GAL4 gene expression imaging using confocal microscopy is a common and powerful technique used to study the nervous system of a model organism such as Drosophila melanogaster. Recent research projects focused on high throughput screenings of thousands of different driver lines, resulting in large image databases. The amount of data generated makes manual assessment tedious or even impossible. The first and most important step in any automatic image processing and data extraction pipeline is to enhance areas with relevant signal. However, data acquired via high throughput imaging tends to be less then ideal for this task, often showing high amounts of background signal. Furthermore, neuronal structures and in particular thin and elongated projections with a weak staining signal are easily lost. In this paper we present a method for enhancing the relevant signal by utilizing a Hessian-based filter to augment thin and weak tube-like structures in the image. To get optimal results, we present a novel adaptive background-aware enhancement filter parametrized with the local background intensity, which is estimated based on a common background model. We also integrate recent research on adaptive image enhancement into our approach, allowing us to propose an effective solution for known problems present in confocal microscopy images. We provide an evaluation based on annotated image data and compare our results against current state-of-the-art algorithms. The results show that our algorithm clearly outperforms the existing solutions.

Behavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we discuss general principles that underlie the tradeoff between encoding cost and inference error. We then derive adaptive encoding schemes that dynamically navigate this tradeoff. These optimal encodings tend to increase the fidelity of the neural representation following a change in the stimulus distribution, and reduce fidelity for stimuli that originate from a known distribution. We predict dynamical signatures of such encoding schemes and demonstrate how known phenomena, such as burst coding and firing rate adaptation, can be understood as hallmarks of optimal coding for accurate inference.

Optimal image quality in light-sheet microscopy requires a perfect overlap between the illuminating light sheet and the focal plane of the detection objective. However, mismatches between the light-sheet and detection planes are common owing to the spatiotemporally varying optical properties of living specimens. Here we present the AutoPilot framework, an automated method for spatiotemporally adaptive imaging that integrates (i) a multi-view light-sheet microscope capable of digitally translating and rotating light-sheet and detection planes in three dimensions and (ii) a computational method that continuously optimizes spatial resolution across the specimen volume in real time. We demonstrate long-term adaptive imaging of entire developing zebrafish (Danio rerio) and Drosophila melanogaster embryos and perform adaptive whole-brain functional imaging in larval zebrafish. Our method improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms.

The past quarter century has witnessed rapid developments of fluorescence microscopy techniques that enable structural and functional imaging of biological specimens at unprecedented depth and resolution. The performance of these methods in multicellular organisms, however, is degraded by sample-induced optical aberrations. Here I review recent work on incorporating adaptive optics, a technology originally applied in astronomical telescopes to combat atmospheric aberrations, to improve image quality of fluorescence microscopy for biological imaging.

Highlights:

• Biological specimens introduce wavefront aberrations and deteriorate the image quality of optical microscopy.
• Adaptive optics is used in optical microscopy to recover ideal imaging performance.
• Adaptive optical imaging improves structural imaging of neurons, allowing for synaptic-level resolution at depth.
• Adaptive optical imaging leads to a more accurate characterization of the functional properties of neurons.

With the ability to correct for the aberrations introduced by biological specimens, adaptive optics—a method originally developed for astronomical telescopes—has been applied to optical microscopy to recover diffraction-limited imaging performance deep within living tissue. In particular, this technology has been used to improve image quality and provide a more accurate characterization of both structure and function of neurons in a variety of living organisms. Among its many highlights, adaptive optical microscopy has made it possible to image large volumes with diffraction-limited resolution in zebrafish larval brains, to resolve dendritic spines over 600μm deep in the mouse brain, and to more accurately characterize the orientation tuning properties of thalamic boutons in the primary visual cortex of awake mice.

Third-harmonic generation microscopy is a powerful label-free nonlinear imaging technique, providing essential information about structural characteristics of cells and tissues without requiring external labelling agents. In this work, we integrated a recently developed compact adaptive optics module into a third-harmonic generation microscope, to measure and correct for optical aberrations in complex tissues. Taking advantage of the high sensitivity of the third-harmonic generation process to material interfaces and thin membranes, along with the 1,300-nm excitation wavelength used here, our adaptive optical third-harmonic generation microscope enabled high-resolution in vivo imaging within highly scattering biological model systems. Examples include imaging of myelinated axons and vascular structures within the mouse spinal cord and deep cortical layers of the mouse brain, along with imaging of key anatomical features in the roots of the model plant Brachypodium distachyon. In all instances, aberration correction led to significant enhancements in image quality.

