iNOS expressing macrophages co-localize with nitrotyrosine staining after myocardial infarction in humans

IntroductionInducible nitric oxide synthase (iNOS) produces micromolar amounts of nitric oxide (NO) upon the right stimuli, whose further reactions can lead to oxidative stress. In murine models of myocardial infarction (MI), iNOS is known to be expressed in infiltrating macrophages, which at early onset enter the infarcted zone and are associated with inflammation. In contrast cardiac tissue resident macrophages are thought to enhance regeneration of tissue injury and re-establish homeostasis. Both detrimental and beneficial effects of iNOS have been described, still the role of iNOS in MI is not fully understood. Our aim was to examine cell expression patterns of iNOS and nitrotyrosine (NT) production in human MI.Material and MethodsWe examined in postmortem human MI hearts the iNOS mRNA expression by means of qPCR. Further we performed immunohistochemical stainings for cell type identification. Afterwards a distance analysis between iNOS and NT was carried out to determine causality between iNOS and NT production.ResultsiNOS mRNA expression was significantly increased in infarcted regions of human MI hearts and iNOS protein expression was detected in resident macrophages in infarcted human hearts as well as in controls hearts, being higher in resident macrophages in MI hearts compared to control. Furthermore in MI and in healthy human hearts cells showing signs of NT production peaked within 10–15 µm proximity of iNOS+ cells.DiscussionThese results indicate that, unexpectedly, resident macrophages are the main source of iNOS expression in postmortem human MI hearts. The peak of NT positive cells within 10–15 µm of iNOS+ cells suggest an iNOS dependent level of NT and therefore iNOS dependent oxidative stress. Our results contribute to understanding the role of iNOS in human MI

SnRNA sequencing defines signaling by RBC-derived extracellular vesicles in the murine heart.

Extracellular vesicles (EVs) mediate intercellular signaling by transferring their cargo to recipient cells, but the functional consequences of signaling are not fully appreciated. RBC-derived EVs are abundant in circulation and have been implicated in regulating immune responses. Here, we use a transgenic mouse model for fluorescence-based mapping of RBC-EV recipient cells to assess the role of this intercellular signaling mechanism in heart disease. Using fluorescent-based mapping, we detected an increase in RBC-EV-targeted cardiomyocytes in a murine model of ischemic heart failure. Single cell nuclear RNA sequencing of the heart revealed a complex landscape of cardiac cells targeted by RBC-EVs, with enrichment of genes implicated in cell proliferation and stress signaling pathways compared with non-targeted cells. Correspondingly, cardiomyocytes targeted by RBC-EVs more frequently express cellular markers of DNA synthesis, suggesting the functional significance of EV-mediated signaling. In conclusion, our mouse model for mapping of EV-recipient cells reveals a complex cellular network of RBC-EV-mediated intercellular communication in ischemic heart failure and suggests a functional role for this mode of intercellular signaling

Supplementary Figures 1-12 from Colorectal Cancer Organoid–Stroma Biobank Allows Subtype-Specific Assessment of Individualized Therapy Responses

Supplementary Figure S1 shows light microscopic images of CRC organoids and CAFs. Supplementary Figure S2 shows immunostaining and RNA sequencing analysis of cultured CAFs. Supplementary Figure S3 shows chromosomal copy number changes in tumors and matched organoids. Supplementary Figure S4 shows the transcriptional variation among tumors and organoids. Supplementary Figure S5 shows the classification of cancer intrinsic subtypes (CRIS) in tumors, matched organoids and xenotransplants. Supplementary Figure S6 shows the tissue microarray analysis of the of tumor samples from the colorectal cancer organoid-stroma biobank cohort. Supplementary Figure S7 shows the association of growth characteristics with molecular features of colorectal cancer organoid-stroma biobank. Supplementary Figure S8 shows the shows CMS and CRIS classifications of tumors and of matched organoids in different contexts. Supplementary Figure S9 describes the establishment of a drug screening workflow in 3D organoid-stroma co-cultures. Supplementary Figure S10 describes the development of a dual luciferase assay to study cell viability simultaneously in organoids and CAFs. Supplementary Figure S11 demonstrates that the MET inhibitor BAY-474 sensitizes Gefitinib resistant co-cultures. Supplementary Figure S12 shows the association of identified sensitivity and resistance signatures with gene expression and prognosis in different CMS.</p

Supplementary Tables 1-12 from Colorectal Cancer Organoid–Stroma Biobank Allows Subtype-Specific Assessment of Individualized Therapy Responses

Supplementary Table S1 shows the clinical data of the CRC organoid-stroma cohort. Supplementary Table S2 shows the inventory of available materials and molecular analyses. Supplementary Table S3 shows the selected variant types for whole exome analysis. Supplementary Table S4 summarizes the genetic characterization of CRC models. Supplementary Table S5 shows the classification according to the consensus molecular subtypes (CMS). Supplementary Table S6 shows classification according to the CRC intrinsic subtypes (CRIS). Supplementary Table S7 summarizes the functional data in mono- and co-cultures. Supplementary Table S8 shows drug sensitivity of CRC organoids in co-culture with autologous and heterologous fibroblasts. Supplementary Table S9 show the drug sensitivity in all biobank models in mono- and co-culture. Supplementary Table S10 shows the cell viability data of the chemogenomic library screens in resistant co-culture models. Supplementary Table S11 shows the identified drug sensitivity and resistance signatures. Supplementary Table S12 lists all antibodies and primer sequences used in this study.</p