While fluorescence microscopy has proven to be an exceedingly useful tool in bioscience, it is difficult to offer simultaneous high resolution, fast speed, large volume, and good biocompatibility in a single imaging technique. Thus, when determining the image data required to quantitatively test a complex biological hypothesis, it often becomes evident that multiple imaging techniques are necessary. Recent years have seen an explosion in development of novel fluorescence microscopy techniques, each of which features a unique suite of capabilities. In this Technical Perspective, we highlight recent studies to illustrate the benefits, and often the necessity, of combining multiple fluorescence microscopy modalities. We provide guidance in choosing optimal technique combinations to effectively address a biological question. Ultimately, we aim to promote a more well-rounded approach in designing fluorescence microscopy experiments, leading to more robust quantitative insight.

Uncertainty is a fundamental aspect of the natural environment, requiring the brain to infer and integrate noisy signals to guide behavior effectively. Sampling-based inference has been proposed as a mechanism for dealing with uncertainty, particularly in early sensory processing. However, it is unclear how to reconcile sampling-based methods with operational principles of higher-order brain areas, such as attractor dynamics of persistent neural representations. In this study, we present a spiking neural network model for the head-direction (HD) system that combines sampling-based inference with attractor dynamics. To achieve this, we derive the required spiking neural network dynamics and interactions to perform sampling from a large family of probability distributions - including variables encoded with Poisson noise. We then propose a method that allows the network to update its estimate of the current head direction by integrating angular velocity samples - derived from noisy inputs - with a pull towards a circular manifold, thereby maintaining consistent attractor dynamics. This model makes specific, testable predictions about the HD system that can be examined in future neurophysiological experiments: it predicts correlated subthreshold voltage fluctuations; distinctive short- and long-term firing correlations among neurons; and characteristic statistics of the movement of the neural activity "bump" representing the head direction. Overall, our approach extends previous theories on probabilistic sampling with spiking neurons, offers a novel perspective on the computations responsible for orientation and navigation, and supports the hypothesis that sampling-based methods can be combined with attractor dynamics to provide a viable framework for studying neural dynamics across the brain.

Tumors are complex ecosystems composed of malignant and non-malignant cells embedded in a dynamic extracellular matrix (ECM). In the tumor microenvironment, molecular phenotypes are controlled by cell-cell and ECM interactions in 3D cellular neighborhoods (CNs). While their inhibition can impede tumor progression, routine molecular tumor profiling fails to capture cellular interactions. Single-cell spatial transcriptomics (ST) maps receptor-ligand interactions but usually remains limited to 2D tissue sections and lacks ECM readouts. Here, we integrate 3D ST with ECM imaging in serial sections from one clinical lung carcinoma to systematically quantify molecular states, cell-cell interactions, and ECM remodeling in CN. Our integrative analysis pinpointed known immune escape and tumor invasion mechanisms, revealing several druggable drivers of tumor progression in the patient under study. This proof-of-principle study highlights the potential of in-depth CN profiling in routine clinical samples to inform microenvironment-directed therapies. A record of this paper's transparent peer review process is included in the supplemental information.

Watanabe et al (Reports, 12 April 2013, p. 195) study the yeast SWR1/SWR-C complex responsible for depositing the histone variant H2A.Z by replacing nucleosomal H2A with H2A.Z. They report that reversal of H2A.Z replacement is mediated by SWR1 and related INO80 on an H2A.Z nucleosome carrying H3K56Q. Using multiple assays and reaction conditions, we find no evidence of such reversal of H2A.Z exchange.

Amyloid-β (Aβ) and human islet amyloid polypeptide (hIAPP) aggregate to form amyloid fibrils that deposit in tissues, and are associated with Alzheimer's disease (AD) and Type-II Diabetes (T2D), respectively. Individuals with T2D have an increased risk of developing AD, and conversely, AD patients have an increased risk of developing T2D. Evidence suggests that this link between AD and T2D might originate from a structural similarity between aggregates of Aβ and hIAPP. Using the cryoEM method Micro-Electron Diffraction (MicroED) we determined the atomic structures of 11-residue segments from both Aβ and hIAPP, termed Aβ 24-34 WT and hIAPP 19-29 S20G, with 64% sequence similarity. We observe a high degree of structural similarity between their backbone atoms (0.96 Å RMSD). Moreover, fibrils of these segments induce amyloid formation through self- and cross-seeding. Furthermore, inhibitors designed for one segment show cross-efficacy for full-length Aβ and hIAPP and reduce cytotoxicity of both proteins, though by apparently blocking different cytotoxic mechanisms. The similarity of the atomic structures of Aβ 24-34 WT and hIAPP 19-29 S20G offers a molecular model for cross-seeding between Aβ and hIAPP.

