DOCSLIB.ORG

Epigenomics and Single-Cell Sequencing Define a Developmental Hierarchy in Langerhans Cell Histiocytosis

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Epigenomics and Single-Cell Sequencing Define a Developmental Hierarchy in Langerhans Cell Histiocytosis Published OnlineFirst July 25, 2019; DOI: 10.1158/2159-8290.CD-19-0138 RESEARCH ARTICLE Epigenomics and Single-Cell Sequencing Define a Developmental Hierarchy in Langerhans Cell Histiocytosis Florian Halbritter1,2, Matthias Farlik1,3, Raphaela Schwentner2, Gunhild Jug2, Nikolaus Fortelny1, Thomas Schnöller2, Hanja Pisa2, Linda C. Schuster1, Andrea Reinprecht4, Thomas Czech4, Johannes Gojo5, Wolfgang Holter2,5,6, Milen Minkov2,7, Wolfgang M. Bauer3, Ingrid Simonitsch-Klupp8, Christoph Bock1,9,10,11, and Caroline Hutter2,5,6 ABSTRACT Langerhans cell histiocytosis (LCH) is a rare neoplasm predominantly affecting children. It occupies a hybrid position between cancers and inflammatory dis- eases, which makes it an attractive model for studying cancer development. To explore the molecular mechanisms underlying the pathophysiology of LCH and its characteristic clinical heterogeneity, we investigated the transcriptomic and epigenomic diversity in primary LCH lesions. Using single-cell RNA sequencing, we identified multiple recurrent types of LCH cells within these biopsies, including puta- tive LCH progenitor cells and several subsets of differentiated LCH cells. We confirmed the presence of proliferative LCH cells in all analyzed biopsies using IHC, and we defined an epigenomic and gene- regulatory basis of the different LCH-cell subsets by chromatin-accessibility profiling. In summary, our single-cell analysis of LCH uncovered an unexpected degree of cellular, transcriptomic, and epigenomic heterogeneity among LCH cells, indicative of complex developmental hierarchies in LCH lesions. SIGNIFICANCE: This study sketches a molecular portrait of LCH lesions by combining single-cell tran- scriptomics with epigenome profiling. We uncovered extensive cellular heterogeneity, explained in part by an intrinsic developmental hierarchy of LCH cells. Our findings provide new insights and hypotheses for advancing LCH research and a starting point for personalizing therapy. See related commentary by Gruber et al., p. 1343. 1CeMM Research Center for Molecular Medicine of the Austrian Acad- Note: Supplementary data for this article are available at Cancer Discovery emy of Sciences, Vienna, Austria. 2St. Anna Children’s Cancer Research Online (http://cancerdiscovery.aacrjournals.org/). 3 Institute (CCRI), Vienna, Austria. Department of Dermatology, Medical F. Halbritter and M. Farlik contributed equally to this work. University of Vienna, Vienna, Austria. 4Department of Neurosurgery, Medi- cal University of Vienna, Vienna, Austria. 5Department of Pediatrics, Medi- Corresponding Authors: Caroline Hutter, St. Anna Children’s Cancer cal University of Vienna, Vienna, Austria. 6St. Anna Children’s Hospital, Research Institute (CCRI), St. Anna Kinderkrebsforschung, Zimmer- St. Anna Kinderspital, Vienna, Austria. 7Department of Pediatrics, Adoles- mannplatz 10, 1090 Vienna, Austria. Phone: 4314-0470-4043; E-mail: cent Medicine and Neonatology, Rudolfstiftung Hospital, Vienna, Austria. [email protected]; and Christoph Bock, CeMM Research Center for 8Clinical Institute of Pathology, Medical University of Vienna, Vienna, Aus- Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, tria. 9Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria. E-mail: [email protected] Vienna, Austria. 10Max Planck Institute for Informatics, Saarland Informat- Cancer Discov 2019;9:1406–21 11 ics Campus, Saarbrücken, Germany. Ludwig Boltzmann Institute for Rare doi: 10.1158/2159-8290.CD-19-0138 and Undiagnosed Diseases, Vienna, Austria. ©2019 American Association for Cancer Research. 