@article {69687, title = {Structural dynamics of human ribosomes in situ reconstructed by exhaustive high-resolution template matching.}, journal = {Mol Cell}, year = {2024}, month = {2024 Nov 25}, abstract = {

Protein synthesis is central to life and requires the ribosome, which catalyzes the stepwise addition of amino acids to a polypeptide chain by undergoing a sequence of structural transformations. Here, we employed high-resolution template matching (HRTM) on cryoelectron microscopy (cryo-EM) images of directly cryofixed living cells to obtain a set of ribosomal configurations covering the entire elongation cycle, with each configuration occurring at its native abundance. HRTM{\textquoteright}s position and orientation precision and ability to detect small targets (\~{}300\ kDa) made it possible to order these configurations along the reaction coordinate and to reconstruct molecular features of any configuration along the elongation cycle. Visualizing the cycle{\textquoteright}s structural dynamics by combining a sequence of \>40 reconstructions into a 3D movie readily revealed component and ligand movements, some of them surprising, such as spring-like intramolecular motion, providing clues about the molecular mechanisms involved in some still mysterious steps during chain elongation.

}, issn = {1097-4164}, doi = {10.1016/j.molcel.2024.11.003}, author = {Rickgauer, J Peter and Choi, Heejun and Moore, Andrew S and Denk, Winfried and Lippincott-Schwartz, Jennifer} } @article {65738, title = {The mind of a mouse.}, journal = {Cell}, volume = {182}, year = {2020}, month = {2020 Sep 17}, pages = {1372-1376}, abstract = {

Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain{\textquoteright}s synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.08.010}, author = {Abbott, Larry F and Bock, Davi D and Callaway, Edward M and Denk, Winfried and Dulac, Catherine and Fairhall, Adrienne L and Fiete, Ila and Harris, Kristen M and Helmstaedter, Moritz and Jain, Viren and Kasthuri, Narayanan and LeCun, Yann and Lichtman, Jeff W and Littlewood, Peter B and Luo, Liqun and Maunsell, John H R and Reid, R Clay and Rosen, Bruce R and Rubin, Gerald M and Sejnowski, Terrence J and Seung, H Sebastian and Svoboda, Karel and Tank, David W and Tsao, Doris and Van Essen, David C} } @article {48460, title = {Single-protein detection in crowded molecular environments in cryo-EM images.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 May 03}, abstract = {

We present an approach to study macromolecular assemblies by detecting component proteins{\textquoteright} characteristic high-resolution projection patterns, calculated from their known 3D structures, in single electron cryo-micrographs. Our method detects single apoferritin molecules in vitreous ice with high specificity and determines their orientation and location precisely. Simulations show that high spatial-frequency information and-in the presence of protein background-a whitening filter are essential for optimal detection, in particular for images taken far from focus. Experimentally, we could detect small viral RNA polymerase molecules, distributed randomly among binding locations, inside rotavirus particles. Based on the currently attainable image quality, we estimate a threshold for detection that is 150 kDa in ice and 300 kDa in 100 nm thick samples of dense biological material.

}, issn = {2050-084X}, doi = {10.7554/eLife.25648}, author = {Rickgauer, J Peter and Grigorieff, Nikolaus and Denk, Winfried} } @article {46327, title = {Space-time wiring specificity supports direction selectivity in the retina.}, journal = {Nature}, volume = {509}, year = {2014}, month = {2014 May 15}, pages = {331-6}, abstract = {

How does the mammalian retina detect motion? This classic problem in visual neuroscience has remained unsolved for 50 years. In search of clues, here we reconstruct Off-type starburst amacrine cells (SACs) and bipolar cells (BCs) in serial electron microscopic images with help from EyeWire, an online community of {\textquoteright}citizen neuroscientists{\textquoteright}. On the basis of quantitative analyses of contact area and branch depth in the retina, we find evidence that one BC type prefers to wire with a SAC dendrite near the SAC soma, whereas another BC type prefers to wire far from the soma. The near type is known to lag the far type in time of visual response. A mathematical model shows how such {\textquoteright}space-time wiring specificity{\textquoteright} could endow SAC dendrites with receptive fields that are oriented in space-time and therefore respond selectively to stimuli that move in the outward direction from the soma.

}, keywords = {Amacrine Cells, Animals, Artificial Intelligence, Brain Mapping, Crowdsourcing, Dendrites, Mice, Models, Neurological, Motion, Neural Pathways, Presynaptic Terminals, Retina, Retinal Bipolar Cells, Spatio-Temporal Analysis}, issn = {1476-4687}, doi = {10.1038/nature13240}, author = {Kim, Jinseop S and Greene, Matthew J and Zlateski, Aleksandar and Lee, Kisuk and Richardson, Mark and Turaga, Srinivas C and Purcaro, Michael and Balkam, Matthew and Robinson, Amy and Behabadi, Bardia F and Campos, Michael and Denk, Winfried and Seung, H Sebastian} } @article {6261, title = {Connectomic reconstruction of the inner plexiform layer in the mouse retina.}, journal = {Nature}, volume = {500}, year = {2013}, month = {2013 Aug 8}, pages = {168-74}, abstract = {

Comprehensive high-resolution structural maps are central to functional exploration and understanding in biology. For the nervous system, in which high resolution and large spatial extent are both needed, such maps are scarce as they challenge data acquisition and analysis capabilities. Here we present for the mouse inner plexiform layer{\textendash}the main computational neuropil region in the mammalian retina{\textendash}the dense reconstruction of 950 neurons and their mutual contacts. This was achieved by applying a combination of crowd-sourced manual annotation and machine-learning-based volume segmentation to serial block-face electron microscopy data. We characterize a new type of retinal bipolar interneuron and show that we can subdivide a known type based on connectivity. Circuit motifs that emerge from our data indicate a functional mechanism for a known cellular response in a ganglion cell that detects localized motion, and predict that another ganglion cell is motion sensitive.

