The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individual neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

The prefrontal cortex (PFC)'s functions are thought to include working memory, as its activity can reflect information that must be temporarily maintained to realize the current goal. We designed a flexible spatial working memory task that required rats to navigate - after distractions and a delay - to multiple possible goal locations from different starting points and via multiple routes. This made the current goal location the key variable to remember, instead of a particular direction or route to the goal. However, across a broad population of PFC neurons, we found no evidence of current-goal-specific memory in any previously reported form - that is differences in the rate, sequence, phase, or covariance of firing. This suggests that such patterns do not hold working memory in the PFC when information must be employed flexibly. Instead, the PFC grouped locations representing behaviorally equivalent task features together, consistent with a role in encoding long-term knowledge of task structure.

The prefrontal cortex (PFC)'s functions are thought to include working memory, as its activity can reflect information that must be temporarily maintained to realize the current goal. We designed a flexible spatial working memory task that required rats to navigate - after distractions and a delay - to multiple possible goal locations from different starting points and via multiple routes. This made the current goal location the key variable to remember, instead of a particular direction or route to the goal. However, across a broad population of PFC neurons, we found no evidence of current-goal-specific memory in any previously reported form - that is differences in the rate, sequence, phase, or covariance of firing. This suggests that such patterns do not hold working memory in the PFC when information must be employed flexibly. Instead, the PFC grouped locations representing behaviorally equivalent task features together, consistent with a role in encoding long-term knowledge of task structure.

Approximately one-third of global CO fixation occurs in a phase-separated algal organelle called the pyrenoid. The existing data suggest that the pyrenoid forms by the phase separation of the CO-fixing enzyme Rubisco with a linker protein; however, the molecular interactions underlying this phase separation remain unknown. Here we present the structural basis of the interactions between Rubisco and its intrinsically disordered linker protein Essential Pyrenoid Component 1 (EPYC1) in the model alga Chlamydomonas reinhardtii. We find that EPYC1 consists of five evenly spaced Rubisco-binding regions that share sequence similarity. Single-particle cryo-electron microscopy of these regions in complex with Rubisco indicates that each Rubisco holoenzyme has eight binding sites for EPYC1, one on each Rubisco small subunit. Interface mutations disrupt binding, phase separation and pyrenoid formation. Cryo-electron tomography supports a model in which EPYC1 and Rubisco form a codependent multivalent network of specific low-affinity bonds, giving the matrix liquid-like properties. Our results advance the structural and functional understanding of the phase separation underlying the pyrenoid, an organelle that plays a fundamental role in the global carbon cycle.

We propose a method to facilitate exploration and analysis of new large data sets. In particular, we give an unsupervised deep learning approach to learning a latent representation that captures semantic similarity in the data set. The core idea is to use data augmentations that preserve semantic meaning to generate synthetic examples of elements whose feature representations should be close to one another.
We demonstrate the utility of our method applied to nano-scale electron microscopy data, where even relatively small portions of animal brains can require terabytes of image data. Although supervised methods can be used to predict and identify known patterns of interest, the scale of the data makes it difficult to mine and analyze patterns that are not known a priori. We show the ability of our learned representation to enable query by example, so that if a scientist notices an interesting pattern in the data, they can be presented with other locations with matching patterns. We also demonstrate that clustering of data in the learned space correlates with biologically-meaningful distinctions. Finally, we introduce a visualization tool and software ecosystem to facilitate user-friendly interactive analysis and uncover interesting biological patterns. In short, our work opens possible new avenues in understanding of and discovery in large data sets, arising in domains such as EM analysis.

DNA double-strand breaks drive genomic instability. However, it remains unknown how these processes may affect the biomechanical properties of the nucleus and what role nuclear mechanics play in DNA damage and repair efficiency. Here, we have used Atomic Force Microscopy to investigate nuclear mechanical changes, arising from externally induced DNA damage. We found that nuclear stiffness is significantly reduced after cisplatin treatment, as a consequence of DNA damage signalling. This softening was linked to global chromatin decondensation, which improves molecular diffusion within the organelle. We propose that this can increase recruitment for repair factors. Interestingly, we also found that reduction of nuclear tension, through cytoskeletal relaxation, has a protective role to the cell and reduces accumulation of DNA damage. Overall, these changes protect against further genomic instability and promote DNA repair. We propose that these processes may underpin the development of drug resistance.

Skin color patterns are ubiquitous in nature, impact social behavior, predator avoidance, and protection from ultraviolet irradiation. A leading model system for vertebrate skin patterning is the zebrafish; its alternating blue stripes and yellow interstripes depend on light-reflecting cells called iridophores. It was suggested that the zebrafish's color pattern arises from a single type of iridophore migrating differentially to stripes and interstripes. However, here we find that iridophores do not migrate between stripes and interstripes but instead differentiate and proliferate in-place, based on their micro-environment. RNA-sequencing analysis further reveals that stripe and interstripe iridophores have different transcriptomic states, while cryogenic-scanning-electron-microscopy and micro-X-ray diffraction identify different crystal-arrays architectures, indicating that stripe and interstripe iridophores are different cell types. Based on these results, we present an alternative model of skin patterning in zebrafish in which distinct iridophore crystallotypes containing specialized, physiologically responsive, organelles arise in stripe and interstripe by in-situ differentiation.

Label-free vibrational imaging of biological samples has attracted significant interest due to its integration of structural and chemical information. Vibrational infrared photothermal amplitude and phase signal (VIPPS) imaging provide label-free chemical identification by targeting the characteristic resonances of biological compounds that are present in the mid-infrared fingerprint region (3 µm - 12 µm). High contrast imaging of subcellular features and chemical identification of protein secondary structures in unlabeled and labeled fibroblast cells embedded in a collagen-rich extracellular matrix is demonstrated by combining contrast from absorption signatures (amplitude signals) with sensitive detection of different heat properties (lock-in phase signals). We present that the detectability of nano-sized cell membranes is enhanced to well below the optical diffraction limit since the membranes are found to act as thermal barriers. VIPPS offers a novel combination of chemical imaging and thermal diffusion characterization that paves the way towards label-free imaging of cell models and tissues as well as the study of intracellular heat dynamics.

Motile cilia power cell locomotion and drive extracellular fluid flow by propagating bending waves from their base to tip. The coordinated bending of cilia requires mechanoregulation by the radial spoke (RS) protein complexes and the microtubule central pair (CP). Despite their importance for ciliary motility across eukaryotes, the molecular function of the RSs is unknown. Here, we reconstituted the Chlamydomonas reinhardtii RS head that abuts the CP and determined its structure using single-particle cryo-EM to 3.1-Å resolution, revealing a flat, negatively charged surface supported by a rigid core of tightly intertwined proteins. Mutations in this core, corresponding to those involved in human ciliopathies, compromised the stability of the recombinant complex, providing a molecular basis for disease. Partially reversing the negative charge on the RS surface impaired motility in C. reinhardtii. We propose that the RS-head architecture is well-suited for mechanoregulation of ciliary beating through physical collisions with the CP.

This protocol provides a two-parameter analysis of single-molecule tracking (SMT) trajectories of Halo-tagged histones in living adherent cell lines and unveils a chromatin mobility landscape composed of five chromatin types, ranging from low to high mobility. When the analysis is applied to Halo-tagged, chromatin-binding proteins, it associates chromatin interaction properties with known functions in a way that previously used SMT parameters did not. For complete information on the use and execution of this protocol, please refer to Lerner et al. (2020).