Using flowViz to visualize flow cytometry data

Optimization of a Cas12a-Driven Synthetic Gene Regulatory Network System

Gene regulatory networks, which control gene expression patterns in development and in response to stimuli, use regulatory logic modules to coordinate inputs and outputs. One example of a regulatory logic module is the gene regulatory cascade (GRC), where a series of transcription factor genes turn on in order. Synthetic biologists have derived artificial systems that encode regulatory rules, including GRCs. Furthermore, the development of single-cell approaches has enabled the discovery of gene regulatory modules in a variety of experimental settings. However, the tools available for validating these observations remain limited. Based on a synthetic GRC using DNA cutting-defective Cas9 (dCas9), we designed and implemented an alternative synthetic GRC utilizing DNA cutting-defective Cas12a (dCas12a). Comparing the ability of these two systems to express a fluorescent reporter, the dCas9 system was initially more active, while the dCas12a system was more streamlined. Investigating the influence of individual components of the systems identified nuclear localization as a major driver of differences in activity. Improving nuclear localization for the dCas12a system resulted in 1.5-fold more reporter-positive cells and a 15-fold increase in reporter intensity relative to the dCas9 system. We call this optimized system the "Synthetic Gene Regulatory Network" (SGRN, pronounced "sojourn").

AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses

Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data. While gating of debris can be conducted using measured optical properties, identifying dead and dying cells often requires utilizing fluorescent stains (e.g. Sytox, a nucleic acid stain that stains cells with compromised cell membranes) to identify cells that should be excluded from downstream analyses. These stains prolong the experimental preparation process and use a flow cytometer's fluorescence channels that could otherwise be used to measure additional fluorescent markers within the cells (e.g. reporter proteins). Here we outline a stain-free method for identifying viable cells for downstream processing by gating cells that are dying or dead. AutoGater is a weakly supervised deep learning model that can separate healthy populations from unhealthy and dead populations using only light-scatter channels. In addition, AutoGater harmonizes different measurements of dead cells such as Sytox and CFUs.

SecMet-FISH: labeling, visualization, and enumeration of secondary metabolite producing microorganisms

Our understanding of the role of secondary metabolites in microbial communities is challenged by intrinsic limitations of culturing bacteria under laboratory conditions and hence cultivation independent approaches are needed. Here, we present a protocol termed Secondary Metabolite FISH (SecMet-FISH), combining advantages of gene-targeted fluorescence in situ hybridization (geneFISH) with in-solution methods (in-solution FISH) to detect and quantify cells based on their genetic capacity to produce secondary metabolites. The approach capitalizes on the conserved nature of biosynthetic gene clusters (BGCs) encoding adenylation (AD) and ketosynthase (KS) domains, and thus selectively targets the genetic basis of non-ribosomal peptide and polyketide biosynthesis. The concept relies on the generation of amplicon pools using degenerate primers broadly targeting AD and KS domains followed by fluorescent labeling, detection, and quantification. Initially, we obtained AD and KS amplicons from Pseuodoalteromonas rubra, which allowed us to successfully label and visualize BGCs within P. rubra cells, demonstrating the feasibility of SecMet-FISH. Next, we adapted the protocol and optimized it for hybridization in both Gram-negative and Gram-positive bacterial cell suspensions, enabling high-throughput single cell analysis by flow cytometry. Ultimately, we used SecMet-FISH to successfully distinguish secondary metabolite producers from non-producers in a five-member synthetic community.

SGRN: A Cas12a-driven Synthetic Gene Regulatory Network System

Gene regulatory networks, which control gene expression patterns in development and in response to stimuli, use regulatory logic modules to coordinate inputs and outputs. One example of a regulatory logic module is the gene regulatory cascade (GRC), where a series of transcription factor genes turn on in order. Synthetic biologists have derived artificial systems that encode regulatory rules, including GRCs. Furthermore, the development of single-cell approaches has enabled the discovery of gene regulatory modules in a variety of experimental settings. However, the tools available for validating these observations remain limited. Based on a synthetic GRC using DNA cutting-defective Cas9 (dCas9), we designed and implemented an alternative synthetic GRC utilizing DNA cutting-defective Cas12a (dCas12a). Comparing the ability of these two systems to express a fluorescent reporter, the dCas9 system was initially more active, while the dCas12a system was more streamlined. Investigating the influence of individual components of the systems identified nuclear localization as a major driver of differences in activity. Improving nuclear localization for the dCas12a system resulted in 1.5-fold more reporter-positive cells and a 15-fold increase in reporter intensity relative to the dCas9 system. We call this optimized system the "Synthetic Gene Regulatory Network" (SGRN, pronounced "sojourn").

Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress

There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of accuracy, uncover patterns in high-dimensional cytometric data that are undetectable by human analysis, facilitate the discovery of cell subpopulations, perform semi-automated immune cell profiling, and demonstrate potential to automate aspects of clinical multiparameter flow cytometric (MFC) diagnostic workflow. Utilizing AI in the analysis of cytometry samples can reduce subjective variability and assist in breakthroughs in understanding diseases. Here we review the diverse types of AI that are being applied to clinical cytometry data and how AI is driving advances in data analysis to improve diagnostic sensitivity and accuracy. We review supervised and unsupervised clustering algorithms for cell population identification, various dimensionality reduction techniques, and their utilities in visualization and machine learning pipelines, and supervised learning approaches for classifying entire cytometry samples.Understanding the AI landscape will enable pathologists to better utilize open source and commercially available tools, plan exploratory research projects to characterize diseases, and work with machine learning and data scientists to implement clinical data analysis pipelines.

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

Advances in automated real-time flow cytometry for monitoring of bioreactor processes

Flow cytometry and its technological possibilities have greatly advanced in the past decade as analysis tool for single cell properties and population distributions of different cell types in bioreactors. Along the way, some solutions for automated real-time flow cytometry (ART-FCM) were developed for monitoring of bioreactor processes without operator interference over extended periods with variable sampling frequency. However, there is still great potential for ART-FCM to evolve and possibly become a standard application in bioprocess monitoring and process control. This review first addresses different components of an ART-FCM, including the sampling device, the sample-processing unit, the unit for sample delivery to the flow cytometer and the settings for measurement of pre-processed samples. Also, available algorithms are presented for automated data analysis of multi-parameter fluorescence datasets derived from ART-FCM experiments. Furthermore, challenges are discussed for integration of fluorescence-activated cell sorting into an ART-FCM setup for isolation and separation of interesting subpopulations that can be further characterized by for instance omics-methods. As the application of ART-FCM is especially of interest for bioreactor process monitoring, including investigation of population heterogeneity and automated process control, a summary of already existing setups for these purposes is given. Additionally, the general future potential of ART-FCM is addressed.

