G-stack modulated probe intensities on expression arrays - sequence corrections and signal calibration

<p>Abstract</p> <p>Background</p> <p>The brightness of the probe spots on expression microarrays intends to measure the abundance of specific mRNA targets. Probes with runs of at least three guanines (G) in their sequence show abnormal high intensities which reflect rather probe effects than target concentrations. This G-bias requires correction prior to downstream expression analysis.</p> <p>Results</p> <p>Longer runs of three or more consecutive G along the probe sequence and in particular triple degenerated G at its solution end ((<it>GGG</it>)₁-effect) are associated with exceptionally large probe intensities on GeneChip expression arrays. This intensity bias is related to non-specific hybridization and affects both perfect match and mismatch probes. The (<it>GGG</it>)₁-effect tends to increase gradually for microarrays of later GeneChip generations. It was found for DNA/RNA as well as for DNA/DNA probe/target-hybridization chemistries. Amplification of sample RNA using T7-primers is associated with strong positive amplitudes of the G-bias whereas alternative amplification protocols using random primers give rise to much smaller and partly even negative amplitudes.</p> <p>We applied positional dependent sensitivity models to analyze the specifics of probe intensities in the context of all possible short sequence motifs of one to four adjacent nucleotides along the 25meric probe sequence. Most of the longer motifs are adequately described using a nearest-neighbor (NN) model. In contrast, runs of degenerated guanines require explicit consideration of next nearest neighbors (GGG terms). Preprocessing methods such as vsn, RMA, dChip, MAS5 and gcRMA only insufficiently remove the G-bias from data.</p> <p>Conclusions</p> <p>Positional and motif dependent sensitivity models accounts for sequence effects of oligonucleotide probe intensities. We propose a positional dependent NN+GGG hybrid model to correct the intensity bias associated with probes containing poly-G motifs. It is implemented as a single-chip based calibration algorithm for GeneChips which can be applied in a pre-correction step prior to standard preprocessing.</p

Hybridization biases of microarray expression data - A model-based analysis of RNA quality and sequence effects

Modern high-throughput technologies like DNA microarrays are powerful tools that are widely used in biomedical research. They target a variety of genomics applications ranging from gene expression profiling over DNA genotyping to gene regulation studies. However, the recent discovery of false positives among prominent research findings indicates a lack of awareness or understanding of the non-biological factors negatively affecting the accuracy of data produced using these technologies. The aim of this thesis is to study the origins, effects and potential correction methods for selected methodical biases in microarray data. The two-species Langmuir model serves as the basal physicochemical model of microarray hybridization describing the fluorescence signal response of oligonucleotide probes. The so-called hook method allows to estimate essential model parameters and to compute summary parameters characterizing a particular microarray sample. We show that this method can be applied successfully to various types of microarrays which share the same basic mechanism of multiplexed nucleic acid hybridization. Using appropriate modifications of the model we study RNA quality and sequence effects using publicly available data from Affymetrix GeneChip expression arrays. Varying amounts of hybridized RNA result in systematic changes of raw intensity signals and appropriate indicator variables computed from these. Varying RNA quality strongly affects intensity signals of probes which are located at the 3\'' end of transcripts. We develop new methods that help assessing the RNA quality of a particular microarray sample. A new metric for determining RNA quality, the degradation index, is proposed which improves previous RNA quality metrics. Furthermore, we present a method for the correction of the 3\'' intensity bias. These functionalities have been implemented in the freely available program package AffyRNADegradation. We show that microarray probe signals are affected by sequence effects which are studied systematically using positional-dependent nearest-neighbor models. Analysis of the resulting sensitivity profiles reveals that specific sequence patterns such as runs of guanines at the solution end of the probes have a strong impact on the probe signals. The sequence effects differ for different chip- and target-types, probe types and hybridization modes. Theoretical and practical solutions for the correction of the introduced sequence bias are provided. Assessment of RNA quality and sequence biases in a representative ensemble of over 8000 available microarray samples reveals that RNA quality issues are prevalent: about 10% of the samples have critically low RNA quality. Sequence effects exhibit considerable variation within the investigated samples but have limited impact on the most common patterns in the expression space. Variations in RNA quality and quantity in contrast have a significant impact on the obtained expression measurements. These hybridization biases should be considered and controlled in every microarray experiment to ensure reliable results. Application of rigorous quality control and signal correction methods is strongly advised to avoid erroneous findings. Also, incremental refinement of physicochemical models is a promising way to improve signal calibration paralleled with the opportunity to better understand the fundamental processes in microarray hybridization

Qualitätskontrolle in der Transplantation hämatopoetischer Progenitorzellen: Optimierung und Standardisierung der Testung klonogenen Wachstums

A rapid and reliable method based on viable CD34+ cells for evaluation of colony growth after cryopreservation of hematopoietic progenitor cell grafts J. Fasold, A.Ketels, R. Repp, M. Gramatzki, A. Humpe Division of Stem cell and Immunotherapy, Second Department of Medicine, Schleswig-Holstein University Hospital Campus Kiel, FRG Purpose: Quality control before and after cryopreservation of hematopoietic progenitor cell grafts (HPC) varies between different centers and still lacks standardization. Especially evaluation of clonogenic growth after cryopreservation is associated with considerable variations of the applied protocol and of the reference parameter, i.e. CD34+ cells, mononuclear cells, or CD45+ cells. We evaluated a method involving a minimum of manipulations of the sample before plating the cells and normalizing for viable CD34+ cells (series 1, S1) or for viable CD45+ cells (series 2, S2). Methods: Satellite tubes of 563 autologous HPC grafts with a concentration of viable CD34+ cells/µL ranging from 30 to 28,220/µL satellite tubes were ranked for the CD34+ cell concentration and divided into 30 sections. From each of the sections one sample (range of CD34+ cell concentration: 140 – 12,710/µL) of different patients was chosen on a random basis for analysis. Cells were thawed rapidly in a 37 °C water bath and diluted depending on cellular concentrations at least 1:10 in IMDM. Cell suspension were further diluted with a final DMSO concentration of less than 0.1% in the assay and plated in quadruplicate with 200 viable CD34+ cells per dish and in parallel with 5x10E+04 viable CD45+ cells per dish. Dishes were incubated at +37°C under fully humidified atmosphere and 5% CO2. Colonies were counted after 14 days of culture under a dissection microscope. Aggregates containing more than 20 cells were considered colonies. Results: In S1 all assays were countable. A median number of 47 CFU-GM (17 – 82)/200 viable CD34+ cells were counted. In S2 only 14 samples were analyzable with a median number of 48 CFU-GM (9 – 153)/dish. The other 16 samples were due to an excess of colonies not countable. In S1, the median standard deviation for the number of CFU-GM in each of the 30 analyzed samples was 8% (1.5 – 21%). One sample was analyzed 24-times with a coefficient of variation of 18.8%. Conclusions: The presented method only involving dilution steps but no washing steps or further manipulation procedures before analysis of CFU-GM growth is comparable to the situation at the time of infusion at the bed-side of the patient. The normalization for 200 viable CD34+ cells leads to consistent colony growth results independent of the concentration of CD34+ cells in the original graft