Sorting minor subpopulations of cells: use of fluorescence as the triggering signal

Flow cytometric cell sorting is commonly used to obtain purified subpopulations of cells for use in in vitro and in vivo assays. This can be time-consuming if the subpopulations of interest represent very low percentages of the cell suspension under study. Often the desired subpopulations are identified by two-color immunofluorescence staining. Generally, cell sorting is performed with a flow cytometer configured to trigger on light scatter signals, then sort windows are set based upon the signals from both fluorescent markers. We demonstrate that triggering the cytometer with the fluorescence signal from antibody staining common to both of the desired subpopulations, then sorting the subpopulations based upon staining of a second marker, substantially increases the speed of cell sorting vis-à-vis traditional methods. This is because undesired events are not analysed, allowing an increase in the throughput rate. While desired subpopulations of cells can be obtained by this method, undesired (i.e., nonstaining) cell "contaminants" increase and may require a second sort. The combined time for the initial enrichment sort and a second sort can be less than sorting once using standard methodology. Alternatively, the degree of contamination may be controlled by adjusting the concentration of the cell suspension and by the sample flow rate.     

System for flow sorting chromosomes on the basis of pulse shape

Sorting on the basis of the complex features resolved by chromosome slit-scan analysis requires rapid and flexible pulse shape acquisition and processing for determining sort decisions before droplet breakoff. Fluorescence scans of chromosome morphology contain centromeric index and banding information suitable for chromosome classification, but these scans are often characterized by variability in length and height and require sophisticated data processing procedures for identification. Setting sort criteria on such complex morphological data requires digitization and subsequent computation by an algorithm tolerant of variations in overall pulse shape. We demonstrate here the capability to sort individual chromosomes based on their morphological features measured by slit-scan flow cytometry. To do this we have constructed a sort controller capable of acquiring an 128 byte chromosome waveform and executing a series of numerical computations resulting in an area-based centromeric index sort decision in less than 2 ms. The system is configured in a NOVIX microprocessor, programmed in FORTH, and interfaced to a slit-scan flow cytometer data acquisition system. An advantage of this configuration is direct control over the machine state during program execution for minimal processing time. Examples of flow sorted chromosomes are shown with their corresponding fluorescence pulse shapes.     

Detecting pulsatile hormone secretions using nonlinear mixed effects partial spline models

Neuroendocrine ensembles communicate with their remote and proximal target cells via an intermittent pattern of chemical signaling. The identification of episodic releases of hormonal pulse signals constitutes a major emphasis of endocrine investigation. Estimating the number, temporal locations, secretion rate, and elimination rate from hormone concentration measurements is of critical importance in endocrinology. In this article, we propose a new flexible statistical method for pulse detection based on nonlinear mixed effects partial spline models. We model pulsatile secretions using biophysical models and investigate biological variation between pulses using random effects. Pooling information from different pulses provides more efficient and stable estimation for parameters of interest. We combine all nuisance parameters including a nonconstant basal secretion rate and biological variations into a baseline function that is modeled nonparametrically using smoothing splines. We develop model selection and parameter estimation methods for the general nonlinear mixed effects partial spline models and an R package for pulse detection and estimation. We evaluate performance and the benefit of shrinkage by simulations and apply our methods to data from a medical experiment.     

Electrooptics studies of Escherichia coli electropulsation: orientation, permeabilization, and gene transfer.

Fast optical transient signals are suitable approaches to the investigation of the behavior of bacteria during an electric pulse. In a previous work, by a dual approach taking advantage of a video method and a fast kinetic study of the light transmitted across a cell suspension, we showed that a field-induced orientation phenomenon was affecting the rod-shaped bacteria during the pulse (Eynard et al., 1992. Eur. J. Biochem. 209:431-436). In the present work, time courses of electro-induced responses of bacteria during a single square-wave pulse are analyzed. Observations of both the orientation step and the permeabilization process are relevant. These two steps are affected by the addition of DNA. They both obey to a first-order kinetic. The conclusion of this work is that Escherichia coli permeabilization and transformation are multistep processes: orientation (step 1) is followed by an envelope alteration (step 2), all steps being affected by plasmid addition. In the case of E. coli, a rod-shaped bacteria, the orientation process (step 1) brings the cell parallel to the field direction. The pulse duration must be longer than the orientation characteristic time (approximately 1 ms) to trigger an effective permeabilization and its associated events. The permeabilization process (step 2) is associated with a field-induced dipole effect.     