Aborting meiosis allows recombination in sterile diploid yeast hybrids

Hybrids between diverged lineages contain novel genetic combinations but an impaired meiosis often makes them evolutionary dead ends. Here, we explore to what extent an aborted meiosis followed by a return-to-growth (RTG) promotes recombination across a panel of 20 Saccharomyces cerevisiae and S. paradoxus diploid hybrids with different genomic structures and levels of sterility. Genome analyses of 275 clones reveal that RTG promotes recombination and generates extensive regions of loss-of-heterozygosity in sterile hybrids with either a defective meiosis or a heavily rearranged karyotype, whereas RTG recombination is reduced by high sequence divergence between parental subgenomes. The RTG recombination preferentially arises in regions with low local heterozygosity and near meiotic recombination hotspots. The loss-of-heterozygosity has a profound impact on sexual and asexual fitness, and enables genetic mapping of phenotypic differences in sterile lineages where linkage analysis would fail. We propose that RTG gives sterile yeast hybrids access to a natural route for genome recombination and adaptation.

The Impact of Variable Horizon Shade on the Growing Season Energy Budget of a Subalpine Headwater Wetland

Surface energy budgets are important to the ecohydrology of complex terrain, where land surfaces cycle in and out of shadows creating distinct microclimates. Shading in such environments can help regulate downstream flow over the course of a growing season, but our knowledge on how shadows impact the energy budget and consequently ecohydrology in montane ecosystems is very limited. We investigated the influence of horizon shade on the surface energy fluxes of a subalpine headwater wetland in the Canadian Rocky Mountains during the growing season. During the study, surface insolation decreased by 60% (32% due to evolving horizon shade and 28% from seasonality). The influence of shade on the energy budget varied between two distinct periods: (1) Stable Shade, when horizon shade was constant and reduced sunlight by 2 h per day; and (2) Dynamic Shade, when shade increased and reduced sunlight by 0.18 h more each day, equivalent to a 13% reduction in incoming shortwave radiation and 16% in net radiation. Latent heat flux, the dominant energy flux at our site, varied temporally because of changes in incoming radiation, atmospheric demand, soil moisture and shade. Horizon shade controlled soil moisture at our site by prolonging snowmelt and reducing evapotranspiration in the late growing season, resulting in increased water storage capacity compared to other mountain wetlands. With the mounting risk of climate-change-driven severe spring flooding and late season droughts downstream of mountain headwaters, shaded subalpine wetlands provide important ecohydrological and mitigation services that are worthy of further study and mapping. This will help us better understand and protect mountain and prairie water resources.

Sickle-trait hemoglobin reduces adhesion to both CD36 and EPCR by Plasmodium falciparum-infected erythrocytes

Sickle-trait hemoglobin protects against severe Plasmodium falciparum malaria. Severe malaria is governed in part by the expression of the Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) that are encoded by var genes, specifically those variants that bind Endothelial Protein C Receptor (EPCR). In this study, we investigate the effect of sickle-trait on parasite var gene expression and function in vitro and in field-collected parasites. We mapped var gene reads generated from RNA sequencing in parasite cultures in normal and sickle-cell trait blood throughout the asexual lifecycle. We investigated sickle-trait effect on PfEMP1 interactions with host receptors CD36 and EPCR using static adhesion assays and flow cytometry. Var expression in vivo was compared by assembling var domains sequenced from total RNA in parasites infecting Malian children with HbAA and HbAS. Sickle-trait did not alter the abundance or type of var gene transcripts in vitro, nor the abundance of overall transcripts or of var functional domains in vivo. In adhesion assays using recombinant host receptors, sickle-trait reduced adhesion by 73-86% to CD36 and 83% to EPCR. Similarly, sickle-trait reduced the surface expression of EPCR-binding PfEMP1. In conclusion, Sickle-cell trait does not directly affect var gene transcription but does reduce the surface expression and function of PfEMP1. This provides a direct mechanism for protection against severe malaria conferred by sickle-trait hemoglobin. Trial Registration: ClinicalTrials.gov Identifier: NCT02645604.

Selective Uptake of Pelagic Microbial Community Members by Caribbean Reef Corals

We identify interactions between coral grazing behavior and the growth rates and cell abundances of pelagic microbial groups found surrounding a Caribbean reef. During incubation experiments with three reef corals, reductions in microbial cell abundance differed according to coral species and suggest specific coral or microbial mechanisms are at play.

Coral reefs are possible sinks for microbes; however, the removal mechanisms at play are not well understood. Here, we characterize pelagic microbial groups at the CARMABI reef (Curaçao) and examine microbial consumption by three coral species: Madracis mirabilis, Porites astreoides, and Stephanocoenia intersepta. Flow cytometry analyses of water samples collected from a depth of 10 m identified 6 microbial groups: Prochlorococcus, three groups of Synechococcus, photosynthetic eukaryotes, and heterotrophic bacteria. Minimum growth rates (μ) for Prochlorococcus, all Synechococcus groups, and photosynthetic eukaryotes were 0.55, 0.29, and 0.45 μ day⁻¹, respectively, and suggest relatively high rates of productivity despite low nutrient conditions on the reef. During a series of 5-h incubations with reef corals performed just after sunset or prior to sunrise, reductions in the abundance of photosynthetic picoeukaryotes, Prochlorococcus and Synechococcus cells, were observed. Of the three Synechococcus groups, one decreased significantly during incubations with each coral and the other two only with M. mirabilis. Removal of carbon from the water column is based on coral consumption rates of phytoplankton and averaged between 138 ng h⁻¹ and 387 ng h⁻¹, depending on the coral species. A lack of coral-dependent reduction in heterotrophic bacteria, differences in Synechococcus reductions, and diurnal variation in reductions of Synechococcus and Prochlorococcus, coinciding with peak cell division, point to selective feeding by corals. Our study indicates that bentho-pelagic coupling via selective grazing of microbial groups influences carbon flow and supports heterogeneity of microbial communities overlying coral reefs.

