The growth in popularity of RNA expression microarrays has been accompanied by concerns about the reliability of the data especially when comparing between different platforms. Here, we present an evaluation of the reproducibility of microarray results using two platforms, Affymetrix GeneChips and Illumina BeadArrays. The study design is based on a dilution series of two human tissues (blood and placenta), tested in duplicate on each platform. The results of a comparison between the platforms indicate very high agreement, particularly for genes which are predicted to be differentially expressed between the two tissues. Agreement was strongly correlated with the level of expression of a gene. Concordance was also improved when probes on the two platforms could be identified as being likely to target the same set of transcripts of a given gene. These results shed light on the causes or failures of agreement across microarray platforms. The set of probes we found to be most highly reproducible can be used by others to help increase confidence in analyses of other data sets using these platforms.

The Affymetrix Human Exon 1.0 ST array was used to measure differential splicing patterns in archived RNA isolated from 26 of 80 children (11 Rejectors and 15 Non-Rejectors). The gene-level probe summaries reported in this series were computed using the Affymetrix Power Tools (APT) software and rma-sketch normalization method. Keywords: Affymetrix 1.0 ST exon array; gene-level analysis Overall design: The gene-level normalized intensities (NI) were computed with the MiDAS algorithm and both the splicing index (SI) and Students t-test p-values (two-sided) were computed on the NI values in R. To remove low expressed gene-level probes, those with values less than 3.5 (log2 scale) in >50% of the samples in either group were filtered out leaving 17,242 of the original 22,011 probes. Those gene-level probes that were highly differentially expressed between groups were also removed using a fold change threshold of log2. This step accounts for the tendency for the genelevel probe set intensities in each group to be "disproportionately affected by background noise or saturation" (Affymetrix Technical notes).

Transcription is a multi-stage process that coordinates several steps within the transcription cycle including chromatin reorganization, RNA polymerase II recruitment, initiation, promoter clearance and elongation. Recent advances have identified the super elongation complex (SEC), containing the eleven nineteen lysine rich leukemia protein (ELL), as a key regulator of transcriptional elongation. We show here that ELL plays a diverse and kinetically distinct role prior to its assembly into the SEC by stabilizing Pol II recruitment/initiation and entry into the pause site. Loss of ELL destabilizes the PIC complexes and results in disruption of early elongation and promoter proximal chromatin structure prior to recruitment of AFF4 and other SEC components. These changes result in significantly reduced transcriptional activation of rapidly induced genes. Thus, ELL plays an early and essential role during rapid high amplitude gene expression that is required for both Pol II pause site entry and release. Total RNA for unstimulated and for simulated with EGF (50 ng/mL) for 1hr from wild type and stably induced ELL KD in HEK293 cells were isolated and purified and submitted to LMT-Affymetrix microarray facility to obtain the raw data. Affymetrix Human Exon ST1.0 platform was used for the microarray analysis. Raw data in CEL files were preprocessed using Affymetrix Expression Console software.

The prognosis of colorectal cancer (CRC) stage II and III patients remains a challenge due to the difficulties of finding robust biomarkers suitable for testing clinical samples. The majority of published gene signatures of CRC have been generated on fresh frozen colorectal tissues. Because collection of frozen tissue is not practical for routine surgical pathology practice, a clinical test that improves prognostic capabilities beyond standard pathological staging of colon cancer will need to be designed for formalin-fixed paraffin-embedded (FFPE) tissues. The NanoString nCounter® platform is a gene expression analysis tool developed for use with FFPE-derived samples. We designed a custom nCounter® codeset based on elements from multiple published fresh frozen tissue microarray-based prognostic gene signatures for colon cancer, and we used this platform to systematically compare gene expression data from FFPE with matched microarray array data from frozen tissues. Our results show moderate correlation of gene expression between two platforms and discovery of a small subset of genes as candidate biomarkers for colon cancer prognosis that are detectable and quantifiable in FFPE tissue sections.

