Volumetric imaging tools that are simple to adopt, flexible, and robust are in high demand in the field of neuroscience, where the ability to image neurons and their networks with high spatiotemporal resolution is essential. Using an axially elongated focus approximating a Bessel beam, in combination with two-photon fluorescence microscopy, has proven successful at such an endeavor. Here, we demonstrate three-photon fluorescence imaging with an axially extended Bessel focus. We use an axicon-based module that allowed for the generation of Bessel foci of varying numerical apertures and axial lengths, and apply this volumetric imaging tool to image mouse brain slices and for in vivo imaging of the mouse brain.

Goal-directed animal behaviors are typically composed of sequences of motor actions whose order and timing are critical for a successful outcome. Although numerous theoretical models for sequential action generation have been proposed, few have been supported by the identification of control neurons sufficient to elicit a sequence. Here, we identify a pair of descending neurons that coordinate a stereotyped sequence of engagement actions during Drosophila melanogaster male courtship behavior. These actions are initiated sequentially but persist cumulatively, a feature not explained by existing models of sequential behaviors. We find evidence consistent with a ramp-to-threshold mechanism, in which increasing neuronal activity elicits each action independently at successively higher activity thresholds.

Genetically-encoded calcium indicators (GECIs) facilitate imaging activity of genetically defined neuronal populations in vivo. The high intracellular GECI concentrations required for in vivo imaging are usually achieved by viral gene transfer using adeno-associated viruses. Transgenic expression of GECIs promises important advantages, including homogeneous, repeatable, and stable expression without the need for invasive virus injections. Here we present the generation and characterization of transgenic mice expressing the GECIs GCaMP6s or GCaMP6f under the Thy1 promoter. We quantified GCaMP6 expression across brain regions and neurons and compared to other transgenic mice and AAV-mediated expression. We tested three mouse lines for imaging in the visual cortex in vivo and compared their performance to mice injected with AAV expressing GCaMP6. Furthermore, we show that GCaMP6 Thy1 transgenic mice are useful for long-term, high-sensitivity imaging in behaving mice.

Calcium imaging is commonly used to measure the neural activity of large groups of neurons in mice. Genetically encoded calcium indicators (GECIs) can be delivered for this purpose using non-invasive genetic methods. Compared to viral gene transfer, transgenic targeting of GECIs provides stable long-term expression and obviates the need for invasive viral injections. Transgenic mice expressing the green GECI GCaMP6 are already widely used. Here we present the generation and characterization of transgenic mice expressing the sensitive red GECI jRGECO1a, driven by the Thy1 promoter. Four transgenic lines with different expression patterns showed sufficiently high expression for cellular in vivo imaging. We used two-photon microscopy to characterize visual responses of individual neurons in the visual cortex in vivo. The signal-to-noise ratio in transgenic mice was comparable to, or better than, mice transduced with adeno-associated virus. In addition, we show that Thy1-jRGECO1a transgenic mice are useful for transcranial population imaging and functional mapping using widefield fluorescence microscopy. We also demonstrate imaging of visual responses in retinal ganglion cells in vitro. Thy1-jRGECO1a transgenic mice are therefore a useful addition to the toolbox for imaging activity in intact neural networks.

Structured illumination microscopy (SIM) is widely used for fast, long-term, live-cell super-resolution imaging. However, SIM images can contain substantial artifacts if the sample does not conform to the underlying assumptions of the reconstruction algorithm. Here we describe a simple, easy to implement, process that can be combined with any reconstruction algorithm to alleviate many common SIM reconstruction artifacts and briefly discuss possible extensions.

A plexus of multidendritic sensory neurons, the dendritic arborization (da) neurons, innervates the epidermis of soft-bodied insects. Previous studies have indicated that the plexus may comprise distinct subtypes of da neurons, which utilize diverse cyclic 3’,5’-guanosine monophosphate signaling pathways and could serve several functions. Here, we identify three distinct classes of da neurons in Manduca, which we term the alpha, beta, and gamma classes. These three classes differ in their sensory responses, branch complexity, peripheral dendritic fields, and axonal projections. The two identified alpha neurons branch over defined regions of the body wall, which in some cases correspond to specific natural folds of the cuticle. These cells project to an intermediate region of the neuropil and appear to function as proprioceptors. Three beta neurons are characterized by long, sinuous dendritic branches and axons that terminate in the ventral neuropil. The function of this group of neurons is unknown. Four neurons belonging to the gamma class have the most complex peripheral dendrites. A representative gamma neuron responds to forceful touch of the cuticle. Although the dendrites of da neurons of different classes may overlap extensively, cells belonging to the same class show minimal dendritic overlap. As a result, the body wall is independently tiled by the beta and gamma da neurons and partially innervated by the alpha neurons. These properties of the da system likely allow insects to discriminate the quality and location of several types of stimuli acting on the cuticle.

The comparison of a pair of electron microscope images recorded at different specimen tilt angles provides a powerful approach for evaluating the quality of images, image-processing procedures, or three-dimensional structures. Here, we analyze tilt-pair images recorded from a range of specimens with different symmetries and molecular masses and show how the analysis can produce valuable information not easily obtained otherwise. We show that the accuracy of orientation determination of individual single particles depends on molecular mass, as expected theoretically since the information in each particle image increases with molecular mass. The angular uncertainty is less than 1° for particles of high molecular mass ( 50 MDa), several degrees for particles in the range 1-5 MDa, and tens of degrees for particles below 1 MDa. Orientational uncertainty may be the major contributor to the effective temperature factor (B-factor) describing contrast loss and therefore the maximum resolution of a structure determination. We also made two unexpected observations. Single particles that are known to be flexible showed a wider spread in orientation accuracy, and the orientations of the largest particles examined changed by several degrees during typical low-dose exposures. Smaller particles presumably also reorient during the exposure; hence, specimen movement is a second major factor that limits resolution. Tilt pairs thus enable assessment of orientation accuracy, map quality, specimen motion, and conformational heterogeneity. A convincing tilt-pair parameter plot, where 60% of the particles show a single cluster around the expected tilt axis and tilt angle, provides confidence in a structure determined using electron cryomicroscopy.

Time-integrated fluorescence cumulant analysis (TIFCA) is a data analysis technique for fluorescence fluctuation spectroscopy (FFS) that extracts information from the cumulants of the integrated fluorescence intensity. It is the first exact theory that describes the effect of sampling time on FFS experiment. Rebinning of data to longer sampling times helps to increase the signal/noise ratio of the experimental cumulants of the photon counts. The sampling time dependence of the cumulants encodes both brightness and diffusion information of the sample. TIFCA analysis extracts this information by fitting the cumulants to model functions. Generalization of TIFCA to multicolor FFS experiment is straightforward. Here, we present an overview of the theory, its implementation, as well as the benefits and requirements of TIFCA. The questions of why, when, and how to use TIFCA will be discussed. We give several examples of practical applications of TIFCA, particularly focused on measuring molecular interaction in living cells.