IMPORTANCE We identify interactions between coral grazing behavior and the growth rates and cell abundances of pelagic microbial groups found surrounding a Caribbean reef. During incubation experiments with three reef corals, reductions in microbial cell abundance differed according to coral species and suggest specific coral or microbial mechanisms are at play. Peaks in removal rates of Prochlorococcus and Synechococcus cyanobacteria appear highest during postsunset incubations and coincide with microbial cell division. Grazing rates and effort vary across coral species and picoplankton groups, possibly influencing overall microbial composition and abundance over coral reefs. For reef corals, use of such a numerically abundant source of nutrition may be advantageous, especially under environmentally stressful conditions when symbioses with dinoflagellate algae break down.

CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data

BACKGROUND

The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge.

RESULTS

Here, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner.

CONCLUSIONS

CytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow.

Computational Analysis of Microbial Flow Cytometry Data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.

Computational flow cytometry of planktonic populations for the evaluation of microbiological-control programs in district cooling plants

Biofouling poses a serious concern for the district cooling (DC) industry. Current industry practises for monitoring biofouling continue to rely on culture-based methods for microbial enumeration, which are ultimately flawed. Computational flow cytometric (cFCM) analyses, which offer enhanced reproducibility and streamlined analytics versus conventional flow cytometry were applied to samples taken from 3 sites in each of 3 plants over a 5-week sampling program. We asked whether the application of cFCM to monitoring planktonic community dynamics in DC plants could be able to provide sufficient information to enhance microbiological-control strategies at site and inform about plant performance impacts. The use of cFCM enabled the evaluation of biocide dosing, deep cleaning treatment efficiencies and routes of microbial ingress into the studied systems. Additionally, inherent risks arising from the reintroduction of microbiological communities into recently cleaned WCT basins from contaminated cooling waters were identified. However, short-term dynamics did not relate with plant performance metrics. In summary, the insights offered by this approach can inform on plant status, enable evaluations of microbial loads during biofouling mitigation programs and, ultimately, enhance industry management of the biofouling process.

Pathogenic Allodiploid Hybrids of Aspergillus Fungi

Interspecific hybridization substantially alters genotypes and phenotypes and can give rise to new lineages. Hybrid isolates that differ from their parental species in infection-relevant traits have been observed in several human-pathogenic yeasts and plant-pathogenic filamentous fungi but have yet to be found in human-pathogenic filamentous fungi. We discovered 6 clinical isolates from patients with aspergillosis originally identified as Aspergillus nidulans (section Nidulantes) that are actually allodiploid hybrids formed by the fusion of Aspergillus spinulosporus with an unknown close relative of Aspergillus quadrilineatus, both in section Nidulantes. Evolutionary genomic analyses revealed that these isolates belong to Aspergillus latus, an allodiploid hybrid species. Characterization of diverse infection-relevant traits further showed that A. latus hybrid isolates are genomically and phenotypically heterogeneous but also differ from A. nidulans, A. spinulosporus, and A. quadrilineatus. These results suggest that allodiploid hybridization contributes to the genomic and phenotypic diversity of filamentous fungal pathogens of humans.

ggCyto: next generation open-source visualization software for cytometry

Motivation

Open source software for computational cytometry has gained in popularity over the past few years. Efforts such as FlowCAP, the Lyoplate and Euroflow projects have highlighted the importance of efforts to standardize both experimental and computational aspects of cytometry data analysis. The R/BioConductor platform hosts the largest collection of open source cytometry software covering all aspects of data analysis and providing infrastructure to represent and analyze cytometry data with all relevant experimental, gating and cell population annotations enabling fully reproducible data analysis. Data visualization frameworks to support this infrastructure have lagged behind.

Results

ggCyto is a new open-source BioConductor software package for cytometry data visualization built on ggplot2 that enables ggplot-like functionality with the core BioConductor flow cytometry data structures. Amongst its features are the ability to transform data and axes on-the-fly using cytometry-specific transformations, plot faceting by experimental meta-data variables and partial matching of channel, marker and cell populations names to the contents of the BioConductor cytometry data structures. We demonstrate the salient features of the package using publicly available cytometry data with complete reproducible examples in a Supplementary Material.

Availability and implementation

https://bioconductor.org/packages/devel/bioc/html/ggcyto.html

Supplementary information

Supplementary data are available at Bioinformatics online.

Flow cytometry data analysis: Recent tools and algorithms

Flow cytometry (FCM) allows scientists to rapidly quantify up to 50 parameters for millions of cells per sample. The bottleneck in the application of the technology is data analysis, and the high number of parameters measured by the current generation of instruments requires the use of advanced computational algorithms to make full use of their capabilities. This review summarizes the main steps of FCM data analysis, focusing on the use of the most recent bioinformatic tools developed for an R-based programming environment. In particular, for each stage of the data analysis, libraries and packages currently available are listed, and a brief description of their functioning is included.

Comparing biotic drivers of litter breakdown across stream compartments

Litter breakdown in the streambed is an important pathway in organic carbon cycling and energy transfer in the biosphere that is mediated by a wide range of streambed organisms. However, most research on litter breakdown to date has focused on a small fraction of the taxa that drive it (e.g. microbial vs. macroinvertebrate-mediated breakdown) and has been limited to the benthic zone (BZ). Despite the importance of the hyporheic zone (HZ) as a bioreactor, little is known about what, or who, mediates litter breakdown in this compartment and whether breakdown rates differ between the BZ and HZ. Here, we explore the relationship between litter breakdown and the variation in community structure of benthic and hyporheic communities by deploying two standardized bioassays (cotton strips and two types of commercially available tea bags) in 30 UK streams that encompass a range of environmental conditions. Then, we modelled these assays as a response of the streambed compartment and the biological features of the streambed assemblage (Prokaryota, Protozoa and Eumetazoa invertebrates) to understand the generality and efficiency of litter processing across communities. Litter breakdown was much faster in the BZ compared with the HZ (around 5 times higher for cotton strips and 1.5 times faster for the tea leaves). However, differences in litter breakdown between the BZ and the HZ were mediated by the biological features of the benthos and the hyporheos. Biomass of all the studied biotic groups, α-diversity of Eumetazoa invertebrates and metabolic diversity of Prokaryota were important predictors that were positively related to breakdown coefficients demonstrating their importance in the functioning of the streambed ecosystem. Our study uses a novel multimetric bioassay that is able to disentangle the contribution by Prokaryota, Protozoa and Eumetazoa invertebrates to litter breakdown. In doing so, our study reveals new insights into how organic matter decomposition is partitioned across biota and streambed compartments.

