Automated detection of acute respiratory distress syndrome from chest X-Rays using Directionality Measure and deep learning features

Acute respiratory distress syndrome (ARDS) is a life-threatening lung injury with global prevalence and high mortality. Chest x-rays (CXR) are critical in the early diagnosis and treatment of ARDS. However, imaging findings may not result in proper identification of ARDS due to a number of reasons, including nonspecific appearance of radiological features, ambiguity in a patient's case due to the pathological stage of the disease, and poor inter-rater reliability from interpretations of CXRs by multiple clinical experts. This study demonstrates the potential capability of methodologies in artificial intelligence, machine learning, and image processing to overcome these challenges and quantitatively assess CXRs for presence of ARDS. We propose and describe Directionality Measure, a novel feature engineering technique used to capture the "cloud-like" appearance of diffuse alveolar damage as a mathematical concept. This study also examines the effectiveness of using an off-the-shelf, pretrained deep learning model as a feature extractor in addition to standard features extracted from the histogram and gray-level co-occurrence matrix (GLCM). Data was collected from hospitalized patients at Michigan Medicine's intensive care unit and the cohort's inclusion criteria was specifically designed to be representative of patients at risk of developing ARDS. Multiple machine learning models were used to evaluate these features with 5-fold cross-validation and the final performance was reported on a hold-out, temporally distinct test set. With AdaBoost, Directionality Measure achieved an accuracy of 78% and AUC of 74% - outperforming classification results using features from the histogram (75% accuracy and 73% AUC), GLCM (76% accuracy and 73% AUC), and ResNet-50 (77% accuracy and 73% AUC). Further experimental results demonstrated that using all feature sets in combination achieved the best overall performance, yielding an accuracy of 83% and AUC of 79% with AdaBoost. These results demonstrate the potential capability of using the proposed methodologies to complement current clinical analysis for detection of ARDS from CXRs.

Valproic acid-induced changes of 4D nuclear morphology in astrocyte cells

Histone deacetylase inhibitors, such as valproic acid (VPA), have important clinical therapeutic and cellular reprogramming applications. They induce chromatin reorganization that is associated with altered cellular morphology. However, there is a lack of comprehensive characterization of VPA-induced changes of nuclear size and shape. Here, we quantify 3D nuclear morphology of primary human astrocyte cells treated with VPA over time (hence, 4D). We compared volumetric and surface-based representations and identified seven features that jointly discriminate between normal and treated cells with 85% accuracy on day 7. From day 3, treated nuclei were more elongated and flattened and then continued to morphologically diverge from controls over time, becoming larger and more irregular. On day 7, most of the size and shape descriptors demonstrated significant differences between treated and untreated cells, including a 24% increase in volume and 6% reduction in extent (shape regularity) for treated nuclei. Overall, we show that 4D morphometry can capture how chromatin reorganization modulates the size and shape of the nucleus over time. These nuclear structural alterations may serve as a biomarker for histone (de-)acetylation events and provide insights into mechanisms of astrocytes-to-neurons reprogramming.

Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome

Background

This study outlines an image processing algorithm for accurate and consistent lung segmentation in chest radiographs of critically ill adults and children typically obscured by medical equipment. In particular, this work focuses on applications in analysis of acute respiratory distress syndrome – a critical illness with a mortality rate of 40% that affects 200,000 patients in the United States and 3 million globally each year.

Methods

Chest radiographs were obtained from critically ill adults (n = 100), adults diagnosed with acute respiratory distress syndrome (ARDS) (n = 25), and children (n = 100) hospitalized at Michigan Medicine. Physicians annotated the lung field of each radiograph to establish the ground truth. A Total Variation-based Active Contour (TVAC) lung segmentation algorithm was developed and compared to multiple state-of-the-art methods including a deep learning model (U-Net), a random walker algorithm, and an active spline model, using the Sørensen–Dice coefficient to measure segmentation accuracy.

Results

The TVAC algorithm accurately segmented lung fields in all patients in the study. For the adult cohort, an averaged Dice coefficient of 0.86 ±0.04 (min: 0.76) was reported for TVAC, 0.89 ±0.12 (min: 0.01) for U-Net, 0.74 ±0.19 (min: 0.15) for the random walker algorithm, and 0.64 ±0.17 (min: 0.20) for the active spline model. For the pediatric cohort, a Dice coefficient of 0.85 ±0.04 (min: 0.75) was reported for TVAC, 0.87 ±0.09 (min: 0.56) for U-Net, 0.67 ±0.18 (min: 0.18) for the random walker algorithm, and 0.61 ±0.18 (min: 0.18) for the active spline model.

Conclusion

The proposed algorithm demonstrates the most consistent performance of all segmentation methods tested. These results suggest that TVAC can accurately identify lung fields in chest radiographs in critically ill adults and children.

Druggable Transcriptional Networks in the Human Neurogenic Epigenome

Chromosome conformation capture methods have revealed the dynamics of genome architecture which is spatially organized into topologically associated domains, with gene regulation mediated by enhancer-promoter pairs in chromatin space. New evidence shows that endogenous hormones and several xenobiotics act within circumscribed topological domains of the spatial genome, impacting subsets of the chromatin contacts of enhancer-gene promoter pairs in cis and trans Results from the National Institutes of Health-funded PsychENCODE project and the study of chromatin remodeling complexes have converged to provide a clearer understanding of the organization of the neurogenic epigenome in humans. Neuropsychiatric diseases, including schizophrenia, bipolar spectrum disorder, autism spectrum disorder, attention deficit hyperactivity disorder, and other neuropsychiatric disorders are significantly associated with mutations in neurogenic transcriptional networks. In this review, we have reanalyzed the results from publications of the PsychENCODE Consortium using pharmacoinformatics network analysis to better understand druggable targets that control neurogenic transcriptional networks. We found that valproic acid and other psychotropic drugs directly alter these networks, including chromatin remodeling complexes, transcription factors, and other epigenetic modifiers. We envision a new generation of CNS therapeutics targeted at neurogenic transcriptional control networks, including druggable parts of chromatin remodeling complexes and master transcription factor-controlled pharmacogenomic networks. This may provide a route to the modification of interconnected gene pathways impacted by disease in patients with neuropsychiatric and neurodegenerative disorders. Direct and indirect therapeutic strategies to modify the master regulators of neurogenic transcriptional control networks may ultimately help extend the life span of CNS neurons impacted by disease.

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

Deep learning in pharmacogenomics: from gene regulation to patient stratification

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.

