Lineage Information Research Articles

Synthetic recording and in situ readout of lineage information in single cells.

Reconstructing the lineage relationships and dynamic event histories of individual cells within their native spatial context is a long-standing challenge in biology. Many biological processes of interest occur in optically opaque or physically inaccessible contexts, necessitating approaches other than direct imaging. Here we describe a synthetic system that enables cells to record lineage information and event histories in the genome in a format that can be subsequently read out of single cells in situ. This system, termed memory by engineered mutagenesis with optical in situ readout (MEMOIR), is based on a set of barcoded recording elements termed scratchpads. The state of a given scratchpad can be irreversibly altered by CRISPR/Cas9-based targeted mutagenesis, and later read out in single cells through multiplexed single-molecule RNA fluorescence hybridization (smFISH). Using MEMOIR as a proof of principle, we engineered mouse embryonic stem cells to contain multiple scratchpads and other recording components. In these cells, scratchpads were altered in a progressive and stochastic fashion as the cells proliferated. Analysis of the final states of scratchpads in single cells in situ enabled reconstruction of lineage information from cell colonies. Combining analysis of endogenous gene expression with lineage reconstruction in the same cells further allowed inference of the dynamic rates at which embryonic stem cells switch between two gene expression states. Finally, using simulations, we show how parallel MEMOIR systems operating in the same cell could enable recording and readout of dynamic cellular event histories. MEMOIR thus provides a versatile platform for information recording and in situ, single-cell readout across diverse biological systems.

Determining Streptococcus suis serotype from short-read whole-genome sequencing data.

BackgroundStreptococcus suis is divided into 29 serotypes based on a serological reaction against the capsular polysaccharide (CPS). Multiplex PCR tests targeting the cps locus are also used to determine S. suis serotypes, but they cannot differentiate between serotypes 1 and 14, and between serotypes 2 and 1/2. Here, we developed a pipeline permitting in silico serotype determination from whole-genome sequencing (WGS) short-read data that can readily identify all 29 S. suis serotypes.ResultsWe sequenced the genomes of 121 strains representing all 29 known S. suis serotypes. We next combined available software into an automated pipeline permitting in silico serotyping of strains by differential alignment of short-read sequencing data to a custom S. suis cps loci database. Strains of serotype pairs 1 and 14, and 2 and 1/2 could be differentiated by a missense mutation in the cpsK gene. We report a 99 % match between coagglutination- and pipeline-determined serotypes for strains in our collection. We used 375 additional S. suis genomes downloaded from the NCBI’s Sequence Read Archive (SRA) to validate the pipeline. Validation with SRA WGS data resulted in a 92 % match. Included pipeline subroutines permitted us to assess strain virulence marker content and obtain multilocus sequence typing directly from WGS data.ConclusionsOur pipeline permits rapid and accurate determination of S. suis serotype, and other lineage information, directly from WGS data. By discriminating between serotypes 1 and 14, and between serotypes 2 and 1/2, our approach solves a three-decade longstanding S. suis typing issue.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-016-0782-8) contains supplementary material, which is available to authorized users.

Erratum to: ‘Genomic structure and marker-derived gene networks for growth and meat quality traits of Brazilian Nelore beef cattle’

