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|Public on May 21, 2015
|MCF10a human breast cancer cells
|cell type: MCF10a human breast cancer cells
|Cells were cultured in DMEM supplemented with 10% FBS.
|RNA was extracted from individual cells in individual microfluidic chambers following cell lysis by Triton X-100 and freeze-thaw.
mRNA from individual cells was reverse transcribed with a primer containing a cell-identifying barcode followed by oligo(dT). Following second strand synthesis using DNA Polymerase I and reagents from the MessageAmp II kit (Ambion), ds-cDNA from all barcoded individual cells was pre-amplified by in vitro transcription using T7 RNA polymerase in a pool. The pools of amplified RNA from each lane of the microfluidic device were individually reverse transcribed using barcoded random hexamers containing both a unique molecular identifier (random 8-base barcode) followed by a lane-identifying barcode (6-base barcode). Illumina adapters were inserted on either end of the library during the two previous reverse transcription steps and were used to then enrich the library by PCR. The pooled library was sequenced on an Illumina NextSeq 500.
|Illumina NextSeq 500
processed data file: PS034_R2_4.txt
|We collected the set of reads that uniquely mapped to the transcriptome and assigned an address comprised of its cell-identifying barcode, gene, UMI, and mapping position. We then filtered the reads to identify unique molecules. Reads with identical addresses were collapsed to a single molecule. In addition, reads with identical cell-identifying barcodes, genes, mapping positions, and with UMIs having a Hamming distance less than or equal to two were collapsed to a single molecule. Finally, because the mapping positions produced by STAR do not necessarily correspond to the beginning of a read, we further considered reads to originate from identical molecules if they had identical genes, cell-identifying barcodes, UMIs with a Hamming distance less than or equal to two, and a mapping position within five bases.
To identify barcodes that correspond to actual individual cells in our device, we filtered the observed cell-identifying barcodes by progressively downsampling the corresponding gene profiles to the same number of total reads and assessing the number of unique molecules detected from each cell-identifying barcode. After excluding cell-identifying barcodes having zero associated molecules, we found the distribution of associated unique molecules to be bimodal, with one small subpopulation having nearly as many unique molecules as reads at low read totals. We found the size of this subpopulation to be in excellent agreement with our device imaging data. We took these 598 profiles to represent the actual individual cells captured in our device with a barcoded bead.
We conducted more detailed analysis on 370 single-cell profiles with the highest coverage in our data set across all five lanes of the microfluidic device. Raw fastq data from read 2 of those 370 cells is provided here. Note that the UMI for each read appears in the comment line of each fastq entry.
The processed data files contain the number of molecules counted for each gene based on counting reads with HTSeq and filtering the UMIs to identify unique molecules. If two UMIs had a Hamming distance less than three, they were considered to be the same UMI. If two reads with identical UMIs mapped to the transcriptome to within 6 bases of each other, they were considered identical molecules.
Supplementary_files_format_and_content: The processed data files contain the number of molecules counted for each gene based on counting reads with HTSeq and filtering the UMIs to identify unique molecules.
|Feb 27, 2015
|Last update date
|May 15, 2019
|Peter A Sims
|3960 Broadway, Lasker 203AC
|Scalable Microfluidics for Single Cell RNA Printing and Sequencing