Evaluation of automated pipelines for tree and plot metric estimation from TLS data in tropical forest areas

Background and AimsTerrestrial LiDAR scanning (TLS) data are of great interest in forest ecology and management because they provide detailed 3-D information on tree structure. Automated pipelines are increasingly used to process TLS data and extract various tree- and plot-level metrics. With these developments comes the risk of unknown reliability due to an absence of systematic output control. In the present study, we evaluated the estimation errors of various metrics, such as wood volume, at tree and plot levels for four automated pipelines.MethodsWe used TLS data collected from a 1-ha plot of tropical forest, from which 391 trees >10 cm in diameter were fully processed using human assistance to obtain control data for tree- and plot-level metrics.Key ResultsOur results showed that fully automated pipelines led to median relative errors in the quantitative structural model (QSM) volume ranging from 39 to 115 % at the tree level and 10 to 134 % at the 1-ha plot level. For tree-level metrics, the median error for the crown-projected area ranged from 46 to 59 % and that for the crown-hull volume varied from 72 to 88 %. This result suggests that the tree isolation step is the weak link in automated pipeline methods. We further analysed how human assistance with automated pipelines can help reduce the error in the final QSM volume. At the tree scale, we found that isolating trees using human assistance reduced the error in wood volume by a factor of 10. At the 1-ha plot scale, locating trees with human assistance reduced the error by a factor of 3.ConclusionsOur results suggest that in complex tropical forests, fully automated pipelines may provide relatively unreliable metrics at the tree and plot levels, but limited human assistance inputs can significantly reduce errors.     

Two-dimensional fluorescence difference gel electrophoresis analysis of the urine proteome in human diabetic nephropathy

Urinary proteins may provide clues regarding pathogenesis of kidney disease as well as providing markers of disease activity. We employed two-dimensional differential in-gel electrophoretic analysis (2-D DIGE) to assess multiple urine samples in patients with diabetic nephropathy. Patient samples were collected as timed overnight collections. All the patients had longstanding diabetes, impaired renal function, and overt proteinuria. Control and patient urinary protein were analyzed by 2-D DIGE and DeCyder analysis. Ninety-nine spots were significantly regulated in the urine proteome of the diabetic samples, with 63 up- and 36 down-regulated. One spot corresponding to a pI 5-6 and a molecular weight between 45 and 66 kDa was consistently up-regulated by 19-fold across individuals in the diabetic group. Surface-enhanced laser desorption/ionization-time of flight analysis of in-gel tryptic digest of this spot identified this protein as alpha 1 antitrypsin (AAT). ELISA of urine samples from a separate group of patients and controls confirmed a marked increase of AAT in diabetic patients. Immunostaining of human diabetic kidneys revealed up-regulation of AAT in areas of renal fibrosis. In conclusion, we developed a method to analyze numerous urine samples from patients and allowed for detection and identification of regulated urine protein spots.     

Robust and systematic drug screening method using chemical arrays and the protein library: identification of novel inhibitors of carbonic anhydrase II

The identification of specific interactions between small molecules and human proteins of interest is a fundamental step in chemical biology and drug development. Here we describe an efficient method to obtain novel binding ligands of human proteins by a chemical array approach. Our method includes large-scale ligand screening with two libraries, proteins and chemicals, the use of cell lysates that express proteins of interest fused with red fluorescent protein, and high-throughput screening by merged display analysis, which removes false positive signals from array experiments. Using our systematic platform, we detected novel inhibitors of carbonic anhydrase II. It is suggested that our systematic platform is a rapid and robust approach to screen novel ligands for human proteins of interest.     

Robust and Systematic Drug Screening Method Using Chemical Arrays and the Protein Library: Identification of Novel Inhibitors of Carbonic Anhydrase II

The identification of specific interactions between small molecules and human proteins of interest is a fundamental step in chemical biology and drug development. Here we describe an efficient method to obtain novel binding ligands of human proteins by a chemical array approach. Our method includes large-scale ligand screening with two libraries, proteins and chemicals, the use of cell lysates that express proteins of interest fused with red fluorescent protein, and high-throughput screening by merged display analysis, which removes false positive signals from array experiments. Using our systematic platform, we detected novel inhibitors of carbonic anhydrase II. It is suggested that our systematic platform is a rapid and robust approach to screen novel ligands for human proteins of interest.     

