Ziemowit Klimonda

Michiel Postema

Andrzej Nowicki

Jerzy Litniewski

Tissue attenuation estimation by mean frequency downshift and bandwidth limitation

Tissue Attenuation Estimation by Mean Frequency Downshift and Bandwidth Limitation

Ziemowit Klimonda, Michiel Postema, Senior Member, IEEE, Andrzej Nowicki, and Jerzy Litniewski,

Abstract-Attenuation of ultrasound in tissue can be estimated from the propagating pulse center frequency downshift. This method assumes that the envelope of the emitted pulse can be approximated by a Gaussian function and that the attenuation depends linearly on frequency. The resulting downshift of the mean frequency depends not only on attenuation, but also on pulse bandwidth and propagation distance. This kind of approach is valid for narrowband pulses and shallow penetration depth. However, for short pulses and deep penetration, the frequency downshift is rather large and the received spectra are modified by the limited bandwidth of the receiving system. In the paper the modified formula modelling the mean frequency of backscattered echoes is presented. The equation takes into account the limitation of the bandwidth due to band-pass filtration of the received echoes. This approach was applied to simulate the variation of the mean frequency of the pulse propagating for both: weakly and strongly attenuating media and for narrowband and wideband pulses. The behavior of both the standard and modified estimates of attenuation has been validated using RF data from a tissuemimicking phantom. The ultrasound attenuation of the phantom, determined with a corrected equation, was close to its true value, while the result obtained using the original formula was lower by as much as 50% at a depth of 8 cm.

Index Terms-Tissue attenuation, frequency downshift, bandwidth limitation

I. INTRODUCTION

T ISSUE attenuation can characterize soft tissue, although natural biological variability limits its effectiveness. Proper knowledge of tissue attenuation is required to reduce shadowing artefacts [START_REF] Treece | Ultrasound attenuation measurement in the presence of scatterer variation for reduction of shadowing and enhancement[END_REF] or determination of scatterer concentration and statistical parameters of the backscatter [START_REF] Oelze | Frequency-dependent attenuationcompensation functions for ultrasonic signals backscattered from random media[END_REF]. Numerous reports suggest the relationship between attenuation and the pathological state of tissue. It has been shown, that the slope of the attenuation coefficient, combined with the statistical parameters of the image texture, can be used to diagnose diffuse liver disease [START_REF] Oosterveld | Ultrasound attenuation and texture analysis of diffuse liver disease: methods and preliminary results[END_REF]. Attenuation values can characterize cirrhotic and fatty human liver [START_REF] Lu | Ultrasound Backscatter and Attenuation in Human Liver With Diffuse Disease[END_REF] and breast tumors [START_REF] Nam | Quantitative Assessment of In Vivo Breast Masses Using Ultrasound Attenuation and Backscatter[END_REF], but also help to discriminate between cancerous and healthy tissue [START_REF] Saijo | High Frequency Acoustic Properties of Tumor Tissue[END_REF]. Changes of attenuation in the cervix have been helpful in prediction of premature delivery in rats [START_REF] Bigelow | In vivo ultrasonic attenuation slope estimates for detection cervical ripening in rats: Preliminary results[END_REF] and in humans [START_REF] Mcfarlin | Ultrasonic attenuation estimation of the pregnant cervix: a preliminary results[END_REF]- [START_REF] Mcfarlin | Beyond Cervical Length: A Pilot Study of Ultrasonic Attenuation for Early Detection of Preterm Birth Risk[END_REF]. Potentially it can be used for skin lesion classification [START_REF] Piotrzkowska | Quantitative sonography of basal cell carcinoma[END_REF]. It has been shown that attenuation changes are related to the degeneration of bovine articular cartilage [START_REF] Nieminen | Ultrasound Attenuation in Normal and Spontaneously Degenerated Articular Cartilage[END_REF], thermal coagulation [START_REF] Worthington | Changes in UltrasoundProperties of Porcine Kidney Tissue During Heating[END_REF] and HIFU treatment [START_REF] Zderic | Attenuation of Porcine Tissues In Vivo After High Intensity Ultrasound Treatment[END_REF], [START_REF] Kemmerer | A New Approach for Detecting Attenuation Changes During High-Intensity Focused Ultrasound[END_REF].

Several approaches to attenuation estimation have been described, such as spectral shift [START_REF] Kuc | Parametric Estimation of the Acoustic Attenuation Coefficient Slope for Soft Tissue[END_REF]- [START_REF] Kim | Attenuation estimation using spectral crosscorrelation[END_REF], spectral difference [START_REF] Kuc | Estimating the acoustic attenuation coefficient slope for liver from reflected ultrasound signals[END_REF]- [START_REF] Kuc | Estimating acoustic attenuation from reflected ultrasound signals: Comparison of spectral-shift and spectral-difference approaches[END_REF], a reference phantom method [START_REF] Yao | Backscatter coefficient measurements using a refernce phantom to extract depth-dependent instrumentation factors[END_REF], and a hybrid spectral domain method [START_REF] Kim | Hybrid spectral domain method for attenuation slope estimation[END_REF]. An extensive overview of spectral methods is presented in [START_REF] Labyed | A theoretical comparison of attenuation measurement techniques from backscattered ultrasound echoes[END_REF] and [START_REF] Bigelow | Attenuation Compensation and Estimation, in Quantitative Ultrasound in Soft Tissues[END_REF].

The spectral shift method exploits the downshift of the center frequency of the ultrasound pulse propagating in an attenuating medium. This downshift is caused by the frequencydependence of attenuation and can be used to determine the attenuation coefficient.

With the spectral difference method, the attenuation is found using the logarithmic spectral difference of power spectra. These spectra are calculated from different segments of received ultrasound signals corresponding to different depths of the medium.

With the reference phantom method, a well-specified, reference phantom is used to correct the ultrasound system and diffraction effects. The power spectra of signals acquired from the examined area of the tissue sample are divided by corresponding spectra acquired from the reference phantom. The local attenuation is estimated from the changes of such normalized spectra with depth.

The hybrid spectral domain method combines the spectral difference and spectral shift methods. The spectra from samples are divided by corresponding reference spectra, corrected for the attenuation in a reference phantom and multiplied by a Gaussian function. The attenuation is estimated from the downshift of such corrected spectra.

In this study we concentrate on the spectral shift method. Generally with frequency downshift, the linear dependence between attenuation and frequency is assumed. A further assumption is a Gaussian spectrum of the interrogating pulse. With these conditions a linear equation can be derived linking the shift of the center frequency with an attenuation coefficient, the trasmited pulse bandwidth, and the propagation distance. However, there are many other factors which might cause a frequency shift of the received signals and render the above assumptions inappropriate. The variations in frequencydependence of backscatter is one of those factors [START_REF] Treece | Ultrasound attenuation measurement in the presence of scatterer variation for reduction of shadowing and enhancement[END_REF], but considered negligible in the present work. Furthermore, differences between real and reference values of sound speed in a medium may introduce a bias in the attenuation estimate [START_REF] Omari | Theoretical and phantom based investigation of the impact of sound speed and backscatter variations on attenuation slope estimation[END_REF]. Other factors include windowing and electronic noise affecting the attenuation estimate [START_REF] Samimi | Performance Evaluation of the Spectral Centroid Downshift Method for Attenuation Estimation[END_REF], [START_REF] Willink | The Effect of Noise and High-Pass Filtering on the Estimation of Mean Blood Velocity Using Wide and Narrow Ultrasound Beams[END_REF], and beam diffraction causing the amplitudes of low frequency components to decrease as the depth of pulse propagation increases [START_REF] Papadakis | Correction for Diffraction Losses in the Ultrasonic Filed of a Piston Source[END_REF]. Furthermore, the received signals always have a limited frequency bandwidth due to band-pass filtration of the ultrasonic system itself, or of the subsequent processing system, deforming the frequency spectra of received signals. The more of the received pulse is out-of-band, the greater is the deformation of the spectrum and the greater is the error in the attenuation estimate.

In this paper we propose a novel algorithm for attenuation estimation from the mean frequency downshift. This modified algorithm inteds to reduce the estimation errors resulting from the noise component and the beam diffraction. Our approach helps to reduce the impact of these factors by proper bandpass filtration. In other words, the mean frequency is estimated only from the part of the spectrum where the distortions are relatively small. Thus, the linear model of mean frequency downshift is modified by taking into account the changes of the signal spectrum due to band-pass filtration. In 2012, we reported the qualitative validation of the method originally proposed [START_REF] Klimonda | Correcting for bounded bandwidth when estimating tissue attenuation from mean frequency downshift[END_REF]. The following work presents the theoretical background of the method and the results of its experimental validation. In Section II, the impact of the noise and of the beam diffraction on the signal mean frequency estimate are discussed. The equation describing the mean frequency of the filtered Gaussian spectrum is derived in Section III. The verification of the equation using simulated data is presented in Section IV. In Section V, experimental data is subjected to the new algorithm. The results are compared with non-modified attenuation estimates.

