Tim J. Stevens

Single-cell Hi-C reveals cell-to-cell variability in chromosome structure

Large-scale chromosome structure and spatial nuclear arrangement have been linked to control of gene expression and DNA replication and repair. Genomic techniques based on chromosome conformation capture (3C) assess contacts for millions of loci simultaneously, but do so by averaging chromosome conformations from millions of nuclei. Here we introduce single-cell Hi-C, combined with genome-wide statistical analysis and structural modelling of single-copy X chromosomes, to show that individual chromosomes maintain domain organization at the megabase scale, but show variable cell-to-cell chromosome structures at larger scales. Despite this structural stochasticity, localization of active gene domains to boundaries of chromosome territories is a hallmark of chromosomal conformation. Single-cell Hi-C data bridge current gaps between genomics and microscopy studies of chromosomes, demonstrating how modular organization underlies dynamic chromosome structure, and how this structure is probabilistically linked with genome activity patterns.

Prediction of Glycosylphosphatidylinositol-Anchored Proteins in Arabidopsis. A Genomic Analysis

Glycosylphosphatidylinositol (GPI) anchoring of proteins provides a potential mechanism for targeting to the plant plasma membrane and cell wall. However, relatively few such proteins have been identified. Here, we develop a procedure for database analysis to identify GPI-anchored proteins (GAP) based on their possession of common features. In a comprehensive search of the annotated Arabidopsis genome, we identified 167 novel putative GAP in addition to the 43 previously described candidates. Many of these 210 proteins show similarity to characterized cell surface proteins. The predicted GAP include homologs of β-1,3-glucanases (16), metallo- and aspartyl proteases (13), glycerophosphodiesterases (6), phytocyanins (25), multi-copper oxidases (2), extensins (6), plasma membrane receptors (19), and lipid-transfer-proteins (18). Classical arabinogalactan (AG) proteins (13), AG peptides (9), fasciclin-like proteins (20), COBRA and 10 homologs, and novel potential signaling peptides that we name GAPEPs (8) were also identified. A further 34 proteins of unknown function were predicted to be GPI anchored. A surprising finding was that over 40% of the proteins identified here have probable AG glycosylation modules, suggesting that AG glycosylation of cell surface proteins is widespread. This analysis shows that GPI anchoring is likely to be a major modification in plants that is used to target a specific subset of proteins to the cell surface for extracellular matrix remodeling and signaling.

3D structures of individual mammalian genomes studied by single-cell Hi-C

The folding of genomic DNA from the beads-on-a-string-like structure of nucleosomes into higher-order assemblies is crucially linked to nuclear processes. Here we calculate 3D structures of entire mammalian genomes using data from a new chromosome conformation capture procedure that allows us to first image and then process single cells. The technique enables genome folding to be examined at a scale of less than 100 kb, and chromosome structures to be validated. The structures of individual topological-associated domains and loops vary substantially from cell to cell. By contrast, A and B compartments, lamina-associated domains and active enhancers and promoters are organized in a consistent way on a genome-wide basis in every cell, suggesting that they could drive chromosome and genome folding. By studying genes regulated by pluripotency factor and nucleosome remodelling deacetylase (NuRD), we illustrate how the determination of single-cell genome structure provides a new approach for investigating biological processes.

Structure calculation, refinement and validation using CcpNmr Analysis

This report describes the working of the program CcpNmr Analysis for both NMR chemical shift assignment and structure determination of biological macromolecules.

MEMOPS: data modelling and automatic code generation.

In recent years the amount of biological data has exploded to the point where much useful information can only be extracted by complex computational analyses. Such analyses are greatly facilitated by metadata standards, both in terms of the ability to compare data originating from different sources, and in terms of exchanging data in standard forms, e.g. when running processes on a distributed computing infrastructure. However, standards thrive on stability whereas science tends to constantly move, with new methods being developed and old ones modified. Therefore maintaining both metadata standards, and all the code that is required to make them useful, is a non-trivial problem. Memops is a framework that uses an abstract definition of the metadata (described in UML) to generate internal data structures and subroutine libraries for data access (application programming interfaces--APIs--currently in Python, C and Java) and data storage (in XML files or databases). For the individual project these libraries obviate the need for writing code for input parsing, validity checking or output. Memops also ensures that the code is always internally consistent, massively reducing the need for code reorganisation. Across a scientific domain a Memops-supported data model makes it easier to support complex standards that can capture all the data produced in a scientific area, share them among all programs in a complex software pipeline, and carry them forward to deposition in an archive. The principles behind the Memops generation code will be presented, along with example applications in Nuclear Magnetic Resonance (NMR) spectroscopy and structural biology.

Are There Knots in Chromosomes

Recent developments have for the first time allowed the determination of three-dimensional structures of individual chromosomes and genomes in nuclei of single haploid mouse embryonic stem (ES) cells based on Hi–C chromosome conformation contact data. Although these first structures have a relatively low resolution, they provide the first experimental data that can be used to study chromosome and intact genome folding. Here we further analyze these structures and provide the first evidence that G1 phase chromosomes are knotted, consistent with the fact that plots of contact probability vs sequence separation show a power law dependence that is intermediate between that of a fractal globule and an equilibrium structure.

