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Sample GSM5387636

Query DataSets for GSM5387636

Status

Public on Jun 17, 2021

Title

Quant-Seq Day 0 iTF-Microglia Rep 03

Sample type

SRA

Source name

Day 0 iTF-Microglia

Organism

Homo sapiens

Characteristics

cell type: iTF-Microglia
treatment: Day 0 LPS

Treatment protocol

For QuantSeq, treatment with PBS (control) or 100 ng/ml 24h LPS treatment on Day 8 iTF-Microglia or Brownjohn-iMicroglia

Growth protocol

For CROP-seq, inducible CRISPRi-iTF-iPSCs were infected with the pooled sgRNA library at <0.15 MOI and then selected for lentiviral integration. For both QuantSeq and CROP-seq, iTF-iPSCs were differentiated into iTF-microglia and cultured with the addition of TMP with the following conditions: iTF-iPSCs were grown in StemFlex until reaching at least 50% confluency and were grown for at least 24h without ROCK inhibitor. They were dissociated with Accutase and centrifuged and pelleted cells were resuspended in Day 0 differentiation medium containing the following: Essential 8™ Basal Medium (Gibco; Cat. No. A15169-01) as a base, 10nM ROCK inhibitor, and 2 mg/ml Doxycycline (Clontech; Cat. No. 631311). iTF-iPSCs were counted and seeded onto double coated plates (Poly-D-Lysine-precoated Bio plates (Corning, assorted Cat. No.) + Matrigel coating) with the following seeding densities: 10,000 cells/well for 96-well plate, 0.1 Mio/well for 12-well plate, 0.15 Mio/well for 6-well plate, 2 Mio/dish for 10-cm dish, and 8 Mio/dish for 15-cm dish. On day 2, media was replaced with Day 2 differentiation media containing Advanced DMEM/F12 Medium (Gibco; Cat. No. 12634-010) as a base medium containing the following: 1X Antibiotic-Antimycotic (Anti-Anti) (Gibco; Cat. No. 15240-062), 1X GlutaMAX™ (Gibco; Cat. No. 35050-061), 2 mg/ml doxycycline, 100 ng/ml Human IL34 (Peprotech; Cat. No. 200-34) and 10 ng/ml Human GM-CSF (Peprotech; Cat. No. 300-03). On day 4 and day 6, the medium was replaced with iTF-Microglia medium, containing Advanced DMEM/F12 as a base medium and the following: 1X Anti-Anti, 1X GlutaMAX, 2 mg/ml doxycycline, 100 ng/ml Human IL-34 and 10 ng/ml Human GM-CSF, 50 ng/ml Human M-CSF (Peprotech; Cat. No. 300-25) and 50 ng/ml Human TGFB1 (Peprotech; Cat. No. 100-21C).

Extracted molecule

total RNA

Extraction protocol

For QuantSeq, cell culture medium was aspirated, cells were washed once with DPBS, and RNA lysis buffer was added directly to wells containing either Day 0 iTF-iPSCs, Day 9 iTF-Microglia or Brownjohn-iMG +/- 100 ng/ml 24h LPS treatment, or Day 15 iTF-Microglia. Biological triplicates for each condition (approximately 0.15 Mio cells each) were pelleted, snap frozen, and stored at -80°C. RNA was extracted using the Quick-RNA Miniprep Kit (Zymo; Cat. No. R1055). For CROP-seq, Day 8 iTF-Microglia were washed 3X with DPBS, dissociated with TrypLE, and resuspended in nuclease-free water before loading onto four wells of the 10x Chromium Controller (10x Genomics, v3.1) according to the manufacturer’s protocol, with 35,000 cells recovered per sample as the target.
For QuantSeq, libraries were prepared from total RNA (473 ng per sample) using the QuantSeq 3‘ mRNA-Seq Library Prep Kit for Illumina (FWD) (Lexogen; Cat. No. 015UG009V0252) following the manufacturer’s instructions. Library amplification was performed with 14 total PCR cycles. mRNA-seq library concentrations (mean of 1.13 ± 0.66 ng/uL) were measured with the Qubit dsDNA HS Assay Kit (Invitrogen; Cat. No. Q32851) on a Qubit 2.0 Fluorometer. Library fragment-length distributions (mean of 287 ± 28 bp) were quantified with High Sensitivity D5000 Reagents (Agilent Technologies; Cat. No. 5067-5593) on the 4200 TapeStation System. The libraries were sequenced on an Illumina NextSeq 2000 instrument with single-end reads. For CROP-seq, sample preparation was performed using the Chromium Next GEM Single Cell 3′ Reagent Kits version 3.1 (10x Genomics, cat. no. PN-1000121) according to the manufacturer’s protocol, reserving 10-30 ng full-length cDNA to facilitate sgRNA assignment by amplifying sgRNA-containing transcripts using hemi-nested PCR reactions adapted from a previously published approach (Hill et al., 2018; Tian et al., 2019). cDNA fragment analysis was performed using the 4200 TapeStation System and sgRNA enrichment libraries were separately indexed and sequenced as spike-ins alongside the whole-transcriptome scRNA-seq libraries using a NovaSeq 6000 using the following configuration: Read 1: 28; i7 index: 8; i5 index: 0; Read 2: 91.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

