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Sample GSM5532211

Query DataSets for GSM5532211

Status

Public on Aug 21, 2024

Title

Matrigel2

Sample type

SRA

Source name

Cells retrieved from 3D Matrigel

Organism

Homo sapiens

Characteristics

cell line: MDA-MB-231 WT
tissue: Breast mammary gland

Treatment protocol

For inhibition experiments, the following small molecule and inhibitors were used: MEK/ERK inhibitor PD98059 (10 mM, Focus Biomolecules), JNKinhibitor SP600125 (10 mM, Focus Biomolecules) and PI3K/Akt inhibitor LY294002 (10 mM, Focus Biomolecules). The inhibitors were mixed with culture medium and refreshed every 2-3 days. To knock down FHL2, MDA-MB-231 cells were transfected with two different FHL2-targeting small interfering RNA (Silencer siRNA, Invitrogen) or non-targeting siRNA (Stealth RNAi negative control, Invitrogen) using Lipofectamine RNAiMAX Reagent (Invitrogen), diluted in Opti-MEM reduced serum medium (Gibco) for a final concentration of 10-20 nM. Cells were transfected 2-3 days before encapsulation following manufacturer’s instructions. To assess MDA-MB-231 response to chemotherapy, Paclitaxel (Tokyo Chemical Industry) was diluted at 0.01, 0.1 and 0.5 mM and added to the culture medium after 3-5 days post-encapsulation, for a drug treatment of 2 days. The highest concentration tested contained around 1% (v/v) of DMSO, which was used as vehicle medium and didn’t show any negative effect on cell viability.

Growth protocol

MDA-MB-231 cells were normally grown in glucose Dulbecco’s Modified Eagle’s Medium (D6046; Sigma-Aldrich) with 10% v/v fetal bovine serum (S0615, Sigma-Aldrich) and 1% penicillin/streptomycin (Gibco), incubated in a 5% CO2 environment at 37 °C and passaged every 3-5 days. Cells were then encapsulated in 3D covalently-crosslinked alginate hydrogels with two different stiffness (stiff vs soft) and 3D Matrigel for 5 days. Cells were then retrieved from the hydrogel, the lysates collected until further processing.

Extracted molecule

total RNA

Extraction protocol

Samples were resuspended in 5 µl 1X NEBNext Cell Lysis Buffer of the NEBNext Single Cell/Low Input RNA Library Prep Kit (NEB, E6420). After 5 min incubation at RT, 2.5X RNA XP Clean Up Beads (Beckman Coulter, A63987) were added to the sample by pipetting up and down. Then, samples were incubated for 5 min at RT, the beads were pelleted on a magnet, the supernatant was removed, the samples were washed twice with 80% ethanol while remaining on the magnet, and finally the beads were resuspended in 8 µl nuclease-free water. The reverse transcription was conducted according to the manual of the NEBNext Single Cell/Low Input RNA Library Prep Kit in the presence of the beads
The lysates were thawed on ice and the RNA precipitated by adding 0.1 volume 3 M sodium acetate (pH 5.5) and 2.5 volumes 100% cold ethanol. After Incubation at − 80 °C for 30 min, the RNA was recovered by centrifugation at 16,000×g for 30 min at 4 °C. The RNA pellet was washed with 1 mL 80% cold ethanol, centrifuged at 16,000×g for 5 min at 4 °C, the ethanol solution carefully removed by aspiration and the RNA pellet dried. The pellet was resuspended in 5 µl 1X NEBNext Cell Lysis Buffer of the NEBNext Single Cell/Low Input RNA Library Prep Kit (NEB, E6420). After 5 min incubation at RT, 2.5X RNA XP Clean Up Beads (Beckman Coulter, A63987) were added to the sample by pipetting up and down. Then, samples were incubated 5 min at RT, the beads were pelleted on a magnet, the supernatant was removed, the samples were washed twice with 80% ethanol while remaining on the magnet, and finally the beads were resuspended in 8 µl nuclease-free water. The reverse transcription was conducted as described in the manual of the NEBNext Single Cell/Low Input RNA Library Prep Kit in the presence of the beads.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina MiSeq

Data processing

After demultiplexing, raw FASTQ data were given to an in-house mRNA analysis pipeline. BBDuk was used to trim the raw sequence data from soft, 3D matrigel and 3D alginate stiff, deleting any residual adapter sequences and low-quality bases at the ends of each read.
BioBloom Tools was used to decontaminate reads from the genomes Mus musculus (mm38), Escherichia coli (BL21), Mycoplasma pneumoniae (M129), Sphingobium sp. (SYK-6), Bradyrhizobium japonicum (USDA 110), Pichia pastoris (GS115), Malessia globosa (CBS 7966), Aspergillus fumigatus (Af293), and a set of viral genomes (RefSeq, 5k+ genomes).
All reads that did not map exclusively to the transcriptome of hg38 (GENCODE version 27, GRCh38.p10) were labeled as potentially contaminated and were removed from further processing. FastQC was used to evaluate sequence quality per sample before as well as after trimming and decontamination. In addition, all samples were examined as a collective with MultiQC.
Following that, the cleaned sample reads were aligned to the hg38 reference genome using STAR. Using featureCounts from Subread, uniquely mapping reads were counted per gene and per sample8. Further quality criteria were evaluated, including library complexity (using Preseq) and the genomic origin of the reads and the 5'-3'-bias (both using QualiMap). The final counts table of 9 samples were utilized for differential expression analysis.
All the subsequent steps after count tables were performed in R programming language. Matrix table containing gene counts were visualized using Principle Component Analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) clustering techniques. For PCA, raw counts were scaled and prcomp function from stats package was employed. tSNE plots were constructed using Rtsne 0.1512. Subsequent steps including Quality Control (QC) analysis, filtering, normalization, feature selection, scaling, regression of unwanted variables and differential expression for the RNAseq count were performed using Seurat package. Default parameters were taken for all used functions unless otherwise mentioned. Normalization was done using ‘log.normalize’ method with a scale factor of 1e-6 to obtain logCPM count values and 2000 most variable genes were used for feature selection. Only mitochondrial genes were regressed out during scaling, cell cycle associated genes did not have an effect on the cell-to-cell clustering. “DESeq2” method was applied to do differential expression analysis between the three groups, soft, stiff and matrigel and in addition testing of genes were limited to a logFoldChange (logFC) cutoff of 1. Further, the differentially expressed genes were manually filtered for an adjusted p-value of 0.05. Finally, volcano plots were constructed using EnhancedVolcano.
Genome_build: GRCh38.p10
Supplementary_files_format_and_content: Matrix table with raw gene counts for every gene and every sample

Submission date

Aug 23, 2021

Last update date

Aug 29, 2024

Contact name

Xin Lu

E-mail(s)

luxgmpg@gmail.com

Organization name

University of Regensburg

Street address

Universitätsstraße 31