Nelore is the major beef cattle breed in Brazil with more than 130 million heads. Genome-wide association studies (GWAS) are often used to associate markers and genomic regions to growth and meat quality traits that can be used to assist selection programs. An alternative methodology to traditional GWAS that involves the construction of gene network interactions, derived from results of several GWAS is the AWM (Association Weight Matrices)/PCIT (Partial Correlation and Information Theory). With the aim of evaluating the genetic architecture of Brazilian Nelore cattle, we used high-density SNP genotyping data (~770,000 SNP) from 780 Nelore animals comprising 34 half-sibling families derived from highly disseminated and unrelated sires from across Brazil. The AWM/PCIT methodology was employed to evaluate the genes that participate in a series of eight phenotypes related to growth and meat quality obtained from this Nelore sample. Our results indicate a lack of structuring between the individuals studied since principal component analyses were not able to differentiate families by its sires or by its ancestral lineages. The application of the AWM/PCIT methodology revealed a trio of transcription factors (comprising VDR, LHX9 and ZEB1) which in combination connected 66 genes through 359 edges and whose biological functions were inspected, some revealing to participate in biological growth processes in literature searches. The diversity of the Nelore sample studied is not high enough to differentiate among families neither by sires nor by using the available ancestral lineage information. The gene networks constructed from the AWM/PCIT methodology were a useful alternative in characterizing genes and gene networks that were allegedly influential in growth and meat quality traits in Nelore cattle.

Developmental analysis of the dopamine-containing neurons of the Drosophila brain.

The Drosophila dopaminergic (DAergic) system consists of a relatively small number of neurons clustered throughout the brain and ventral nerve cord. Previous work shows that clusters of DA neurons innervate different brain compartments, which in part accounts for functional diversity of the DA system. We analyzed the association between DA neuron clusters and specific brain lineages, developmental and structural units of the Drosophila brain that provide a framework of connections that can be followed throughout development. The hatching larval brain contains six groups of primary DA neurons (born in the embryo), which we assign to six distinct lineages. We can show that all larval DA clusters persist into the adult brain. Some clusters increase in cell number during late larval stages, whereas others do not become DA positive until early pupa. Ablating neuroblasts with hydroxyurea (HU) prior to onset of larval proliferation (generates secondary neurons) confirms that these added DA clusters are primary neurons born in the embryo, rather than secondary neurons. A single cluster that becomes DA positive in the late pupa, PAM1/lineage DALcm1/2, forms part of a secondary lineage that can be ablated by larval HU application. By supplying lineage information for each DA cluster, our analysis promotes further developmental and functional analyses of this important system of neurons. J. Comp. Neurol. 525:363-379, 2017. © 2016 Wiley Periodicals, Inc.

Whole-organism lineage tracing by combinatorial and cumulative genome editing.

Multicellular systems develop from single cells through distinct lineages. However, current lineage-tracing approaches scale poorly to whole, complex organisms. Here, we use genome editing to progressively introduce and accumulate diverse mutations in a DNA barcode over multiple rounds of cell division. The barcode, an array of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 target sites, marks cells and enables the elucidation of lineage relationships via the patterns of mutations shared between cells. In cell culture and zebrafish, we show that rates and patterns of editing are tunable and that thousands of lineage-informative barcode alleles can be generated. By sampling hundreds of thousands of cells from individual zebrafish, we find that most cells in adult organs derive from relatively few embryonic progenitors. In future analyses, genome editing of synthetic target arrays for lineage tracing (GESTALT) can be used to generate large-scale maps of cell lineage in multicellular systems for normal development and disease.

Fluoroquinolone-Resistant Enteric Bacteria in Sub-Saharan Africa: Clones, Implications and Research Needs.

Fluoroquinolones came into widespread use in African countries in the early 2000s, after patents for the first generation of these drugs expired. By that time, quinolone antibacterial agents had been used intensively worldwide and resistant lineages of many bacterial species had evolved. We sought to understand which Gram negative enteric pandemic lineages have been reported from Africa, as well as the nature and transmission of any indigenous resistant clones. A systematic review of articles indexed in the Medline and AJOL literature databases was conducted. We report on the findings of 43 eligible studies documenting local or pandemic fluoroquinolone-resistant enteric clones in sub-Sahara African countries. Most reports are of invasive non-typhoidal Salmonella and Escherichia coli lineages and there have been three reports of cholera outbreaks caused by fluoroquinolone-resistant Vibrio cholerae O1. Fluoroquinolone-resistant clones have also been reported from commensals and animal isolates but there are few data for non-Enterobacteriaceae and almost none for difficult-to-culture Campylobacter spp. Fluoroquinolone-resistant lineages identified in African countries were universally resistant to multiple other classes of antibacterial agents. Although as many as 972 non-duplicate articles refer to fluoroquinolone resistance in enteric bacteria from Africa, most do not report on subtypes and therefore information on the epidemiology of fluoroquinolone-resistant clones is available from only a handful of countries in the subcontinent. When resistance is reported, resistance mechanisms and lineage information is rarely investigated. Insufficient attention has been given to molecular and sequence-based methods necessary for identifying and tracking resistant clones in Africa and more research is needed in this area.

