Two-photon spectrometer

Type: Optics / Microscopy,

Keywords: Fluorescent proteins, Genetically encoded sensors, Two-photon absorption spectra, Cross sections, Brightness, Fluorescence lifetime, Quantum yields, Extinction coefficients, Three-photon absorption spectra, Multiphoton bleaching rates

Resource for multiphoton characterization of genetically-encoded fluorescent probes

This resource provides the service of characterization of multiphoton absorption properties and multiphoton stability to a large number of protein engineers and neuroscientists involved in the BRAIN initiative. This information is indispensable because it makes it possible to choose the best probe and best excitation conditions (wavelength, laser power, etc.) for deep high-resolution multiphoton microscopy of the brain. Although there are few laboratories around the world that are able to quantitatively characterize the multiphoton properties of organic molecules, they are either not dealing with the probes utilized in neuroscience or they are not providing the service for all interested BRAIN researchers.

* Methods and protocols of two-photon characterization of fluorescent probes and biosensors in a broad NIR range of wavelengths.

* Deciphering molecular mechanisms of fluorescence change in genetically-encoded sensors.

* Detailed characterization of multiphoton photobleaching parameters of genetically-encoded probes.

* Three-photon absorption characterization of fluorescent probes and proteins.

* Quantutative characterization of fluorescence quantum yields, lifetimes, spectra, anisotropy of fluorescent proteins.

* Characterize two-photon absorption properties of fluorescent molecules in the NIR spectral range.

* Elucidate the role of different factors in fluorescence signal change in genetically-encoded fluorescent proteins upon binding ligands.

* Understand the role of internal protein electrostatics in photophysical properties of fluorescent proteins.

* Characterize two-photon absorption properties of green, red, and near-IR genetically encoded probes in the 680-1300 nm spectral range.

* Understand the role of fluorescent quantum yield, extinction coefficient, and fractional concentration of anionic form of chromophore in fluorescence signal change of genetically-encoded green and red GECIs.

* Understand the role of internal protein electric field in two-photon cross section, spectral shift of absorption peak, and quantum yield of green and red fluorescent proteins.

* Well established methods and reference standards for two-photon spectral and cross sectional characterization.

* Automated, computer controlled systems for multiphoton spectroscopy of purified fluorescent molecules and FP solutions.

* Automated system for multiphoton photostability (bleaching) characterization of fluorescent proteins expressed in E. coli colonies.

* Spectral range available for multiphoton spectroscopy is limited to 680 – 1300 nm.

* Fluorescence lifetime measurements are limited to lifetimes > 50 ps.

* Typical concentrations of purified proteins should be > 1 mM.

* Drobizhev et al. 2020, Characterizing the Two-Photon Absorption Properties of Fluorescent Molecules in the 680 – 1300 nm Spectral Range, Bio-Protocol,

* Molina et al. 2019 Understanding the Fluorescence Change in Red Genetically Encoded Calcium Ion Indicators, Biophys. J.,

* Piatkevich et al. 2018, A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters, Nat. Chem. Biol.,

* Qian et al. 2019, A genetically encoded near-infrared fluorescent calcium ion indicator, Nat. Meth.,

* Zhang et al. 2020, An ultrasensitive biosensor for high-resolution kinase activity imaging in awake mice. Nat. Chem. Biol.,

* Drobizhev et al. 2021, Local Electric Field Controls Fluorescence Quantum Yields of Red and Far-Red Fluorescent Proteins, Frontiers Mol. Biosciences,

* Dalangin et al., 2020, Far-red fluorescent genetically encoded calcium ion indicators, bioRxiv,


Mikhail Drobizhev, Associate Research Professor


Montana State University, Bozeman, MT



Huwang Ai, Professor, University of Virginia; Ed Boyden, Professor, MIT; Robert Campbell, Professor, University of Alberta, Canada; Thom Hughes, Professor Montana State University; Yusuke Nasu, Research Professor, University of Tokyo, Japan; Nathan Shaner, Professor, University of San Diego