A recurrent neural circuit in Drosophila deblurs visual inputs

A critical goal of vision is to detect changes in light intensity, even when these changes are blurred by the spatial resolution of the eye and the motion of the animal. Here we describe a recurrent neural circuit in Drosophila that compensates for blur and thereby selectively enhances the perceived contrast of moving edges. Using in vivo , two-photon voltage imaging, we measured the temporal response properties of L1 and L2, two cell types that receive direct synaptic input from photoreceptors. These neurons have biphasic responses to brief flashes of light, a hallmark of cells that encode changes in stimulus intensity. However, the second phase was often much larger than the first, creating an unusual temporal filter. Genetic dissection revealed that recurrent neural circuitry strongly shapes the second phase of the response, informing the structure of a dynamical model. By applying this model to moving natural images, we demonstrate that rather than veridically representing stimulus changes, this temporal processing strategy systematically enhances them, amplifying and sharpening responses. Comparing the measured responses of L2 to model predictions across both artificial and natural stimuli revealed that L2 tunes its properties as the model predicts in order to deblur images. Since this strategy is tunable to behavioral context, generalizable to any time-varying sensory input, and implementable with a common circuit motif, we propose that it could be broadly used to selectively enhance sharp and salient changes.

An efficient behavioral screening platform classifies natural products and other chemical cues according to their chemosensory valence in C. elegans

Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these compounds into the rhizosphere and atmosphere where they affect animal and microbe behavior. To survive, nematodes must have evolved the sensory capacity to distinguish plant-made small molecules (SMs) that are harmful and must be avoided from those that are beneficial and should be sought. This ability to classify chemical cues as a function of their value is fundamental to olfaction, and represents a capacity shared by many animals, including humans. Here, we present an efficient platform based on multi-well plates, liquid handling instrumentation, inexpensive optical scanners, and bespoke software that can efficiently determine the valence (attraction or repulsion) of single SMs in the model nematode, Caenorhabditis elegans. Using this integrated hardware-wetware-software platform, we screened 90 plant SMs and identified 37 that attracted or repelled wild-type animals, but had no effect on mutants defective in chemosensory transduction. Genetic dissection indicates that for at least 10 of these SMs, response valence emerges from the integration of opposing signals, arguing that olfactory valence is often determined by integrating chemosensory signals over multiple lines of information. This study establishes that C. elegans is an effective discovery engine for determining chemotaxis valence and for identifying natural products detected by the chemosensory nervous system.

Temporal scaling of motor cortical dynamics reveals hierarchical control of vocal production

Neocortical activity is thought to mediate voluntary control over vocal production, but the underlying neural mechanisms remain unclear. In a highly vocal rodent, the male Alston's singing mouse, we investigate neural dynamics in the orofacial motor cortex (OMC), a structure critical for vocal behavior. We first describe neural activity that is modulated by component notes (~100 ms), probably representing sensory feedback. At longer timescales, however, OMC neurons exhibit diverse and often persistent premotor firing patterns that stretch or compress with song duration (~10 s). Using computational modeling, we demonstrate that such temporal scaling, acting through downstream motor production circuits, can enable vocal flexibility. These results provide a framework for studying hierarchical control circuits, a common design principle across many natural and artificial systems.

Mapping the neural dynamics of locomotion across the Drosophila brain

Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.

Transforming a head direction signal into a goal-oriented steering command

To navigate, we must continuously estimate the direction we are headed in, and we must correct deviations from our goal1. Direction estimation is accomplished by ring attractor networks in the head direction system2,3. However, we do not fully understand how the sense of direction is used to guide action. Drosophila connectome analyses4,5 reveal three cell populations (PFL3R, PFL3L and PFL2) that connect the head direction system to the locomotor system. Here we use imaging, electrophysiology and chemogenetic stimulation during navigation to show how these populations function. Each population receives a shifted copy of the head direction vector, such that their three reference frames are shifted approximately 120° relative to each other. Each cell type then compares its own head direction vector with a common goal vector; specifically, it evaluates the congruence of these vectors via a nonlinear transformation. The output of all three cell populations is then combined to generate locomotor commands. PFL3R cells are recruited when the fly is oriented to the left of its goal, and their activity drives rightward turning; the reverse is true for PFL3L. Meanwhile, PFL2 cells increase steering speed, and are recruited when the fly is oriented far from its goal. PFL2 cells adaptively increase the strength of steering as directional error increases, effectively managing the tradeoff between speed and accuracy. Together, our results show how a map of space in the brain can be combined with an internal goal to generate action commands, via a transformation from world-centric coordinates to body-centric coordinates.

Brain-wide neural activity underlying memory-guided movement

Behavior relies on activity in structured neural circuits that are distributed across the brain, but most experiments probe neurons in a single area at a time. Using multiple Neuropixels probes, we recorded from multi-regional loops connected to the anterior lateral motor cortex (ALM), a circuit node mediating memory-guided directional licking. Neurons encoding sensory stimuli, choices, and actions were distributed across the brain. However, choice coding was concentrated in the ALM and subcortical areas receiving input from the ALM in an ALM-dependent manner. Diverse orofacial movements were encoded in the hindbrain; midbrain; and, to a lesser extent, forebrain. Choice signals were first detected in the ALM and the midbrain, followed by the thalamus and other brain areas. At movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

Feedforward amplification in recurrent networks underlies paradoxical neural coding