The pharmacoepigenomics informatics pipeline defines a pathway of novel and known warfarin pharmacogenomics variants

AIM

'Pharmacoepigenomics' methods informed by omics datasets and pre-existing knowledge have yielded discoveries in neuropsychiatric pharmacogenomics. Now we evaluate the generality of these methods by discovering an extended warfarin pharmacogenomics pathway.

MATERIALS & METHODS

We developed the pharmacoepigenomics informatics pipeline, a scalable multi-omics variant screening pipeline for pharmacogenomics, and conducted an experiment in the genomics of warfarin.

RESULTS

We discovered known and novel pharmacogenomics variants and genes, both coding and regulatory, for warfarin response, including adverse events. Such genes and variants cluster in a warfarin response pathway consolidating known and novel warfarin response variants and genes.

CONCLUSION

These results can inform a new warfarin test. The pharmacoepigenomics informatics pipeline may be able to discover new pharmacogenomics markers in other drug-disease systems.

Mining the topography and dynamics of the 4D Nucleome to identify novel CNS drug pathways

The pharmacoepigenome can be defined as the active, noncoding province of the genome including canonical spatial and temporal regulatory mechanisms of gene regulation that respond to xenobiotic stimuli. Many psychotropic drugs that have been in clinical use for decades have ill-defined mechanisms of action that are beginning to be resolved as we understand the transcriptional hierarchy and dynamics of the nucleus. In this review, we describe spatial, temporal and biomechanical mechanisms mediated by psychotropic medications. Focus is placed on a bioinformatics pipeline that can be used both for detection of pharmacoepigenomic variants that discretize drug response and adverse events to improve pharmacogenomic testing, and for the discovery of novel CNS therapeutics. This approach integrates the functional topology and dynamics of the transcriptional hierarchy of the pharmacoepigenome, gene variant-driven identification of pharmacogenomic regulatory domains, and mesoscale mapping for the discovery of novel CNS pharmacodynamic pathways in human brain. Examples of the application of this pipeline are provided, including the discovery of valproic acid (VPA) mediated transcriptional reprogramming of neuronal cell fate following injury, and mapping of a CNS pathway glutamatergic pathway for the mood stabilizer lithium. These examples in regulatory pharmacoepigenomics illustrate how ongoing research using the 4D nucleome provides a foundation to further insight into previously unrecognized psychotropic drug pharmacodynamic pathways in the human CNS.

Network Reconstruction Reveals that Valproic Acid Activates Neurogenic Transcriptional Programs in Adult Brain Following Traumatic Injury

Objectives

To determine the mechanism of action of valproic acid (VPA) in the adult central nervous system (CNS) following traumatic brain injury (TBI) and hemorrhagic shock (HS).

Methods

Data were analyzed from different sources, including experiments in a porcine model, data from postmortem human brain, published studies, public and commercial databases.

Results

The transcriptional program in the CNS following TBI, HS, and VPA treatment includes activation of regulatory pathways that enhance neurogenesis and suppress gliogenesis. Genes which encode the transcription factors (TFs) that specify neuronal cell fate, including MEF2D, MYT1L, NEUROD1, PAX6 and TBR1, and their target genes, are induced by VPA. VPA represses genes responsible for oligodendrogenesis, maintenance of white matter, T-cell activation, angiogenesis, and endothelial cell proliferation, adhesion and chemotaxis. NEUROD1 has regulatory interactions with 38% of the genes regulated by VPA in a swine model of TBI and HS in adult brain. Hi-C spatial mapping of a VPA pharmacogenomic SNP in the GRIN2B gene shows it is part of a transcriptional hub that contacts 12 genes that mediate chromatin-mediated neurogenesis and neuroplasticity.

Conclusions

Following TBI and HS, this study shows that VPA administration acts in the adult brain through differential activation of TFs responsible for neurogenesis, genes responsible for neuroplasticity, and repression of TFs that specify oligodendrocyte cell fate, endothelial cell chemotaxis and angiogenesis.

Short title: Mechanism of action of valproic acid in traumatic brain injury

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-017-2130-6) contains supplementary material, which is available to authorized users.

Linking MedDRA(®)-Coded Clinical Phenotypes to Biological Mechanisms by the Ontology of Adverse Events: A Pilot Study on Tyrosine Kinase Inhibitors

INTRODUCTION

A translational bioinformatics challenge exists in connecting population and individual clinical phenotypes in various formats to biological mechanisms. The Medical Dictionary for Regulatory Activities (MedDRA(®)) is the default dictionary for adverse event (AE) reporting in the US Food and Drug Administration Adverse Event Reporting System (FAERS). The ontology of adverse events (OAE) represents AEs as pathological processes occurring after drug exposures.

OBJECTIVES

The aim of this work was to establish a semantic framework to link biological mechanisms to phenotypes of AEs by combining OAE with MedDRA(®) in FAERS data analysis. We investigated the AEs associated with tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs) targeting tyrosine kinases. The five selected TKIs/mAbs (i.e., dasatinib, imatinib, lapatinib, cetuximab, and trastuzumab) are known to induce impaired ventricular function (non-QT) cardiotoxicity.

RESULTS

Statistical analysis of FAERS data identified 1053 distinct MedDRA(®) terms significantly associated with TKIs/mAbs, where 884 did not have corresponding OAE terms. We manually annotated these terms, added them to OAE by the standard OAE development strategy, and mapped them to MedDRA(®). The data integration to provide insights into molecular mechanisms of drug-associated AEs was performed by including linkages in OAE for all related AE terms to MedDRA(®) and the existing ontologies, including the human phenotype ontology (HP), Uber anatomy ontology (UBERON), and gene ontology (GO). Sixteen AEs were shared by all five TKIs/mAbs, and each of 17 cardiotoxicity AEs was associated with at least one TKI/mAb. As an example, we analyzed "cardiac failure" using the relations established in OAE with other ontologies and demonstrated that one of the biological processes associated with cardiac failure maps to the genes associated with heart contraction.

CONCLUSION

By expanding the existing OAE ontological design, our TKI use case demonstrated that the combination of OAE and MedDRA(®) provides a semantic framework to link clinical phenotypes of adverse drug events to biological mechanisms.

Stress and glucocorticoid receptor transcriptional programming in time and space: Implications for the brain-gut axis

BACKGROUND

Chronic psychological stress is associated with enhanced abdominal pain and altered intestinal barrier function that may result from a perturbation in the hypothalamic-pituitary-adrenal (HPA) axis. The glucocorticoid receptor (GR) exploits diverse mechanisms to activate or suppress congeneric gene expression, with regulatory variation associated with stress-related disorders in psychiatry and gastroenterology.

