TREM1 deficiency alters the tumor myeloid landscape
Ontology highlight
ABSTRACT: To comprehensively characterize the impact of TREM1 deficiency specifically within the tumor myeloid populations, we selectively enriched the CD45+CD11b+ tumor-infiltrating myeloid cells from tumor-bearing Trem1+/+ and Trem1-/- mice for scRNA-seq analysis.
Project description:To gain a global understanding of the impact of TREM1 silencing, we analyzed the CD45+ tumor infiltrating cells (TICs) of B16F10 tumor-bearing Trem1+/+ and Trem1-/- mice. Utilizing the 10x Genomics Chromium Platform, we analyzed approximately 5390 cells per sample with a coverage rate of 15493 genes per cell.
Project description:To comprehensively characterize the changes within the TME during TREM1 deficiency and anti-PD-1 immune checkpoint blockade therapy, we performed scRNA-seq analysis of the CD45+ TICs in melanoma-bearing C57BL/6 mice receiving the various treatments. We analyzed approximately 8,249 CD45+ cells from the treatment groups with t-SNE analysis, identifying 10 distinct clusters of tumor-infiltrating immune cells
Project description:Langerhans cells (LC) in skin help initiate the immune response to locally presented antigens. We performed high-resolution single-cell RNA-sequencing (scRNAseq) analysis for antigen presenting cells including LC in normal mouse skin, and in mouse skin expressing the human papillomavirus (HPV) 16 E7 oncogene. Ear skin was collected from normal and trangenic mice. Dissociated CD45+ cells were processed for scRNA-seq using the 10X Genomics Chromium 3' gene expression kit (v2).
Project description:Adipose tissue mass and adiposity change throughout the lifespan. During aging, while visceral adipose tissue (VAT) tends to increase, peripheral subcutaneous adipose tissue (SAT) decreases significantly. Unlike VAT, which is linked to metabolic diseases, SAT has beneficial effects. However, the molecular details behind aging-associated loss of SAT remain unclear. Here we compare scRNA-seq of total SVF of SAT from young and aging mice to identify a novel Aging-dependent Regulatory Cell (ARC) that emerges in SAT of aged mice. Inguinal white adipose tissue (iWAT) was used as a representative SAT; iWAT pads of 2 mice from each age group were subjected to collagenase digestion and treated with a hypotonic buffer to remove red blood cells before subjection to scRNA-seq by 10X Genomics Chromium Single Cell Kit. The findings showed that ARCs express adipogenic markers but lack adipogenic capacity and inhibit differentiation of neighboring adipose precursors.
Project description:The aim of this study is to analyze the change in genome wide expression levels in HAP1 cells upon loss of SMARCB1, SMARCA4 or both these genes together. The SMARCB1 and SMARCA4 genes were the hits from a genome wide screen involving genetrap mutagenesis to find new players that are involved in sensitivity to Doxorubicin (Dox). It was found that loss of SMARCB1 and SMARCA4 genes impart resistance in HAP1 cells to Dox. To validate this, the genes were knocked out in HAP1 cells with CRISPR-Cas9 technology. Gene expression levels in SMARCB1 null, SMARCA4 null and SMARCB1-SMARCA4 double null cells were compared to wildtype HAP1 cells using RNAseq. From these experiments it was found that SMARCB1 loss caused several fold increase in ABCB1 gene levels. ABCB1 is an efflux pump in cells responsible for flushing out many small-molecule drugs. Further analysis of this gene confirmed that ABCB1 was the main factor responsible for Dox resistance upon SMARCB1 loss. In total there are four different cell types with two replicates for each cell type. Therefore, 8 samples in total.
Project description:The spinal cord neural stem cell potential is contained within the ependymal cells lining the central canal. This neural stem cell potential is known to decline with age in the mouse. Here, we microdissected and dissociated into single cells the central canal region from the spinal cord of 4 young adult (3-to-4-month old) and 4 aged (18-to-19-month old) C57BL/6J mice to profile the transcriptomes of cells in and around the central canal using 10x Genomics technology.
