An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input–output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.

SummaryUnderstanding neural circuits requires deciphering the interactions of myriad cell types defined by anatomy, spatial organization, gene expression, and functional properties. Resolving these cell types requires both single neuron resolution and high-throughput, a combination that is challenging to achieve with conventional anatomical methods. Here we introduce BARseq, a method for mapping the projections of thousands of spatially resolved neurons by combining the high throughput of DNA sequencing with the high spatial resolution of microscopy. We used BARseq to determine the projections of 1309 neurons in mouse auditory cortex to 11 targets. We observed 264 distinct projection patterns. Hierarchical clustering confirmed the major classical classes of projection neurons, segregated across cortical laminae. Further analysis revealed 25 subclasses, largely intermingled across laminae. Unlike cell types defined by gene expression, projection subclasses beyond the major classes were rarely enriched in specific laminae, raising the possibility that the organization of projection patterns in mature neurons is orthogonal to that of gene expression. In this way, downstream brain areas could receive information from multiple cell types through parallel pathways. By sequencing in situ, BARseq has the potential to bridge anatomical, transcriptomic, functional, and other approaches at single neuron resolution with high throughput, and thereby offer unprecedented insight of the structure and function of a neural circuit.. CC-BY-NC 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/294637 doi: bioRxiv preprint first posted online Apr. 3, 2018; 2 An important challenge in neuroscience is to relate diverse characteristics of single neurons, in a co-registered fashion, within single brains 1 . Even simultaneous co-registration of two characteristics can be challenging, and has led to insights about the functional organization of neural circuits 2,3 . A high-throughput method capable of such multimodal co-registration would yield a "Rosetta Brain"-an integrative dataset that could constrain theoretical efforts to bridge across levels of structure and function in the nervous system 1 .As a first step toward this goal we began with MAPseq 4,5 (Fig. 1A, left), a sequencing-based method capable of mapping long-range projections of thousands of single neurons in a single brain. MAPseq achieves multiplexing by uniquely labeling individual neurons with random RNA sequences, or "barcodes". Because MAPseq, like most other sequencing methods, relies on tissue homogenization, it cannot resolve the spatial organization of the neuronal somata. This spatial organization, however, potentially allows the registration of distinct neuronal characteristics. We therefore sought to develop a method that would preserve the spatial organization of barc...

Functional circuits consist of neurons with diverse axonal projections and gene expression. Understanding the molecular signature of projections requires high-throughput interrogation of both gene expression and projections to multiple targets in the same cells at cellular resolution, which is difficult to achieve using current technology. Here, we introduce BARseq2, a technique that simultaneously maps projections and detects multiplexed gene expression by in situ sequencing. We determined the expression of cadherins and cell-type markers in 29,933 cells, and the projections of 3,164 cells in both the mouse motor cortex and auditory cortex. Associating gene expression and projections in 1,349 neurons revealed shared cadherin signatures of homologous projections across the two cortical areas. These cadherins were enriched across multiple branches of the transcriptomic taxonomy. By correlating multi-gene expression and projections to many targets in single neurons with high throughput, BARseq2 provides a potential path to uncovering the molecular logic underlying neuronal circuits.

An essential step toward understanding brain function is to establish a cellular-resolution structural framework upon which multi-scale and multi-modal information spanning molecules, cells, circuits and systems can be integrated and interpreted. Here, through a collaborative effort from the Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based description of one brain structure - the primary motor cortex upper limb area (MOp-ul) of the mouse. Applying state-of-the-art labeling, imaging, computational, and neuroinformatics tools, we delineated the MOp-ul within the Mouse Brain 3D Common Coordinate Framework (CCF). We defined over two dozen MOp-ul projection neuron (PN) types by their anterograde targets; the spatial distribution of their somata defines 11 cortical sublayers, a significant refinement of the classic notion of cortical laminar organization. We further combine multiple complementary tracing methods (classic tract tracing, cell type-based anterograde, retrograde, and transsynaptic viral tracing, high-throughput BARseq, and complete single cell reconstruction) to systematically chart cell type-based MOp input-output streams. As PNs link distant brain regions at synapses as well as host cellular gene expression, our construction of a PN type resolution MOp-ul wiring diagram will facilitate an integrated analysis of motor control circuitry across the molecular, cellular, and systems levels. This work further provides a roadmap towards a cellular resolution description of mammalian brain architecture.

Cellular DNA/RNA tags (barcodes) allow for multiplexed cell lineage tracing and neuronal projection mapping with cellular resolution. Conventional approaches to reading out cellular barcodes trade off spatial resolution with throughput. Bulk sequencing achieves high throughput but sacrifices spatial resolution, whereas manual cell picking has low throughput. In situ sequencing could potentially achieve both high spatial resolution and high throughput, but current in situ sequencing techniques are inefficient at reading out cellular barcodes. Here we describe BaristaSeq, an optimization of a targeted, padlock probe-based technique for in situ barcode sequencing compatible with Illumina sequencing chemistry. BaristaSeq results in a five-fold increase in amplification efficiency, with a sequencing accuracy of at least 97%. BaristaSeq could be used for barcode-assisted lineage tracing, and to map long-range neuronal projections.

No abstract

To forecast solar power's short-term fluctuation stemming from changes in cloud coverage, a convolutional neural networks (CNN) is used to correlate PV output to contemporaneous sky images.

Studies of the energy return on investment (EROI) for oil production generally rely on aggregated statistics for large regions or countries. In order to better understand the drivers of the energy productivity of oil production, we use a novel approach that applies a detailed field-level engineering model of oil and gas production to estimate energy requirements of drilling, producing, processing, and transporting crude oil. We examine 40 global oilfields, utilizing detailed data for each field from hundreds of technical and scientific data sources. Resulting net energy return (NER) ratios for studied oil fields range from ≈2 to ≈100 MJ crude oil produced per MJ of total fuels consumed. External energy return (EER) ratios, which compare energy produced to energy consumed from external sources, exceed 1000:1 for fields that are largely self-sufficient. The lowest energy returns are found to come from thermally-enhanced oil recovery technologies. Results are generally insensitive to reasonable ranges of assumptions explored in sensitivity analysis. Fields with very large associated gas production are sensitive to assumptions about surface fluids processing due to the shifts in energy consumed under different gas treatment configurations. This model does not currently include energy invested in building oilfield capital equipment (e.g., drilling rigs), nor does it include other indirect energy uses such as labor or services.