Organ function requires coordinated activities of thousands of genes in distinct, spatially organized cell types. Understanding the basis of emergent tissue function requires approaches to dissect the genetic control of diverse cellular and tissue phenotypes in vivo. Here, we develop paired imaging and sequencing methods to construct large-scale, multi-modal genotype-phenotypes maps in tissue with pooled genetic perturbations. Using imaging, we identify genetic perturbations in individual cells while simultaneously measuring their gene expression and subcellular morphology. Using single-cell sequencing, we measure transcriptomic responses to the same genetic perturbations. We apply this approach to study hundreds of genetic perturbations in the mouse liver. Our study reveals regulators of hepatocyte zonation and liver unfolded protein response, as well as distinct pathways that cause hepatocyte steatosis. Our approach enables new ways of interrogating the genetic basis of complex cellular and organismal physiology and provides crucial training data for emerging machine-learning models of cellular function.

Transcription factors (TFs) regulate gene programs, thereby controlling diverse cellular processes and cell states. To comprehensively understand TFs and the programs they control, we created a barcoded library of all annotated human TF splice isoforms (>3,500) and applied it to build a TF Atlas charting expression profiles of human embryonic stem cells (hESCs) overexpressing each TF at single-cell resolution. We mapped TF-induced expression profiles to reference cell types and validated candidate TFs for generation of diverse cell types, spanning all three germ layers and trophoblasts. Targeted screens with subsets of the library allowed us to create a tailored cellular disease model and integrate mRNA expression and chromatin accessibility data to identify downstream regulators. Finally, we characterized the effects of combinatorial TF overexpression by developing and validating a strategy for predicting combinations of TFs that produce target expression profiles matching reference cell types to accelerate cellular engineering efforts.

Animals perform flexible goal-directed behaviours to satisfy their basic physiological needs1-12. However, little is known about how unitary behaviours are chosen under conflicting needs. Here we reveal principles by which the brain resolves such conflicts between needs across time. We developed an experimental paradigm in which a hungry and thirsty mouse is given free choices between equidistant food and water. We found that mice collect need-appropriate rewards by structuring their choices into persistent bouts with stochastic transitions. High-density electrophysiological recordings during this behaviour revealed distributed single neuron and neuronal population correlates of a persistent internal goal state guiding future choices of the mouse. We captured these phenomena with a mathematical model describing a global need state that noisily diffuses across a shifting energy landscape. Model simulations successfully predicted behavioural and neural data, including population neural dynamics before choice transitions and in response to optogenetic thirst stimulation. These results provide a general framework for resolving conflicts between needs across time, rooted in the emergent properties of need-dependent state persistence and noise-driven shifts between behavioural goals.

Summary The cellular diversity and complex organization of the brain have hindered systematic characterization of age-related changes in its cellular and molecular architecture, limiting our ability to understand the mechanisms underlying its functional decline during aging. Here we generated a high-resolution cell atlas of brain aging within the frontal cortex and striatum using spatially resolved single-cell transcriptomics and quantified the changes in gene expression and spatial organization of major cell types in these brain regions over the lifespan of mice. We observed substantially more pronounced changes in the composition, gene expression and spatial organization of non-neuronal cells over neurons. Our data revealed molecular and spatial signatures of glial and immune cell activation during aging, particularly enriched in subcortical white matter, and identified both similarities and notable differences in cell activation patterns induced by aging and systemic inflammatory challenge. These results provide critical insights into age-related decline and inflammation in the brain.

Computational analysis of cellular activity has developed largely independently of modern transcriptomic cell typology, but integrating these approaches may be essential for full insight into cellular-level mechanisms underlying brain function and dysfunction. Applying this approach to the habenula (a structure with diverse, intermingled molecular, anatomical, and computational features), we identified encoding of reward-predictive cues and reward outcomes in distinct genetically defined neural populations, including TH+ cells and Tac1+ cells. Data from genetically targeted recordings were used to train an optimized nonlinear dynamical systems model and revealed activity dynamics consistent with a line attractor. High-density, cell-type-specific electrophysiological recordings and optogenetic perturbation provided supporting evidence for this model. Reverse-engineering predicted how Tac1+ cells might integrate reward history, which was complemented by in vivo experimentation. This integrated approach describes a process by which data-driven computational models of population activity can generate and frame actionable hypotheses for cell-type-specific investigation in biological systems.

The diversity and complex organization of cells in the brain have hindered systematic characterization of age-related changes in its cellular and molecular architecture, limiting our ability to understand the mechanisms underlying its functional decline during aging. Here, we generated a high-resolution cell atlas of brain aging within the frontal cortex and striatum using spatially resolved single-cell transcriptomics and quantified changes in gene expression and spatial organization of major cell types in these regions over the mouse lifespan. We observed substantially more pronounced changes in cell state, gene expression, and spatial organization of non-neuronal cells over neurons. Our data revealed molecular and spatial signatures of glial and immune cell activation during aging, particularly enriched in the subcortical white matter, and identified both similarities and notable differences in cell-activation patterns induced by aging and systemic inflammatory challenge. These results provide critical insights into age-related decline and inflammation in the brain.

Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.

Despite social distancing and shelter-in-place policies, COVID-19 continues to spread in the United States. A lack of timely information about factors influencing COVID-19 spread and testing has hampered agile responses to the pandemic. We developed How We Feel, an extensible web and mobile application that aggregates self-reported survey responses, to fill gaps in the collection of COVID-19-related data. How We Feel collects longitudinal and geographically localized information on users' health, behavior, and demographics. Here we report results from over 500,000 users in the United States from April 2, 2020 to May 12, 2020. We show that self- reported surveys can be used to build predictive models of COVID-19 test results, which may aid in identification of likely COVID-19 positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation, as well as for household and community exposure, occupation, and demographics being strong risk factors for COVID-19. We further reveal factors for which users have been SARS-CoV-2 PCR tested, as well as the temporal dynamics of self- reported symptoms and self-isolation behavior in positive and negative users. These results highlight the utility of collecting a diverse set of symptomatic, demographic, and behavioral self- reported data to fight the COVID-19 pandemic.

Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC→PAG (periaqueductal gray). By comparing Ca2+-activity of the molecularly homogeneous PFC→PAG type against two heterogeneous classes in several two-alternative choice tasks in freely-moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.

To advance the measurement of distributed neuronal population representations of targeted motor actions on single trials, we developed an optical method (COSMOS) for tracking neural activity in a largely uncharacterized spatiotemporal regime. COSMOS allowed simultaneous recording of neural dynamics at ∼30 Hz from over a thousand near-cellular resolution neuronal sources spread across the entire dorsal neocortex of awake, behaving mice during a three-option lick-to-target task. We identified spatially distributed neuronal population representations spanning the dorsal cortex that precisely encoded ongoing motor actions on single trials. Neuronal correlations measured at video rate using unaveraged, whole-session data had localized spatial structure, whereas trial-averaged data exhibited widespread correlations. Separable modes of neural activity encoded history-guided motor plans, with similar population dynamics in individual areas throughout cortex. These initial experiments illustrate how COSMOS enables investigation of large-scale cortical dynamics and that information about motor actions is widely shared between areas, potentially underlying distributed computations.

We call upon the research community to standardize efforts to use daily self-reported data about COVID-19 symptoms in the response to the pandemic and to form a collaborative consortium to maximize global gain while protecting participant privacy.

How have complex brains evolved from simple circuits? Here we investigated brain region evolution at cell-type resolution in the cerebellar nuclei, the output structures of the cerebellum. Using single-nucleus RNA sequencing in mice, chickens, and humans, as well as STARmap spatial transcriptomic analysis and whole-central nervous system projection tracing, we identified a conserved cell-type set containing two region-specific excitatory neuron classes and three region-invariant inhibitory neuron classes. This set constitutes an archetypal cerebellar nucleus that was repeatedly duplicated to form new regions. The excitatory cell class that preferentially funnels information to lateral frontal cortices in mice becomes predominant in the massively expanded human lateral nucleus. Our data suggest a model of brain region evolution by duplication and divergence of entire cell-type sets.

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text I. V. Kauvar, T. A. Machado, E. Yuen, J. Kochalka, W. E. Allen, C. Raja, N. Pichamoorthy, G. Wetzstein, and K. Deisseroth, "Multifocal panoptic recording of cross-cortical neuronal dynamics in behaving mice," in Biophotonics Congress: Optics in the Life Sciences Congress 2019 (BODA,BRAIN,NTM,OMA,OMP), OSA Technical Digest (Optica Publishing Group, 2019), paper JW3B.2. Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article