The activity of single neurons encodes behavioral variables, such as sensory stimuli (Hubel & Wiesel 1959) and behavioral choice (Britten et al. 1992; Guo et al. 2014), but their influence on behavior is often mysterious. We estimated the influence of a unit of neural activity on behavioral choice from recordings in anterior lateral motor cortex (ALM) in mice performing a memory-guided movement task (H. K. Inagaki et al. 2018). Choice selectivity grew as it flowed through a sequence of directions in activity space. Early directions carried little selectivity but were predicted to have a large behavioral influence, while late directions carried large selectivity and little behavioral influence. Consequently, estimated behavioral influence was only weakly correlated with choice selectivity; a large proportion of neurons selective for one choice were predicted to influence choice in the opposite direction. These results were consistent with models in which recurrent circuits produce feedforward amplification (Goldman 2009; Ganguli et al. 2008; Murphy & Miller 2009) so that small amplitude signals along early directions are amplified to produce low-dimensional choice selectivity along the late directions, and behavior. Targeted photostimulation experiments (Daie et al. 2021b) revealed that activity along the early directions triggered sequential activity along the later directions and caused predictable behavioral biases. These results demonstrate the existence of an amplifying feedforward dynamical motif in the motor cortex, explain paradoxical responses to perturbation experiments (Chettih & Harvey 2019; Daie et al. 2021b; Russell et al. 2019), and reveal behavioral relevance of small amplitude neural dynamics.

A high-performance speech neuroprosthesis

Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text1,2 or sound3,4. Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary1-7. Here we demonstrate a speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant-who can no longer speak intelligibly owing to amyotrophic lateral sclerosis-achieved a 9.1% word error rate on a 50-word vocabulary (2.7 times fewer errors than the previous state-of-the-art speech BCI2) and a 23.8% word error rate on a 125,000-word vocabulary (the first successful demonstration, to our knowledge, of large-vocabulary decoding). Our participant's attempted speech was decoded  at 62 words per minute, which is 3.4 times as fast as the previous record8 and begins to approach the speed of natural conversation (160 words per minute9). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for restoring rapid communication to people with paralysis who can no longer speak.

Not everything, not everywhere, not all at once: a study of brain-wide encoding of movement

Activity related to movement is found throughout sensory and motor regions of the brain. However, it remains unclear how movement-related activity is distributed across the brain and whether systematic differences exist between brain areas. Here, we analyzed movement related activity in brain-wide recordings containing more than 50,000 neurons in mice performing a decision-making task. Using multiple techniques, from markers to deep neural networks, we find that movement-related signals were pervasive across the brain, but systematically differed across areas. Movement-related activity was stronger in areas closer to the motor or sensory periphery. Delineating activity in terms of sensory- and motor-related components revealed finer scale structures of their encodings within brain areas. We further identified activity modulation that correlates with decision-making and uninstructed movement. Our work charts out a largescale map of movement encoding and provides a roadmap for dissecting different forms of movement and decision-making related encoding across multi-regional neural circuits.

A high-performance speech neuroprosthesis

Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text or sound. Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrainted sentences from a large vocabulary. Here, we demonstrate the first speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant, who can no longer speak intelligibly due amyotrophic lateral sclerosis (ALS), achieved a 9.1% word error rate on a 50 word vocabulary (2.7 times fewer errors than the prior state of the art speech BCI2) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4 times faster than the prior record for any kind of BCI and begins to approach the speed of natural conversation (160 words per minute). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for using intracortical speech BCIs to restore rapid communication to people with paralysis who can no longer speak.

Hypothalamic neurons that mirror aggression

Social interactions require awareness and understanding of the behavior of others. Mirror neurons, cells representing an action by self and others, have been proposed to be integral to the cognitive substrates that enable such awareness and understanding. Mirror neurons of the primate neocortex represent skilled motor tasks, but it is unclear if they are critical for the actions they embody, enable social behaviors, or exist in non-cortical regions. We demonstrate that the activity of individual VMHvl^PR neurons in the mouse hypothalamus represents aggression performed by self and others. We used a genetically encoded mirror-TRAP strategy to functionally interrogate these aggression-mirroring neurons. We find that their activity is essential for fighting and that forced activation of these cells triggers aggressive displays by mice, even toward their mirror image. Together, we have discovered a mirroring center in an evolutionarily ancient region that provides a subcortical cognitive substrate essential for a social behavior.

Long-term unsupervised recalibration of cursor BCIs

Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time. Compensating for this nonstationarity would enable consistently high performance without the need for supervised recalibration periods, where users cannot engage in free use of their device. Here we introduce a hidden Markov model (HMM) to infer what targets users are moving toward during iBCI use. We then retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms the state of the art in large-scale, closed-loop simulations over two months and in closed-loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we further show how recently proposed data distribution-matching approaches to recalibration fail over long time scales; only target-inference methods appear capable of enabling long-term unsupervised recalibration. Our results demonstrate how task structure can be used to bootstrap a noisy decoder into a highly-performant one, thereby overcoming one of the major barriers to clinically translating BCIs.

Neural dynamics in the rodent motor cortex enables flexible control of vocal timing

Neocortical activity is thought to mediate voluntary control over vocal production, but the underlying neural mechanisms remain unclear. In a highly vocal rodent, the Alston's singing mouse, we investigate neural dynamics in the orofacial motor cortex (OMC), a structure critical for vocal behavior. We first describe neural activity that is modulated by component notes (approx. 100 ms), likely representing sensory feedback. At longer timescales, however, OMC neurons exhibit diverse and often persistent premotor firing patterns that stretch or compress with song duration (approx. 10 s). Using computational modeling, we demonstrate that such temporal scaling, acting via downstream motor production circuits, can enable vocal flexibility. These results provide a framework for studying hierarchical control circuits, a common design principle across many natural and artificial systems.

