Fig 1.
Schematic of the 8-spot single-molecule FRET setup.
A freely-diffusing single molecule sample is probed via 8 independent excitation spots generated by a 532 nm CW laser and an LCOS spatial-light modulator. The fluorescent signal from each excitation spot is optically conjugated to a pair of pixels in the two detectors. The donor and acceptor emission are separated in two distinct spectral bands and detected by two different SPAD arrays.
Table 1.
Dark count rates (DCR, in Hz) for individual SPADs of the two SPAD arrays used in this work. D-channel and A-channel indicates the 8-SPAD array used to collect, respectively, the donor and acceptor signal from each spot. The smallest (24 Hz) and largest (7 kHz) DCR are highlighted. The top row indicates the pixel number in each array. Under the assumption of Poisson statistics, the standard error is equal to the square root of the estimated rates.
Table 2.
Estimated afterpulsing probabilities (in percent) and their standard deviation for each SPADs of the two arrays. The top row indicates the pixel number in each array. The standard deviation is based on three independent measurements.
Fig 2.
Single-spot μs-ALEX smFRET efficiency for 5 dsDNA samples estimated using different methods.
Population-level FRET efficiency estimated for each of the 5 dsDNA samples: 7d, 12d, 17d, 22d and 27 (the number represents the separation in base-pair between D and A dyes). FRET is estimated using the following methods. E KDE–KDE maximum from the corrected E distribution. E Gauss–Gaussian fit of the corrected E histogram. PR KDE–KDE maximum of the PR distribution, PR value converted to E. PR Gauss–Gaussian fit of the PR histogram, PR value converted to E (all previous estimation are described in Appendix 9 in S1 File). SNA mean E–Mean of the FRET distribution returned by SNA analysis. SNA max E–Mode of the FRET distribution returned by SNA analysis (SNA analysis described in Appendix 11 in S1 File). For full details on the analysis (including number of bursts and fit errors) see section of μs-ALEX: Corrected E figure of the accompanying Jupyter notebook (view online). An overview of the computational notebooks can be found in Appendix 2 in S1 File.
Fig 3.
DCR-corrected total count rate as a function of the pixel.
Characterization of the excitation × detection profile as a function of spot. Values computed from DCR-corrected count rate in each pixel for different samples. (A) Signal detected by the SPAD array in the D emission channel. (B) Signal detected by the SPAD array in the A emission channel. The samples are the 5 doubly-labeled dsDNA samples (7d - 27d) plus the D-only dsDNA sample (DO). For each sample, the signal is normalized to 1 for the detector with the highest signal. For computational details see section Signal vs spot (view online) of the accompanying Jupyter notebook.
Fig 4.
For each sample, 8 donor autocorrelation functions (ACFs) and for samples with sufficient acceptor signal (7d, 12d and 17d), 8 acceptor ACFs, were fitted with a 2D diffusion model with multi-exponential afterpulsing components. Additionally, for all samples, the cross-correlation function (CCF) of the 8 donor and acceptor signals were computed. Each curve was fitted with the respective model described in the text. (A) Diffusion times for the donor ACFs. (B) Diffusion times for the acceptor ACFs. (C) Diffusion times for the donor-acceptor CCFs. A significantly shorter diffusion time is observed for the 22d sample in both the ACFs and CCFs. Sample 7d is characterized by a significantly larger diffusion time in the CCF only.
Table 3.
8-spot average of diffusion times (μs) fitted from ACF and 2-color CCF curves.
Average diffusion times (± 1 standard deviation) obtained from correlation function fits (in μs). τG: donor-ACF, τR: acceptor ACF, τGR: donor-acceptor CCF diffusion time obtained for a 2D diffusion model with no offset. No fit was performed for the acceptor ACF (A-ACF) of sample DO, 27d and 22d due to lack of sufficient acceptor signal.
Fig 5.
Burst statistics extracted for the D-only population in the series of 6 dsDNA samples (7d, 12d, 17d, 22d, 27d and DO) using burst search on Dem photons. The first row (A, B, C) shows results obtained with a minimum SBR criterion burst search. The second row (D, E, F) show results obtained with a fixed threshold burst search. The three columns report different burst statistics as a function of the spot number. A, D: Mean burst size. B, E: Mean burst duration. C, F: Mean burst peak count rate. While the peak count rate is largely invariant from the type of burst search (C & F), burst size and duration are affected. In particular, the uneven DCR distribution of the donor SPAD array (maximum DCR in pixel 3), causes a dip in both mean burst size and duration (A & B). The DCR influence is eliminated when using a fixed threshold (D & E). Graphs D—F show the fairly uniform properties of the excitation-detection PSFs across the different spots (see main text). Additional computational details can be found in Section Burst statistics vs spot (view online) of the accompanying Jupyter notebook.
