One of the challenges in modern fluorescence microscopy is to reconcile the conventional utilization of microscopes as exploratory instruments with their emerging and rapidly expanding role as a quantitative tools. The contribution of microscopy to observational biology will remain enormous owing to the improvements in acquisition speed, imaging depth, resolution and biocompatibility of modern imaging instruments. However, the use of fluorescence microscopy to facilitate the quantitative measurements necessary to challenge hypotheses is a relatively recent concept, made possible by advanced optics, functional imaging probes and rapidly increasing computational power. We argue here that to fully leverage the rapidly evolving application of microscopes in hypothesis-driven biology, we not only need to ensure that images are acquired quantitatively but must also re-evaluate how microscopy-based experiments are designed. In this Opinion, we present a reverse logic that guides the design of quantitative fluorescence microscopy experiments. This unique approach starts from identifying the results that would quantitatively inform the hypothesis and map the process backward to microscope selection. This ensures that the quantitative aspects of testing the hypothesis remain the central focus of the entire experimental design.

We present hysteresis phenomena of the intelligent driver model for traffic flow in a circular one-lane roadway. We show that the microscopic structure of traffic flow is dependent on its initial state by plotting the fraction of congested vehicles over the density, which shows a typical hysteresis loop, and by investigating the trajectories of vehicles on the velocity-over-headway plane. We find that the trajectories of vehicles on the velocity-over-headway plane, which usually show a hysteresis loop, include multiple loops. We also point out the relations between these hysteresis loops and the congested jams or high-density clusters in traffic flow.

A new type of wide-field fluorescence microscopy is described, which produces 100-nm-scale spatial resolution in all three dimensions, by using structured illumination in a microscope that has two opposing objective lenses. Illumination light is split by a grating and a beam splitter into six mutually coherent beams, three of which enter the specimen through each objective lens. The resulting illumination intensity pattern contains high spatial frequency components both axially and laterally. In addition, the emission is collected by both objective lenses coherently, and combined interferometrically on a single camera, resulting in a detection transfer function with axially extended support. These two effects combine to produce near-isotropic resolution. Experimental images of test samples and biological specimens confirm the theoretical predictions.

We developed a significantly improved genetically encoded quantitative adenosine triphosphate (ATP) sensor to provide real-time dynamics of ATP levels in subcellular compartments. iATPSnFR2 is a variant of iATPSnFR1, a previously developed sensor that has circularly permuted super-folder GFP inserted between the ATP-binding helices of the ε-subunit of a bacterial F0-F1 ATPase. Optimizing the linkers joining the two domains resulted in a ∼ 5-6 fold improvement in the dynamic range compared to the previous generation sensor, with excellent discrimination against other analytes and affinity variants varying from 4 μM to 500 μM. A chimeric version of this sensor fused to either the HaloTag protein or a suitably spectrally separated fluorescent protein, provides a ratiometric readout allowing comparisons of ATP across cellular regions. Subcellular targeting of the sensor to nerve terminals reveals previously uncharacterized single synapse metabolic signatures, while targeting to the mitochondrial matrix allowed direct quantitative probing of oxidative phosphorylation dynamics.

The glycolytic pathway of the hyperthermophilic archaea that belong to the order Thermococcales (Pyrococcus, Thermococcus and Palaeococcus) differs significantly from the canonical Embden-Meyerhof pathway in bacteria and eukarya. This archaeal glycolysis variant consists of several novel enzymes, some of which catalyze unique conversions. Moreover, the enzymes appear not to be regulated allosterically, but rather at transcriptional level. To elucidate details of the gene expression control, the transcription initiation sites of the glycolytic genes in Pyrococcus furiosus have been mapped by primer extension analysis and the obtained promoter sequences have been compared with upstream regions of non-glycolytic genes. Apart from consensus sequences for the general transcription factors (TATA-box and BRE) this analysis revealed the presence of a potential transcription factor binding site (TATCAC-N(5)-GTGATA) in glycolytic and starch utilizing promoters of P. furiosus and several thermococcal species. The absence of this inverted repeat in Pyrococcus abyssi and Pyrococcus horikoshii probably reflects that their reduced catabolic capacity does not require this regulatory system. Moreover, this phyletic pattern revealed a TrmB-like regulator (PF0124 and TK1769) which may be involved in recognizing the repeat. This Thermococcales glycolytic regulon, with more than 20 genes, is the largest regulon that has yet been described for Archaea.

Nitrogen-containing-bisphosphonates (N-BPs) are a class of drugs widely prescribed to treat osteoporosis and other bone-related diseases. Although previous studies have established that N-BPs function by inhibiting the mevalonate pathway in osteoclasts, the mechanism by which N-BPs enter the cytosol from the extracellular space to reach their molecular target is not understood. Here we implemented a CRISPRi-mediated genome-wide screen and identified (solute carrier family 37 member A3) as a gene required for the action of N-BPs in mammalian cells. We observed that SLC37A3 forms a complex with ATRAID (all-trans retinoic acid-induced differentiation factor), a previously identified genetic target of N-BPs. SLC37A3 and ATRAID localize to lysosomes and are required for releasing N-BP molecules that have trafficked to lysosomes through fluid-phase endocytosis into the cytosol. Our results elucidate the route by which N-BPs are delivered to their molecular target, addressing a key aspect of the mechanism of action of N-BPs that may have significant clinical relevance.