The suppression of tumorigenicity 2/IL-33 (ST2/IL-33) pathway has been implicated in several immune and inflammatory diseases. ST2 is produced as 2 isoforms. The membrane-bound isoform (ST2L) induces an immune response when bound to its ligand, IL-33. The other isoform is a soluble protein (sST2) that is thought to be a decoy receptor for IL-33 signaling. Elevated sST2 levels in serum are associated with an increased risk for cardiovascular disease. We investigated the determinants of sST2 plasma concentrations in 2,991 Framingham Offspring Cohort participants. While clinical and environmental factors explained some variation in sST2 levels, much of the variation in sST2 production was driven by genetic factors. In a genome-wide association study (GWAS), multiple SNPs within IL1RL1 (the gene encoding ST2) demonstrated associations with sST2 concentrations. Five missense variants of IL1RL1 correlated with higher sST2 levels in the GWAS and mapped to the intracellular domain of ST2, which is absent in sST2. In a cell culture model, IL1RL1 missense variants increased sST2 expression by inducing IL-33 expression and enhancing IL-33 responsiveness (via ST2L). Our data suggest that genetic variation in IL1RL1 can result in increased levels of sST2 and alter immune and inflammatory signaling through the ST2/IL-33 pathway.

The pea aphid, Acyrthosiphon pisum, exhibits several environmentally cued polyphenisms, in which discrete, alternative phenotypes are produced. At low-density, parthenogenetic females produce unwinged female progeny, but at high-density females produce progeny that develop with wings. These alternative phenotypes represent a solution to the competing demands of dispersal and reproduction. Males also develop as either winged or unwinged, but these alternatives are determined by a genetic polymorphism. Winged and unwinged males are morphologically less distinct from each other than winged and unwinged females, possibly because males experience fewer trade-offs between dispersal and reproduction. To assess whether shared physiological differences mirror the shared morphological differences that characterize the wing polyphenism and polymorphism, we used a cDNA microarray representing an estimated 10% of the coding genome (1734 genes) to examine differential transcript accumulation between winged and unwinged females and males. We identified several transcripts that differentially accumulate between winged and unwinged morphs in both sexes, the majority of which are involved in energy production. Unexpectedly, the extent of differential transcript accumulation between winged and unwinged morphs was greater for adult males than for adult females. Together, these results suggest not only that similar physiological differences underlie the polyphenism and polymorphism, but that male morphs, like females, are subject to trade-offs between reproduction and dispersal that are reflected in levels of transcript accumulation and possibly genome-wide patterns of gene regulation. These data also provide a baseline for future studies of the molecular and physiological basis of life-history trade-offs.

The sense of taste is a specialized chemosensory system dedicated to the evaluation of food and drink. Despite the fact that vertebrates and insects have independently evolved distinct anatomic and molecular pathways for taste sensation, there are clear parallels in the organization and coding logic between the two systems. There is now persuasive evidence that tastant quality is mediated by labeled lines, whereby distinct and strictly segregated populations of taste receptor cells encode each of the taste qualities.

The behavioral response to a sensory stimulus may depend on both learned and innate neuronal representations. How these circuits interact to produce appropriate behavior is unknown. In Drosophila, the lateral horn (LH) and mushroom body (MB) are thought to mediate innate and learned olfactory behavior, respectively, although LH function has not been tested directly. Here we identify two LH cell types (PD2a1 and PD2b1) that receive input from an MB output neuron required for recall of aversive olfactory memories. These neurons are required for aversive memory retrieval and modulated by training. Connectomics data demonstrate that PD2a1 and PD2b1 neurons also receive direct input from food odor-encoding neurons. Consistent with this, PD2a1 and PD2b1 are also necessary for unlearned attraction to some odors, indicating that these neurons have a dual behavioral role. This provides a circuit mechanism by which learned and innate olfactory information can interact in identified neurons to produce appropriate behavior.

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.