A Bacterial Component to Alzheimer's-Type Dementia Seen via a Systems Biology Approach that Links Iron Dysregulation and Inflammagen Shedding to Disease.

J Alzheimers Dis. 2016 Jun 18;

Authors: Pretorius E, Bester J, Kell DB

Abstract
The progression of Alzheimer's disease (AD) is accompanied by a great many observable changes, both molecular and physiological. These include oxidative stress, neuroinflammation, and (more proximal to cognitive decline) the death of neuronal and other cells. A systems biology approach seeks to organize these observed variables into pathways that discriminate those that are highly involved (i.e., causative) from those that are more usefully recognized as bystander effects. We review the evidence that iron dysregulation is one of the central causative pathway elements here, as this can cause each of the above effects. In addition, we review the evidence that dormant, non-growing bacteria are a crucial feature of AD, that their growth in vivo is normally limited by a lack of free iron, and that it is this iron dysregulation that is an important factor in their resuscitation. Indeed, bacterial cells can be observed by ultrastructural microscopy in the blood of AD patients. A consequence of this is that the growing cells can shed highly inflammatory components such as lipopolysaccharides (LPS). These too are known to be able to induce (apoptotic and pyroptotic) neuronal cell death. There is also evidence that these systems interact with elements of vitamin D metabolism. This integrative systems approach has strong predictive power, indicating (as has indeed been shown) that both natural and pharmaceutical iron chelators might have useful protective roles in arresting cognitive decline, and that a further assessment of the role of microbes in AD development is more than highly warranted.

PMID: 27340854 [PubMed - as supplied by publisher]

Gut microbiota drive the development of neuro-inflammatory response in cirrhosis.

Hepatology. 2016 Jun 23;

Authors: Kang DJ, Betrapally NS, Ghosh SA, Sartor RB, Hylemon PB, Gillevet PM, Sanyal AJ, Heuman DM, Carl D, Zhou H, Liu R, Wang X, Yang J, Jiao C, Herzog J, Lippmann HR, Sikaroodi M, Brown RR, Bajaj JS

Abstract
The mechanisms behind the development of hepatic encephalopathy (HE) are unclear although hyperammonemia and systemic inflammation through gut dysbiosis have been proposed.
AIM: Define the individual contribution of hyperammonemia and systemic inflammation on neuro-inflammation in cirrhosis using germ-free (GF) and conventional mice.
METHODS: GF and conventional C57BL/6 mice were made cirrhotic using CCl4 gavage. These were compared to their non-cirrhotic counterparts. Intestinal microbiota, systemic and neuro-inflammation (including microglial and glial activation), serum ammonia, intestinal glutaminase activity and cecal glutamine content were compared between groups.
RESULTS: GF-cirrhotic mice developed similar cirrhotic changes to the conventional mice after four extra weeks (16 vs. 12 weeks) of CCL4 gavage. GF-cirrhotic mice exhibited higher ammonia compared to the GF controls but this was not associated with systemic or neuro-inflammation. Ammonia was generated through increased small intestinal glutaminase activity with concomitantly reduced intestinal glutamine levels. However, conventional cirrhotic mice had intestinal dysbiosis as well as systemic inflammation, associated with increased serum ammonia compared to conventional controls. This was associated with neuro-inflammation and glial/microglial activation. Correlation network analysis in conventional mice showed significant linkages between systemic/neuro-inflammation, intestinal microbiota and ammonia. Specifically beneficial, autochthonous taxa were negatively linked with brain and systemic inflammation, ammonia and with Staphylococcaceae, Lactobacillaceae and Streptococcaceae. Enterobacteriaceae were positively linked with serum inflammatory cytokines Conclusions: Gut microbiota changes drive the development of neuro- and systemic inflammatory responses in cirrhotic animals. This article is protected by copyright. All rights reserved.

PMID: 27339732 [PubMed - as supplied by publisher]

Reverse Engineering of Gene Regulatory Network Using Restricted Gene Expression Programming.