Ultra high-speed sorting.

BACKGROUND: Cell sorting has a history dating back approximately 40 years. The main limitation has been that, although flow cytometry is a science, cell sorting has been an art during most of this time. Recent advances in assisting technologies have helped to decrease the amount of expertise necessary to perform sorting. METHODS: Droplet-based sorting is based on a controlled disturbance of a jet stream dependent on surface tension. Sorting yield and purity are highly dependent on stable jet break-off position. System pressures and orifice diameters dictate the number of droplets per second, which is the sort rate limiting step because modern electronics can more than handle the higher cell signal processing rates. RESULTS: Cell sorting still requires considerable expertise. Complex multicolor sorting also requires new and more sophisticated sort decisions, especially when cell subpopulations are rare and need to be extracted from background. High-speed sorting continues to pose major problems in terms of biosafety due to the aerosols generated. CONCLUSIONS: Cell sorting has become more stable and predictable and requires less expertise to operate. However, the problems of aerosol containment continue to make droplet-based cell sorting problematical. Fluid physics and cell viability restraints pose practical limits for high-speed sorting that have almost been reached. Over the next 5 years there may be advances in fluidic switching sorting in lab-on-a-chip microfluidic systems that could not only solve the aerosol and viability problems but also make ultra high-speed sorting possible and practical through massively parallel and exponential staging microfluidic architectures.     

Allelematch: an R package for identifying unique multilocus genotypes where genotyping error and missing data may be present

We present allelematch, an R package, to automate the identification of unique multilocus genotypes in data sets where the number of individuals is unknown, and where genotyping error and missing data may be present. Such conditions commonly occur in noninvasive sampling protocols. Output from the software enables a comparison of unique genotypes and their matches, and facilitates the review of differences between profiles. The software has a variety of applications in molecular ecology, and may be valuable where a large number of samples must be processed, unique genotypes identified, and repeated observations made over space and time. We used simulations to assess the performance of allelematch and found that it can reliably and accurately determine the correct number of unique genotypes (± 3%) across a broad range of data set properties. We found that the software performs with highest accuracy when genotyping error is below 4%. The R package is available from the Comprehensive R Archive Network (http://cran.r-project.org/). Supplementary documentation and tutorials are provided.     

A model of motion adaptation and motion after-effects based upon principal component regression

A computational model to help explain effects of adaptation to moving signals is compared with established energy (linear regression) models of motion detection. The proposed model assumes that processed image signals are subject to error in both dimensions of space and time. This assumption constrains models of motion perception to be based upon principal component regression rather than linear regression. It is shown that response suppression of model complex cell neurons that input into the model may account for (1) increases in perceived speed after adaptation to static patterns and testing with slowly moving patterns, (2) significant increases in perceived speed after adaptation to patterns moving at a medium speed and testing at high speed, and (3) decreases in perceived speed in the opponent direction to a quickly moving adapting signal. Neither of predictions (2) or (3) are general features of established accounts of motion detection by visual processes based upon linear regression. Comparisons of the proposed model's speed transfer function with existing psychophysical data suggests that the visual system processes motion signals with the tacit assumption that image measurements are subject to error in both space and time. Received: 24 January 2000 / Accepted in revised form: 8 May 2000     

