scholarly journals Isolation and Characterization of Heparan Sulfate from Human Lung Tissues

Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5512
Author(s):  
Rupert Derler ◽  
Nikola Kitic ◽  
Tanja Gerlza ◽  
Andreas J. Kungl
Keyword(s):  
Heparan Sulfate ◽  
Immune Cell ◽  
Human Lung ◽  
Expression Profiles ◽  
Experimental Investigations ◽  
Binding Affinities ◽  
Isolation And Characterization ◽  
Core Proteins ◽  
Therapeutic Concepts ◽  
Disaccharide Composition

Glycosaminoglycans are a class of linear, highly negatively charged, O-linked polysaccharides that are involved in many (patho)physiological processes. In vitro experimental investigations of such processes typically involve porcine-derived heparan sulfate (HS). Structural information about human, particularly organ-specific heparan sulfate, and how it compares with HS from other organisms, is very limited. In this study, heparan sulfate was isolated from human lung tissues derived from five donors and was characterized for their overall size distribution and disaccharide composition. The expression profiles of proteoglycans and HS-modifying enzymes was quantified in order to identify the major core proteins for HS. In addition, the binding affinities of human HS to two chemokines—CXCL8 and CCL2—were investigated, which represent important inflammatory mediators in lung pathologies. Our data revealed that syndecans are the predominant proteoglycan class in human lungs and that the disaccharide composition varies among individuals according to sex, age, and health stage (one of the donor lungs was accidentally discovered to contain a solid tumor). The compositional difference of the five human lung HS preparations affected chemokine binding affinities to various degrees, indicating selective immune cell responses depending on the relative chemokine–glycan affinities. This represents important new insights that could be translated into novel therapeutic concepts for individually treating lung immunological disorders via HS targets.

scholarly journals A molecular cell atlas of the human lung from single cell RNA sequencing

2019 ◽  
Author(s):  
Kyle J. Travaglini ◽  
Ahmad N. Nabhan ◽  
Lolita Penland ◽  
Rahul Sinha ◽  
Astrid Gillich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Human Lung ◽  
Expression Profiles ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Trafficking ◽  
Single Cell Rna Sequencing

AbstractAlthough single cell RNA sequencing studies have begun providing compendia of cell expression profiles, it has proven more difficult to systematically identify and localize all molecular cell types in individual organs to create a full molecular cell atlas. Here we describe droplet- and plate-based single cell RNA sequencing applied to ∼75,000 human lung and blood cells, combined with a multi-pronged cell annotation approach, which have allowed us to define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 of 45 previously known cell types or subtypes and 14 new ones. This comprehensive molecular atlas elucidates the biochemical functions of lung cell types and the cell-selective transcription factors and optimal markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signaling interactions including sources and targets of chemokines in immune cell trafficking and expression changes on lung homing; and identifies the cell types directly affected by lung disease genes and respiratory viruses. Comparison to mouse identified 17 molecular types that appear to have been gained or lost during lung evolution and others whose expression profiles have been substantially altered, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions, and interactions are achieved in development and tissue engineering and altered in disease and evolution.

scholarly journals Molecular cloning of a phosphatidylinositol-anchored membrane heparan sulfate proteoglycan from human lung fibroblasts.

1990 ◽  
Vol 111 (6) ◽  
pp. 3165-3176 ◽  
Author(s):  
G David ◽  
V Lories ◽  
B Decock ◽  
P Marynen ◽  
J J Cassiman ◽  
...  
Keyword(s):  
Heparan Sulfate ◽  
Human Lung ◽  
Core Protein ◽  
Heparan Sulfate Proteoglycan ◽  
Amino Acid Sequences ◽  
Lung Fibroblasts ◽  
Sulfate Proteoglycan ◽  
Human Lung Fibroblast ◽  
Human Lung Fibroblasts ◽  
Core Proteins

Two mAbs raised against the 64-kD core protein of a membrane heparan sulfate proteoglycan from human lung fibroblasts also recognize a nonhydrophobic proteoglycan which accumulates in the culture medium of the cells. Pulse-chase studies suggest that the hydrophobic cell-associated forms act as precursors for the nonhydrophobic medium-released species. The core proteins of the medium-released proteoglycans are slightly smaller than those of the hydrophobic cell-associated species, but the NH2-terminal amino acid sequences of both forms are identical. The characterization of human lung fibroblast cDNAs that encode the message for these core proteins and the effect of bacterial phosphatidylinositol-specific phospholipase C suggest that the hydrophobic proteoglycan is membrane-anchored through a phospholipid tail. These data identify a novel membrane proteoglycan in human lung fibroblasts and imply that the shedding of this proteoglycan may be related to the presence of the phospholipid anchor.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

scholarly journals Predicting Host Immune Cell Dynamics and Key Disease-Associated Genes Using Tissue Transcriptional Profiles

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 301
Author(s):  
Muying Wang ◽  
Satoshi Fukuyama ◽  
Yoshihiro Kawaoka ◽  
Jason E. Shoemaker
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Mean Squared Error ◽  
Expression Profiles ◽  
Statistical Tests ◽  
Critical Factor ◽  
Expression Data ◽  
Cell Dynamics ◽  
Cell Counts

Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests.

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