Atomic Force Microscopy Study of Nano-Physiological Response of Ladybird Beetles to Photostimuli

Background: Insects are of interest not only as the most numerous and diverse group of animals but also as highly efficient bio-machines varying greatly in size. They are the main human competitors for crop, can transmit various diseases, etc. However, little study of insects with modern nanotechnology tools has been done. Methodology/Principal Findings: Here we applied an atomic force microscopy (AFM) method to study stimulation of ladybird beetles with light. This method allows for measuring of the internal physiological responses of insects by recording surface oscillations in different parts of the insect at sub-nanometer amplitude level and sub-millisecond time. Specifically, we studied the sensitivity of ladybird beetles to light of different wavelengths. We demonstrated previously unknown blindness of ladybird beetles to emerald color (,500nm) light, while being able to see UV-blue and green light. Furthermore, we showed how one could study the speed of the beetle adaptation to repetitive flashing light and its relaxation back to the initial stage. Conclusions: The results show the potential of the method in studying insects. We see this research as a part of what might be a new emerging area of ‘‘nanophysiology’ ’ of insects

Morphology and Nanomechanics of Sensory Neurons Growth Cones following Peripheral Nerve Injury

A prior peripheral nerve injury in vivo, promotes a rapid elongated mode of sensory neurons neurite regrowth in vitro. This in vitro model of conditioned axotomy allows analysis of the cellular and molecular mechanisms leading to an improved neurite re-growth. Our differential interference contrast microscopy and immunocytochemistry results show that conditioned axotomy, induced by sciatic nerve injury, did not increase somatic size of adult lumbar sensory neurons from mice dorsal root ganglia sensory neurons but promoted the appearance of larger neurites and growth cones. Using atomic force microscopy on live neurons, we investigated whether membrane mechanical properties of growth cones of axotomized neurons were modified following sciatic nerve injury. Our data revealed that neurons having a regenerative growth were characterized by softer growth cones, compared to control neurons. The increase of the growth cone membrane elasticity suggests a modification in the ratio and the inner framework of the main structural proteins

Quantifying cellular mechanics and adhesion in renal tubular injury using single cell force spectroscopy

Abstract Tubulointerstitial fibrosis represents the major underlying pathology of diabetic nephropathy where loss of cell-to-cell adhesion is a critical step. To date, research has predominantly focussed on the loss of cell surface molecular binding events that include altered protein ligation. In the current study, atomic force microscopy single cell force spectroscopy (AFM- SCFS) was used to quantify changes in cellular stiffness and cell adhesion in TGF-β1 treated kidney cells of the human proximal tubule (HK2). AFM indentation of TGF-β1 treated HK2 cells showed a significant increase (42%) in the Elastic modulus (stiffness) compared to control. Fluorescence microscopy confirmed that increased cell stiffness is accompanied by reorganization of the cytoskeleton. The corresponding changes in stiffness, due to F-actin rearrangement, affected the work of detachment by changing the separation distance between two adherent cells. Overall, our novel data quantitatively demonstrate a correlation between cellular elasticity, adhesion and early morphologic/phenotypic changes associated with tubular injury

Optimality of mutation and selection in germinal centers

The population dynamics theory of B cells in a typical germinal center could play an important role in revealing how affinity maturation is achieved. However, the existing models encountered some conflicts with experiments. To resolve these conflicts, we present a coarse-grained model to calculate the B cell population development in affinity maturation, which allows a comprehensive analysis of its parameter space to look for optimal values of mutation rate, selection strength, and initial antibody-antigen binding level that maximize the affinity improvement. With these optimized parameters, the model is compatible with the experimental observations such as the ~100-fold affinity improvements, the number of mutations, the hypermutation rate, and the "all or none" phenomenon. Moreover, we study the reasons behind the optimal parameters. The optimal mutation rate, in agreement with the hypermutation rate in vivo, results from a tradeoff between accumulating enough beneficial mutations and avoiding too many deleterious or lethal mutations. The optimal selection strength evolves as a balance between the need for affinity improvement and the requirement to pass the population bottleneck. These findings point to the conclusion that germinal centers have been optimized by evolution to generate strong affinity antibodies effectively and rapidly. In addition, we study the enhancement of affinity improvement due to B cell migration between germinal centers. These results could enhance our understandings to the functions of germinal centers.Comment: 5 figures in main text, and 4 figures in Supplementary Informatio

Force and Compliance Measurements on Living Cells Using Atomic Force Microscopy (AFM)

We describe the use of atomic force microscopy (AFM) in studies of cell adhesion and cell compliance. Our studies use the interaction between leukocyte function associated antigen-1 (LFA-1)/intercellular adhesion molecule-1 (ICAM-1) as a model system. The forces required to unbind a single LFA-1/ICAM-1 bond were measured at different loading rates. This data was used to determine the dynamic strength of the LFA-1/ICAM-1 complex and characterize the activation potential that this complex overcomes during its breakage. Force measurements acquired at the multiple- bond level provided insight about the mechanism of cell adhesion. In addition, the AFM was used as a microindenter to determine the mechanical properties of cells. The applications of these methods are described using data from a previous study

Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer

Ate van der Zee and colleagues analyze the gene expression profiles of ovarian cancer samples from 157 patients, and identify an 86-gene expression profile that seems to predict overall survival

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data