Prediction of cervical neoplasia diagnosis groups. Discriminant analysis on digitized cell images

The purpose of this study was to develop discriminant analysis models for predicting cervical dysplasia/neoplasia case diagnoses using cytometric features derived from the digital image analysis of cell monolayers. The data base consisted of 925 cells from 27 cases diagnosed either as moderate dysplasia (n = 10), severe dysplasia (n = 5), carcinoma in situ (n = 8) or invasive carcinoma (n = 4) on both tissue biopsy and monolayer preparations. Cell features examined were cell diameter, nuclear diameter, nuclear mean optical density (OD), nuclear integrated OD (IOD), nuclear OD standard deviation, normalized IOD, nuclear texture and nuclear-cytoplasmic ratio. Features derived from cells visually classified as moderate dysplasia correctly predicted the case diagnosis of moderate dysplasia versus more severe disease for 85% of the cells. Prediction models using summary measures (mean and variance) derived from all visually classified abnormal cells within each case correctly separated all cases into their respective diagnostic categories. These findings suggest that dysplastic cells in a cytologic sample have features that collectively reflect the tissue diagnosis, regardless of the visual differences among the cells. Such information has potential use for diagnosis and possibly for prognosis.     

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.     

The dynamics of gene amplification described as a multitype compartmental model and as a branching process.

The present work is aimed at developing the mathematical tools by which the dynamics of gene amplification (GA) can be described in detail. Some discrete compartmental models of GA by disproportionate replication and a general model for other putative GA mechanisms are presented and analyzed. The dynamical distribution of gene copy number in the cell population is calculated with the loss of cells taken either as constant or as copy-number-dependent. Our analysis shows that for a one-copy GA process with constant loss of cells, the relative frequency of single-gene-copy cells (sensitive cells) converges to zero, with the rate of convergence depending on the amplification probability. In contrast, for a one-copy GA process with copy-number-dependent loss of cells, the relative frequency of single-copy cells is bounded, implying a bounded compartment of many-gene-copy cells. Using branching processes theory we calculate the dynamical distribution of the single-gene-copy compartment as well as its extinction probability. Our models are used for estimating treatment prognosis as affected by drug resistance due to GA, showing significant differences in prognosis resulting from small changes in drug dose.     

Spontaneous regression of residual tumour burden: prediction by Monte Carlo simulation.

Current cancer treatment protocols are designed to release the tumour burden down to a small number of cells. In this study, we use Monte Carlo simulations to show that small populations of cells with intrinsic cell loss rates comparable to the cell loss rates observed clinically in human tumours, may regress spontaneously. Large populations of cells tend to grow under the same conditions of cell loss that result in extinction of small clones. Furthermore, minor variations in the intrinsic cell death probability near 0.50 result in large differences in the number of surviving cells calculated at the 100th generation. When Monte Carlo simulations of clonal growth resulted in clones with large populations (> 50 cells), the population as a whole behaved in a deterministic fashion (logarithmic growth) similar to those observed in clinically observed neoplasms and consistent with other published models of tumour growth. These findings provide a plausible explanation for the clinically observed failure of tumours to recur in instances where tumour burden remains following cancer therapy. The findings also demonstrate the usefulness of the Monte Carlo method to simulate biologic events in populations where the fate of each member of a population can be modeled probabilistically.     

Immunophenotyping of Waldenstr?ms Macroglobulinemia Cell Lines Reveals Distinct Patterns of Surface Antigen Expression: Potential Biological and Therapeutic Implications

