Spatial proteomics in neurons at single-protein resolution

To fully understand biological processes and functions, it is necessary to reveal the molecular heterogeneity of cells and even subcellular assemblies by gaining access to the location and interaction of all biomolecules. The study of protein arrangements has seen significant advancements through super-resolution microscopy, but such methods are still far from reaching the multiplexing capacity of spatial proteomics. Here, we introduce Secondary label-based Unlimited Multiplexed DNA-PAINT (SUM-PAINT), a high-throughput imaging method capable of achieving virtually unlimited multiplexing at better than 15 nm spatial resolution. Using SUM-PAINT, we generated the most extensive multiprotein dataset to date at single-protein spatial resolution, comprising up to 30 distinct protein targets in parallel and adapted omics-inspired analysis workflows to explore these feature-rich datasets. Remarkably, by examining the multiplexed protein content of almost 900 individual synapses at single-protein resolution, we revealed the complexity of synaptic heterogeneity, ultimately leading to the discovery of a new synapse type. This work provides not only a feature-rich resource for researchers, but also an integrated data acquisition and analysis workflow for comprehensive spatial proteomics at single-protein resolution, paving the way for ‘Localizomics’.

[1]  X. Zhuang,et al.  Molecular and spatial signatures of mouse brain aging at single-cell resolution , 2022, Cell.

[2]  A. Brunner,et al.  Deep Visual Proteomics defines single-cell identity and heterogeneity , 2022, Nature Biotechnology.

[3]  T. Schikorski,et al.  A large-scale nanoscopy and biochemistry analysis of postsynaptic dendritic spines , 2021, Nature Neuroscience.

[4]  M. Guttman,et al.  Integrated spatial genomics reveals global architecture of single nuclei , 2020, Nature.

[5]  P. De Koninck,et al.  Activity-Dependent Remodeling of Synaptic Protein Organization Revealed by High Throughput Analysis of STED Nanoscopy Images , 2020, Frontiers in Neural Circuits.

[6]  R. Jungmann,et al.  Circumvention of common labeling artifacts using secondary nanobodies , 2019, bioRxiv.

[7]  Mike Heilemann,et al.  Automated highly multiplexed super-resolution imaging of protein nano-architecture in cells and tissues , 2019, Nature Communications.

[8]  Maximilian T. Strauss,et al.  An order of magnitude faster DNA-PAINT imaging by optimized sequence design and buffer conditions , 2019, Nature Methods.

[9]  Guo-Cheng Yuan,et al.  Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+ , 2019, Nature.

[10]  Ulf Matti,et al.  Nuclear pores as versatile reference standards for quantitative superresolution microscopy , 2019, Nature Methods.

[11]  Maximilian T. Strauss,et al.  Direct Visualization of Single Nuclear Pore Complex Proteins Using Genetically‐Encoded Probes for DNA‐PAINT , 2019, bioRxiv.

[12]  Lai Guan Ng,et al.  Dimensionality reduction for visualizing single-cell data using UMAP , 2018, Nature Biotechnology.

[13]  Julia Behnsen,et al.  Alpha shapes: determining 3D shape complexity across morphologically diverse structures , 2018, BMC Evolutionary Biology.

[14]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection , 2018, J. Open Source Softw..

[15]  E. Fornasiero,et al.  Newly produced synaptic vesicle proteins are preferentially used in synaptic transmission , 2018, The EMBO journal.

[16]  Salil S. Bhate,et al.  Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging , 2017, Cell.

[17]  S. Hell,et al.  Fluorescence nanoscopy in cell biology , 2017, Nature Reviews Molecular Cell Biology.

[18]  R. Franzen,et al.  Puzzling Out Synaptic Vesicle 2 Family Members Functions , 2017, Front. Mol. Neurosci..

[19]  Maximilian T. Strauss,et al.  Super-resolution microscopy with DNA-PAINT , 2017, Nature Protocols.

[20]  M. Heilemann,et al.  Single-Molecule Localization Microscopy in Eukaryotes. , 2017, Chemical reviews.

[21]  Thomas A. Blanpied,et al.  A transsynaptic nanocolumn aligns neurotransmitter release to receptors , 2016, Nature.

[22]  Marco Cuturi,et al.  On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests , 2015, Entropy.

[23]  X. Zhuang,et al.  Spatially resolved, highly multiplexed RNA profiling in single cells , 2015, Science.

[24]  Henry Pinkard,et al.  Advanced methods of microscope control using μManager software. , 2014, Journal of biological methods.

[25]  F. Benfenati,et al.  Functional Role of ATP Binding to Synapsin I In Synaptic Vesicle Trafficking and Release Dynamics , 2014, The Journal of Neuroscience.

[26]  Sean C. Bendall,et al.  Multiplexed ion beam imaging of human breast tumors , 2014, Nature Medicine.

[27]  J. Buhmann,et al.  Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry , 2014, Nature Methods.

[28]  Masaki Tanaka,et al.  Differential Expression of Alpha-Synuclein in Hippocampal Neurons , 2014, PloS one.

[29]  Johannes B. Woehrstein,et al.  Multiplexed 3D Cellular Super-Resolution Imaging with DNA-PAINT and Exchange-PAINT , 2014, Nature Methods.

[30]  X. Zhuang,et al.  Actin, Spectrin, and Associated Proteins Form a Periodic Cytoskeletal Structure in Axons , 2013, Science.

[31]  M. Gudheti,et al.  Single Molecule Localization Microscopy , 2012 .

[32]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[33]  Daniel Müllner,et al.  Modern hierarchical, agglomerative clustering algorithms , 2011, ArXiv.

[34]  X. Zhuang,et al.  Superresolution Imaging of Chemical Synapses in the Brain , 2010, Neuron.

[35]  F. Simmel,et al.  Single-molecule kinetics and super-resolution microscopy by fluorescence imaging of transient binding on DNA origami. , 2010, Nano letters.

[36]  Qikai Xu,et al.  Design of 240,000 orthogonal 25mer DNA barcode probes , 2009, Proceedings of the National Academy of Sciences.

[37]  A. Plastino,et al.  JENSEN–SHANNON DIVERGENCE AS A MEASURE OF THE DEGREE OF ENTANGLEMENT , 2008, 0804.3662.

[38]  S. Kaech,et al.  Culturing hippocampal neurons , 2006, Nature Protocols.

[39]  J. A. Ferreira,et al.  On the Benjamini-Hochberg method , 2006, math/0611265.

[40]  S. Lange,et al.  Adjusting for multiple testing--when and how? , 2001, Journal of clinical epidemiology.

[41]  A. Turberfield,et al.  A DNA-fuelled molecular machine made of DNA , 2022 .

[42]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[43]  R. Scheller,et al.  Differential expression of synaptic vesicle protein 2 (SV2) isoforms , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[44]  N. Hirokawa,et al.  Structure of the peripheral domains of neurofilaments revealed by low angle rotary shadowing. , 1988, Journal of molecular biology.

[45]  G. Banker,et al.  The establishment of polarity by hippocampal neurons in culture , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[46]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[47]  Peng Yin,et al.  Optical imaging of individual biomolecules in densely packed clusters , 2016 .

[48]  Xin Jin,et al.  K-Means Clustering , 2010, Encyclopedia of Machine Learning.