LVTree Viewer: An Interactive Display for the All-Species Living Tree Incorporating Automatic Comparison with Prokaryotic Systematics

We describe an interactive viewer for the All-Species Living Tree (LVTree). The viewer incorporates treeing and lineage information from the ARB-SILVA website. It allows collapsing the tree branches at different taxonomic ranks and expanding the collapsed branches as well, keeping the overall topology of the tree unchanged. It also enables the user to observe the consequence of trial lineage modifications by re-collapsing the tree. The system reports taxon statistics at all ranks automatically after each collapsing and re-collapsing. These features greatly facilitate the comparison of the 16S rRNA sequence phylogeny with prokaryotic taxonomy in a taxon by taxon manner. In view of the fact that the present prokaryotic systematics is largely based on 16S rRNA sequence analysis, the current viewer may help reveal discrepancies between phylogeny and taxonomy. As an application, we show that in the latest release of LVTree, based on 11,939 rRNA sequences, as few as 24 lineage modifications are enough to bring all but two phyla (Proteobacteria and Firmicutes) to monophyletic clusters.

Genomic structure and marker-derived gene networks for growth and meat quality traits of Brazilian Nelore beef cattle.

BackgroundNelore is the major beef cattle breed in Brazil with more than 130 million heads. Genome-wide association studies (GWAS) are often used to associate markers and genomic regions to growth and meat quality traits that can be used to assist selection programs. An alternative methodology to traditional GWAS that involves the construction of gene network interactions, derived from results of several GWAS is the AWM (Association Weight Matrices)/PCIT (Partial Correlation and Information Theory). With the aim of evaluating the genetic architecture of Brazilian Nelore cattle, we used high-density SNP genotyping data (~770,000 SNP) from 780 Nelore animals comprising 34 half-sibling families derived from highly disseminated and unrelated sires from across Brazil. The AWM/PCIT methodology was employed to evaluate the genes that participate in a series of eight phenotypes related to growth and meat quality obtained from this Nelore sample.ResultsOur results indicate a lack of structuring between the individuals studied since principal component analyses were not able to differentiate families by its sires or by its ancestral lineages. The application of the AWM/PCIT methodology revealed a trio of transcription factors (comprising VDR, LHX9 and ZEB1) which in combination connected 66 genes through 359 edges and whose biological functions were inspected, some revealing to participate in biological growth processes in literature searches.ConclusionsThe diversity of the Nelore sample studied is not high enough to differentiate among families neither by sires nor by using the available ancestral lineage information. The gene networks constructed from the AWM/PCIT methodology were a useful alternative in characterizing genes and gene networks that were allegedly influential in growth and meat quality traits in Nelore cattle.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2535-3) contains supplementary material, which is available to authorized users.

Distinguishing between stochasticity and determinism: Examples from cell cycle duration variability.

We describe a recent approach for distinguishing between stochastic and deterministic sources of variability, focusing on the mammalian cell cycle. Variability between cells is often attributed to stochastic noise, although it may be generated by deterministic components. Interestingly, lineage information can be used to distinguish between variability and determinism. Analysis of correlations within a lineage of the mammalian cell cycle duration revealed its deterministic nature. Here, we discuss the sources of such variability and the possibility that the underlying deterministic process is due to the circadian clock. Finally, we discuss the "kicked cell cycle" model and its implication on the study of the cell cycle in healthy and cancerous tissues.