Waldenströms macroglobulinemia (WM) is a subtype of Non-Hodgkin’s lymphoma in which the tumor cell population is markedly heterogeneous, consisting of immunoglobulin-M secreting B-lymphocytes, plasmacytoid lymphocytes and plasma cells. Due to rarity of disease and scarcity of reliable preclinical models, many facets of WM molecular and phenotypic architecture remain incompletely understood. Currently, there are 3 human WM cell lines that are routinely used in experimental studies, namely, BCWM.1, MWCL-1 and RPCI-WM1. During establishment of RPCI-WM1, we observed loss of the CD19 and CD20 antigens, which are typically present on WM cells. Intrigued by this observation and in an effort to better define the immunophenotypic makeup of this cell line, we conducted a more comprehensive analysis for the presence or absence of other cell surface antigens that are present on the RPCI-WM1 model, as well as those on the two other WM cell lines, BCWM.1 and MWCL-1. We examined expression of 65 extracellular and 4 intracellular antigens, comprising B-cell, plasma cell, T-cell, NK-cell, myeloid and hematopoietic stem cell surface markers by flow cytometry analysis. RPCI-WM1 cells demonstrated decreased expression of CD19, CD20, and CD23 with enhanced expression of CD28, CD38 and CD184, antigens that were differentially expressed on BCWM.1 and MWCL-1 cells. Due to increased expression of CD184/CXCR4 and CD38, RPCI-WM1 represents a valuable model in which to study the effects anti-CXCR4 or anti-CD38 targeted therapies that are actively being developed for treatment of hematologic cancers. Overall, differences in surface antigen expression across the 3 cell lines may reflect the tumor clone population predominant in the index patients, from whom the cell lines were developed. Our analysis defines the utility of the most commonly employed WM cell lines as based on their immunophenotype profiles, highlighting unique differences that can be further studied for therapeutic exploit.     

A simplified approach for modeling diffusion into cells

Regulation of the intracellular concentration of substrates is essential for the maintenance of a stable cellular environment. Diffusion and reaction processes supply and consume substrates within cells and determine their steady-state concentrations. To realistically represent these processes by computer simulation they must be modeled in three dimensions. Yet three-dimensional models are inherently computing intensive. This study describes a method, which substantially simplifies the modeling of diffusion into a polyhedral body (a cube), that was used as a model representation of a cell. The method is applied to a case study of oxygen diffusion into nitrogen-fixing, rhizobia-infected cells in legume nodules. The method involved generating a one-dimensional representation of the three-dimensional problem to provide a "surface area profile" of three-dimensional diffusion. The one-dimensional models were significantly easier to program, several orders of magnitude faster to solve and in this study were validated by assessing their results against those of comparable three-dimensional models of diffusion into the same body. The results show the one-dimensional method to be a close approximation of a three-dimensional source-sink problem with systematic differences below 10% for fractional oxygenation of leghemoglobin, cell respiration and nitrogenase activity. Larger differences between models (up to 45%) in the predicted average and innermost O(2)concentrations had no effects on the physiological conclusions of the study, but were attributed to the poorer resolution of the three- than the one-dimensional model, and to an inherent simplification in the derivation of the one-dimensional surface area profiles. The one-dimensional modeling approach was found to be a simple, yet powerful tool for the study of diffusion and reaction in biological systems.     

Synchronized firing in the retina

Synchronized firing in neural populations has been proposed to constitute an elementary aspect of the neural code, but a complete understanding of its origins and significance has been elusive. Synchronized firing has been extensively documented in retinal ganglion cells, the output neurons of the retina. However, differences in synchronized firing across species and cell types have led to varied conclusions about its mechanisms and role in visual signaling. Recent work on two identified cell populations in the primate retina, the ON-parasol and OFF-parasol cells, permits a more unified understanding. Intracellular recordings reveal that synchronized firing in these cell types arises primarily from common synaptic input to adjacent pairs of cells. Statistical analysis indicates that local pairwise interactions can explain the pattern of synchronized firing in the entire parasol cell population. Computational analysis reveals that the aggregate impact of synchronized firing on the visual signal is substantial. Thus, in the parasol cells, the origin and impact of synchronized firing on the neural code may be understood as locally shared input which influences the visual signals transmitted from eye to brain.     

Establishment and optimization of epithelial cell cultures from human ectocervix,transformation zone,and endocervix optimization of epithelial cell cultures

Cervical cancer is a major public health problem and research using cell culture models has improved understanding of this disease. The human cervix contains three anatomic regions; ectocervix with stratified squamous epithelium, endocervix with secretory epithelium, and transformation zone (TZ) with metaplastic cells. Most cervical cancers originate within the TZ. However, little is known about the biology of TZ cells or why they are highly susceptible to carcinogenesis. The goal of this study was to develop and optimize methods to compare growth and differentiation of cells cultured from ectocervix, TZ or endocervix. We examined the effects of different serum-free media on cell attachment, cell growth and differentiation, and cell population doublings in monolayer culture. We also optimized conditions for organotypic culture of cervical epithelial cells using collagen rafts with human cervical stromal cells. Finally, we present a step-by-step protocol for culturing cells from each region of human cervix.     

