Multiparametric Time-Correlated Single Photon Counting Luminescence Microscopy
V I Shcheslavskiy1,2, M V Shirmanova2, A Jelzow3
1Becker&Hickl GmbH, Berlin, 12277, Germany. vis@becker-hickl.de.
Biochemistry. Biokhimiia
|June 20, 2019
Summary
Time-correlated single photon counting (TCSPC) precisely measures photon arrival times for high-resolution imaging. Modern multi-dimensional TCSPC expands capabilities for advanced biological studies using fluorescence lifetime imaging.
Area of Science:
- Optics and Photonics
- Biophysics
- Biotechnology
Background:
- Classic time-correlated single photon counting (TCSPC) offers high time resolution and detection efficiency.
- Modern TCSPC integrates multiple data dimensions beyond photon arrival time.
Purpose of the Study:
- To review classic and multi-dimensional TCSPC microscopy techniques.
- To highlight applications in fluorescence lifetime imaging for biological research.
Main Methods:
- Detection of single photons from periodic optical signals.
- Registration of photon arrival times relative to reference pulses.
- Construction of multi-dimensional photon distributions.
Main Results:
- TCSPC achieves extremely high time resolution and near-ideal detection efficiency.
- Multi-dimensional TCSPC captures spatial, spectral, and temporal data for each photon.
- Enables advanced fluorescence lifetime imaging.
Conclusions:
- TCSPC is a powerful technique for precise timing in optical measurements.
- Multi-dimensional TCSPC significantly enhances fluorescence lifetime imaging capabilities.
- These advancements are crucial for diverse biological studies.
Related Concept Videos
Drug Concentration Versus Time Correlation
2.1K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
2.1K
Correlation and Causation
42.3K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
42.3K
Correlations
35.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.8K
2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)
1.4K
Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
1.4K
Correlation
14.8K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
14.8K
Correlation and Regression
3.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.2K


