Epigenomic Profiling
Epigenomic Profiling - Related Videos
07:28
TChIP-Seq: Cell-Type-Specific Epigenome Profiling
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Cited by 1 •
2019
We describe a step-by-step protocol for tandem chromatin immunoprecipitation sequencing (tChIP-Seq) that enables the analysis of cell-type-specific genome-wide histone modification.
09:06
Spatially Resolved, Integrated Single-Cell Multiomic Profiling of the Transcriptome and Epigenomic Targets in Frozen Tissue Sections
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2026
This study presents a spatial Trekker–CUT&Tag multiome protocol combining direct spatial barcoding of a tissue section with single-cell multiome analysis. This approach enables spatially resolved single-cell profiling of gene expression and the histone H3K27ac modification simultaneously, offering mechanistic insights into gene regulation in the context of tissue microanatomy.
10:03
Nuclei Isolation from Mouse Cardiac Progenitor Cells for Epigenome and Gene Expression Profiling at Single-Cell Resolution
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Cited by 5 •
2023
Here, we present a protocol describing cell nuclei preparation. After microdissection and enzymatic dissociation of cardiac tissue into single cells, the progenitor cells were frozen, followed by isolation of pure viable cells, which were used for single-nucleus RNA sequencing and the single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing analyses.
06:38
Pattern-based Search of Epigenomic Data Using GeNemo
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2017
Unlike DNA sequence data, epigenomic data are not readily subjected to text-based searches. Presented here are the procedures to use an upgraded version of GeNemo, a web-based bioinformatics tool, to conduct pattern-based searches for similarities in epigenomic data comparing available online databases including Encyclopedia of DNA Elements with user's data.
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...
FAQs
Epigenomic profiling separates several layers of gene regulation rather than treating them as a single signal. DNA methylation and histone modifications represent chemical marks, while chromatin accessibility reflects how readily regulatory DNA can be reached. Regulatory interactions add information about contacts influencing gene activity. Considering these layers together helps explain distinct cellular states in tumors.
Comparisons reveal which regulatory patterns are associated with malignancy rather than with normal tissue or treatment exposure. Examining tumors against normal tissues can identify tumor-specific signatures, while comparing treatment conditions can show epigenomic changes linked to cellular adaptation or resistance. These contrasts make it easier to connect altered gene regulation with cancer behavior and therapeutic response.
Changes in gene-control patterns can help cancer cells adopt and maintain abnormal cellular states without changing their DNA sequence. Profiling these states shows how regulatory alterations may accompany tumor development, adaptation, and resistance to treatment. This perspective is valuable because it links cancer phenotypes to controllable gene-regulatory mechanisms rather than focusing only on sequence changes.
A study selects the regulatory features relevant to its biological question, then applies sequencing-based or related molecular assays to measure them. The resulting profiles can cover DNA methylation, histone modifications, chromatin accessibility, regulatory interactions, or several of these features. Researchers then compare the measurements across defined sample groups or treatment conditions to identify meaningful patterns.
Comparison groups should reflect the question being tested, such as tumor versus normal tissue or one treatment condition versus another. Consistent comparisons help distinguish tumor-associated signatures from broader tissue differences and treatment-associated changes. Organizing samples around these contrasts allows the analysis to connect epigenomic variation with disease classification, cellular adaptation, or response-related outcomes.
The resulting profiles can reveal patterns that distinguish tumors from normal tissues or separate cancer states from one another. Such tumor-specific signatures may support biomarker discovery and disease classification. When profiles are compared across treatment conditions, they can also identify regulatory changes relevant to therapeutic response, providing evidence for mechanisms that may be considered in treatment development.
By identifying abnormal patterns of DNA methylation, histone modification, chromatin accessibility, or regulatory interaction, the approach highlights disrupted gene-control mechanisms in cancer cells. These findings can clarify how tumors develop and adapt, including changes associated with treatment resistance. The information supports investigation of therapies directed at abnormal epigenomic regulation and helps prioritize relevant biological mechanisms.