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ref: -2011 tags: two photon cross section fluorescent protein photobleaching Drobizhev gcamp date: 11-04-2020 18:07 gmt revision:9 [8] [7] [6] [5] [4] [3] [head]

PMID-21527931 Two-photon absorption properties of fluorescent proteins

  • Significant 2-photon cross section of red fluorescent proteins (same chromophore as DsRed) in the 700 - 770nm range, accessible to Ti:sapphire lasers ...
    • This corresponds to a S 0S nS_0 \rightarrow S_n transition
    • But but, photobleaching is an order of magnitude slower when excited by the direct S 0S 1S_0 \rightarrow S_1 transition (but the fluorophores can be significantly less bright in this regime).
      • Quote: the photobleaching of DsRed slows down by an order of magnitude when the excitation wavelength is shifted to the red, from 750 to 950 nm (32).
    • See also PMID-18027924
  • Further work by same authors: Absolute Two-Photon Absorption Spectra and Two-Photon Brightness of Orange and Red Fluorescent Proteins
    • " TagRFP possesses the highest two-photon cross section, σ2 = 315 GM, and brightness, σ2φ = 130 GM, where φ is the fluorescence quantum yield. At longer wavelengths, 1000–1100 nm, tdTomato has the largest values, σ2 = 216 GM and σ2φ = 120 GM, per protein chain. Compared to the benchmark EGFP, these proteins present 3–4 times improvement in two-photon brightness."
    • "Single-photon properties of the FPs are poor predictors of which fluorescent proteins will be optimal in two-photon applications. It follows that additional mutagenesis efforts to improve two-photon cross section will benefit the field."
  • 2P cross-section in both the 700-800nm and 1000-1100 nm range corresponds to the chromophore polarizability, and is not related to 1p cross section.
  • This can be useflu for multicolor imaging: excitation of the higher S0 → Sn transition of TagRFP simultaneously with the first, S0 → S1, transition of mKalama1 makes dual-color two-photon imaging possible with a single excitation laser wavelength (13)
  • Why are red GECIs based on mApple (rGECO1) or mRuby (RCaMP)? dsRed2 or TagRFP are much better .. but maybe they don't have CP variants.
  • from https://elifesciences.org/articles/12727

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ref: -2017 tags: two photon holographic imaging Arch optogenetics GCaMP6 date: 09-12-2019 19:24 gmt revision:1 [0] [head]

PMID-28053310 Simultaneous high-speed imaging and optogenetic inhibition in the intact mouse brain.

  • Bovetti S1, Moretti C1, Zucca S1, Dal Maschio M1, Bonifazi P2,3, Fellin T1.
  • Image GCamp6 in either scanned mode (high resolution, slow) or holographically (SLM, redshirt 80x80 NeuroCCD, activate opsin Arch, simultaneously record juxtasomal action potentials.

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ref: -2019 tags: three photon imaging visual cortex THG chirp NOPA mice GCaMP6 MIT date: 03-01-2019 18:46 gmt revision:2 [1] [0] [head]

PMID-30635577 Functional imaging of visual cortical layers and subplate in awake mice with optimized three photon microscopy

  • Murat Yildirim, Hiroki Sugihara, Peter T.C. So & Mriganka Sur'
  • Used a fs Ti:Saphirre 16W pump into a non-colinear optical parametric amplifier (both from Spectra-Physics) to generate the 1300nm light.
  • Used pulse compensation to get the pulse width at the output of the objective to 40 fS.
    • Three-photon cross section is inverse quadratic in pulse width:
    • NP 3δ(τR) 2(NA 22hcλ) 3 N \sim \frac{P^3 \delta}{(\tau R)^2} (\frac{NA^2}{2hc\lambda})^3
    • P is power, δ\delta is 3p cross-section, τ\tau is pulse width, R repetition rate, NA is the numerical aperture (sixth power of NA!!!), h c and λ\lambda Planks constant, speed of light, and wavelength respectively.
  • Optimized excitation per depth by monitoring damage levels. varied from 0.5nJ to 5 nJ.
  • Imaged up to 1.5mm deep! All the way to the white matter / subplate.
  • Allegedly used a custom scan and tube lens to minimize aberrations in the excitation path (hence improve 3p excitation)
  • Layer 5 neurons are more broadly tuned for orientation than other layers. But the data is not dramatic.
  • Used straightforward metrics for tuning, using a positive and negative bump gaussian fit, then vector averaging to get global orientation selectivity.
  • Interesting that the variance between layers seems higher than between mice.