Project description:BACKGROUND & AIMS- More frequent interaction of bacteria with the colonic epithelium is associated with ulcerative colitis (UC). The identities of all proteins which promote bacterial clearance in colonic epithelial cells are unknown. Previously, we discovered that dCAP-D3 (Chromosome Associated Protein-D3), regulates responses to bacterial infection. We examined whether CAP-D3 promotes bacterial clearance in human colonic epithelium. METHODS- Clearance of Salmonella or adherent-invasive Escherichia coli LF82 was assessed by gentamycin protection assays in HT-29 and Caco-2 cells expressing CAP-D3 shRNA. CAP-D3 levels in colonic epithelial cells from healthy and UC patient tissues were analyzed by immunoblot. RNA-sequencing identified bacterially-induced CAP-D3 target genes. The role of CAP-D3 target genes in bacterial clearance was analyzed by gentamycin protection assays, immunofluorescent staining, and by using pharmacologic inhibitors. RESULTS- CAP-D3 expression was reduced in colonic epithelial cells from UC patients with active disease. Reduction of CAP-D3 expression inhibited autophagy and decreased intracellular bacterial clearance. The components of the heterodimeric SLC7A5/SLC3A2 amino acid transporter were identified as CAP-D3 target genes; their levels increased in infected, CAP-D3 deficient cell lines and in cells from UC patients. In HT-29 cells, this resulted in earlier SLC7A5 recruitment to Salmonella-containing vacuoles, increased mTOR activity, and enhanced bacterial survival. Inhibition of SLC7A5/SLC3A2 or mTOR activity rescued the bacterial clearance defect in CAP-D3 deficient cells. CONCLUSIONS- CAP-D3 attenuates amino acid transporter transcription to promote bacterial autophagy in colon epithelial cells. CAP-D3 protein levels are decreased in patients with active UC, suggesting that CAP-D3 is a potential therapeutic target to restore mucosal homeostasis in UC patients. Three RNA samples from 3 independent experiments including timepoints taken at 0, 0.5 and 7 hours post-infection were analyzed on a bioanalyzer for quality; one of the 0.5 hour post-infection samples was excluded at this time due to poor RNA purity. Directional, cDNA libraries made from cellular mRNAs were generated from the other 8 samples and sequenced (paired-end sequencing of 100 bp reads) in the Genomics Core at the University of Chicago on an Illumina HiSeq2000.
Project description:MSCs comprise several percent of pluripotent-like cells named Multilineage-differentiating stress enduring (Muse) cells that express pluripotent markers at moderate levels, are collectable as cells positive for the pluripotent surface marker SSEA-3, are able to differentiate into triploblastic lineage cells, and self-renew at the single cell level (Kuroda et al, PNAS, 2010; Wakao et al, PNAS, 2011). Importantly, MSCs also comprise cells other than SSEA-3(+)-Muse cells, namely multipotent SSEA-3(-)-non-Muse MSCs that correspond to ~98% of the total MSC population. These SSEA-3(-)-non-Muse MSCs exhibit the same properties as conventional MSCs, although the non-Muse MSCs are multipotent, they are not pluripotent. In the present study, to clarify the key molecules that characterize pluripotent-like vs multipotent somatic stem cells, we separated human MSCs into SSEA-3(+)-Muse cells and SSEA-3(-)-non Muse MSCs, and analyzed both populations by scRNA-seq. SSEA-3(+) Muse cells and SSEA-3(-) non-Muse MSCs were sorted from human BM-MSCs. Sequencing libraries were prepared using Chromium single cell 3' Kit v3 (10x Genomics, Pleasanton, CA, USA) and sequenced on HiSeq2500 (Illumina, San Diego, CA, USA). Transcripts were mapped with CellRanger pipeline v3 (10x Genomics). Library construction, sequencing, and initial analysis were performed by GENEWIZ (South Plainfield, NJ, USA).
Project description:Two medulloblastoma cell lines (ONS-76 and HDMB-03) were grown in 3D hyaluronan hydrogels for three weeks. We observed nodules forming showing different behavior and wanted to evaluate if these different nodules (slow vs fast vs non-growing, migrating and invading cells) are also characterised by different gene expression patterns. We performed this experiment on a SHH (ONS-76) and on a group 3 MB (HDMB-03) cell line to compare if certain subpopulations would be unique for the subgroups.