The Affymetrix GeneChip® system is a commonly used platform for microarray analysis but the technology is inherently expensive. Unfortunately, changes in experimental planning and execution, such as the unavailability of previously anticipated samples or a shift in research focus, may render significant numbers of pre-purchased GeneChip® microarrays unprocessed before their manufacturer’s expiration dates. Researchers and microarray core facilities wonder whether expired microarrays are still useful for gene expression analysis. Our results demonstrated that microarray data generated using U133A microarrays, which were more than four years past the manufacturer’s expiration date, were highly specific and consistent with those from unexpired microarrays in identifying DEGs despite some appreciable fold change compression and decrease in sensitivity. Our data also suggested that the MAQC reference RNA samples, stored at -80°C, were stable over a time frame of at least four years. The new gene expression data were generated with 12 microarrays (2 types of microarrays × 2 samples × 3 replicates). Three replicates for each of the two MAQC samples (A = Stratagene’s Universal Human Reference RNA; B = Ambion’s Human Brain Reference RNA) were profiled in 2009 by using both the expired U133A microarrays (expired in 2004) and the unexpired U133Plus2 microarrays. In addition, gene expression data generated with unexpired U133Plus2 microarrays (AFX), other microarray platforms, and TaqMan® assays by the MAQC project in 2005 were used as references to assess the stability of the MAQC samples stored at -80°C for four years by comparing new microarray data with those obtained four years ago. The MAQC reference data also allowed for further evaluation of the usefulness of the data generated with expired U133A microarrays.

Sources of Nanostring codeset of 414-gene list.

In the present study, to identify potential paracrine factor for the stromal regulation of E2-induced epithelial cell proliferation, we treated epithelial and stromal cell populations of mouse uterine primary co-culture with either oil or E2. Three independent RNA pools prepared for each population were then subjected to the Affymetrix gene chip analysis for the whole mouse genome transcripts. Our data revealed up-regulation of 119 genes and down-regulation of 28 genes in epithelial cell populations and up-regulation of 144 genes and down-regulation of 192 genes in stromal cell population. We analyzed whole gnenome transcription from epithelial and stromal cell populations of mouse uterine primary co-culture using the Affymetrix GeneChip® Mouse Gene 1.0 ST Array (vehicle vs. E2). 3 replicates were performed.

To shed light on the molecular bases of B-lineage acute lymphoblastic leukemia lacking known rearrangements (B-NEG ALL) and the differences between children and adults, we analyzed 168 B-NEG ALLs - including children, adolescents/young adults (AYA) and adults by genome-wide technologies, namely Next-generation sequencing and copy number aberration (CNA). Affymetrix SNP array analysis was performed according to the manufacturer's directions on DNA extracted from bone marrow sampled at diagnosis and paired germline DNA extracted from peripheral blood/bone marrow at complete remission or saliva. Copy number analysis of Affymetrix SNP 6.0 arrays was performed for 13 B-NEG ALL samples and their paired normal (non-tumoral) DNA samples, included in the discovery panel, processed in the same experiment and deposited.

In this study, to obtain a complete registry of genetic lesions in MDS and to identify novel therapeutic targets, we performed SNP array analysis and whole exome analysis for novel mutations using high-throughput sequencing technologies. In whole exome analysis, paired CD3-positive T cells were used as a normal control. By comparing sequences in tumors and paired T cells, 268 non-synonymous somatic mutations were confirmed with an overall true positive rate of 53.9 %, including 206 missense, 25 nonsense, and 10 splice site mutations, and 27 frameshift-causing insertions/deletions (indels). The mutations of the known gene targets, however, accounted for only 12.3 % of all detected mutations (N = 33), and the remaining 235 mutations involved previously unreported genes. Combined with the genomic copy number profile obtained by SNP array karyotyping, this array of somatic mutations provided a landscape of myelodysplasia genomes. Copy number analysis of Affymetrix 250K SNP arrays was performed for 29 MDS or related neoplasms and paired 29 germline samples.

