Chapter 4 – Making Light Work Harder in Biology  119

of UV light to activate and/​or switch fluorophores, and of visible excitation light for fluor­

escence imaging, over several thousand cycles substantially increases concentration of free

radicals in cellular samples. This potentially impairs the viability of non-​fixed cells.

The phrase time-​correlated single-​molecule localization microscopy (tcSMLM) is some­

times used to describe the subset of localization microscopy techniques, which render

time-​resolved information. This is generally from using tracking of positional data, which

estimate the peak of the fluorophore’s 2D intensity profile, but other methods also utilize

time-​dependent differences in photophysical fluorophore properties such fluctuations of

brightness and fluorescence lifetimes, which may be applied to multiple fluorescent emitters

in a field of view, including super-​resolution optical fluctuation imaging (SOFI) (Dertinger

et al, 2009) which uses temporal intensity statistics to generate super-​resolved image data,

and a range of recent algorithms which use statistical analysis of dye brightness fluctuations,

for example, Bayesian analysis of bleaching and blinking (3B) (Cox et al, 2012), which can

overcome several of the issues associated with high fluorophore density, which straightfor­

ward tracking methods can be limited by.

The most widely applied tcSMLM approach uses PALM instrumentation, called time-​

correlated PALM (tcPALM), in which PALM is used to provided time-​resolved information

from individual tracks (Cissé et al. 2013). As with normal PALM, only a fraction of the tagged

molecules present can be localized so this does not render a definitive estimate for the total

number of molecules present of that particular type but does yield quantitative details of

their dynamics from the subset that are labeled and detected once stochastically activated.

Here, for example, PALM can image a region of a cell expected to have a high concentration

of molecular clusters comprising a specific protein labeled with a photoactivatable dye, and

then a time-​series is acquired recording the time from the start of activation at which every

track is subsequently detected. The peak time from this distribution, inferred from many

such fields of view form different cells, is then used as a characteristic arrival time for that

protein into a molecular complex, which can then be compared against other estimates in

which the system is perturbed in some way at which characteristic time for fluorophore to

be detected time, and which can be a very tool to understand the reactions that occur in the

cluster assembly process.

4.2.9  STOCHASTIC BLINKING

Reversible photobleaching of fluorophores, or blinking, can also be utilized (for a good review

of the fluorophore photophysics, see Ha and Tinnefeld, 2012). The physical mechanism of

blinking is heterogeneous, in that several potential photophysical mechanisms can lead to the

appearance of reversible photobleaching. Occupancy of the triplet state, or triplet blinking,

is one of such; however, the triplet state lifetime is ~10⁻⁶ s, which is too small to account

for observed blinking in fluorescence imaging with a sampling time window of ~10⁻³ s and

above. Redox blinking is another possible mechanism in that an excited electron is removed

(one of the definitions of oxidation), which induces a dark state that is transient up until the

time that a new electron is elevated to the excited state energy level. However, many different

fluorophores also appear to have nonredox photochemical mechanisms to generate blinking.

The stochastic nature of photoblinking can be carefully selected using different chemical

redox conditions but also, in some cases, through a dependence of the blinking kinetics on

the excitation light intensity. High-​intensity light, in excess of several kW cm⁻², can give rise

to several reversible blinking cycles before succumbing to irreversible photobleaching. This

reduces the local concentration of photoactive fluorophores in any given image frame, facili­

tating super-​resolution localization microscopy. This technique has been applied to living

bacterial cells to map out DNA binding proteins using YFP (Lee et al., 2011).

An alternative stochastic super-​resolution imaging method is called “point accumula­

tion for imaging in nanoscale topography.” Here, fluorescence imaging is performed using

diffusing fluorophore-​tagged biomolecules, which are known to interact only transiently

with the sample. This method is relatively straightforward to implement compared to

PALM/​STORM. This method has several variants, for example, it has also been adapted to a