Conventional sequencing methods are unable to measure sequence and spatial information simultaneously, limiting our understanding of the spatial heterogeneity of molecules within cells and intact tissues. To address this issue, multiplexed in situ hybridization and a new generation of sequencing methods termed in situ sequencing have emerged, aiming to spatially resolve RNAs inside cells. Such spatial transcriptomic profiling is critical to resolving cell identity in intact issues, a key question in neuroscience today as the brain consists of a three‐dimensional (3D) tapestry of cell types whose functional diversity arises from the spatial heterogeneity of gene expression. We developed a 3D intact‐tissue RNA sequencing technique in complex organs, termed STARmap, enabling targeted single‐cell gene expression mapping via in situ sequencing within mouse cortex. This new analytical approach has great promises for addressing a series of fundamental questions in neuroscience (e.g. spatial organization of cell types, molecular markers of functional circuits, activity‐triggered gene expression) that are hard to be studied otherwise. Support or Funding Information X.W. is supported by a Life Science Research Foundation fellowship and the Gordon and Betty Moore Foundation. W.E.A. is supported by a Fannie and John Hertz Foundation Fellowship and an NSF Graduate Research Fellowship. K.D. is supported by NIMH (R01MH099647), NIDA (P50DA042012), the DARPA NeuroFAST program W911NF‐14‐2‐0013, the NSF NeuroNex program, the Gatsby Foundation, the AE Foundation, the NOMIS Foundation, the Fresenius Foundation, the Wiegers Family Fund, the James Grosfeld Foundation, the Sam and Betsy Reeves Foundation, and the HL Snyder Foundation. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Memories of fearful events can last a lifetime. The prelimbic (PL) cortex, a subregion of prefrontal cortex, plays a critical role in fear memory retrieval over time. Most studies have focused on acquisition, consolidation, and retrieval of recent memories, but much less is known about the neural mechanisms of remote memory. Using a new knock-in mouse for activity-dependent genetic labeling ( TRAP2 ), we demonstrate that neuronal ensembles in the PL cortex are dynamic. PL neurons TRAPed during later memory retrievals are more likely to be reactivated and make larger behavioral contributions to remote memory retrieval compared to those TRAPed during learning or early memory retrieval. PL activity during learning is required to initiate this time-dependent reorganization in PL ensembles underlying memory retrieval. Finally, while neurons TRAPed during earlier and later retrievals have similar broad projections throughout the brain, PL neurons TRAPed later have a stronger functional recruitment of cortical targets.

Physiological needs produce motivational drives, such as thirst and hunger, that regulate behaviors essential to survival. Hypothalamic neurons sense these needs and must coordinate relevant brainwide neuronal activity to produce the appropriate behavior. We studied dynamics from ~24,000 neurons in 34 brain regions during thirst-motivated choice behavior in 21 mice as they consumed water and became sated. Water-predicting sensory cues elicited activity that rapidly spread throughout the brain of thirsty animals. These dynamics were gated by a brainwide mode of population activity that encoded motivational state. After satiation, focal optogenetic activation of hypothalamic thirst-sensing neurons returned global activity to the pre-satiation state. Thus, motivational states specify initial conditions that determine how a brainwide dynamical system transforms sensory input into behavioral output.

Haploinsufficiency of Retinoic Acid Induced 1 (RAI1) causes Smith-Magenis syndrome (SMS), a syndromic autism spectrum disorder associated with craniofacial abnormalities, intellectual disability, and behavioral problems. There is currently no cure for SMS. Here, we generated a genetic mouse model to determine the reversibility of SMS-like neurobehavioral phenotypes in Rai1 heterozygous mice. We show that normalizing the Rai1 level 3-4 wk after birth corrected the expression of genes related to neural developmental pathways and fully reversed a social interaction deficit caused by Rai1 haploinsufficiency. In contrast, Rai1 reactivation 7-8 wk after birth was not beneficial. We also demonstrated that the correct Rai1 dose is required in both excitatory and inhibitory neurons for proper social interactions. Finally, we found that Rai1 heterozygous mice exhibited a reduction of dendritic spines in the medial prefrontal cortex (mPFC) and that optogenetic activation of mPFC neurons in adults improved the social interaction deficit of Rai1 heterozygous mice. Together, these results suggest the existence of a postnatal temporal window during which restoring Rai1 can improve the transcriptional and social behavioral deficits in a mouse model of SMS. It is possible that circuit-level interventions would be beneficial beyond this critical window.

Both designed and natural anion-conducting channelrhodopsins (dACRs and nACRs, respectively) have been widely applied in optogenetics (enabling selective inhibition of target-cell activity during animal behaviour studies), but each class exhibits performance limitations, underscoring trade-offs in channel structure-function relationships. Therefore, molecular and structural insights into dACRs and nACRs will be critical not only for understanding the fundamental mechanisms of these light-gated anion channels, but also to create next-generation optogenetic tools. Here we report crystal structures of the dACR iC++, along with spectroscopic, electrophysiological and computational analyses that provide unexpected insights into pH dependence, substrate recognition, channel gating and ion selectivity of both dACRs and nACRs. These results enabled us to create an anion-conducting channelrhodopsin integrating the key features of large photocurrent and fast kinetics alongside exclusive anion selectivity.

The salience of behaviorally relevant stimuli is dynamic and influenced by internal state and external environment. Monitoring such changes is critical for effective learning and flexible behavior, but the neuronal substrate for tracking the dynamics of stimulus salience is obscure. We found that neurons in the paraventricular thalamus (PVT) are robustly activated by a variety of behaviorally relevant events, including novel ("unfamiliar") stimuli, reinforcing stimuli and their predicting cues, as well as omission of the expected reward. PVT responses are scaled with stimulus intensity and modulated by changes in homeostatic state or behavioral context. Inhibition of the PVT responses suppresses appetitive or aversive associative learning and reward extinction. Our findings demonstrate that the PVT gates associative learning by providing a dynamic representation of stimulus salience.