Towards functional phosphoproteomics by mapping differential phosphorylation events in signaling networks

Protein phosphorylation plays a central role in many signal transduction pathways that mediate biological processes. Novel quantitative mass spectrometry-based methods have recently revealed phosphorylation dynamics in animals, yeast, and plants. These methods are important for our understanding of how differential phosphorylation participates in translating distinct signals into proper physiological responses, and shifted research towards screening for potential cancer therapies and in-depth analysis of phosphoproteomes. In this review, we aim to describe current progress in quantitative phosphoproteomics. This emerging field has changed numerous static pathways into dynamic signaling networks, and revealed protein kinase networks that underlie adaptation to environmental stimuli. Mass spectrometry enables high-throughput and high-quality analysis of differential phosphorylation at a site-specific level. Although determination of differential phosphorylation between treatments is analogous to detecting differential gene expression, the large body of statistical techniques that has been developed for analysis of differential gene expression is not generally applied for detecting differential phosphorylation. We suggest possible improvements for analysis of quantitative phosphorylation by increasing the number of biological replicates and adapting statistical tests used for gene expression profiling and widely implemented in freely available software tools.     

Enrichment and identification of glycoproteins in human saliva using lectin magnetic bead arrays

Aberrant glycosylation of proteins is a hallmark of tumorigenesis and could provide diagnostic value in cancer detection. Human saliva is an ideal source of glycoproteins due to the relatively high proportion of glycosylated proteins in the salivary proteome. Moreover, saliva collection is noninvasive and technically straightforward, and the sample collection and storage is relatively easy. Although differential glycosylation of proteins can be indicative of disease states, identification of differential glycosylation from clinical samples is not trivial. To facilitate salivary glycoprotein biomarker discovery, we optimized a method for differential glycoprotein enrichment from human saliva based on lectin magnetic bead arrays (saLeMBA). Selected lectins from distinct reactivity groups were used in the saLeMBA platform to enrich salivary glycoproteins from healthy volunteer saliva. The technical reproducibility of saLeMBA was analyzed with liquid chromatography–tandem mass spectrometry (LC–MS/MS) to identify the glycosylated proteins enriched by each lectin. Our saLeMBA platform enabled robust glycoprotein enrichment in a glycoprotein- and lectin-specific manner consistent with known protein-specific glycan profiles. We demonstrated that saLeMBA is a reliable method to enrich and detect glycoproteins present in human saliva.     

A complex standard for protein identification, designed by evolution

Shotgun proteomic investigations rely on the algorithmic assignment of mass spectra to peptides. The quality of these matches is therefore a cornerstone in the analysis and has been the subject of numerous recent developments. In order to establish the benefits of novel algorithms, they are applied to reference samples of known content. However, these were recently shown to be either too simple to resemble typical real-life samples or as leading to results of lower accuracy as the method itself. Here, we describe how to use the proteome of Pyrococcus furiosus , a hyperthermophile, as a standard to evaluate proteomics identification workflows. Indeed, we prove that the Pyrococcus furiosus proteome provides a valid method for detecting random hits, comparable to the decoy databases currently in popular use, but we also prove that the Pyrococcus furiosus proteome goes squarely beyond the decoy approach by also providing many hundreds of highly reliable true positive hits. Searching the Pyrococcus furiosus proteome can thus be used as a unique test that provides the ability to reliably detect both false positives as well as proteome-scale true positives, allowing the rigorous testing of identification algorithms at the peptide and protein level.     

A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data

MOTIVATION: Microarray experiments are affected by numerous sources of non-biological variation that contribute systematic bias to the resulting data. In a dual-label (two-color) cDNA or long-oligonucleotide microarray, these systematic biases are often manifested as an imbalance of measured fluorescent intensities corresponding to Sample A versus those corresponding to Sample B. Systematic biases also affect between-slide comparisons. Making effective corrections for these systematic biases is a requisite for detecting the underlying biological variation between samples. Effective data normalization is therefore an essential step in the confident identification of biologically relevant differences in gene expression profiles. Several normalization methods for the correction of systemic bias have been described. While many of these methods have addressed intensity-dependent bias, few have addressed both intensity-dependent and spatiality-dependent bias. RESULTS: We present a neural network-based normalization method for correcting the intensity- and spatiality-dependent bias in cDNA microarray datasets. In this normalization method, the dependence of the log-intensity ratio (M) on the average log-intensity (A) as well as on the spatial coordinates (X,Y) of spots is approximated with a feed-forward neural network function. Resistance to outliers is provided by assigning weights to each spot based on how distant their M values is from the median over the spots whose A values are similar, as well as by using pseudospatial coordinates instead of spot row and column indices. A comparison of the robust neural network method with other published methods demonstrates its potential in reducing both intensity-dependent bias and spatial-dependent bias, which translates to more reliable identification of truly regulated genes.     

