Dual Omics Profiling
Dual Omics Profiling - Related Videos
08:07
Profiling of Surface Protein Epitopes on Viral Particles by Multiplex Dual-Reporter Strategy
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2024
Here, we describe a newly developed multiplex fluorescent immunoassay that uses a dual-reporter flow cytometric system to concurrently detect two unique spike protein epitopes on intact severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral particles that had been captured by angiotensin-converting enzyme-2-coupled magnetic microspheres.
08:29
Expression Profiling with Microarrays
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2023
Microarrays are important tools for profiling gene expression, and are based on complementary binding between probes that are attached to glass chips and nucleic acids derived from samples. Using these arrays, scientists can simultaneously evaluate the expression of thousands of genes. In addition, the expression profiles of different cells or tissue types can be compared, allowing researchers to deduce how the expression of different genes change during biological processes, and thus gain...
11:35
Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function
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Cited by 16 •
2010
A dual-mode imaging system was developed for non-contact assessment of cutaneous tissue oxygenation and vascular function.
07:33
In vivo Dual Substrate Bioluminescent Imaging
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Cited by 20 •
2011
Herein we describe the methods to construct, visualize, and quantify the bioluminescent reactions of both firefly and renilla luciferase enzymes expressed in metastatic breast cancer cells during their growth and metastasis in vivo.
16:24
Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
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Cited by 9 •
2014
Molecular signaling through both estrogen and microRNAs are critical in breast cancer development and growth. Estrogen activates the estrogen receptors, which are transcription factors. Many transcription factors can regulate the expression of microRNAs, and estrogen-regulated microRNAs can be profiled using different large-scale techniques.
FAQs
The approach connects measurements from two molecular layers within the same biological system, allowing researchers to examine whether changes occur together. For example, gene-expression shifts can be interpreted alongside chromatin accessibility or DNA methylation patterns. This comparison can expose regulatory relationships and coordinated developmental changes that would remain ambiguous if each molecular layer were analyzed separately.
Matched measurements preserve the connection between molecular states instead of comparing unrelated populations. This makes it possible to ask whether a particular expression pattern coincides with a specific regulatory or protein state in the same biological context. In developmental studies, that alignment strengthens cell-state classification and helps associate molecular changes with lineage commitment or tissue formation.
No. Coordination identifies relationships and candidate regulatory events, but it does not by itself establish causation. A molecular feature that changes alongside a developmental transition may contribute to that transition or simply accompany it. Researchers can use the integrated result to prioritize possible regulators and distinguish stronger developmental hypotheses from patterns that require further investigation.
The paired layers may include gene expression with chromatin accessibility, DNA methylation, or protein abundance, among other molecular features. The most informative pairing depends on the developmental question. Expression can describe active cellular programs, while a regulatory or protein layer provides complementary context for interpreting cell identity, lineage changes, and tissue development.
Researchers first obtain two molecular measurements from matched cells or samples, then analyze the datasets together rather than treating them as unrelated experiments. Integrated analysis can identify coordinated changes, classify cell states, and examine how molecular programs vary over developmental time. The resulting relationships can then support trajectory analysis and selection of candidate regulators.
By linking molecular states across paired datasets, the approach helps characterize how cells change over time and how distinct states relate to one another. Researchers can use these linked patterns to study lineage commitment, identify transitions associated with tissue formation, and compare regulatory programs between developmental states. The same strategy can also highlight candidate changes associated with developmental disease.