In vivo generation of DNA sequence diversity for cellular barcoding

Heterogeneity is a ubiquitous feature of biological systems. A complete understanding of such systems requires a method for uniquely identifying and tracking individual components and their interactions with each other. We have developed a novel method of uniquely tagging individual cells in vivo with a genetic 'barcode' that can be recovered by DNA sequencing. Our method is a two-component system comprised of a genetic barcode cassette whose fragments are shuffled by Rci, a site-specific DNA invertase. The system is highly scalable, with the potential to generate theoretical diversities in the billions. We demonstrate the feasibility of this technique in Escherichia coli. Currently, this method could be employed to track the dynamics of populations of microbes through various bottlenecks. Advances of this method should prove useful in tracking interactions of cells within a network, and/or heterogeneity within complex biological samples

Sequencing the Connectome

Connectivity determines the function of neural circuits. Historically, circuit mapping has usually been viewed as a problem of microscopy, but no current method can achieve high-throughput mapping of entire circuits with single neuron precision. Here we describe a novel approach to determining connectivity. We propose BOINC ("barcoding of individual neuronal connections"), a method for converting the problem of connectivity into a form that can be read out by high-throughput DNA sequencing. The appeal of using sequencing is that its scale--sequencing billions of nucleotides per day is now routine--is a natural match to the complexity of neural circuits. An inexpensive high-throughput technique for establishing circuit connectivity at single neuron resolution could transform neuroscience research

Rosetta Brains: A Strategy for Molecularly-Annotated Connectomics

We propose a neural connectomics strategy called Fluorescent In-Situ Sequencing of Barcoded Individual Neuronal Connections (FISSEQ-BOINC), leveraging fluorescent in situ nucleic acid sequencing in fixed tissue (FISSEQ). FISSEQ-BOINC exhibits different properties from BOINC, which relies on bulk nucleic acid sequencing. FISSEQ-BOINC could become a scalable approach for mapping whole-mammalian-brain connectomes with rich molecular annotations

Using high-throughput barcode sequencing to efficiently map connectomes

The function of a neural circuit is determined by the details of its synaptic connections. At present, the only available method for determining a neural wiring diagram with single synapse precision-a 'connectome'-is based on imaging methods that are slow, labor-intensive and expensive. Here, we present SYNseq, a method for converting the connectome into a form that can exploit the speed and low cost of modern high-throughput DNA sequencing. In SYNseq, each neuron is labeled with a unique random nucleotide sequence-an RNA 'barcode'-which is targeted to the synapse using engineered proteins. Barcodes in pre- and postsynaptic neurons are then associated through protein-protein crosslinking across the synapse, extracted from the tissue, and joined into a form suitable for sequencing. Although our failure to develop an efficient barcode joining scheme precludes the widespread application of this approach, we expect that with further development SYNseq will enable tracing of complex circuits at high speed and low cost

Quadratic optimal functional quantization of stochastic processes and numerical applications

In this paper, we present an overview of the recent developments of functional quantization of stochastic processes, with an emphasis on the quadratic case. Functional quantization is a way to approximate a process, viewed as a Hilbert-valued random variable, using a nearest neighbour projection on a finite codebook. A special emphasis is made on the computational aspects and the numerical applications, in particular the pricing of some path-dependent European options.Comment: 41 page

Spatial representation of temporal information through spike timing dependent plasticity

We suggest a mechanism based on spike time dependent plasticity (STDP) of synapses to store, retrieve and predict temporal sequences. The mechanism is demonstrated in a model system of simplified integrate-and-fire type neurons densely connected by STDP synapses. All synapses are modified according to the so-called normal STDP rule observed in various real biological synapses. After conditioning through repeated input of a limited number of of temporal sequences the system is able to complete the temporal sequence upon receiving the input of a fraction of them. This is an example of effective unsupervised learning in an biologically realistic system. We investigate the dependence of learning success on entrainment time, system size and presence of noise. Possible applications include learning of motor sequences, recognition and prediction of temporal sensory information in the visual as well as the auditory system and late processing in the olfactory system of insects.Comment: 13 pages, 14 figures, completely revised and augmented versio