PURPOSE

During acute and chronic stress, corticotropin-releasing hormone drives secretion of adrenocorticotropic hormone from the pituitary, ultimately leading to the release of cortisol (human) and corticosterone (rodent) from the adrenal glands. Cortisol binds with the GR in the cytosol, translocates to the nucleus, and activates the NR3C1 (nuclear receptor subfamily 3, group C, member 1 [GR]) gene. This review focuses on the rapidly developing observations that cortisol is responsible for driving circadian and ultradian bursts of transcriptional activity in the CLOCK (clock circadian regulator) and PER (period circadian clock 1) gene families, and this rhythm is disrupted in major depressive disorder, bipolar disorder, and stress-related gastrointestinal and immune disorders. Glucocorticoid receptor regulates different sets of transcripts in a tissue-specific manner, through pulsatile waves of gene expression that includes occupancy of glucocorticoid response elements located within constitutively open spatial domains in chromatin. Emerging evidence supports a potentially pivotal role for epigenetic regulation of how GR interacts with other chromatin regulators to control the expression of its target genes. Dysregulation of the central and peripheral GR regulome has potentially significant consequences for stress-related disorders affecting the brain-gut axis.

A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis

AIM

A regulatory network in the human brain mediating lithium response in bipolar patients was revealed by analysis of functional SNPs from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping.

METHODS

An initial set of 23,312 SNPs in linkage disequilibrium with lead SNPs, and sub-threshold GWAS SNPs rescued by pathway analysis, were studied in the same populations. These were assessed using our workflow and annotation by the epigenome roadmap consortium.

RESULTS

Twenty-seven percent of 802 SNPs that were associated with lithium response (13 published studies gene association studies and two GWAS) were shared in common with 1281 SNPs from 18 GWAS examining psychiatric disorders and adverse events associated with lithium treatment. Nineteen SNPs were annotated as active regulatory elements such as enhancers and promoters in a tissue-specific manner. They were located within noncoding regions of ten genes: ANK3, ARNTL, CACNA1C, CACNG2, CDKN1A, CREB1, GRIA2, GSK3B, NR1D1 and SLC1A2. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p = 10(-27); Fisher exact test) with an AMPA2 glutamate receptor network in human brain. Our workflow results showed concordance with annotation of regulatory elements from the epigenome roadmap. Analysis of cognate mRNA and enhancer RNA exhibited patterns consistent with an integrated pathway in human brain.

CONCLUSION

This pharmacoepigenomic regulatory pathway is located in the same brain regions that exhibit tissue volume loss in bipolar disorder. Although in silico analysis requires biological validation, the approach provides value for identification of candidate variants that may be used in pharmacogenomic testing to identify bipolar patients likely to respond to lithium.

Epigenomic mapping and effect sizes of noncoding variants associated with psychotropic drug response

AIM

To provide insight into potential regulatory mechanisms of gene expression underlying addiction, analgesia, psychotropic drug response and adverse drug events, genome-wide association studies searching for variants associated with these phenotypes has been undertaken with limited success. We undertook analysis of these results with the aim of applying epigenetic knowledge to aid variant discovery and interpretation.

METHODS

We applied conditional imputation to results from 26 genome-wide association studies and three candidate gene-association studies. The analysis workflow included data from chromatin conformation capture, chromatin state annotation, DNase I hypersensitivity, hypomethylation, anatomical localization and biochronicity. We also made use of chromatin state data from the epigenome roadmap, transcription factor-binding data, spatial maps from published Hi-C datasets and 'guilt by association' methods.

RESULTS

We identified 31 pharmacoepigenomic SNPs from a total of 2024 variants in linkage disequilibrium with lead SNPs, of which only 6% were coding variants. Interrogation of chromatin state using our workflow and the epigenome roadmap showed agreement on 34 of 35 tissue assignments to regulatory elements including enhancers and promoters. Loop boundary domains were inferred by association with CTCF (CCCTC-binding factor) and cohesin, suggesting proximity to topologically associating domain boundaries and enhancer clusters. Spatial interactions between enhancer-promoter pairs detected both known and previously unknown mechanisms. Addiction and analgesia SNPs were common in relevant populations and exhibited large effect sizes, whereas a SNP located in the promoter of the SLC1A2 gene exhibited a moderate effect size for lithium response in bipolar disorder in patients of European ancestry. SNPs associated with drug-induced organ injury were rare but exhibited the largest effect sizes, consistent with the published literature.

CONCLUSION

This work demonstrates that an in silico bioinformatics-based approach using integrative analysis of a diversity of molecular and morphological data types can discover pharmacoepigenomic variants that are suitable candidates for further validation in cell lines, animal models and human clinical trials.

The epigenome, 4D nucleome and next-generation neuropsychiatric pharmacogenomics

The 4D nucleome has the potential to render challenges in neuropsychiatric pharmacogenomics more tractable. The epigenome roadmap consortium has demonstrated the critical role that noncoding regions of the human genome play in determination of human phenotype. Chromosome conformation capture methods have revealed the 4D organization of the nucleus, bringing interactions between distant regulatory elements into close spatial proximity in a periodic manner. These functional interactions have the potential to elucidate mechanisms of CNS drug response and side effects that previously have been unrecognized. This perspective assesses recent advances likely to reveal novel pharmacodynamic regulatory pathways in human brain, charting a future new avenue of pharmacogenomics research, using the spatial and temporal architecture of the human epigenome as its foundation.

CLO: The cell line ontology

Background

Cell lines have been widely used in biomedical research. The community-based Cell Line Ontology (CLO) is a member of the OBO Foundry library that covers the domain of cell lines. Since its publication two years ago, significant updates have been made, including new groups joining the CLO consortium, new cell line cells, upper level alignment with the Cell Ontology (CL) and the Ontology for Biomedical Investigation, and logical extensions.

Construction and content

Collaboration among the CLO, CL, and OBI has established consensus definitions of cell line-specific terms such as ‘cell line’, ‘cell line cell’, ‘cell line culturing’, and ‘mortal’ vs. ‘immortal cell line cell’. A cell line is a genetically stable cultured cell population that contains individual cell line cells. The hierarchical structure of the CLO is built based on the hierarchy of the in vivo cell types defined in CL and tissue types (from which cell line cells are derived) defined in the UBERON cross-species anatomy ontology. The new hierarchical structure makes it easier to browse, query, and perform automated classification. We have recently added classes representing more than 2,000 cell line cells from the RIKEN BRC Cell Bank to CLO. Overall, the CLO now contains ~38,000 classes of specific cell line cells derived from over 200 in vivo cell types from various organisms.

