Flow Cell Sequencing
Flow Cell Sequencing - Related Videos
08:53
Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma
0 Views •
Cited by 9 •
2017
Here, we describe a combined flow cytometric cell sorting and low-input, next-generation library construction protocol designed to produce high-quality, whole-exome data from the Hodgkin Reed-Sternberg (HRS) cells of classical Hodgkin lymphoma (CHL).
10:43
Flow Cytometry Purification of Mouse Meiotic Cells
0 Views •
Cited by 65 •
2011
An efficient method to obtain highly purified viable meiotic fractions from mouse testis is described, which combines a refined cell dissociation protocol with fluorescent activated cell sorting (FACS). This method takes advantage of differences in the DNA content and nuclear density of discrete meiotic fractions.
09:45
Detection of Copy Number Alterations Using Single Cell Sequencing
0 Views •
Cited by 9 •
2017
Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.
08:57
Amplicon Sequencing using the Long-Read Sequencing Technologies
0 Views •
2025
This protocol was optimized for targeted deep sequencing of 18 drug-resistance regions in Mycobacterium tuberculosis using a long-read sequencing platform, followed by analysis with a tuberculosis-specific bioinformatics pipeline designed for long-read data.
05:38
Isolating and Analyzing Cells of the Pancreas Mesenchyme by Flow Cytometry
0 Views •
Cited by 13 •
2017
Here, we describe a method for the isolation of cells in the pancreas microenvironment from embryonic, neonatal and adult mouse tissue, focusing on the isolation of mesenchymal cells. This method allows profiling of cell gene expression and protein secretion in order to elucidate the extrinsic signals that regulate pancreas development, function, and tumorigenesis.
FAQs
Once DNA fragments bind to the coated surface, bridge amplification forms clusters from those bound molecules. The clusters create many discrete targets for subsequent nucleotide-incorporation and fluorescence-imaging cycles. Because numerous clusters can form across one flow cell, the instrument can process many DNA molecules in parallel, supporting high-throughput sequence determination.
Reagents flow across the cell and expose incorporated nucleotides to fluorescence imaging. The recorded signal identifies the nucleotide added at that position. Chemical removal of terminators then prepares the molecules for the next incorporation cycle. Repeating this sequence of addition, imaging, and removal builds the read incrementally.
The coated surface provides a location where DNA fragments can bind and where amplified clusters remain available for reading. Reagent flow brings the chemistry needed for nucleotide incorporation and terminator removal into the chamber. Together, these features support repeated imaging cycles across many spatially separated molecular clusters and enable parallel data collection.
Each imaging cycle records fluorescence associated with nucleotide incorporation at a particular position. The sequence of signals across repeated cycles is converted into sequence data, which can then be aligned and analyzed. This step translates optical measurements from the flow cell into ordered nucleotide information suitable for biological interpretation.
Following cyclic imaging, fluorescence signals are converted into sequence data. Researchers then align those sequences and analyze them according to the biological question. This progression connects molecular measurements in the flow cell with interpretable results, such as genome sequence patterns, transcriptome profiles, genetic variation, or microbial-community composition.
Flow Cell Sequencing can support investigations of genomes, transcriptomes, genetic variation, gene expression, and microbial communities. These outputs make the method relevant to diverse biological questions, including disease research and evolutionary studies. The same sequencing results can also contribute to personalized medicine, where characterizing an individual's genetic information is scientifically relevant.
For transcriptome studies, the resulting sequence data can be used to examine gene-expression information. In microbial-community research, those data support analysis of community composition. In both settings, parallel processing handles many nucleic acid molecules, while alignment and downstream analysis connect the generated sequences to the biological system under study.