973 lines
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74 KiB
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<meta name="description" content="Behavioral modeling and optogenetics elucidate mechanisms of subjective, history-dependent decision bias… Advanced transgenic approach improves light-based study of neuronal circuit dynamics… Current state of computational methods in single-cell functional genomics… Sleep promotes communication between association cortices and hippocampus… Experimentally manipulating neural activity in behaving mice implicates the posterior parietal cortex in history-based decision bias" />
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<h1 class="title"><span class="field field--name-title field--type-string field--label-hidden">BRAIN Publication Roundup – January 2018</span>
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<div class="field field--name-field-simple-date field--type-datetime field--label-hidden field__item"><time datetime="2018-01-16T12:00:00Z" class="datetime">January 16, 2018</time>
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<div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p><em>Behavioral modeling and optogenetics elucidate mechanisms of subjective, history-dependent decision bias… Advanced transgenic approach improves light-based study of neuronal circuit dynamics… Current state of computational methods in single-cell functional genomics… Sleep promotes communication between association cortices and hippocampus…</em></p>
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||
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<p><strong>Experimentally manipulating neural activity in behaving mice implicates the posterior parietal cortex in history-based decision bias</strong></p>
|
||
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||
<p>Making decisions based on past outcomes is a key adaptive behavior, but how do neural circuits track choice-outcome history and form subjective bias to inform decision-making? At the University of California, San Diego, <a href="http://biology.ucsd.edu/research/faculty/tkomiyama">Dr. Takaki Komiyama</a> and colleagues explored the neural circuit mechanisms that underlie <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=29089500">subjective, history-dependent decision bias</a>. They used two-photon calcium imaging to record neural activity in the posterior parietal cortex (PPC), while mice performed an action selection task. The researchers sought to identify idiosyncratic relationships between choice-outcome history and subsequent action selection bias. They showed that action decisions were swayed by past outcomes, and that this behavior was modulated by a subpopulation of PPC neurons. Furthermore, to examine the temporal specificity of these effects, they used precise optogenetic application on PPC (i.e., light directed towards PPC to inactivate the region). Inactivation of PPC before but not during the trial diminished the extent to which an animal depended on past outcomes to make a current choice. This study is the first to demonstrate an essential role for PPC neural circuitry in using an animal’s past history of choices to influence subjective biases on subsequent actions.</p>
|
||
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||
<figure role="group" class="caption caption-drupal-media align-center">
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<div alt="Inactivating pre-stimulus activity in PPC alters bias. ">
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||
<figcaption>Inactivating pre-stimulus activity in PPC alters bias. <strong>(a)</strong> Control (blue light directed away from PPC) and inactivation (light directed toward PPC) sessions alternated day-to-day. Continuous blue light was applied during the inter-trial interval (ITI) in randomly selected trials in both control and inactivation sessions. <strong>(b)</strong> An example session. The mouse tended to alternate choice and exhibited history dependency in light-off trials, but this tendency was reduced in light-on trials. <strong>(c)</strong> The effect of PPC ITI inactivation on the model fit in seven separate inactivation sessions in one mouse. Black, mean across sessions; gray, individual sessions.</figcaption>
|
||
</figure>
|
||
<strong>Advanced transgenic strategies enable individual neurons within dense networks to be imaged</strong><p>A key focus in neuroscience is to decipher neuronal circuit dynamics, for the sake of understanding how the brain drives perception, emotion, cognition, and behavior. Recent progress in genetically-encoded voltage and calcium indicators (GEVIs and GECIs, respectively) has benefited this effort by allowing <em>in vivo</em> monitoring of large identified neuronal populations. Indeed, advanced transgenic approaches now achieve high levels of indicator expression. However, targeting non-sparse cell populations leads to dense expression patterns, preventing researchers from assigning optical signal read-out to individual neurons. At Imperial College London, <a href="http://www.imperial.ac.uk/people/t.knopfel">Dr. Thomas Knöpfel</a> and colleagues have developed a <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=28686207">genetic technique for sparse but strong cellular indicator expression,</a> which allows for the resolution and segregation of individual cells and their processes within densely populated neuronal networks. The expression is termed “strong” because the expression level in individual cells is high, and it is termed “sparse” because only a fraction of the neuronal population of interest expresses the fluorescent indicator. The concept resembles Golgi staining, an important histological technique for imaging a low percentage of neurons—in their entirety—within a dense network. Utilizing GCaMP6f (GECI) or the voltage-sensitive fluorescent protein (VSFP) Butterfly 1.2 (GEVI), the researchers achieved strong intensity but sparsely-distributed indicator expression in cortical layer II/III pyramidal neurons within the mouse brain. Morphologies of individual neurons and subcellular structures were successfully resolved<em>.</em> Furthermore, using these fluorescent proteins and a modular transgenic approach, the team produced dual GEVI/GECI neuronal labelling, which enables monitoring of concurrent voltage and calcium activity in either the same neuron or in neighboring neurons. By enabling successful visualization of large neuronal populations, this methodological approach has the translational potential to inform animal models of neurological disease by providing rapid evaluation of cellular indicators of dysfunction.</p>
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||
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||
<figure role="group" class="caption caption-drupal-media align-center">
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||
<div alt="Dual-controlled co-expression of GECI and GEVI in somatosensory cortical layer II/III pyramidal neurons in vivo. ">
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<figcaption>Dual-controlled co-expression of GECI and GEVI in somatosensory cortical layer II/III pyramidal neurons in vivo. <strong>(i)</strong> Strong GCaMP6f fluorescence signal observed in individual pyramidal neurons. <strong>(ii)</strong> Strong VSFPB1.2 fluorescence signal observed in two pyramidal neurons. <strong>(iii)</strong> Merged GCaMP6f and VSFPB1.2 fluorescence signals on nuclei counterstaining (blue) showing sparse indicator expression. Inset: neuron (outlined by the dotted box in b-ii) showing optimal GEVI membrane targeting, and co-expressing GCaMP6f and VSFPB1.2.</figcaption>
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|
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<strong>Advances in single-cell genomics are revealing the diverse facets of cellular identity</strong><p>Although cells of the same ‘type’ can exhibit significant heterogeneity, most genomic profiling studies have analyzed cell populations rather than single cells. However, technological advances have enabled genome-wide profiling of molecular information in single cells. While the field is still in its infancy, single-cell genomics are beginning to make it feasible to create a comprehensive atlas of human cells. At Harvard University, <a href="http://sysbiophd.harvard.edu/people/faculty/aviv-regev">Dr. Aviv Regev</a> and colleagues reviewed the current state of <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=27824854">computational methods in single-cell functional genomics</a>, focusing on single-cell RNA sequencing (scRNA-seq). The authors report that biological and technical aspects merge to determine the measured genomic profiles of cells. Sources of variation that affect single-cell genomics data are (1) technical (unwanted) factors that reflect variance due to the experimental process, and random biological factors that are either (2) intrinsic or (3) extrinsic to the molecular mechanisms of gene expression. Experimental, statistical, and computational strategies are used to reduce technical variation, so that biological variation can then be more confidently studied. The authors define a cell’s identity, which is reflected in its molecular profile, as the instantaneous intersection of all factors affecting it. These factors include multiple time-dependent processes that take place simultaneously, the cell’s response to local environmental signals, and the precise tissue location in which the cell resides. The more permanent aspects of a cell’s identity indicate its <em>type</em> (i.e., cell types are organized into taxonomies), but this approach also provides snapshots of the dynamic temporal transitions that cells undergo as well. Combining single-cell genomics with computational models that relate cells to one another in space and time could eventually yield an integrated understanding of how these cells function in health and disease. Importantly, this approach holds potential in contributing to a complete census of cell types in the complex mammalian brain.</p>
|
||
|
||
<figure role="group" class="caption caption-drupal-media align-center">
|
||
<div alt="Computational methods dissect the molecular profile and tease apart facets of the cell’s identity.">
|
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The referenced media source is missing and needs to be re-embedded.