This dataset contains a re-analysis of the raw microarray data originally published by Petrich AM et al in 2012 (citation details are provided through the link to the GEO record). We were interested in re-analyzing the data because the list of differentially expressed genes that were identified when comparing rapamycin resistant DLBCL cell lines to rapamycin sensitive cell lines was not included in the original article or supplemental materials. We were interested in validating and expanding upon the findings from the original article by reevaluating the raw microarray data. Our reanalysis identified over 200 genes that were significantly differentially expressed between rapamycin resistant and sensitive cells. Importantly, our analysis highlighted a gene that was highly upregulated in rapamycin resistant cells, CD247, that was not the focus on the original publication, and is the target of a drug currently in clinical trials for refractory DLBCL. Our reanalysis also highlighted the role of SYK, a kinase upregulated in rapamycin resistant cell lines, that has a direct molecular relationship with CD247, and is a potential biomarker of drug response in DLBCL. Methods The methods used to generate the original microarray data are described in the GEO record where the data were originally published: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27255 We downloaded the raw Affymetrix data (CEL files) from GEO for renalysis. Statistical and quality analysis was performed using the data analysis pipeline in iReport (http://www.ingenuity.com/products/ireport) which utilizes packages such as RMA and Limma from Bioconductor, in the R programming language. A full description of the analysis packages used by this pipeline are included in the word document "GSE27255 stats and QC details" included in this DataShare record. Analysis of microarray data in iReport identified 229 differentially expressed genes (DEGs) with a p-value <0.05, fold change >1.5. These DEGs were then uploaded and analyzed in Ingenuity Pathway Analysis (www.ingenuity.com) for functional enrichment, pathway analysis, and drug target/biomarker analysis. Our novel findings with respect to the role of CD247 and SYK in rapamycin resistant DLBCL cell lines is depicted in the pathway image fine "Rap resist network with CD247" which is included in this DataShare record.