}, keywords = {Amacrine Cells, Animals, Biological, Cell Communication, Computer-Assisted, Connectome, Electron, Image Processing, Inbred C57BL, Mice, Microscopy, Models, Neuropil, Retina, Retinal Ganglion Cells}, issn = {1476-4687}, doi = {10.1038/nature12346}, author = {Helmstaedter, Moritz and Briggman, Kevin L and Turaga, Srinivas C and Jain, Viren and Seung, H Sebastian and Denk, Winfried} } @article {4326, title = {Large-scale automated histology in the pursuit of connectomes.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {31}, year = {2011}, month = {2011 Nov 9}, pages = {16125-38}, abstract = {

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain{\textquoteright}s computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4077-11.2011}, author = {Kleinfeld, David and Bharioke, Arjun and Blinder, Pablo and Bock, Davi D and Briggman, Kevin L and Chklovskii, Dmitri B and Denk, Winfried and Helmstaedter, Moritz and Kaufhold, John P and Lee, Wei-Chung Allen and Meyer, Hanno S and Micheva, Kristina D and Oberlaender, Marcel and Prohaska, Steffen and Reid, R Clay and Smith, Stephen J and Takemura, Shinya and Tsai, Philbert S and Sakmann, Bert} } @article {3846, title = {Learning to agglomerate superpixel hierarchies.}, journal = {Neural Information Processing Systems}, volume = {24}, year = {2011}, month = {2011}, pages = {648-56}, abstract = {

An agglomerative clustering algorithm merges the most similar pair of clusters at every iteration. The function that evaluates similarity is traditionally handdesigned, but there has been recent interest in supervised or semisupervised settings in which ground-truth clustered data is available for training. Here we show how to train a similarity function by regarding it as the action-value function of a reinforcement learning problem. We apply this general method to segment images by clustering superpixels, an application that we call Learning to Agglomerate Superpixel Hierarchies (LASH). When applied to a challenging dataset of brain images from serial electron microscopy, LASH dramatically improved segmentation accuracy when clustering supervoxels generated by state of the boundary detection algorithms. The naive strategy of directly training only supervoxel similarities and applying single linkage clustering produced less improvement.

}, author = {Jain, Viren and Turaga, Srinivas and Briggman, Kevin and Helmstaedter, Moritz N. and Denk, Winfried and Seung, Sebastian} } @article {4305, title = {Two-photon calcium imaging of evoked activity from L5 somatosensory neurons in vivo.}, journal = {Nature Neuroscience}, volume = {14}, year = {2011}, month = {2011 Aug}, pages = {1089-93}, abstract = {

Multiphoton imaging (MPI) is widely used for recording activity simultaneously from many neurons in superficial cortical layers in vivo. We combined regenerative amplification multiphoton microscopy (RAMM) with genetically encoded calcium indicators to extend MPI of neuronal population activity into layer 5 (L5) of adult mouse somatosensory cortex. We found that this approach could be used to record and quantify spontaneous and sensory-evoked activity in populations of L5 neuronal somata located as much as 800 μm below the pia. In addition, we found that RAMM could be used to simultaneously image activity from large (80) populations of apical dendrites and follow these dendrites down to their somata of origin.

}, keywords = {Animals, Axons, Calcium, Dendrites, Evoked Potentials, Green Fluorescent Proteins, Inbred C57BL, Mice, Nerve Net, Neurons, Newborn, Photons, Physical Stimulation, Probability, Reaction Time, Somatosensory, Somatosensory Cortex, Statistics as Topic, Transgenic}, issn = {1546-1726}, doi = {10.1038/nn.2879}, author = {Mittmann, Wolfgang and Wallace, Damian J and Czubayko, Uwe and Herb, Jan T and Schaefer, Andreas T and Looger, Loren L and Denk, Winfried and Kerr, Jason N D} } @conference {3858, title = {Boundary learning by optimization with topological constraints.}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, year = {2010}, month = {2010}, abstract = {

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topological differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.

}, author = {Jain, Viren and Bollmann, Benjamin and Richardson, Mark and Berger, Daniel R. and Helmstaedter, Moritz N. and Briggman, Kevin L and Denk, Winfried and Bowden, Jared B. and Mendenhall, John M. and Abraham, Wickliffe C. and Harris, Kristen M and Kasthuri, Narayanan and Hayworth, Ken J. and Schalek, Richard and Tapia, Juan Carlos and Lichtman, Jeff W. and Seung, Hyunjune Sebastian} } @article {3844, title = {Convolutional networks can learn to generate affinity graphs for image segmentation.}, journal = {Neural Computation}, volume = {22}, year = {2010}, month = {2010 Feb}, pages = {511-38}, abstract = {

Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Electron, Image Processing, Mathematical Computing, Mathematical Concepts, Mathematics, Microscopy, Neural Networks (Computer), Pattern Recognition}, issn = {1530-888X}, doi = {10.1162/neco.2009.10-08-881}, author = {Turaga, Srinivas C and Murray, Joseph F and Jain, Viren and Roth, Fabian and Helmstaedter, Moritz and Briggman, Kevin and Denk, Winfried and Seung, Hyunjune Sebastian} } @proceedings {4528, title = {Supervised learning of image restoration with convolutional networks.}, volume = {2}, year = {2007}, month = {2007 Oct 14}, pages = {1-8}, publisher = {IEEE}, edition = {ICCV}, author = {Jain, Viren and Murray, Joseph F and Roth, Fabian and Turaga, Srinivas and Zhigulin, Valentin and Briggman, Kevin L and Helmstaedter, Moritz N. and Denk, Winfried and Seung, H Sebastian} }