Anoxia and Gene Regulation: Your Standard in Reference Gene Validation for qPCR
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Anoxia is committed to improving gene expression analysis by providing validated reference genes for real-time PCR. Our expert team offers reliable tools and services to ensure accurate and reproducible qPCR results across various research fields.
We deliver quality reference gene databases, validation services, and ready-to-use kits, backed by scientific support and resources to help you achieve precise gene expression normalization.
Our Mission
Our mission at Anoxia is to empower researchers with reliable and validated reference genes that ensure precise and reproducible gene expression analysis by real-time PCR. We strive to advance molecular biology research through innovative solutions, rigorous validation, and dedicated scientific support, helping scientists unlock accurate insights across diverse biological systems.
Next-Generation Approaches to Gene Expression Profiling
Reference genes in real-time PCR
Single-cell RNA sequencing (scRNA-Seq) allows researchers to measure gene expression in individual cells, rather than averaging signals from bulk tissues. This technique offers an unprecedented view into the cellular diversity within tissues, enabling the discovery of rare or unknown cell types and tracking cell lineage and developmental processes.
By using microfluidics, droplets, or fluorescence-activated cell sorting, individual cells are isolated and processed to capture their transcriptome. This makes it possible to study disease progression at the cellular level, analyze cell-type-specific drug responses, and investigate gene regulatory networks with high-resolution.
scRNA-Seq is especially powerful in heterogeneous systems like the immune system, tumors, or brain tissue, where it helps to uncover subtle differences between cell types or states that would otherwise be masked in traditional gene expression studies.
GAPDH : one of the most commonly used reference genes
GAPDH is one of the most commonly used reference genes, frequently cited in major scientific journals as part of the classical normalization strategy . While its use brings consistent results in many studies, in others it is not recommended due to variability in expression influenced by specific experimental factors, casting doubt on its classification as a true housekeeping gene (HKG). Large-scale studies on 72 human tissue types, analyzing 1595 samples from over 600 donorsrevealed a 14-fold difference in GAPDH expression between skeletal muscle and breast cells. A similar observation was made in medaka (Oryzias latipes), where a 5.45-fold difference was found between intestine and muscle tissue . Although such variability doesn't exclude its usefulness within the same tissue, it highlights the necessity of validation. For instance, in tuberculosis studies where blood samples were analyzed to monitor IL-4 expression post-treatment, normalization with GAPDH produced misleading results masking gene regulation and leading to both false negatives and false positives . Further, in studies on adipose-derived stem cells , although GAPDH expression remained stable across passages, additional conditions such as hypoxia and induced differentiation (chondrogenesis, osteogenesis, adipogenesis) affected its expression. Specifically, during chondrogenesis and under hypoxic conditions, positive regulation of GAPDH was observed, rendering it unsuitable as a reference gene. These findings underline the absolute need to validate GAPDH under each specific experimental context, regardless of prior successful use.
Spatial transcriptomics in current biomedical research
Spatial transcriptomics is transforming gene expression analysis by enabling researchers to study gene activity within intact tissue architecture at single-cell resolution. Once limited to specialized labs, platforms like Visium (10x Genomics), GeoMx, and CosMx (NanoString) have made spatial transcriptomics more accessible for biomedical research. These technologies reveal spatial gene expression patterns in complex biological systems, helping to identify tissue-specific signaling, disease-associated cell niches, and novel biomarkers. Applications span from cancer biology, where spatial mapping distinguishes immune, stromal, and tumor cell regions, to neuroscience, where spatial transcriptomics preserves fragile brain tissue to uncover local gene expression changes near amyloid plaques in Alzheimer’s disease. In reproductive biology, they reveal molecular gradients in tissues like the endometrium. Spatial proteomics and multiplex imaging techniques, such as CyCIF and RNAscope, further enhance understanding by linking gene and protein expression to histological features. These insights are driving discoveries in tumor immunology, neuroinflammation, tissue regeneration, and developmental biology. As spatial -omics technologies continue to evolve, they offer powerful tools for mapping gene expression with high precision in both healthy and diseased human and animal tissues.
CRISPR-based transcriptional reporters
CRISPR-based transcriptional reporters are advanced molecular tools that harness the precision of the CRISPR/Cas system to monitor or modulate gene expression in live cells in real time. Unlike traditional gene expression assays that rely on fixed or lysed samples, these systems use engineered CRISPR components typically a catalytically inactive Cas9 (dCas9) fused to a transcriptional activator or reporter protein to target specific genomic loci without cutting DNA. When guided by custom-designed gRNAs, these reporters can either activate endogenous gene expression or illuminate transcriptional activity through fluorescent or luminescent signals. This live-cell, real-time monitoring enables dynamic analysis of gene regulation, response to stimuli, and cellular state transitions with unparalleled spatial and temporal resolution. CRISPR-based transcriptional reporters have become powerful tools in functional genomics, synthetic biology, and gene regulatory network studies, allowing researchers to map regulatory elements, screen gene function, and program synthetic circuits. Their adaptability, precision, and compatibility with high-throughput platforms make them essential for studying complex gene expression patterns in both basic research and applied biotechnology.
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Smart-Seq3
Smart-Seq3 is a next-generation single-cell RNA sequencing (scRNA-seq) technology designed to provide full-length transcript coverage from individual cells, offering enhanced resolution for transcriptomic analysis. Building on earlier Smart-Seq protocols, Smart-Seq3 introduces improved sensitivity and precision by capturing the 5' end of transcripts, enabling more accurate quantification of gene expression and the identification of transcript isoforms, allele-specific expression, and rare genetic variants. Unlike droplet-based methods that typically capture only transcript fragments, Smart-Seq3 allows researchers to reconstruct full-length mRNA molecules, providing deep insights into alternative splicing events and transcript diversity at the single-cell level. This makes it particularly valuable in studies of complex tissues, cancer heterogeneity, developmental biology, and any research requiring fine-resolution analysis of gene expression. With its ability to link expression changes to specific transcript variants, Smart-Seq3 is a powerful tool for dissecting gene regulation and understanding how different isoforms contribute to cell function and disease mechanisms. .
Single-Cell RNA Sequencing
Single-cell RNA sequencing (scRNA-Seq) is a cutting-edge method that enables the analysis of gene expression at the resolution of individual cells, offering a detailed view of cellular diversity within complex tissues. Unlike traditional bulk RNA techniques that average gene expression across thousands of cells, scRNA-Seq reveals subtle differences between cell types, states, or responses that would otherwise be masked. This approach is particularly valuable for identifying rare cell populations, mapping developmental trajectories, and understanding disease mechanisms at the cellular level, such as in cancer, neuroscience, or immunology. By isolating single cells and sequencing their RNA transcripts, scRNA-Seq provides high-resolution insights into gene regulatory networks and cell-specific functions.
microRNA RT-qPCR data normalization
In microRNA RT-qPCR analysis, accurate normalization is essential for reliable gene expression results. A robust method involves using the mean expression value of all detected microRNAs within each sample as the normalization factor. This approach avoids relying on unstable housekeeping genes or small non-coding RNAs, which may vary under different experimental conditions. By averaging the expression levels of all expressed microRNAs, this method provides a stable baseline that reflects the overall microRNA profile of the sample. It significantly reduces technical variation and improves the detection of true biological differences, making it ideal for both large-scale microRNA profiling and targeted validation studies. This normalization strategy enhances the reliability of differential expression analysis in cancer research, biomarker discovery, and clinical diagnostics.
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