A causal role of mutations in genes encoding for multiple general transcription factors in neurodevelopmental disorders including autism suggested that alterations at the global level of gene expression regulation might also relate to disease risk in sporadic cases of autism. This premise can be tested by evaluating for global changes in the overall distribution of gene expression levels. For instance, in mice, we recently showed that variability in hippocampal-dependent behaviors was associated with variability in the pattern of the overall distribution of gene expression levels, as assessed by variance in the distribution of gene expression levels in the hippocampus. We hypothesized that a similar change in the variance in gene expression levels might be found in children with autism. Gene expression microarrays covering greater than 47,000 unique RNA transcripts were done on purified RNA from peripheral blood lymphocytes of children with autism (n=82) and controls (n=64). The variance in the distribution of gene expression levels from each microarray was compared between groups of children. Also tested was whether a risk factor for autism, increased paternal age, was associated with variance in the overall distribution of gene expression levels. A decrease in the variance in the distribution of gene expression levels in peripheral blood lymphocytes (PBL) was associated with the diagnosis of autism and a risk factor for autism, increased paternal age. Traditional approaches to microarray analysis of gene expression suggested a possible mechanism for decreased variance in gene expression. Gene expression pathways involved in transcriptional regulation were down-regulated in the blood of children with autism and children of older fathers. Thus, results from global and gene specific approaches to studying microarray data were complimentary and supported the hypothesis that alterations at the global level of gene expression regulation are related to autism and increased paternal age. Regulation of transcription, thus, represents a possible point of convergence for multiple etiologies of autism and other neurodevelopmental disorders. The study was designed to compare gene expression profiles in peripheral blood lymphocytes of children with autism (n=82) and controls(n=64). Expression profiling: Expression profiling was performed at Translational Genomics (TGen), a member of the NIMH Neuroscience Microarray Consortium. Total RNA was extracted from peripheral blood lymphocytes (PBL) within 30 minutes of the blood draw using the Qiagen Qiaquick kit (Germantown, MD). Isolated total RNA was double round amplified, cleaned, and biotin-labeled using Affymetrix’s GeneChip Two-Cycle Target Labeling kit (Santa Clara, CA) with a T7 promoter and Ambion’s MEGAscript T7 High Yield Transcription kit (Austin, TX) as per manufacturer’s protocol. Amplified and labeled cRNA was quantified on a spectrophotometer and run on a 1% TAE gel to check for an evenly distributed range of transcript sizes. Twenty micrograms of cRNA was fragmented to approximately 35-200bp by alkaline treatment (200 mM Tris-acetate, pH 8.2, 500 mM KOAc, 150 mM MgOAc) and run on a 1% TAE gell to verify fragmentation. Separate hybridization cocktails were made using 15 micrograms of fragmented cRNA from each sample as per Affymetrix’s protocol. Two hundred microliters (containing 10 micrograms of fragmented cRNA) of each cocktail was separately hybridized to an Affymetrix Human Genome U133 Plus 2.0 Array for 16h at 45 degree Celsius in the Hybridization Oven 640. The Affymetrix Human Genome Arrays measure the expression of over 47,000 transcripts and variants, including 38,500 characterized human genes. Arrays were washed on Affymetrix’s GeneChip Fluidics Station 450 using a primary streptavidin phycoerythrin (SAPE) stain, subsequent biotinylated antibody stain, and secondary SAPE stain. Arrays were scanned on Affymetrix’s GeneChip Scanner 3000 7G with AutoLoader. Scanned images obtained by the Affymetrix GeneChip Operating Software (GCOS) v1.2 were used to extract raw signal intensity values per probe set on the array. A scaling factor of 150 was used to normalize array signal intensity in MAS 5.0. Arrays were scanned over 1 day on 2 different machines. Arrays scanned on the same machine and in the same day were considered to be from the same scan batch. Rescanning of a limited number of samples indicated that there were no significant differences between machines, nonetheless, for all comparisons groups were balanced for the scan batch. Gene expression levels were not adjusted for possible batch effects as algorithms that attempt to adjust for batch effects also alter the gene expression distribution. When samples could not be prepped simultaneously they were balanced for group membership (autism vs. control). To statistically control for possible confounds related to scan batches in our analysis of gene expression variance, batch number was entered into an analysis of covariance. For traditional analysis of gene expression, experimental groups were balanced with respect to batch membership. Microarray data analysis: Affymetrix .cel files were imported into Affymetrix Expression Console version 1.1. Data was pre-processed and summarized by Microarray Analysis Suite (MAS) 5.0 and Robust Multiarray Analysis (RMA). For the analysis of gene expression distributions, MAS 5.0 was used because the algorithm does not alter the gene expression distribution, whereas, RMA utilizes quantile normalization of probes prior to summarization and, therefore, has the potential to remove group level differences in gene expression distributions. Because of the numerous advantages in its handling of noise in gene expression and background subtraction, RMA was used for traditional gene expression analyses looking for specific gene expression differences between groups. Because, we found group level differences in the distribution of gene expression levels between groups, for traditional gene expression analyses summarized gene expression levels were also quantile normalized after the summarization step. Quantile normalization adjusts all data sets such that they have identical distribution patterns. Probesets were then filtered for those that were called present in at least 50 out of the 146 subjects (n = 25,146 probesets). A p-value of .05 was used as a threshold for significance. A fold-change of 1.1 was used as a cut off for magnitude of change. All microarrays met manufacturers recommended quality control criteria. Present calls ranged from 37.4% to 49%, mean 43.7%, SD 2.7%. Actin 3’to5’ ratios ranged from .726 to 5.15, mean1.37, SD 0.5. There were no significant group level differences in quality control measures.

