Whole-organ imaging and characterization at a submicron level provide abundant information on development and diseases while remaining a big challenge, especially in the context of time load. Herein, a quantitative light-sheet microscopy platform that enabled highly time-efficient assessments of fibrous structures within the intact cleared tissue is developed. Dual-view inverted selective plane illumination microscopy (diSPIM), followed by improved registration and deconvolution, led to submicron isotropic imaging of mouse upper genital tract with one hundred-fold speed-ups than previous efforts. Further, optical metrics quantifying 3D local density and structural complexity of targets based on parallel and vectorized convolution in both spatial and frequency domains are developed. Collectively, ≈400–2000 fold increases in time efficiency counting for imaging, postprocessing, and quantitative characterization compared to the traditional method is gained. Using this platform, automatic identification of medulla and cortex within the mouse ovary at over 90% overlap with manual selection by anatomy experts is achieved. Additionally, heterogeneous distributions of immune cells in the mouse ovary and fallopian tube, offering a unique perspective for understanding the immune microenvironment are discovered. This work paves the way for future whole-organ study, and exhibits potential with promise for offering mechanistic insights into physiological and pathological alterations of biological tissues.

Many insects manipulate plants by injecting effector proteins. In one extreme example of this molecular “hijacking”, Hormaphis cornu aphids inject bicycle proteins into Hamamelis virginiana (Witch Hazel), contributing to the development of novel organs called galls. Bicycle proteins share no amino acid sequence similarity with proteins of known function. Here, we report the crystal structures of two divergent bicycle proteins. Both proteins contain saposin-like folds: one with multiple disulfide bonds exhibits a helix swap; the other has no disulfide bonds and possesses two tandem domains. To explore the structural evolution of bicycle proteins, we predicted bicycle protein structures with Alphafold2 (AF2). While AF2 did not recover the two experimental structures using existing databases, it succeeded after we provided multiple sequence alignments (MSAs) containing protein sequences encoded in new genome sequences from closely related aphid species. Using this customized approach at scale, we generated 2400 high-confidence predictions for bicycle proteins from seven aphid species. This dataset revealed that bicycle proteins without cysteines are outliers in fold space and appear to have evolved from ancestral proteins with disulfide-bonded saposin-like folds. While all bicycle proteins contain predicted saposin-like folds, they display a vast diversity of structural and physicochemical properties. While this diversity thwarts prediction of conserved functions encoded in structure, it suggests that bicycle proteins have evolved to target diverse plant processes and/or to evade plant immune surveillance.Significance statement Parasites introduce specialized “effector” proteins into hosts, both to suppress host immunity and to release nutrients. The molecular functions and structures of most effector proteins are unknown. Effector proteins often evolve rapidly and share no similarity with proteins of known function. Here, we demonstrate that machine learning algorithms can accurately predict the structures of aphid “bicycle” effector proteins when supplemented with data from closely related species. We exploit this finding to generate predictions of 2400 bicycle protein structures. These proteins exploit a common motif, yet exhibit diverse structures that form distinct structural clusters. Despite the clustering of these proteins in structure space, they occupy a nearly uniformly physicochemical space, suggesting that they encode a large diversity of molecular functions.

Inducible and reversible perturbation of the activity of selected neurons in vivo is critical to understanding the dynamics of brain circuits. Several genetically encoded systems for rapid inducible neuronal silencing have been developed in the past few years offering an arsenal of tools for in vivo experiments. Some systems are based on ion-channels or pumps, others on G protein coupled receptors, and yet others on modified presynaptic proteins. Inducers range from light to small molecules to peptides. This diversity results in differences in the various parameters that may determine the applicability of each tool to a particular biological question. Although further development would be beneficial, the current silencing tool kit already provides the ability to make specific perturbations of circuit function in behaving animals.

Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers listening to spikes in real time and noticing patterns of activity related to ongoing stimuli or behaviors. With the advent of large-scale recordings, such close observation of data has become difficult. To find patterns in large-scale neural data, we developed 'Rastermap', a visualization method that displays neurons as a raster plot after sorting them along a one-dimensional axis based on their activity patterns. We benchmarked Rastermap on realistic simulations and then used it to explore recordings of tens of thousands of neurons from mouse cortex during spontaneous, stimulus-evoked and task-evoked epochs. We also applied Rastermap to whole-brain zebrafish recordings; to wide-field imaging data; to electrophysiological recordings in rat hippocampus, monkey frontal cortex and various cortical and subcortical regions in mice; and to artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.