Accurate design of translational output by a neural network model of ribosome distribution

Synonymous codon choice can have dramatic effects on ribosome speed and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs. Here, we have modeled this variation in translation elongation by using a feed-forward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. Our approach revealed sequence features affecting translation elongation and characterized large technical biases in ribosome profiling. We applied our model to design synonymous variants of a fluorescent protein spanning the range of translation speeds predicted with our model. Levels of the fluorescent protein in budding yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo; that this information can be harnessed to design coding sequences; and that control of translation elongation alone is sufficient to produce large quantitative differences in protein output.

Identifying and exploiting genes that potentiate the evolution of antibiotic resistance

There is an urgent need to develop novel approaches for predicting and preventing the evolution of antibiotic resistance. Here we show that the ability to evolve de novo resistance to a clinically important β-lactam antibiotic, ceftazidime, varies drastically across the genus Pseudomonas. This variation arises because strains possessing the ampR global transcriptional regulator evolve resistance at a high rate. This does not arise because of mutations in ampR. Instead, this regulator potentiates evolution by allowing mutations in conserved peptidoglycan biosynthesis genes to induce high levels of β-lactamase expression. Crucially, blocking this evolutionary pathway by co-administering ceftazidime with the β-lactamase inhibitor avibactam can be used to eliminate pathogenic P. aeruginosa populations before they can evolve resistance. In summary, our study shows that identifying potentiator genes that act as evolutionary catalysts can be used to both predict and prevent the evolution of antibiotic resistance.

Comparison of phagocytosis in three Caribbean Sea urchins

In 1983 large numbers of the sea urchin Diadema antillarum unexplainably began showing signs of illness and dying in the Caribbean, and over the next year they came close to extinction, making it one of the worst mass mortality events on record. Present evidence suggests a water-borne pathogen as the etiological agent. Decades later Diadema densities remain low, and its near extinction has been a major factor in transforming living coral reefs in the Caribbean to barren algae-covered rock. In the ensuing decades, no solid explanation has been found to the questions: what killed Diadema; why did Diadema succumb while other species of urchins on the same reefs did not; and why has Diadema still not recovered? A recent hypothesis posited by our lab as to Diadema's vulnerability was directed at possible compromised immunity in Diadema, and experimental results found a significantly impaired humoral response to a key component of gram-negative bacteria. Here we use flow cytometry to examine the cellular arm of invertebrate immunity. We performed cytotoxicity and phagocytosis assays as a measure of the cellular immune responses of cells from Diadema and two other species of sea urchins not affected by the die-off. Despite our previous findings of in impaired humoral response, our study found no apparent difference in the cellular phagocytic response of Diadema compared to the other urchin species studied.

Gut dysbiosis and neuroimmune responses to brain infection with Theiler's murine encephalomyelitis virus

Recent studies have begun to point out the contribution of microbiota to multiple sclerosis (MS) pathogenesis. Theiler's murine encephalomyelitis virus induced demyelinating disease (TMEV-IDD) is a model of progressive MS. Here, we first analyze the effect of intracerebral infection with TMEV on commensal microbiota and secondly, whether the early microbiota depletion influences the immune responses to TMEV on the acute phase (14 dpi) and its impact on the chronic phase (85 dpi). The intracranial inoculation of TMEV was associated with a moderate dysbiosis. The oral administration of antibiotics (ABX) of broad spectrum modified neuroimmune responses to TMEV dampening brain CD4⁺ and CD8⁺ T infiltration during the acute phase. The expression of cytokines, chemokines and VP2 capsid protein was enhanced and accompanied by clusters of activated microglia disseminated throughout the brain. Furthermore, ABX treated mice displayed lower levels of CD4⁺ and CD8⁺T cells in cervical and mesenteric lymph nodes. Increased mortality to TMEV was observed after ABX cessation at day 28pi. On the chronic phase, mice that survived after ABX withdrawal and recovered microbiota diversity showed subtle changes in brain cell infiltrates, microglia and gene expression of cytokines. Accordingly, the surviving mice of the group ABX-TMEV displayed similar disease severity than TMEV mice.

Evaluating flow cytometer performance with weighted quadratic least squares analysis of LED and multi-level bead data

We developed a fully automated procedure for analyzing data from LED pulses and multilevel bead sets to evaluate backgrounds and photoelectron scales of cytometer fluorescence channels. The method improves on previous formulations by fitting a full quadratic model with appropriate weighting and by providing standard errors and peak residuals as well as the fitted parameters themselves. Here we describe the details of the methods and procedures involved and present a set of illustrations and test cases that demonstrate the consistency and reliability of the results. The automated analysis and fitting procedure is generally quite successful in providing good estimates of the Spe (statistical photoelectron) scales and backgrounds for all the fluorescence channels on instruments with good linearity. The precision of the results obtained from LED data is almost always better than that from multilevel bead data, but the bead procedure is easy to carry out and provides results good enough for most purposes. Including standard errors on the fitted parameters is important for understanding the uncertainty in the values of interest. The weighted residuals give information about how well the data fits the model, and particularly high residuals indicate bad data points. Known photoelectron scales and measurement channel backgrounds make it possible to estimate the precision of measurements at different signal levels and the effects of compensated spectral overlap on measurement quality. Combining this information with measurements of standard samples carrying dyes of biological interest, we can make accurate comparisons of dye sensitivity among different instruments. Our method is freely available through the R/Bioconductor package flowQB. © 2017 International Society for Advancement of Cytometry.