J Bioinform Comput Biol. 2016 May 26;:1650021

Authors: Yang B, Liu S, Zhang W

Abstract
Inference of gene regulatory networks has been becoming a major area of interest in the field of systems biology over the past decade. In this paper, we present a novel representation of S-system model, named restricted gene expression programming (RGEP), to infer gene regulatory network. A new hybrid evolutionary algorithm based on structure-based evolutionary algorithm and cuckoo search (CS) is proposed to optimize the architecture and corresponding parameters of model, respectively. Two synthetic benchmark datasets and one real biological dataset from SOS DNA repair network in E. coli are used to test the validity of our method. Experimental results demonstrate that our proposed method performs better than previously proposed popular methods.

PMID: 27338130 [PubMed - as supplied by publisher]

Prediction of core cancer genes using a hybrid of feature selection and machine learning methods.

Genet Mol Res. 2015;14(3):8871-82

Authors: Liu YX, Zhang NN, He Y, Lun LJ

Abstract
Machine learning techniques are of great importance in the analysis of microarray expression data, and provide a systematic and promising way to predict core cancer genes. In this study, a hybrid strategy was introduced based on machine learning techniques to select a small set of informative genes, which will lead to improving classification accuracy. First feature filtering algorithms were applied to select a set of top-ranked genes, and then hierarchical clustering and collapsing dense clusters were used to select core cancer genes. Through empirical study, our approach is capable of selecting relatively few core cancer genes while making high-accuracy predictions. The biological significance of these genes was evaluated using systems biology analysis. Extensive functional pathway and network analyses have confirmed findings in previous studies and can bring new insights into common cancer mechanisms.

PMID: 26345818 [PubMed - indexed for MEDLINE]

TarNet: An Evidence-Based Database for Natural Medicine Research.

PLoS One. 2016;11(6):e0157222

Authors: Hu R, Ren G, Sun G, Sun X

Abstract
BACKGROUND: Complex diseases seriously threaten human health. Drug discovery approaches based on "single genes, single drugs, and single targets" are limited in targeting complex diseases. The development of new multicomponent drugs for complex diseases is imperative, and the establishment of a suitable solution for drug group-target protein network analysis is a key scientific problem that must be addressed. Herbal medicines have formed the basis of sophisticated systems of traditional medicine and have given rise to some key drugs that remain in use today. The search for new molecules is currently taking a different route, whereby scientific principles of ethnobotany and ethnopharmacognosy are being used by chemists in the discovery of different sources and classes of compounds.
RESULTS: In this study, we developed TarNet, a manually curated database and platform of traditional medicinal plants with natural compounds that includes potential bio-target information. We gathered information on proteins that are related to or affected by medicinal plant ingredients and data on protein-protein interactions (PPIs). TarNet includes in-depth information on both plant-compound-protein relationships and PPIs. Additionally, TarNet can provide researchers with network construction analyses of biological pathways and protein-protein interactions (PPIs) associated with specific diseases. Researchers can upload a gene or protein list mapped to our PPI database that has been manually curated to generate relevant networks. Multiple functions are accessible for network topological calculations, subnetwork analyses, pathway analyses, and compound-protein relationships.
CONCLUSIONS: TarNet will serve as a useful analytical tool that will provide information on medicinal plant compound-affected proteins (potential targets) and system-level analyses for systems biology and network pharmacology researchers. TarNet is freely available at http://www.herbbol.org:8001/tarnet, and detailed tutorials on the program are also available.

PMID: 27337171 [PubMed - as supplied by publisher]

Principles of Systems Biology-No. 6.

Cell Syst. 2016 Jun 22;2(6):356-359

Authors:

Abstract
This month's Cell Systems Call (Cell Systems 1, 307) shows how interdisciplinary approaches can provide leverage against problems as diverse as tracing cell lineage and understanding massive cellular machines.

PMID: 27336964 [PubMed - as supplied by publisher]

Schizophrenia interactome with 504 novel protein-protein interactions.

NPJ Schizophr. 2016;2:16012

Authors: Ganapathiraju MK, Thahir M, Handen A, Sarkar SN, Sweet RA, Nimgaonkar VL, Loscher CE, Bauer EM, Chaparala S

Abstract
Genome-wide association studies of schizophrenia (GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein-protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at http://severus.dbmi.pitt.edu/schizo-pi with advanced search capabilities.

PMID: 27336055 [PubMed]

Proteomic systems evaluation of the molecular validity of preclinical psychosis models compared to schizophrenia brain pathology.