Buprenorphine versus dihydrocodeine for opiate detoxification in primary care: a randomised controlled trial

Background Many drug users present to primary care requesting detoxification from illicit opiates. There are a number of detoxification agents but no recommended drug of choice. The purpose of this study is to compare buprenorphine with dihydrocodeine for detoxification from illicit opiates in primary care. Methods Open label randomised controlled trial in NHS Primary Care (General Practices), Leeds, UK. Sixty consenting adults using illicit opiates received either daily sublingual buprenorphine or daily oral dihydrocodeine. Reducing regimens for both interventions were at the discretion of prescribing doctor within a standard regimen of not more than 15 days. Primary outcome was abstinence from illicit opiates at final prescription as indicated by a urine sample. Secondary outcomes during detoxification period and at three and six months post detoxification were recorded. Results Only 23% completed the prescribed course of detoxification medication and gave a urine sample on collection of their final prescription. Risk of non-completion of detoxification was reduced if allocated buprenorphine (68% vs 88%, RR 0.58 CI 0.35–0.96, p = 0.065). A higher proportion of people allocated to buprenorphine provided a clean urine sample compared with those who received dihydrocodeine (21% vs 3%, RR 2.06 CI 1.33–3.21, p = 0.028). People allocated to buprenorphine had fewer visits to professional carers during detoxification and more were abstinent at three months (10 vs 4, RR 1.55 CI 0.96–2.52) and six months post detoxification (7 vs 3, RR 1.45 CI 0.84–2.49). Conclusion Informative randomised trials evaluating routine care within the primary care setting are possible amongst drug using populations. This small study generates unique data on commonly used treatment regimens

Quantifying impacts of short-term plasticity on neuronal information transfer

Short-term changes in efficacy have been postulated to enhance the ability of synapses to transmit information between neurons, and within neuronal networks. Even at the level of connections between single neurons, direct confirmation of this simple conjecture has proven elusive. By combining paired-cell recordings, realistic synaptic modelling and information theory, we provide evidence that short-term plasticity can not only improve, but also reduce information transfer between neurons. We focus on a concrete example in rat neocortex, but our results may generalise to other systems. When information is contained in the timings of individual spikes, we find that facilitation, depression and recovery affect information transmission in proportion to their impacts upon the probability of neurotransmitter release. When information is instead conveyed by mean spike rate only, the influences of short-term plasticity critically depend on the range of spike frequencies that the target network can distinguish (its effective dynamic range). Our results suggest that to efficiently transmit information, the brain must match synaptic type, coding strategy and network connectivity during development and behaviour.Comment: Accepted for publication in Phys Rev E. 42 pages in referee format, 9 figure

Identification of a brainstem locus that inhibits tumor necrosis factor

In the brain, compact clusters of neuron cell bodies, termed nuclei, are essential for maintaining parameters of host physiology within a narrow range optimal for health. Neurons residing in the brainstem dorsal motor nucleus (DMN) project in the vagus nerve to communicate with the lungs, liver, gastrointestinal tract, and other organs. Vagus nerve-mediated reflexes also control immune system responses to infection and injury by inhibiting the production of tumor necrosis factor (TNF) and other cytokines in the spleen, although the function of DMN neurons in regulating TNF release is not known. Here, optogenetics and functional mapping reveal cholinergic neurons in the DMN, which project to the celiacsuperior mesenteric ganglia, significantly increase splenic nerve activity and inhibit TNF production. Efferent vagus nerve fibers terminating in the celiac-superior mesenteric ganglia form varicose-like structures surrounding individual nerve cell bodies innervating the spleen. Selective optogenetic activation of DMN cholinergic neurons or electrical activation of the cervical vagus nerve evokes action potentials in the splenic nerve. Pharmacological blockade and surgical transection of the vagus nerve inhibit vagus nerve-evoked splenic nerve responses. These results indicate that cholinergic neurons residing in the brainstem DMN control TNF production, revealing a role for brainstem coordination of immunity

Catalyzing next-generation Artificial Intelligence through NeuroAI

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI