基于单分子量子相干调制的细胞温度成像技术

目的 细胞温度成像可以帮助科学家研究和理解细胞内部的温度分布,揭示细胞代谢和生物化学过程的关键信息。目前,基于荧光温度探针的细胞温度成像技术存在低温度分辨率和有限测量范围等限制。本文旨在利用单分子量子相干过程依赖温度的特性,开发一种单细胞温度成像和实时检测技术。方法 基于飞秒脉冲激光制备延时和相位可调的飞秒脉冲对,调制的脉冲对通过显微系统激发细胞内标记的荧光单分子,之后收集并记录每个荧光光子的到达时间。利用单分子相干过程与周围环境温度的关系,定义单分子量子相干可视度（V）,建立V与环境温度的对应关系。通过调制解调荧光光子的到达时间,获取单分子周围环境温度,结合扫描成像,实现细胞的温度成像和实时检测。结果 该方法可以实现高精度（温度分辨率<0.1℃）和大范围温度（10～50℃）的温度成像和测量,并观测到了单个细胞代谢相关的温度变化。结论 该研究有助于深入了解细胞代谢、蛋白质功能和疾病机制,为生物医学研究提供重要工具。     

Kinesin moving through the spotlight: single-motor fluorescence microscopy with submillisecond time resolution

Kinesin-1 is one of the motor proteins that drive intracellular transport in eukaryotes. This motor makes hundreds of 8-nm steps along a microtubule before releasing. Kinesin-1 can move at velocities of up to approximately 800 nm/s, which means that one turnover on average takes 10 ms. Important details, however, concerning the coordination between the two motor domains have not been determined due to limitations of the techniques used. In this study, we present an approach that allows the observation of fluorescence intensity changes on individual kinesins with a time resolution far better than the duration of a single step. In our approach, the laser focus of a confocal fluorescence microscope is pointed at a microtubule and the photons emitted by fluorescently labeled kinesin motors walking through the spot are detected with submicrosecond accuracy. We show that the autocorrelation of a fluorescence time trace of an individual kinesin motor contains information at time lags down to 0.1 ms. The quality and time resolution of the autocorrelation is primarily determined by the amount of signal photons used. By adding the autocorrelations of several tens of kinesins, fluorescence intensity changes can be observed at a timescale below 100 micros.     

Studying Individual Patterns of Development Using I-States as Objects Analysis (ISOA)

An approach is presented for studying individual pattern development in person-oriented terms focusing on the concept of i-state, i.e. an individual's configuration of information at a specific point in time. The procedure is called I-States as Objects Analysis (ISOA). First common i-states (typical states) are identified using cluster analysis of subindividuals and then this information is used for describing typical developmental patterns. Both a general procedure and a specific procedure used on a demonstration data set were developed. Using ISOA, change and stability can be studied both with regard to structure and with regard to individual variation. An empirical example was given which concerned longitudinal data about school grades at four different ages for 333 boys and girls. The data were split into a test sample and a replication sample of equal sizes. It was contended from the empirical study that ISOA functioned reasonably well on the sample studied. In the discussion, it was pointed out that ISOA can be a powerful method to use for small samples with many measurement occasions and that the method is optimal for studying short-term change.     

Pioneers of post-agricultural forest successions are adapted for herbivory avoidance but not biotic seed dispersal

Question

Natural reforestation is an important component of climate mitigation and adaptation, but the ecological processes promoting or constraining it are poorly understood. In this study we employ a stand reconstruction approach (which uses ages of extant trees to estimate year of establishment for each individual tree) to test for general trait-based effects on tree species arrival order in post-agricultural forest successions.

Location

Naturally reforesting post-agricultural landscapes throughout New Zealand.

Methods

Ages were obtained for 2434 individuals spanning 30 tree species across a nationwide network of 128 plots in 14 naturally reforesting post-agricultural sites. These ages were used to calculate individual-level arrival times (relative to the oldest individual in each plot). We estimated species-level arrival times by fitting linear mixed-effects (LME) regressions (with species identity as the fixed effect, and plots nested within sites as the random effects) to individual arrival time data. We used back-casting (where arrival time data are used to document individual-level presence in plots through time) to track annual changes in species abundance and community-weighted mean (CWM) trait values. We used standardised major axis (SMA) regressions to examine the effect of traits related to resource use strategy, herbivory avoidance, seed dispersal and disturbance response on species-level arrival times. We used LME regressions to test for changes in CWM trait values with stand age.

Results

The earliest-arriving species had traits associated with herbivory avoidance, were abiotically dispersed and had short predicted dispersal distances. There was no evidence that traits linked to resource use strategy or disturbance response affected species arrival times. Every significant species-level relationship was recovered in community-level LME analyses.

Conclusions

Our findings suggest that mammalian herbivore control and enhancement of biotic (bird) seed dispersal may be key management interventions in realising the full climate mitigation and adaptation potential of natural reforestation in post-agricultural landscapes.     