II. NOISE AND BEAM DIFFRACTION EFFECTS ON SPECTRAL MEAN FREQUENCY

A. Effect of noise

When the amplitude of a propagating pulse decreases due to attenuation, the signal-to-noise ratio (SNR) also drops. For a low SNR, the noise frequency component is relatively high and the combined spectrum of the received signal (signal and noise) is shifted towards the noise mean frequency [START_REF] Willink | The Effect of Noise and High-Pass Filtering on the Estimation of Mean Blood Velocity Using Wide and Narrow Ultrasound Beams[END_REF]. This is also the case with signals from large depth. The signal spectrum is downshifted due to attenuation but the noise component increases the received signal mean frequency. Accordingly, the mean frequency estimate is overestimated and the overestimation increases with SNR decrease.

Figure 1 demonstrates an ilustrative example of normalized spectra from signals backscattered in a tissue-mimicking phantom at a 1-cm (black line) and 8-cm depth (gray line). The spectral noise level is indicated by arrow. In the case of 8-cm penetration depth, the mean frequency calculated in the 0 -25 MHz frequency range equals 5.0 MHz, while after low-pass filtering with a cut-off frequency of 8 MHz, the mean frequency dropped to 2.9 MHz, corresponding to the peak frequency of the spectrum (gray line). Figure 1 also shows some distortions for frequency between 0 and 0.6 MHz. The source of these distortions is attributed to the receiving system. Thus, there is a part of the signal spectrum which clearly does not fit into the Gaussian pulse model. Therefore, bandpass filtration should be used to isolate signal from noise. Bandpass filtration is a standard method used in medical ultrasound techniques to increase the SNR or to isolate the desired frequency range of a received signal [START_REF] Szabo | Diagnostic Ultrasound Imaging: Inside Out[END_REF].

B. The effect of beam diffraction

Beam diffraction is an important factor affecting the ultrasonic wave propagation. The diffraction correction D L for a piston source is given by [START_REF] Rogers | An exact expression for the Lommel diffraction correction integral[END_REF]

D L = 1 -e -i(2π/s) [J 0 (2π/s) + iJ 1 (2π/s)] (1)
where J 0 and J 1 are the zeroth and first order Bessel functions of the first kind, s = ∆xc/(f a 2 ), in which c is a speed of sound and a is transducer radius. The diffraction correction is defined as a normalized integral of the acoustic field over the circle with the surface equal to the surface of the piston source and located coaxially at a distance ∆x. The integral is normalized by a similar integral of the plane wave acoustic field [START_REF] Rogers | An exact expression for the Lommel diffraction correction integral[END_REF]. It describes how the beam diffraction affects the received signal comparing to the plane wave. Figure 2 shows the absolute value of the D L as a function of the frequency, at a depth of 1 cm and 8 cm. Both plots were calculated for a transducer radius equal to 7.5 mm used in experiments and described further in Section V. Figure 2 shows, that the diffraction mostly distorts the lower part of the spectrum. In our case, the part of the spectrum below 2 MHz is strongly affected by the beam diffraction, while for the higher frequencies, the distortion is substantially smaller. The pulse spectrum is moved to the low frequency range where the correction for diffraction effects are especially significant, particularly in the case of deep penetration, due to the pulse mean frequency downshift caused by attenuation.

In short, noise and beam diffraction affects the mean frequency estimate. This impact could be limited by considering only that part of the received signal spectrum, where the distortions are small. It can be done by band-pass filtering prior to the mean frequency estimation. However, such filtering modifies the signal spectrum whilst the Gaussian pulse model is no longer valid. Thus, the model should be modified to take into account the band-pass filtration of the received signal and the resulted distortion of its spectrum. The new equation allows for the recovery of the Gaussian pulse mean frequency when the ultrasonic backscatter is band-pass filtered, which is described in the next section.

III. SUMMARY OF ALGORITHM

Let us assume a Gaussian pulse with carrier frequency f 0 propagating through the attenuating medium and described by the following equation

p(t) = sin (2πf 0 t)e -t 2 2σ 2 t , ( 2
)
where t is time and σ 2 t is variance of the Gaussian envelope. The Fourier amplitude spectrum is

P (f ) = σ t 2 √ 2π e - (f -f 0 ) 2 2 ( 1 2πσ t ) 2 -e - (f +f 0 ) 2 2 ( 1 2πσ t ) 2 . (3)
Thus, the amplitude spectrum of p(t) is even and has the form of absolute value of the difference of two Gaussian functions with peaks at f 0 and -f 0 , respectively. For f 0 0, the one sided spectrum can be approximated by a single Gaussian function with a peak at f 0 . Assuming that the attenuation varies linearly with frequency f , the spectrum of the propagating pulse is a product of the Gaussian function and the exponential function representing the attenuation. Thus, the one sided power spectrum S(f ) of p(t) can be approximated as follows

S(f ) = Ae - (f -f 0 ) 2 σ 2 e (-2αf ∆x) , (4)
where f 0 corresponds to the spectral peak of the transmitted pulse, σ 2 = 1/(2πσ t ) 2 is the variance of the amplitude spectrum, α is the attenuation coefficient, ∆x is the distance traveled by scanning pulse and A = σ 2 t /8π 2 is a scaling factor. Expression (4) can be transformed as follows:

S(f ) = Ae - f 2 +(2σ 2 α∆x-2f 0 )f +f 2 0 σ 2 = = Ae (σ 2 α 2 ∆x 2 -2α∆xf0) e - f -fp σ 2 . (5)
Expression [START_REF] Nam | Quantitative Assessment of In Vivo Breast Masses Using Ultrasound Attenuation and Backscatter[END_REF] shows that the power spectrum of the scanning pulse traveling in linear attenuating medium is given by a Gaussian function, but with a new peak frequency f p instead of f 0 . Peak frequency f p is given by expression ( 6)

f p = f 0 -σ 2 α∆x . (6)
This common expression is often used in the spectral shift method [START_REF] Bigelow | Attenuation Compensation and Estimation, in Quantitative Ultrasound in Soft Tissues[END_REF]. In this case, the local attenuation coefficient α is equal to [START_REF] Bigelow | Correcting for Focusing when Estimating Attenuation for Tissue Characterization based on Gaussian Approximations of the Beam Profie[END_REF]:

α = - 1 σ 2 ∆f p ∆x → ∆x→0 - 1 σ 2 df p dx . ( 7
)
For a Gaussian pulse, the frequency of the spectral peak f p is close to the pulse mean frequency f m . Thus, in this case f m can be used as an estimate of f p and replace it in [START_REF] Bigelow | In vivo ultrasonic attenuation slope estimates for detection cervical ripening in rats: Preliminary results[END_REF] in order to estimate the attenuation coefficient which is convenient as the mean frequency f m can be readily estimated from the ultrasound data.

f m ≈ f p (8)
However, approximation [START_REF] Mcfarlin | Ultrasonic attenuation estimation of the pregnant cervix: a preliminary results[END_REF] is not always appropriate. The basic idea is graphically explained in Figure 3 for arbitrary Gaussian spectra and two filter bandwidths, one wide and the other narrow.

frequency [a.u.] f1 f2 [a.u.] 0 0.5 1 f p < f m f1 f2 [a.u.] 0 0.5 1 f p : f m (a) (b)
Fig. 3. The difference between the peak and mean frequencies of a virtual Gaussian spectrum computed assuming (a) a wide and (b) a narrow filter bandwidth.

Assuming that the received signal is band-pass filtered with the cut-off frequencies f 1 and f 2 , approximation (8) is appropriate only if the received pulse spectrum is inside the filter bandwidth (Fig. 3). It is not valid if a significant amount of spectral energy is outside of the filter bandwidth (Fig. 3b) yielding an incorrect attenuation estimation.

Assuming that the pulse is perfectly reflected and subsequently detected by the receiving system which introduces band-pass filtration, the mean frequency can be determined using [START_REF] Szabo | Diagnostic Ultrasound Imaging: Inside Out[END_REF]

f m = ∞ 0 f S(f ) ∞ 0 S(f ) . (9)
Assuming that the band-pass filtration can be described by ideal filter with a rectangular transfer function with cut-off frequencies f 1 and f 2 , the mean frequency f m is given by ( 10)

f m = f2 f1 f S(f ) f2 f1 S(f ) , (10)
where the denominator can be expressed by

f2 f1 S(f )df = B f2 f1 e - f -fp σ 2 df = B √ π 2 σ erf f 2 -f p σ -erf f 1 -f p σ , (11)
where erf() is the error function and B is given by ( 12)

