Searching events in AFM force-extension curves: A wavelet approach

New insights into external layers of cyanobacteria and microalgae based on multiscale analysis of AFM force-distance curves

External layers, the outermost structures around cells, perform essential eco-physiological functions to support cyanobacteria and microalgae in aquatic environments. These layers have been recognized as adaptations to turbulence, a ubiquitous and inherent physical process occurring in the environments of most cyanobacteria and microalgae. However, the underlying biophysical mechanism of these layers is still poorly understood. Force measurements were performed directly on the external layers of eight living cyanobacterial and green algal strains in situ using atomic force microscopy (AFM). We developed a wavelet analysis method based on a multiscale decomposition of derivative force-distance curves to quantify the elastic responses of various external layers upon mechanical deformation. Such analysis has the advantages of detecting singularities and distinguishing the biomechanical contributions of each external layer. The elastic modulus of the same type of external layer follows the same statistical distribution. However, the elastic response among different types of external layers is challenged by our method, indicating the heterogeneity of the mechanical properties of inner and outer layers in multilayer strains. This discrepancy was due to the thickness and texture of each external layer, especially the chemical presence of ribose, hydroxyproline and glutamic acid. This study highlights a new way to elucidate more precise information about external layers and provides a biophysical mechanistic explanation for the functioning of the various external layers to protect cyanobacterial and microalgal cells in a turbulent environment.

Learning-based event locating for single-molecule force spectroscopy

Acquiring events massively from single-molecule force spectroscopy (SMFS) experiments, which is crucial for revealing important biophysical information, is usually not straightforward. A significant amount of human labor is usually required to identify events in the measured force spectrum during measuring or before performing further data analysis. This prevents the experiment from being done in a fully-automated manner or scaling with the throughput of the measuring setup. In this work, we attempt to tackle this problem with a deep learning approach. A deep neural network model is developed to infer the occurrence of the events using the data stream from the measuring setup. We demonstrated that the proposed method could achieve high accuracy with force spectrums of a variety of samples from both optical tweezers and AFMs by learning from user-given samples instead of complicated manual algorithm designing or parameter tuning. Furthermore, we found that the trained model can be used to perform event detection on datasets measured from a different optical tweezer setup, showing the potential of being leveraged in more complex deep learning schemes.

FEATHER: Automated Analysis of Force Spectroscopy Unbinding and Unfolding Data via a Bayesian Algorithm

Single-molecule force spectroscopy (SMFS) provides a powerful tool to explore the dynamics and energetics of individual proteins, protein-ligand interactions, and nucleic acid structures. In the canonical assay, a force probe is retracted at constant velocity to induce a mechanical unfolding/unbinding event. Next, two energy landscape parameters, the zero-force dissociation rate constant (k_o) and the distance to the transition state (Δx^‡), are deduced by analyzing the most probable rupture force as a function of the loading rate, the rate of change in force. Analyzing the shape of the rupture force distribution reveals additional biophysical information, such as the height of the energy barrier (ΔG^‡). Accurately quantifying such distributions requires high-precision characterization of the unfolding events and significantly larger data sets. Yet, identifying events in SMFS data is often done in a manual or semiautomated manner and is obscured by the presence of noise. Here, we introduce, to our knowledge, a new algorithm, FEATHER (force extension analysis using a testable hypothesis for event recognition), to automatically identify the locations of unfolding/unbinding events in SMFS records and thereby deduce the corresponding rupture force and loading rate. FEATHER requires no knowledge of the system under study, does not bias data interpretation toward the dominant behavior of the data, and has two easy-to-interpret, user-defined parameters. Moreover, it is a linear algorithm, so it scales well for large data sets. When analyzing a data set from a polyprotein containing both mechanically labile and robust domains, FEATHER featured a 30-fold improvement in event location precision, an eightfold improvement in a measure of the accuracy of the loading rate and rupture force distributions, and a threefold reduction of false positives in comparison to two representative reference algorithms. We anticipate FEATHER being leveraged in more complex analysis schemes, such as the segmentation of complex force-extension curves for fitting to worm-like chain models and extended in future work to data sets containing both unfolding and refolding transitions.

Tibial Nerve Stimulation to Drive Genital Sexual Arousal in an Anesthetized Female Rat

BACKGROUND

There is clinical evidence that percutaneous tibial nerve stimulation can positively benefit women with female sexual interest/arousal disorder, yet no studies have explored the potential mechanisms further.

AIM

To investigate the effect of tibial nerve stimulation on vaginal blood perfusion (VBP) in an anesthetized rat model.

METHODS

16 ketamine-anesthetized rats were surgically implanted with a nerve cuff electrode on 1 tibial nerve. The tibial nerve was stimulated for 30 minutes continuously or non-continuously at a frequency of 10 to 25 Hz.

OUTCOMES

VBP was measured with laser Doppler flowmetry and analyzed using a wavelet transform of time-frequency representations with a focus on the neurogenic energy range (0.076-0.200 Hz).

RESULTS

25 of 33 (75.8%) stimulation periods had at least a 500% increase in laser Doppler flowmetry neurogenic energy compared with baseline. This increase was most common within 20 to 35 minutes after the start of stimulation. There was no statistically significant difference for frequency used or estrous cycle stage.

CLINICAL TRANSLATION

The results of this study provide further support for percutaneous tibial nerve stimulation as an alternative treatment option for women with genital arousal aspects of female sexual interest/arousal disorder.

STRENGTHS AND LIMITATIONS

This study successfully demonstrates the ability of tibial nerve stimulation to increase VBP. However, further studies to determine parameter optimization and to illuminate neural mechanisms are needed. Further studies also are necessary to determine effects of repeated stimulation sessions.

CONCLUSION

Long-duration tibial stimulation was successful at driving increases in the neurogenic component of VBP, providing evidence that tibial nerve stimulation could be used to treat genital arousal aspects of female sexual interest/arousal disorder by improving pelvic blood flow. Zimmerman LL, Rice IC, Berger MB, Bruns TM. Tibial Nerve Stimulation to Drive Genital Sexual Arousal in an Anesthetized Female Rat. J Sex Med 2018;15:296-303.