Evaluation of bias associated with high-multiplex, target-specific pre-amplification

Okino, Steven T.; Kong, Michelle; Sarras, Haya; Wang, Yan

doi:10.1016/j.bdq.2015.12.001

Cited by 29 publications

(22 citation statements)

References 19 publications

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“…However, using plasma samples, miRNA yield without preamplification is too low for use in profiling experiments. Also, variability introduced by the preamplification step has been proven to be lower than variability introduced by reverse transcription [ 20 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

MicroRNA profiling in plasma samples using qPCR arrays: Recommendations for correct analysis and interpretation

et al. 2018

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MicroRNA (miRNA) regulate gene expression through posttranscriptional mRNA degradation or suppression of translation. Many (pre)analytical issues remain to be resolved for miRNA screening with TaqMan Low Density Arrays (TLDA) in plasma samples, such as optimal RNA isolation, preamplification and data normalization. We optimized the TLDA protocol using three RNA isolation protocols and preamplification dilutions. By using 100μL elution volume during RNA isolation and adding a preamplification step without dilution, 49% of wells were amplified. Informative target miRNA were defined as having quantification cycle values ≤35 in at least 20% of samples and low technical variability (CV across 2 duplicates of 1 sample <4%). A total of 218 miRNA was considered informative (= 59% of all target miRNA). Different normalization strategies were compared: exogenous Ath-miR-159a, endogenous RNA U6, and three mathematical normalization techniques: geNorm (Qbase, QB) and NormFinder (NF) normalization algorithms, and global mean calculation. To select the best normalization method, technical variability, biological variability, stability, and the extent to which the normalization method reduces data dispersion were calculated. The geNorm normalization algorithm reduced data dispersion to the greatest extent, while endogenous RNA U6 performed worst. In conclusion, for miRNA profiling in plasma samples using TLDA cards we recommend: 1. Implementing a preamplification step in the TLDA protocol without diluting the final preamplification product 2. A stepwise approach to exclude non-informative miRNA based on quality control parameters 3. Against using snoRNA U6 as normalization method for relative quantification 4. Using the geNorm algorithm as normalization method for relative quantification.

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Section: Discussionmentioning

confidence: 99%

MicroRNA profiling in plasma samples using qPCR arrays: Recommendations for correct analysis and interpretation

et al. 2018

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“…It is important to mention that every method based on PCR are subjected to amplification biases, due to a preferential amplification of some alleles compared to others [45]. Moreover, the sequencing process has an intrinsic error rate that is independent of the library preparation, but dependent on sequencing depth of the choice platform.…”

Section: Methodologies For Tcr Repertoire Analysismentioning

confidence: 99%

Molecular T-Cell Repertoire Analysis as Source of Prognostic and Predictive Biomarkers for Checkpoint Blockade Immunotherapy

Aversa

Malanga

Fiume

et al. 2020

IJMS

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The T cells are key players of the response to checkpoint blockade immunotherapy (CBI) and monitoring the strength and specificity of antitumor T-cell reactivity remains a crucial but elusive component of precision immunotherapy. The entire assembly of T-cell receptor (TCR) sequences accounts for antigen specificity and strength of the T-cell immune response. The TCR repertoire hence represents a “footprint” of the conditions faced by T cells that dynamically evolves according to the challenges that arise for the immune system, such as tumor neo-antigenic load. Hence, TCR repertoire analysis is becoming increasingly important to comprehensively understand the nature of a successful antitumor T-cell response, and to improve the success and safety of current CBI.

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“…Multiplex . In Ig-Seq experiments, it refers to simultaneously measure multiple samples in a single sequencing run by adding multiple primer pairs of known sequences in a PCR reaction mixture (28, 92).…”

Section: Glossary Of Termsmentioning

confidence: 99%

The Pipeline Repertoire for Ig-Seq Analysis

López-Santibáñez-Jácome

Avendaño‐Vázquez

Flores-Jasso

2019

Front. Immunol.

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With the advent of high-throughput sequencing of immunoglobulin genes (Ig-Seq), the understanding of antibody repertoires and their dynamics among individuals and populations has become an exciting area of research. There is an increasing number of computational tools that aid in every step of the immune repertoire characterization. However, since not all tools function identically, every pipeline has its unique rationale and capabilities, creating a rich blend of useful features that may appear intimidating for newcomer laboratories with the desire to plunge into immune repertoire analysis to expand and improve their research; hence, all pipeline strengths and differences may not seem evident. In this review we provide a practical and organized list of the current set of computational tools, focusing on their most attractive features and differences in order to carry out the characterization of antibody repertoires so that the reader better decides a strategic approach for the experimental design, and computational pathways for the analyses of immune repertoires.

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Evaluation of bias associated with high-multiplex, target-specific pre-amplification

Cited by 29 publications

References 19 publications

MicroRNA profiling in plasma samples using qPCR arrays: Recommendations for correct analysis and interpretation

MicroRNA profiling in plasma samples using qPCR arrays: Recommendations for correct analysis and interpretation

Molecular T-Cell Repertoire Analysis as Source of Prognostic and Predictive Biomarkers for Checkpoint Blockade Immunotherapy

The Pipeline Repertoire for Ig-Seq Analysis

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