A cell size- and cell cycle-aware stochastic model for predicting time-dynamic gene network activity in individual cells.

BackgroundDespite the development of various modeling approaches to predict gene network activity, a time dynamic stochastic model taking into account real-time changes in cell volume and cell cycle stages is still missing.ResultsHere we present a stochastic single-cell model that can be applied to any eukaryotic gene network with any number of components. The model tracks changes in cell volume, DNA replication, and cell division, and dynamically adjusts rates of stochastic reactions based on this information. By tracking cell division, the model can maintain cell lineage information, allowing the researcher to trace the descendants of any single cell and therefore study cell lineage effects. To test the predictive power of our model, we applied it to the canonical galactose network of the yeast Saccharomyces cerevisiae. Using a minimal set of free parameters and across several galactose induction conditions, the model effectively captured several details of the experimentally-obtained single-cell network activity levels as well as phenotypic switching rates.ConclusionOur model can readily be customized to model any gene network in any of the commonly used cells types, offering a novel and user-friendly stochastic modeling capability to the systems biology field.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0240-5) contains supplementary material, which is available to authorized users.

Inferring epigenetic dynamics from kin correlations

Populations of isogenic embryonic stem cells or clonal bacteria often exhibit extensive phenotypic heterogeneity that arises from intrinsic stochastic dynamics of cells. The phenotypic state of a cell can be transmitted epigenetically in cell division, leading to correlations in the states of cells related by descent. The extent of these correlations is determined by the rates of transitions between the phenotypic states. Therefore, a snapshot of the phenotypes of a collection of cells with known genealogical structure contains information on phenotypic dynamics. Here, we use a model of phenotypic dynamics on a genealogical tree to define an inference method that allows extraction of an approximate probabilistic description of the dynamics from observed phenotype correlations as a function of the degree of kinship. The approach is tested and validated on the example of Pyoverdine dynamics in Pseudomonas aeruginosa colonies. Interestingly, we find that correlations among pairs and triples of distant relatives have a simple but nontrivial structure indicating that observed phenotypic dynamics on the genealogical tree is approximately conformal--a symmetry characteristic of critical behavior in physical systems. The proposed inference method is sufficiently general to be applied in any system where lineage information is available.

Phylogeny and Taxonomy of Archaea: A Comparison of the Whole-Genome-Based CVTree Approach with 16S rRNA Sequence Analysis.

A tripartite comparison of Archaea phylogeny and taxonomy at and above the rank order is reported: (1) the whole-genome-based and alignment-free CVTree using 179 genomes; (2) the 16S rRNA analysis exemplified by the All-Species Living Tree with 366 archaeal sequences; and (3) the Second Edition of Bergey’s Manual of Systematic Bacteriology complemented by some current literature. A high degree of agreement is reached at these ranks. From the newly proposed archaeal phyla, Korarchaeota, Thaumarchaeota, Nanoarchaeota and Aigarchaeota, to the recent suggestion to divide the class Halobacteria into three orders, all gain substantial support from CVTree. In addition, the CVTree helped to determine the taxonomic position of some newly sequenced genomes without proper lineage information. A few discrepancies between the CVTree and the 16S rRNA approaches call for further investigation.