B = Ae σ 2 α 2 ∆x 2 -2α∆xf0 . ( 12
)
The numerator of (10) can be transformed as follows

f2 f1 f S(f ) df = B f2 f1 f e - f -fp σ 2 df = B t2 t1 σ 2 t e -t 2 dt + f p t2 t1 σ e -t 2 dt = B σ 2 2 e - f 1 -fp σ 2 -e - f 2 -fp σ 2 (13)
+ B f p √ π 2 σ erf f 2 -f p σ -erf f 1 -f p σ .
Finally, the f m is given by

f m = f p - σ √ π e - f 2 -fp σ 2 -e - f 1 -fp σ 2 erf f2-fp σ -erf f1-fp σ ( 14
)
where the f p is given by equation [START_REF] Saijo | High Frequency Acoustic Properties of Tumor Tissue[END_REF]. It is clear that the pulse mean frequency differs from the frequency of the spectral peak frequency. The expression describing the measured mean frequency f m is the sum of the frequency f p and the bias component which is a function of f p . If f 1 approaches -∞ and f 2 approaches ∞, the bias component disappears and f m = f p . Nevertheless, the frequency f 1 is always greater than or equal to zero and f 2 is finite for pulses received by real system. Therefore the mean frequency is only an approximation of the peak frequency. Figure 4 shows the function f m (f p ) calculated for three different σ parameters. We have empirically verified that equation [START_REF] Worthington | Changes in UltrasoundProperties of Porcine Kidney Tissue During Heating[END_REF] gives unique and stable solutions for the values of f p corresponding with the attenuation values occurring in soft tissue, frequencies up to 15 MHz and pulse bandwidth up to 90%. To determine the attenuation coefficient from band-pass filtrated data using the estimated f m and equation [START_REF] Bigelow | In vivo ultrasonic attenuation slope estimates for detection cervical ripening in rats: Preliminary results[END_REF], the true peak frequency f p should first be calculated. Peak frequency f p is an implicit function of f m . Therefore it can be calculated by numerically solving equation [START_REF] Worthington | Changes in UltrasoundProperties of Porcine Kidney Tissue During Heating[END_REF]. Then, the attenuation can be estimated from f p using (7). In the following section the results of simulations of ( 14) are presented.

IV. COMPUTER SIMULATIONS

A. Simulation procedure Equation ( 14) describes the mean frequency changes for band-pass filtrated backscattered signals. It was validated using simulated RF data. For simulations of the signal received at the transducer a simple, 1-dimensional (1D) geometry model of tissue and incidence of plane wave was assumed. The 1D model was applied as a direct consequence of the 180 • backscattering assumption. It was also assumed that the attenuation is the only factor influencing the mean frequency shift. It is important to emphasize here that the only reason for the development of a simulation model was to test whether [START_REF] Worthington | Changes in UltrasoundProperties of Porcine Kidney Tissue During Heating[END_REF] correctly predicts the mean frequency down shift calculated from the band-filtered backscatter. The final validation of the method proposed including filtering of the noise and diffraction distortions was performed in phantom experiments. The backscattered signal from tissue was simulated as follows. A set of point scatterers was uniformly distributed along the Z axis up to a depth of 10 cm in a medium attenuating linearly with frequency. The average number of scatterers per wavelength equaled 53 as discussed in [START_REF] Wagner | Statistics of speckle in ultrasound b-scans[END_REF], [START_REF] Evans | Doppler Ultrasound: Physics, Instrumentation and Signal Processing[END_REF]. The speed of sound in the simulated medium was 1540 m/s. The scatterers reflectivity was also uniformly randomly distributed. Each scatterer was considered a secondary sound source. The transmit pulse had a Gaussian envelope and 5.6 MHz carrier frequency and a 50% or a 76% bandwidth (-6 dB). The transducer transfer function was identical to the transmit pulse amplitude spectrum. The backscattered echoes were simulated by superposition of all of the elementary, individually scattered wavelets, taking into account the transducer transfer function, phase differences caused by spatial location of individual scatterer and attenuation. The tissue attenuation coefficients α = 0.5 and 1.5 dB/(cm • MHz)) were chosen in subsequent simulations. 33 RF lines were simulated for each value of attenuation coefficient. The band-pass filtering was performed using the fourth order Butterworth filters with passband 1--12 MHz and 3 -7 MHz. The variation of mean frequency f m along the penetration path was estimated from filtrated RF lines by means of I/Q algorithm [START_REF] Barber | A new time domain technique for velocity measurement using Doppler ultrasound[END_REF], [START_REF] Evans | Doppler Ultrasound: Physics, Instrumentation and Signal Processing[END_REF] taking 10mm long sample window. Next, the average from 33 mean frequency f m lines was calculated and compared with the mean frequency predicted from ( 8) and ( 14).

B. Simulation results and discussion

The results of the simulations are presented in Figures 567. The f m lines (gray) were estimated from simulated echoes while the predictions of the linear (8) and the new equation ( 14) are represented by black dashed line and solid lines, respectively.

The results for the medium attenuation of 0.5 dB/(cm • MHz)) and a 50% pulse bandwidth are presented in Figure 5. For wideband filtration (Fig. 5a), there are no visible differences between predictions of both equations. For narrowband filtration (Fig. 5b), some small differences are visible and the corrected equation fits a slightly better to the estimate. The estimates of mean frequencies for the medium attenuation of 0.5 dB/(cm • MHz) and pulses of 76% bandwidth are presented in Figure 6. Here, the use of wideband filtration (Fig. 6a) results in very small differences between predictions of both equations, as previously (Fig. 5a). However, the difference between the old and the new approach is clearly visible for narrowband filtration (Fig. 6b), where at the distance of 8 cm the difference exceeds 1.2 MHz and modified equation predicts correctly mean frequency of simulated echoes.

The downshifts of mean frequency in highly attenuating medium (1.5 dB/(cm • MHz)) for pulses of 76% bandwidth are shown in Figures 7a and7b. In this case the differences between old and new equations are clearly visible even for wideband filtration. The corrected equation fits the estimated mean frequency well along the whole penetration depth, while the linear equation fits the f m line only for wideband filtration and a depth of less than 2 cm (Fig. 7a).

Presented results indicate that equation ( 14) correctly describes the f m shift caused by attenuation even for the narrow band filtering of the simulated echoes.

V. PHANTOM EXPERIMENT A. Measurement setup

The experimental setup consisted of a flat 15 mm diameter transducer, 5.6 MHz center frequency, (IMASONIC ME5 -1/2 , France), a pulser-receiver (Panametric 5900 PR) and a sampling oscilloscope (Agilent Infinium 54810A). First, the pulse spectrum in water was examined to measure the transmitted pulse bandwidth and then calculate an estimate of standard variation σ in ( 8) and ( 14). The transducer was driven by short pulse and the ultrasound pulse reflected from acoustic reflector placed in water at 60 mm depth was recorded. The amplitude spectrum of the pulse was calculated and Full Width at Half Maximum (FWHM) was determined. Then the standard deviation σ was calculated using the relation FWHM ≈ 2.35σ. The standard deviation was equal 1.68 MHz and was used as an estimate of σ in equations ( 8) and ( 14). 33 RF backscattered echoes from the tissue mimicking phantom (Dansk Phantom Service, model 1054, attenuation coefficient equal 0.7 dB/(cm • MHz)) were recorded. Each echo was acquired in different phantom location assuring statistical independence of ultrasonic data. The measured RF data were band-pass (2 -8 MHz) filtered, to limit the impact of the beam diffraction and presence of noise on the final attenuation estimate. The cut-off frequencies were chosen basing on the plots of the backscattered spectra (Fig. 1) and diffraction correction D L (Fig. 2). The lower cut-off frequency was chosen to remove the part of the signal spectrum which is the most distorted by beam diffraction. The higher cutoff frequency was chosen to increase the SNR especially in signals from greater depths. As a reference, the measured RF data without initial band-pass filtering were processed in the same way. The mean frequency variation with the depth was estimated using exactly the same procedures as in the case of simulated echoes i.e. using the I/Q algorithm with a window length corresponding to a 1 cm depth. Data from a depth of up to 0.5 cm were not taken into account because of the strong and multiple reflections from the surface of the phantom. The resulting 33 mean frequency lines were averaged and compared with the models given by ( 8) and ( 14). The attenuation distribution was estimated as follows. Each mean frequency line was smoothed using the Singular Spectrum Analysis (SSA) algorithm [START_REF] Golyandina | Analysis of time Series Structure: SSA and related techniques[END_REF]. Next, the attenuation was estimated using equations ( 7), (8) (linear equation) and ( 14) (corrected equation). Equation ( 14) was numerically solved for f p using Matlab R (The MathWorks, Ltd., Natick, MA) function fzero(). Finally, the attenuation lines were averaged and the standard error of the mean was calculated for each point of the averaged attenuation line.

B. Measurement results and discussion

The reference plot of the mean frequency changes estimated without the initial band-pass filtration of RF data are shown in Figure 8 (gray line). The predictions from both equations for f m (linear 8 and corrected 14) are plotted by dashed and solid black lines respectively. For this plot the cut-off frequencies f 1 and f 2 in 14 equaled 0 and 25 MHz respectively, where the upper band corresponds to sampling frequency (50 MHz). For low penetration depth both models predict similar drop of the mean frequency that agrees well with the estimated mean frequency line. For larger depths the estimated values are clearly higher and the difference between the estimated and both predicted lines increases with penetration depth. The frequency range of filtration band is (0 -25 MHz) which can be considered large, thus both approaches operate very similarly. The corrected method does not cut out the low frequency components of the signal spectrum responsible for the diffraction distortion. The upper limit of filtration is too high to limit the influence of noise. Consequently, the frequency line calculated using the corrected equation is not consistent with the mean frequency estimate.