A Monte Carlo method for the simulation of kinetie models

The purpose of this note is to illustrate the feasibility of simulating kinetic systems, such as commonly encountered in photosynthesis research, using the Monte Carlo (MC) method. In this approach, chemical events are considered at the molecular level where they occur randomly and the macroscopic kinetic evolution results from averaging a large number of such events. Their repeated simulation is easily accomplished using digital computing. It is shown that the MC approach is well suited to the capabilities and resources of modern microcomputers. A software package is briefly described and discussed, allowing a simple programming of any kinetic model system and its resolution. The execution is reasonably fast and accurate; it is not subject to such instabilities as found with the conventional analytical approach.Abbreviations MC Monte Carlo - RN random number - PSU photosynthetic unit Dedicated to Prof. L.N.M. Duysens on the occasion of his retirement.     

Tripled Readout Slices in Multi Time-Point pCASL Using Multiband Look-Locker EPI

Multi time-point pseudo-continuous arterial spin labelling (pCASL) with a Look-Locker EPI readout can sample the signal curve of blood kinetics at multiple time points after the labelling pulse. However, due to signal relaxation of labelled blood, the number of readout slices is limited. The aim of this study is to employ a multiband excitation technique to triple the number of readout slices in multi time-point pCASL. The multiband technique, along with 2-fold in-plane parallel imaging, was incorporated into the Look-Locker EPI for the multi time-point sampling of blood kinetic behaviour following the pCASL labelling scheme. The performance evaluation of the multiband and the single-band techniques were performed on four healthy subjects using a 32-channel head RF coil at 3T. Quantitative perfusion maps were analysed using a combination of labelling with and without flow suppression gradients. The perfusion maps provided by the multiband accelerated multi time-point pCASL were in good agreement with the conventional single-band technique. Multiband acceleration caused SNR loss but offered quantitative perfusion maps in 6.23 min with 18 slices compared with 6 slices within the same time period for the single-band method. As conclusion, the multiband technique can successfully triple the number of readout slices while achieving comparable perfusion data in the same measurement time as the conventional single-band readout.     

Pore disappearance in a cell after electroporation: theoretical simulation and comparison with experiments.

The process of pore disappearance after cell electroporation is analyzed theoretically. On the basis of the kinetic model, in which the formation and annihilation of a metastable hydrophilic pore are considered as random one-step processes, a distribution function of cell resealing times, Fr(t), is derived. Two cases are studied: 1) the rate of pore resealing, k(r), is significantly greater than the rate of pore formation, k(f); and 2) the rate of pore formation, k(f), is comparable with k(r). It is determined that the shape of the distribution function depends on the initial number of pores in a cell, n(i). If in the absence of an external electric field the rate of pore formation, k(f), is significantly less than the rate of pore resealing, k(r) (case 1), pores disappear completely, whereas when k(f) approximately k(r) (case 2), the cell achieves a steady state in which the number of pores is equal to k(f)/k(r). In case 1, when n(i) = 1, the distribution function Fr(t) is exponential. The developed theory is compared with experimental data available in the literature. Increasing the time of incubation at elevated temperature increases the fraction of resealed cells. This indicates that the time necessary for the resealing varies from cell to cell. Although the shape of experimental relationships depends on the electroporation conditions they can be described by theoretical curves quite well. Thus it can be concluded that the disappearance of pores in the cell membrane after electroporation is a random process. It is shown that from the comparison of presented theory with experiments, the following parameters can be estimated: the average number of pores, n(i), that appeared in a cell during an electric pulse; the rate of pore disappearance, k(r); the ratio k(f)/k(r); and the energy barrier to pore disappearance deltaWr(0). Estimated numerical values of the parameters show that increasing the amplitude of an electric pulse increases either the apparent number of pores created during the pulse (the rate of pore resealing remains the same) or the rate of pore resealing (the average number of pores remains the same).     