Adding Geospatial Data Provenance into SDI—A Service-Oriented Approach

Geospatial data provenance records the derivation history of a geospatial data product. It is important in evaluating the quality of data products. In a Geospatial Web Service environment where data are often disseminated and processed widely and frequently in an unpredictable way, it is even more important in identifying original data sources, tracing workflows, updating or reproducing scientific results, and evaluating reliability and quality of geospatial data products. Geospatial data provenance has become a fundamental issue in establishing the spatial data infrastructure (SDI). This paper investigates how to support provenance awareness in SDI. It addresses key issues including provenance modeling, capturing, and sharing in a SDI enabled by interoperable geospatial services. A reference architecture for provenance tracking is proposed, which can accommodate geospatial feature provenance at different levels of granularity. Open standards from ISO, World Wide Web Consortium (W3C), and OGC are leveraged to facilitate the interoperability. At the feature type level, this paper proposes extensions of W3C PROV-XML for ISO 19115 lineage and “Parent Level” provenance registration in the geospatial catalog service. At the feature instance level, light-weight lineage information entities for feature provenance are proposed and managed by Web Feature Services. Experiments demonstrate the applicability of the approach for creating provenance awareness in an interoperable geospatial service-oriented environment.

Factor graph analysis of live cell-imaging data reveals mechanisms of cell fate decisions.

Cell fate decisions have a strong stochastic component. The identification of the underlying mechanisms therefore requires a rigorous statistical analysis of large ensembles of single cells that were tracked and phenotyped over time. We introduce a probabilistic framework for testing elementary hypotheses on dynamic cell behavior using time-lapse cell-imaging data. Factor graphs, probabilistic graphical models, are used to properly account for cell lineage and cell phenotype information. Our model is applied to time-lapse movies of murine granulocyte-macrophage progenitor (GMP) cells. It decides between competing hypotheses on the mechanisms of their differentiation. Our results theoretically substantiate previous experimental observations that lineage instruction, not selection is the cause for the differentiation of GMP cells into mature monocytes or neutrophil granulocytes. The Matlab source code is available at http://treschgroup.de/Genealogies.html.

Segmentation of occluded hematopoietic stem cells from tracking.

Image sequences of live proliferating cells often contain visual ambiguities that are difficult even for human domain experts to resolve. Here we present a new approach to analyzing image sequences that capture the development of clones of hematopoietic stem cells (HSCs) from live cell time lapse microscopy. The HSCs cannot survive long term imaging unless they are cultured together with a secondary cell type, OP9 stromal cells. The HSCs frequently disappear under the OP9 cell layer, making segmentation difficult or impossible from a single image frame, even for a human domain expert. We have developed a new approach to the segmentation of HSCs that captures these occluded cells. Starting with an a priori segmentation that uses a Monte Carlo technique to estimate the number of cells in a clump of touching cells, we proceed to track and lineage the image data. Following user validation of the lineage information, an a posteriori resegmentation step utilizing tracking results delineates the HSCs occluded by the OP9 layer. Resegmentation has been applied to 3031 occluded segmentations from 77 tracks, correctly recovering over 84% of the occluded segmentations.

Integrating B Cell Lineage Information into Statistical Tests for Detecting Selection in Ig Sequences

Detecting selection in B cell Ig sequences is critical to understanding affinity maturation and can provide insights into Ag-driven selection in normal and pathologic immune responses. The most common sequence-based methods for detecting selection analyze the ratio of replacement and silent mutations using a binomial statistical analysis. However, these approaches have been criticized for low sensitivity. An alternative method is based on the analysis of lineage trees constructed from sets of clonally related Ig sequences. Several tree shape measures have been proposed as indicators of selection that can be statistically compared across cohorts. However, we show that tree shape analysis is confounded by underlying experimental factors that are difficult to control for in practice, including the sequencing depth and number of generations in each clone. Thus, although lineage tree shapes may reflect selection, their analysis alone is an unreliable measure of in vivo selection. To usefully capture the information provided by lineage trees, we propose a new method that applies the binomial statistical framework to mutations identified based on lineage tree structure. This hybrid method is able to detect selection with increased sensitivity in both simulated and experimental data sets. We anticipate that this approach will be especially useful in the analysis of large-scale Ig sequencing data sets generated by high-throughput sequencing technologies.