Utility and discussion

The CLO has been applied to different biomedical research studies. Example case studies include annotation and analysis of EBI ArrayExpress data, bioassays, and host-vaccine/pathogen interaction. CLO’s utility goes beyond a catalogue of cell line types. The alignment of the CLO with related ontologies combined with the use of ontological reasoners will support sophisticated inferencing to advance translational informatics development.

tranSMART: An Open Source and Community-Driven Informatics and Data Sharing Platform for Clinical and Translational Research

tranSMART is an emerging global open source public private partnership community developing a comprehensive informatics-based analysis and data-sharing cloud platform for clinical and translational research. The tranSMART consortium includes pharmaceutical and other companies, not-for-profits, academic entities, patient advocacy groups, and government stakeholders. The tranSMART value proposition relies on the concept that the global community of users, developers, and stakeholders are the best source of innovation for applications and for useful data. Continued development and use of the tranSMART platform will create a means to enable "pre-competitive" data sharing broadly, saving money and, potentially accelerating research translation to cures. Significant transformative effects of tranSMART includes 1) allowing for all its user community to benefit from experts globally, 2) capturing the best of innovation in analytic tools, 3) a growing 'big data' resource, 4) convergent standards, and 5) new informatics-enabled translational science in the pharma, academic, and not-for-profit sectors.

Ontology-based combinatorial comparative analysis of adverse events associated with killed and live influenza vaccines

Vaccine adverse events (VAEs) are adverse bodily changes occurring after vaccination. Understanding the adverse event (AE) profiles is a crucial step to identify serious AEs. Two different types of seasonal influenza vaccines have been used on the market: trivalent (killed) inactivated influenza vaccine (TIV) and trivalent live attenuated influenza vaccine (LAIV). Different adverse event profiles induced by these two groups of seasonal influenza vaccines were studied based on the data drawn from the CDC Vaccine Adverse Event Report System (VAERS). Extracted from VAERS were 37,621 AE reports for four TIVs (Afluria, Fluarix, Fluvirin, and Fluzone) and 3,707 AE reports for the only LAIV (FluMist). The AE report data were analyzed by a novel combinatorial, ontology-based detection of AE method (CODAE). CODAE detects AEs using Proportional Reporting Ratio (PRR), Chi-square significance test, and base level filtration, and groups identified AEs by ontology-based hierarchical classification. In total, 48 TIV-enriched and 68 LAIV-enriched AEs were identified (PRR>2, Chi-square score >4, and the number of cases >0.2% of total reports). These AE terms were classified using the Ontology of Adverse Events (OAE), MedDRA, and SNOMED-CT. The OAE method provided better classification results than the two other methods. Thirteen out of 48 TIV-enriched AEs were related to neurological and muscular processing such as paralysis, movement disorders, and muscular weakness. In contrast, 15 out of 68 LAIV-enriched AEs were associated with inflammatory response and respiratory system disorders. There were evidences of two severe adverse events (Guillain-Barre Syndrome and paralysis) present in TIV. Although these severe adverse events were at low incidence rate, they were found to be more significantly enriched in TIV-vaccinated patients than LAIV-vaccinated patients. Therefore, our novel combinatorial bioinformatics analysis discovered that LAIV had lower chance of inducing these two severe adverse events than TIV. In addition, our meta-analysis found that all previously reported positive correlation between GBS and influenza vaccine immunization were based on trivalent influenza vaccines instead of monovalent influenza vaccines.

Researchers' needs for resource discovery and collaboration tools: a qualitative investigation of translational scientists

Background

A critical aspect of clinical and translational science (CTS) is interdisciplinary and collaborative research, which increasingly requires a wide range of computational and human resources. However, few studies have systematically analyzed such resource needs of CTS researchers.

Objective

To improve our understanding of CTS researchers’ needs for computational and human resources in order to build useful and useable supporting informatics tools.

Methods

We conducted semistructured interviews of 30 CTS researchers from the University of Michigan, followed by qualitative analysis of the interview transcripts.

Results

The analysis identified three recurring themes: the need for the federation of information, the need to address information overload, and the need to humanize computing, including strong and well-informed views about the use of social networking tools for research collaboration. These findings helped us to narrow down the available design choices for assisting CTS researchers, and helped to identify potential deficiencies of well-known theoretical frameworks used to guide our study, with suggestions for future remedies.

Conclusions

The user needs identified through the study, along with concrete design suggestions, provided key design, methodological, and theoretical insights, which are being used to guide the design and development of a CTS resource portal. The results and interview instrument should be useful to other institutions with Clinical and Translational Science Awards that face similar challenges related to helping CTS researchers make more effective use of computational and human resources.

The NIH National Center for Integrative Biomedical Informatics (NCIBI)

The National Center for Integrative and Biomedical Informatics (NCIBI) is one of the eight NCBCs. NCIBI supports information access and data analysis for biomedical researchers, enabling them to build computational and knowledge models of biological systems to address the Driving Biological Problems (DBPs). The NCIBI DBPs have included prostate cancer progression, organ-specific complications of type 1 and 2 diabetes, bipolar disorder, and metabolic analysis of obesity syndrome. Collaborating with these and other partners, NCIBI has developed a series of software tools for exploratory analysis, concept visualization, and literature searches, as well as core database and web services resources. Many of our training and outreach initiatives have been in collaboration with the Research Centers at Minority Institutions (RCMI), integrating NCIBI and RCMI faculty and students, culminating each year in an annual workshop. Our future directions include focusing on the TranSMART data sharing and analysis initiative.

GSearcher: agile attribute querying for biological networks

Summary: GSearcher provides a highly interactive user experience in navigating attribute data associated with large and complex biological networks. The user may either perform a quick search using keywords, phrases or regular expressions, or build a complex query with a group of filters for efficient and flexible exploration of large datasets.

Availability:http://brainarray.mbni.med.umich.edu/gsearcher/

Contact:sugang@umich.edu

2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research

Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine.

Unified translation repression mechanism for microRNAs and upstream AUGs

Background

MicroRNAs (miRNAs) are endogenous small RNAs that modulate gene expression at the post-transcriptional level by binding complementary sites in the 3'-UTR. In a recent genome-wide study reporting a new miRNA target class (miBridge), we identified and validated interactions between 5'-UTRs and miRNAs. Separately, upstream AUGs (uAUGs) in 5'-UTRs are known to regulate genes translationally without affecting mRNA levels, one of the mechanisms for miRNA-mediated repression.