We investigate a practical approach to solving one instantiation of a distributed hypothesis testing problem under severe rate constraints that shows up in a wide variety of applications such as camera calibration, biometric authentication and video hashing: given two distributed continuous-valued random sources, determine if they satisfy a certain Euclidean distance criterion. We show a way to convert the problem from continuous-valued to binary-valued using binarized random projections and obtain rate savings by applying a linear syndrome code. In finding visual correspondences, our approach uses just 49% of the rate of scalar quantization to achieve the same level of retrieval performance. To perform video hashing, our approach requires only a hash rate of 0.0142 bpp to identify corresponding groups of pictures correctly.

Light-mediated chemical reactions are powerful methods for manipulating and interrogating biological systems. Photosensitizers, compounds that generate reactive oxygen species upon excitation with light, can be utilized for numerous biological experiments, but the repertoire of bioavailable photosensitizers is limited. Here, we describe the synthesis, characterization, and utility of two photosensitizers based upon the widely used rhodamine scaffold and demonstrate their efficacy for chromophore-assisted light inactivation, cell ablation in culture and in vivo, and photopolymerization of diaminobenzidine for electron microscopy. These chemical tools will facilitate a broad range of applications spanning from targeted destruction of proteins to high-resolution imaging.

Rhodamine dyes exist in equilibrium between a fluorescent zwitterion and a nonfluorescent lactone. Tuning this equilibrium toward the nonfluorescent lactone form can improve cell-permeability and allow creation of "fluorogenic" compounds-ligands that shift to the fluorescent zwitterion upon binding a biomolecular target. An archetype fluorogenic dye is the far-red tetramethyl-Si-rhodamine (SiR), which has been used to create exceptionally useful labels for advanced microscopy. Here, we develop a quantitative framework for the development of new fluorogenic dyes, determining that the lactone-zwitterion equilibrium constant () is sufficient to predict fluorogenicity. This rubric emerged from our analysis of known fluorophores and yielded new fluorescent and fluorogenic labels with improved performance in cellular imaging experiments. We then designed a novel fluorophore-Janelia Fluor 526 (JF)-with SiR-like properties but shorter fluorescence excitation and emission wavelengths. JF is a versatile scaffold for fluorogenic probes including ligands for self-labeling tags, stains for endogenous structures, and spontaneously blinking labels for super-resolution immunofluorescence. JF constitutes a new label for advanced microscopy experiments, and our quantitative framework will enable the rational design of other fluorogenic probes for bioimaging.

Genetically encoded pH sensors based on fluorescent proteins are valuable tools for the imaging of cellular events that are associated with pH changes, such as exocytosis and endocytosis. Superecliptic pHluorin (SEP) is a pH-sensitive green fluorescent protein (GFP) variant widely used for such applications. Here, we report the rational design, development, structure, and applications of Lime, an improved SEP variant with higher fluorescence brightness and greater pH sensitivity. The X-ray crystal structure of Lime supports the mechanistic rationale that guided the introduction of beneficial mutations. Lime provides substantial improvements relative to SEP for imaging of endocytosis and exocytosis. Furthermore, Lime and its variants are advantageous for a broader range of applications including the detection of synaptic release and neuronal voltage changes.

Green-to-red photoconvertible fluorescent proteins (pcFPs) are powerful tools for super-resolution localization microscopy and protein tagging. Recently, they have been found to undergo efficient photoconversion not only by the traditional 400-nm illumination but also by an alternative method termed primed conversion, employing dual wavelength illumination with blue and far-red/near-infrared light. Primed conversion has been reported only for Dendra2 and its mechanism has remained elusive. Here, we uncover the molecular mechanism of primed conversion by reporting the intermediate "primed" state to be a triplet dark state formed by intersystem crossing. We show that formation of this state can be influenced by the introduction of serine or threonine at sequence position 69 (Eos notation) and use this knowledge to create "pr"- (for primed convertible) variants of most known green-to-red pcFPs.

The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images. Here we demonstrate that rDL structured illumination microscopy eliminates spectral bias-induced resolution degradation and reduces model uncertainty by five-fold, improving the super-resolution information by more than ten-fold over other computational approaches. Moreover, rDL applied to LLSM enables self-supervised training by using the spatial or temporal continuity of noisy data itself, yielding results similar to those of supervised methods. We demonstrate the utility of rDL by imaging the rapid kinetics of motile cilia, nucleolar protein condensation during light-sensitive mitosis and long-term interactions between membranous and membrane-less organelles.