Schizophr Res. 2016 Jun 19;

Authors: Cox DA, Gottschalk MG, Wesseling H, Ernst A, Cooper JD, Bahn S

Abstract
Pharmacological and genetic rodent models of schizophrenia play an important role in the drug discovery pipeline, but quantifying the molecular similarity of such models with the underlying human pathophysiology has proved difficult. We developed a novel systems biology methodology for the direct comparison of anterior prefrontal cortex tissue from four established glutamatergic rodent models and schizophrenia patients, enabling the evaluation of which model displays the greatest similarity to schizophrenia across different pathophysiological characteristics of the disease. Liquid chromatography coupled tandem mass spectrometry (LC-MS(E)) proteomic profiling was applied comparing healthy and "disease state" in human post-mortem samples and rodent brain tissue samples derived from models based on acute and chronic phencyclidine (PCP) treatment, ketamine treatment or NMDA receptor knockdown. Protein-protein interaction networks were constructed from significant abundance changes and enrichment analyses enabled the identification of five functional domains of the disease such as "development and differentiation", which were represented across all four rodent models and were thus subsequently used for cross-species comparison. Kernel-based machine learning techniques quantified that the chronic PCP model represented schizophrenia brain changes most closely for four of these functional domains. This is the first study aiming to quantify which rodent model recapitulates the neuropathological features of schizophrenia most closely, providing an indication of face validity as well as potential guidance in the refinement of construct and predictive validity. The methodology and findings presented here support recent efforts to overcome translational hurdles of preclinical psychiatric research by associating functional dimensions of behaviour with distinct biological processes.

PMID: 27335180 [PubMed - as supplied by publisher]

Drug repositioning through network pharmacology.

Curr Top Med Chem. 2016 May 30;

Authors: Ye H, Wei J, Tang K, Feuers R, Hong H

Abstract
Low drug productivity has been a significant problem of the pharmaceutical industry for several decades even though numerous novel technologies were introduced during this period. Currently pharmacologic dogma, "single drug, single target, single disease", is at the root of the lack of drug productivity. From a systems biology viewpoint, network pharmacology has been proposed to complement established and guiding pharmacologic approaches. The rationale for network pharmacology as a major component of drug discovery and development is that a disease can be caused by perturbation of the disease-causing network and a drug may be designed to interact with multiple targets for modulation of such a network from the disease status toward normal status. Therefore, network pharmacology has been applied to guide and assist in drug repositioning. Drugs exerting their therapeutic effects may directly target disease-associated proteins, but they may also modulate the pathways involved in the pathological process. In this review, we discuss the progresses and prospects in network pharmacology, focusing on drug off-targets discovery, disease-associated protein identification, and pathway analysis for elucidating relationships between drug targets and disease-associated proteins.

PMID: 27334200 [PubMed - as supplied by publisher]

JAK inhibition in the treatment of diabetic kidney disease.

Diabetologia. 2016 Jun 22;

Authors: Brosius FC, Tuttle KR, Kretzler M

Abstract
Diabetic kidney disease (DKD) is the most common cause of kidney failure in many countries today, but treatments have not improved in the last 20 years. Recently, systems biology methods have allowed the elucidation of signalling pathways and networks involved in the progression of DKD that were not well appreciated previously. A prominent pathway found to be integrally associated with DKD progression is the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway. Increased expression of JAK-STAT genes was found in multiple cells in the kidney, including glomerular podocytes, in both early and progressive DKD. Subsequent experiments in a mouse diabetic model showed that enhanced expression of JAK2 selectively in glomerular podocytes increased functional and pathological features of DKD. Finally, a yet unpublished Phase 2 multicentre, randomised, double-blind, placebo-controlled study of the efficacy of a selective JAK1 and JAK2 inhibitor has been conducted in type 2 diabetic participants with DKD. In this trial there was a reduction of albuminuria in participants who received the active inhibitor compared with those who received a placebo These results support the further study of JAK inhibitors as a new therapy for DKD. This review summarises a presentation given at the 'Anti-inflammatory interventions in diabetes' symposium at the 2015 annual meeting of the EASD. It is accompanied by an overview by the Session Chair, Hiddo Heerspink (DOI: 10.1007/s00125-016-4030-4 ).

PMID: 27333885 [PubMed - as supplied by publisher]

Comprehensive analysis of high-throughput screens with HiTSeekR.