Unraveling the Entangled Brain: How Do We Go About It?

An impactful understanding of the brain will require entirely new approaches and unprecedented collaborative efforts. The next steps will require brain researchers to develop theoretical frameworks that allow them to tease apart dependencies and causality in complex dynamical systems, as well as the ability to maintain awe while not getting lost in the effort. The outstanding question is: How do we go about it?

Regional cytoarchitecture of the adult and developing mouse enteric nervous system

The organization and cellular composition of tissues are key determinants of their biological function. In the mammalian gastrointestinal (GI) tract, the enteric nervous system (ENS) intercalates between muscular and epithelial layers of the gut wall and can control GI function independent of central nervous system (CNS) input.1 As in the CNS, distinct regions of the GI tract are highly specialized and support diverse functions, yet the regional and spatial organization of the ENS remains poorly characterized.2 Cellular arrangements,3,4 circuit connectivity patterns,5,6 and diverse cell types7-9 are known to underpin ENS functional complexity and GI function, but enteric neurons are most typically described only as a uniform meshwork of interconnected ganglia. Here, we present a bird's eye view of the mouse ENS, describing its previously underappreciated cytoarchitecture and regional variation. We visually and computationally demonstrate that enteric neurons are organized in circumferential neuronal stripes. This organization emerges gradually during the perinatal period, with neuronal stripe formation in the small intestine (SI) preceding that in the colon. The width of neuronal stripes varies throughout the length of the GI tract, and distinct neuronal subtypes differentially populate specific regions of the GI tract, with stark contrasts between SI and colon as well as within subregions of each. This characterization provides a blueprint for future understanding of region-specific GI function and identifying ENS structural correlates of diverse GI disorders.

Towards a more general understanding of the algorithmic utility of recurrent connections

Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such “tag propagation” algorithms perform repeated, local propagation of information and were originally introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and construct hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to propagating multiple interacting tags and demonstrate that these are efficient computational substrates for more general computations of connectedness by introducing and solving an abstracted biologically inspired decision-making task. Our work thus clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks.

In striking contrast to the majority of current-day artificial neural network research which primarily focuses on feedforward architectures, biological brains make extensive use of lateral and recurrent connections. This raises the possibility that this difference makes a fundamental contribution to the gap in computational power between real neural circuits and artificial neural networks. Thus, despite the difficulty of making effective comparisons between different network architectures, developing a more detailed understanding of the computational role played by such connections is a pressing challenge. Here, we leverage the computational capabilities of large-scale machine learning to robustly explore how differences in architectures affect a network’s ability to learn tasks that require propagation of global information. We first focus on the task of determining whether two pixels are connected in an image which has an elegant and efficient recurrent solution: propagate a connected label or tag along paths. Inspired by this solution, we show that it can be generalized in many ways, including propagating multiple tags at once and changing the computation performed on the result of the propagation. Strikingly, this simple expansion of the tag propagation network is sufficient to solve a crucial abstraction to temporal connectedness at the core of many decision-making problems, which we illustrate for an abstracted competitive foraging task. Our results shed light on the set of computational tasks that can be solved efficiently by recurrent computation and how these solutions may relate to the structure of neural activity.

Transforming representations of movement from body- to world-centric space

When an animal moves through the world, its brain receives a stream of information about the body's translational velocity from motor commands and sensory feedback signals. These incoming signals are referenced to the body, but ultimately, they must be transformed into world-centric coordinates for navigation1,2. Here we show that this computation occurs in the fan-shaped body in the brain of Drosophila melanogaster. We identify two cell types, PFNd and PFNv3-5, that conjunctively encode translational velocity and heading as a fly walks. In these cells, velocity signals are acquired from locomotor brain regions6 and are multiplied with heading signals from the compass system. PFNd neurons prefer forward-ipsilateral movement, whereas PFNv neurons prefer backward-contralateral movement, and perturbing PFNd neurons disrupts idiothetic path integration in walking flies7. Downstream, PFNd and PFNv neurons converge onto hΔB neurons, with a connectivity pattern that pools together heading and translation direction combinations corresponding to the same movement in world-centric space. This network motif effectively performs a rotation of the brain's representation of body-centric translational velocity according to the current heading direction. Consistent with our predictions, we observe that hΔB neurons form a representation of translational velocity in world-centric coordinates. By integrating this representation over time, it should be possible for the brain to form a working memory of the path travelled through the environment8-10.

Targeted photostimulation uncovers circuit motifs supporting short-term memory

Short-term memory is associated with persistent neural activity that is maintained by positive feedback between neurons. To explore the neural circuit motifs that produce memory-related persistent activity, we measured coupling between functionally characterized motor cortex neurons in mice performing a memory-guided response task. Targeted two-photon photostimulation of small (<10) groups of neurons produced sparse calcium responses in coupled neurons over approximately 100 μm. Neurons with similar task-related selectivity were preferentially coupled. Photostimulation of different groups of neurons modulated activity in different subpopulations of coupled neurons. Responses of stimulated and coupled neurons persisted for seconds, far outlasting the duration of the photostimuli. Photostimuli produced behavioral biases that were predictable based on the selectivity of the perturbed neuronal population, even though photostimulation preceded the behavioral response by seconds. Our results suggest that memory-related neural circuits contain intercalated, recurrently connected modules, which can independently maintain selective persistent activity.