Hydrocarbons Are Essential for Optimal Cell Size, Division, and Growth of Cyanobacteria

Optimal growth and division of cyanobacteria depends upon hydrocarbon induced flexibility in the thylakoid membranes of cyanobacteria, via accumulation of these compounds within the lipid bilayer. Cyanobacteria are intricately organized, incorporating an array of internal thylakoid membranes, the site of photosynthesis, into cells no larger than other bacteria. They also synthesize C15-C19 alkanes and alkenes, which results in substantial production of hydrocarbons in the environment. All sequenced cyanobacteria encode hydrocarbon biosynthesis pathways, suggesting an important, undefined physiological role for these compounds. Here, we demonstrate that hydrocarbon-deficient mutants of Synechococcus sp. PCC 7002 and Synechocystis sp. PCC 6803 exhibit significant phenotypic differences from wild type, including enlarged cell size, reduced growth, and increased division defects. Photosynthetic rates were similar between strains, although a minor reduction in energy transfer between the soluble light harvesting phycobilisome complex and membrane-bound photosystems was observed. Hydrocarbons were shown to accumulate in thylakoid and cytoplasmic membranes. Modeling of membranes suggests these compounds aggregate in the center of the lipid bilayer, potentially promoting membrane flexibility and facilitating curvature. In vivo measurements confirmed that Synechococcus sp. PCC 7002 mutants lacking hydrocarbons exhibit reduced thylakoid membrane curvature compared to wild type. We propose that hydrocarbons may have a role in inducing the flexibility in membranes required for optimal cell division, size, and growth, and efficient association of soluble and membrane bound proteins. The recent identification of C15-C17 alkanes and alkenes in microalgal species suggests hydrocarbons may serve a similar function in a broad range of photosynthetic organisms.

Prion‐like protein aggregates exploit the RHO GTPase to cofilin‐1 signaling pathway to enter cells

Protein aggregation is a hallmark of diverse neurodegenerative diseases. Multiple lines of evidence have revealed that protein aggregates can penetrate inside cells and spread like prions. How such aggregates enter cells remains elusive. Through a focused siRNA screen targeting genes involved in membrane trafficking, we discovered that mutant SOD1 aggregates, like viruses, exploit cofilin‐1 to remodel cortical actin and enter cells. Upstream of cofilin‐1, signalling from the RHO GTPase and the ROCK1 and LIMK1 kinases controls cofilin‐1 activity to remodel actin and modulate aggregate entry. In the spinal cord of symptomatic SOD1G93A transgenic mice, cofilin‐1 phosphorylation is increased and actin dynamics altered. Importantly, the RHO to cofilin‐1 signalling pathway also modulates entry of tau and α‐synuclein aggregates. Our results identify a common host cell signalling pathway that diverse protein aggregates exploit to remodel actin and enter cells.

Combining fluorescence imaging with Hi-C to study 3D genome architecture of the same single cell.

Fluorescence imaging and chromosome conformation capture assays such as Hi-C are key tools for studying genome organization. However, traditionally, they have been carried out independently, making integration of the two types of data difficult to perform. By trapping individual cell nuclei inside a well of a 384-well glass-bottom plate with an agarose pad, we have established a protocol that allows both fluorescence imaging and Hi-C processing to be carried out on the same single cell. The protocol identifies 30,000-100,000 chromosome contacts per single haploid genome in parallel with fluorescence images. Contacts can be used to calculate intact genome structures to better than 100-kb resolution, which can then be directly compared with the images. Preparation of 20 single-cell Hi-C libraries using this protocol takes 5 d of bench work by researchers experienced in molecular biology techniques. Image acquisition and analysis require basic understanding of fluorescence microscopy, and some bioinformatics knowledge is required to run the sequence-processing tools described here.

Multiple marker abundance profiling: combining selected reaction monitoring and data-dependent acquisition for rapid estimation of organelle abundance in subcellular samples

Summary Measuring changes in protein or organelle abundance in the cell is an essential, but challenging aspect of cell biology. Frequently‐used methods for determining organelle abundance typically rely on detection of a very few marker proteins, so are unsatisfactory. In silico estimates of protein abundances from publicly available protein spectra can provide useful standard abundance values but contain only data from tissue proteomes, and are not coupled to organelle localization data. A new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower‐abundance proteins in Arabidopsis. NPAS was combined with subcellular protein localization data, facilitating quantitative estimations of organelle abundance during routine experimental procedures. A suite of targeted proteomics markers for subcellular compartment markers was developed, enabling independent verification of in silico estimates for relative organelle abundance. Estimation of relative organelle abundance was found to be reproducible and consistent over a range of tissues and growth conditions. In silico abundance estimations and localization data have been combined into an online tool, multiple marker abundance profiling, available in the SUBA4 toolbox (http://suba.live).