Noise and correlations in parallel perceptual decision making

Perceptual decisions involve the accumulation of sensory evidence over time, a process that is corrupted by noise [1]. Here, we extend the decision-making framework to crossmodal research [2, 3] and the parallel processing of two distinct signals presented to different sensory modalities like vision and audition. Contrary to the widespread view that multisensory signals are integrated prior to a single decision [4-10], we show that evidence is accumulated for each signal separately and that consequent decisions are flexibly coupled by logical operations. We find that the strong correlation of response latencies from trial to trial is critical to explain the short latencies of multisensory decisions. Most critically, we show that increased noise in multisensory decisions is needed to explain the mean and the variability of response latencies. Precise knowledge of these key factors is fundamental for the study and understanding of parallel decision processes with multisensory signals.     

GeneCluster 2.0: an advanced toolset for bioarray analysis

SUMMARY: GeneCluster 2.0 is a software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding classification, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting and k-nearest neighbor algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater. AVAILABILITY: http://www.broad.mit.edu/cancer/software     

Estimation of ion channel kinetics from fluctuations of macroscopic currents

For single channel recordings, the maximum likelihood estimation (MLE) of kinetic rates and conductance is well established. A direct extrapolation of this method to macroscopic currents is computationally prohibitive: it scales as a power of the number of channels. An approximated MLE that ignored the local time correlation of the data has been shown to provide estimates of the kinetic parameters. In this article, an improved approximated MLE that takes into account the local time correlation is proposed. This method estimates the channel kinetics using both the time course and the random fluctuations of the macroscopic current generated by a homogeneous population of ion channels under white noise. It allows arbitrary kinetic models and stimulation protocols. The application of the proposed algorithm to simulated data from a simple three-state model on nonstationary conditions showed reliable estimates of all the kinetic constants, the conductance and the number of channels, and reliable values for the standard error of those estimates. Compared to the previous approximated MLE, it reduces by a factor of 10 the amount of data needed to secure a given accuracy and it can even determine the kinetic rates in macroscopic stationary conditions.     

Theory of the kinetic analysis of patch-clamp data.

This paper describes a theory of the kinetic analysis of patch-clamp data. We assume that channel gating is a Markov process that can be described by a model consisting of n kinetic states and n(n - 1) rate constants at each voltage, and that patch-clamp data describe the occupancy of x different conductance levels over time. In general, all the kinetic information in a set of patch-clamp data is found in either two-dimensional dwell time histograms describing the frequency of observation of sequential dwell times of durations tau 1 and tau 2 (Fredkin, D. R., M. Montal, and J. A. Rice, 1985, Proceedings of the Berkeley Conference in Honor of Jerzy Neyman and Jack Kiefer, vol. 1, 269-289) or in three-point joint probability functions describing the probability that a channel is in a given conductance at time t, and at time t + tau 1, and at time t + tau 1 + tau 2. For the special case of channels with a single open state plus multiple closed states, one-dimensional analyses provide all of the kinetic information. Stationary patch-clamp data have information that can be used to determine H rate constants, where H = n(n - 1) - G and G is the number of intraconductance rate constants. Thus, to calculate H rate constants, G rate constants must be fixed. In general there are multiple sets of G rate constants that can be fixed to allow the calculation of H rate constants although not every set of G rate constants will work. Arbitrary assignment of the G intraconductance rate constants equal to zero always provides a solution and the calculation of H rate constants. Nonstationary patch-clamp data have information for the determination of H rate constants at a reference voltage plus n(n - 1) rate constants at all test voltages. Thus, nonstationary data have extra information about the voltage dependencies of rate constants that can be used to rule out kinetic models that cannot be disqualified on the basis of stationary data.     

