Title |
Preparing Unbiased T-Cell Receptor and Antibody cDNA Libraries for the Deep Next Generation Sequencing Profiling
|
---|---|
Published in |
Frontiers in immunology, January 2013
|
DOI | 10.3389/fimmu.2013.00456 |
Pubmed ID | |
Authors |
Ilgar Z. Mamedov, Olga V. Britanova, Ivan V. Zvyagin, Maria A. Turchaninova, Dmitriy A. Bolotin, Ekaterina V. Putintseva, Yuriy B. Lebedev, Dmitriy M. Chudakov |
Abstract |
High-throughput sequencing has the power to reveal the nature of adaptive immunity as represented by the full complexity of T-cell receptor (TCR) and antibody (IG) repertoires, but is at present severely compromised by the quantitative bias, bottlenecks, and accumulated errors that inevitably occur in the course of library preparation and sequencing. Here we report an optimized protocol for the unbiased preparation of TCR and IG cDNA libraries for high-throughput sequencing, starting from thousands or millions of live cells in an investigated sample. Critical points to control are revealed, along with tips that allow researchers to minimize quantitative bias, accumulated errors, and cross-sample contamination at each stage, and to enhance the subsequent bioinformatic analysis. The protocol is simple, reliable, and can be performed in 1-2 days. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Czechia | 1 | 33% |
United States | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 3 | 1% |
United States | 2 | <1% |
Japan | 1 | <1% |
Unknown | 260 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 62 | 23% |
Student > Ph. D. Student | 58 | 22% |
Student > Master | 27 | 10% |
Student > Bachelor | 20 | 8% |
Student > Doctoral Student | 19 | 7% |
Other | 44 | 17% |
Unknown | 36 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 79 | 30% |
Biochemistry, Genetics and Molecular Biology | 57 | 21% |
Immunology and Microbiology | 42 | 16% |
Medicine and Dentistry | 25 | 9% |
Engineering | 4 | 2% |
Other | 20 | 8% |
Unknown | 39 | 15% |