An expression tag toolbox for microbial production of membrane bound plant cytochromes P450

Membrane-associated Cytochromes P450 (P450s) are one of the most important enzyme families for biosynthesis of plant-derived medicinal compounds. However, the hydrophobic nature of P450s makes their use in robust cell factories a challenge. Here, we explore a small library of N-terminal expression tag chimeras of the model plant P450 CYP79A1 in different Escherichia coli strains. Using a high-throughput screening platform based on C-terminal GFP fusions, we identify several highly expressing and robustly performing chimeric designs. Analysis of long-term cultures by flow cytometry showed homogeneous populations for some of the conditions. Three chimeric designs were chosen for a more complex combinatorial assembly of a multigene pathway consisting of two P450s and a redox partner. Cells expressing these recombinant enzymes catalyzed the conversion of the substrate to highly different ratios of the intermediate and the final product of the pathway. Finally, the effect of a robustly performing expression tag was explored with a library of 49 different P450s from medicinal plants and nearly half of these were improved in expression by more than twofold. The developed toolbox serves as a platform to tune P450 performance in microbial cells, thereby facilitating recombinant production of complex plant P450-derived biochemicals. Biotechnol. Bioeng. 2017;114: 751-760. © 2016 Wiley Periodicals, Inc.

Hypoxia and classical activation limits Mycobacterium tuberculosis survival by Akt-dependent glycolytic shift in macrophages

Cellular reactive oxygen species (ROS) is a major antibacterial defense mechanism used by macrophages upon activation. Exposure of Mycobacterium tuberculosis (Mtb)-infected macrophages to hypoxia is known to compromise the survival of the pathogen. Here we report that the hypoxia-induced control of intracellular Mtb load in RAW 264.7 macrophages was mediated by regulating the cellular ROS levels. We show that similar to classical activation, hypoxia incubation of macrophages resulted in decreased mitochondrial outer membrane potential (MOMP) and a concomitant increase in the cellular ROS levels. Mitochondrial depolarization and consequently higher ROS could be blocked by knocking down Akt using siRNAs, which acted by inhibiting the switch to glycolytic mode of metabolism, an essential adaptive response upon classical activation or hypoxic incubation of macrophages. Moreover, in the classically activated macrophages or in the macrophages under hypoxia incubation, supplementation with additional glucose had similar effects as Akt knockdown. Interestingly, in both the cases, the reversal of phenotype was linked with the ability of the mitochondrial F₀–F₁ ATP synthase activity to maintain the MOMP in the absence of oxidative phosphorylation. Both Akt knockdown and glucose supplementation were also able to rescue Mtb survival in these macrophages upon classical activation or hypoxia incubation. These results provide a framework for better understanding of how the interplay between oxygen supply, which is limiting in the human tubercular granulomas, and nutrient availability could together direct the outcome of infections in vivo.

Pharmacodynamics of anti-HIV gene therapy using viral vectors and targeted endonucleases

OBJECTIVES

A promising curative approach for HIV is to use designer endonucleases that bind and cleave specific target sequences within latent genomes, resulting in mutations that render the virus replication incompetent. We developed a mathematical model to describe the expression and activity of endonucleases delivered to HIV-infected cells using engineered viral vectors in order to guide dose selection and predict therapeutic outcomes.

METHODS

We developed a mechanistic model that predicts the number of transgene copies expressed at a given dose in individual target cells from fluorescence of a reporter gene. We fitted the model to flow cytometry datasets to determine the optimal vector serotype, promoter and dose required to achieve maximum expression.

RESULTS

We showed that our model provides a more accurate measure of transduction efficiency compared with gating-based methods, which underestimate the percentage of cells expressing reporter genes. We identified that gene expression follows a sigmoid dose-response relationship and that the level of gene expression saturation depends on vector serotype and promoter. We also demonstrated that significant bottlenecks exist at the level of viral uptake and gene expression: only ∼1 in 220 added vectors enter a cell and, of these, depending on the dose and promoter used, between 1 in 15 and 1 in 1500 express transgene.

CONCLUSIONS

Our model provides a quantitative method of dose selection and optimization that can be readily applied to a wide range of other gene therapy applications. Reducing bottlenecks in delivery will be key to reducing the number of doses required for a functional cure.

flowAI: automatic and interactive anomaly discerning tools for flow cytometry data

Motivation: Flow cytometry (FCM) is widely used in both clinical and basic research to characterize cell phenotypes and functions. The latest FCM instruments analyze up to 20 markers of individual cells, producing high-dimensional data. This requires the use of the latest clustering and dimensionality reduction techniques to automatically segregate cell sub-populations in an unbiased manner. However, automated analyses may lead to false discoveries due to inter-sample differences in quality and properties.

Results: We present an R package, flowAI, containing two methods to clean FCM files from unwanted events: (i) an automatic method that adopts algorithms for the detection of anomalies and (ii) an interactive method with a graphical user interface implemented into an R shiny application. The general approach behind the two methods consists of three key steps to check and remove suspected anomalies that derive from (i) abrupt changes in the flow rate, (ii) instability of signal acquisition and (iii) outliers in the lower limit and margin events in the upper limit of the dynamic range. For each file analyzed our software generates a summary of the quality assessment from the aforementioned steps. The software presented is an intuitive solution seeking to improve the results not only of manual but also and in particular of automatic analysis on FCM data.

Availability and implementation: R source code available through Bioconductor: http://bioconductor.org/packages/flowAI/

Contacts:  mongianni1@gmail.com or Anis_Larbi@immunol.a-star.edu.sg

Supplementary information:  Supplementary data are available at Bioinformatics online.

Multi-parametric cytometry from a complex cellular sample: Improvements and limits of manual versus computational-based interactive analyses

The wide possibilities opened by the developments of multi-parametric cytometry are limited by the inadequacy of the classical methods of analysis to the multi-dimensional characteristics of the data. While new computational tools seemed ideally adapted and were applied successfully, their adoption is still low among the flow cytometrists. In the purpose to integrate unsupervised computational tools for the management of multi-stained samples, we investigated their advantages and limits by comparison to manual gating on a typical sample analyzed in immunomonitoring routine. A single tube of PBMC, containing 11 populations characterized by different sizes and stained with 9 fluorescent markers, was used. We investigated the impact of the strategy choice on manual gating variability, an undocumented pitfall of the analysis process, and we identified rules to optimize it. While assessing automatic gating as an alternate, we introduced the Multi-Experiment Viewer software (MeV) and validated it for merging clusters and annotating interactively populations. This procedure allowed the finding of both targeted and unexpected populations. However, the careful examination of computed clusters in standard dot plots revealed some heterogeneity, often below 10%, that was overcome by increasing the number of clusters to be computed. MeV facilitated the identification of populations by displaying both the MFI and the marker signature of the dataset simultaneously. The procedure described here appears fully adapted to manage homogeneously high number of multi-stained samples and allows improving multi-parametric analyses in a way close to the classic approach. © 2016 International Society for Advancement of Cytometry.