1406 | CANCER DISCOVERY OCTOBER 2019 www.aacrjournals.org Downloaded from cancerdiscovery.aacrjournals.org on September 27, 2021. © 2019 American Association for Cancer Research. Published OnlineFirst July 25, 2019; DOI: 10.1158/2159-8290.CD-19-0138 INTRODUCTION BRAFV600E mutation (6–8), but the pathophysiology of LCH is different from cancers with oncogenic BRAFV600E mutations, Langerhans cell histiocytosis (LCH) is a rare hematopoietic such as melanoma, non–small cell lung cancer, and colorec- neoplasm driven by gain-of-function mutations in the MAPK tal cancer. LCH cells carry few genetic alterations beyond pathway (1, 2). The name of the disease originates from the BRAFV600E or another oncogenic driver in the MAPK pathway characteristic expression of CD207, also known as langerin, (MAP2K1, ARAF), and they show no progressive accumula- which is conventionally associated with Langerhans cells—a tion of recurrent somatic mutations (9). Instead, high levels type of resident epidermal immune cell (3). However, the rela- of inflammatory infiltrate suggest that cell-extrinsic, immu- tionship between LCH cells and Langerhans cells remains con- nologic stimuli could be key contributors to the formation of troversial, and LCH has also been linked to dendritic cells and LCH lesions (10–12). LCH is lethal in up to 20% of patients other myeloid cell types (4–6). LCH is phenotypically character- with risk organ–positive, multisystem disease treated accord- ized by an accumulation of CD1A and CD207 double-positive ing to current standard of care. In contrast, the disease often cells in various tissues (Fig. 1A). These lesions can develop in resolves spontaneously following unknown mechanisms in almost any organ, but are most common in skin and bone (1). patients with single lesions (1, 12). LCH severity is clinically assessed by the extent of the dis- ease (involvement of one or more organs, i.e., single-system RESULTS vs. multisystem disease) and the involvement of known risk organs (liver, spleen, hematopoietic system). Treatment deci- Langerhans Cell Histiocytosis Lesions Are sions are currently based solely on clinical presentation, as Composed of Distinct Cell Populations no reliable molecular markers for risk stratification have LCH cells within the same lesion show wide variability in been identified yet. LCH is most commonly driven by the their CD1A and CD207 levels based on IHC (Fig. 1B) and flow OCTOber 2019 CANCER DISCOVERY | 1407 Downloaded from cancerdiscovery.aacrjournals.org on September 27, 2021. © 2019 American Association for Cancer Research. Published OnlineFirst July 25, 2019; DOI: 10.1158/2159-8290.CD-19-0138 RESEARCH ARTICLE Halbritter et al. A Langerhans cells histiocytosis (LCH) is a rare BELCH cells are Marker genes identify multiple cell types in single-cell transcriptome data of LCH lesions paediatric disease that affects various organs heterogeneous CD1A CD207 MAP2K1 CD33 BRAF Lesions formed by immune Bone lesion within biopsies Brain cells of unknown origin CD1A Cell surface markers: Lymph node CD1A, CD207 High Skin Genetic drivers: Lung Gain of MAPK signaling, CD3D IL32 FOXP3 CD19 CD79A V600E Liver 60% BRAF CD207 Spleen Incidence: Bone ∼4.6/million (age 0–15) Hematoxylin and eosin stain Skin lesion t-SNE dimension 2 IL3RACLEC4C TCF4 CD14 CD163 BRAFV600E Normalized UMI count Low t-SNE dimension 1 CDLCH biopsy samples analyzed using LCH biopsy samples analyzed using F Single-cell transcriptomics captures LCH cells single-cell transcriptome sequencing single-cell transcriptome sequencing and their microenvironment Identifier Biopsy Sex Age Extent Plasmacytoid CD1A+ CD207 + LCH_ELymph FemaleChild Multi- dendritic cells (LCH cells) node system LCH_H Skin Female Infant Multi- B cells system LCH_I Bone Male Child Single- system LCH_J Skin Female Aged Multi- system LCH_K Bone Male Child Single- system t-SNE dimension 2 LCH_N Skin Male Adult Multi- t-SNE dimension 2 system T cells LCH_O Bone Male Child Single- 19,044 single-cell Monocytes/ system transcriptomes macrophages t-SNE dimension 1 t-SNE dimension 1 Patient: EHIJKNO LCH cellImmune cell Figure 1. Single-cell transcriptome analysis captures cellular and molecular diversity of LCH lesions. A, Clinical presentation and incidence of LCH. Shown is an X-ray image of LCH bone lesions in the skull (top right), a photograph of extensive LCH skin lesions (bottom right), and a hematoxylin and eosin staining of an LCH biopsy (bottom left). B, Cellular heterogeneity in an LCH biopsy (lymph node), as revealed by immunostaining for CD1A, CD207, and an antibody that specifically detects mutated BRAFV600E protein. C, Overview of patient biopsy samples analyzed by single-cell RNA sequencing (RNA-seq) in this study. D, Low-dimensional projection (t-SNE plot) of the combined single-cell RNA-seq dataset across all analyzed biopsies, comprising a total of 19,044 single-cell transcriptome profiles. E, Molecular heterogeneity in LCH illustrated by low-dimensional projection (as in D) of all single-cell transcriptome profiles overlaid with the expression levels of selected marker genes (dark blue