The mean frequency curves estimated from the band-pass filtered (2-8 MHz) echoes together with the frequency values predicted by [START_REF] Worthington | Changes in UltrasoundProperties of Porcine Kidney Tissue During Heating[END_REF] dotted lines) increases at larger depths compared to the error of the non-modified estimate (black dotted lines), it is still more accurate than the estimate obtained using non-modified method. The presented plots indicate that the estimate errors caused by the beam diffraction and the noise component can be reduced by proper band-bass filtration and use of the nonlinear model (eq. ( 14)) of the mean frequency drop. However, the presented method have some limitations. The method assumes the propagation of a Gaussian pulse. Therefore, it will be not appropriate, when the shape of the interrogating pulse is far from Gaussian. For example, such situation occures in coded transmission technique. Moreover, the assymptotic behavior of the function f m (f p ) (Fig. 4) indicates, that for large frequency shift the small inaccuracy in f m estimation results in large error in f p . This is the probable reason of the increase of the standard error in the new estimate (Fig. 10, gray dotted lines). Finally, this method require more calculations compared to non-modified frequency shift method, thus it is computationally more expensive.

Additionally, we used the I/Q algorithm in order to estimate the mean frequency of the signal. The I/Q algorithm is known and used mostly in Doppler techniques. The algorithm was selected due to its simplicity and ease of implementation. However, it should be noted here, that the frequency shift can be also estimated by other techniques which often perform better and characterize by higher accuracy, for example energy-balanced [START_REF] Bamler | Doppler frequency estimation and the Cramér-Rao bound[END_REF] or correlation based [START_REF] Kim | Attenuation estimation using spectral crosscorrelation[END_REF], [START_REF] Kim | Hybrid spectral domain method for attenuation slope estimation[END_REF], [START_REF] Samimi | Optimum diffraction-corrected frequencyshift estimator of the ultrasonic attenuation coefficient[END_REF].

VI. CONCLUSIONS

A new method for determination of ultrasound attenuation coefficient has been presented. The method is a modification of the common attenuation estimation from the downshift of the mean frequency of the propagating pulse. The assumptions are the same: a Gaussian spectrum of the scanning pulse and medium attenuation varying linearly with frequency. In practice, the diffraction phenomenon and noise significantly affect the frequency estimate and consequently the value of the determined attenuation. However, these errors can be reduced by band-pass filtering with properly selected cutoff frequencies. The proposed method removes the disrupted portion of the spectrum of the received signal and uses the filtered data for the calculation of the mean frequency of the spectrum. To this end a new equation was derived, describing the relation between the pulse spectrum shift and pulse spectral width, tissue attenuation and frequency limits.

The method has been examined using simulated and experimental data. Simulation results revealed the usefulness of the new equation for the mean frequency calculation from the limited Gaussian spectrum. In some cases, this mean frequency differed significantly from the frequency determined from nonfiltered data. The measurements were performed using a single circular transducer and a tissue phantom. Phantom experiments indicated that the estimate errors caused by the beam diffraction and noise could be reduced by proper band-pass filtering. The improved accuracy of the attenuation estimate was shown. The proposed method is especially useful for processing data from echoes returned from a large depth, where the SNR is low and distortion of the spectrum due to beam diffraction is relatively large.

Fig. 1 .

 1 Fig.1. Normalized spectra of the signal acquired from A tissue-mimicking phantom at 1-cm and 8-cm depths.

Fig. 2 .

 2 Fig. 2. The absolute values of diffraction correction |D L | for a piston source with radius 7.5 mm, calculated for depths of 1 cm and 8 cm.

Fig. 4 .

 4 Fig. 4. The function fm(fp) calculated for three different σ parameters of the Gaussian pulse. The cut-off frequencies f 1 and f 2 were 2 and 8 MHz, respectively.

Fig. 5 .

 5 Fig. 5. Mean frequency estimate and predictions of both equations -linear and corrected. The RF data were simulated. Medium with attenuation coefficient equal 0.5 dB/(cm • MHz) and 5.6 MHz pulse with 50% bandwidth was assumed. The filter passband was equal to (a) 1 -12M Hz and (b) 3 -7M Hz.

Fig. 6 .Fig. 7 .

 67 Fig. 6. Mean frequency estimate and predictions of both equations -linear and corrected. The RF data were simulated. Medium with attenuation coefficient equal 0.5 dB/(cm • MHz) and 5.6 MHz pulse with 76% bandwidth was assumed. The filter passband was equal to (a) 1 -12 MHz and (b) 3 -7M Hz.

Fig. 8 .

 8 Fig.8. Mean frequency estimate and predictions of both equations -linear and corrected. The RF data were acquired from tissue-mimicking phantom and were processed without initial filtration.

Fig. 9 .Fig. 10 .

 910 Fig. 9. Mean frequency estimate and prediction of the corrected equation. The RF data were initially processed using band-pass (2 -8 MHz) filter.of the Figures8 and 9indicates, that band-pass filtration have clearly limited the difference between the mean frequency estimate and the prediction of the new model.The plots of attenuation distribution corresponding to measured mean frequency data are shown in Figure10. The attenu-

Andrzej

  Nowicki graduated from the Dept. of Electronics, Warsaw University of Technology in 1969. He received his Ph.D. and D.Sc. from Institute of Fundamental Technological Research IFTR, Polish Academy of Sciences in 1976 and 1980. He became a full Professor in 1992. From 1995 till 2013 he was Director for Research and Director of IFTR. In 1972 he conducted research in the field of ultrasound at CNRS, Paris, France. In 1978 and later in1981 he joined a research team at the Institute of Applied Physiology and Medicine, Seattle, WA, USA. In 1986 and 1997 he worked at Drexel University, Philadelphia, PA, USA, being involved in breast Doppler imaging and also being responsible for teaching graduate courses in Advanced Ultrasound. His research interests include pulsed Doppler, cardiac imaging, transcranial and tissue flow Doppler, high frequency ultrasonic imaging, speckles statistics in tumor diagnostics and flow mediated dilation. He was awarded with the Pioneer Award from AIUM in 1988 and became a Honorary Fellow of AIUM in 1990. He was President of The Polish Ultrasound Society 2002-2008. Jerzy Litniewski is a Professor at the Polish Academy of Science heading the Bioacoustics Laboratory in the Institute of Fundamental Technological Research. He received M.Sc. degree from Warsaw University in Physics, Ph.D. degree in scanning acoustic microscopy and D.Sc. degree in bone sonography from the Polish Academy of Science. His research interests involve the many areas of ultrasound-tissue interaction, including soft and bone tissue characterization, acoustic microscopy, in vivo imaging of tissue attenuation and quantitative sonography of breast and skin lesions.

ACKNOWLEDGMENT

This work has been supported by Polish National Science Centre (grant 2011/01/B/ST7/06728) and by the European Regional Development Fund under the Innovative Economy Operational Programme (grant POIG.01.03.01-14-012/08-00)


Anthony Delalande
email: anthony.delalande@cnrs-orleans.fr

Chloe Leduc

Patrick Midoux

Michiel Postema

Chantal Pichon
email: chantal.pichon@cnrs-orleans.fr

Chloé Leduc

Efficient gene delivery by sonoporation is associated with microbubble entry into cells and the clathrin-dependent endocytosis pathway

Keywords: ultrasound, microbubble, sonoporation, gene delivery, endocytosis, clathrin-mediated endocytosis pathway

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Although being less efficient than gene delivery by viral vectors; non-viral gene transfer still offers a great promise for gene therapy because of its safety compared to its viral counterparts. Almost twenty years ago, an original drug/gene delivery method based on ultrasound stimulation coupled to gaseous microbubbles was proposed [START_REF] Bao | Transfection of a reporter plasmid into cultured cells by sonoporation in vitro[END_REF]. Upon ultrasound exposure, microbubbles can oscillate, move, coalesce, fragment, or be disrupted otherwise [START_REF] Postema | Ultrasound-directed drug delivery[END_REF].