|
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</div>
|
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|
||
<figcaption><strong>a)</strong> A cell (blue) in the multiple concurrent contexts that shape its identity. <strong>(b)</strong> The biological factors affecting the cell combine to create its unique, instantaneous identity. Computational methods dissect the molecular profile and tease apart facets of the cell’s identity. Examples include (counterclockwise from top): (1) division into discrete types; (2) continuous phenotypes (e.g., pro-inflammatory potential of each T cell, quantified through a gene expression signature derived from bulk T cell profiles (N.Y., A.R. and colleagues1)); (3) temporal progression (e.g., differentiation, such as hematopoiesis); (4) temporal vacillation between cellular states (e.g., cell cycle oscillation; A.R. and colleagues2); (5) physical locations: a schematic representation of an embryo (in situ hybridization data of landmark genes allows inferring of spatial bins) (A.R. and colleagues3).<br>
|
||
1 Gaublomme JT, et al. Single-cell genomics unveils critical regulators of Th17 Cell pathogenicity. Cell. 2015; 163:1400–1412. <br>
|
||
2 Kowalczyk MS, et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 2015; 25:1860–1872. <br>
|
||
3 Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015; 33:495–502.</figcaption>
|
||
</figure>
|
||
<strong>Hippocampal-neocortical communication increases during sleep after spatial learning</strong><p>Hippocampal sharp-wave ripples, which are high-frequency oscillations seen on an electroencephalograph (EEG) during sleep, have long been thought to be involved in the conversion of short-term memories into long-term memories. Little is known about how ripples transfer hippocampal information to the neocortex, which is required for the consolidation of memories that can be consciously, intentionally recalled (i.e., declarative memories). At New York University, <a href="https://med.nyu.edu/faculty/gyorgy-buzsaki">Dr. György Buzsáki</a> and colleagues have developed a <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=29051381">microelectrode system (NeuroGrid) for simultaneous electrophysiological monitoring of multiple sites in the rat neocortex</a>. NeuroGrid is a conformable array of tiny linked electrodes that can be laid across a brain area, for large-scale and spatially-continuous monitoring of a large population of neurons. With this technology, the researchers made a novel observation in rats during non-rapid eye movement (NREM) sleep, the longest stage of sleep. Ripples in the association neocortex—a brain area responsible for processing complex sensory information—and the hippocampus occurred concurrently, suggesting communication between the two regions. Then, the team monitored NREM sleep brain activity in rats trained in a cheeseboard maze versus untrained rats that explored the maze randomly. The maze training actually increased the hippocampal-cortical ripple coupling, suggesting that such communication is important for memory consolidation. This is a fundamental discovery, with potential importance to the understanding of memory disorders.</p>
|
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|
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<figure role="group" class="caption caption-drupal-media align-center">
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<div alt="A micrograph and an anatomical map of a NeuroGrid">
|
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The referenced media source is missing and needs to be re-embedded.
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<figcaption><strong>(A)</strong> Micrograph of a NeuroGrid consisting of uniformly-distributed 15µm by 15µm electrodes (scale bar, 1.5mm). Inset: NeuroGrid conforming to the dorsal surface of rat cortex (scale bar, 1mm). <strong>(B)</strong> Anatomical map of neocortical ripple occurrence relative to somatosensory and visual cortex, in a sample rat. Regions with somatosensory and visual evoked potential amplitude >3 standard deviations above the mean amplitude are in blue and green, respectively. Regions with an occurrence rate of cortical ripples >0.05 Hz are in red. Ripple oscillations were rarely observed in primary somatosensory, visual, or motor cortices (rate <0.05 Hz), but were prevalent in the posterior parietal (association) cortex and midline structures (rate: 0.1-0.5 Hz).</figcaption>
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