color indicates high expression levels). F, Cellular hetero- geneity in LCH illustrated by low-dimensional projection (as in D), annotated with cell types inferred from marker-gene expression. cytometry (Supplementary Fig. S1). To characterize the cel- Focusing initially on LCH-specific transcriptional profiles lular and molecular landscape of LCH lesions, we performed that were shared across cells and across patients, we com- droplet-based single-cell transcriptome sequencing of seven pared CD1A and CD207 marker–positive LCH cells with four LCH lesions. The biopsies were obtained from patients with immune-cell populations identified in all biopsies (Supple- multisystem disease (n = 4) and single-system disease (n = 3), mentary Fig. S2D). The LCH cells showed high expression of and they were collected
Load more

Recommended publications
  • Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
    Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • Cd1a [O10] Concentrated and Prediluted Monoclonal Antibody 901-3158-061719
    CD1a [O10] Concentrated and Prediluted Monoclonal Antibody 901-3158-061719 Catalog Number: ACI 3158 A, B API 3158 AA VLTM 3158 G20 Description: 0.1, 0.5 mL conc. 6.0 mL, RTU 20 mL, RTU Dilution: 1:100 Ready-to-use Ready-to-use Diluent: Van Gogh Yellow N/A N/A Intended Use: Protocol Recommendations (VALENT® Automated Slide For In Vitro Diagnostic Use Staining Platform) Cont’d: CD1a [O10] is a mouse monoclonal antibody that is intended for Protein Block (Optional): Incubate for 10-20 minutes at RT with Val laboratory use in the qualitative identification of CD1a protein by Background Block. immunohistochemistry (IHC) in formalin-fixed paraffin-embedded Primary Antibody: Incubate for 30 minutes. (FFPE) human tissues. The clinical interpretation of any staining or its Secondary: Incubate for 10 minutes with Val Mouse Secondary. absence should be complemented by morphological studies using proper Linker: Incubate for 10 minutes with Val Universal Linker. controls and should be evaluated within the context of the patient’s Polymer: Incubate for 10 minutes with Val Universal Polymer. clinical history and other diagnostic tests by a qualified pathologist. Chromogen: Incubate for 5 minutes with Val DAB. Summary and Explanation: Counterstain: Counterstain for 5 minutes with Val Hematoxylin. CD1a is a protein of 43 to 49 kDa and is expressed on dendritic cells and cortical thymocytes (1,2). CD1a [O10] staining has been shown to be Protocol Recommendations (intelliPATH FLX® and manual use): useful in the differentiation of Langerhans cells from interdigitating cells. Peroxide Block: Block for 5 minutes with Peroxidazed 1. It has also proved useful for phenotyping Langerhans cell histiocytosis Pretreatment: Perform heat retrieval using Diva or Reveal Decloaker.
    [Show full text]
  • Transcriptional Control of Tissue-Resident Memory T Cell Generation
    Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Filip Cvetkovski All rights reserved ABSTRACT Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Tissue-resident memory T cells (TRM) are a non-circulating subset of memory that are maintained at sites of pathogen entry and mediate optimal protection against reinfection. Lung TRM can be generated in response to respiratory infection or vaccination, however, the molecular pathways involved in CD4+TRM establishment have not been defined. Here, we performed transcriptional profiling of influenza-specific lung CD4+TRM following influenza infection to identify pathways implicated in CD4+TRM generation and homeostasis. Lung CD4+TRM displayed a unique transcriptional profile distinct from spleen memory, including up-regulation of a gene network induced by the transcription factor IRF4, a known regulator of effector T cell differentiation. In addition, the gene expression profile of lung CD4+TRM was enriched in gene sets previously described in tissue-resident regulatory T cells. Up-regulation of immunomodulatory molecules such as CTLA-4, PD-1, and ICOS, suggested a potential regulatory role for CD4+TRM in tissues. Using loss-of-function genetic experiments in mice, we demonstrate that IRF4 is required for the generation of lung-localized pathogen-specific effector CD4+T cells during acute influenza infection. Influenza-specific IRF4−/− T cells failed to fully express CD44, and maintained high levels of CD62L compared to wild type, suggesting a defect in complete differentiation into lung-tropic effector T cells.
    [Show full text]
  • Single-Cell RNA Sequencing Demonstrates the Molecular and Cellular Reprogramming of Metastatic Lung Adenocarcinoma
    ARTICLE https://doi.org/10.1038/s41467-020-16164-1 OPEN Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma Nayoung Kim 1,2,3,13, Hong Kwan Kim4,13, Kyungjong Lee 5,13, Yourae Hong 1,6, Jong Ho Cho4, Jung Won Choi7, Jung-Il Lee7, Yeon-Lim Suh8,BoMiKu9, Hye Hyeon Eum 1,2,3, Soyean Choi 1, Yoon-La Choi6,10,11, Je-Gun Joung1, Woong-Yang Park 1,2,6, Hyun Ae Jung12, Jong-Mu Sun12, Se-Hoon Lee12, ✉ ✉ Jin Seok Ahn12, Keunchil Park12, Myung-Ju Ahn 12 & Hae-Ock Lee 1,2,3,6 1234567890():,; Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell tran- scriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions. 1 Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea.
    [Show full text]
  • View Dendritic Cell Development Poster
    Overview of Dendritic Cell Development Lineage–, CD45+, Common CD117 (c-kit) Common MHCII+, CD11c+ – + CD207 (Langerin) Myeloid CD117 (c-kit) Lineage , CD45 , Myeloid Progenitor MHCII (HLA-DR)+, CD11c+ Progenitor CD324 (E-Cadherin) Human Mouse CD326 (EpCAM) CD207 (Langerin) TGFb1 Cells CD11b, CD115 Cells CD14 Monocyte CD324 (E-Cadherin) Monocyte M-CSF CD11b – + Ly6C Langerhans CD24 Lineage , CD45 , M-CSF CD326 (EpCAM) MHCII (HLA-DR)+, CD11c+ Langerhans CD11blo Zbtb46– Cells CD172a (Sirp-α) CD16 CD1ahi, CD1c CD205 (DEC-205) Cells CSF F4/80 CD64 CD172a (Sirp-α) Lineage–, CD45+, FLT3L TLR3, TLR11 CD1a, CD1c Inflammatory CD369 (Dectin-1/CLEC7A) MHCII+, CD11c+ +/– CSF IL-15 CD8–, CD14– CD11b, CD14 CD371 (CLEC12A) CD64 Monocyte- FLT3L Inflammatory CD370 (Clec9a)– CD172a (Sirp-α) IL-15 CLEC6A CD11b derived lo Monocyte- CD206, CD209 (DC-SIGN) TLR1, TLR2, TLR3 , TLR6 CD209a (DC-SIGN) CD367 (DCIR/CLEC4A) DCs CD14– CD272 (BTLA)lo derived CD369 (Dectin-1/CLEC7A) DCs Common Ly-6C – + CD371 (CLEC12A) CD117 (c-kit) Lineage , CD45 , IL-1β, IL-6, IL-10, TLR1-6, TLR7-8, TLR10 Dendritic + lo CLEC6A – – CD135/FLT3 MHCII , CD11c IL-12, IL-23, TNF CD8a , CD14 IL-1β, IL-6 IL-10, Precursor TLR3lo, TLR4, TLR7, TLR8 CD45R (B220) IL-12, IL-23, TNF Plasmacytoid CD207 (Langerin)– Cells CD317 (BST-2) Common Lineage–, CD45+, FLT3L DCs Lineage–, CD45+, + Ly6C + lo/– CD207 IFN Type I + + Dendritic CD135/FLT3 MHCII (HLA-DR) , CD11c Lineage–, CD45+, IRF7, IRF8, BATF3hi Siglec-H MHCII (HLA-DR) , CD11c hi – + CD123 + + Dermal SpiB, Zbtb46 CD1a, CD64 CD1a Precursor CD117 (c-kit)
    [Show full text]
  • Hepatitis C Virus As a Unique Human Model Disease to Define
    viruses Review Hepatitis C Virus as a Unique Human Model Disease to Define Differences in the Transcriptional Landscape of T Cells in Acute versus Chronic Infection David Wolski and Georg M. Lauer * Liver Center at the Gastrointestinal Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA * Correspondence: [email protected]; Tel.: +1-617-724-7515 Received: 27 June 2019; Accepted: 23 July 2019; Published: 26 July 2019 Abstract: The hepatitis C virus is unique among chronic viral infections in that an acute outcome with complete viral elimination is observed in a minority of infected patients. This unique feature allows direct comparison of successful immune responses with those that fail in the setting of the same human infection. Here we review how this scenario can be used to achieve better understanding of transcriptional regulation of T-cell differentiation. Specifically, we discuss results from a study comparing transcriptional profiles of hepatitis C virus (HCV)-specific CD8 T-cells during early HCV infection between patients that do and do not control and eliminate HCV. Identification of early gene expression differences in key T-cell differentiation molecules as well as clearly distinct transcriptional networks related to cell metabolism and nucleosomal regulation reveal novel insights into the development of exhausted and memory T-cells. With additional transcriptional studies of HCV-specific CD4 and CD8 T-cells in different stages of infection currently underway, we expect HCV infection to become a valuable model disease to study human immunity to viruses. Keywords: viral hepatitis; hepatitis C virus; T cells; transcriptional regulation; transcription factors; metabolism; nucleosome 1.
    [Show full text]
  • CD Markers Are Routinely Used for the Immunophenotyping of Cells
    ptglab.com 1 CD MARKER ANTIBODIES www.ptglab.com Introduction The cluster of differentiation (abbreviated as CD) is a protocol used for the identification and investigation of cell surface molecules. So-called CD markers are routinely used for the immunophenotyping of cells. Despite this use, they are not limited to roles in the immune system and perform a variety of roles in cell differentiation, adhesion, migration, blood clotting, gamete fertilization, amino acid transport and apoptosis, among many others. As such, Proteintech’s mini catalog featuring its antibodies targeting CD markers is applicable to a wide range of research disciplines. PRODUCT FOCUS PECAM1 Platelet endothelial cell adhesion of blood vessels – making up a large portion molecule-1 (PECAM1), also known as cluster of its intracellular junctions. PECAM-1 is also CD Number of differentiation 31 (CD31), is a member of present on the surface of hematopoietic the immunoglobulin gene superfamily of cell cells and immune cells including platelets, CD31 adhesion molecules. It is highly expressed monocytes, neutrophils, natural killer cells, on the surface of the endothelium – the thin megakaryocytes and some types of T-cell. Catalog Number layer of endothelial cells lining the interior 11256-1-AP Type Rabbit Polyclonal Applications ELISA, FC, IF, IHC, IP, WB 16 Publications Immunohistochemical of paraffin-embedded Figure 1: Immunofluorescence staining human hepatocirrhosis using PECAM1, CD31 of PECAM1 (11256-1-AP), Alexa 488 goat antibody (11265-1-AP) at a dilution of 1:50 anti-rabbit (green), and smooth muscle KD/KO Validated (40x objective). alpha-actin (red), courtesy of Nicola Smart. PECAM1: Customer Testimonial Nicola Smart, a cardiovascular researcher “As you can see [the immunostaining] is and a group leader at the University of extremely clean and specific [and] displays Oxford, has said of the PECAM1 antibody strong intercellular junction expression, (11265-1-AP) that it “worked beautifully as expected for a cell adhesion molecule.” on every occasion I’ve tried it.” Proteintech thanks Dr.