Approaches to inferring multi-regional interactions from simultaneous population recordings: Inferring multi-regional interactions from simultaneous population recordings

Most past studies of neural representations and dynamics have focused on recordings from single brain areas. However, growing evidence of brain-wide, parallel representations of cognitive variables suggests that analyzing neural representations and dynamics in individual brain areas can benefit from understanding the context of multi-regional interactions that support them. Moreover, perturbation experiments revealed that the manner in which these parallel representations interact with each other can differ dramatically across different pairs of brain areas. Recent advances in recording technology offer a potentially powerful substrate to study how multi-regional interactions coordinate neural representations in individual brain areas and dictate behavior on a single-trial basis through simultaneous recordings of multiple brain areas. We review pragmatic approaches to studying multi-regional interactions and illustrate them in the concrete context of a rodent delayed response task paradigm.

Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus

OBJECTIVE

To evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of decoders trained to discriminate a comprehensive basis set of 39 English phonemes and to synthesize speech sounds via a neural pattern matching method. We decoded neural correlates of spoken-out-loud words in the 'hand knob' area of precentral gyrus, a step toward the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak.

APPROACH

Neural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrode's binned action potential counts or high-frequency local field potential power. Speech synthesis was performed using the 'Brain-to-Speech' pattern matching method. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes' onset times.

MAIN RESULTS

A linear decoder achieved up to 29.3% classification accuracy (chance = 6%) across 39 phonemes, while an RNN classifier achieved 33.9% accuracy. Parameter sweeps indicated that performance did not saturate when adding more electrodes or more training data, and that accuracy improved when utilizing time-varying structure in the data. Microphonic contamination and phoneme onset differences modestly increased decoding accuracy, but could be mitigated by acoustic artifact subtraction and using a neural speech onset marker, respectively. Speech synthesis achieved r = 0.523 correlation between true and reconstructed audio.

SIGNIFICANCE

The ability to decode speech using intracortical electrode array signals from a nontraditional speech area suggests that placing electrode arrays in ventral speech areas is a promising direction for speech BCIs.

A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology

Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and low-pass filtering, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, including changes in single-neuron selectivity and population decoding. These were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data.

Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis

Speaking is a sensorimotor behavior whose neural basis is difficult to study with single neuron resolution due to the scarcity of human intracortical measurements. We used electrode arrays to record from the motor cortex 'hand knob' in two people with tetraplegia, an area not previously implicated in speech. Neurons modulated during speaking and during non-speaking movements of the tongue, lips, and jaw. This challenges whether the conventional model of a 'motor homunculus' division by major body regions extends to the single-neuron scale. Spoken words and syllables could be decoded from single trials, demonstrating the potential of intracortical recordings for brain-computer interfaces to restore speech. Two neural population dynamics features previously reported for arm movements were also present during speaking: a component that was mostly invariant across initiating different words, followed by rotatory dynamics during speaking. This suggests that common neural dynamical motifs may underlie movement of arm and speech articulators.

Single-Cell Reconstruction of Emerging Population Activity in an Entire Developing Circuit

Animal survival requires a functioning nervous system to develop during embryogenesis. Newborn neurons must assemble into circuits producing activity patterns capable of instructing behaviors. Elucidating how this process is coordinated requires new methods that follow maturation and activity of all cells across a developing circuit. We present an imaging method for comprehensively tracking neuron lineages, movements, molecular identities, and activity in the entire developing zebrafish spinal cord, from neurogenesis until the emergence of patterned activity instructing the earliest spontaneous motor behavior. We found that motoneurons are active first and form local patterned ensembles with neighboring neurons. These ensembles merge, synchronize globally after reaching a threshold size, and finally recruit commissural interneurons to orchestrate the left-right alternating patterns important for locomotion in vertebrates. Individual neurons undergo functional maturation stereotypically based on their birth time and anatomical origin. Our study provides a general strategy for reconstructing how functioning circuits emerge during embryogenesis. VIDEO ABSTRACT.

Author Correction: Kilohertz frame-rate two-photon tomography

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Kilohertz frame-rate two-photon tomography

Point-scanning two-photon microscopy enables high-resolution imaging within scattering specimens such as the mammalian brain, but sequential acquisition of voxels fundamentally limits its speed. We developed a two-photon imaging technique that scans lines of excitation across a focal plane at multiple angles and computationally recovers high-resolution images, attaining voxel rates of over 1 billion Hz in structured samples. Using a static image as a prior for recording neural activity, we imaged visually evoked and spontaneous glutamate release across hundreds of dendritic spines in mice at depths over 250 µm and frame rates over 1 kHz. Dendritic glutamate transients in anesthetized mice are synchronized within spatially contiguous domains spanning tens of micrometers at frequencies ranging from 1-100 Hz. We demonstrate millisecond-resolved recordings of acetylcholine and voltage indicators, three-dimensional single-particle tracking and imaging in densely labeled cortex. Our method surpasses limits on the speed of raster-scanned imaging imposed by fluorescence lifetime.

Active dendritic integration and mixed neocortical network representations during an adaptive sensing behavior

Animals strategically scan the environment to form an accurate perception of their surroundings. Here we investigated the neuronal representations that mediate this behavior. Ca²⁺ imaging and selective optogenetic manipulation during an active sensing task reveals that layer 5 pyramidal neurons in the vibrissae cortex produce a diverse and distributed representation that is required for mice to adapt their whisking motor strategy to changing sensory cues. The optogenetic perturbation degraded single-neuron selectivity and network population encoding through a selective inhibition of active dendritic integration. Together the data indicate that active dendritic integration in pyramidal neurons produces a nonlinearly mixed network representation of joint sensorimotor parameters that is used to transform sensory information into motor commands during adaptive behavior. The prevalence of the layer 5 cortical circuit motif suggests that this is a general circuit computation.

