Project description:Current spatial transcriptomics methods provide molecular and spatial information but no morphological readout. Here, we present STEM - a method that correlates multiplexed error-robust FISH with electron microscopy from neighboring tissue sections of the same sample. STEM links transcriptional and spatial organization of single cells with ultrastructural morphology of the tissue in vivo. Using STEM to characterize demyelinated white-matter lesions allowed us to link morphology of myelin-laden foamy microglia to transcriptional signature. Moreover, we revealed that interferon-response microglia have unique morphology and are enriched near CD8 T-cells.
Project description:Current spatial transcriptomics methods identify cell types and states in a spatial context but lack morphological information. Electron microscopy, in contrast, provides structural details at nanometer resolution without decoding the diverse cellular states and identity. STEM address this limitation by correlating multiplexed error-robust FISH with electron microscopy from adjacent tissue sections. Using STEM to characterize demyelinated lesions in mice, we were able to bridge spatially resolved transcriptional data with morphological information on cell identities. This approach allowed us to link the morphology of foamy microglia and interferon-response microglia with their transcriptional signatures.
Project description:Amyloid fibrils are filamentous protein aggregates implicated in several common diseases such as Alzheimer's disease and type II diabetes. Similar structures are also the molecular principle of the infectious spongiform encephalopathies such as Creutzfeldt-Jakob disease in humans, scrapie in sheep, and of the so-called yeast prions, inherited non-chromosomal elements found in yeast and fungi. Scanning transmission electron microscopy (STEM) is often used to delineate the assembly mechanism and structural properties of amyloid aggregates. In this review we consider specifically contributions and limitations of STEM for the investigation of amyloid assembly pathways, fibril polymorphisms and structural models of amyloid fibrils. This type of microscopy provides the only method to directly measure the mass-per-length (MPL) of individual filaments. Made on both in vitro assembled and ex vivo samples, STEM mass measurements have illuminated the hierarchical relationships between amyloid fibrils and revealed that polymorphic fibrils and various globular oligomers can assemble simultaneously from a single polypeptide. The MPLs also impose strong constraints on possible packing schemes, assisting in molecular model building when combined with high-resolution methods like solid-state nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR).
Project description:BackgroundDe novo genome assembly of next-generation sequencing data is one of the most important current problems in bioinformatics, essential in many biological applications. In spite of significant amount of work in this area, better solutions are still very much needed.ResultsWe present a new program, SAGE, for de novo genome assembly. As opposed to most assemblers, which are de Bruijn graph based, SAGE uses the string-overlap graph. SAGE builds upon great existing work on string-overlap graph and maximum likelihood assembly, bringing an important number of new ideas, such as the efficient computation of the transitive reduction of the string overlap graph, the use of (generalized) edge multiplicity statistics for more accurate estimation of read copy counts, and the improved use of mate pairs and min-cost flow for supporting edge merging. The assemblies produced by SAGE for several short and medium-size genomes compared favourably with those of existing leading assemblers.ConclusionsSAGE benefits from innovations in almost every aspect of the assembly process: error correction of input reads, string-overlap graph construction, read copy counts estimation, overlap graph analysis and reduction, contig extraction, and scaffolding. We hope that these new ideas will help advance the current state-of-the-art in an essential area of research in genomics.
Project description:Scanning transmission electron microscopy (STEM) was successfully applied to the analysis of silicon nanowires (SiNWs) that were self-assembled during an inductively coupled plasma (ICP) process. The ICP-synthesized SiNWs were found to present a Si-SiO2 core-shell structure and length varying from ≈100 nm to 2-3 μm. The shorter SiNWs (maximum length ≈300 nm) were generally found to possess a nanoparticle at their tip. STEM energy dispersive X-ray (EDX) spectroscopy combined with electron tomography performed on these nanostructures revealed that they contain iron, clearly demonstrating that the short ICP-synthesized SiNWs grew via an iron-catalyzed vapor-liquid-solid (VLS) mechanism within the plasma reactor. Both the STEM tomography and STEM-EDX analysis contributed to gain further insight into the self-assembly process. In the long-term, this approach might be used to optimize the synthesis of VLS-grown SiNWs via ICP as a competitive technique to the well-established bottom-up approaches used for the production of thin SiNWs.
Project description:Filamentous proteins are responsible for the superior mechanical strength of our cells and tissues. The remarkable mechanical properties of protein filaments are tied to their complex molecular packing structure. However, since these filaments have widths of several to tens of nanometers, it has remained challenging to quantitatively probe their molecular mass density and three-dimensional packing order. Scanning transmission electron microscopy (STEM) is a powerful tool to perform simultaneous mass and morphology measurements on filamentous proteins at high resolution, but its applicability has been greatly limited by the lack of automated image processing methods. Here, we demonstrate a semi-automated tracking algorithm that is capable of analyzing the molecular packing density of intra- and extracellular protein filaments over a broad mass range from STEM images. We prove the wide applicability of the technique by analyzing the mass densities of two cytoskeletal proteins (actin and microtubules) and of the main protein in the extracellular matrix, collagen. The high-throughput and spatial resolution of our approach allow us to quantify the internal packing of these filaments and their polymorphism by correlating mass and morphology information. Moreover, we are able to identify periodic mass variations in collagen fibrils that reveal details of their axially ordered longitudinal self-assembly. STEM-based mass mapping coupled with our tracking algorithm is therefore a powerful technique in the characterization of a wide range of biological and synthetic filaments.
Project description:Traditional single-molecule methods do not report whole-molecule kinetic conformations, and their adaptive shape changes during the process of self-assembly. Here, using graphene liquid-cell electron microscopy with electrons of low energy at low dose, we show that this approach resolves the time dependence of conformational adaptations of macromolecules for times up to minutes, the resolution determined by motion blurring, with DNA as the test case. Single-stranded DNA molecules are observed in real time as they hybridize near the solid surface to form double-stranded helices; we contrast molecules the same length but differing in base-pair microstructure (random, blocky, and palindromic hairpin) whose key difference is that random sequences possess only one stable final state, but the others offer metastable intermediate structures. Hybridization is observed to couple with enhanced translational mobility and torsion-induced rotation of the molecule. Prevalent transient loops are observed in error-correction processes. Transient melting and other failed encounters are observed in the competitive binding of multiple single-stranded molecules. Among the intermediate states reported here, some were predicted but not observed previously, and the high incidence of looping and enhanced mobility come as surprises. The error-producing mechanisms, failed encounters, and transient intermediate states would not be easily resolved by traditional single-molecule methods. The methods generalize to visualize motions and interactions of other organic macromolecules.