Intra Cluster Heterogeneity
Intra Cluster Heterogeneity - Related Videos
07:54
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
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Cited by 27 •
2011
Here we describe a method to quantify molecular heterogeneity in histological sections of tumor material using quantitative immunofluorescence, image analysis, and a statistical measure of heterogeneity. The method is intended for use in clinical biomarker development and analysis.
01:22
Heterogeneous Catalysis
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2026
Heterogeneous catalysis involves a catalyst in a different phase from the reactants. It is a process where the catalyst and the reactants are in distinct phases, typically solid and gas or liquid.Most heterogeneous catalysts are metals, metal oxides, or acids. The list includes transition metals like iron (Fe), cobalt (Co), nickel (Ni), palladium (Pd), platinum (Pt), chromium (Cr), manganese (Mn), tungsten (W), silver (Ag), and copper (Cu). These metals possess partially vacant d orbitals that...
11:15
HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin
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Cited by 12 •
2016
The conversion of trans-ferulic acid to vanillin was achieved by heterogeneous catalysis. HKUST-1 was employed in this synthesis and the essential step in the catalytic process was the generation of unsaturated metal sites. Thus, when the catalyst was activated under vacuum, full vanillin conversion (yield of 95%) was obtained.
10:37
Spatial Separation of Molecular Conformers and Clusters
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Cited by 3 •
2014
We present a technique that allows the spatial separation of different conformers or clusters present in a molecular beam. An electrostatic deflector is used to separate species by their mass-to-dipole moment ratio, leading to the production of gas-phase ensembles of a single conformer or cluster stoichiometry.
04:20
Intra-uterine Cancer Cell Injection: A Technique to Deliver Cancer Cells for Establishing Intra-peritoneal Ovarian Cancer Mouse Model
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2023
In this video, we demonstrate intra-uterine injection of ovarian cancer cells in a mouse model. This orthotopic murine model helps us to non-invasively monitor tumor growth and evaluate the efficacy of new therapies for the prevention and treatment of recurrent ovarian cancer.
FAQs
Residual variation matters because it can separate a broad cluster-level signal into biologically meaningful subpopulations. Differences in mutations, epigenetic states, or transcriptional programs may indicate distinct cellular behavior rather than random measurement scatter. Local microenvironmental signals add another source of divergence. Examining these patterns helps investigators avoid treating every cell in one cluster as biologically equivalent.
Single-cell profiling measures variation from cell to cell, while spatial profiling adds information about each cell’s local tissue context. Used together, these approaches can reveal whether residual differences follow molecular states, physical neighborhoods, or both. This helps identify transitional states and distinguish stable tumor phenotypes from variation more consistent with technical noise.
Variation within a cluster can expose cells that occupy transitional states rather than fitting a single stable phenotype. These intermediate patterns provide evidence that tumor cell populations may contain related but biologically distinct states. Mapping such variation helps researchers interpret how tumor phenotypes are organized and investigate changes associated with tumor evolution and therapy resistance.
Researchers first examine cell-resolved measurements using single-cell or spatial profiling, then quantify residual variation among cells assigned to the same cluster. They can assess whether the differences form recognizable subpopulations or transitional states and whether local microenvironmental signals coincide with them. This workflow supports separation of meaningful biological structure from technical noise.
Assessing within-cluster variation can reveal that cells grouped together do not share identical biological states. In cancer samples, this additional resolution helps distinguish malignant and immune cell populations more accurately and can expose meaningful subpopulations that a broad cluster label obscures. Better classification strengthens interpretation of tumor composition and cellular interactions.
Recognizing residual differences can identify subpopulations with distinct biological programs, including states associated with therapy resistance. Those patterns may improve biomarker discovery by linking molecular variation to clinically relevant tumor features. They also support more precise treatment design, because strategies can account for heterogeneous cellular states instead of assuming that one cluster will respond uniformly.