Nucleic Acids Res. 2016 Jun 21;

Authors: List M, Schmidt S, Christiansen H, Rehmsmeier M, Tan Q, Mollenhauer J, Baumbach J

Abstract
High-throughput screening (HTS) is an indispensable tool for drug (target) discovery that currently lacks user-friendly software tools for the robust identification of putative hits from HTS experiments and for the interpretation of these findings in the context of systems biology. We developed HiTSeekR as a one-stop solution for chemical compound screens, siRNA knock-down and CRISPR/Cas9 knock-out screens, as well as microRNA inhibitor and -mimics screens. We chose three use cases that demonstrate the potential of HiTSeekR to fully exploit HTS screening data in quite heterogeneous contexts to generate novel hypotheses for follow-up experiments: (i) a genome-wide RNAi screen to uncover modulators of TNFα, (ii) a combined siRNA and miRNA mimics screen on vorinostat resistance and (iii) a small compound screen on KRAS synthetic lethality. HiTSeekR is publicly available at http://hitseekr.compbio.sdu.dk It is the first approach to close the gap between raw data processing, network enrichment and wet lab target generation for various HTS screen types.

PMID: 27330136 [PubMed - as supplied by publisher]

Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature.

Pharmacol Res. 2016 Jun 18;

Authors: Poornima P, Kumar JD, Zhao Q, Blunder M, Efferth T

Abstract
Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g. polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples (Green tea polyphenolics, Eleutherococcus senticosus, Rhodiola rosea, and Schisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer.

PMID: 27329331 [PubMed - as supplied by publisher]

Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli.

Microb Cell Fact. 2016;15(1):112

Authors: Jahan N, Maeda K, Matsuoka Y, Sugimoto Y, Kurata H

Abstract
BACKGROUND: A kinetic model provides insights into the dynamic response of biological systems and predicts how their complex metabolic and gene regulatory networks generate particular functions. Of many biological systems, Escherichia coli metabolic pathways have been modeled extensively at the enzymatic and genetic levels, but existing models cannot accurately reproduce experimental behaviors in a batch culture, due to the inadequate estimation of a specific cell growth rate and a large number of unmeasured parameters.
RESULTS: In this study, we developed a detailed kinetic model for the central carbon metabolism of E. coli in a batch culture, which includes the glycolytic pathway, tricarboxylic acid cycle, pentose phosphate pathway, Entner-Doudoroff pathway, anaplerotic pathway, glyoxylate shunt, oxidative phosphorylation, phosphotransferase system (Pts), non-Pts and metabolic gene regulations by four protein transcription factors: cAMP receptor, catabolite repressor/activator, pyruvate dehydrogenase complex repressor and isocitrate lyase regulator. The kinetic parameters were estimated by a constrained optimization method on a supercomputer. The model estimated a specific growth rate based on reaction kinetics and accurately reproduced the dynamics of wild-type E. coli and multiple genetic mutants in a batch culture.
CONCLUSIONS: This model overcame the intrinsic limitations of existing kinetic models in a batch culture, predicted the effects of multilayer regulations (allosteric effectors and gene expression) on central carbon metabolism and proposed rationally designed fast-growing cells based on understandings of molecular processes.

PMID: 27329289 [PubMed - in process]

Lipid metabolism in mycobacteria--Insights using mass spectrometry-based lipidomics.

Biochim Biophys Acta. 2016 Jan;1861(1):60-7

Authors: Crick PJ, Guan XL

Abstract
Diseases including tuberculosis and leprosy are caused by species of the Mycobacterium genus and are a huge burden on global health, aggravated by the emergence of drug resistant strains. Mycobacteria have a high lipid content and complex lipid profile including several unique classes of lipid. Recent years have seen a growth in research focused on lipid structures, metabolism and biological functions driven by advances in mass spectrometry techniques and instrumentation, particularly the use of electrospray ionization. Here we review the contributions of lipidomics towards the advancement of our knowledge of lipid metabolism in mycobacterial species.

PMID: 26515252 [PubMed - indexed for MEDLINE]

Inflammatory gene networks in term human decidual cells define a potential signature for cytokine-mediated parturition.