Fig 6.
Leakage coefficient for different samples and spots.
Leakage coefficient estimated for each spot for dsDNA samples 7d, 12d, 17d and DO. Each dot is the estimated leakage coefficient for a given spot and sample (the color indicates the sample). The left panel (A) shows the dependence of leakage versus sample, while the right panel (B) shows the dependence of leakage versus spot number. Black lines are weighted mean with weights proportional to the number of bursts detected in each sample (A) or spot (B). Both graphs show a good uniformity across samples and spots, with leakage factors in the 3–4% range. For computational details (including the numerical values used in this figure) see section Leakage coefficient (view online) of the accompanying Jupyter notebook.
Table 4.
Line 1–3: multispot γm values and their obtained from Table 5 and Fig 2, using Eq. (SI.61) with PR estimates obtained by Gaussian fit, KDE or SNA, respectively. Values computed for samples 22d and 27d (indicated in italics) are unreliable due to the overlap between DO and DA PR peaks. Last line: factor K (Eq. (SI.65)), proportional to the uncertainty on γm, computed for the Gaussian fir approach. Values for the other methods are comparable. A common set of parameters ds = 0.061, βs = 0.81, lm = 0.033 was used, average of the values obtained with the various analysis methods.
Table 5.
Mean and standard deviation of PR histogram peak values obtained using Gaussian fit, KDE or SNA analysis (SNA mean value).
Fig 7.
FRET efficiency versus dye separation in the multispot experiments.
The γ-corrected FRET efficiency values obtained by Gaussian fit are represented as a function of dye separation (in base pair unit). Spots are indicated by different colors. The single-spot μs-ALEX results are represented as a connected line. The distance dependence of E predicted by a simple model of the DNA double helix, to which dyes are attached by a linker at a fixed position, is shown as a guide to the eye. The parameters used in the model are defined in Appendix 16 in S1 File. For further details see section FRET vs distance (view online) of the accompanying Jupyter notebook.
Fig 8.
μs-ALEX vs multispot burst data distributions.
Distributions of acceptor counts after donor excitation, burst duration and peak photon rate in each burst for both single-spot μs-ALEX (left column) and multispot (right column) experiments. Bursts were searched using the donor-excitation stream and a constant count rate threshold (rmin = 25 kHz for both single-spot and multi-spot measurements), followed by a selection with γ-corrected burst size ≥ 15. All the distributions (i.e. histograms) are normalized so that their integral is equal to 1. For the multispot setup, acceptor counts and peak photon rates distributions (first and last row) are reported for each channel separately; for readability, for the multispot burst duration distribution (second row, right), we report the mean across the channels. For more details see section Burst statistics vs usALEX accompanying Jupyter notebook (view online).
Fig 9.
Schematic of the reaction observed in real time with the 8-spot smFRET setup.
(A) The RNAP-promoter initially transcribed complex (RPITC) is prepared with an initiating dinucleotide (red “π” symbol) as the nascent RNA chain. Complementary DNA strands are labeled at DNA promoter bases with donor (D, green, position -5) and acceptor (A, red, position -8) dyes. After formation of a transcription initiation bubble, the dyes are separated, resulting in medium FRET. The initial state remains in stationary conditions until the addition of the four missing nucleotides (NTPs, yellow arrow), which triggers transcription initiation and elongation. (B) During elongation, the transcriptional bubble moves downstream (to the right), causing hybridization of the sequence of the initial transcriptional bubble and a corresponding decrease of the D-A distance (FRET increase).
Fig 10.
Real time transcriptional bubble closure kinetics results.
(A) Evolution of FRET efficiency distribution as function of time (one curve per 30 s). The curves represent Gaussian fit of the FRET histograms. (B) Fraction of high FRET population obtained in the real time kinetics measurement (grey and blue dots) and fraction of probe hybridization to a run-off transcript from quenched kinetics assays (red squares). Dots are computed as a function of time using either a 5 s (grey) or 30 s (blue) moving integration window. The solid black curve is a single-exponential model fitted to the 30 s moving integration window. Quenched kinetics data (from ref. [49]) are normalized to fit initial and final values of real time kinetics trajectory. For more details on the analysis see accompanying Realtime Kinetics Analysis Jupyter notebook (view online).