Sensitivity enhancement in fluorescence correlation spectroscopy of multiple species using time-gated detection

Fluorescence correlation spectroscopy (FCS) is a powerful technique to measure chemical reaction rates and diffusion coefficients of molecules in thermal equilibrium. The capabilities of FCS can be enhanced by measuring the energy, polarization, or delay time between absorption and emission of the collected fluorescence photons in addition to their arrival times. This information can be used to change the relative intensities of multiple fluorescent species in FCS measurements and, thus, the amplitude of the intensity autocorrelation function. Here we demonstrate this strategy using lifetime gating in FCS experiments. Using pulsed laser excitation and laser-synchronized gating in the detection channel, we suppress photons emitted within a certain time interval after excitation. Three applications of the gating technique are presented: suppression of background fluorescence, simplification of FCS reaction studies, and investigation of lifetime heterogeneity of fluorescently labeled biomolecules. The usefulness of this technique for measuring forward and backward rates of protein fluctuations in equilibrium and for distinguishing between static and dynamic heterogeneity makes it a promising tool in the investigation of chemical reactions and conformational fluctuations in biomolecules.     

Measurement of single macromolecule orientation by total internal reflection fluorescence polarization microscopy

A new approach is presented for measuring the three-dimensional orientation of individual macromolecules using single molecule fluorescence polarization (SMFP) microscopy. The technique uses the unique polarizations of evanescent waves generated by total internal reflection to excite the dipole moment of individual fluorophores. To evaluate the new SMFP technique, single molecule orientation measurements from sparsely labeled F-actin are compared to ensemble-averaged orientation data from similarly prepared densely labeled F-actin. Standard deviations of the SMFP measurements taken at 40 ms time intervals indicate that the uncertainty for individual measurements of axial and azimuthal angles is approximately 10 degrees at 40 ms time resolution. Comparison with ensemble data shows there are no substantial systematic errors associated with the single molecule measurements. In addition to evaluating the technique, the data also provide a new measurement of the torsional rigidity of F-actin. These measurements support the smaller of two values of the torsional rigidity of F-actin previously reported.     

Metabolic isotopomer labeling systems. Part II: structural flux identifiability analysis

Metabolic flux analysis using carbon labeling experiments (CLEs) is an important tool in metabolic engineering where the intracellular fluxes have to be computed from the measured extracellular fluxes and the partially measured distribution of 13C labeling within the intracellular metabolite pools. The relation between unknown fluxes and measurements is described by an isotopomer labeling system (ILS) (see Part I [Math. Biosci. 169 (2001) 173]). Part II deals with the structural flux identifiability of measured ILSs in the steady state. The central question is whether the measured data contains sufficient information to determine the unknown intracellular fluxes. This question has to be decided a priori, i.e. before the CLE is carried out. In structural identifiability analysis the measurements are assumed to be noise-free. A general theory of structural flux identifiability for measured ILSs is presented and several algorithms are developed to solve the identifiability problem. In the particular case of maximal measurement information, a symbolical algorithm is presented that decides the identifiability question by means of linear methods. Several upper bounds of the number of identifiable fluxes are derived, and the influence of the chosen inputs is evaluated. By introducing integer arithmetic this algorithm can even be applied to large networks. For the general case of arbitrary measurement information, identifiability is decided by a local criterion. A new algorithm based on integer arithmetic enables an a priori local identifiability analysis to be performed for networks of arbitrary size. All algorithms have been implemented and flux identifiability is investigated for the network of the central metabolic pathways of a microorganism. Moreover, several small examples are worked out to illustrate the influence of input metabolite labeling and the paradox of information loss due to network simplification.     

Stochastic modelling of a single ion channel: an alternating renewal approach with application to limited time resolution

Stochastic models of ion channels have been based largely on Markov theory where individual states and transition rates must be specified, and sojourn-time densities for each state are constrained to be exponential. This study presents an approach based on random-sum methods and alternating-renewal theory, allowing individual states to be grouped into classes provided the successive sojourn times in a given class are independent and identically distributed. Under these conditions Markov models form a special case. The utility of the approach is illustrated by considering the effects of limited time resolution (modelled by using a discrete detection limit, xi) on the properties of observable events, with emphasis on the observed open-time (xi-open-time). The cumulants and Laplace transform for a xi-open-time are derived for a range of Markov and non-Markov models; several useful approximations to the xi-open-time density function are presented. Numerical studies show that the effects of limited time resolution can be extreme, and also highlight the relative importance of the various model parameters. The theory could form a basis for future inferential studies in which parameter estimation takes account of limited time resolution in single channel records. Appendixes include relevant results concerning random sums and a discussion of the role of exponential distributions in Markov models.     