Labour-Efficient In Vitro Lymphocyte Population Tracking and Fate Prediction Using Automation and Manual Review

Interest in cell heterogeneity and differentiation has recently led to increased use of time-lapse microscopy. Previous studies have shown that cell fate may be determined well in advance of the event. We used a mixture of automation and manual review of time-lapse live cell imaging to track the positions, contours, divisions, deaths and lineage of 44 B-lymphocyte founders and their 631 progeny in vitro over a period of 108 hours. Using this data to train a Support Vector Machine classifier, we were retrospectively able to predict the fates of individual lymphocytes with more than 90% accuracy, using only time-lapse imaging captured prior to mitosis or death of 90% of all cells. The motivation for this paper is to explore the impact of labour-efficient assistive software tools that allow larger and more ambitious live-cell time-lapse microscopy studies. After training on this data, we show that machine learning methods can be used for realtime prediction of individual cell fates. These techniques could lead to realtime cell culture segregation for purposes such as phenotype screening. We were able to produce a large volume of data with less effort than previously reported, due to the image processing, computer vision, tracking and human-computer interaction tools used. We describe the workflow of the software-assisted experiments and the graphical interfaces that were needed. To validate our results we used our methods to reproduce a variety of published data about lymphocyte populations and behaviour. We also make all our data publicly available, including a large quantity of lymphocyte spatio-temporal dynamics and related lineage information.

Dissecting genealogy and cell cycle as sources of cell-to-cell variability in MAPK signaling using high-throughput lineage tracking

Cells, even those having identical genotype, exhibit variability in their response to external stimuli. This variability arises from differences in the abundance, localization, and state of cellular components. Such nongenetic differences are likely heritable between successive generations and can also be influenced by processes such as cell cycle, age, or interplay between different pathways. To address the contribution of nongenetic heritability and cell cycle in cell-to-cell variability we developed a high-throughput and fully automated microfluidic platform that allows for concurrent measurement of gene expression, cell-cycle periods, age, and lineage information under a large number of temporally changing medium conditions and using multiple strains. We apply this technology to examine the role of nongenetic inheritance in cell heterogeneity of yeast pheromone signaling. Our data demonstrate that the capacity to respond to pheromone is passed across generations and that the strength of the response correlations between related cells is affected by perturbations in the signaling pathway. We observe that a ste50Δ mutant strain exhibits highly heterogeneous response to pheromone originating from a unique asymmetry between mother and daughter response. On the other hand, fus3Δ cells were found to exhibit an unusually high correlation between mother and daughter cells that arose from a combination of extended cell-cycle periods of fus3Δ mothers, and decreased cell-cycle modulation of the pheromone pathway. Our results contribute to the understanding of the origins of cell heterogeneity and demonstrate the importance of automated platforms that generate single-cell data on several parameters.

