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Bioinformatics and single-cell CRISPRi-based screen reveals effector genes and implicates multi-tissue etiology for BMD [scCRISPR screen]

GSE261283 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2024/04/17 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 16 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
16 samples: 8 × GDO_hFOB 1.19, transfected with custom CRISPRi sgRNA pool vs 8 × GEX_hFOB 1.19, transfected with custom CRISPRi sgRNA poolGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
GWAS-Informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral densitybioRxiv : the preprint server for biology · 2024
Summary (written by the submitters)
To nominate identify tissues relevant to the etiology of bone mineral density we generated ATAC-seq in pediatric hMSC-osteoblasts, hFOB 1.19 cells (hFOBs), osteoclasts, and primary chondrocytes. Leveraging the results of this experiment, we designed a CRISPRi screen in hFOBs with scRNA-seq expression read out. The targets selected for the screen were informed by newly generated Capture-C and bulk RNA-seq from hFOBs.
Published in
GWAS-Informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density
Conery M, Pippin JA, Wagley Y et al. · bioRxiv : the preprint server for biology 2024 · PMID 38562830 · doi:10.1101/2024.03.19.585778
This dataset
Open GSE261283 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE261283_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1086393 and SRA study SRP494461. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38562830) ↗
Study design
8 × GDO_hFOB 1.19, transfected with custom CRISPRi sgRNA pool vs 8 × GEX_hFOB 1.19, transfected with custom CRISPRi sgRNA pool

Supports a between-group comparison across 16 samples.

How we read this: 2 replicated groups read from 16 sample titles; they account for 16 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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