Response properties of motion-sensitive visual interneurons in the lobula plate of Drosophila melanogaster

The crystalline-like structure of the optic lobes of the fruit fly Drosophila melanogaster has made them a model system for the study of neuronal cell-fate determination, axonal path finding, and target selection. For functional studies, however, the small size of the constituting visual interneurons has so far presented a formidable barrier. We have overcome this problem by establishing in vivo whole-cell recordings from genetically targeted visual interneurons of Drosophila. Here, we describe the response properties of six motion-sensitive large-field neurons in the lobula plate that form a network consisting of individually identifiable, directionally selective cells most sensitive to vertical image motion (VS cells). Individual VS cell responses to visual motion stimuli exhibit all the characteristics that are indicative of presynaptic input from elementary motion detectors of the correlation type. Different VS cells possess distinct receptive fields that are arranged sequentially along the eye's azimuth, corresponding to their characteristic cellular morphology and position within the retinotopically organized lobula plate. In addition, lateral connections between individual VS cells cause strongly overlapping receptive fields that are wider than expected from their dendritic input. Our results suggest that motion vision in different dipteran fly species is accomplished in similar circuitries and according to common algorithmic rules. The underlying neural mechanisms of population coding within the VS cell network and of elementary motion detection, respectively, can now be analyzed by the combination of electrophysiology and genetic intervention in Drosophila.     

Spatiotemporal elements of macaque v1 receptive fields

Neurons in primary visual cortex (V1) are commonly classified as simple or complex based upon their sensitivity to the sign of stimulus contrast. The responses of both cell types can be described by a general model in which the outputs of a set of linear filters are nonlinearly combined. We estimated the model for a population of V1 neurons by analyzing the mean and covariance of the spatiotemporal distribution of random bar stimuli that were associated with spikes. This analysis reveals an unsuspected richness of neuronal computation within V1. Specifically, simple and complex cell responses are best described using more linear filters than the one or two found in standard models. Many filters revealed by the model contribute suppressive signals that appear to have a predominantly divisive influence on neuronal firing. Suppressive signals are especially potent in direction-selective cells, where they reduce responses to stimuli moving in the nonpreferred direction.     

Do Pioneer Cells Exist?

Most mathematical models of collective cell spreading make the standard assumption that the cell diffusivity and cell proliferation rate are constants that do not vary across the cell population. Here we present a combined experimental and mathematical modeling study which aims to investigate how differences in the cell diffusivity and cell proliferation rate amongst a population of cells can impact the collective behavior of the population. We present data from a three-dimensional transwell migration assay that suggests that the cell diffusivity of some groups of cells within the population can be as much as three times higher than the cell diffusivity of other groups of cells within the population. Using this information, we explore the consequences of explicitly representing this variability in a mathematical model of a scratch assay where we treat the total population of cells as two, possibly distinct, subpopulations. Our results show that when we make the standard assumption that all cells within the population behave identically we observe the formation of moving fronts of cells where both subpopulations are well-mixed and indistinguishable. In contrast, when we consider the same system where the two subpopulations are distinct, we observe a very different outcome where the spreading population becomes spatially organized with the more motile subpopulation dominating at the leading edge while the less motile subpopulation is practically absent from the leading edge. These modeling predictions are consistent with previous experimental observations and suggest that standard mathematical approaches, where we treat the cell diffusivity and cell proliferation rate as constants, might not be appropriate.     