The RNA polymerase II general transcription factor TFIID is a complex containing the TATA-binding protein (TBP) and associated factors (TAFs). We have used a mutant allele of the gene encoding yeast TAF(II)68/61p to analyze its function in vivo. We provide biochemical and genetic evidence that the C-terminal alpha-helix of TAF(II)68/61p is required for its direct interaction with TBP, the stable incorporation of TBP into the TFIID complex, the integrity of the TFIID complex, and the transcription of most genes in vivo. This is the first evidence that a yeast TAF(II) other than TAF(II)145/130 interacts with TBP, and the implications of this on the interpretation of data obtained studying TAF(II) mutants in vivo are discussed. We have identified a high copy suppressor of the TAF68/61 mutation, TSG2, that has sequence similarity to a region of the SAGA subunit Ada1. We demonstrate that it directly interacts with TAF(II)68/61p in vitro, is a component of TFIID, is required for the stability of the complex in vivo, and is necessary for the transcription of many yeast genes. On the basis of these functions, we propose that Tsg2/TAF(II)48p is the histone 2A-like dimerization partner for the histone 2B-like TAF(II)68/61p in the yeast TFIID complex.

Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the "cell types" that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type. As datasets continue to grow in size and complexity, computational challenges abound, requiring analytical methods to be scalable, flexible, and robust. Moreover, careful consideration needs to be paid to experimental biases and statistical challenges that are unique to these measurements to avoid artifacts. This chapter introduces these topics in the context of cell-type identification, and outlines an instructive step-by-step example bioinformatic pipeline for researchers entering this field.

Bacterial small non-coding RNAs (sRNAs) are being recognized as novel widespread regulators of gene expression in response to environmental signals. Here, we present the first search for sRNA-encoding genes in the nitrogen-fixing endosymbiont Sinorhizobium meliloti, performed by a genome-wide computational analysis of its intergenic regions. Comparative sequence data from eight related alpha-proteobacteria were obtained, and the interspecies pairwise alignments were scored with the programs eQRNA and RNAz as complementary predictive tools to identify conserved and stable secondary structures corresponding to putative non-coding RNAs. Northern experiments confirmed that eight of the predicted loci, selected among the original 32 candidates as most probable sRNA genes, expressed small transcripts. This result supports the combined use of eQRNA and RNAz as a robust strategy to identify novel sRNAs in bacteria. Furthermore, seven of the transcripts accumulated differentially in free-living and symbiotic conditions. Experimental mapping of the 5’-ends of the detected transcripts revealed that their encoding genes are organized in autonomous transcription units with recognizable promoter and, in most cases, termination signatures. These findings suggest novel regulatory functions for sRNAs related to the interactions of alpha-proteobacteria with their eukaryotic hosts.

An adaptive transition from exploring the environment in search of vital resources to exploiting these resources once the search is successful is important to all animals. Here we study the neuronal circuitry that allows larval of either sex to negotiate this exploration-exploitation transition. We do so by combining Pavlovian conditioning with high-resolution behavioral tracking, optogenetic manipulation of individually identified neurons, and EM-data-based analyses of synaptic organization. We find that optogenetic activation of the dopaminergic neuron DAN-i1 can both establish memory during training, and acutely terminate learned search behavior in a subsequent recall test. Its activation leaves innate behavior unaffected, however. Specifically, DAN-i1 activation can establish associative memories of opposite valence upon paired and unpaired training with odor, and its activation during the recall test can terminate the search behavior resulting from either of these memories. Our results further suggest that in its behavioral significance DAN-i1 activation resembles but does not equal sugar reward. Dendrogram analyses of all the synaptic connections between DAN-i1 and its two main targets, the Kenyon cells and the mushroom body output neuron MBON-i1, further suggest that the DAN-i1 signals during training and during the recall test could be delivered to the Kenyon cells and to MBON-i1, respectively, within previously unrecognized, locally confined branching structures. This would provide an elegant circuit motif to terminate search upon its successful completion.In the struggle for survival animals have to explore their environment in search of food. Once food is found, however, it is adaptive to prioritize exploiting it over continuing a search that would now be as pointless as searching for the glasses you are wearing. This exploration-exploitation trade-off is important for animals and humans, as well as for technical search devices. We investigate which of the only 10,000 neurons of a fruit fly larva can tip the balance in this trade-off, and identify a single dopamine neuron called DAN-i1 that can do so. Given the similarities in dopamine neuron function across the animal kingdom, this may reflect a general principle of how search is terminated once it is successful.