Utilization of AFFX spike-in control probes to monitor sample identity throughout Affymetrix GeneChip Array processing

Sample tracking in microbiome community profiling assays using synthetic 16S rRNA gene spike-in controls

Workflows for microbiome community profiling by high-throughput sequencing are prone to sample mix-ups and cross-contamination due to the complexity of the procedures and large number of samples typically analyzed in parallel. We employed synthetic 16S rRNA gene spike-in controls to establish a method for tracking of sample identity and detection of cross-contamination in microbiome community profiling assays based on 16S rRNA gene amplicon sequencing (16S-seq). Results demonstrated that combinatorial sample tracking mixes (STMs) can be reliably resolved by Illumina sequencing and faithfully represent their sample of origin. In a single-blinded experiment, addition of STMs at low levels was shown to be sufficient to unambiguously identify and resolve swapped samples. Using artificial admixtures of individually SMT-tagged samples, we further established the ability to detect and quantify cross-contamination down to a level of approximately 1%. The utility of our technique was underscored through detection of an unplanned case of cross-contamination that occurred during this study. By enabling detection of sample mix-ups and cross-contamination throughout 16S-seq workflows, the present technique thus assures provenance of sequence data on a per-sample basis. The method can be readily implemented in standard 16S-seq workflows and its routine application is expected to enhance the reliability of 16S-seq data.

Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study

In a mouse intercross with more than 500 animals and genome-wide gene expression data on six tissues, we identified a high proportion (18%) of sample mix-ups in the genotype data. Local expression quantitative trait loci (eQTL; genetic loci influencing gene expression) with extremely large effect were used to form a classifier to predict an individual's eQTL genotype based on expression data alone. By considering multiple eQTL and their related transcripts, we identified numerous individuals whose predicted eQTL genotypes (based on their expression data) did not match their observed genotypes, and then went on to identify other individuals whose genotypes did match the predicted eQTL genotypes. The concordance of predictions across six tissues indicated that the problem was due to mix-ups in the genotypes (although we further identified a small number of sample mix-ups in each of the six panels of gene expression microarrays). Consideration of the plate positions of the DNA samples indicated a number of off-by-one and off-by-two errors, likely the result of pipetting errors. Such sample mix-ups can be a problem in any genetic study, but eQTL data allow us to identify, and even correct, such problems. Our methods have been implemented in an R package, R/lineup.

Calling sample mix-ups in cancer population studies

Sample tracking errors have been and always will be a part of the practical implementation of large experiments. It has recently been proposed that expression quantitative trait loci (eQTLs) and their associated effects could be used to identify sample mix-ups and this approach has been applied to a number of large population genomics studies to illustrate the prevalence of the problem. We had adopted a similar approach, termed ‘BADGER’, in the METABRIC project. METABRIC is a large breast cancer study that may have been the first in which eQTL-based detection of mismatches was used during the study, rather than after the event, to aid quality assurance. We report here on the particular issues associated with large cancer studies performed using historical samples, which complicate the interpretation of such approaches. In particular we identify the complications of using tumour samples, of considering cellularity and RNA quality, of distinct subgroups existing in the study population (including family structures), and of choosing eQTLs to use. We also present some results regarding the design of experiments given consideration of these matters. The eQTL-based approach to identifying sample tracking errors is seen to be of value to these studies, but requiring care in its implementation.

Quality assurance of RNA expression profiling in clinical laboratories

RNA expression profiles are increasingly used to diagnose and classify disease, based on expression patterns of as many as several thousand RNAs. To ensure quality of expression profiling services in clinical settings, a standard operating procedure incorporates multiple quality indicators and controls, beginning with preanalytic specimen preparation and proceeding thorough analysis, interpretation, and reporting. Before testing, histopathological examination of each cellular specimen, along with optional cell enrichment procedures, ensures adequacy of the input tissue. Other tactics include endogenous controls to evaluate adequacy of RNA and exogenous or spiked controls to evaluate run- and patient-specific performance of the test system, respectively. Unique aspects of quality assurance for array-based tests include controls for the pertinent outcome signatures that often supersede controls for each individual analyte, built-in redundancy for critical analytes or biochemical pathways, and software-supported scrutiny of abundant data by a laboratory physician who interprets the findings in a manner facilitating appropriate medical intervention. Access to high-quality reagents, instruments, and software from commercial sources promotes standardization and adoption in clinical settings, once an assay is vetted in validation studies as being analytically sound and clinically useful. Careful attention to the well-honed principles of laboratory medicine, along with guidance from government and professional groups on strategies to preserve RNA and manage large data sets, promotes clinical-grade assay performance.

Clinical implementation of RNA signatures for pharmacogenomic decision-making

RNA profiling is increasingly used to predict drug response, dose, or toxicity based on analysis of drug pharmacokinetic or pharmacodynamic pathways. Before implementing multiplexed RNA arrays in clinical practice, validation studies are carried out to demonstrate sufficient evidence of analytic and clinical performance, and to establish an assay protocol with quality assurance measures. Pathologists assure quality by selecting input tissue and by interpreting results in the context of the input tissue as well as the technologies that were used and the clinical setting in which the test was ordered. A strength of RNA profiling is the array-based measurement of tens to thousands of RNAs at once, including redundant tests for critical analytes or pathways to promote confidence in test results. Instrument and reagent manufacturers are crucial for supplying reliable components of the test system. Strategies for quality assurance include careful attention to RNA preservation and quality checks at pertinent steps in the assay protocol, beginning with specimen collection and proceeding through the various phases of transport, processing, storage, analysis, interpretation, and reporting. Specimen quality is checked by probing housekeeping transcripts, while spiked and exogenous controls serve as a check on analytic performance of the test system. Software is required to manipulate abundant array data and present it for interpretation by a laboratory physician who reports results in a manner facilitating therapeutic decision-making. Maintenance of the assay requires periodic documentation of personnel competency and laboratory proficiency. These strategies are shepherding genomic arrays into clinical settings to provide added value to patients and to the larger health care system.