Encoding and processing of sensory information in neuronal spike trains

Recently, a statistical signal-processing technique has allowed the information carried by single spike trains of sensory neurons on time-varying stimuli to be characterized quantitatively in a variety of preparations. In weakly electric fish, its application to first-order sensory neurons encoding electric field amplitude (P-receptor afferents) showed that they convey accurate information on temporal modulations in a behaviorally relevant frequency range (<80 Hz). At the next stage of the electrosensory pathway (the electrosensory lateral line lobe, ELL), the information sampled by first-order neurons is used to extract upstrokes and downstrokes in the amplitude modulation waveform. By using signal-detection techniques, we determined that these temporal features are explicitly represented by short spike bursts of second-order neurons (ELL pyramidal cells). Our results suggest that the biophysical mechanism underlying this computation is of dendritic origin. We also investigated the accuracy with which upstrokes and downstrokes are encoded across two of the three somatotopic body maps of the ELL (centromedial and lateral). Pyramidal cells of the centromedial map, in particular I-cells, encode up- and downstrokes more reliably than those of the lateral map. This result correlates well with the significance of these temporal features for a particular behavior (the jamming avoidance response) as assessed by lesion experiments of the centromedial map

Understanding Health and Disease with Multidimensional Single-Cell Methods

Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.Comment: 25 pages, 7 figures; revised version with minor changes. To appear in J. Phys.: Cond. Mat

A novel 3D human glioblastoma cell culture system for modeling drug and radiation responses

Background. Glioblastoma (GBM) is the most common primary brain tumor, with dismal prognosis. The failure of drug–radiation combinations with promising preclinical data to translate into effective clinical treatments may relate to the use of simplified 2-dimensional in vitro GBM cultures. Methods. We developed a customized 3D GBM culture system based on a polystyrene scaffold (Alvetex) that recapitulates key histological features of GBM and compared it with conventional 2D cultures with respect to their response to radiation and to molecular targeted agents for which clinical data are available. Results. In 3 patient-derived GBM lines, no difference in radiation sensitivity was observed between 2D and 3D cultures, as measured by clonogenic survival. Three different molecular targeted agents, for which robust clinical data are available were evaluated in 2D and 3D conditions: (i) temozolomide, which improves overall survival and is standard of care for GBM, exhibited statistically significant effects on clonogenic survival in both patient-derived cell lines when evaluated in the 3D model compared with only one cell line in 2D cells; (ii) bevacizumab, which has been shown to increase progression-free survival when added to standard chemoradiation in phase III clinical trials, exhibited marked radiosensitizing activity in our 3D model but had no effect on 2D cells; and (iii) erlotinib, which had no efficacy in clinical trials, displayed no activity in our 3D GBM model, but radiosensitized 2D cells. Conclusions. Our 3D model reliably predicted clinical efficacy, strongly supporting its clinical relevance and potential value in preclinical evaluation of drug–radiation combinations for GBM

Automated artemia length measurement using U-shaped fully convolutional networks and second-order anisotropic Gaussian kernels

The brine shrimp Artemia, a small crustacean zooplankton organism, is universally used as live prey for larval fish and shrimps in aquaculture. In Artemia studies, it would be highly desired to have access to automated techniques to obtain the length information from Anemia images. However, this problem has so far not been addressed in literature. Moreover, conventional image-based length measurement approaches cannot be readily transferred to measure the Artemia length, due to the distortion of non-rigid bodies, the variation over growth stages and the interference from the antennae and other appendages. To address this problem, we compile a dataset containing 250 images as well as the corresponding label maps of length measuring lines. We propose an automated Anemia length measurement method using U-shaped fully convolutional networks (UNet) and second-order anisotropic Gaussian kernels. For a given Artemia image, the designed UNet model is used to extract a length measuring line structure, and, subsequently, the second-order Gaussian kernels are employed to transform the length measuring line structure into a thin measuring line. For comparison, we also follow conventional fish length measurement approaches and develop a non-learning-based method using mathematical morphology and polynomial curve fitting. We evaluate the proposed method and the competing methods on 100 test images taken from the dataset compiled. Experimental results show that the proposed method can accurately measure the length of Artemia objects in images, obtaining a mean absolute percentage error of 1.16%

The Multiscale Morphology Filter: Identifying and Extracting Spatial Patterns in the Galaxy Distribution

We present here a new method, MMF, for automatically segmenting cosmic structure into its basic components: clusters, filaments, and walls. Importantly, the segmentation is scale independent, so all structures are identified without prejudice as to their size or shape. The method is ideally suited for extracting catalogues of clusters, walls, and filaments from samples of galaxies in redshift surveys or from particles in cosmological N-body simulations: it makes no prior assumptions about the scale or shape of the structures.}Comment: Replacement with higher resolution figures. 28 pages, 17 figures. For Full Resolution Version see: http://www.astro.rug.nl/~weygaert/tim1publication/miguelmmf.pd

Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis

The aim of this thesis is to develop automated methods for the analysis of the spatial patterns, and the functional behaviour of endothelial cells, viewed under microscopy, with applications to the understanding of atherosclerosis. Initially, a radial search approach to segmentation was attempted in order to trace the cell and nuclei boundaries using a maximum likelihood algorithm; it was found inadequate to detect the weak cell boundaries present in the available data. A parametric cell shape model was then introduced to fit an equivalent ellipse to the cell boundary by matching phase-invariant orientation fields of the image and a candidate cell shape. This approach succeeded on good quality images, but failed on images with weak cell boundaries. Finally, a support vector machines based method, relying on a rich set of visual features, and a small but high quality training dataset, was found to work well on large numbers of cells even in the presence of strong intensity variations and imaging noise. Using the segmentation results, several standard shear-stress dependent parameters of cell morphology were studied, and evidence for similar behaviour in some cell shape parameters was obtained in in-vivo cells and their nuclei. Nuclear and cell orientations around immature and mature aortas were broadly similar, suggesting that the pattern of flow direction near the wall stayed approximately constant with age. The relation was less strong for the cell and nuclear length-to-width ratios. Two novel shape analysis approaches were attempted to find other properties of cell shape which could be used to annotate or characterise patterns, since a wide variability in cell and nuclear shapes was observed which did not appear to fit the standard parameterisations. Although no firm conclusions can yet be drawn, the work lays the foundation for future studies of cell morphology. To draw inferences about patterns in the functional response of cells to flow, which may play a role in the progression of disease, single-cell analysis was performed using calcium sensitive florescence probes. Calcium transient rates were found to change with flow, but more importantly, local patterns of synchronisation in multi-cellular groups were discernable and appear to change with flow. The patterns suggest a new functional mechanism in flow-mediation of cell-cell calcium signalling

Shape mode analysis exposes movement patterns in biology: flagella and flatworms as case studies

We illustrate shape mode analysis as a simple, yet powerful technique to concisely describe complex biological shapes and their dynamics. We characterize undulatory bending waves of beating flagella and reconstruct a limit cycle of flagellar oscillations, paying particular attention to the periodicity of angular data. As a second example, we analyze non-convex boundary outlines of gliding flatworms, which allows us to expose stereotypic body postures that can be related to two different locomotion mechanisms. Further, shape mode analysis based on principal component analysis allows to discriminate different flatworm species, despite large motion-associated shape variability. Thus, complex shape dynamics is characterized by a small number of shape scores that change in time. We present this method using descriptive examples, explaining abstract mathematics in a graphic way.Comment: 20 pages, 6 figures, accepted for publication in PLoS On

An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies

This paper presents an automated system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies. Initially, features are extracted from colour-corrected biopsy images at positions of interest identified by adaptive thresholding and clump decomposition. A sequential floating search method and principal component analysis are used to reduce dimensionality. Manually annotated training images allow supervised training. The performance of Gaussian parametric and mixture models is compared when used to classify regions as either inflammatory or healthy. The system is optimized using a response surface method that maximises the area under the receiver operating characteristic curve. This system is then tested on images previously ranked by a number of observers with varying levels of expertise. These results are compared to the automated system using Spearman rank correlation. Results show that this system can rank 15 test images, with varying degrees of inflammation, in strong agreement with five expert pathologists

Appropriately differentiated ARPE-19 cells regain phenotype and gene expression profiles similar to those of native RPE cells.

PurposeThe RPE cell line ARPE-19 provides a dependable and widely used alternative to native RPE. However, replication of the native RPE phenotype becomes more difficult because these cells lose their specialized phenotype after multiple passages. Compounding this problem is the widespread use of ARPE-19 cells in an undifferentiated state to attempt to model RPE functions. We wished to determine whether suitable culture conditions and differentiation could restore the RPE-appropriate expression of genes and proteins to ARPE-19, along with a functional and morphological phenotype resembling native RPE. We compared the transcriptome of ARPE-19 cells kept in long-term culture with those of primary and other human RPE cells to assess the former's inherent plasticity relative to the latter.MethodsARPE-19 cells at passages 9 to 12 grown in DMEM containing high glucose and pyruvate with 1% fetal bovine serum were differentiated for up to 4 months. Immunocytochemistry was performed on ARPE-19 cells grown on filters. Total RNA extracted from ARPE-19 cells cultured for either 4 days or 4 months was used for RNA sequencing (RNA-Seq) analysis using a 2 × 50 bp paired end protocol. The RNA-Seq data were analyzed to identify the affected pathways and recognize shared ontological classification among differentially expressed genes. RPE-specific mRNAs and miRNAs were assessed with quantitative real-time (RT)-PCR, and proteins with western blotting.ResultsARPE-19 cells grown for 4 months developed the classic native RPE phenotype with heavy pigmentation. RPE-expressed genes, including RPE65, RDH5, and RDH10, as well as miR-204/211, were greatly increased in the ARPE-19 cells maintained at confluence for 4 months. The RNA-Seq analysis provided a comprehensive view of the relative abundance and differential expression of the genes in the differentiated ARPE-19 cells. Of the 16,757 genes with detectable signals, nearly 1,681 genes were upregulated, and 1,629 genes were downregulated with a fold change of 2.5 or more differences between 4 months and 4 days of culture. Gene Ontology analysis showed that the upregulated genes were associated with visual cycle, phagocytosis, pigment synthesis, cell differentiation, and RPE-related transcription factors. The majority of the downregulated genes play a role in cell cycle and proliferation.ConclusionsThe ARPE-19 cells cultured for 4 months developed a phenotype characteristic of native RPE and expressed proteins, mRNAs, and miRNAs characteristic of the RPE. Comparison of the ARPE-19 RNA-Seq data set with that of primary human fetal RPE, embryonic stem cell-derived RPE, and native RPE revealed an important overall similar expression ratio among all the models and native tissue. However, none of the cultured models reached the absolute values in the native tissue. The results of this study demonstrate that low-passage ARPE-19 cells can express genes specific to native human RPE cells when appropriately cultured and differentiated