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Heart valve anomalies are some of the most common congenital heart defects, yet neither the genetic nor the epigenetic forces guiding heart valve development are well understood. When functioning normally, mature heart valves prevent intracardiac retrograde blood flow; before valves develop, there is considerable regurgitation, resulting in reversing (or oscillatory) flows between the atrium and ventricle. As reversing flows are particularly strong stimuli to endothelial cells in culture, an attractive hypothesis is that heart valves form as a developmental response to retrograde blood flows through the maturing heart. Here, we exploit the relationship between oscillatory flow and heart rate to manipulate the amount of retrograde flow in the atrioventricular (AV) canal before and during valvulogenesis, and find that this leads to arrested valve growth. Using this manipulation, we determined that klf2a is normally expressed in the valve precursors in response to reversing flows, and is dramatically reduced by treatments that decrease such flows. Experimentally knocking down the expression of this shear-responsive gene with morpholine antisense oligonucleotides (MOs) results in dysfunctional valves. Thus, klf2a expression appears to be necessary for normal valve formation. This, together with its dependence on intracardiac hemodynamic forces, makes klf2a expression an early and reliable indicator of proper valve development. Together, these results demonstrate a critical role for reversing flows during valvulogenesis and show how relatively subtle perturbations of normal hemodynamic patterns can lead to both major alterations in gene expression and severe valve dysgenesis.

In vivo fluorescence microscopy is a powerful tool to image the beating heart in its early development stages. A high acquisition frame rate is necessary to study its fast contractions, but the limited fluorescence intensity requires sensitive cameras that are often too slow. Moreover, the problem is even more complex when imaging distinct tissues in the same sample using different fluorophores. We present Paired Alternating AcQuisitions, a method to image cyclic processes in multiple channels, which requires only a single (possibly slow) camera. We generate variable temporal illumination patterns in each frame, alternating between channel-specific illuminations (fluorescence) in odd frames and a motion-encoding brightfield pattern as a common reference in even frames. Starting from the image pairs, we find the position of each reference frame in the cardiac cycle through a combination of image-based sorting and regularized curve fitting. Thanks to these estimated reference positions, we assemble multichannel videos whose frame rate is virtually increased. We characterize our method on synthetic and experimental images collected in zebrafish embryos, showing quantitative and visual improvements in the reconstructed videos over existing nongated sorting-based alternatives. Using a 15 Hz camera, we showcase a reconstructed video containing two fluorescence channels at 100 fps.

The embryonic vertebrate heart begins pumping blood long before the development of discernable chambers and valves. At these early stages, the heart tube has been described as a peristaltic pump. Recent advances in confocal laser scanning microscopy and four-dimensional visualization have warranted another look at early cardiac structure and function. We examined the movement of cells in the embryonic zebrafish heart tube and the flow of blood through the heart and obtained results that contradict peristalsis as a pumping mechanism in the embryonic heart. We propose a more likely explanation of early cardiac dynamics in which the pumping action results from suction due to elastic wave propagation in the heart tube.

Significance: Despite recent developments in microscopy, temporal aliasing can arise when imaging dynamic samples. Modern sampling frameworks, such as generalized sampling, mitigate aliasing but require measurement of temporally overlapping and potentially negative-valued inner products. Conventional cameras cannot collect these directly as they operate sequentially and are only sensitive to light intensity. Aim: We aim to mitigate aliasing in microscopy of dynamic monochrome samples by implementing generalized sampling via the use of a color camera and modulated color illumination. Approach: We solve the overlap problem by spectrally multiplexing the acquisitions and using (positive) B-spline segments as projection kernels. Reconstruction involves spectral unmixing and inverse filtering. We implemented this method using a color LED illuminator. We evaluated its performance by imaging a rotating grid and its applicability by imaging the beating zebrafish embryo heart in transmission and light-sheet microscopes. Results: Compared to stroboscopic imaging, our method mitigates aliasing with performance improving as the projection order increases. The approach can be implemented in conventional microscopes but is limited by the number of available LED colors and camera channels. Conclusions: Generalized sampling can be implemented via color modulation in microscopy to mitigate temporal aliasing. The simple hardware requirements could make it applicable to other optical imaging modalities.

