scholarly journals Single cell and spatial transcriptomics in human tendon disease indicate dysregulated immune homeostasis

2021 ◽  
pp. annrheumdis-2021-220256
Author(s):  
Moeed Akbar ◽  
Lucy MacDonald ◽  
Lindsay A N Crowe ◽  
Konstantin Carlberg ◽  
Mariola Kurowska-Stolarska ◽  
...  
2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Adrian R. Kendal ◽  
Thomas Layton ◽  
Hussein Al-Mossawi ◽  
Louise Appleton ◽  
Stephanie Dakin ◽  
...  

Cell Reports ◽  
2014 ◽  
Vol 7 (4) ◽  
pp. 1130-1142 ◽  
Author(s):  
Bidesh Mahata ◽  
Xiuwei Zhang ◽  
Aleksandra A. Kolodziejczyk ◽  
Valentina Proserpio ◽  
Liora Haim-Vilmovsky ◽  
...  

2021 ◽  
Vol 12 ◽  
Author(s):  
Brittany Rocque ◽  
Arianna Barbetta ◽  
Pranay Singh ◽  
Cameron Goldbeck ◽  
Doumet Georges Helou ◽  
...  

The liver is unique in both its ability to maintain immune homeostasis and in its potential for immune tolerance following solid organ transplantation. Single-cell RNA sequencing (scRNA seq) is a powerful approach to generate highly dimensional transcriptome data to understand cellular phenotypes. However, when scRNA data is produced by different groups, with different data models, different standards, and samples processed in different ways, it can be challenging to draw meaningful conclusions from the aggregated data. The goal of this study was to establish a method to combine ‘human liver’ scRNA seq datasets by 1) characterizing the heterogeneity between studies and 2) using the meta-atlas to define the dominant phenotypes across immune cell subpopulations in healthy human liver. Publicly available scRNA seq data generated from liver samples obtained from a combined total of 17 patients and ~32,000 cells were analyzed. Liver-specific immune cells (CD45+) were extracted from each dataset, and immune cell subpopulations (myeloid cells, NK and T cells, plasma cells, and B cells) were examined using dimensionality reduction (UMAP), differential gene expression, and ingenuity pathway analysis. All datasets co-clustered, but cell proportions differed between studies. Gene expression correlation demonstrated similarity across all studies, and canonical pathways that differed between datasets were related to cell stress and oxidative phosphorylation rather than immune-related function. Next, a meta-atlas was generated via data integration and compared against PBMC data to define gene signatures for each hepatic immune subpopulation. This analysis defined key features of hepatic immune homeostasis, with decreased expression across immunologic pathways and enhancement of pathways involved with cell death. This method for meta-analysis of scRNA seq data provides a novel approach to broadly define the features of human liver immune homeostasis. Specific pathways and cellular phenotypes described in this human liver immune meta-atlas provide a critical reference point for further study of immune mediated disease processes within the liver.


2021 ◽  
Author(s):  
Adrian R Kendal ◽  
Antonina Lach ◽  
Pierre-Alexis Mouthuy ◽  
Richard Brown ◽  
Constantinos Loizou ◽  
...  

Chronic tendinopathy represents a growing burden to healthcare services in an active and ageing global population. The ability to identify, isolate and interrogate, in vitro, key pathogenic and reparative tendon cell populations is essential to developing precision therapies and implantable materials. Human hamstring tendon cells were cultured for 8 days on either tissue culture plastic or aligned electrospun fibres made of polydioxanone (absorbable polymer). Combined single cell surface proteomics and unbiased single cell transcriptomics (CITE-Seq) revealed six discrete cell clusters, four of which shared key gene expression determinants with ex vivo human cell clusters. These were PTX3_PAPPA, POST_SCX, DCN_LUM and ITGA7_NES cell clusters. Surface proteomics found that PTX3_PAPPA cells were CD10+CD26+CD54+. ITGA7_NES cells were CD146+, and POSTN_SCX cells were CD90+CD95+CD10+. Three clusters preferentially survived and proliferated on the aligned electrospun fibres; DCN_LUM, POSTN_SCX, and PTX3_PAPPA. They maintained high expression of tendon matrix associated genes, including COL1A1, COL1A2, COL3A1, ELN, FBLN1, and up-regulated genesets enriched for TNF-α signalling via NFκB, IFN-γ signalling and IL-6/STAT3 signalling. When cells were pre-selected based on surface protein markers, a similar up-regulation of pro-inflammatory signalling pathways was observed, particularly in PTX3 gene expressing CD10+CD26+CD54+ cells, with increased expression of genes associated with TNF-α signalling and IFN-γ signalling. Discrete human tendon cell sub populations persist in vitro culture and can be recognised by specific gene and surface protein signatures. Aligned PDO fibres promote the survival of three clusters, including pro-inflammatory PTX3 expressing CD10+CD26+CD54+ cells found in chronic tendon disease.


Author(s):  
Debby A. Jennings ◽  
Michael J. Morykwas ◽  
Louis C. Argenta

Grafts of cultured allogenic or autogenic keratlnocytes have proven to be an effective treatment of chronic wounds and burns. This study utilized a collagen substrate for keratinocyte and fibroblast attachment. The substrate provided mechanical stability and augmented graft manipulation onto the wound bed. Graft integrity was confirmed by light and transmission electron microscopy.Bovine Type I dermal collagen sheets (100 μm thick) were crosslinked with 254 nm UV light (13.5 Joules/cm2) to improve mechanical properties and reduce degradation. A single cell suspension of third passage neonatal foreskin fibroblasts were plated onto the collagen. Five days later, a single cell suspension of first passage neonatal foreskin keratinocytes were plated on the opposite side of the collagen. The grafts were cultured for one month.The grafts were fixed in phosphate buffered 4% formaldehyde/1% glutaraldehyde for 24 hours. Graft pieces were then washed in 0.13 M phosphate buffer, post-fixed in 1% osmium tetroxide, dehydrated, and embedded in Polybed 812.


Author(s):  
Alexander Lind ◽  
Falastin Salami ◽  
Anne‐Marie Landtblom ◽  
Lars Palm ◽  
Åke Lernmark ◽  
...  

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