Related Experiment Video

Updated: Feb 2, 2026

Far-Red Fluorescent Senescence-Associated β-Galactosidase Probe for Identification and Enrichment of Senescent Tumor Cells by Flow Cytometry
14:01

Far-Red Fluorescent Senescence-Associated β-Galactosidase Probe for Identification and Enrichment of Senescent Tumor Cells by Flow Cytometry

Published on: September 13, 2022

5.8K

Alarmin Detection in Senescent Cells

Dong Eun Kim1, Albert R Davalos2

  • 1Buck Institute for Research on Aging, Novato, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|November 27, 2018
PubMed

Keywords:
AlarminDamage-associated molecular pattern (DAMP)High Mobility Group Box 1 protein (HMGB1)ImmunofluorescenceImmunohistochemistryProtein precipitationWestern blot

More Related Videos

Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells08:56

Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells

Published on: July 12, 2022

3.5K
A Sensitive Method to Quantify Senescent Cancer Cells09:18

A Sensitive Method to Quantify Senescent Cancer Cells

Published on: August 2, 2013

21.3K

Related Concept Videos

Replicative Cell Senescence02:15

Replicative Cell Senescence

Replicative cell senescence is a property of cells that allows them to divide a finite number of times throughout the organism's lifespan while preventing excessive proliferation. Replicative senescence is associated with the gradual loss of the telomere — short, repetitive DNA sequences found at the end of the chromosomes. Telomeres are bound by a group of proteins to form a protective cap on the ends of chromosomes. Embryonic stem cells express telomerase — an enzyme that adds...
4.4K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.0K
Detection of Black Holes01:10

Detection of Black Holes

Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.6K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.3K
Effects of EDTA on End-Point Detection Methods01:18

Effects of EDTA on End-Point Detection Methods

Different methods, such as visual observance of metal-ion indicators, spectroscopic techniques, and potentiometric methods, can determine the endpoint of an EDTA titration.
In the visual method, metal-ion indicators (metallochromic dyes), which have distinct colors in their free and complex forms, are added to the mixture to signal the titration's end point. They form stable complexes with metal ions, but these complexes are weaker than the corresponding metal–EDTA complexes. As a...
665
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
11.4K