Results

Using sequence data from whole-genome cDNA alignments we identified 1418 uAUG sequences on the 5'-UTR that specifically interact with 3'-ends of conserved miRNAs. We computationally identified miRNAs that can target six genes through their uAUGs that were previously reported to suppress translation. We extended this meta-analysis by confirming expression of these miRNAs in cell-lines used in the uAUG studies. Similarly, seven members of the KLF family of genes containing uAUGs were computationally identified as interacting with several miRNAs. Using KLF9 as an example (whose protein expression is limited to brain tissue despite the mRNA being expressed ubiquitously), we show computationally that miRNAs expressed only in HeLa cells and not in neuroblastoma (N2A) cells can bind the uAUGs responsible for translation inhibition. Our computed results demonstrate that tissue- or cell-line specific repression of protein translation by uAUGs can be explained by the presence or absence of miRNAs that target these uAUG sequences. We propose that these uAUGs represent a subset of miRNA interaction sites on 5'-UTRs in miBridge, whereby a miRNA binding a uAUG hinders the progression of ribosome scanning the mRNA before it reaches the open reading frame (ORF).

Conclusions

While both miRNAs and uAUGs are separately known to down-regulate protein expression, we show that they may be functionally related by identifying potential interactions through a sequence-specific binding mechanism. Using prior experimental evidence that shows uAUG effects on translation repression together with miRNA expression data specific to cell lines, we demonstrate through computational analysis that cell-specific down-regulation of protein expression (while maintaining mRNA levels) correlates well with the simultaneous presence of miRNA and target uAUG sequences in one cell type and not others, suggesting tissue-specific translation repression by miRNAs through uAUGs.

Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks

SUMMARY

Metscape is a plug-in for Cytoscape, used to visualize and interpret metabolomic data in the context of human metabolic networks. We have developed a metabolite database by extracting and integrating information from several public sources. By querying this database, Metscape allows users to trace the connections between metabolites and genes, visualize compound networks and display compound structures as well as information for reactions, enzymes, genes and pathways. Applying the pathway filter, users can create subnetworks that consist of compounds and reactions from a given pathway. Metscape allows users to upload experimental data, and visualize and explore compound networks over time, or experimental conditions. Color and size of the nodes are used to visualize these dynamic changes. Metscape can display the entire metabolic network or any of the pathway-specific networks that exist in the database.

AVAILABILITY

Metscape can be installed from within Cytoscape 2.6.x under 'Network and Attribute I/O' category. For more information, please visit http://metscape.ncibi.org/tryplugin.html.

The University of Michigan Honest Broker: a Web-based service for clinical and translational research and practice

For the success of clinical and translational science, a seamless interoperation is required between clinical and research information technology. Addressing this need, the Michigan Clinical Research Collaboratory (MCRC) was created. The MCRC employed a standards-driven Web Services architecture to create the U-M Honest Broker, which enabled sharing of clinical and research data among medical disciplines and separate institutions. Design objectives were to facilitate sharing of data, maintain a master patient index (MPI), deidentification of data, and routing data to preauthorized destination systems for use in clinical care, research, or both. This article describes the architecture and design of the U-M HB system and the successful demonstration project. Seventy percent of eligible patients were recruited for a prospective study examining the correlation between interventional cardiac catheterizations and depression. The U-M Honest Broker delivered on the promise of using structured clinical knowledge shared among providers to help clinical and translational research.

High-throughput next-generation sequencing technologies foster new cutting-edge computing techniques in bioinformatics

The advent of high-throughput next generation sequencing technologies have fostered enormous potential applications of supercomputing techniques in genome sequencing, epi-genetics, metagenomics, personalized medicine, discovery of non-coding RNAs and protein-binding sites. To this end, the 2008 International Conference on Bioinformatics and Computational Biology (Biocomp) – 2008 World Congress on Computer Science, Computer Engineering and Applied Computing (Worldcomp) was designed to promote synergistic inter/multidisciplinary research and education in response to the current research trends and advances. The conference attracted more than two thousand scientists, medical doctors, engineers, professors and students gathered at Las Vegas, Nevada, USA during July 14–17 and received great success. Supported by International Society of Intelligent Biological Medicine (ISIBM), International Journal of Computational Biology and Drug Design (IJCBDD), International Journal of Functional Informatics and Personalized Medicine (IJFIPM) and the leading research laboratories from Harvard, M.I.T., Purdue, UIUC, UCLA, Georgia Tech, UT Austin, U. of Minnesota, U. of Iowa etc, the conference received thousands of research papers. Each submitted paper was reviewed by at least three reviewers and accepted papers were required to satisfy reviewers' comments. Finally, the review board and the committee decided to select only 19 high-quality research papers for inclusion in this supplement to BMC Genomics based on the peer reviews only. The conference committee was very grateful for the Plenary Keynote Lectures given by: Dr. Brian D. Athey (University of Michigan Medical School), Dr. Vladimir N. Uversky (Indiana University School of Medicine), Dr. David A. Patterson (Member of United States National Academy of Sciences and National Academy of Engineering, University of California at Berkeley) and Anousheh Ansari (Prodea Systems, Space Ambassador). The theme of the conference to promote synergistic research and education has been achieved successfully.

Synergies and distinctions between computational disciplines in biomedical research: perspective from the Clinical andTranslational Science Award programs

Clinical and translational research increasingly requires computation. Projects may involve multiple computationally oriented groups including information technology (IT) professionals, computer scientists, and biomedical informaticians. However, many biomedical researchers are not aware of the distinctions among these complementary groups, leading to confusion, delays, and suboptimal results. Although written from the perspective of Clinical and Translational Science Award (CTSA) programs within academic medical centers, this article addresses issues that extend beyond clinical and translational research. The authors describe the complementary but distinct roles of operational IT, research IT, computer science, and biomedical informatics using a clinical data warehouse as a running example. In general, IT professionals focus on technology. The authors distinguish between two types of IT groups within academic medical centers: central or administrative IT (supporting the administrative computing needs of large organizations) and research IT (supporting the computing needs of researchers). Computer scientists focus on general issues of computation such as designing faster computers or more efficient algorithms, rather than specific applications. In contrast, informaticians are concerned with data, information, and knowledge. Biomedical informaticians draw on a variety of tools, including but not limited to computers, to solve information problems in health care and biomedicine. The paper concludes with recommendations regarding administrative structures that can help to maximize the benefit of computation to biomedical research within academic health centers.