Am J Obstet Gynecol. 2016 Feb;214(2):284.e1-284.e47

Authors: Ibrahim SA, Ackerman WE, Summerfield TL, Lockwood CJ, Schatz F, Kniss DA

Abstract
BACKGROUND: Inflammation is a proximate mediator of preterm birth and fetal injury. During inflammation several microRNAs (22 nucleotide noncoding ribonucleic acid (RNA) molecules) are up-regulated in response to cytokines such as interleukin-1β. MicroRNAs, in most cases, fine-tune gene expression, including both up-regulation and down-regulation of their target genes. However, the role of pro- and antiinflammatory microRNAs in this process is poorly understood.
OBJECTIVE: The principal goal of the work was to examine the inflammatory genomic profile of human decidual cells challenged with a proinflammatory cytokine known to be present in the setting of preterm parturition. We determined the coding (messenger RNA) and noncoding (microRNA) sequences to construct a network of interacting genes during inflammation using an in vitro model of decidual stromal cells.
STUDY DESIGN: The effects of interleukin-1β exposure on mature microRNA expression were tested in human decidual cell cultures using the multiplexed NanoString platform, whereas the global inflammatory transcriptional response was measured using oligonucleotide microarrays. Differential expression of select transcripts was confirmed by quantitative real time-polymerase chain reaction. Bioinformatics tools were used to infer transcription factor activation and regulatory interactions.
RESULTS: Interleukin-1β elicited up- and down-regulation of 350 and 78 nonredundant transcripts (false discovery rate < 0.1), respectively, including induction of numerous cytokines, chemokines, and other inflammatory mediators. Whereas this transcriptional response included marked changes in several microRNA gene loci, the pool of fully processed, mature microRNA was comparatively stable following a cytokine challenge. Of a total of 6 mature microRNAs identified as being differentially expressed by NanoString profiling, 2 (miR-146a and miR-155) were validated by quantitative real time-polymerase chain reaction. Using complementary bioinformatics approaches, activation of several inflammatory transcription factors could be inferred downstream of interleukin-1β based on the overall transcriptional response. Further analysis revealed that miR-146a and miR-155 both target genes involved in inflammatory signaling, including Toll-like receptor and mitogen-activated protein kinase pathways.
CONCLUSION: Stimulation of decidual cells with interleukin-1β alters the expression of microRNAs that function to temper proinflammatory signaling. In this setting, some microRNAs may be involved in tissue-level inflammation during the bulk of gestation and assist in pregnancy maintenance.

PMID: 26348374 [PubMed - indexed for MEDLINE]

Using quantitative systems pharmacology for novel drug discovery.

Expert Opin Drug Discov. 2015 Dec;10(12):1315-31

Authors: Pérez-Nueno VI

Abstract
INTRODUCTION: Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.
AREAS COVERED: This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.
EXPERT OPINION: The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

PMID: 26328768 [PubMed - indexed for MEDLINE]

Defining bacterial regulons using ChIP-seq.

Methods. 2015 Sep 15;86:80-8

Authors: Myers KS, Park DM, Beauchene NA, Kiley PJ

Abstract
Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is a powerful method that identifies protein-DNA binding sites in vivo. Recent studies have illustrated the value of ChIP-seq in studying transcription factor binding in various bacterial species under a variety of growth conditions. These results show that in addition to identifying binding sites, correlation of ChIP-seq data with expression data can reveal important information about bacterial regulons and regulatory networks. In this chapter, we provide an overview of the current state of knowledge about ChIP-seq methodology in bacteria, from sample preparation to raw data analysis. We also describe visualization and various bioinformatic analyses of processed ChIP-seq data.

PMID: 26032817 [PubMed - indexed for MEDLINE]

Systems biology approaches to defining transcription regulatory networks in halophilic archaea.