Comparison of analytical and inverse finite element approaches to estimate cell viscoelastic properties by micropipette aspiration

The viscoelastic properties of cells are important in predicting cell deformation under mechanical loading and may reflect cell phenotype or pathological transition. Previous studies have demonstrated that viscoelastic parameters estimated by finite element (FE) analyses of micropipette aspiration (MA) data differ from those estimated by the analytical half-space model. However, it is unclear whether these differences are statistically significant, as previous studies have been based on average cell properties or parametric analyses that do not reflect the inherent experimental and biological variability of real experimental data. To determine whether cell material parameters estimated by the half-space model are significantly different from those predicted by the FE method, we implemented an inverse FE method to estimate the viscoelastic parameters of a population of primary porcine aortic valve interstitial cells tested by MA. We found that inherent differences between the analytical and inverse FE estimation methods resulted in statistically significant differences in individual cell properties. However, in cases with small pipette to cell radius ratios and short loading periods, model-dependent differences were masked by experimental and cell-to-cell variability. Analytical models that account for finite cell-size and loading rate further relaxed the experimental conditions for which accurate cell material parameter estimates could be obtained. These data provide practical guidelines for analysis of MA data that account for the wide range of conditions encountered in typical experiments.     

Aldehyde dehydrogenase activity selects for the holoclone phenotype in prostate cancer cells

Aldehyde dehydrogenase 1 (ALDH) activity is considered to be a marker of cancer stem cells (CSCs) in many tumour models, since these cells are more proliferative and tumourigenic than ALDH^Lo cells in experimental models. However it is unclear whether all CSC-like cells are within the ALDH^Hi population, or whether all ALDH^Hi cells are highly proliferative and tumourigenic. The ability to establish a stem cell hierarchy in vitro, whereby sub-populations of cells have differing proliferative and differentiation capacities, is an alternate indication of the presence of stem cell-like populations within cell lines. In this study, we have examined the interaction between ALDH status and the ability to establish a stem cell hierarchy in PC3 prostate cancer cells. We demonstrate that PC3 cells contain a stem cell hierarchy, and isolation of ALDH^Hi cells enriches for the most primitive holoclone population, however holoclone formation is not restricted to ALDH^Hi cells. In addition, we show that ALDH activity undergoes phenotypic plasticity, since the ALDH^Lo population can develop ALDH^Hi populations comparable to parental cells within 2 weeks in culture. Furthermore, we show that the majority of ALDH^Hi cells are found within the least primitive paraclone population, which is circumvented by culturing PC3 cells as spheroids in defined medium favouring stem cell characteristics. Although ALDH^Hi status enriches for holoclone formation, this activity may be mediated by a minority of ALDH^Hi cells.     

Subsets of myeloid-derived suppressor cells in tumor-bearing mice

Myeloid-derived suppressor cells (MDSC) are a heterogeneous group of cells that play a critical role in tumor associated immune suppression. In an attempt to identify a specific subset of MDSC primarily responsible for immunosuppressive features of these cells, 10 different tumor models were investigated. All models showed variable but significant increase in the population of MDSC. Variability of MDSC expansion in vivo matched closely the effect of tumor cell condition medium in vitro. MDSC consists of two major subsets of Ly6G(+)Ly6C(low) granulocytic and Ly6G(-)Ly6C(high) monocytic cells. Granulocytic MDSC have increased level of reactive oxygen species and undetectable level of NO whereas monocytic MDSC had increased level of NO but undetectable levels of reactive oxygen species. However, their suppressive activity per cell basis was comparable. Almost all tumor models demonstrated a preferential expansion of granulocytic subset of MDSC. We performed a phenotypical and functional analysis of several surface molecules previously suggested to be involved in MDSC-mediated suppression of T cells: CD115, CD124, CD80, PD-L1, and PD-L2. Although substantial proportion of MDSC expressed those molecules no differences in the level of their expression or the proportion, positive cells were found between MDSC and cells from tumor-free mice that lack immune suppressive activity. The level of MDSC-mediated T cell suppression did not depend on the expression of these molecules. These data indicate that suppressive features of MDSC is caused not by expansion of a specific subset but more likely represent a functional state of these cells.     

Heterocellular cultures of pulmonary alveolar epithelial cells grown on laminin-5 supplemented matrix

