Immune-interacting lymphatic endothelial subtype at capillary terminals drives lymphatic malformation.

"data availability the single-cell raw sequencing data and processed counts tables as well as r files containing raw counts and metadata have been deposited in the gene expression omnibus (accession no gse201916 ). further analysis of innate and adaptive immune cells by flow cytometry showed increase in the frequency ( fig 4 g and data s1 ) and number ( fig s3 d ) of cd45 + cd11b + f4/80 + myeloid cells in the ears of pik3ca h1047r ; vegfr3-creer t2 mice compared to controls.; ptx3 high dermal lecs shared a set of their marker genes and were enriched in transcripts encoding regulators of innate and adaptive immune responses including ptx3 itself as well as phagocytic pathogen ( mrc1 ) and chemokine ( ackr2 ) receptors and regulators of t cell activation ( cd276 cd200 ; fig 5 c and data s2 ).; additional cluster markers are shown ( fig 5 d ) and listed ( data s2 ) and the data are available for browsing at https://makinenlab shinyapps io/dermalymphaticendothelialcells/ .; ordering of cells based on similarities in their expression patterns generated a linear trajectory across the clusters with ptx3 high lecs at the end of the trajectory ( fig 5 b and d ; and data s3 ).; based on the expression of the lec subtype markers identified in the control skin dataset ( data s2 ) we defined clusters of valve collecting vessel and capillary lecs.; enriched genes for each cluster are listed in data s4 .; marker expression defined six clusters corresponding to the same identities than those in the pik3ca h1047r mutant mice including non-proliferating and proliferating ptx3 high capillary lecs ( fig 6 d and e ; fig s4 c ; and data s5 ) but also an additional ptx3 high cluster characterized by high expression of metabolic genes ( fig s4 c and data s5 ).; to avoid the confounding effect of the cell cycle ( ) we determined differentially expressed genes (deg) between the non-proliferative ptx3 clusters in mutant mice in comparison with ptx3 capillary lecs from control mice ( data s6 ).; both mutant clusters also showed enrichment of processes and genes related to immune regulation ( fig 8 a and b ; and data s7 and s8 ).; the latter include upregulation of genes encoding pro-inflammatory cytokines ( ccl2 ccl7 ) (scavenger) receptors ( ackr2 l1cam ) as well as extracellular matrix proteins ( lgals3 ) and proteinases ( adam17 adam8 mmp14 mmp2 ) implicated in inflammatory processes ( fig 8 c and data s6 ).; the absolute cell numbers and cell frequencies presented as % of all live cells are provided in data s1 .; the data were further processed in two steps in which an lec population was extracted from the control (including cre - littermates and wild-type c57bl/6j) and mutant datasets individually after removal of contaminating cells identified as epithelial cells/keratinocytes fibroblasts mural cells and immune cells based on deg analysis ( data s9 ).; in the second step lecs from the control and mutant mice were integrated and additional small clusters with neuronal immune cell and fibroblast identities were removed ( data s9 ).; data s1 shows immune cell frequencies and cell counts for facs data.; data s2 shows cluster gene markers for dermal lecs.; data s3 shows zonation markers for dermal lecs.; data s4 shows cluster gene markers for dermal lecs from the pik3ca h1047r ; cdh5-creer t2 mice.; data s5 shows cluster gene markers for dermal lecs from control and pik3ca h1047r ; cdh5-creer t2 mice.; data s6 shows degs between non-proliferative ptx3 capillary lecs from pik3ca h1047r mutant in comparison with ptx3 capillary lecs from control mice.; data s7 shows go enrichment analysis of degs in non-proliferative ptx3 metabolic capillary lecs from pik3ca h1047r mutant in comparison with ptx3 capillary lecs from control mice.; data s8 shows go enrichment analysis of degs in non-proliferative ptx3 lecs from pik3ca h1047r mutant and control mice.; data s9 shows marker genes of clusters removed during processing of lec dataset from pik3ca h1047r mutant and control mice.; data s1 shows immune cell frequencies and cell counts for facs data.; data s2 shows cluster gene markers for dermal lecs defined by comparing cells in each cluster with all other cells in the other clusters.; data s3 shows zonation markers for dermal lecs.; data s4 shows cluster gene markers for dermal lecs isolated from the pik3ca h1047r ; cdh5-creer t2 mice defined by comparing cells in each cluster with all other cells in the other clusters.; data s5 shows cluster gene markers for dermal lecs from the control and pik3ca h1047r ; cdh5-creer t2 mice defined by comparing cells in each cluster with all other cells in the other clusters.; data s6 shows degs between non-proliferative ptx3 capillary lecs from pik3ca h1047r mutant in comparison with ptx3 capillary lecs from control mice degs were selected by p value with bonferroni correction less than 0 05 and logarithmic fold change > 0 5 or < -0 5.; data s7 shows go enrichment analysis of degs in non-proliferative ptx3 metabolic capillary lecs from pik3ca h1047r mutant in comparison with ptx3 capillary lecs from control mice.; data s8 shows go enrichment analysis of degs in non-proliferative ptx3 lecs from pik3ca h1047r mutant and control mice.; data s9 shows marker genes of clusters removed during processing of lec dataset from pik3ca h1047r mutant and control mice. data availability the single-cell raw sequencing data and processed counts tables as well as r files containing raw counts and metadata have been deposited in the gene expression omnibus (accession no gse201916"