Background: Adrenocortical carcinoma (ACC) is associated with poor survival rates. The objective of the study was to analyze ACC gene expression profiling data prognostic biomarkers and novel therapeutic targets. Methods: 44 ACC and 4 normal adrenal glands were profiled on Affymetrix U133 Plus 2 expression microarrays and pathway and transcriptional enrichment analysis performed. Protein levels were determined by western blot. Drug efficacy was assessed against ACC cell lines. Previously published expression datasets were analyzed as validation data sets. Results: Pathway enrichment analysis identified marked dysregulation of cyclin-dependent kinases and mitosis. Over-expression of PTTG1, which encodes securin, a negative regulator of p53, was identified as a marker of poor survival. Median survival for patients with tumors expressing high PTTG1 levels (log2 ratio of PTTG1 to average beta-actin <-3.04 ) was 1.8 years compared to 9.0 years if tumors expressed lower levels of PTTG1 (P<0.0001). These findings were confirmed by our analysis of previously published datasets. Treatment of ACC cell lines with vorinostat decreased securin levels and inhibited cell growth (IC50s of 1.69 uM and 0.891 uM, for SW-13 and H295R, respectively). Conclusion: Over-expression of PTTG1 is correlated with poor survival in ACC. PTTG1/securin is a prognostic biomarker and warrants investigation as a therapeutic target. Overall design: RNA from forty-four adrenocortical carcinomas and four normal adrenal glands was extracted, labeled, and hybridized to Affymetrix U133 Plus 2 arrays. The resulting data was normalized by gcRMA with quantile normalization and background subtraction after using the ExpressionFileCreator in GenePattern. Data was then floored at 5.5 using PreprocessDataset, and filtered to remove 1) probes with more than 35 floored values and/or 2) probes where all values from one batch were floored while values from the other batch were not. Further batch effects were minimized using ComBat with the parametric option. Data was then floored at 2. Differentially expressed genes were determined using a T-test with multiple comparison correction as implemented by Comparative Marker Selection in Gene Pattern. Genes with the corrected p-value < 0.005 and the FDR < 0.075 were selected for further study. For comparing high to low grade or primary to recurrence, the FDR cut-off was increased to < 0.13. Survival analysis was conducted using Prism 6 (GraphPad) to generate Kaplan-Meier curves that were compared by log-rank.

Diffuse intrinsic pontine glioma (DIPG) is one of the most devastating of paediatric malignancies and one for which no effective therapy exists. A major contributor to the failure of therapeutic trials is the assumption that biologic properties of brainstem tumours in children are identical to cerebral high-grade gliomas of adults. A better understanding of the biology of DIPG itself is needed in order to develop agents targeted more specifically to these children’s disease. Here we address this lack of knowledge by performing the first high-resolution SNP-based DNA microarray analysis of a series of DIPGs. Eleven samples (nine post-mortem and two pre-treatment surgical samples), the largest series thus far examined, were hybridized to Affymetrix SNP arrays (250k or 6.0). The study was approved by the Research Ethics Board at our institution (Hospital for Sick Children, Toronto, Ontario, Canada). All Array findings were validated using quantitative-PCR, fluorescence in-situ hybridization, immunohistochemistry and/or microsatellite analysis. Analysis of DIPG copy number alterations showed recurrent changes distinct from those of paediatric supratentorial high-grade astrocytomas. 36% of DIPGs had gains in PDGFRA and all showed PDGF-R-α expression. Gains in PARP-1 were identified in 3 cases. Pathway analysis revealed genes with loss of heterozygosity were enriched for DNA repair pathways. Our data provides the first, comprehensive high-resolution genomic analysis of paediatric DIPG. Our findings of recurrent involvement of the PDGFR pathway as well as defects in DNA repair pathways coupled with gain of PARP-1 highlight two potential, biologically-based, therapeutic targets directed specifically at this devastating disease. Overall design: Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from snap frozen biopsy and autopsy brain tissue from DIPG patients. Copy number analysis of Affymetrix 250K and 6.0 SNP arrays was performed for 11 paediatric DIPG samples, 7 matched normal brain samples, and HapMap samples.