New relationships of human hematopoietic lineages facilitate detection of multipotent hematopoietic stem and progenitor cells

Three important goals of hematopoietic stem cell research are to understand of how hematopoietic stem cells (HSCs) self-renew, how lineage commitment takes place, and how HSCs can be expanded ex vivo. Research in this area requires a reliable model of hematopoiesis. Performing detailed functional analyses of human hematopoietic progenitor subsets, we recently gained evidence for new hematopoietic lineage relationships.¹ According to our data, neutrophils belong to the same branch of the hematopoietic tree as lymphocytes. In contrast, eosinophils and basophils derive from another branch, the erythro-myeloid branch. Here, after introducing the newly proposed hematopoietic model, we discuss its consequences for the identification and expansion of human multipotent progenitors and suggest a fast and reliable method to screen for multipotent hematopoietic cells in vitro.     

Use of Composite Protein Database including Search Result Sequences for Mass Spectrometric Analysis of Cell Secretome

Mass spectrometric (MS) data of human cell secretomes are usually run through the conventional human database for identification. However, the search may result in false identifications due to contamination of the secretome with fetal bovine serum (FBS) proteins. To overcome this challenge, here we provide a composite protein database including human as well as 199 FBS protein sequences for MS data search of human cell secretomes. Searching against the human-FBS database returned more reliable results with fewer false-positive and false-negative identifications compared to using either a human only database or a human-bovine database. Furthermore, the improved results validated our strategy without complex experiments like SILAC. We expect our strategy to improve the accuracy of human secreted protein identification and to also add value for general use.     

Main-chain conformational tendencies of amino acids

A Ramachandran plot is a visual representation of the main-chain conformational tendencies of an amino acid. Despite forty years of research, the shape of Ramachandran plots is still a matter of debate. The issue in making a Ramachandran plot based on experimental data is deciding whether sparse data represent genuine conformations. We present here a simple solution to settle the ambiguities of the sparse data, and explain how we verified the accuracies of our plots using an independent dataset. To obtain our results, we then measured the pair-wise distances of main-chain conformational tendencies among amino acids, and showed that the conformational relationships of amino acids are well preserved in a two-dimensional map, leading to the conclusion that the conformational diversity space of amino acids is largely two dimensional. We further noticed that amino acids in early and late evolutionary stages are located in different zones in the two-dimensional map. In addition to these conclusions, we here present an amino acid substitution table derived from experimental data.     

Purification and direct transformation of epithelial progenitor cells from primary human prostate

Epithelial cell transformation has been demonstrated in numerous animal models for the study of solid tumor biology. However, little evidence exists for human epithelial cell transformation without previous immortalization via genetic influences such as SV40 T-antigen, thus limiting our knowledge of the events that can transform naive human epithelium. Here we describe a system developed in our laboratory to directly transform freshly isolated primary human prostate epithelial cells without previous culture or immortalization. Prostate tissue is obtained from patients and benign tissue is separated from malignant tissue. Benign and malignant tissues are mechanically and enzymatically dissociated to single cells overnight, and immune cells and epithelial subsets are isolated on the basis of differential expression of surface antigens. Epithelial progenitor cells are transduced with lentiviruses expressing oncogenes and combined with inductive stroma for in vivo studies. At 8-16 weeks after transplantation into immune-deficient mice, the development of lesions, histologically classified as benign prostate, prostatic intraepithelial neoplasia and adenocarcinoma, can be evaluated.     