These properties modify the cell membrane permeability by a process known as sonoporation [START_REF] Bao | Transfection of a reporter plasmid into cultured cells by sonoporation in vitro[END_REF][START_REF] Miller | Sonoporation of cultured cells in the rotating tube exposure system[END_REF][START_REF] Taniyama | Local delivery of plasmid DNA into rat carotid artery using ultrasound[END_REF]. It offers the opportunity of site-specific local delivery of a drug or gene. In the field of gene transfer, several studies have reported an improvement of gene delivery efficacy by sonoporation [START_REF] Bao | Transfection of a reporter plasmid into cultured cells by sonoporation in vitro[END_REF][START_REF] Wang | Gene transfer with microbubble ultrasound and plasmid DNA into skeletal muscle of mice: comparison between commercially available microbubble contrast agents[END_REF]. Numerous in-vitro and in-vivo studies on sonoporation and its exploitation for gene transfer have been published (Delalande et al., 2011;[START_REF] Delalande | Sonoporation: Mechanistic insights and ongoing challenges for gene transfer[END_REF][START_REF] Mehier-Humbert | Plasma membrane poration induced by ultrasound exposure: implication for drug delivery[END_REF][START_REF] Noble | Ultrasound-targeted microbubble destruction-mediated gene delivery into canine livers[END_REF][START_REF] Pichon | Recent advances in gene delivery with ultrasound and microbubbles[END_REF]. The main hypothesis assumes a direct entry of the molecules through transient pores in the plasma membrane as a consequence of interaction of oscillating microbubbles with the plasma membrane [START_REF] Mehier-Humbert | Plasma membrane poration induced by ultrasound exposure: implication for drug delivery[END_REF][START_REF] Miller | Sonoporation of cultured cells in the rotating tube exposure system[END_REF][START_REF] Taniyama | Local delivery of plasmid DNA into rat carotid artery using ultrasound[END_REF]. Transient pores formed at the plasma membrane are responsible for the direct intracellular delivery of small molecules but also the outward transport of intracellular molecules [START_REF] Kaddur | Transient transmembrane release of green fluorescent proteins with sonoporation[END_REF]. According to Zhou et al., the pore formed during sonoporation is estimated to be 100 nm wide and opened during 5 seconds [START_REF] Zhou | The Size of Sonoporation Pores on the Cell Membrane[END_REF]. The pore formation may be the trigger of many intracellular mechanisms [START_REF] Juffermans | Ultrasound and microbubble-targeted delivery of therapeutic compounds: ICIN Report Project 49: Drug and gene delivery through ultrasound and microbubbles[END_REF]. Several mechanisms of pore membrane formation could occur during sonoporation (for a review see [START_REF] Delalande | Ultrasound and microbubbleassisted gene delivery: recent advances and ongoing challenges[END_REF]). However, the mechanisms of gene transfer from microbubble stimulation, its interaction with cells to gene expression are still not well understood. Recently, some studies have shown that endocytosis process could be involved in the sonoporation mechanism for large molecules [START_REF] Meijering | Ultrasound and microbubble-targeted delivery of macromolecules is regulated by induction of endocytosis and pore formation[END_REF][START_REF] Paula | Therapeutic ultrasound promotes plasmid DNA uptake by clathrin-mediated endocytosis[END_REF], intracellular delivery of liposomes [START_REF] Afadzi | Mechanisms of the Ultrasound-Mediated Intracellular Delivery of Liposomes and Dextrans[END_REF] and lipoplexes [START_REF] Kurosaki | Development of anionic bubble lipopolyplexes for efficient and safe gene transfection with ultrasound exposure in mice[END_REF]. As pDNA are big molecules over 3 MDa, we suppose that endocytosis should be involved in gene transfer by sonoporation. None of those studies have delineated the complete the process of sonoporation. A better knowledge of the key steps occurring during and after sonoporation could allow for an improvement of this method. This is the first study making a correlation between optimal ultrasound parameters determination for efficient gene transfer in vitro and the processes that govern this efficacy. We observed that under optimal ultrasound parameters that lead to gene transfer, microbubbles interacted preferentially with lipid raft areas of the cell and some of them were found inside cells. Sonoporation-induced gene transfer is not a purely mechanical process but biological as well. Here, we focused on the role of endocytosis in cellular gene uptake. This hypothesis has been confirmed by the use of a clathrin-dependent endocytosis inhibitor that abolished gene transfer efficacy. The plasmid DNA was followed in the cell and was found mainly routed to rab7 positive endosomes 3 hours after sonoporation and close to the nucleus after 6 hours.

Materials and Methods

Cell culture

Human epithelial cervix adenocarcinoma HeLa cells were used in this study (CCL2, ATCC, Rockville, MD). HeLa cells stably expressing the fusion protein Rab7-GFP and Nup153-GFP were developed to localize the fluorescent-labelled pDNA into specific cellular structures (late endosomes and nucleus respectively). HeLa cells were grown in MEM with Earl's salts medium (PAA Laboratories GmbH, Pashing, Austria) supplemented with 10% v/v of FCS and 1% of non-essential amino acids (PAA), penicillin (100 U/mL) and streptomycin (100 µg/mL) at 37°C in a humidified atmosphere containing 5% CO 2 . To select Rab7-eGFP and Nup153-eGFP transgene expressing HeLa cells, cell medium was supplemented with G418 (200 μg/mL, PAA).

Cells were free from mycoplasma as evidenced by MycoAlert® Mycoplasma Detection Kit (Lonza, Levallois Perret, France).

Plasmid DNA preparation and labelling

A 7.5-kb homemade reporter plasmid that encoded the firefly luciferase gene (pLuc) under the control of the strong cytomegalovirus promoter was used. Five consecutive NFB motifs that recognized the NFB transcription factor were inserted upstream of the promoter. All plasmids vectors were propagated from Escherichia coli DH5α cultures. Supercoiled plasmid was isolated from bacteria by the standard alkaline lysis method, and purification was carried out with the Qiagen Mega Kit (Qiagen, Courtaboeuf, France) according to the manufacturer's instructions. Each pDNA was labelled with the Label IT nucleic acid labelling kit (Mirus, Madison, WI, USA) at 1:2 reagent/pDNA weight ratios according to manufacturer's instructions. When indicated, plasmids were labelled with cyanine 3 or cyanine 5 fluorescein probes followed by ethanol precipitation for DNA purification. The labelling densities were determined by spectroscopy according to the manufacturer's protocol and corresponded to approximately 1 fluorophore per 80 base pair. We verified that the labelling process of the plasmid did not affect the gene expression.

Ultrasound exposure system for gene transfer

US were generated from a 0.5-inch diameter, IBMF-014 planar transducer with a center frequency of 1 MHz (Sofranel, Sartrouville, France). The transducer was placed in a custom made polystyrene tank filled with degassed pure water. A signal consisting of 1.0 MHz center frequency, 40% duty cycle and a pulse repetition frequency (PRF) of 10 kHz was generated by a 33220A arbitrary function generator (Agilent technologies, Les Ulis, France) and amplified by a RF power amplifier (ADECE, Artannes sur Indre, France) was used as the input for the transducer. Peak negative pressure of 150 kPa was used corresponding to an MI of 0.15. Ultrasound stimulation time was set to 60 seconds. The transducer was calibrated in a Perspex container using an HGL-200 PVDF bullet type hydrophone (Onda, Sunnyvale, CA) placed at 3 cm, the natural focal distance of the transducer and behind the first cuvette wall. Measures made with and without the cuvette wall conducted to an average of 5% attenuation. Reflections from the cuvette were measured in a separate experiment in our sonoporation setup. The transducer was driven using a 103 SPR pulser-receiver (Accu-Tron Inc). The reflections from the cuvette were measured to be lower than -13 dB, and could therefore be neglected. Furthermore, we investigated the influence of potential reflections from the 3-mm thick container wall using the same pulser-receiver. Here, we found, that reflections from the inner surface of the container were mostly cancelled out by the inverted-phase reflections from the outer surface of the container. The transducer was positioned in front of the sonoporation cuvette as depicted in the figure 1 (Sarstedt AG & Co, Nümbrecht, Germany). 10 5 cells were grown on a resized Opticell ® membrane (10×15 mm) (Nunc GmbH & Co. KG, Langenselbold, Germany). Opticell ® membranes were transferred into polystyrene cuvettes (Sarstedt, Nümbrecht, Germany) containing 1.5 mL of OptiMEM medium (Invitrogen, Carlsbad, CA, USA) supplemented with 1% FCS.

During sonoporation the cell medium was supplemented with 2.5 µg of pDNA and 5 µL of microbubbles and the cuvette was placed under gentle agitation using a magnetic stirrer. After sonoporation the membrane was placed in the incubator with the sonoporation medium on a 12-well cell culture plate.

Ultrasound exposure system for high-speed imaging

The behaviour of microbubbles during sonoporation was studied by high-speed imaging methods. We performed real-time visualization of sonoporation events to investigate how microbubbles behave towards cells as a function of the acoustic pressure. The different possibility of interactions between MicroMarker ® microbubbles and cells was investigated using the optimal acoustic parameters (1 MHz, 40% DC, 10 kHz PRF), at 100, 150 and 200 kPa.

For fast camera imaging experiments, the transducer was placed in a custom-made aluminum sonication chamber with internal dimensions of 130×170×35 (mm) 3 was locked into to the xy-stage of a 200M inverted microscope (Carl Zeiss AG, Oberkochen, Germany) coupled with a LSM Axiovert 510 confocal scanning device (Carl Zeiss), using an EC Plan-Neofluar 40× magnification and 1.30 numerical aperture oil objective (DIC M27, Carl Zeiss AG), with automated z-stack functionality as shown in the figure 2. The color charge coupled device (CCD) of a Photron FastCam MC-2.1 high-speed camera (VKT Video Kommunikation GmbH, Pfullingen, Germany) was connected to the microscope. The sensor was rotated to make sure that in all recorded movies, the ultrasound is directed from the bottom right to the top left of the frame. The peak-negative acoustic pressures were measured in the insonation tank at the objective's field of view and corresponded to a mechanical index (MI) of 0.1, 0.15 or 0.2. The day before experiment, 1.6×10 6 cells were seeded in an Opticell ® chamber. Ultrasound contrast agents were injected into the cell culture chamber before each experiment.