Gene expression is perturbated by drugs to different extent. Analyzing genes whose expression is frequently regulated by drugs would be useful for the screening of candidate therapeutic targets and genes implicated in side effect. Here, we obtained the differential expression number (DEN) for genes profiled in Affymetrix microarrays from the Connectivity Map project, and conducted systemic comparative computational analysis between high DEN genes and other genes. Results indicated that genes with higher down-/up-regulation number (down_h/up_h) tended to be clustered in genome, and have lower homologous gene number, higher SNP density and more disease-related SNP. Down_h and up_h were significantly enriched in cancer related pathways, while genes with lower down-/up-regulation number (down_l/up_l) were mainly involved in the development of nervous system diseases. Besides, up_h had lower interaction network degree, later developmental stage to express, higher tissue expression specificity than up_l, while down_h showed reversed tendency in comparison with down_l. Together, our analysis suggests that genes frequently regulated by drugs are more likely to be associated with disease-related functions, but the extensive activation of conserved and widely expressed genes by drugs is disfavored.

A major component of the cardiac stress response is the simultaneous activation of several gene regulatory networks. Interestingly, the transcriptional regulator steroid receptor coactivator-2, SRC-2 is often decreased during cardiac failure in humans. We postulated that SRC-2 suppression plays a mechanistic role in the stress response and that SRC-2 activity is an important regulator of the adult heart gene expression profile. Genome-wide microarray analysis, confirmed with targeted gene expression analyses revealed that genetic ablation of SRC-2 activates the “fetal gene program” in adult mice as manifested by shifts in expression of a) metabolic and b) sarcomeric genes, as well as associated modulating transcription factors. While these gene expression changes were not accompanied by changes in left ventricular weight or cardiac function, imposition of transverse aortic constriction (TAC) predisposed SRC-2 knockout (KO) mice to stress-induced cardiac dysfunction. In addition, SRC-2 KO mice lacked the normal ventricular hypertrophic response as indicated through heart weight, left ventricular wall thickness, and blunted molecular signaling known to activate hypertrophy. Our results indicate that SRC-2 is involved in maintenance of the steady-state adult heart transcriptional profile, with its ablation inducing transcriptional changes that mimic a stressed heart. These results further suggest that SRC-2 deletion interferes with the timing and integration needed to respond efficiently to stress through disruption of metabolic and sarcomeric gene expression and hypertrophic signaling, the three key stress responsive pathways. For microarray analysis, 250ng of RNA isolated from total heart (RNeasy kit, Qiagen) for each sample was labeled using the new standard Affymetrix linear amplification protocol using the 3' IVT Express Kit. This was reverse-transcribed and cRNA was produced and biotinylated via in vitro transcription. A hybridization cocktail containing Affymetrix spike-in controls and 15 μg fragmented, labeled cRNA was loaded onto a GeneChip® Mouse 430 2.0 array. The array was hybridized for 16 hours at 45°C with rotation at 60 rpm then washed and stained with a strepavidin, R-phycoerythrin conjugate stain using the FS 450_0001 Fluidics protocol setting. Signal amplification was done using biotinylated antistreptavidin. The stained array was scanned on the Affymetrix GeneChip® Scanner 3000. The images were analyzed and quality control metrics recorded using Affymetrix Command Console v3. Experiments were run using Affymetrix MG 430 2.0 chip with 45,101 probesets representing 20,757 unique genes. There were 8 experiments in 2 groups: WT-unstressed – 4 experiments, and KO-unstressed – 4 experiments. QC parameters for all experiments were within the acceptable limits. We used the following software packages for data QC, statistical analysis and presentation of the results: Affymetrix Expression Console (www.affymetrix.com), Partek (www.partek.com), BRB Array Tools (linus.nci.nih.gov/BRB-ArrayTools.html), and dChip (biosun1.harvard.edu/complab/dchip). Expressions were estimated using the RMA (Multi-Array Analysis) method [38] with Partek software. Differentially expressed genes were found using the RVM (Random Variance Model) t-test, which is designed for small sample size experiments [39]. We used BRB Array Tools software, developed by Dr. Richard Simon and the BRB-ArrayTools Development Team. All genes were included in the comparison. For the genes represented by more than one probeset, we used the most highly expressed probeset. The cutoffs for differentially expressed genes were False Discovery Rate (FDR) = 0.05 [40].

