|
| Status |
Public on May 02, 2023 |
| Title |
Dataset D3 lib7 |
| Sample type |
SRA |
| |
|
| Source name |
Whole bacteria
|
| Organism |
Staphylococcus aureus |
| Characteristics |
tissue: Whole bacteria
cell line: USA300 LAC
cell type: Bacteria
genotype: WT
treatment: TSB
|
| Treatment protocol |
After growth as described, most bacteria were not subjected to further treatment with the exception of some cells in Dataset D6 that were treated with 10 mM N-acetylcysteine for 90 min, and for cells in Dataset D7 that were treated with 10 µg/ml vancomycin for variable times.
|
| Growth protocol |
Bacteria were streaked out onto agar from frozen stocks overnight then grown overnight in LB or TSB as indicated. Samples were then diluted back to grow in the medium and to the densities indicated. With the exception of Datasets D3 and D4, there was an initial "back-dilution" step prior to harvesting to improve population homogeneity.
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Samples were fixed and processed according to the protocol of Blattman et al. (PMID: 32451472). Briefly, cells were fixed overnight with 4% formaldehyde, permeabilized with 50% ethanol followed by enzymatic cell wall digestion (100 µg/ml, 15 min using lysozyme for E. coli and lysostaphin for S. aureus), then treated with DNase to remove genomic DNA (this step was ommited for some samples in Dataset D4).
Reverse transcription and split-pool barcode ligation were carried out in situ in fixed cells according to the protocol of Blattman et al. (PMID: 32451472). After barcoding, cells were separated into aliquots of ~20,000 cells for further processing. After lysis and reversal of crosslinking, second strand synthesis was performed using the NebNext Second Strand Synthesis module (New England Biolabs) and tagmentation was carried out using the EZ-Tn5 transposase (Lucigen), before final amplification with multiplex indexing primers.
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic single cell |
| Library selection |
cDNA |
| Instrument model |
Illumina NextSeq 500 |
| |
|
| Description |
S. aureus grown to an OD600 of 1 in TSB
|
| Data processing |
All analysis was performed using the Tavazoie lab computational pipeline (https://tavazoielab.c2b2.columbia.edu/PETRI-seq/PETRI_Seq_Computational.pdf).
Initial QC was performed using FastQC then unique molecular identifiers were extracted with UMI tools. Initial demultiplexing by cell barcode was performed iteratively using Cutadapt.
After selecting barcodes associated with the highest number of reads, reads from individual barcodes were aligned to the E. coli MG1655 and S. aureus USA300-FPR3757 reference genomes using BWA.
Numbers of reads mapping to individual genes were quantified using FeatureCounts before counts were collapsed into unique reads based on UMI and used to make the final counts matrix.
Sample annotations were extracted from the number of barcode 1, since this corresponds to the way samples were added in the initial plate during reverse transcription.
Assembly: E. coli reads were aligned to the K-12 MG1655 reference assembly (GCA_000005845.2) and S. aureus to the USA300_FPR3757 reference assembly (GCF_000013465.1).
Supplementary files format and content: Counts matrices: gzip-compressed tab-separated text files of counts by genes (columns) by cell barcode (rows) ("_counts.txt.gz")
Supplementary files format and content: Metadata files: tab-separated files containing library and sample information for each cell barcode ("_metadata.txt").
|
| |
|
| Submission date |
Nov 10, 2022 |
| Last update date |
May 02, 2023 |
| Contact name |
Itai Yanai |
| E-mail(s) |
itai.yanai@nyulangone.org
|
| Organization name |
NYU Langone Health
|
| Street address |
435 East 30th Street
|
| City |
New York City |
| State/province |
NY |
| ZIP/Postal code |
10016 |
| Country |
USA |
| |
|
| Platform ID |
GPL24034 |
| Series (1) |
| GSE217715 |
A quantitative model for the transcriptional landscape of the bacterial cell cycle |
|
| Relations |
| BioSample |
SAMN31682575 |
| SRA |
SRX18230924 |