Ultrasound contrast agent

In this study, Micromarker ® (VisualSonics, La Frette S/seine, France) microbubbles were used. Micromarker ® contrast agents contain a perfluorobutane (C 4 F 10 ) gas encapsulated by a phospholipid shell and have a median diameter between 2.3 to 2.9 μm. Microbubbles were prepared according to manufacturer's protocol, Micromarker ® suspension contained approximately 2×10 9 MB/mL.

Gene transfer efficacy

We determined the optimal acoustic parameters that give the maximal gene transfer on adherent HeLa cells using our set-up described in Figure 1. Four parameters were tested: the sound pressure (kPa), the time of ultrasound exposure (sec), the duty cycle (%) and the microbubble concentration (%). We tested different values of acoustic pressure (from 100 to 200 kPa), duty cycles from 10 to 60% and ultrasound exposure times ranging from 10 to 300 seconds. The importance of the amount of microbubbles was also evaluated. When a parameter was tested, the other parameters were fixed as follows: 150 kPa, 60 sec, 40% DC, 5 µL microbubbles.

The efficacy of sonoporation to deliver luciferase gene was determined by measuring the luciferase activity 24 h after sonoporation of HeLa cells in the presence of a plasmid encoding the luciferase reporter gene. Cells were harvested, lysed and the luciferase activity was measured using a luminometer (Lumat LB9507, Berthold, Wildbach, Germany) after adding 100 µL of luciferase substrate (1 mM luciferin containing ATP) to 60 µL of cell lysate. The values were expressed as RLU (Relative Luciferase Unit), this level was reported to the protein concentration in RLU/mg prot.

The protein concentration was measured by the bicinchoninic acid assay.

The gene transfer efficacy was also evaluated by flow cytometry when a plasmid encoding for the GFP reporter gene was used (pMAX, Lonza). 24 h after sonoporation cells were harvested, pelleted and re-suspended in PBS before flow cytometry analysis; 10000 cells were analyzed for each condition.

Cell viability assay

The cell viability was determined 24 h after sonoporation using the MTT colorimetric assay. Cells were incubated during 4 h at 37°C, 5% CO 2 in presence of 3-(4,5dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (Sigma-Aldrich, St. Louis, USA) at 500 µg/mL. Cells were washed and incubated in a lysis buffer consisting of acidified isopropanol containing 3% of SDS (Sigma-Aldrich) allowing the solubilization of tetrazolium salts. Quantification was made measuring the optic density at 560 nm; the control optical density was made using cells having received the same manipulations without any treatment.

Microbubble fluorescent labelling

To probe MicroMarker ® microbubbles, their envelope was stained with the fluorescent lipophilic probe DiD ( ex = 633 nm,  em = 643-670 nm) (Vybrant™, Molecular Probes ® , Invitrogen, San Diego, CA, USA). A ratio of 1 µL of DiD to 40 µL MicroMarker ® was used. The solution was homogenized by pipetting and it was used after 5 min incubation at room temperature.

Lipid raft labelling

To get more insight on interactions between microbubbles and cells, we identified if the region where microbubbles clustered on the cell surface could be some specific area of the plasma membrane as lipid rafts. Microbubbles were labelled with DiD lipophilic dye and lipid rafts were stained with Rhodamine B-tagged cholera toxin Bsubunit (CTB, Molecular Probes,  ex = 543 nm,  em = 553-600 nm) known to interact with GM1 gangliosides, one constituent of lipid rafts [START_REF] Antes | Detection of protein mediated glycosphingolipid clustering by the use of resonance energy transfer between fluorescent labelled lipids. A method established by applying the system ganglioside GM1 and cholera toxin B subunit[END_REF]. HeLa cells were incubated with 2.5 µg/mL of CTB during 30 minutes at 37°C before sonoporation. Immediately after sonoporation cells were fixed using a 2% paraformaldehyde solution in PBS. After three washes, resized Opticell ® membranes were then mounted on glass slides for confocal microscopy observations. Images were analyzed for colocalization by the plugin JACoP in ImageJ [START_REF] Bolte | A guided tour into subcellular colocalization analysis in light microscopy[END_REF].

Importance of endocytosis during sonoporation

To assess if endocytosis is needed to get an efficient gene transfer following sonoporation a first experiment was the evaluation of incubation time of cells and plasmid DNA on gene transfer efficacy. Cells were sonoporated and the medium was retrieved and renewed with fresh medium immediately (0 h) or at different times up to 4 hours. Cells were harvested 24 hours later and the luciferase expression was measured as previously described.

To validate the involvement of endocytosis in the process of gene transfer by sonoporation, we tested the effect of depletion of cellular ATP and inhibitors of clathrin and caveolae-mediated endocytosis pathways on the sonoporation gene transfer efficacy. Cells were ATP-depleted by 30 minutes incubation in presence of ATP depletion buffer (50 mM 2-deoxy-ATP, 50 mM sodium azide, 1.8 mM CaCl 2 in PBS pH 7.4) before the sonoporation. Cells were pre-treated for 30 minutes with either chlorpromazine (2.5 to 20 µg/mL; Sigma-Aldrich) an inhibitor of clathrinmediated endocytosis pathway, Filipin III (1 to 4 µg/mL; Sigma-Aldrich) or Genistein (1 to 10 µg/mL), both inhibitors of caveolin-mediated endocytosis pathway [START_REF] Ivanov | Pharmacological inhibition of endocytic pathways: is it specific enough to be useful? Methods in molecular biology[END_REF]. Then, sonoporation was performed in presence of indicated inhibitor and the cell medium was only replaced two hours later to maintain the inhibition effect.

Plasmid DNA internalization assay

The pDNA uptake by cells was evaluated by flow cytometry using a FITC-labelled plasmid. Trypan blue was used as a quencher of the FITC-fluorescence at the plasma membrane to distinguish pDNA attached to the plasma from internalized pDNA [START_REF] Van Amersfoort | Evaluation of a flow cytometric fluorescence quenching assay of phagocytosis of sensitized sheep erythrocytes by polymorphonuclear leukocytes[END_REF]. Cells were harvested immediately (0), 3 or 6 hours after sonoporation and analyzed by flow cytometry (LSR, BD). 10 000 events were used for each condition.

Statistical analysis

Data was expressed as mean ± SD. Statistical differences between groups was analyzed by the bilateral Mann-Whitney U-test and the correlation analyses were conducted using the Spearman test using XLStat 2014 software (Addinsoft, Paris, France). The significance was defined as a p-value ≤ 0.05.

Results

Optimal acoustic parameters determination for efficient gene transfer.

Figure 3A shows the effect of the acoustic pressure on the gene transfer efficacy.

Increasing the acoustic pressure from 100 to 160 kPa caused a linear improvement of the gene transfer efficacy (from 3.2×10 4 to 2×10 6 RLU/mg prot, 62-fold increase).

The maximum level of gene expression was obtained at 160 kPa and it decreased with further increase in acoustic pressure. At 200 kPa, the efficacy was 10-fold lower than the level obtained at 160 kPa (1.9×10 5 RLU/mg prot). In our study, the cell viability was preserved (90% of cell viability) under the optimal condition for gene transfer.

As observed in Figure 3B, the gene transfer efficacy was positively improved with ultrasound exposure time between 10 and 60 seconds (2.1×10 4 and 5.4×10 5 RLU/mg prot respectively). A plateau was observed between 60 and 300 seconds. However, it should be noted that a high variability was observed for long stimulation times. Indeed, we observed a cell toxicity ranging from 30 to 60% for the respective ultrasound stimulation time of 10 and 300 seconds.

Regarding the duty cycle (Figure 3C), a threshold value of 40% duty cycle was required for an efficient gene transfer. Between 10 and 30% duty cycle, a very low expression level of luciferase gene was obtained (around 10 4 RLU/mg prot). At 40% duty cycle, there was 75-fold increase of gene transfer efficacy as compared to 10% duty cycle. Then, a plateau was reached between 40 and 60% duty cycle. However, experiments conducted at 60% duty cycle led to a low viability (40%).

Figure 3D shows that a minimum volume of microbubble of 2.5 µL is required to get an increased gene expression compared to control without microbubbles (6.2×10 5 RLU/ mg prot using 2.5 µL of microbubbles vs. 5.8×10 4 RLU/ mg prot for the control without microbubbles). No significant improvement was achieved when higher volumes were injected.

Microbubble behavior during sonoporation

Depending on the acoustic pressure used three different types of microbubble-cell interactions were observed (figure 4 and supplementary video). At 100 kPa, a cellular massage was the main observed phenomenon. Ultrasound-activated microbubbles remained in contact with the cell membrane during the whole stimulation (Figure 4A). At this acoustic pressure, a low level of gene transfer was obtained as seen on Figure 3A. At 150 kPa, an additional phenomenon consisting of the microbubble entry into cells was depicted (figure 4B). The microbubble was first interacting with cells and then disappeared after few milliseconds. In the presented sequence, two microbubbles (indicated by arrow heads) penetrated inside cells after 600 ms of ultrasonic stimulation. This observation is in accordance with our previous study (Delalande et al., 2011). Note that the optimal gene transfer efficacy was obtained at this acoustic pressure range (Figure 3A). For a sound pressure of 200 kPa, microbubbles were either driven out from the field of view due to excessive radiation force or interacted violently with the cell. The latter induced the cell detachment from the support. At 200 kPa, the gene transfer efficacy was ten times lower than at 150 kPa that is likely due to the above-described phenomena.