New class of microRNA targets containing simultaneous 5'-UTR and 3'-UTR interaction sites

MicroRNAs (miRNAs) are known to post-transcriptionally regulate target mRNAs through the 3'-UTR, which interacts mainly with the 5'-end of miRNA in animals. Here we identify many endogenous motifs within human 5'-UTRs specific to the 3'-ends of miRNAs. The 3'-end of conserved miRNAs in particular has significant interaction sites in the human-enriched, less conserved 5'-UTR miRNA motifs, while human-specific miRNAs have significant interaction sites only in the conserved 5'-UTR motifs, implying both miRNA and 5'-UTR are actively evolving in response to each other. Additionally, many miRNAs with their 3'-end interaction sites in the 5'-UTRs turn out to simultaneously contain 5'-end interaction sites in the 3'-UTRs. Based on these findings we demonstrate combinatory interactions between a single miRNA and both end regions of an mRNA using model systems. We further show that genes exhibiting large-scale protein changes due to miRNA overexpression or deletion contain both UTR interaction sites predicted. We provide the predicted targets of this new miRNA target class, miBridge, as an efficient way to screen potential targets, especially for nonconserved miRNAs, since the target search space is reduced by an order of magnitude compared with the 3'-UTR alone. Efficacy is confirmed by showing SEC24D regulation with hsa-miR-605, a miRNA identified only in primate, opening the door to the study of nonconserved miRNAs. Finally, miRNAs (and associated proteins) involved in this new targeting class may prevent 40S ribosome scanning through the 5'-UTR and keep it from reaching the start-codon, preventing 60S association.

Interactive Design Strategy for a Multi-Functional PAMAM Dendrimer-Based Nano-Therapeutic Using Computational Models and Experimental Analysis

Molecular dynamics simulations of nano-therapeutics as a final product and of all intermediates in the process of generating a multi-functional nano-therapeutic based on a poly(amidoamine) (PAMAM) dendrimer were performed along with chemical analyses of each of them. The actual structures of the dendrimers were predicted, based on potentiometric titration, gel permeation chromatography, and NMR. The chemical analyses determined the numbers of functional molecules, based on the actual structure of the dendrimer. Molecular dynamics simulations calculated the configurations of the intermediates and the radial distributions of functional molecules, based on their numbers. This interactive process between the simulation results and the chemical analyses provided a further strategy to design the next reaction steps and to gain insight into the products at each chemical reaction step.

Delivering informatics capabilities to an AHC research community through public/private partnerships (PPP)

Velos eResearch is a commercially-developed, regulatory-compliant, web-based clinical research information system from Velos Inc. Aithent Inc. is a software development services company. The University of Michigan (UM) has public/private partnerships with Velos and Aithent to collaborate on development of additional capabilities, modules, and new products to better support the needs of clinical and translational research communities. These partnerships provide UM with a mechanism for obtaining high-quality functionally comprehensive capabilities more quickly and at lower cost, while the corporate partners get a quality advisory and development partner--this benefits all parties. The UM chose to partner with Velos in part because of its commitment to interoperability. Velos is an active participant in the NCI caBIG project and is committed to caBIG compatibility. Velos already provides interoperability with other Velos sites in the CTSA context. One example of the partnership is co-development of integrated specimen management capabilities. UM spent more than a year defining business requirements and technical specifications for, and is funding development of, this capability. UM also facilitates an autonomous user community (20+ institutions, 7 CTSA awardees); the broad goal of the group is to share experiences, expertise, identify collaborative opportunities, and support one another as well as provide a source of future needs identification to Velos. Advantages and risks related to delivering informatics capabilities to an AHC research community through a public/private partnership will be presented. The UM, Velos and Aithent will discuss frameworks, agreements and other factors that have contributed to a successful partnership.

A bioinformatics analysis of the cell line nomenclature

MOTIVATION

Cell lines are used extensively in biomedical research, but the nomenclature describing cell lines has not been standardized. The problems are both linguistic and experimental. Many ambiguous cell line names appear in the published literature. Users of the same cell line may refer to it in different ways, and cell lines may mutate or become contaminated without the knowledge of the user. As a first step towards rationalizing this nomenclature, we created a cell line knowledgebase (CLKB) with a well-structured collection of names and descriptive data for cell lines cultured in vitro. The objectives of this work are: (i) to assist users in extracting useful information from biomedical text and (ii) to highlight the importance of standardizing cell line names in biomedical research. This CLKB contains a broad collection of cell line names compiled from ATCC, Hyper CLDB and MeSH. In addition to names, the knowledgebase specifies relationships between cell lines. We analyze the use of cell line names in biomedical text. Issues include ambiguous names, polymorphisms in the use of names and the fact that some cell line names are also common English words. Linguistic patterns associated with the occurrence of cell line names are analyzed. Applying these patterns to find additional cell line names in the literature identifies only a small number of additional names. Annotation of microarray gene expression studies is used as a test case. The CLKB facilitates data exploration and comparison of different cell lines in support of clinical and experimental research.

AVAILABILITY

The web ontology file for this cell line collection can be downloaded at http://www.stateslab.org/data/celllineOntology/cellline.zip.

MiSearch adaptive pubMed search tool

Summary: MiSearch is an adaptive biomedical literature search tool that ranks citations based on a statistical model for the likelihood that a user will choose to view them. Citation selections are automatically acquired during browsing and used to dynamically update a likelihood model that includes authorship, journal and PubMed indexing information. The user can optionally elect to include or exclude specific features and vary the importance of timeliness in the ranking.

Availability:http://misearch.ncibi.org

Contact:dstates@umich.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Discriminating single-base difference miRNA expressions using microarray Probe Design Guru (ProDeG)

MicroRNAs (miRNA) are endogenous tissue-specific short RNAs that regulate gene expression. Discriminating each let-7 family member expression is especially important due to let-7's abundance and connection with development and cancer. However, short lengths (22 nt) and similarities between multiple sequences have prevented identification of individual members. Here, we present ProDeG, a computational algorithm which designs imperfectly matched sequences (previously yielding only noise levels in microarray experiments) for genome-wide microarray “signal” probes to discriminate single nucleotide differences and to improve probe qualities. Our probes for the entire let-7 family are both homogeneous and specific, verified using microarray signals from fluorescent dye-tagged oligonucleotides corresponding to the let-7 family, demonstrating the power of our algorithm. In addition, false let-7c signals from conventional perfectly-matched probes were identified in lymphoblastoid cell-line samples through comparison with our probe-set signals, raising concerns about false let-7 family signals in conventional microarray platform.