The DNA microarray market, valued at $1206.4 million in 2025, is projected to experience steady growth, driven by increasing applications in genomics research, personalized medicine, and drug discovery. The market's Compound Annual Growth Rate (CAGR) of 4.1% from 2025 to 2033 indicates a consistent expansion, fueled by advancements in microarray technology, leading to improved sensitivity, accuracy, and throughput. Key application areas like gene expression analysis, genotyping, and genome cytogenetics are experiencing significant growth, particularly in oligonucleotide DNA microarrays (oDNA) which are favored for their high specificity and sensitivity. The market is segmented geographically, with North America holding a dominant share due to robust research infrastructure and high healthcare expenditure. Europe and Asia Pacific are also showing promising growth, driven by increasing investments in life sciences and expanding diagnostic capabilities. While regulatory hurdles and the emergence of next-generation sequencing (NGS) technologies pose some challenges, the DNA microarray market's versatility, established reliability, and cost-effectiveness in specific applications ensures its continued relevance within the broader genomics landscape. The competitive landscape is characterized by established players like Illumina, Affymetrix, Agilent Technologies, and Roche NimbleGen, along with smaller, specialized companies. These companies are actively engaged in developing innovative microarray technologies and expanding their product portfolios to cater to the growing demand from various research and clinical settings. Furthermore, strategic collaborations, partnerships, and acquisitions are expected to shape the competitive dynamics in the coming years. Continued technological advancements, such as the development of high-density microarrays and improved data analysis tools, will further drive market growth. The focus on personalized medicine, coupled with increasing government funding for genomic research, will play a crucial role in sustaining the market's upward trajectory throughout the forecast period.

BackgroundTumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes. Original SNP array signals are usually corrupted by noise, and need to be de-convoluted into absolute copy number profile by analytical methods. Unfortunately, in contrast with the popularity of tumor Affymetrix SNP array, the methods that are specifically designed for this platform are still limited. The complicated characteristics of noise in signals is one of the difficulties for dissecting tumor Affymetrix SNP array data, as they inevitably blur the distinction between aberrations and create an obstacle for the copy number aberration (CNA) identification.ResultsWe propose a tool named TAFFYS for comprehensive analysis of tumor Affymetrix SNP array data. TAFFYS introduce a wavelet-based de-noising approach and copy number-specific signal variance model for suppressing and modelling the noise in signals. Then a hidden Markov model is employed for copy number inference. Finally, by using the absolute copy number profile, statistical significance of each aberration region is calculated in term of different aberration types, including amplification, deletion and loss of heterozygosity (LOH). The result shows that copy number specific-variance model and wavelet de-noising algorithm fits well with the Affymetrix SNP array signals, leading to more accurate estimation for diluted tumor sample (even with only 30% of cancer cells) than other existed methods. Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms. Application on the 35 breast tumor samples shows that TAFFYS can automatically dissect the tumor samples and reveal statistically significant aberration regions where cancer-related genes locate.ConclusionsTAFFYS provide an efficient and convenient tool for identifying the copy number alteration and allelic imbalance and assessing the recurrent aberrations for the tumor Affymetrix SNP array data.

Summary from GEO:

"Regulatory T (Treg) cells characterized by expression of the transcription factor forkhead box P3 (Foxp3) maintain immune homeostasis by suppressing self-destructive immune responses1-4. Foxp3 operates as a late acting differentiation factor controlling Treg cell homeostasis and function5, whereas the early Treg cell lineage commitment is regulated by the Akt kinase and the forkhead box O (Foxo) family of transcription factors6-10. However, whether Foxo proteins act beyond the Treg cell commitment stage to control Treg cell homeostasis and function remains largely unexplored. Here we show that Foxo1 is a pivotal regulator of Treg cell function. Treg cells express high amounts of Foxo1, and display reduced T-cell receptor-induced Akt activation, Foxo1 phosphorylation, and Foxo1 nuclear exclusion. Mice with Treg cell-specific deletion of Foxo1 develop a fatal inflammatory disorder similar in severity to Foxp3-deficient mice, but without the loss of Treg cells. Genome-wide analysis of Foxo1 binding sites reveals ~300 Foxo1-bound target genes, including the proinflammatory cytokine Ifng, that do not appear to be directly regulated by Foxp3. These findings demonstrate that the evolutionarily ancient Akt-Foxo1 signaling module controls a novel genetic program indispensable for Treg cell function."