Bovine viral diarrhea virus structural protein E2 as a complement regulatory protein

Bovine viral diarrhea virus (BVDV) is a member of the genus Pestivirus, family Flaviviridae, and is one of the most widely distributed viruses in cattle worldwide. Approximately 60 % of cattle in endemic areas without control measures are infected with BVDV during their lifetime. This wide prevalence of BVDV in cattle populations results in significant economic losses. BVDV is capable of establishing persistent infections in its host due to its ability to infect fetuses, causing immune tolerance. However, this cannot explain how the virus evades the innate immune system. The objective of the present work was to test the potential activity of E2 as a complement regulatory protein. E2 glycoprotein, produced both in soluble and transmembrane forms in stable CHO-K1 cell lines, was able to reduce complement-mediated cell lysis up to 40 % and complement-mediated DNA fragmentation by 50 %, in comparison with cell lines not expressing the glycoprotein. This work provides the first evidence of E2 as a complement regulatory protein and, thus, the finding of a mechanism of immune evasion by BVDV. Furthermore, it is postulated that E2 acts as a self-associated molecular pattern (SAMP), enabling the virus to avoid being targeted by the immune system and to be recognized as self.

Standardized Multi-Color Flow Cytometry and Computational Biomarker Discovery

Multi-color flow cytometry has become a valuable and highly informative tool for diagnosis and therapeutic monitoring of patients with immune deficiencies or inflammatory disorders. However, the method complexity and error-prone conventional manual data analysis often result in a high variability between different analysts and research laboratories. Here, we provide strategies and guidelines aiming at a more standardized multi-color flow cytometric staining and unsupervised data analysis for whole blood patient samples.

AKT mediated glycolytic shift regulates autophagy in classically activated macrophages

Autophagy is considered as an innate defense mechanism primarily due to its role in the targeting of intracellular pathogens for lysosomal degradation. Here we report inhibition of autophagy as an adaptive response in classically activated macrophages that helps achieve high cellular ROS production and cell death-another hallmark of innate mechanisms. We show prolonged classical activation of Raw 264.7 macrophages by treating them with IFN-γ and LPS inhibited autophagy. The inhibition of autophagy was dependent on nitric oxide (NO) production which activated the AKT-mTOR signaling, the known negative regulators of autophagy. Autophagy inhibition in these cells was accompanied with a shift to aerobic glycolysis along with a decline in the mitochondrial membrane potential (MOMP). The decline in MOMP coupled with autophagy inhibition led to increased mitochondrial content and considerably elevated cellular ROS, eventually causing cell death. Next, using specific siRNA mediated knockdowns we show AKT was responsible for the glycolytic shift and autophagy inhibition in activated macrophages. Surprisingly, AKT knockdown in activated macrophages also rescued them from cell death. Finally we show that AKT mediated autophagy inhibition in the activated macrophages correlated with the depletion of glucose from the extracellular medium, and glucose supplementation not only rescued autophagy levels and reversed other phenotypes of activated macrophages, but also inhibited cell death. Thus we report here a novel link between AKT mediated glycolytic metabolism and autophagy in the activated macrophages, and provide a possible mechanism for sustained macrophage activation in vivo.

Polar Fixation of Plasmids during Recombinant Protein Production in Bacillus megaterium Results in Population Heterogeneity

During the past 2 decades, Bacillus megaterium has been systematically developed for the gram-per-liter scale production of recombinant proteins. The plasmid-based expression systems employed use a xylose-controlled promoter. Protein production analyses at the single-cell level using green fluorescent protein as a model product revealed cell culture heterogeneity characterized by a significant proportion of less productive bacteria. Due to the enormous size of B. megaterium, such bistable behavior seen in subpopulations was readily analyzed by time lapse microscopy and flow cytometry. Cell culture heterogeneity was not caused simply by plasmid loss: instead, an asymmetric distribution of plasmids during cell division was detected during the exponential-growth phase. Multicopy plasmids are generally randomly distributed between daughter cells. However, in vivo and in vitro experiments demonstrated that under conditions of strong protein production, plasmids are retained at one of the cell poles. Furthermore, it was found that cells with accumulated plasmids and high protein production ceased cell division. As a consequence, the overall protein production of the culture was achieved mainly by the subpopulation with a sufficient plasmid copy number. Based on our experimental data, we propose a model whereby the distribution of multicopy plasmids is controlled by polar fixation under protein production conditions. Thereby, cell lines with fluctuating plasmid abundance arise, which results in population heterogeneity. Our results provide initial insights into the mechanism of cellular heterogeneity during plasmid-based recombinant protein production in a Bacillus species.

NetworkPainter: dynamic intracellular pathway animation in Cytobank

BACKGROUND

High-throughput technologies such as flow and mass cytometry have the potential to illuminate cellular networks. However, analyzing the data produced by these technologies is challenging. Visualization is needed to help researchers explore this data.

RESULTS

We developed a web-based software program, NetworkPainter, to enable researchers to analyze dynamic cytometry data in the context of pathway diagrams. NetworkPainter provides researchers a graphical interface to draw and "paint" pathway diagrams with experimental data, producing animated diagrams which display the activity of each network node at each time point.

CONCLUSION

NetworkPainter enables researchers to more fully explore multi-parameter, dynamical cytometry data.

Cell-surface receptor control that depends on the size of a synthetic equilateral-triangular RNA-protein complex

A human cell surface displays many complex-structured receptors for receiving extracellular signals to regulate cellular functions. The use of precisely regulated signal-controls of the receptors could have possibilities beyond the current synthetic biology research that begins with the transfection of exogenous molecules to rewire intracellular circuits. However, by using a current ligand-receptor technique, the configuration of the artificially assembled cell surface molecules has been undefined because the assemblage is an unsystematic molecular clustering. Thus, the system bears improvements for precisely regulating receptor functions. We report here a new tool that refines stereochemically-controlled positioning of an assembled surface receptor. The tool performs rationally as an ON/OFF switch and is finely tunable so that a 3 to 6 nm size difference of the device precisely distinguishes the efficiency of apoptosis induced via cell-surface receptor binding. We discuss the potential use of the device in next-generation synthetic biology and in cell surface studies.

OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis

Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.

flowFit: a Bioconductor package to estimate proliferation in cell-tracking dye studies

SUMMARY

Herein we introduce flowFit, a Bioconductor package designed to perform quantitative analysis of cell proliferation in tracking dye-based experiments. The software, distributed as an R Bioconductor library, is based on a mathematical model that takes into account the height of each peak, the size and position of the parental population (labeled but not proliferating) and the estimated distance between the brightness of a cell and the brightness of its daughter (in which the dye is assumed to undergo a 2-fold dilution). Although the algorithm does not make any inference on cell types, rates of cell divisions or rates of cell death, it deconvolutes the actual collected data into a set of peaks, whereby each peak corresponds to a subpopulation of cells that have divided N times. We validated flowFit by retrospective analysis of published proliferation-tracking experiments and demonstrated that the algorithm predicts the same percentage of cells/generation either in samples with discernible peaks (in which the peaks are visible in the collected raw data) or in samples with non-discernible peaks (in which the peaks are fused together). To the best of our knowledge, flowFit represents the first open-source algorithm in its category and might be applied to numerous areas of cell biology in which quantitative deconvolution of tracking dye-based experiments is desired, including stem cell research.

AVAILABILITY AND IMPLEMENTATION

http://www.bioconductor.org/packages/devel/bioc/html/flowFit.html (Bioconductor software page). http://www.bioconductor.org/packages/2.13/bioc/vignettes/flowFit/inst/doc/HowTo-flowFit.pdf (package vignette). http://rpubs.com/tucano/flowFit (online tutorial).

Cell sorting in cancer research--diminishing degree of cell heterogeneity

Increasing evidence of intratumor heterogeneity and its augmentation due to selective pressure of microenvironment and recent achievements in cancer therapeutics lead to the need to investigate and track the tumor subclonal structure. Cell sorting of heterogeneous subpopulations of tumor and tumor-associated cells has been a long established strategy in cancer research. Advancement in lasers, computer technology and optics has led to a new generation of flow cytometers and cell sorters capable of high-speed processing of single cell suspensions. Over the last several years cell sorting was used in combination with molecular biological methods, imaging and proteomics to characterize primary and metastatic cancer cell populations, minimal residual disease and single tumor cells. It was the principal method for identification and characterization of cancer stem cells. Analysis of single cancer cells may improve early detection of tumors, monitoring of circulating tumor cells, evaluation of intratumor heterogeneity and chemotherapeutic treatments. The aim of this review is to provide an overview of major cell sorting applications and approaches with new prospective developments such as microfluidics and microchip technologies.

Computational analysis of high-dimensional flow cytometric data for diagnosis and discovery

Recent technological advancements have enabled the flow cytometric measurement of tens of parameters on millions of cells. Conventional manual data analysis and bioinformatics tools cannot provide a complete analysis of these datasets due to this complexity. In this chapter we will provide an overview of a general data analysis pipeline both for automatic identification of cell populations of known importance (e.g., diagnosis by identification of predefined cell population) and for exploratory analysis of cohorts of flow cytometry assays (e.g., discovery of new correlates of a malignancy). We provide three real-world examples of how unsupervised discovery has been used in basic and clinical research. We also discuss challenges for evaluation of the algorithms developed for (1) identification of cell populations using clustering, (2) identification of specific cell populations, and (3) supervised analysis for discriminating between patient subgroups.

Adaptation to a new environment allows cooperators to purge cheaters stochastically

Cooperation via production of common goods is found in diverse life forms ranging from viruses to social animals. However, natural selection predicts a "tragedy of the commons": Cheaters, benefiting from without producing costly common goods, are more fit than cooperators and should destroy cooperation. In an attempt to discover novel mechanisms of cheater control, we eliminated known ones using a yeast cooperator-cheater system engineered to supply or exploit essential nutrients. Surprisingly, although less fit than cheaters, cooperators quickly dominated a fraction of cocultures. Cooperators isolated from these cocultures were superior to the cheater isolates they had been cocultured with, even though these cheaters were superior to ancestral cooperators. Resequencing and phenotypic analyses revealed that evolved cooperators and cheaters all harbored mutations adaptive to the nutrient-limited cooperative environment, allowing growth at a much lower concentration of nutrient than their ancestors. Even after the initial round of adaptation, evolved cooperators still stochastically dominated cheaters derived from them. We propose the "adaptive race" model: If during adaptation to an environment, the fitness gain of cooperators exceeds that of cheaters by at least the fitness cost of cooperation, the tragedy of the commons can be averted. Although cooperators and cheaters sample from the same pool of adaptive mutations, this symmetry is soon broken: The best cooperators purge cheaters and continue to grow, whereas the best cheaters cause rapid self-extinction. We speculate that adaptation to changing environments may contribute to the persistence of cooperative systems before the appearance of more sophisticated mechanisms of cheater control.

QUAliFiER: an automated pipeline for quality assessment of gated flow cytometry data

Background

Effective quality assessment is an important part of any high-throughput flow cytometry data analysis pipeline, especially when considering the complex designs of the typical flow experiments applied in clinical trials. Technical issues like instrument variation, problematic antibody staining, or reagent lot changes can lead to biases in the extracted cell subpopulation statistics. These biases can manifest themselves in non–obvious ways that can be difficult to detect without leveraging information about the study design or other experimental metadata. Consequently, a systematic and integrated approach to quality assessment of flow cytometry data is necessary to effectively identify technical errors that impact multiple samples over time. Gated cell populations and their statistics must be monitored within the context of the experimental run, assay, and the overall study.

