Single-cell transcriptomics reveal a unique memory-like NK cell subset that accumulates with aging and correlates with disease severity in COVID-19
Ontology highlight
ABSTRACT: For the scRNA-seq analysis of human blood lymphocytes and NK cells, lymphocytes were sorted as CD45+ cells, and NK cells were sorted as CD3/CD19/CD20/CD14−CD7+ cells using FACS (SONY SH800S); the purity was above 95%. The cells from 4 elderly individuals were pooled in one tube and the cells from 4 young individuals were pooled in another tube. We then counted and resuspended the pooled cells at a concentration of 1000 cells/μL, aiming for an estimated 6000 cells per library, following the instructions of the single-cell 3ʹ solution v3 reagent kit (10X Genomics). Single-cell libraries were constructed strictly according to the manufacturer’s standard protocols. Each sequencing library was generated with a unique sample index. Libraries were sequenced on the Illumina NovaSeq 6000 system.
Project description:The goal of this study is to identify novel target genes of DVL3 in two breast cancer cell lines Methods:Total RNA was isolated using the Aurum™ Total RNA Mini Kit (Bio Rad) and library preparation and sequencing were performed at Center for Biotechnology & Genomics of Texas Tech University. RNA quality was determined using RNA Screen Tape (Agilent). Ribosomal RNA depletion was achieved using NEB Next rRNA Depletion Kit (Human/Mouse/Rat) (NEB # E6310X). RNA fragmentation, double stranded cDNA and adaptor ligation was generated using NEBNext Ultra II Directional RNA Library Prep according to the manufacturer’s protocol (NEB # E7760L). PCR enriched libraries were quantified by Qubit and equimolar indexed libraries (different samples had different indexes for multiplexing) were pooled. Pooled libraries were quantitatively checked using the Agilent Tapestation 2200 and quantified using Qubit. The libraries were then diluted to 200 pM and spiked with 2% phiX libraries (Illumina control). The transcriptome sequencing was performed on the barcoded stranded RNA-Seq libraries using Illumina NovaSeq 6000 SP flow cell, paired-end reads (2 × 50 bp).
Project description:We performed tiling CRISPR activation (CRISPRa) screens in Jurkat T cells to discover enhancers controlling IL2RA and CD69 expression. For each target gene, we constructed a pooled lentiviral library of sgRNAs that targeted sites at all Cas9 PAMs throughout the window starting 100 kb upstream of the transcription start site, extending through the gene body, and 25 kb downstream. A separate library was constructed for each gene, and a separate screen was performed for each gene. For each gene, Jurkat cells stably expressing the dCas9-VP64 transcriptional activator were transduced with the lentiviral sgRNA library. Cells were stained for the protein of interested and sorted by flow cytometry into four bins of expression. Distribution of sgRNAs in each of the sorted populations was compared to the unsorted population. Regions where dCas9-VP64 recruitment was sufficient to drive target gene expression were identified as putative enhancers. The hg19 coordinates of the CD69 library window were chr12:9,880,082-10,013,497. The library contained 2,244 negative control sgRNAs taken from the CRISPRi/a libraries described in Gilbert et al., Cell 150, 647-661 (2014). The hg19 coordinates of the IL2RA library window were chr10:6,027,657- 6,204,333. The library contained 2,244 negative control sgRNAs taken from the CRISPRi/a libraries described in Gilbert et al., Cell 150, 647-661 (2014).
Project description:The following lymphocytes were sorted from the lamin propria of the small intestine of EomesGfg/+ RORgtCreTGg Rosa26Yfp/+ by using the markers Lineage- CD45+ Nkp46+ NK1.1+ : 1.convential NK cells (Eomes GFP+ RORgt YFP-) 2. ILC1 (Eomes GFP- RORgt YFP-) 3. exRORgt ILC3 (Eomes GFP- RORgt YFP+). Conventional NK cells from the bone marrow (cNK BM) were sorted from Eomes Gfp/+ mice with the markers Lineage- CD45+ NK1.1+ Eomes GFP+.
Project description:DNA methylation assessments of peripheral blood DNA can be used to accurately estimate the relative proportions of underlying leukocyte subtypes. Such cell deconvolution analysis relies on libraries of discriminating differentially methylated regions that are developed for each specific cell type measured. The relationship between estimated cell type proportions can then be tested for their association with phenotypes, disease states, and subject outcomes, or used in multivariable models as terms for adjustment in epigenome-wide association studies (EWAS). We obtained purified neutrophils, monocytes, B-lymphocytes, natural killer (NK) cells, CD4+ T-cells, and CD8+ T-cells from healthy subjects and measured DNA methylation with the Illumina HumanMethylationEPIC array platform. In addition, we measured DNA methylation with the EPIC array in two sets of artificial DNA mixtures comprising the above cell types. We compared three separate approaches to select reference differentially methylated region libraries (DMR library), for cell type proportion inference. The IDOL algorithm identified an optimal DMR library consisting of 450 CpG sites for inferring leukocyte subtype proportions (average R2=99.2). Importantly, the majority of CpG sites (69%) in the IDOL DMR library were unique to the new EPIC methylation array, in that they were not present on the 450K array. Our new reference DMR library is available as a Bioconductor package, has the potential to reduce any unintended technical differences arising from the combination of different generations of array platforms, and may be helpful in generating larger DMR libraries that include novel cell subtypes. A dataset of six whole blood samples were FACS sorted and the DNA was processed as a validation dataset.
