Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and new loss function which phrases detection and localization as a Bayesian inference problem, and thus allows the network to provide uncertainty-estimates. In contrast to standard methods which independently process imaging frames, our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. We demonstrate the power of our method across a variety of datasets, imaging modalities, signal to noise ratios, and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy at low fluorophore densities, they are confounded by high densities. Our method is the first deep-learning based approach which achieves state-of-the-art on the SMLM2016 challenge. It achieves the best scores on 12 out of 12 data-sets when comparing both detection accuracy and precision, and excels at high densities. Finally, we investigate how unsupervised learning can be used to make the network robust against mismatch between simulated and real data. The lessons learned here are more generally relevant for the training of deep networks to solve challenging Bayesian inverse problems on spatially extended domains in biology and physics.

Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and new loss function which phrases detection and localization as a Bayesian inference problem, and thus allows the network to provide uncertainty-estimates. In contrast to standard methods which independently process imaging frames, our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. We demonstrate the power of our method across a variety of datasets, imaging modalities, signal to noise ratios, and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy at low fluorophore densities, they are confounded by high densities. Our method is the first deep-learning based approach which achieves state-of-the-art on the SMLM2016 challenge. It achieves the best scores on 12 out of 12 data-sets when comparing both detection accuracy and precision, and excels at high densities. Finally, we investigate how unsupervised learning can be used to make the network robust against mismatch between simulated and real data. The lessons learned here are more generally relevant for the training of deep networks to solve challenging Bayesian inverse problems on spatially extended domains in biology and physics.

Small-molecule fluorophores, such as fluorescein and rhodamine derivatives, are critical tools in modern biochemical and biological research. The field of chemical dyes is old; colored molecules were first discovered in the 1800s, and the fluorescein and rhodamine scaffolds have been known for over a century. Nevertheless, there has been a renaissance in using these dyes to create tools for biochemistry and biology. The application of modern chemistry, biochemistry, molecular genetics, and optical physics to these old structures enables and drives the development of novel, sophisticated fluorescent dyes. This critical review focuses on an important example of chemical biology-the melding of old and new chemical knowledge-leading to useful molecules for advanced biochemical and biological experiments. Expected final online publication date for the Annual Review of Biochemistry Volume 86 is June 20, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Conventional microscopy collects data from individual planes and cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point-spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for processing and analyzing volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics and helping elucidate how brain regions work in concert to support behavior.

In this paper, we present a novel error measure to compare a computer-generated segmentation of images or volumes against ground truth. This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations that we usually encounter in biomedical image processing: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be explicitly expressible in the measure. (2) Non-tolerable errors have to be corrected manually. The effort needed to do so should be reflected by the error measure. Our measure is the minimal weighted sum of split and merge operations to apply to one segmentation such that it resembles another segmentation within specified tolerance bounds. This is in contrast to other commonly used measures like Rand index or variation of information, which integrate small, but tolerable, differences. Additionally, the TED provides intuitive numbers and allows the localization and classification of errors in images or volumes. We demonstrate the applicability of the TED on 3D segmentations of neurons in electron microscopy images where topological correctness is arguable more important than exact boundary locations. Furthermore, we show that the TED is not just limited to evaluation tasks. We use it as the loss function in a max-margin learning framework to find parameters of an automatic neuron segmentation algorithm. We show that training to minimize the TED, i.e., to minimize crucial errors, leads to higher segmentation accuracy compared to other learning methods.

In many reptiles, including the red-eared slider turtle (), sex is determined by ambient temperature during embryogenesis. We previously showed that the epigenetic regulator is elevated at the male-producing temperature and essential to activate the male pathway. In this work, we established a causal link between temperature and transcriptional regulation of We show that signal transducer and activator of transcription 3 (STAT3) is phosphorylated at the warmer, female-producing temperature, binds the locus, and represses transcription, blocking the male pathway. Influx of Ca, a mediator of STAT3 phosphorylation, is elevated at the female temperature and acts as a temperature-sensitive regulator of STAT3 activation.

During development, regulatory factors appear in a precise order to determine cell fates over time. Consequently, to investigate complex tissue development, it is necessary to visualize and manipulate cell lineages with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here, we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labeling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation and inactivation of reporters and/or effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.

During development, regulatory factors appear in a precise order to determine cell fates over time. Consequently, to investigate complex tissue development, it is necessary to visualize and manipulate cell lineages with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here, we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labeling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation and inactivation of reporters and/or effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.

During development, regulatory factors appear in a precise order to determine cell fates over time. To investigate complex tissue development, one should not just label cell lineages but further visualize and manipulate cells with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labelling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation/inactivation of reporters/effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.One-Sentence Summary Gaining sequential genetic access to vertebrate cell lineages.Competing Interest StatementThe authors have declared no competing interest.