Results

We have developed two new packages, flowWorkspace and QUAliFiER to construct a pipeline for quality assessment of gated flow cytometry data. flowWorkspace makes manually gated data accessible to BioConductor’s computational flow tools by importing pre–processed and gated data from the widely used manual gating tool, FlowJo (Tree Star Inc, Ashland OR). The QUAliFiER package takes advantage of the manual gates to perform an extensive series of statistical quality assessment checks on the gated cell sub–populations while taking into account the structure of the data and the study design to monitor the consistency of population statistics across staining panels, subject, aliquots, channels, or other experimental variables. QUAliFiER implements SVG–based interactive visualization methods, allowing investigators to examine quality assessment results across different views of the data, and it has a flexible interface allowing users to tailor quality checks and outlier detection routines to suit their data analysis needs.

Conclusion

We present a pipeline constructed from two new R packages for importing manually gated flow cytometry data and performing flexible and robust quality assessment checks. The pipeline addresses the increasing demand for tools capable of performing quality checks on large flow data sets generated in typical clinical trials. The QUAliFiER tool objectively, efficiently, and reproducibly identifies outlier samples in an automated manner by monitoring cell population statistics from gated or ungated flow data conditioned on experiment–level metadata.

Computational analysis of high-throughput flow cytometry data

INTRODUCTION

Flow cytometry has been around for over 40 years, but only recently has the opportunity arisen to move into the high-throughput domain. The technology is now available and is highly competitive with imaging tools under the right conditions. Flow cytometry has, however, been a technology that has focused on its unique ability to study single cells and appropriate analytical tools are readily available to handle this traditional role of the technology.

AREAS COVERED

Expansion of flow cytometry to a high-throughput (HT) and high-content technology requires both advances in hardware and analytical tools. The historical perspective of flow cytometry operation as well as how the field has changed and what the key changes have been discussed. The authors provide a background and compelling arguments for moving toward HT flow, where there are many innovative opportunities. With alternative approaches now available for flow cytometry, there will be a considerable number of new applications. These opportunities show strong capability for drug screening and functional studies with cells in suspension.

EXPERT OPINION

There is no doubt that HT flow is a rich technology awaiting acceptance by the pharmaceutical community. It can provide a powerful phenotypic analytical toolset that has the capacity to change many current approaches to HT screening. The previous restrictions on the technology, based on its reduced capacity for sample throughput, are no longer a major issue. Overcoming this barrier has transformed a mature technology into one that can focus on systems biology questions not previously considered possible.

A computational framework to emulate the human perspective in flow cytometric data analysis

BACKGROUND

In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation.

RESULTS

To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods.

CONCLUSIONS

The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics.

Adaptive evolution of the lactose utilization network in experimentally evolved populations of Escherichia coli

Adaptation to novel environments is often associated with changes in gene regulation. Nevertheless, few studies have been able both to identify the genetic basis of changes in regulation and to demonstrate why these changes are beneficial. To this end, we have focused on understanding both how and why the lactose utilization network has evolved in replicate populations of Escherichia coli. We found that lac operon regulation became strikingly variable, including changes in the mode of environmental response (bimodal, graded, and constitutive), sensitivity to inducer concentration, and maximum expression level. In addition, some classes of regulatory change were enriched in specific selective environments. Sequencing of evolved clones, combined with reconstruction of individual mutations in the ancestral background, identified mutations within the lac operon that recapitulate many of the evolved regulatory changes. These mutations conferred fitness benefits in environments containing lactose, indicating that the regulatory changes are adaptive. The same mutations conferred different fitness effects when present in an evolved clone, indicating that interactions between the lac operon and other evolved mutations also contribute to fitness. Similarly, changes in lac regulation not explained by lac operon mutations also point to important interactions with other evolved mutations. Together these results underline how dynamic regulatory interactions can be, in this case evolving through mutations both within and external to the canonical lactose utilization network.

Flow-based combinatorial antibody profiling: an integrated approach to cell characterization

BD FACS™ CAP (CAP = combinatorial antibody profile) is a screening tool for rapid characterization of human cell surface protein expression profiles using semi-automated high-throughput flow cytometry. The current configuration consists of 229 directly conjugated antibodies arrayed in a 96-well plate as three-color cocktails, which enables the characterization of each of the 229 individual surface markers. Each individual cell type of interest is analyzed on the 96-well screening plates and the data are acquired on a flow cytometer equipped with a high-throughput sampler. The expression level of each marker for each cell type is then calculated using semiautomated custom flow cytometry software. The process of characterizing these surface markers in a highly efficient manner using BD FACS™ CAP is enabled by automated liquid handling for staining, automated flow cytometry for data acquisition, and standardized algorithms for automated data analysis.

The curvHDR method for gating flow cytometry samples

Background

High-throughput flow cytometry experiments produce hundreds of large multivariate samples of cellular characteristics. These samples require specialized processing to obtain clinically meaningful measurements. A major component of this processing is a form of cell subsetting known as gating. Manual gating is time-consuming and subjective. Good automatic and semi-automatic gating algorithms are very beneficial to high-throughput flow cytometry.

Results

We develop a statistical procedure, named curvHDR, for automatic and semi-automatic gating. The method combines the notions of significant high negative curvature regions and highest density regions and has the ability to adapt well to human-perceived gates. The underlying principles apply to dimension of arbitrary size, although we focus on dimensions up to three. Accompanying software, compatible with contemporary flow cytometry infor-matics, is developed.

Conclusion

The method is seen to adapt well to nuances in the data and, to a reasonable extent, match human perception of useful gates. It offers big savings in human labour when processing high-throughput flow cytometry data whilst retaining a good degree of efficacy.

iFlow: A Graphical User Interface for Flow Cytometry Tools in Bioconductor

Flow cytometry (FCM) has become an important analysis technology in health care and medical research, but the large volume of data produced by modern high-throughput experiments has presented significant new challenges for computational analysis tools. The development of an FCM software suite in Bioconductor represents one approach to overcome these challenges. In the spirit of the R programming language (Tree Star Inc., “FlowJo,” http://www.owjo.com), these tools are predominantly console-driven, allowing for programmatic access and rapid development of novel algorithms. Using this software requires a solid understanding of programming concepts and of the R language. However, some of these tools|in particular the statistical graphics and novel analytical methods|are also useful for nonprogrammers. To this end, we have developed an open source, extensible graphical user interface (GUI) iFlow, which sits on top of the Bioconductor backbone, enabling basic analyses by means of convenient graphical menus and wizards. We envision iFlow to be easily extensible in order to quickly integrate novel methodological developments.