Simplifying access to a Clinical Data Repository using schema summarization

The University of Michigan Clinical Data Repository (CDR) integrates over 25 data sources, and as a result has a schema that is too complex to be directly queried by clinical researchers. Schema summarization uses abstract elements and links to summarize a complex schema and allows users with limited knowledge of the underlying database structure to effectively issue queries to the CDR for clinical and translational research.

The Cell Line Ontology and its use in tagging cell line names in biomedical text

Cell lines are used extensively throughout biomedical research, but the nomenclature describing cell lines has not been standardized; many ambiguous names appear in the published literature. The Cell Line Ontology is a well-structured collection of names for cell lines cultured in vitro. This ontology collates names from ATCC, HyperCLDB and MeSH in an informative format and specifies relationships between cell lines including derivation and homolog. Tagging cell line names in text requires an integrated approach.

An 'Honest Broker' mechanism to maintain privacy for patient care and academic medical research

PURPOSE

From the Hippocratic Oath to the World Medical Association's Declaration of Geneva, physicians have sworn to protect patients' privacy. However, as systems move to more integrated architectures, protecting this medical data becomes more of a challenge. The increase in complexity of IT environments, the aggregation of data, and the desire of other entities to access this data, often 24 h/day x 7 day/week x 365 day/year, is putting serious strains on our ability to maintain its security. This problem cuts across all electronic record sources from patient care records to academic medical research records.

APPROACH

In order to address this issue, we are rethinking the way we store, transmit, process, access, and federate patient data from clinical and research applications. Our groups at the University of Michigan are developing a system called the "Honest Broker" to help manage this problem. The Honest Broker will offload the burden of housing identifiable data elements of protected health information (PHI) (e.g., name and address) as well as manage data transfer between clinical and research systems. Lab results and other non-identifiable data will be stored in separate systems with either a research study ID or clinical ID number. This two-component architecture increases the burden on attackers who now need to compromise two systems, one of which is seriously hardened, in order to match health data with a patient's actual identity.

CONCLUSIONS

While no security system is truly intrusion-proof, this architecture provides a high security choke point reducing the likelihood of a breach. By redesigning the method of integrating clinical care and research, we have enabled projects that would be cost prohibitive to conduct otherwise. The scalability of this mechanism is dependant on nature of the heterogenous nature of the clinical systems serving patients.

miBLAST: scalable evaluation of a batch of nucleotide sequence queries with BLAST

A common task in many modern bioinformatics applications is to match a set of nucleotide query sequences against a large sequence dataset. Exis-ting tools, such as BLAST, are designed to evaluate a single query at a time and can be unacceptably slow when the number of sequences in the query set is large. In this paper, we present a new algorithm, called miBLAST, that evaluates such batch workloads efficiently. At the core, miBLAST employs a q-gram filtering and an index join for efficiently detecting similarity between the query sequences and database sequences. This set-oriented technique, which indexes both the query and the database sets, results in substantial performance improvements over existing methods. Our results show that miBLAST is significantly faster than BLAST in many cases. For example, miBLAST aligned 247 965 oligonucleotide sequences in the Affymetrix probe set against the Human UniGene in 1.26 days, compared with 27.27 days with BLAST (an improvement by a factor of 22). The relative performance of miBLAST increases for larger word sizes; however, it decreases for longer queries. miBLAST employs the familiar BLAST statistical model and output format, guaranteeing the same accuracy as BLAST and facilitating a seamless transition for existing BLAST users.

Challenges in presenting high dimensional data to aid in triage in the DARPA virtual soldier project

One of the goals of the DARPA Virtual Soldier Project is to aid the field medic in the triage of a casualty. In Phase I, we are currently collecting 12 baseline experimental physiological variables and a cardiac gated Computed Tomography (CT) imagery for use in an prototyping a futuristic electronic medical record, the "Holomer". We are using physiological models and Kalman filtering to aid in diagnosis and predict outcomes in relation to cardiac injury. The physiological modeling introduces another few hundred variables. Reducing the complexity of the above into easy-to-read text to aid in the triage by the field medic is the challenge with multiple display solutions. A description of the possible techniques follows.

The "Honest Broker" method of integrating interdisciplinary research data

Multiple clinical informatics systems have been developed within separate departments of the University of Michigan Medical School. We are in the process of creating an "Honest Broker" method of safely and securely linking together data from different clinical systems for a research project studying the co-morbidity of depression and cardiovascular disease. The Michigan Clinical Research Collaboratory (MCRC) is an NIH/NHLBI Roadmap initiative funded to re-engineer the clinical research enterprise.

Noise suppression and optical sectioning by non-phase-recording interferometry

It has long been known that image plane holography with low-coherence illumination achieves optical sectioning of a volume object. A method is analyzed that is similar to image plane holography, but the interferometric arrangement utilizes the interference between two object-bearing beams instead of the basic object and reference beams.

Guidelines for incorporating non-perfectly matched oligonucleotides into target-specific hybridization probes for a DNA microarray

Sequence-specific oligonucleotide probes play a crucial role in hybridization techniques including PCR, DNA microarray and RNA interference. Once the entire genome becomes the search space for target genes/genomic sequences, however, cross-hybridization to non-target sequences becomes a problem. Large gene families with significant similarity among family members, such as the P450s, are particularly problematic. Additionally, accurate single nucleotide polymorphism (SNP) detection depends on probes that can distinguish between nearly identical sequences. Conventional oligonucleotide probes that are perfectly matched to target genes/genomic sequences are often unsuitable in such cases. Carefully designed mismatches can be used to decrease cross-hybridization potential, but implementing all possible mismatch probes is impractical. Our study provides guidelines for designing non-perfectly matched DNA probes to target DNA sequences as desired throughout the genome. These guidelines are based on the analysis of hybridization data between perfectly matched and non-perfectly matched DNA sequences (single-point or double-point mutated) calculated in silico. Large changes in hybridization temperature predicted by these guidelines for non-matched oligonucleotides fit independent experimental data very well. Applying the guidelines to find oligonucleotide microarray probes for P450 genes, we confirmed the ability of our point mutation method to differentiate the individual genes in terms of thermodynamic calculations of hybridization and sequence similarity.

Optical sectioning by holographic coherence imaging: a generalized analysis

A theory of optical sectioning by image plane holography is developed, emphasizing the use of broad-spectrum holographic methods to enhance the process. It is shown that a broad-spectrum source in a grating interferometer imitates the behavior of a monochromatic broad source.