"Disclosures: E. Baselga reported a patent for TOPIAL PIK3CA inhibitor issued. M. Graupera reported “other” from ArQule, Inc., a wholly owned subsidiary of Merck & Co., Inc and “other” from Venthera during the conduct of the study. No other disclosures were reported."

"We thank Dieter Saur (Technische Universität München, Munich, Germany) for the R26-LSL-Pik3caH1047R mice, Sagrario Ortega (Centro Nacional de Investigaciones Oncológicas, Madrid, Spain) for the Vegfr3-CreERT2 mice and Ralf Adams (Max Planck Institute for Molecular Biomedicine, Münster, Germany) for the Cdh5-CreERT2 mice. We also thank the BioVis facility (Uppsala University) for flow cytometer usage and support, the Single Cell Core Facility at Flemingsberg campus, Karolinska Institute, for single-cell sequencing services, Maria Globisch for advice and help with the Multiplex ELISA, and Sofie Lunell Segerqvist and Aissatu Mami Camara for technical assistance. The computations of scRNA-seq data were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) partially funded by the Swedish Research Council through grant agreement no. 2018-05973, under projects SNIC 2022/23-461 and SNIC 2022/22-133. Prasoon Agarwal and Ying Sun (Uppsala University) are acknowledged for assistance with the analysis of scRNA-seq data. This work was supported by grants from Knut and Alice Wallenberg Foundation (2018.0218 [T. Mäkinen] and 2020.0057 [T. Mäkinen and C. Betsholtz]), the Swedish Research Council (2020-0269; T. Mäkinen), the Göran Gustafsson Foundation (T. Mäkinen), the Swedish Cancer Society (19 0220 Pj, 19 0219 Us; T. Mäkinen), the European Research Council (ERC-2014-CoG-646849; T. Mäkinen), the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement no. 814316 (T. Mäkinen, M. Kraft), La Caixa Foundation (HR18-00120; M. Graupera and E. Baselga), and La Caixa Banking Foundation (LCF/BQ/PR20/11770002; S.D. Castillo). S. Stritt was supported by a research fellowship from the Deutsche Forschungsgemeinschaft (STR 1538/1-1) and a non-stipendiary long-term fellowship from the European Molecular Biology Organization (ALTF 86-2017). S.D. Castillo is a recipient of a fellowship from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement no. 749731. Author contributions: Conceptualization: M. Petkova, M. Kraft, and T. Mäkinen. Investigation: M. Petkova, M. Kraft, S. Stritt, I. Martinez-Corral, H. Ortsäter, and B. Jakic. Formal analysis: M. Petkova, M. Kraft, T. Mäkinen; Resources: M. Vanlandewijck, and C. Betsholtz (scRNA-seq); E. Baselga, S.D. Castillo, and M. Graupera (human samples). Data curation: M. Kraft. Writing—original draft: M. Petkova and T. Mäkinen. Writing—review and editing: all authors. Visualization: M. Petkova, M. Kraft, and T. Mäkinen. Supervision: T. Mäkinen. Project administration: T. Mäkinen. Funding acquisition: T. Mäkinen."