    [Show full text]
  • Gene List HTG Edgeseq Immuno-Oncology Assay
    Gene List HTG EdgeSeq Immuno-Oncology Assay Adhesion ADGRE5 CLEC4A CLEC7A IBSP ICAM4 ITGA5 ITGB1 L1CAM MBL2 SELE ALCAM CLEC4C DST ICAM1 ITGA1 ITGA6 ITGB2 LGALS1 MUC1 SVIL CDH1 CLEC5A EPCAM ICAM2 ITGA2 ITGAL ITGB3 LGALS3 NCAM1 THBS1 CDH5 CLEC6A FN1 ICAM3 ITGA4 ITGAM ITGB4 LGALS9 PVR THY1 Apoptosis APAF1 BCL2 BID CARD11 CASP10 CASP8 FADD NOD1 SSX1 TP53 TRAF3 BCL10 BCL2L1 BIRC5 CASP1 CASP3 DDX58 NLRP3 NOD2 TIMP1 TRAF2 TRAF6 B-Cell Function BLNK BTLA CD22 CD79A FAS FCER2 IKBKG PAX5 SLAMF1 SLAMF7 SPN BTK CD19 CD24 EBF4 FASLG IKBKB MS4A1 RAG1 SLAMF6 SPI1 Cell Cycle ABL1 ATF1 ATM BATF CCND1 CDK1 CDKN1B NCL RELA SSX1 TBX21 TP53 ABL2 ATF2 AXL BAX CCND3 CDKN1A EGR1 REL RELB TBK1 TIMP1 TTK Cell Signaling ADORA2A DUSP4 HES1 IGF2R LYN MAPK1 MUC1 NOTCH1 RIPK2 SMAD3 STAT5B AKT3 DUSP6 HES5 IKZF1 MAF MAPK11 MYC PIK3CD RNF4 SOCS1 STAT6 BCL6 ELK1 HEY1 IKZF2 MAP2K1 MAPK14 NFATC1 PIK3CG RORC SOCS3 SYK CEBPB EP300 HEY2 IKZF3 MAP2K2 MAPK3 NFATC3 POU2F2 RUNX1 SPINK5 TAL1 CIITA ETS1 HEYL JAK1 MAP2K4 MAPK8 NFATC4 PRKCD RUNX3 STAT1 TCF7 CREB1 FLT3 HMGB1 JAK2 MAP2K7 MAPKAPK2 NFKB1 PRKCE S100B STAT2 TYK2 CREB5 FOS HRAS JAK3 MAP3K1 MEF2C NFKB2 PTEN SEMA4D STAT3 CREBBP GATA3 IGF1R KIT MAP3K5 MTDH NFKBIA PYCARD SMAD2 STAT4 Chemokine CCL1 CCL16 CCL20 CCL25 CCL4 CCR2 CCR7 CX3CL1 CXCL12 CXCL3 CXCR1 CXCR6 CCL11 CCL17 CCL21 CCL26 CCL5 CCR3 CCR9 CX3CR1 CXCL13 CXCL5 CXCR2 MST1R CCL13 CCL18 CCL22 CCL27 CCL7 CCR4 CCRL2 CXCL1 CXCL14 CXCL6 CXCR3 PPBP CCL14 CCL19 CCL23 CCL28 CCL8 CCR5 CKLF CXCL10 CXCL16 CXCL8 CXCR4 XCL2 CCL15 CCL2 CCL24 CCL3 CCR1 CCR6 CMKLR1 CXCL11 CXCL2 CXCL9 CXCR5
    [Show full text]
  • Virtual Chip-Seq: Predicting Transcription Factor Binding
    bioRxiv preprint doi: https://doi.org/10.1101/168419; this version posted March 12, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Virtual ChIP-seq: predicting transcription factor binding 2 by learning from the transcriptome 1,2,3 1,2,3,4,5 3 Mehran Karimzadeh and Michael M. Hoffman 1 4 Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada 2 5 Princess Margaret Cancer Centre, Toronto, ON, Canada 3 6 Vector Institute, Toronto, ON, Canada 4 7 Department of Computer Science, University of Toronto, Toronto, ON, Canada 5 8 Lead contact: michael.hoff[email protected] 9 March 8, 2019 10 Abstract 11 Motivation: 12 Identifying transcription factor binding sites is the first step in pinpointing non-coding mutations 13 that disrupt the regulatory function of transcription factors and promote disease. ChIP-seq is 14 the most common method for identifying binding sites, but performing it on patient samples is 15 hampered by the amount of available biological material and the cost of the experiment. Existing 16 methods for computational prediction of regulatory elements primarily predict binding in genomic 17 regions with sequence similarity to known transcription factor sequence preferences. This has limited 18 efficacy since most binding sites do not resemble known transcription factor sequence motifs, and 19 many transcription factors are not even sequence-specific. 20 Results: 21 We developed Virtual ChIP-seq, which predicts binding of individual transcription factors in new 22 cell types using an artificial neural network that integrates ChIP-seq results from other cell types 23 and chromatin accessibility data in the new cell type.
