We established a collection of 7,000 transgenic lines of Drosophila melanogaster. Expression of GAL4 in each line is controlled by a different, defined fragment of genomic DNA that serves as a transcriptional enhancer. We used confocal microscopy of dissected nervous systems to determine the expression patterns driven by each fragment in the adult brain and ventral nerve cord. We present image data on 6,650 lines. Using both manual and machine-assisted annotation, we describe the expression patterns in the most useful lines. We illustrate the utility of these data for identifying novel neuronal cell types, revealing brain asymmetry, and describing the nature and extent of neuronal shape stereotypy. The GAL4 lines allow expression of exogenous genes in distinct, small subsets of the adult nervous system. The set of DNA fragments, each driving a documented expression pattern, will facilitate the generation of additional constructs for manipulating neuronal function.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.

Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1–5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

Analyzing Drosophila neural expression patterns in thousands of 3D image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology. Given a target brain labeled with predefined landmarks, the BrainAligner program automatically finds the corresponding landmarks in a subject brain and maps it to the coordinate system of the target brain via a deformable warp. Using a neuropil marker (the antibody nc82) as a reference of the brain morphology and a target brain that is itself a statistical average of 295 brains, we achieved a registration accuracy of 2µm on average, permitting assessment of stereotypy, potential connectivity, and functional mapping of the adult fruitfly brain. We used BrainAligner to generate an image pattern atlas of 2,954 registered brains containing 470 different expression patterns that cover all the major compartments of the fly brain.

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

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SummaryMitochondrial dynamics is highly involved in muscle atrophy, the slow twitch muscle as soleus, preferentially affected by hindlimb unloading (HU), was well characterized by mitochondrial dysfunction in biogenesis. However, the fast twitch muscles like tibialis anterior (TA) and gastrocnemius (GAS), which are the most massive parts of the hindlimb muscles, are less elucidated on mitochondrial adaptations responding to HU. To investigate the mitochondrial dynamic responses and the involved molecules mediating atrophy in TA and GAS, we studied a 4-week HU mouse model. We found GAS was preferentially affected to atrophy by unloading compared with TA. Furthermore, the depressed mitochondrial biogenesis occurred, accounting for mitochondrial loss in GAS by unloading. PGC-1a, as well as its transcriptional/post-translational modification regulators, such as p-CREB, SIRT1, and p-AMPK, were consistently reduced in response to unloading in GAS. Molecules relevant to autophagy, mitochondrial fusion, and fission, were compromised following unloading both in TA and GAS. These results suggested that TA exhibited resistance to unloading induced muscle atrophy while GAS presented significant mitochondrial loss, which might be due to the mitochondrial biogenesis suppressed by the inactivation of PGC-1a. However, both in TA and GAS muscles, a similar sedentary mitochondrial dynamics of mitochondrial fusion and fission was induced by unloading though TA exhibited little muscle atrophy. IUBMBIUBMB Life, 64(11): 901-910, 2012

: Recent whole brain mapping projects are collecting large-scale 3D images using powerful and informative modalities, such as STPT, fMOST, VISoR, or MRI. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modality image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulted from different sample preparation methods and imaging modalities. We introduced a cross-modality registration method, called mBrainAligner, which uses coherent landmark mapping as well as deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We also built a single cell resolution atlas using the fMOST modality, and used our method to generate whole brain map of 3D full single neuron morphology and neuron cell types. INTRODUCTIONRecent advances in high-resolution light microscopy 1-6 , tissue clearing 7-13 , sparse labeling techniques 2,14-17 and full morphology neuron tracing methods [18][19][20][21] now make it feasible to map the mammalian whole-brain at single cell resolution [22][23][24] . Large international efforts such as the BRAIN Initiative Cell Census Network (BICCN) 25 , MouseLight project 19 , Allen Mouse Brain Connectivity Atlas 26 and Mouse Connectome project [27][28][29] , are engaged in cell typing, mapping long-range axonal projection and microcircuit connection analyses. Massive image datasets are acquired through a variety of high-resolution, high-throughput imaging techniques, such as serial two-photon tomography (STPT), fluorescence micro-optical sectioning tomography (fMOST), light-sheet fluorescence microscopy (LSFM), volumetric imaging with synchronous on-the-flyscan and readout (VISoR). A critical technique required to use these data is registration (alignment) of brains, their compartments, and various neuronal structures including somas, dendrites, and axon arbors. Data that are acquired across subjects, modalities, time or even species must be mapped onto a canonical coordinate space. Its importance is seen in multiple ways. First, registration of datasets of different modalities generated in different projects provides a valuable resource for everexpanding studies of neuron wiring and functions. Second, the registration to a common standard 'atlas' enables digital delineation and quantification of brain anatomy and neurons' morphology and other attributes such as transcriptomics. Third, registration allows quantitative comparison of features of neurons across samples under different conditions, thus enabling analysis of distinct aspects of neurobiology in a coordinated manner. Established brain atlases such as the Allen mouse brain atlas (using STPT imaging) 30,31 have helped neurobiologists tremendously to study gene expression patterns, brain anatomy, and neural circuits. With the newly produced whole brain single neuron morphology database (e.g.,