Size Heterogeneity Analysis
Size Heterogeneity Analysis - Related Videos
03:06
Two-Dimensional Semi-Denaturing Agarose Gel Electrophoresis: A Technique to Separate Polymorphic Amyloids Fibers Based on Size Heterogeneity
0 Views •
2025
This video demonstrates the two-dimensional semi-denaturing detergent agarose gel electrophoresis-based separation of amyloid fibers. The technique separates the polymorphic amyloid aggregates based on their heterogeneous size.
04:51
Size Exclusion Chromatography Based Separation of Bacterial Extracellular Vesicles: A Technique to Separate Heterogenous Population of Gram-Negative Bacterial Outer Membrane Vesicles Based on Their Size
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2025
In this video, we demonstrate a technique–size exclusion chromatography–to separate a heterogenous population of outer membrane vesicles or OMVs derived from bacteria. These vesicles are nanosized spherical structures and are separated into large and small subpopulations based on their size through Size Exclusion Chromatography.
07:26
Size Exclusion Chromatography to Analyze Bacterial Outer Membrane Vesicle Heterogeneity
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Cited by 23 •
2021
Bacterial vesicles play important roles in pathogenesis and have promising biotechnological applications. The heterogeneity of vesicles complicates analysis and use; therefore, a simple, reproducible method to separate varying sizes of vesicles is necessary. Here, we demonstrate the use of size exclusion chromatography to separate heterogeneous vesicles produced by Aggregatibacter actinomycetemcomitans.
01:25
The Anchoring-and-Adjustment Heuristic
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2020
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
07:54
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
0 Views •
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.
FAQs
The approach connects a measured signal to physical size through a size-dependent behavior, such as elution, diffusion, imaging dimensions, or light scattering. Measurements from individual components or distinguishable sample features are then represented across a distribution. This preserves variation within the sample and can expose groups that a single summary value would conceal.
Controlled conditions help ensure that differences in elution behavior, diffusion, imaging dimensions, or light scattering primarily reflect size rather than inconsistent measurement circumstances. This improves comparability between samples and experiments. In biological techniques and bioprocessing, consistent conditions are especially important when assessing whether a treatment or process has changed sample composition.
An average size summarizes a sample with one central value, whereas Size Heterogeneity Analysis retains information about the range and distribution of sizes. Two samples can therefore share a similar average while differing in subpopulations, aggregation, fragmentation, or overall consistency. Examining the distribution provides a more sensitive view of sample structure and change.
Changes in the distribution can reveal the appearance or enlargement of distinct size groups, which may be consistent with aggregation, or the emergence of smaller components, which may indicate fragmentation. The analysis does not reduce the sample to one value, so these compositional shifts can be detected and compared across biological conditions or treatments.
A typical workflow establishes controlled measurement conditions, selects a size-dependent signal appropriate to the sample, captures measurements from the cells, particles, vesicles, or biomolecules, and converts those measurements into a size distribution. The resulting profile is then examined for subpopulations, aggregation, fragmentation, or inconsistency rather than interpreted only through a bulk average.
The method is useful when researchers need to characterize sample composition, monitor quality, or evaluate reproducibility. It can compare distributions before and after biological conditions or treatments and identify changes that bulk measurements may miss. In bioprocessing, these results support quality-control decisions by showing whether a sample remains consistent or develops distinct size-related components.