Project description:Peripheral blood mononuclear cells (PBMCs) were isolated from healthy controls and patients with various diseases.. Cells were stained with CD45 and sample tags for identification purposes. CD45+ live cells were enriched through flow sorting and samples were pooled. Samples were then stained with 30 AbSeq antibodies and loaded onto 4 cartridges to account for interplate differences. Pooled sequencing libraries were then sequenced on NovaSeq 6000 (Illumina) using a S2 Reagent Kit v1.5 (200 cycles).
Project description:The goal of this study is to identify genomic signatures predicitve of cell-of-origin in acute myeloid leukemia 50K bulk leukemia (GFP+) cells from the spleen of recipient mice were sorted directly into 350μl of RLT buffer (Qiagen) and flash-frozen. Total RNA was isolated according to manufacturer’s protocols (Qiagen) including DNase treatment, and quality was assessed using an Agilent 2100 Bioanalyzer and RNA 6000 Nano kit. Amplified cDNA was sheared to approximately 300bp using a Covaris E220 Focused Ultrasonicator. RNA-seq library preparation used the TruSeq DNA sample prep kit v2 (Illumina). Libraries were sequenced on the Illumina HiSeq 2000 platform.
Project description:Three technical replicates of FACS-sorted T cells (CD45+CD3+) and one replicate of FACS-sorted tumor cells (MCSP+) were loaded to a targeted 10,000 cells per lane on the 10X Genomics Chromium Controler with the single cell 5’ Immune Repertoire and Gene Expression profiling kit. In total, we loaded ~30,000 individual tumor infiltrating lymphocytes (TILs) and ~10,000 melanoma cells on the 10X platform (10X Genomics, CA, USA). Reverse transcription, TCR enrichment, and library preparations were performed according to the 10X Genomics 5’ V(D)J protocol revision C. Transcriptome libraries were pooled and sequenced on the Illumina NovaSeq 6000 S2 flow cell with 26 R1, 8 i7, and 91 R2 cycles respectively. The TCR libraries were pooled and sequenced on the Illumina MiSeq V2 150 cycles paired-end. Single cell transcriptomic and TCR data was processed with the 10X Genomics Cell Ranger Pipeline version 2.2.0 with the software-provided GRCh38 reference transcriptomes. After quality control, there was RNAseq profile data available from 6267 immune and 4303 melanoma cells. Downstream processing and visualization was encompassed through Seurat and tSNE plots.
Project description:Purpose: Identification of differentially methylated regions (DMRs) in murine innate lymphoid cells (ILC) Methods: Numerous preparations of lymph node cells from mice were independently sorted by flow cytometry to isolate Natural killer (NK) cells, ILC1, ILC2, ILC3 and lymphoid tissue inducer (LTi) cells. Genomic DNA from the sorted, fixed cells was extracted using the NucleoSpin Tissue kit (Macherey-Nagel), including a crosslink removal step that was described recently (Kyburz et al., J Allergy Clin Immunol 143, 1496-1512 e1411 (2019)). The resulting single-stranded DNA was converted with bisulfite using the EZ DNA Methylation-Direct Kit (Zymo Research) and fragmented by sonication (Covaris S220, 10% duty cycle, 175W peak incident power, intensity 5, 200 cycles per burst, 120 seconds). The fragmented DNA served as input for the Accel-NGS Methyl-Seq DNA Library Kit (Swift Biosciences) and resulted in libraries that were sequenced on an Illumina NovaSeq 6000 . After quality control, trimming and mapping by using BSMAP, we identified differentially methylated regions by pairwise comparison of the samples by using metilene. Results: The sequencing depth of our libraries varies between 210 and 282 million paired end reads. More than 98% of the murine CpG motifs were covered one time and 76% of the motifs at least five times. The pairwise comparison of the methylomes identified most of the differential methylated regions around the transcriptional start sites. Less differences were found between NK and ILC1 as well as between ILC3 and LTi cells. Among the DMRs showing meaningful differences we identified known demethylated regions in Tbx21, Ifng, Gata3, Il5 and Ccr6 as well as so far uncharacterized regions in Gpr18, Ptgir, Il22 or Il23r. Conclusions: Here we report for the first time a genome-wide methylation study on ILC subpopulations that will help to understand development and function of these cells.
Project description:Small RNAs isolated from RBCs were size fractionated by gel electrophoresis and used for the creation of 6 libraries. For the library from healthy children (library 1), RNA from 4 individuals was pooled. Libraries were multiplexed and analyzed on a 454 sequencing platform 26 yielding 569,621 sequence reads after demultiplexing into the 6 libraries
Project description:We recently introduced CUT&Tag, an epigenomic profiling strategy in which antibodies are bound to chromatin proteins in situ in permeabilized nuclei, and then used to tether the cut-and-paste transposase Tn5. Activation of the transposase simultaneously cleaves DNA and adds DNA sequencing adapters (“tagmentation”) for paired-end DNA sequencing. Here, we introduce a streamlined CUT&Tag protocol that suppresses exposure artifacts to ensure high-fidelity mapping of the antibody-targeted protein and improves signal-to-noise over current chromatin profiling methods. Streamlined CUT&Tag can be performed in a single PCR tube from cells to amplified libraries, providing low-cost high-resolution genome-wide chromatin maps. By simplifying library preparation, CUT&Tag requires less than a day at the bench from live cells to sequencing-ready barcoded libraries. Because of low background levels, barcoded and pooled CUT&Tag libraries can be sequenced for ~$25 per sample, enabling routine genome-wide profiling of chromatin proteins and modifications that requires no special skills or equipment.