Our real-time sonoporation result indicate that three event types could be recorded: microbubbles stuck on cell membrane during the insonation corresponding to the cellular massage; microbubbles entering into cells (translation) and microbubbles having a violent interaction with cells. Table 1 summarizes the correlation between gene transfer efficacy and the different types of events recorded during sonoporation and the acoustic pressure used. A positive correlation could be measured between the gene transfer efficiency and the microbubble entry to cells (p<0.0001). Moreover the incidence of violent interactions was found positively correlated with the acoustic pressure (p<0.0001).

Lipids rafts and microbubble-cell interactions.

At optimal parameters for gene transfer, microbubbles were found mainly colocalized with lipid rafts immediately after sonoporation with a coefficient of colocalization of 0.706 and an overlapping coefficient of 0.803. This is shown by the purple color corresponding to a colocalization of DiD-labelled microbubbles presented in blue and Rhodamine B-labelled cholera toxin B presented in red (figure 5). Figure 8 presents the kinetics of a FITC-labelled pDNA uptake after sonoporation by flow cytometry. When analyzing the cells immediately after sonoporation, 4% of cells were found positive for FITC fluorescence. The maximum of cells carrying sonoporated pDNA is reached (12.5%) upon 3 hours post-sonoporation. This value drops to 9.6% when trypan blue was added to quench the extracellular fluorescence meaning that most of pDNA was internalized at this time. After 6 hours postsonoporation the percentage of cells carrying pDNA decreased to 3.5%. The mean fluorescence intensity values (MFI) obtained in different conditions without trypan blue treatment followed the same trend as the percentage of fluorescent cells. After 6 hours, a decrease of MFI value by 1.5 fold times was observed.

Plasmid DNA and microbubble intracellular routing upon sonoporation

Endocytosis process involvement during sonoporation

Figure 9 shows the influence of time incubation on gene transfer efficacy. No significant gene transfer efficacy increase was found during the first hour of incubation. However, it was improved 15-times greater when the medium was removed upon 2 hours after sonoporation compared to an immediate washing. When the sonoporation medium was replaced after 4 hours post-sonoporation, it resulted in the highest gene transfer efficacy (1.82×10 8 RLU/mg prot), 328 times higher than when the medium was changed immediately following sonoporation process.

A sonoporation in an ATP-depletion condition conducted to a gene transfer efficacy 28-fold lower than the control (figure 10A). The use of caveolae pathway inhibitors Filipin III or Genistein had no significant effect on gene transfer by sonoporation (figures 10B and 10C). By contrast, there was a significant and dose-dependent inhibition when the clathrin-dependent endocytosis inhibitor used was chlorpromazine. The gene transfer was significantly decreased by 3, 5 and 140-fold when cells were respectively incubated in presence of 7.5, 10 and 20 μg/mL chlorpromazine (figure 10D) corresponding to a decrease of 70, 82 and 99% of the luciferase expression obtained in control cells (p-value<0.05). No significant inhibition was observed with chlorpromazine concentration of 5 μg/mL. It is worth to note that a significant toxicity of chlorpromazine was obtained using a concentration of 20 μg/mL (approximately 30% of cell viability).

We validated the effectiveness of the endocytosis inhibition protocol by testing the effect on the internalization of transferrin, a positive control of the clathrin-dependent endocytosis pathway (data not shown). These conditions led also to an inhibition of transferrin uptake whilst little effect was obtained with cells incubated in the presence of Filipin III.

Discussion

The aim of this study was to investigate cellular events that occur during sonoporation from microbubble-cell interactions, intracellular plasmid DNA routing and transgene expression.

Optimal parameters determination

The levels of luciferase activity obtained were in the same range as those reported in our previous results obtained with HeLa cells in suspension in the presence of BR14 microbubbles [START_REF] Kaddur | Transient transmembrane release of green fluorescent proteins with sonoporation[END_REF]. In this study, a plateau was also reached at 40% duty cycle.

Duvshani et al. obtained also similar results when they performed sonoporation on mouse prostate adenocarcinoma cells although their experiments have been conducted with Optison™ [START_REF] Duvshani-Eshet | Efficient transfection of tumors facilitated by long-term therapeutic ultrasound in combination with contrast agent: from in vitro to in vivo setting[END_REF]. The ultrasound settings used in this study were similar in acoustic pressure, duty cycle and frequency but not for the insonation time that was between 10 to 30 minutes. In another study performed on HeLa cells using SonoVue ® , Li et al. have found that a maximum efficacy was obtained for an ultrasonic stimulation of 240 kPa for 7 minutes with a duty cycle of 60%. However, they measured a high toxicity (30% of cell viability) [START_REF] Li | Optimising ultrasound-mediated gene transfer (sonoporation) in vitro and prolonged expression of a transgene in vivo: potential applications for gene therapy of cancer[END_REF]. The high variability that we observed after increasing stimulation time could be explained by an increase in the toxicity induced by sonoporation with time of stimulation. A minimum volume of microbubbles was necessary to reach a good gene transfer efficacy. The microbubble concentration used was similar to that previously obtained on HeLa cells in suspension in the presence of microbubbles BR14 [START_REF] Kaddur | Transient transmembrane release of green fluorescent proteins with sonoporation[END_REF] and the study of Rahim et al. performed on CHO cells adherent in the presence of SonoVue ® [START_REF] Rahim | Physical parameters affecting ultrasound/microbubble-mediated gene delivery efficiency in vitro[END_REF].

The gene transfer efficacy was often decreased when too many microbubbles (above 10 µL) were in the presence of cells due to the formation of microbubble clusters reducing microbubble-cell interactions or either to the cell toxicity as observed previously [START_REF] Nomikou | Studies on neutral, cationic and biotinylated cationic microbubbles in enhancing ultrasound-mediated gene delivery in vitro and in vivo[END_REF].

Taken together, the results obtained from the optimal acoustic parameters determination reflect the importance of each parameter used in ultrasound effectiveness for gene transfer mediated by sonoporation.

Microbubble-cell interactions

Depending on the settings used, the microbubble-cell interactions could be changed.

Therefore, we established a sonoporation device coupled to a high-speed camera to observe these interactions in real time. The high speed imaging results showed that during ultrasound stimulation (1 MHz, 150 kPa, 40% duty cycle), microbubbles were interacting with cell membranes as observed in our previous studies [START_REF] Delalande | Sonoporation: Mechanistic insights and ongoing challenges for gene transfer[END_REF]Delalande et al., 2011). At this precise acoustic setting the microbubble entry in cells was preferentially observed and this parameter gave the highest gene transfer.

A positive correlation between these two events could mean that the entry of the microbubble while entering directly or indirectly promotes the pDNA delivery into the cell. The microbubble entrance could allow extracellular calcium entry [START_REF] Fan | Spatiotemporally controlled single cell sonoporation[END_REF] and this calcium could trigger endocytosis [START_REF] Macdonald | Calcium increases endocytotic vesicle size and accelerates membrane fission in insulin-secreting INS-1 cells[END_REF].

As we could expect microbubbles do not interact with random areas of the cell. The preferential localization of microbubbles during sonoporation is in lipid raft region.

Because oscillating microbubbles are attracted to each other this localization might be explained by the fact that microbubbles are attracted to acoustically active areas in the cell. At this location, plasma membrane is composed of cluster of lipids enriched with cholesterol and sphingomyelin occupying at least 50% of the plasma the membrane [START_REF] Jurak | Properties of Langmuir and solid supported lipid films with sphingomyelin[END_REF]. These areas are dynamic membrane domains tightly packed by lipid-lipid interactions. Moreover, cholesterol and sphingolipids are known to rigidify the plasma membrane; thus microbubbles could be attracted to these rigid parts of the plasma membrane. Note that lipid raft area has been reported to be active in terms of endocytosis process [START_REF] Ares | Dynamin2, Clathrin, and Lipid Rafts Mediate Endocytosis of the Apical Na/K/2Cl Cotransporter NKCC2 in Thick Ascending Limbs[END_REF]. This hypothesis is in line with the sonophore formation in the plasma membrane described by [START_REF] Krasovitski | Intramembrane cavitation as a unifying mechanism for ultrasound-induced bioeffects[END_REF]. Sonophores are the expansion of a gap between the two membranes leaflets due to cavitation during the ultrasound negative peak pressure; this expansion stretches the plasma membrane until rupture to create a pore. Sonophores could be mainly formed either in the vicinity or in lipid raft areas and therefore attract microbubbles to this location. Microbubbles could be attracted to these areas because oscillating sonophores act like oscillating microbubble, due to the clustering phenomenon because in-phase oscillating objects are clustering [START_REF] Kotopoulis | Microfoam formation in a capillary[END_REF]. Moreover oscillating microbubble interacts differently when in contact to a surface for example microbubbles will collapse asymmetrically next to rigid surface [START_REF] Prentice | Membrane disruption by optically controlled microbubble cavitation[END_REF]. We could imagine that the cell surface rigidity mismatch between lipid raft areas and the rest of the cell could create a preferential interaction site with microbubbles.