Overall design from GEO:

"Regulatory T cells were FACS sorted in WT mice (2 reps), Foxo1 KO mice (2 reps), mice expressing a constitutively active form of Foxo1 (1 rep), and Foxo1 KO mice expressing constitutively active Foxo1. We identified genes differentially expressed in WT vs. KO mice and assessed whether expression was recovered in the KO in presence of constitutively active Foxo1"

The DNA biochip market is experiencing robust growth, driven by advancements in genomics research, personalized medicine, and diagnostics. The increasing prevalence of chronic diseases and the rising demand for faster, more accurate diagnostic tools are key factors fueling market expansion. Technological innovations, such as the development of higher-density chips and improved data analysis software, are enhancing the efficiency and capabilities of DNA biochips, leading to wider adoption across various applications. While the market is currently dominated by established players like Illumina, Affymetrix, and Agilent Technologies, smaller companies are also contributing with specialized solutions and innovative technologies. The segment of oligonucleotide DNA chips currently holds a larger market share compared to complementary DNA chips due to their versatility and cost-effectiveness. Gene expression analysis remains the leading application, followed by genotyping, with the "others" segment demonstrating steady growth potential as new applications emerge. The North American market currently holds a significant share due to robust research infrastructure and high healthcare expenditure. However, the Asia-Pacific region is expected to witness significant growth in the coming years, driven by rising investments in healthcare and increasing genomic research initiatives in countries like China and India. The market faces challenges such as high initial investment costs, stringent regulatory approvals, and the need for specialized expertise. However, ongoing technological advancements and the increasing demand for high-throughput screening are expected to overcome these obstacles and fuel continued market expansion. The forecast period of 2025-2033 presents significant opportunities for market expansion. Assuming a conservative CAGR (Compound Annual Growth Rate) of 8% (a reasonable estimate given the industry's growth trajectory), and a 2025 market size of $2 Billion (a plausible estimate based on industry reports and available data), the market is projected to reach approximately $4 Billion by 2033. This growth will be influenced by factors such as increased government funding for research, the rising adoption of point-of-care diagnostics, and the growing need for rapid and accurate infectious disease detection. The continued development of advanced biochip technologies, along with the integration of artificial intelligence and machine learning for data analysis, will further accelerate market growth. Competition among existing players and the entry of new companies will further shape the market landscape, fostering innovation and making DNA biochips increasingly accessible and affordable.

Transient abnormal myelopoiesis (TAM) is a clonal pre-leukemic disorder that progresses to myeloid leukemia of Down syndrome (ML-DS) through the accumulation of genetic alterations. To investigate the mechanism of leukemogenesis in this disorder, a xenograft model of TAM was established using NOD/Shi-scid, IL-2Rγnull mice. In serial transplantations, engrafted TAM-derived cells showed the emergence of divergent subclones with another GATA1 mutation and various CNAs, including a 16q deletion and 1q gain, which are clinically associated with ML-DS. These results suggest that genetically heterogeneous subclones with varying leukemia-initiating potential already exist in the neonatal TAM phase, and ML-DS may develop from a pool of such minor clones through clonal selection. Overall design: Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from murine bone marrow (hCD45 sorted) or human peripheral blood samples. Copy number analysis of Affymetrix 500K SNP arrays was performed for xenografted TAM samples of 3 patients. There are also 3 samples from TAM patients in remission, which were used as references for copy number inference.