The pulmonary alveolar epithelium consists of alveolar type I (AT1) and alveolar type II (AT2) cells. Interactions between these two cell types are necessary for alveolar homeostasis and remodeling. These interactions have been difficult to study in vitro because current cell culture models of the alveolar epithelium do not provide a heterocellular population of AT1 and AT2 cells for an extended period of time in culture. In this study, a new method for obtaining heterocellular cultures of AT1- and AT2-like alveolar epithelial cells maintained for 7 d on a rat tail collagen-fibronectin matrix supplemented with laminin-5 is described. These cultures contain cells that appear by their morphology to be either AT1 cells (larger flattened cells without lamellar bodies) or AT2 cells (smaller cuboidal cells with lamellar bodies). AT1-like cells stain for the type I cell marker aquaporin-5, whereas AT2-like cells stain for the type II cell markers surfactant protein C or prosurfactant protein C. AT1/AT2 cell ratios, cell morphology, and cell phenotype-specific staining patterns seen in 7-d-old heterocellular cultures are similar to those seen in alveoli in situ. This culture system, in which a mixed population of phenotypically distinct alveolar epithelial cells are maintained, may facilitate in vitro studies that are more representative of AT1-AT2 cell interactions that occur in vivo.     

Linking Macroscopic with Microscopic Neuroanatomy Using Synthetic Neuronal Populations

Dendritic morphology has been shown to have a dramatic impact on neuronal function. However, population features such as the inherent variability in dendritic morphology between cells belonging to the same neuronal type are often overlooked when studying computation in neural networks. While detailed models for morphology and electrophysiology exist for many types of single neurons, the role of detailed single cell morphology in the population has not been studied quantitatively or computationally. Here we use the structural context of the neural tissue in which dendritic trees exist to drive their generation in silico. We synthesize the entire population of dentate gyrus granule cells, the most numerous cell type in the hippocampus, by growing their dendritic trees within their characteristic dendritic fields bounded by the realistic structural context of (1) the granule cell layer that contains all somata and (2) the molecular layer that contains the dendritic forest. This process enables branching statistics to be linked to larger scale neuroanatomical features. We find large differences in dendritic total length and individual path length measures as a function of location in the dentate gyrus and of somatic depth in the granule cell layer. We also predict the number of unique granule cell dendrites invading a given volume in the molecular layer. This work enables the complete population-level study of morphological properties and provides a framework to develop complex and realistic neural network models.     

Pooling strategies in V1 can account for the functional and structural diversity across species

Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant cells and the existence of orientation maps in higher mammals’ V1. These models, however, do not explain the emergence of complex cells in animals that do not show orientation maps. In this study, we build a theoretical model based on a convolutional network called Sparse Deep Predictive Coding (SDPC) and show that a single computational mechanism, pooling, allows the SDPC model to account for the emergence in V1 of complex cells with or without that of orientation maps, as observed in distinct species of mammals. In particular, we observed that pooling in the feature space is directly related to the orientation map formation while pooling in the retinotopic space is responsible for the emergence of a complex cells population. Introducing different forms of pooling in a predictive model of early visual processing as implemented in SDPC can therefore be viewed as a theoretical framework that explains the diversity of structural and functional phenomena observed in V1.     

The MYpop toolbox: Putting yeast stress responses in cellular context on single cell and population scales

Systems biology holds the promise to integrate multiple sources of information in order to build ever more complete models of cellular function. To do this, the field must overcome two significant challenges. First, the current strategy to model average cells must be replaced with population based models accounting for cell‐to‐cell variability. Second, models must be integrated with each other and with basic cellular function. This requires a core model of cellular physiology as well as a multiscale simulation platform to support large‐scale simulation of culture or tissues from single cells. Here, we present such a simulation platform with a core model of yeast physiology as scaffold to integrate and simulate SBML models. The software automates this integration helping users simulate their model of choice in context of the cell division cycle. We benchmark model merging, simulation and analysis by integrating a minimal model of osmotic stress into the core model and analyzing it. We characterize the effect of single cell differences on the dynamics of osmoadaptation, estimating when normal cell growth is resumed and obtaining an explanation for experimentally observed glycerol dynamics based on population dynamics. Hence, the platform can be used to reconcile single cell and population level data.     

Therapeutic implications of cancer stem cells

Most cancers comprise a heterogenous population of cells with marked differences in their proliferative potential as well as the ability to reconstitute the tumor upon transplantation. Cancer stem cells are a minor population of tumor cells that possess the stem cell property of self-renewal. In addition, dysregulation of stem cell self-renewal is a likely requirement for the development of cancer. This new model for cancer will have significant ramifications for the way we study and treat cancer. In addition, through targeting the cancer stem cell and its dysregulated self-renewal, our therapies for treating cancer are likely to improve.