TESTING INFERENCES ABOUT MICRO-EVOLUTIONARY PROCESSES BY MEANS OF SPATIAL AUTOCORRELATION ANALYSIS

We generated numerous simulated gene-frequency surfaces subjected to 200 generations of isolation by distance with, in some cases, added migration or selection. From these surfaces we assembled six data sets comprising from 12 to 15 independent allele-frequency surfaces, to simulate biologically plausible population samples. The purpose of the study was to investigate whether spatial autocorrelation analysis will correctly infer the microevolutionary processes involved in each data set. The correspondence between the simulated processes and the inferences made concerning them is close for five of the six data sets. Errors in inference occurred when the effect of migration was weak, due to low gene frequency differential or low migration strength; when selection was weak and against a background with a complex pattern; and when a random process—isolation by distance—was the only one acting. Spatial correlograms proved more sensitive to detecting trends than inspection of gene-frequency surfaces by the human eye. Joint interpretation of the correlograms and their clusters proved most reliable in leading to the correct inference. The inspection and clustering of surfaces were useful for determining directional components. Because this method relies on common patterns across loci, as many gene frequencies as feasible should be used. We recommend spatial autocorrelation analysis for the detection of microevolutionary processes in natural populations.     

Empirical evaluation of data transformations and ranking statistics for microarray analysis

There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics outperform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.     

A Laboratory Information Management System (LIMS) for a high throughput genetic platform aimed at candidate gene mutation screening

High throughput mutation screening in an automated environment generates large data sets that have to be organized and stored reliably. Complex multistep workflows require strict process management and careful data tracking. We have developed a Laboratory Information Management Systems (LIMS) tailored to high throughput candidate gene mutation scanning and resequencing that respects these requirements. Designed with a client/server architecture, our system is platform independent and based on open-source tools from the database to the web application development strategy. Flexible, expandable and secure, the LIMS, by communicating with most of the laboratory instruments and robots, tracks samples and laboratory information, capturing data at every step of our automated mutation screening workflow. An important feature of our LIMS is that it enables tracking of information through a laboratory workflow where the process at one step is contingent on results from a previous step. AVAILABILITY: Script for MySQL database table creation and source code of the whole JSP application are freely available on our website: http://www-gcs.iarc.fr/lims/. SUPPLEMENTARY INFORMATION: System server configuration, database structure and additional details on the LIMS and the mutation screening workflow are available on our website: http://www-gcs.iarc.fr/lims/     

Novel egg white-based 3-D cell culture system

Although three dimensional (3-D) cell culture systems have numerous advantages over traditional monolayer culture, the currently available 3-D cell culture media are cost-prohibitive for regular use by the majority of research laboratories. Here we show a simple system based on avian egg white that supports growth of cells in 3-D, at a significantly decreased cost. Specifically, we show that growth of immortalized human breast epithelial cells (MCF10A) in egg white-based medium results in formation of acini with hollow lumens, apoptotic clearance of the cells in the lumen, and apicobasal polarization comparable to what has been described using established 3-D culture media such as reconstituted basement membrane preparations (BM). There was no significant difference in MCF10A proliferation and acinar size between egg white and BM. We also cultured different established cell lines, oncogene-transformed MCF10A, and mouse mammary epithelial cells in egg white and BM, and observed similar morphology. In summary, our data convincingly argue that egg white can be used as a suitable alternative model for 3-D cell culture studies. We strongly believe that this simple and inexpensive method should allow researchers to perform 3-D cell culture experiments on a regular basis, and result in a dramatic increase of use of the 3-D cell culture in research. Thus, this finding lays the foundation for significantly increased, cost-effective use of 3-D cultures in cell biology.     

Power and potential bias in field studies of natural selection

The advent of multiple regression analyses of natural selection has facilitated estimates of both the direct and indirect effects of selection on many traits in numerous organisms. However, low power in selection studies has possibly led to a bias in our assessment of the levels of selection shaping natural populations. Using calculations and simulations based on the statistical properties of selection coefficients, we find that power to detect total selection (the selection differential) depends on sample size and the strength of selection relative to the opportunity of selection. The power of detecting direct selection (selection gradients) is more complicated and depends on the relationship between the correlation of each trait and fitness and the pattern of correlation among traits. In a review of 298 previously published selection differentials, we find that most studies have had insufficient power to detect reported levels of selection acting on traits and that, in general, the power of detecting weak levels of selection is low given current study designs. We also find that potential publication bias could explain the trend that reported levels of direct selection tend to decrease as study sizes increase, suggesting that current views of the strength of selection may be inaccurate and biased upward. We suggest that studies should be designed so that selection is analyzed on at least several hundred individuals, the total opportunity of selection be considered along with the pattern of selection on individual traits, and nonsignificant results be actively reported combined with an estimate of power.