We have conducted a genetic screen for mutations that decrease the effectiveness of signaling by a protein tyrosine kinase, the product of the Drosophila melanogaster sevenless gene. These mutations define seven genes whose wild-type products may be required for signaling by sevenless. Four of the seven genes also appear to be essential for signaling by a second protein tyrosine kinase, the product of the Ellipse gene. The putative products of two of these seven genes have been identified. One encodes a ras protein. The other locus encodes a protein that is homologous to the S. cerevisiae CDC25 protein, an activator of guanine nucleotide exchange by ras proteins. These results suggest that the stimulation of ras protein activity is a key element in the signaling by sevenless and Ellipse and that this stimulation may be achieved by activating the exchange of GTP for bound GDP by the ras protein.

Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers setting up experiments while listening to spikes in real time and observing a pattern of consistent firing when certain stimuli or behaviors happened. With the advent of large-scale recordings, such close observation of data has become harder because high-dimensional spaces are impenetrable to our pattern-finding intuitions. To help ourselves find patterns in neural data, our lab has been openly developing a visualization framework known as “Rastermap” over the past five years. Rastermap takes advantage of a new global optimization algorithm for sorting neural responses along a one-dimensional manifold. Displayed as a raster plot, the sorted neurons show a variety of activity patterns, which can be more easily identified and interpreted. We first benchmark Rastermap on realistic simulations with multiplexed cognitive variables. Then we demonstrate it on recordings of tens of thousands of neurons from mouse visual and sensorimotor cortex during spontaneous, stimulus-evoked and task-evoked epochs, as well as on whole-brain zebrafish recordings, widefield calcium imaging data, population recordings from rat hippocampus and 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.

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.

We consider the problem of establishing visual correspondences in a distributed and rate-efficient fashion by broadcasting compact descriptors. Establishing visual correspondences is a critical task before other vision tasks can be performed in a camera network. We use coarsely quantized random projections of descriptors to build binary hashes, and use the hamming distance between binary hashes as a matching criterion. In this work, we show that the hamming distance between the binary hashes has a binomial distribution, with parameters that are a function of the number of random projections and the euclidean distance between the original descriptors. We present experimental results that verify our result, and show that for the task of finding visual correspondences, sending binary hashes is more rate-efficient than prior approaches.

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.