Extending three-dimensional (3D) single-molecule localization microscopy away from the coverslip and into thicker specimens will greatly broaden its biological utility. However, because of the limitations of both conventional imaging modalities and conventional labeling techniques, it is a challenge to localize molecules in three dimensions with high precision in such samples while simultaneously achieving the labeling densities required for high resolution of densely crowded structures. Here we combined lattice light-sheet microscopy with newly developed, freely diffusing, cell-permeable chemical probes with targeted affinity for DNA, intracellular membranes or the plasma membrane. We used this combination to perform high-localization precision, ultrahigh-labeling density, multicolor localization microscopy in samples up to 20 μm thick, including dividing cells and the neuromast organ of a zebrafish embryo. We also demonstrate super-resolution correlative imaging with protein-specific photoactivable fluorophores, providing a mutually compatible, single-platform alternative to correlative light-electron microscopy over large volumes.

A neuronal population encodes information most efficiently when its activity is uncorrelated and high-dimensional, and most robustly when its activity is correlated and lower-dimensional. Here, we analyzed the correlation structure of natural image coding, in large visual cortical populations recorded from awake mice. Evoked population activity was high dimensional, with correlations obeying an unexpected power-law: the n-th principal component variance scaled as 1/n. This was not inherited from the 1/f spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that the variance spectrum must decay at least this fast if a population code is smooth, i.e. if small changes in input cannot dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness represents a fundamental constraint governing correlations in neural population codes.

Live-cell super-resolution microscopy enables the imaging of biological structure dynamics below the diffraction limit. Here we present enhanced super-resolution radial fluctuations (eSRRF), substantially improving image fidelity and resolution compared to the original SRRF method. eSRRF incorporates automated parameter optimization based on the data itself, giving insight into the trade-off between resolution and fidelity. We demonstrate eSRRF across a range of imaging modalities and biological systems. Notably, we extend eSRRF to three dimensions by combining it with multifocus microscopy. This realizes live-cell volumetric super-resolution imaging with an acquisition speed of ~1 volume per second. eSRRF provides an accessible super-resolution approach, maximizing information extraction across varied experimental conditions while minimizing artifacts. Its optimal parameter prediction strategy is generalizable, moving toward unbiased and optimized analyses in super-resolution microscopy.

The ability to probe the membrane potential of multiple genetically defined neurons simultaneously would have a profound impact on neuroscience research. Genetically encoded voltage indicators are a promising tool for this purpose, and recent developments have achieved a high signal-to-noise ratio in vivo with 1-photon fluorescence imaging. However, these recordings exhibit several sources of noise and signal extraction remains a challenge. We present an improved signal extraction pipeline, spike-guided penalized matrix decomposition-nonnegative matrix factorization (SGPMD-NMF), which resolves supra- and subthreshold voltages in vivo. The method incorporates biophysical and optical constraints. We validate the pipeline with simultaneous patch-clamp and optical recordings from mouse layer 1 in vivo and with simulated and composite datasets with realistic noise. We demonstrate applications to mouse hippocampus expressing paQuasAr3-s or SomArchon1, mouse cortex expressing SomArchon1 or Voltron, and zebrafish spines expressing zArchon1.

Calcium imaging with genetically encoded calcium indicators (GECIs) is routinely used to measure neural activity in intact nervous systems. GECIs are frequently used in one of two different modes: to track activity in large populations of neuronal cell bodies, or to follow dynamics in subcellular compartments such as axons, dendrites and individual synaptic compartments. Despite major advances, calcium imaging is still limited by the biophysical properties of existing GECIs, including affinity, signal-to-noise ratio, rise and decay kinetics and dynamic range. Using structure-guided mutagenesis and neuron-based screening, we optimized the green fluorescent protein-based GECI GCaMP6 for different modes of in vivo imaging. The resulting jGCaMP7 sensors provide improved detection of individual spikes (jGCaMP7s,f), imaging in neurites and neuropil (jGCaMP7b), and may allow tracking larger populations of neurons using two-photon (jGCaMP7s,f) or wide-field (jGCaMP7c) imaging.

l-Lactate is a monocarboxylate produced during the process of cellular glycolysis and has long generally been considered a waste product. However, studies in recent decades have provided new perspectives on the physiological roles of l-lactate as a major energy substrate and a signaling molecule. To enable further investigations of the physiological roles of l-lactate, we have developed a series of high-performance (Δ/ = 15 to 30 ), intensiometric, genetically encoded green fluorescent protein (GFP)-based intracellular l-lactate biosensors with a range of affinities. We evaluated these biosensors in cultured cells and demonstrated their application in an preparation of brain tissue. Using these biosensors, we were able to detect glycolytic oscillations, which we analyzed and mathematically modeled.

We describe an engineered family of highly antigenic molecules based on GFP-like fluorescent proteins. These molecules contain numerous copies of peptide epitopes and simultaneously bind IgG antibodies at each location. These 'spaghetti monster' fluorescent proteins (smFPs) distributed well in neurons, notably into small dendrites, spines and axons. smFP immunolabeling localized weakly expressed proteins not well resolved with traditional epitope tags. By varying epitope and scaffold, we generated a diverse family of mutually orthogonal antigens. In cultured neurons and mouse and fly brains, smFP probes allowed robust, orthogonal multicolor visualization of proteins, cell populations and neuropil. smFP variants complement existing tracers and greatly increase the number of simultaneous imaging channels, and they performed well in advanced preparations such as array tomography, super-resolution fluorescence imaging and electron microscopy. In living cells, the probes improved single-molecule image tracking and increased yield for RNA-seq. These probes facilitate new experiments in connectomics, transcriptomics and protein localization.

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known whether the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher order visual areas and measured stimulus discrimination thresholds of 0.35° and 0.37°, respectively, in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, behavioral variability during a sensory discrimination task could not be explained by neural variability in V1. Instead, behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that perceptual discrimination in mice is limited by downstream decoders, not by neural noise in sensory representations.

Understanding how ethanol influences behavior is key to deciphering the mechanisms of ethanol action and alcoholism. In mammals, low doses of ethanol stimulate locomotion, whereas high doses depress it. The acute stimulant effect of ethanol has been proposed to be a manifestation of its rewarding effects. In Drosophila, ethanol exposure transiently potentiates locomotor activity in a biphasic dose- and time-dependent manner. An initial short-lived peak of activity corresponds to an olfactory response to ethanol. A second, longer-lasting period of increased activity coincides with rising internal ethanol concentrations; these closely parallel concentrations that stimulate locomotion in mammals. High-resolution analysis of the walking pattern of individual flies revealed that locomotion consists of bouts of activity; bout structure can be quantified by bout frequency, bout length, and the time spent walking at high speeds. Ethanol exposure induces both dramatic and dynamic changes in bout structure. Mutants with increased ethanol sensitivity show distinct changes in ethanol-induced locomotor behavior, as well as genotype-specific changes in activity bout structure. Thus, the overall effect of ethanol on locomotor behavior in Drosophila is caused by changes in discrete quantifiable parameters of walking pattern. The effects of ethanol on locomotion are comparable in flies and mammals, suggesting that Drosophila is a suitable model system to study the underlying mechanisms.