NextSeq 2000

Data processing

QuantSeq: Alignment and mapping were performed using Salmon version 1.4.0 (Patro et al., 2017), with the --noLengthCorrection flag and either the human reference genome GRCh38 (Gencode, release 37), or a custom GRCh38 reference genome containing the references for each 3TF transgene integrated in iTF-iPSCs, to obtain transcript abundance counts. CROP-Seq: Alignment and gene expression quantification was performed on scRNAseq libraries and sgRNA-enriched libraries using Cell Ranger (version 5.0.1, 10X Genomics) with default parameters and reference genome GRCh38-3.0.0. Cellranger aggr was used to aggregate counts belonging to the same sample across different GEM wells. The resulting gene vs. cell barcode matrix contained 58,302 cells which had on average 41,827 reads per cell, and a median of 3,346 genes per cell.
QuantSeq: Tximport version 1.18.0 (Soneson et al., 2015) was used to obtain gene-level count estimates. Genes with zero counts across all samples were removed from the analysis. CROP-seq: sgRNA unique molecular identifier (UMI) counts for each cell barcode were obtained using a previously described mapping workflow (Hill et al., 2018). To facilitate sgRNA identity assignment, a combination of demuxEM (Gaublomme et al., 2019) and a z-score cut-off method we previously described (Tian et al., 2019) were used such that only cells with a single sgRNA as determined by both methods were carried forward in the analysis.
QuantSeq: For differential gene expression analysis of LPS-treated vs. PBS-treated iTF-Microglia samples, DESeq2 version 1.30.1 (Love et al., 2014) was used to calculate the log-fold-change and p-values and perform shrinkage of log-fold-change for downstream visualization using ggplot2 version 3.3.3. CROP-seq: The raw gene vs. barcode matrix outputted by Cell Ranger was converted into a SingleCellExperiment (SCE) object using the read10xCounts function from the DropletUtils package version 1.10.3 (Lun et al., 2019) in R (v 4.0.3). sgRNA assignments were appended to the SCE metadata and filtered to only include cells with a single sgRNA, resulting in 28,905 cells. The SCE was converted into a Seurat object using Seurat::as.Seurat version 4.0.1 (Hao et al., 2020).
CROP-Seq: The data was normalized and highly variable genes were identified using Seurat::SCTransform (Hafemeister and Satija, 2019). For initial data exploration, principal-component analysis was performed using Seurat::RunPCA to determine the number of principal components to retain. UMAP dimensional reduction using Seurat::RunUMAP and clustering using Seurat::FindNeighbors and Seurat::FindClusters were performed on the retained principal components with resolution = 0.7.
CROP-Seq: To determine the differentially expressed genes between UMAP clusters, Seurat::FindAllMarkers was used.
Genome_build: GRCh38
Supplementary_files_format_and_content: The processed data files for CROP-seq are hdf5 formatted matrices of UMI counts, with rows as genes and columns as cell barcodes, obtained from running cellranger count.

Submission date

Jun 16, 2021

Last update date

Jun 17, 2021

Contact name

Martin Kampmann

E-mail(s)

[email protected]

Organization name

UCSF

Lab

Martin Kampmann