Ring attractor dynamics in the Drosophila central brain

Ring attractors are a class of recurrent networks hypothesized to underlie the representation of heading direction. Such network structures, schematized as a ring of neurons whose connectivity depends on their heading preferences, can sustain a bump-like activity pattern whose location can be updated by continuous shifts along either turn direction. We recently reported that a population of fly neurons represents the animal's heading via bump-like activity dynamics. We combined two-photon calcium imaging in head-fixed flying flies with optogenetics to overwrite the existing population representation with an artificial one, which was then maintained by the circuit with naturalistic dynamics. A network with local excitation and global inhibition enforces this unique and persistent heading representation. Ring attractor networks have long been invoked in theoretical work; our study provides physiological evidence of their existence and functional architecture.

Angular velocity integration in a fly heading circuit

Many animals maintain an internal representation of their heading as they move through their surroundings. Such a compass representation was recently discovered in a neural population in the Drosophila melanogaster central complex, a brain region implicated in spatial navigation. Here, we use two-photon calcium imaging and electrophysiology in head-fixed walking flies to identify a different neural population that conjunctively encodes heading and angular velocity, and is excited selectively by turns in either the clockwise or counterclockwise direction. We show how these mirror-symmetric turn responses combine with the neurons' connectivity to the compass neurons to create an elegant mechanism for updating the fly's heading representation when the animal turns in darkness. This mechanism, which employs recurrent loops with an angular shift, bears a resemblance to those proposed in theoretical models for rodent head direction cells. Our results provide a striking example of structure matching function for a broadly relevant computation.

Robust neuronal dynamics in premotor cortex during motor planning

Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in the mouse premotor cortex to probe the robustness of persistent neural representations during motor planning. We show that preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of the premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in the premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.

Dynamical feature extraction at the sensory periphery guides chemotaxis

Behavioral strategies employed for chemotaxis have been described across phyla, but the sensorimotor basis of this phenomenon has seldom been studied in naturalistic contexts. Here, we examine how signals experienced during free olfactory behaviors are processed by first-order olfactory sensory neurons (OSNs) of the Drosophila larva. We find that OSNs can act as differentiators that transiently normalize stimulus intensity—a property potentially derived from a combination of integral feedback and feed-forward regulation of olfactory transduction. In olfactory virtual reality experiments, we report that high activity levels of the OSN suppress turning, whereas low activity levels facilitate turning. Using a generalized linear model, we explain how peripheral encoding of olfactory stimuli modulates the probability of switching from a run to a turn. Our work clarifies the link between computations carried out at the sensory periphery and action selection underlying navigation in odor gradients.

DOI:http://dx.doi.org/10.7554/eLife.06694.001

Fruit flies are attracted to the smell of rotting fruit, and use it to guide them to nearby food sources. However, this task is made more challenging by the fact that the distribution of scent or odor molecules in the air is constantly changing. Fruit flies therefore need to cope with, and exploit, this variation if they are to use odors as cues.

Odor molecules bind to receptors on the surface of nerve cells called olfactory sensory neurons, and trigger nerve impulses that travel along these cells. While many studies have investigated how fruit flies can distinguish between different odors, less is known about how animals can use variation in the strength of an odor to guide them towards its source.

Optogenetics is a technique that allows neuroscientists to control the activities of individual nerve cells, simply by shining light on to them. Because fruit fly larvae are almost transparent, optogenetics can be used on freely moving animals. Now, Schulze, Gomez-Marin et al. have used optogenetics in these larvae to trigger patterns of activity in individual olfactory sensory neurons that mimic the activity patterns elicited by real odors. These virtual realities were then used to study, in detail, some of the principles that control the sensory navigation of a larva—as it moves using a series of forward ‘runs’ and direction-changing ‘turns’.

Olfactory sensory neurons responded most strongly whenever light levels changed rapidly in strength (which simulated a rapid change in odor concentration). On the other hand, these neurons showed relatively little response to constant light levels (i.e., constant odors). This indicates that the activity of olfactory sensory neurons typically represents the rate of change in the concentration of an odor. An independent study by Kim et al. found that olfactory sensory neurons in adult fruit flies also respond in a similar way.

Schulze, Gomez-Marin et al. went on to show that the signals processed by a single type of olfactory sensory neuron could be used to predict a larva's behavior. Larvae tended to turn less when their olfactory sensory neurons were highly active. Low levels and inhibition of activity in the olfactory sensory neurons had the opposite effect; this promoted turning. It remains to be determined how this relatively simple control principle is implemented by the neural circuits that connect sensory neurons to the parts of a larva's nervous system that are involved with movement.

DOI:http://dx.doi.org/10.7554/eLife.06694.002

From a meso- to micro-scale connectome: array tomography and mGRASP

Mapping mammalian synaptic connectivity has long been an important goal of neuroscience because knowing how neurons and brain areas are connected underpins an understanding of brain function. Meeting this goal requires advanced techniques with single synapse resolution and large-scale capacity, especially at multiple scales tethering the meso- and micro-scale connectome. Among several advanced LM-based connectome technologies, Array Tomography (AT) and mammalian GFP-Reconstitution Across Synaptic Partners (mGRASP) can provide relatively high-throughput mapping synaptic connectivity at multiple scales. AT- and mGRASP-assisted circuit mapping (ATing and mGRASPing), combined with techniques such as retrograde virus, brain clearing techniques, and activity indicators will help unlock the secrets of complex neural circuits. Here, we discuss these useful new tools to enable mapping of brain circuits at multiple scales, some functional implications of spatial synaptic distribution, and future challenges and directions of these endeavors.

Neuronal circuits underlying persistent representations despite time varying activity

BACKGROUND

Our brains are capable of remarkably stable stimulus representations despite time-varying neural activity. For instance, during delay periods in working memory tasks, while stimuli are represented in working memory, neurons in the prefrontal cortex, thought to support the memory representation, exhibit time-varying neuronal activity. Since neuronal activity encodes the stimulus, its time-varying dynamics appears to be paradoxical and incompatible with stable network stimulus representations. Indeed, this finding raises a fundamental question: can stable representations only be encoded with stable neural activity, or, its corollary, is every change in activity a sign of change in stimulus representation?

RESULTS

Here we explain how different time-varying representations offered by individual neurons can be woven together to form a coherent, time-invariant, representation. Motivated by two ubiquitous features of the neocortex-redundancy of neural representation and sparse intracortical connections-we derive a network architecture that resolves the apparent contradiction between representation stability and changing neural activity. Unexpectedly, this network architecture exhibits many structural properties that have been measured in cortical sensory areas. In particular, we can account for few-neuron motifs, synapse weight distribution, and the relations between neuronal functional properties and connection probability.

CONCLUSIONS

We show that the intuition regarding network stimulus representation, typically derived from considering single neurons, may be misleading and that time-varying activity of distributed representation in cortical circuits does not necessarily imply that the network explicitly encodes time-varying properties.

A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis

Although the diversity of cortical interneuron electrical properties is well recognized, the number of distinct electrical types (e-types) is still a matter of debate. Recently, descriptions of interneuron variability were standardized by multiple laboratories on the basis of a subjective classification scheme as set out by the Petilla convention (Petilla Interneuron Nomenclature Group, PING). Here, we present a quantitative, statistical analysis of a database of nearly five hundred neurons manually annotated according to the PING nomenclature. For each cell, 38 features were extracted from responses to suprathreshold current stimuli and statistically analyzed to examine whether cortical interneurons subdivide into e-types. We showed that the partitioning into different e-types is indeed the major component of data variability. The analysis suggests refining the PING e-type classification to be hierarchical, whereby most variability is first captured within a coarse subpartition, and then subsequently divided into finer subpartitions. The coarse partition matches the well-known partitioning of interneurons into fast spiking and adapting cells. Finer subpartitions match the burst, continuous, and delayed subtypes. Additionally, our analysis enabled the ranking of features according to their ability to differentiate among e-types. We showed that our quantitative e-type assignment is more than 90% accurate and manages to catch several human errors.

Effective stimuli for constructing reliable neuron models

The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for selecting specific stimuli has never been rigorously justified. The present experimental and theoretical study proposes a novel framework, inspired by learning theory, for objectively selecting the stimuli that best unravel the neuron's dynamics. The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron's dynamics as attested by their ability to generalize well to the neuron's response to novel experimental stimuli. We used this framework to evaluate a variety of stimuli in different types of cortical neurons, ages and animals. Despite their simplicity, a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons. The general framework that we propose paves a new way for defining, evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose.

Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data

Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such procedures is using a previously developed model to generate surrogate data (e.g., traces of spikes following step current pulses) and then challenging the algorithm to recover the original parameters (e.g., the value of maximal ion channel conductances) that were used to generate the data. In this fashion, the success or failure of the model fitting procedure to find the original parameters can be easily determined. Here we show that some model fitting procedures that provide an excellent fit in the case of such model-to-model comparisons provide ill-balanced results when applied to experimental data. The main reason is that surrogate and experimental data test different aspects of the algorithm's function. When considering model-generated surrogate data, the algorithm is required to locate a perfect solution that is known to exist. In contrast, when considering experimental target data, there is no guarantee that a perfect solution is part of the search space. In this case, the optimization procedure must rank all imperfect approximations and ultimately select the best approximation. This aspect is not tested at all when considering surrogate data since at least one perfect solution is known to exist (the original parameters) making all approximations unnecessary. Furthermore, we demonstrate that distance functions based on extracting a set of features from the target data (such as time-to-first-spike, spike width, spike frequency, etc.)--rather than using the original data (e.g., the whole spike trace) as the target for fitting-are capable of finding imperfect solutions that are good approximations of the experimental data.

A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data

We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.

The back photoreaction of the M intermediate in the photocycle of bacteriorhodopsin: mechanism and evidence for two M species

The back photoreaction of the M intermediate in the photocycle of bacteriorhodopsin is investigated both for the native pigment and its D96N mutant. The experimental setup is based on creating the M intermediate by a first pulse, followed by a (blue) laser pulse which drives the back photoreaction of M. Experiments are carried out varying the delay between the two pulses, as well as the temperature over the -25 degrees C-20 degrees C range. It is found that the kinetic patterns of the M back photoreaction change with time after the generation of this intermediate. The data provide independent evidence for the suggestion of a photocycle mechanism based on two distinct M intermediates. They are thus in keeping with the consecutive model of Varo and Lanyi (Biochemistry 30, 5016-5022; 1991), although they cannot exclude other models such as those based on branched or parallel cycles. More generally, we offer a "photochemical" approach to discriminating between intermediate stages in the photocycle which does not depend on spectroscopic and/or kinetic data. While markedly affecting the rate of the M --> N transition in the photocycle, the rate of the thermal step in back photoreaction of M, at both room and low temperatures, is not significantly affected by the D96N mutation. It is proposed that while Asp 96 is the Schiff-base protonating moiety in the M --> N transition, another residue (most probably Asp 85) reprotonates the Schiff base following light absorption by M.

Time-resolved titrations of ASP-85 in bacteriorhodopsin: the multicomponent kinetic mechanism

The Asp-85 residue, located in the vicinity of the retinal chromophore, plays a key role in the function of bacteriorhodopsin (bR) as a light-driven proton pump. In the unphotolyzed pigment the protonation of Asp-85 is responsible for the transition from the purple form (lambdamax = 570 nm) to the blue form (lambdamax = 605 nm) of bR (pKa = 3.5 in 20 mM NaCl). The Purple <=> Blue transition can also be induced by deionization (cation removal). These color changes offer a unique opportunity for time resolving the titration of a protein residue using conventional stopped-flow methodologies. We have studied the Purple <=> Blue equilibration kinetics in bR by exposing the system to pH and to cation jumps. Independently of the equilibration direction (Purple-->Blue or Blue-->Purple) and of the inducing concentration jump ([H+] or [cation]), the kinetics are found to exhibit analogous multicomponent features. Analysis of the data over a range of cation concentrations and pH values leads to the conclusion that the rate-determining step in the overall titration of Asp-85 is proton translocation through a specific proton channel. The multicomponent kinetics, extending over a wide time range (10(-2)-10(4) s), are accounted for in terms of a pH-dependent heterogeneity of proton channels. A model is presented in which the relative weight of four proton channels is determined by the state of protonation of two interacting, channel-controlling, protein residues A1 and A2. These findings bear on the mechanism of the vectorial proton translocation associated with the photocycle of bR.

An optical submicrometer calcium sensor with conductance sensing capability

The identification of chemical species and the measurement of their concentrations with high (submicrometer) spatial resolution are of considerable importance in cell biology. In this article we report the first successful development of a > or = 0.1-micron Ca2+ sensor based on a pulled micropipet, filled with a conducting porous sol-gel glass which was doped with the fluorescent calcium green 1 Ca2+ indicator. Such sensors are potentially capable of measuring Ca2+ concentrations as low as 10(-8) M, in confined volumes, with a three-dimensional resolution which exceeds approximately 0.1 micron. A major advantage of the sensor is its capability to be integrated into a multifunctional probe which will measure chemical analyte concentrations and ion conductance.

Time-resolved titrations of the Schiff base and of the Asp85 residue in artificial bacteriorhodopsins

Deprotonation/protonation processes involving the retinal Schiff base and the Asp85 residue play dominant roles in the light-induced proton pump of bacteriorhodopsin (bR). Although the pKa values of these two moieties in unphotolyzed bR are well established, the kinetics of the respective titrations in the native pigment are difficult to interpret, primarily due to the extreme (nonphysiological) pKa values of the two moieties (12.2 +/- 0.2 and 2.7, in 0.1 M NaCl, for the Schiff base and for Asp85, respectively). These difficulties are circumvented by applying stopped-flow techniques, time resolving the titrations of several artificial bRs in which the pKa values of the above two residues are substantially modified: 13-CF3 bR, pKa (Schiff base) = 8.2 +/- 0.2; 13-demethyl-11,14-epoxy bR, pKa (Schiff base) = 8.2 +/- 0.1 (in 0.1 M NaCl); aromatic bR, pKa (Asp85) = 5.2 +/- 0.1 (in water). The R82Q bR mutant, pKa (Asp85) congruent to 7.2 was also employed. A major objective was to verify whether the basic relationships of homogeneous kinetics obeyed by elementary acid/base systems in solution (primarily, the possibility to express the equilibrium constant as the ratio of the forward and back rate constants) are also obeyed by the Schiff base and Asp85 moieties. We found that this is the case for the Schiff base in the pH range between 7 and 9 but not at lower pH. These observations led to the conclusion that the Schiff base is titrable from the outside medium via a proton channel, which becomes saturated, and thus rate determining, below pH approximately equal to 7.(ABSTRACT TRUNCATED AT 250 WORDS)

A covalent link between the chromophore and the protein backbone of bacteriorhodopsin is not required for forming a photochemically active pigment analogous to the wild type

Bacteriorhodopsin pigments lacking the retinal-Lys-216 covalent bond were prepared by reconstituting the K216G mutant protein with retinal alkylamine Schiff bases. The procedure follows the approach of Zhukovsky et al. [Zhukovsky, E., Robinson, P., & Oprian, D. (1991) Science 251, 558-560] in the case of visual (rhodopsin) pigments. Reconstitution leads to a mixture of three pigments. One of them, bR(K216G)/566a, absorbs (pH = 6.9) at 566 nm. Its absorption is pH-dependent, exhibiting a purple to blue transition. The pigment's laser-induced photocycle patterns are similar to those of wild-type all-trans-bR. A second component, bR(K216G)/566b, exhibits an independent photocycle reminiscent of that of wild-type 13-cis-bR. A third pigment component, bR(K216G)/630, absorbs around 630 nm. Experiments in the presence of a pH dye indicator show that illumination of bR(K216G)/566 produces a detectable proton gradient. It is concluded that a covalent bond between the retinal chromophore and the protein backbone is not a prerequisite for the basic structure and photochemical features of bR or for its proton pump activity.