Adjusting the objective correction collar is a widely used approach to correct spherical aberrations (SA) in optical microscopy. In this work, we characterized and compared its performance with adaptive optics in the context of in vivo brain imaging with two-photon fluorescence microscopy. We found that the presence of sample tilt had a deleterious effect on the performance of SA-only correction. At large tilt angles, adjusting the correction collar even worsened image quality. In contrast, adaptive optical correction always recovered optimal imaging performance regardless of sample tilt. The extent of improvement with adaptive optics was dependent on object size, with smaller objects having larger relative gains in signal intensity and image sharpness. These observations translate into a superior performance of adaptive optics for structural and functional brain imaging applications in vivo, as we confirmed experimentally.

Biological specimens are rife with optical inhomogeneities that seriously degrade imaging performance under all but the most ideal conditions. Measuring and then correcting for these inhomogeneities is the province of adaptive optics. Here we introduce an approach to adaptive optics in microscopy wherein the rear pupil of an objective lens is segmented into subregions, and light is directed individually to each subregion to measure, by image shift, the deflection faced by each group of rays as they emerge from the objective and travel through the specimen toward the focus. Applying our method to two-photon microscopy, we could recover near-diffraction-limited performance from a variety of biological and nonbiological samples exhibiting aberrations large or small and smoothly varying or abruptly changing. In particular, results from fixed mouse cortical slices illustrate our ability to improve signal and resolution to depths of 400 microm.

Biological specimens are rife with optical inhomogeneities that seriously degrade imaging performance under all but the most ideal conditions. Measuring and then correcting for these inhomogeneities is the province of adaptive optics. Here we introduce an approach to adaptive optics in microscopy wherein the rear pupil of an objective lens is segmented into subregions, and light is directed individually to each subregion to measure, by image shift, the deflection faced by each group of rays as they emerge from the objective and travel through the specimen toward the focus. Applying our method to two-photon microscopy, we could recover near-diffraction-limited performance from a variety of biological and nonbiological samples exhibiting aberrations large or small and smoothly varying or abruptly changing. In particular, results from fixed mouse cortical slices illustrate our ability to improve signal and resolution to depths of 400 microm.

Commentary: Introduces a new, zonal approach to adaptive optics (AO) in microscopy suitable for highly inhomogeneous and/or scattering samples such as living tissue. The method is unique in its ability to handle large amplitude aberrations (>20 wavelengths), including spatially complex aberrations involving high order modes beyond the ability of most AO actuators to correct. As befitting a technique designed for in vivo fluorescence imaging, it is also photon efficient.
Although used here in conjunction with two photon microscopy to demonstrate correction deep into scattering tissue, the same principle of pupil segmentation might be profitably adapted to other point-scanning or widefield methods. For example, plane illumination microscopy of multicellular specimens is often beset by substantial aberrations, and all far-field superresolution methods are exquisitely sensitive to aberrations.

Alcohol abuse is a pervasive problem known to be influenced by genetic factors, yet our understanding of the mechanisms underlying alcohol addiction is far from complete. Drosophila melanogaster has been established as a model for studying the molecular mechanisms that mediate the acute and chronic effects of alcohol. However, the Drosophila model has not yet been extended to include more complex alcohol-related behaviors such as self-administration. We recently established a paradigm to characterize ethanol consumption and preference in flies. We demonstrated that flies prefer to consume ethanol-containing food over regular food, and this preference exhibits several features of alcohol addiction: flies increase ethanol consumption over time, they consume ethanol to pharmacologically relevant concentrations, they will overcome an aversive stimulus in order to consume ethanol, and they exhibit relapse after a period of ethanol deprivation. Thus, ethanol preference in flies provides a new model for studying important aspects of addiction and their underlying mechanisms. One mutant that displayed decreased ethanol preference, krasavietz, may represent a first step toward uncovering those mechanisms.