx

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

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

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. </p

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

Hypothermia Promotes Interleukin-22 Expression and Fine-Tunes Its Biological Activity

The emergence and propagation of cracks in the asphalt layer is the main factor of deterioration of roads, so in this work the method of discrete elements (MED) was used to simulate the propagation of cracks in asphalt mixtures with the Indirect Traction test. This simulation was carried out considering the viscoelastic (bulk) medium with the Burger model combined with the cohesive zone model to simulate the initiation and propagation of fissures. The characterization of the asphalt concrete (AC) is essential to obtain adequate parameters and, in turn, ensure a better projection of asphalt pavements thus obtaining a good performance on the roads. By the simulation of the indirect traction test or Brazilian test with the method of the discrete elements (MED), these mechanical parameters were obtained. The irregular shape of the aggregate was modeled by means of the Clumps, this technique consists in the grouping of particles connected to each other that act as a single rigid body. Asphalt concrete has the viscoelastic characteristic which is an important factor considered in the simulation, adopting the viscoelastic Burger contact model combined with the cohesive zone model to simulate the initiation and propagation of cracks. The computational simulation by the discrete element method in the present work was carried out using the commercial program PFC2D v. 4.0. The experimental indirect traction tests were carried out in the work of Noreña (2008) in samples of 101.6 mm in diameter by 63.5 mm in height, then a servo- control system is employed to apply the efforts, applying a speed of 50.8 mm/min. The results of the indirect traction test obtained in the laboratory were compared with the numerical results obtained in this work. From the curves Forces vs Displacements obtained in the simulation of the Indirect Traction test, it can be concluded that the MED technique in the simulation of problems, is efficient, on the qualitative and quantitative points of view; showing itself as a promising tool for the investigation of the behavior of asphalt pavements subjected to fracturing processes.TesisEl surgimiento y propagación de fisuras en la camada asfáltica es el principal factor de deterioración de las carreteras, por lo cual, en este trabajo se empleó el método de elementos discretos (MED) para simular la propagación de fisuras en las mezclas asfálticas con el ensayo de Tracción Indirecta. Esta simulación fue realizada considerando el medio (bulk) viscoelástico con el modelo Burger combinado con el modelo de zona cohesiva para simular el inicio y la propagación de las fisuras. La caracterización del concreto asfaltico (CA) es primordial para la obtención de parámetros adecuados y que, a su vez, garantizaran una mejor proyección de los pavimentos asfalticos obteniéndose así un buen desempeño en las carreteras. Mediante la simulación del ensayo de tracción indirecta o ensayo brasileño con el método de elementos discretos (MED) se obtuvo estos parámetros mecánicos. La forma irregular del agregado fue modelada por medio de los Clumps, esta técnica consiste en la agrupación de partículas conectadas unas a las otras que actúan como un cuerpo rígido único. El Concreto asfaltico tiene la característica viscoelástica que es un factor importante considerado en la simulación adoptándose el modelo de contacto viscoelástico Burger combinado con el modelo de zona cohesiva para simular el inicio y la propagación de las fisuras. La simulación computacional por el método de elementos discretos en el presente trabajo fue realizada utilizado el programa comercial PFC2D v. 4.0. Los ensayos de tracción indirecta experimental fueron realizados en el trabajo de Noreña (2008) en muestras de 101.6 mm de diámetro por 63.5 mm de altura, posteriormente se aplica un sistema de servo-control para aplicar los esfuerzos, aplicándose una velocidad de 50.8 mm/min. Los resultados del ensayo de tracción indirecta obtenidos en laboratorio fueron comparados con los resultados numéricos obtenidos en este trabajo. De las curvas Fuerzas vs Desplazamientos obtenidos en la simulación del ensayo de Tracción Indirecta, se puede concluir que la técnica MED en la simulación de problemas, se muestra eficiente, sobre el punto de vista cualitativo y cuantitativo; mostrándose como una herramienta prometedora para la investigación del comportamiento de pavimentos asfálticos sometidos a procesos de fracturamiento

Hypothermia promotes interleukin-22 expression and fine-tunes its biological activity

Disturbed homeostasis as a result of tissue stress can provoke leukocyte responses enabling recovery. Since mild hypothermia displays specific clinically relevant tissue-protective properties and interleukin (IL)-22 promotes healing at host/environment interfaces, effects of lowered ambient temperature on IL-22 were studied. We demonstrate that a 5-h exposure of endotoxemic mice to 4°C reduces body temperature by 5.0° and enhances splenic and colonic il22 gene expression. In contrast, tumor necrosis factor-α and IL-17A were not increased. In vivo data on IL-22 were corroborated using murine splenocytes and human peripheral blood mononuclear cells (PBMC) cultured upon 33°C and polyclonal T cell activation. Upregulation by mild hypothermia of largely T-cell-derived IL-22 in PBMC required monocytes and associated with enhanced nuclear T-cell nuclear factor of activated T cells (NFAT)-c2. Notably, NFAT antagonism by cyclosporin A or FK506 impaired IL-22 upregulation at normothermia and entirely prevented its enhanced expression upon hypothermic culture conditions. Data suggest that intact NFAT signaling is required for efficient IL-22 induction upon normothermic and hypothermic conditions. Hypothermia furthermore boosted early signal transducer and activator of transcription 3 activation by IL-22 and shaped downstream gene expression in epithelial-like cells. Altogether, data indicate that hypothermia supports and fine-tunes IL-22 production/action, which may contribute to regulatory properties of low ambient temperature

Diagnosing Acute Cellular Rejection after Paediatric Liver Transplantation—Is There Room for Interleukin Profiles?

Background: The current gold standard to diagnose T-cell-mediated acute rejection (TCMR) requires liver histology. Using data from the ChilSFree study on immune response after paediatric liver transplantation (pLT), we aimed to assess whether soluble cytokines can serve as an alternative diagnostic tool in children suspected to have TCMR. Methods: A total of n = 53 blood samples obtained on the day of or up to 3 days before liver biopsy performed for suspected TCMR at median 18 days (range 7–427) after pLT in n = 50 children (38% female, age at pLT 1.8 (0.5–17.5) years) were analysed for circulating cytokine levels using Luminex-based Multiplex technology. Diagnostic accuracy of cytokine concentrations was assessed using a multivariable model based on elastic net regression and gradient boosting machine analysis. Results: TCMR was present in 68% of biopsies. There was strong evidence that patients with TCMR had increased levels of soluble CXCL8, CXCL9, CXCL10, IL-16, IL-18, HGF, CCL4, MIF, SCGF-β, and HGF before biopsy. There was some evidence for increased levels of sCD25, ICAM-1, IL-6, IL-3, and CCL11. Diagnostic value of both single cytokine levels and a combination of cytokines and clinical markers was poor, with AUROCs not exceeding 0.7. Conclusion: Patients with TCMR showed raised levels of cytokines and chemokines reflective of T-cell activation and chemotaxis. Despite giving insight into the mechanisms of TCMR, the diagnostic value of soluble cytokines for the confirmation of TCMR in a clinical scenario of suspected TCMR is poor

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

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