Thermal equilibration between the M and N intermediates in the photocycle of bacteriorhodopsin

The stages in the photocycle of bacteriorhodopsin (BR) involving the M and N intermediates are investigated using a double pulse excitation method. A first (cycling) pulse at 532 nm is followed, with an appropriate time delay, by a second pulse (337, 406, 446, or 470 nm) which induces the M-->BR back-photoreaction. After depletion by the second pulse a repopulation of M in the millisecond range is observed which is interpreted in terms of a thermal N-->M relaxation. It is thus concluded that a (thermal) M<-->N equilibrium accounts for the biphasic decay of M in the BR photocycle. Other models for this stage of the light-driven proton-pump are therefore unnecessary.

Pharmacokinetic and imaging studies in patients receiving a formulation of liposome-associated adriamycin

Pharmacokinetic and imaging studies in 19 patients receiving liposome-entrapped adriamycin (L-ADM) were carried out within the framework of a Phase I clinical trial (Gabizon et al., 1989a). The formulation of L-ADM tested consisted of 0.2 microM-extruded multilamellar vesicles composed of egg phosphatidylcholine, egg-derived phosphatidyl-glycerol (PG), cholesterol, and ADM intercalated in the fluid lipid bilayer. Plasma clearance of total drug extracted from the plasma after L-ADM infusion followed a biexponential curve with a pattern similar to that reported for free ADM. The plasma concentration of drug circulating in liposome-associated from was also measured in a subgroup of seven patients. Liposome-associated drug was found to be rapidly cleared from plasma. Its ratio to non-liposome-associated drug appeared to correlate with liver reserve, with highest ratios in patients with normal liver function. Liposome clearance, as measured by the plasma concentration of PG in three patients was slower than the clearance of liposome-associated ADM, suggesting that liposomes lose part of their drug payload during circulation. To learn about the liposome organ distribution, imaging studies were carried out with 111Indium-deferoxamine labelled liposomes of the same composition. Liposomes were cleared predominantly by liver and spleen and to a lesser extent by bone marrow in seven out of nine patients. In two patients with active hepatitis and severe liver dysfunction, there was minimal liver uptake and increased spleen and bone marrow uptake. Except for one hepatoma patient, intrahepatic and extrahepatic tumours were not imaged by liposomes, suggesting that liposome uptake is restricted to cells of the reticulo-endothelial system (RES).(ABSTRACT TRUNCATED AT 250 WORDS)

Separation of liposome-associated doxorubicin from non-liposome-associated doxorubicin in human plasma: implications for pharmacokinetic studies

To characterize the pharmacokinetics of liposome-associated drugs, the fraction of drug circulating in liposome-associated form and the absolute plasma drug levels must be determined. In this report, we describe our methodological approach to quantitate plasma liposome-associated doxorubicin separately from protein-bound and free doxorubicin. The method is based on the affinity of a cation-exchange resin for doxorubicin and the repulsion by the same resin of negatively-charged liposomes. The methodology is technically simple and reproducible, and lends itself to the analysis of multiple plasma samples as required in pharmacokinetic studies. The validity of this approach was confirmed by separation of liposome-associated from non-liposome-associated drug using gel exclusion chromatography.

Immunofractionation of chromatin regions associated with histone H1o

Two monoclonal antibodies, which were elicited against histone H5, bind to purified rat liver chromatin and to rat liver H1o but not to rat liver H1. The monoclonal antibodies were immobilized on CNBr-Sepharose and the resulting immunoaffinity column was used to fractionate rat liver oligonucleosomes. Enzyme-linked immunoabsorbant assay (ELISA) and immunoblotting experiments indicate that the nucleosomes bound to the column were tenfold enriched in their content of H1o. Oligonucleosomes, prepared from the livers of either untreated or 3-methylcholanthrene-treated adult rats, were fractionated on the anti-H1o affinity column. The DNA purified from the unfractionated nucleosomes, from the unbound nucleosomes and from the nucleosomes which were bound to the column was examined with various 32P-labeled probes. A slight enrichment in H1o was detected in the coding region of the rat albumin gene. In contrast DNA which was bound to the column was significantly depleted in sequences hybridizing with total cellular RNA (which contains mostly ribosomal RNA) and with sequences hybridizing to the 3'-terminal region of a cytochrome P-450 gene, which is inducible by the chemical carcinogen 3-methylcholanthrene, regardless of whether isolated from control or from carcinogen-treated rat livers. Our experiments clearly demonstrate that chromatin can be efficiently immunofractionated. The results suggest that the H1o content of chromatin regions containing genes which are constitutively transcribed is not necessarily different from that of regions containing non-transcribed genes and that highly inducible genes may be segregated into chromatin regions which are depleted of H1o.

Immunofractionation of DNA sequences associated with HMG-17 in chromatin

Antibodies specific for chromosomal protein HMG-17 were immobilized on CNBr-Sepharose and the resulting immunoaffinity column was used to purify chromatin segments enriched in HMG-17. The DNA was purified from both the nucleosomal fraction which was bound to the column and from the fraction which was not bound, and examined with DNA probes representing repetitive DNA, non-transcribed genes, transcribed genes and inducible genes. The results suggest that HMG-17 is preferentially associated with DNA sequences coding for genes, regardless of whether they are transcribed, and therefore support the notion that HMG-17 confers specific structural characteristics on selected regions in the genome.