    [Show full text]
  • Assessing Various Control Samples for Microarray Gene Expression Profiling of Laryngeal Squamous Cell Carcinoma
    biomolecules Communication Assessing Various Control Samples for Microarray Gene Expression Profiling of Laryngeal Squamous Cell Carcinoma Adam Ustaszewski 1 , Magdalena Kostrzewska-Poczekaj 1, Joanna Janiszewska 1 , Malgorzata Jarmuz-Szymczak 1,2, Malgorzata Wierzbicka 1,3 , Joanna Marszal 3 , Reidar Grénman 4 and Maciej Giefing 1,* 1 Institute of Human Genetics, Polish Academy of Sciences, Strzeszy´nska32, 60-479 Pozna´n,Poland; [email protected] (A.U.); [email protected] (M.K.-P.); [email protected] (J.J.); [email protected] (M.J.-S.); [email protected] (M.W.) 2 Department of Oncology, Hematology and Bone Marrow Transplantation, Poznan University of Medical Sciences, 61-001 Pozna´n,Poland 3 Department of Otolaryngology and Laryngological Oncology, Pozna´nUniversity of Medical Sciences, 60-355 Pozna´n,Poland; [email protected] 4 Department of Otorhinolaryngology-Head and Neck Surgery, University of Turku and Turku University Hospital, 20520 Turku, Finland; seija.grenman@tyks.fi * Correspondence: maciej.giefi[email protected]; Tel.: +48-61-6579-138 Abstract: Selection of optimal control samples is crucial in expression profiling tumor samples. To address this issue, we performed microarray expression profiling of control samples routinely used in head and neck squamous cell carcinoma studies: human bronchial and tracheal epithelial cells, Citation: Ustaszewski, A.; squamous cells obtained by laser uvulopalatoplasty and tumor surgical margins. We compared Kostrzewska-Poczekaj, M.; the results using multidimensional scaling and hierarchical clustering versus tumor samples and Janiszewska, J.; Jarmuz-Szymczak, M.; laryngeal squamous cell carcinoma cell lines. A general observation from our study is that the Wierzbicka, M.; Marszal, J.; Grénman, R.; Giefing, M.
    [Show full text]
  • Distribution Patterns of Dendritic Cells and T Cells in Diffuse Large B-Cell
    Imaging, Diagnosis, Prognosis Distribution Patterns of Dendritic Cells and T Cells in Diffuse Large B-Cell Lymphomas Correlate with Prognoses Kung-Chao Chang,1, 2 Guan-Cheng Huang,3 DanJones,5 and Ya-Hui Lin4 Abstract Purpose: Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin’s lymphomas, accounts for 30% to 40% of all lymphoma cases. However, long-term survival by current chemotherapy was achieved in only 40% of patients, warranting the development of novel therapeutic strategies including T-cell immunotherapy. However, the level of baseline immune activation in DLBCL is unclear. Experimental Design: The density and distribution of dendritic cells and Tcells in 48 cases of primary DLBCL was evaluated by immunohistochemistry. Results: Increased numbers of intratumoral CD1a+ dendritic cells and increased S100+ cells and CD45RO+ T cells around the edges of the tumors were seen in 10 of 48 (21%), 9 of 48 (19%), and 10 of 48 (21%) cases and these were correlated with a favorable prognosis (P =0.015; P =0.070,andP = 0.017, respectively), along with increased granzyme B+ T cells in tumor beds (P = 0.013). Increased peritumoral T cells were correlated with tumor expression of HL A-DR (r = 0.446; P = 0.002). Extranodal lymphomas showed fewer tumor-associated CD45RO+ Tcells (r =-0.407;P = 0.001) and less conspicuous dendritic cell infiltrates. Conclusions: In DLBCL, the presence of baseline antitumor immune response is associated with favorable clinical outcome, and thus adjuvant T-cell immunotherapy may further boost treatment responses. The most common subtype of non-Hodgkin’s lymphoma is In animal models, the mice, which were immunized with diffuse large B-cell lymphoma (DLBCL; ref.
    [Show full text]