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Nuclear oligo hashing improves differential analysis of single-cell RNA-seq

ABSTRACT: Nuclear oligo hashing improves differential analysis of single-cell RNA-seq

PROVIDER: PRJNA701020 | ENA |

REPOSITORIES: ENA

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Nuclear oligo hashing improves differential analysis of single-cell RNA-seq

Project description:Single-cell RNA sequencing (scRNA-seq) offers a high-resolution molecular view into complex tissues, but suffers from high levels of technical noise which frustrates efforts to compare the gene expression programs of different cell types. “Spike-in” RNA standards help control for technical variation in scRNA-seq, but using them with recently developed, ultra-scalable scRNA-seq methods based on combinatorial indexing is not feasible. Here, we describe a simple and cost-effective method for normalizing transcript counts and subtracting technical variability that improves differential expression analysis in scRNA-seq. The method affixes a ladder of synthetic single-stranded DNA oligos to each cell that appears in its RNA-seq library. With improved normalization we explore chemical perturbations with broad or highly specific effects on gene regulation, including RNA pol II elongation, histone deacetylation, and activation of the glucocorticoid receptor. Our methods reveal that inhibiting histone deacetylation prevents cells from executing their canonical program of changes following glucocorticoid stimulation.

2021-12-01 | GSE166470 | GEO

Comparison of different hashing methods on human PBMCs and primary mice tissues

Project description:the aim 1 was to evaluate hashing (sample multiplexing) accuracy of TotalSeq-B antibodies and CellPlex (a multiplexing solution from 10x Genomics) on 2 pools of PBMCs from 3 different donors. Thus the genetic difference between donors was used as a ground truth for sample demultiplexing based on antibody or lipid hashing hashing. the aim 2 was to evaluate TotalSeq antibody and lipid hashing on different mice primary tissues using different hashing protocols.

2022-02-12 | E-MTAB-11401 | biostudies-arrayexpress

Hashing (sample multiplexing) of MCF7, PC3, DU145 and MDA-MB-231 cells and nuclei using different hashing methods

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The contribution of cell hashing for single-cell analysis of acute leukemia.

Project description:Although classical single cell RNA analysis is a very powerful tool, it nevertheless has several drawbacks. To overcome these two disadvantages, the cell hashing technique seems interesting. In this work, we propose to test the feasibility and the reliability of the single cell hashing technique on primary AML cells. For this purpose, we compared the transcriptomic profile of AML cells analyzed by the classical single-cell RNA sequencing approach versus the cell hashing technique.

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Analysis of hashing quality in a PBMC sample

Project description:We used PIP-seq single cell RNA-sequencing to understand hashing efficiency in a standard set of healthy human PBMCs

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KDM6A improves the efficiency of nuclear transfer reprogramming in bovine

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scRNA-seq of total mouse aortic cells with CITE-seq and cell Hashing

Project description:We performed single-cell RNA-seq analysis with CITE-seq and cell hashing of total viable aortic cells from Ldlr-/- mice fed a high fat diet for 13 weeks

2021-12-14 | GSE182950 | GEO

Cell hashing enable sample multiplexing, multiplet identification and super-loading on droplet-based single cell RNA-sequencing platforms

Project description:We reasoned that by using a distinct set of oligo-tagged antibodies against ubiquitously expressed proteins, we could uniquely label multiple populations of cells, multiplex them together, and use the barcoded antibody signal as a fingerprint. We refer to this approach as cellular "hashing", as our set of oligos defines a "look up table" to assign each multiplexed cell to its original sample. We demonstrate application of the technique to combine eight samples and run them simultaneously in a single droplet based scRNA-seq run. We show that cell hashtags allow sample multiplexing, confident multiplet identification and super-loading in the context of a commonly used droplet-based scRNA-seq method to drive down the per-cell cost of large-scale scRNA-seq experiments

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scRNA-seq of mouse CD45+ aortic cells with CITE-seq and cell Hashing after continuous or alternate diet

Project description:scRNA-seq of mouse CD45+ aortic cells with CITE-seq and cell Hashing after continuous or alternate diet

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Single cell 5’ transcript, TCRαβ enriched, and Cell hashing/CITE-seq library preparation on the 10x chromium, and sequencing

Project description:Single cells from human colorectal cancer and normal adjacent colon of 16 patients were used for single-cell RNA-seq, TCR-seq, CITE-seq and Cell hashing. In brief, single cells were incubated for 3h with or without PMA/Ionomycin, and were treated with Cell hashing and CITE-seq antibodies to distinguish samples, stimulation/non-stimulation, and cell surface proteins. Sorted viable CD3+TCRαβ+ single cells were loaded into 10x genomics ChromiumTM controller to make nanoliter-scale droplets with uniquely barcoded 5’ gel beads called GEMs. After GEM-RT and the following some cDNA amplification steps, cDNAs derived from cellular mRNA were pooled for downstream processing and library preparation according to the manufacturer’s instructions. The 5’ transcript library was sequenced with Illumina Novaseq. The single cell TCR enriched library was sequenced with Illumina Miseq using 150 paired-end reads. HTO/ADTs from Cell hashing or CITE-seq were amplified using specific primers that append P5 and P7 sequences for illumina sequencing (Miseq or Nextseq). All fastq files were demultiplexed. Cell hashing and CITE-seq barcodes are available in attached text files. Fastq files from RNA-seq and TCR-seq can be processed through cellranger and vdjranger by 10xgenomics. The datasets include the data of independent experiments at May 29, June 16, June 23, and Aug 13, 2019. Details are available in Masuda et al., bioRxiv, 2020, The functional and phenotypic diversity of single T-cell infiltrates in human colorectal cancer as correlated with clinical outcome.

2022-03-02 | E-MTAB-9455 | biostudies-arrayexpress

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