New technologies in imaging

Visualization of cellular and molecular processes is an indispensable tool for cell biologists, and innovations in microscopy methods unfailingly lead to new biological discoveries. Today, light microscopy (LM) provides ever-higher spatial and temporal resolution and visualization of biological process over enormous ranges. Electron microscopy (EM) is moving into the atomic resolution regime and allowing cellular analyses that are more physiological and sophisticated in scope. Importantly, much is being gained by combining multiple approaches, (e.g., LM and EM) to take advantage of their complementary strengths. The advent of high-throughput microscopies has led to a common need for sophisticated computational methods to quantitatively analyze huge amounts of data and translate images into new biological insights. In vivo imaging requires carefully balancing conflicting parameters to achieve high imaging speed, low photobleaching and phototoxicity, good three-dimensional resolution, high signal-to-noise ratio, and excellent physical coverage. Light-sheet microscopy provides outstanding performance in all of these categories and has become a key imaging method for the life sciences. Philipp Keller (Janelia Farm) showed how entire Drosophila embryos can be imaged throughout embryogenesis at a spatiotemporal resolution that enables comprehensive cell tracking. The use of automated approaches for computational image analysis enables systematical reconstruction of cell lineage information from such recordings in real time. Progress in the light-sheet microscopy field is faster than ever, and further improvements in temporal and spatial resolution are expected in the near future. These capabilities will directly synergize with the rapid progress in related fields, such as the development of advanced fluorescent reporter strategies, powerful approaches to high-throughput data analysis, and computational tools for biophysical modeling, opening up exciting new opportunities for microscopy-based research in the life sciences. Superresolution imaging is enabling the visualization of intracellular relationships unobtainable using traditional fluorescent microscopy. However, different superresolution techniques are based on distinct physical principles to break conventional light microscopy limitations, and these principles determine the apparent size of the biological structure being imaged. The 25-nm-microtubule diameter appears to be between 30 and 120 nm, depending on the technique used. These specific principles also define the acquisition speed of each method, resulting in a family of techniques, with each technique optimized for both size (e.g., organelles vs. transmembrane receptors) and dynamics (e.g., slow transport vs. diffusion). Recent advances that combine multiple approaches to address a biological question show great promise for overcoming these limitations. Catherine Galbraith (National Institutes of Health) showed how live-cell superresolution imaging of single molecules can be combined with computer-vision tracking and conventional microscopy. This integration provides a dense functional dynamics map that can be used to correlate the behavior of multiple proteins to that of the entire cell, while visualizing biochemistry at the single-molecule level. Because biology is the complex integration of multiple systems, the combination of multiple imaging approaches presents the greatest opportunity for making meaningful discoveries. Cellular transmission EM has traditionally suffered from the need to section the sample. Sectioning of resin-embedded material is relatively easy but can suffer from poor sample preservation, while cryosectioning of frozen-hydrated samples is inefficient and hampered by cutting and compression artifacts. A recent breakthrough is the use of focus ion beam (FIB) methodology to carve out cellular slices, thus opening a window into deep regions of the eukaryotic cell without displaying the shortcomings of previous methods. Although EM methodologies cannot provide live imaging, they can produce snapshots that allow inference of biological transitions. The resolution and number of snapshots have been increasing with the high-throughput automated data collection and analysis. Eva Nogales (University of California–Berkeley, Howard Hughes Medical Institute, and Lawrence Berkeley National Laboratory) reported the structure of GMPCPP and GDP microtubules at 5-A resolution, showing how hydrolysis of GTP results in an accordion-like deformation of protofilaments that is overcome by the presence of Taxol. New developments (silicon-based cameras for direct electron detection, several phase-plate implementations for in-focus EM imaging, and innovative image analysis algorithms) will extend the scope of molecular and cellular EM studies, opening the door for higher-quality structures and quantitative conformational descriptions.

Pre-disposition and epigenetics govern variation in bacterial survival upon stress.

Bacteria suffer various stresses in their unpredictable environment. In response, clonal populations may exhibit cell-to-cell variation, hypothetically to maximize their survival. The origins, propagation, and consequences of this variability remain poorly understood. Variability persists through cell division events, yet detailed lineage information for individual stress-response phenotypes is scarce. This work combines time-lapse microscopy and microfluidics to uniformly manipulate the environmental changes experienced by clonal bacteria. We quantify the growth rates and RpoH-driven heat-shock responses of individual Escherichia coli within their lineage context, stressed by low streptomycin concentrations. We observe an increased variation in phenotypes, as different as survival from death, that can be traced to asymmetric division events occurring prior to stress induction. Epigenetic inheritance contributes to the propagation of the observed phenotypic variation, resulting in three-fold increase of the RpoH-driven expression autocorrelation time following stress induction. We propose that the increased permeability of streptomycin-stressed cells serves as a positive feedback loop underlying this epigenetic effect. Our results suggest that stochasticity, pre-disposition, and epigenetic effects are at the source of stress-induced variability. Unlike in a bet-hedging strategy, we observe that cells with a higher investment in maintenance, measured as the basal RpoH transcriptional activity prior to antibiotic treatment, are more likely to give rise to stressed, frail progeny.