Methods. 2015 Sep 15;86:102-14

Authors: Darnell CL, Schmid AK

Abstract
To survive complex and changing environmental conditions, microorganisms use gene regulatory networks (GRNs) composed of interacting regulatory transcription factors (TFs) to control the timing and magnitude of gene expression. Genome-wide datasets; such as transcriptomics and protein-DNA interactions; and experiments such as high throughput growth curves; facilitate the construction of GRNs and provide insight into TF interactions occurring under stress. Systems biology approaches integrate these datasets into models of GRN architecture as well as statistical and/or dynamical models to understand the function of networks occurring in cells. Previously, these types of studies have focused on traditional model organisms (e.g. Escherichia coli, yeast). However, recent advances in archaeal genetics and other tools have enabled a systems approach to understanding GRNs in these relatively less studied archaeal model organisms. In this report, we outline a systems biology workflow for generating and integrating data focusing on the TF regulator. We discuss experimental design, outline the process of data collection, and provide the tools required to produce high confidence regulons for the TFs of interest. We provide a case study as an example of this workflow, describing the construction of a GRN centered on multi-TF coordinate control of gene expression governing the oxidative stress response in the hypersaline-adapted archaeon Halobacterium salinarum.

PMID: 25976837 [PubMed - indexed for MEDLINE]

Point process models for localization and interdependence of punctate cellular structures.

Cytometry A. 2016 Jun 21;

Authors: Li Y, Majarian TD, Naik AW, Johnson GR, Murphy RF

Abstract
Accurate representations of cellular organization for multiple eukaryotic cell types are required for creating predictive models of dynamic cellular function. To this end, we have previously developed the CellOrganizer platform, an open source system for generative modeling of cellular components from microscopy images. CellOrganizer models capture the inherent heterogeneity in the spatial distribution, size, and quantity of different components among a cell population. Furthermore, CellOrganizer can generate quantitatively realistic synthetic images that reflect the underlying cell population. A current focus of the project is to model the complex, interdependent nature of organelle localization. We built upon previous work on developing multiple non-parametric models of organelles or structures that show punctate patterns. The previous models described the relationships between the subcellular localization of puncta and the positions of cell and nuclear membranes and microtubules. We extend these models to consider the relationship to the endoplasmic reticulum (ER), and to consider the relationship between the positions of different puncta of the same type. Our results do not suggest that the punctate patterns we examined are dependent on ER position or inter- and intra-class proximity. With these results, we built classifiers to update previous assignments of proteins to one of 11 patterns in three distinct cell lines. Our generative models demonstrate the ability to construct statistically accurate representations of puncta localization from simple cellular markers in distinct cell types, capturing the complex phenomena of cellular structure interaction with little human input. This protocol represents a novel approach to vesicular protein annotation, a field that is often neglected in high-throughput microscopy. These results suggest that spatial point process models provide useful insight with respect to the spatial dependence between cellular structures. © 2016 International Society for Advancement of Cytometry.

PMID: 27327612 [PubMed - as supplied by publisher]

Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images.

BMC Bioinformatics. 2015;16:397

Authors: Chiang M, Hallman S, Cinquin A, de Mochel NR, Paz A, Kawauchi S, Calof AL, Cho KW, Fowlkes CC, Cinquin O

Abstract
BACKGROUND: Analysis of single cells in their native environment is a powerful method to address key questions in developmental systems biology. Confocal microscopy imaging of intact tissues, followed by automatic image segmentation, provides a means to conduct cytometric studies while at the same time preserving crucial information about the spatial organization of the tissue and morphological features of the cells. This technique is rapidly evolving but is still not in widespread use among research groups that do not specialize in technique development, perhaps in part for lack of tools that automate repetitive tasks while allowing experts to make the best use of their time in injecting their domain-specific knowledge.
RESULTS: Here we focus on a well-established stem cell model system, the C. elegans gonad, as well as on two other model systems widely used to study cell fate specification and morphogenesis: the pre-implantation mouse embryo and the developing mouse olfactory epithelium. We report a pipeline that integrates machine-learning-based cell detection, fast human-in-the-loop curation of these detections, and running of active contours seeded from detections to segment cells. The procedure can be bootstrapped by a small number of manual detections, and outperforms alternative pieces of software we benchmarked on C. elegans gonad datasets. Using cell segmentations to quantify fluorescence contents, we report previously-uncharacterized cell behaviors in the model systems we used. We further show how cell morphological features can be used to identify cell cycle phase; this provides a basis for future tools that will streamline cell cycle experiments by minimizing the need for exogenous cell cycle phase labels.
CONCLUSIONS: High-throughput 3D segmentation makes it possible to extract rich information from images that are routinely acquired by biologists, and provides insights - in particular with respect to the cell cycle - that would be difficult to derive otherwise.

PMID: 26607933 [PubMed - indexed for MEDLINE]