脉冲耦合神经网络的并行加速优化算法研究

并行编程技术可以有效提高算法的执行效率。文中分别利用CPU的单指令多数据流扩展指令集（Streaming SIMD Extensions,SSE）技术和多核并行编程技术,对脉冲耦合神经网络（Pulse Coupled Neural Network,PCNN）分割算法进行并行编程优化,以减少算法的运行时间。实验结果表明,SSE技术以及多核并行编程技术大大加快了PCNN分割算法的运行速度,有效提高了算法的执行效率,在一定程度上解决了该方法计算量大、耗时多的问题,具有应用于医学图像处理的潜在价值。     

Using input minimization to train a cerebellar model to simulate regulation of smooth pursuit

Cerebellar learning appears to be driven by motor error, but whether or not error signals are provided by climbing fibers (CFs) remains a matter of controversy. Here we show that a model of the cerebellum can be trained to simulate the regulation of smooth pursuit eye movements by minimizing its inputs from parallel fibers (PFs), which carry various signals including error and efference copy. The CF spikes act as “learn now” signals. The model can be trained to simulate the regulation of smooth pursuit of visual objects following circular or complex trajectories and provides insight into how Purkinje cells might encode pursuit parameters. In minimizing both error and efference copy, the model demonstrates how cerebellar learning through PF input minimization (InMin) can make movements more accurate and more efficient. An experimental test is derived that would distinguish InMin from other models of cerebellar learning which assume that CFs carry error signals.     

Too much of a good thing? Variety is confusing in mate choice

Choice variety is supposed to increase the likelihood that a chooser's preferences are satisfied. To assess the effects of variety on real-world mate choice, we analysed human dating decisions across 84 speed-dating events (events in which people go on a series of sequential 'mini-dates'). Results showed that choosers made fewer proposals (positive dating decisions) at events in which the available dates showed greater variety across such attributes as age, height, occupation and education, and this effect was particularly strong when choosers were confronted with a larger number of opposite-sex speed daters. Additionally, participants attending events in which the available options showed greater variety across these attributes were less likely to choose the consensually preferred mate option and more likely to choose no one at all. In contexts in which time is a limited resource, choice variety-rather than facilitating choice quality or increasing choosiness-is confusing and potentially detrimental to choice quality.     

Rational polynomial equation as an unbiased approach for the kinetic studies of Drosophila melanogaster acetylcholinesterase reaction mechanism

The hydrolysis of substrates by cholinesterases does not follow the Michaelis–Menten reaction mechanism. The well-known inhibition by excess substrate is often accompanied by an unexpectedly high activity at low substrate concentrations. It appears that these peculiarities are the consequence of an unusual architecture of the active site, which conducts the substrate molecule over many stages before it is cleaved and released. Structural and kinetic data also suggest that two substrate molecules can attach at the same time to the free, as well as to the acetylated, enzyme. We present a procedure which provides an unbiased framework for mathematical modelling of such complex reaction mechanisms. It is based on regression analysis of a rational polynomial using classical initial rate data. The determination of polynomial degree reveals the number of independent parameters that can be evaluated from the available information. Once determined, these parameters can substantially facilitate the construction and evaluation of a kinetic model reflecting the expected molecular events in an enzymic reaction. We also present practical suggestions for testing the postulated kinetic model, using an original thermodynamic approach and an isolated effect in a specifically mutated enzyme.     

Biogeo: an R package for assessing and improving data quality of occurrence record datasets

Occurrence data from museum and herbarium collections are valuable for mapping biodiversity patterns in space and time. Unfortunately these collections datasets contain many errors and suffer from several data quality issues that can influence the quality of the products derived from them. It is up to the user to identify these errors and data quality issues when using these data. Despite the large number of potential users of these datasets there are few software tools dedicated to error detection and correction of collections datasets. The R package biogeo was developed for detecting and correcting errors and for assessing of data quality of collections datasets consisting of occurrence records. Features of the package include error detection, such as mismatches between the recorded country and the country where the record is plotted, records of terrestrial species that fall into the sea and outlier detection. A key feature of the package is the ability to identify likely alternative positions for points that represent obvious errors in the dataset and functions to explore records in geographical and environmental space in order to identify possible errors in the dataset. Functions are also available for converting coordinates that are in various text formats into degrees, minutes and seconds and then into decimal degrees.