Analysis of time-gated imaging through scattering media by a Fourier optics approach

The method of Fourier optics is applied to the problem of time-gated imaging through scattering media. Tb adapt the problem to this treatment, appropriate alterations are made: The continuous medium is replaced by a cascade of thin scatterers, and a spatial filtering process is substituted for the conventional gating processes. Closed-form solutions are derived.

Correlations among angular wave component amplitudes in elastic multiple-scattering random media

The propagation of scalar waves through random media that provide multiple elastic scattering is considered by derivation of an expression for the angular correlation of the scattered wave amplitudes. Coherent wave transmission is shown to occur through a mechanism similar to that responsible for coherent backscattering. While the properties of the scattered wave are generally consistent with radiative-transfer theory for sufficiently small incident and scattering angles, coherent transmission provides corrections to radiative-transfer results at larger angles. The theoretical angular correlation curves are fit, by specifying the probability densities of two random variables that correspond to material parameters, to measured data of laser light scattering from various polymer microsphere suspensions.

Investigation of ultrafast time gating by spatial filtering

With a spatial-filtering method of gating, we explore image formation through scattering media using first-arriving light. Gating times of a few femtoseconds and less are produced, and the resolution at these extremely short gating times is investigated.

F-actin, tubulin and spectrin in the organ of Corti: comparative distribution in different cell types and mammalian species

Laser scanning confocal microscopy was used to determine the distribution of actin, spectrin and tubulin in whole mounts of the organ of Corti of guinea pig, monkey, rat and chinchilla. Actin, spectrin and tubulin were localized in all cell types in the auditory epithelium. No specialized cytoskeletal organization of tubulin was detected in the cytoplasmic domain of hair cells. The only specialized organization of actin and spectrin in the cytoplasmic domain was the infra-cuticular network, found exclusively in apical guinea pig outer hair cells. In contrast, the lateral wall of inner and outer hair cells contained a homogeneous distribution of label specific for actin and spectrin. The label intensity was similar in the base and the apex of the cochlea. These results indicate that the distribution of spectrin and actin in the auditory epithelium is similar to that in other epithelial cells, suggesting that actin and spectrin participate in the formation of cellular shape and possibly in docking molecules to the membrane.

The diameters of frozen-hydrated chromatin fibers increase with DNA linker length: evidence in support of variable diameter models for chromatin

The diameters of chromatin fibers from Thyone briareus (sea cucumber) sperm (DNA linker length, n = 87 bp) and Necturus maculosus (mudpuppy) erythrocytes (n = 48 bp) were investigated. Soluble fibers were frozen into vitrified aqueous solutions of physiological ionic strength (124 mM), imaged by cryo-EM, and measured interactively using quantitative computer image-processing techniques. Frozen-hydrated Thyone and Necturus fibers had significantly different mean diameters of 43.5 nm (SD = 4.2 nm; SEM = 0.61 nm) and 32.0 nm (SD = 3.0 nm; SEM = 0.36 nm), respectively. Evaluation of previously published EM data shows that the diameters of chromatin from a large number of sources are proportional to linker length. In addition, the inherent variability in fiber diameter suggests a relationship between fiber structure and the heterogeneity of linker length. The cryo-EM data were in quantitative agreement with space-filling double-helical crossed-linker models of Thyone and Necturus chromatin. The data, however, do not support solenoid or twisted-ribbon models for chromatin that specify a constant 30 nm diameter. To reconcile the concept of solenoidal packing with the data, we propose a variable-diameter solid-solenoid model with a fiber diameter that increases with linker length. In principle, each of the variable diameter models for chromatin can be reconciled with local variations in linker length.

Radial density distribution of chromatin: evidence that chromatin fibers have solid centers

Fiber diameter, radial distribution of density, and radius of gyration were determined from scanning transmission electron microscopy (STEM) of unstained, frozen-dried chromatin fibers. Chromatin fibers isolated under physiological conditions (ionic strength, 124 mM) from Thyone briareus sperm (DNA linker length, n = 87 bp) and Necturus maculosus erythrocytes (n = 48 bp) were analyzed by objective image-processing techniques. The mean outer diameters were determined to be 38.0 nm (SD = 3.7 nm; SEM = 0.36 nm) and 31.2 nm (SD = 3.6 nm; SEM = 0.32 nm) for Thyone and Necturus, respectively. These data are inconsistent with the twisted-ribbon and solenoid models, which predict constant diameters of approximately 30 nm, independent of DNA linker length. Calculated radial density distributions of chromatin exhibited relatively uniform density with no central hole, although the 4-nm hole in tobacco mosaic virus (TMV) from the same micrographs was visualized clearly. The existence of density at the center of chromatin fibers is in strong disagreement with the hollow-solenoid and hollow-twisted-ribbon models, which predict central holes of 16 and 9 nm for chromatin of 38 and 31 nm diameter, respectively. The cross-sectional radii of gyration were calculated from the radial density distributions and found to be 13.6 nm for Thyone and 11.1 nm for Necturus, in good agreement with x-ray and neutron scattering. The STEM data do not support the solenoid or twisted-ribbon models for chromatin fiber structure. They do, however, support the double-helical crossed-linker models, which exhibit a strong dependence of fiber diameter upon DNA linker length and have linker DNA at the center.

Chromatin fibers are left-handed double helices with diameter and mass per unit length that depend on linker length

Four classes of models have been proposed for the internal structure of eukaryotic chromosome fibers--the solenoid, twisted-ribbon, crossed-linker, and superbead models. We have collected electron image and x-ray scattering data from nuclei, and isolated chromatin fibers of seven different tissues to distinguish between these models. The fiber diameters are related to the linker lengths by the equation: D(N) = 19.3 + 0.23 N, where D(N) is the external diameter (nm) and N is the linker length (base pairs). The number of nucleosomes per unit length of the fibers is also related to linker length. Detailed studies were done on the highly regular chromatin from erythrocytes of Necturus (mud puppy) and sperm of Thyone (sea cucumber). Necturus chromatin fibers (N = 48 bp) have diameters of 31 nm and have 7.5 +/- 1 nucleosomes per 10 nm along the axis. Thyone chromatin fibers (N = 87 bp) have diameters of 39 nm and have 12 +/- 2 nucleosomes per 10 nm along the axis. Fourier transforms of electron micrographs of Necturus fibers showed left-handed helical symmetry with a pitch of 25.8 +/- 0.8 nm and pitch angle of 32 +/- 3 degrees, consistent with a double helix. Comparable conclusions were drawn from the Thyone data. The data do not support the solenoid, twisted-ribbon, or supranucleosomal particle models. The data do support two crossed-linker models having left-handed double-helical symmetry and conserved nucleosome interactions.