Because peptide nucleic acids (PNAs) are capable of blocking amplification of deoxyribonucleic acid (DNA) by Taq DNA polymerase in vitro, we postulated that PNAs might be able to block replication in vivo. To explore this possibility, we assessed the ability of PNA to specifically block the replication of pUC19 plasmids by allowing a PNA, directed against segments of the Ampr sequence to bind to pUC19 prior to electroporation into Escherichia coli, strain DH10B. Colonies produced by this maneuver not only remained sensitive to ampicillin but were also incapable of blue color production on X-gal-containing media, thus demonstrating true blockade of pUC19 replication, rather than antisense activity. The ability of the PNA to prevent pUC19 replication in these experiments was shown to be dose related. Attempts to prevent the replication of E. coli using a PNA directed against a portion of the lac Z sequence found within the bacterial genome were not uniformly successful. Subsequent experiments showed that the electroporated PNA did not consistently enter a sufficient number of cells for an effect to be demonstrated in the assays used. Nonetheless, this is the first demonstration of in vivo complete replication blockade by a PNA and opens up the potential for new forms of specific antibiosis in both prokaryotic and eukaryotic cells.

Optical microscopy is an essential tool in biology and medicine. Imaging thin, yet non-flat objects in a single shot (without relying on more sophisticated sectioning setups) remains challenging as the shallow depth of field that comes with high-resolution microscopes leads to unsharp image regions and makes depth localization and quantitative image interpretation difficult. Here, we present a method that improves the resolution of light microscopy images of such objects by locally estimating image distortion while jointly estimating object distance to the focal plane. Specifically, we estimate the parameters of a spatially-variant Point-Spread function (PSF) model using a Convolutional Neural Network (CNN), which does not require instrument- or object-specific calibration. Our method recovers PSF parameters from the image itself with up to a squared Pearson correlation coefficient of 0.99 in ideal conditions, while remaining robust to object rotation, illumination variations, or photon noise. When the recovered PSFs are used with a spatially-variant and regularized Richardson-Lucy deconvolution algorithm, we observed up to 2.1 dB better signal-to-noise ratio compared to other blind deconvolution techniques. Following microscope-specific calibration, we further demonstrate that the recovered PSF model parameters permit estimating surface depth with a precision of 2 micrometers and over an extended range when using engineered PSFs. Our method opens up multiple possibilities for enhancing images of non-flat objects with minimal need for a priori knowledge about the optical setup.

In microscopy, the time burden and cost of acquiring and annotating large datasets that many deep learning models take as a prerequisite, often appears to make these methods impractical. Can this requirement for annotated data be relaxed? Is it possible to borrow the knowledge gathered from datasets in other application fields and leverage it for microscopy? Here, we aim to provide an overview of methods that have recently emerged to successfully train learning-based methods in bio-microscopy.

Autofocus (AF) methods are extensively used in biomicroscopy, for example to acquire timelapses, where the imaged objects tend to drift out of focus. AD algorithms determine an optimal distance by which to move the sample back into the focal plane. Current hardware-based methods require modifying the microscope and image-based algorithms either rely on many images to converge to the sharpest position or need training data and models specific to each instrument and imaging configuration. Here we propose DeepFocus, an AF method we implemented as a Micro-Manager plugin, and characterize its Convolutional neural network-based sharpness function, which we observed to be depth co-variant and sample-invariant. Sample invariance allows our AF algorithm to converge to an optimal axial position within as few as three iterations using a model trained once for use with a wide range of optical microscopes and a single instrument-dependent calibration stack acquisition of a flat (but arbitrary) textured object. From experiments carried out both on synthetic and experimental data, we observed an average precision, given 3 measured images, of 0.30 +- 0.16 micrometers with a 10x, NA 0.3 objective. We foresee that this performance and low image number will help limit photodamage during acquisitions with light-sensitive samples.