Plasmid DNA cellular routing after sonoporation

A key to success of this delivery method lies in understanding not only the mechanism governing microbubble and cell interactions but also the fate of pDNA after sonoporation. The internalization process of the plasmid DNA after sonoporation was found to be a slow phenomenon. 4 hours of interaction time between plasmid DNA and the cells was needed to get the maximum of gene transfer efficacy and flow cytometry analyses showed that the plasmid DNA was mainly internalized after 3 hours. Real-time confocal microscopy investigations showed that pDNA was mainly localized at the plasma membrane straightaway following sonoporation and these spots could progressively move inside cells as we have observed previously [START_REF] Delalande | Sonoporation: Mechanistic insights and ongoing challenges for gene transfer[END_REF]. These structures could correspond either to vesicles (endosome) or aggregates-like structures upon sonoporation. The presence of free pDNA not co-localized with Rab7-eGFP vesicles observed in figures 6A and 6C indicates that some of them might be able to escape the lysosome pathway or they could also be the result of a direct entrance across the plasma membrane during the sonoporation. The presence of microbubbles and pDNA inside cells raises the question about their cellular entry pathway. The kinetics of pDNA uptake shows that only the combination of ultrasound and microbubbles was able to efficiently drive pDNA inside the cells.

Our observations indicated also that pDNA were not observed into the nucleus, spots were close to the nucleus as indicated by their localization in the vicinity of nuclear envelope delimited by Nup153-eGFP. However, we found in the same conditions a good gene transfer efficiency meaning that pDNA reach the nucleus this discrepancy could be due to a weaker fluorescent signal when the plasmid DNA reaches the nucleus [START_REF] Maury | Influence of pDNA availability on transfection efficiency of polyplexes in nonproliferative cells[END_REF]. The amount of pDNA found close to or inside the nucleus was not abundant compared to the pDNA entered into the cell observed at earlier time points suggesting a massive degradation or efflux of pDNA as observed after 6 hours by the decrease of MFI value by 1.5 fold times. However, this decrease was not as much as the one observed for the cell percentage which dropped from 9.6 at 3 hours to 3.5% at 6 hours. This reveals that some cells, likely those that had internalized a low amount of pDNA, have lost completely their content as indicated by the reduction of the cell percentage. The sonoporation efficacy could be improved at this step. In our study, the routing of the pDNA to the nucleus was also facilitated by the presence of optimal NFB binding consensus sequence put upstream of the CMV promoter of the luciferase coding sequence [START_REF] Goncalves | An optimized extended DNA kappa B site that enhances plasmid DNA nuclear import and gene expression[END_REF].

The maximum of cells carrying sonoporated pDNA is reached (12.5%) upon 3 hours post-sonoporation as a consequence of internalization process. When considering the values obtained after trypan blue treatment, the increase of MFI value after 3 hours was not as much as that observed with the percentage of cells. This observation suggests that during 3 hours after sonoporation, a new cell population took up pDNA via endocytosis process as validated by the increase of FITC-positive cell percentage. However, the amount of pDNA internalized during 3 hours was not very much higher than that occurred during the sonoporation process as indicated by the very slight increase of MFI value. This is in agreement with the fact that free pDNA were hardly up taken by cells and that microbubbles could not bind to pDNA to improve its internalization.

The main limitation of this study lies in the fact that all the experiments have been done in separate experiments. A single-cell study of the sonoporation process would be the best to prove the correlation between all the phenomena observed.

Conclusion

We have found the optimal ultrasound parameters in our set-up for an efficient gene delivery on HeLa adherent cells. This study showed the cell-microbubbles interactions and the intracellular fate of microbubbles and pDNA during and after the sonoporation process. A set-up combining high speed camera coupled with confocal fluorescence microscopy has allowed us to observe that depending on the acoustic pressure used, some microbubble were forced to enter the cell during sonoporation whilst other were stuck at the plasma membrane. The microbubbles were found to enter in cells only in the best conditions of transfection suggesting that the microbubble entry could be closely correlated to the pDNA entry into the cell. By combining fluorescent pDNA with cellular tools exhibiting fluorescent intracellular compartments, confocal microscopy observations and uptake experiments indicated that the pDNA were taken up by cells through clathrin-mediated-endocytosis pathway as confirmed by their localization inside late endosomes and the effect of the pharmacological inhibitors. It is worth noting that the observations made in this study may not be translated to other nucleic acids and particularly for small molecules oligonucleotides as siRNA. Knowing this intracellular fate of microbubble and pDNA will be helpful for a rational design of potent sonoporation for gene delivery that might be combined with molecular imaging.

Figure captions

Figure 6

 6 Figure 6 presents the localization of plasmid DNA (red) in HeLa cells expressing

Figure 7

 7 Figure 7 presents the localization of plasmid DNA (red) and DiD-labelled

Figure 1 -

 1 Figure 1 -In vitro sonoporation set-up used for gene transfer experiments.

Figure 2 -

 2 Figure 2 -In vitro set-up used for high-speed imaging experiments. The top panel

Figure 3 -

 3 Figure 3 -Optimal sonoporation parameters determination. (A) Influence of the

Figure 4 -

 4 Figure 4 -Sequences of images of HeLa cells in the presence of microbubbles; (A)

Figure 5 -

 5 Figure 5 -Localization of MB to cell membrane interaction area after sonoporation

Figure 6 -

 6 Figure 6 -Localization of pDNA relative to late endosomes. HeLaRab7-eGFP cells

Figure 7 -

 7 Figure 7 -Localization of pDNA relative to the nucleus 6 h post-sonoporation.

Figure 8 -

 8 Figure 8 -Analysis of plasmid DNA internalization after sonoporation. Cells were

Table 1 .

 1

	Peak negative acoustic pressure (kPa)	Gene transfer efficacy (RLU/mg proteins)	(%) to cell membrane Microbubble stuck	Microbubble-cell interaction type observed cell (%) interactions (%) Microbubble entry to Violent microbubble-cell	Videos recorded (n)
	100	3.2×10	4	86	14	0	9
	150	1.8×10	6	39	43	18	45
	200	1.9×10	5	33	24	53	21

Table 1 .

 1 Comparison between gene transfer efficacy values and microbubble-cell interaction type observed by high speed imaging.

Acknowledgements

The authors thank the French "Région Centre" and National Research Agency for funding.

Involvement of endocytosis during sonoporation process

The impact of endocytosis on the sonoporation efficacy was shown in two ways.

After 6 hours the quantity of cells carrying plasmid DNA drops to 3.5% probably because of the degradation of the internalized pDNA. Free pDNA lifetime in cytosol has been estimated to be between 50 and 90 min [START_REF] Lechardeur | Metabolic instability of plasmid DNA in the cytosol: a potential barrier to gene transfer[END_REF]. A part of pDNA endocytosed in Rab7 vesicles, as observed in figure 6C, may also reach the lysosome pathway to be degraded [START_REF] Bucci | Rab7: a key to lysosome biogenesis[END_REF].

The use of endocytosis inhibitors showed that the main endocytosis pathway routing plasmid DNA was the clathrin-dependent pathway. The lack of significant gene transfer inhibition using caveolae inhibitors was unexpected because of the preferential localization of microbubbles observed in the lipid raft area. A correlation between this localization and caveolae endocytosis would have been expected [START_REF] Lajoie | LIPID RAFTS, CAVEOLAE, AND THEIR ENDOCYTOSIS[END_REF]. The results obtained with chlorpromazine are in the same line as those obtained by Meijering et al.; they have reported that a 50% inhibition of 500-kDa dextran delivery by sonoporation was obtained when endothelial cells were treated with chlorpromazine [START_REF] Meijering | Ultrasound and microbubble-targeted delivery of macromolecules is regulated by induction of endocytosis and pore formation[END_REF]. In contrast to our results, these authors have observed an inhibition with the Filipin III, this difference could be due to the size of the molecule transferred that was smaller (500 kDa for the dextran versus 3 MDa for the plasmid) and/or to the difference in cell type used. The reason why clathrin-dependent endocytosis pathway is more effective to deliver plasmid DNA rather than the caveolae pathway remains unknown. For example, the endocytosis route of lipoplexes or polyplexes can be different in function of their chemical composition [START_REF] Billiet | Gene transfer by chemical vectors, and endocytosis routes of polyplexes, lipoplexes and lipopolyplexes in a myoblast cell line[END_REF].

Supplementary video 1.

The video shows 3 sequences of images of HeLa cells in the presence of microbubbles; (1) images taken during an ultrasound stimulation at 100 kPa, the microbubble surrounded interacts with the plasma membrane by cellular "massage";

(2) images taken during an ultrasound stimulation at 150 kPa, the arrowed microbubbles shrink before entering in the cell; (3) images taken during an ultrasound stimulation at 200 kPa, a microbubble interacts with a cell and detaches it from its support.

HeLa cells seeded on a resized Opticell ® membrane 1 MHz ultrasound transducer

Magnetic stirrer

Cuvette filled with OptiMEM ® medium Water tank