A method for directly fitting the time derivative of sedimentation velocity data and an alternative algorithm for calculating sedimentation coefficient distribution functions

The time-derivative method for deriving the sedimentation coefficient distribution, g(s*), from sedimentation velocity data that was developed by Walter Stafford has many advantages and is now widely used. By fitting Gaussian functions to the g(s*) distribution both sedimentation and diffusion coefficients (and therefore molecular masses) for individual species can be obtained. However, some of the approximations used in these procedures limit the accuracy of the results. An alternative approach is proposed in which the dc/dt data are fitted rather than g(s*). This new approach gives improved accuracy, extends the range to sedimentation coefficients below 1 S, and enhances resolution of multiple species. For both approaches the peaks from individual species are broadened when the data cover too wide a time span, and this effect is explored and quantified. An alternative algorithm for calculating ?(s*) from the dc/dt curves is presented and discussed. Rather than first averaging the dc/dt data for individual scan pairs and then calculating ?(s*) from that average, the ?(s*) distributions are calculated for every scan pair and then subsequently averaged. This alternative procedure yields smaller error bars for g(s*) and somewhat greater accuracy for fitted hydrodynamic properties when the time span becomes large.     

A Note on Opportunism and Parsimony in Data Collection

ABSTRACT Out of precaution, opportunism, and a general tendency towards thoroughness, researchers studying wildlife often collect multiple, sometimes highly correlated measurements or samples. Although such redundancy has its benefits in terms of quality control, increased resolution, and unforeseen future utility, it also comes at a cost if animal welfare (e.g., duration of handling) or time and resource limitation are a concern. Using principle components analysis and bootstrapping, we analyzed sets of morphometric measurements collected on 171 brown bears in Sweden during a long-term monitoring study (1984–2006). We show that of 11 measurements, 7 were so similar in terms of their predictive power for an overall size index that each individual measurement provided little additional information. We argue that when multiple research objectives or data collection goals compete for a limited amount of time or resources, it is advisable to critically evaluate the amount of additional information contributed by extra measurements. We recommend that wildlife researchers look critically at the data they collect not just in terms of quality but also in terms of need.     

Fluorescence intensity and lifetime distribution analysis: toward higher accuracy in fluorescence fluctuation spectroscopy

Fluorescence fluctuation methods such as fluorescence correlation spectroscopy and fluorescence intensity distribution analysis (FIDA) have proven to be versatile tools for studying molecular interactions with single molecule sensitivity. Another well-known fluorescence technique is the measurement of the fluorescence lifetime. Here, we introduce a method that combines the benefits of both FIDA and fluorescence lifetime analysis. It is based on fitting the two-dimensional histogram of the number of photons detected in counting time intervals of given width and the sum of excitation to detection delay times of these photons. Referred to as fluorescence intensity and lifetime distribution analysis (FILDA), the technique distinguishes fluorescence species on the basis of both their specific molecular brightness and the lifetime of the excited state and is also able to determine absolute fluorophore concentrations. The combined information yielded by FILDA results in significantly increased accuracy compared to that of FIDA or fluorescence lifetime analysis alone. In this paper, the theory of FILDA is elaborated and applied to both simulated and experimental data. The outstanding power of this technique in resolving different species is shown by quantifying the binding of calmodulin to a peptide ligand, thus indicating the potential for application of FILDA to similar problems in the life sciences.     

Denoising two-photon calcium imaging data

Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, we present a signal plus colored noise model in which the signal is represented as harmonic regression and the correlated noise is represented as an order autoregressive process. We provide an efficient cyclic descent algorithm to compute approximate maximum likelihood parameter estimates by combing a weighted least-squares procedure with the Burg algorithm. We use Akaike information criterion to guide selection of the harmonic regression and the autoregressive model orders. Our flexible yet parsimonious modeling approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio. This refined separation leads to appreciably enhanced image contrast for individual cells including clear delineation of subcellular details and network activity. The application of our approach to in vivo imaging data recorded in the ferret primary visual cortex demonstrates that our method yields substantially denoised signal estimates. We also provide a general Volterra series framework for deriving this and other signal plus correlated noise models for imaging. This approach to analyzing two-photon calcium imaging data may be readily adapted to other computational biology problems which apply correlated noise models.     

Analysis of the effect of fluorescence intensity on distributions obtained by flow cytofluorometry

It is shown that one of the major resolution limiting factors in the rapid measurement of fluorescence from individual cells with “fast flow cytofluorometers” is the small number of photons which are counted in each light pulse. A method is described for evaluating this factor for individual systems and for specific cells and stains. Once evaluated, this contribution to the broadening of the distribution can be stripped from the observed distribution to give a closer estimate of the actual distribution of dye in the cell poulation.     

Enhanced resolution of fluorescence anisotropy decays by simultaneous analysis of progressively quenched samples. Applications to anisotropic rotations and to protein dynamics.

Enhanced resolution of rapid and complex anisotropy decays was obtained by measurement and analysis of data from progressively quenched samples. Collisional quenching by acrylamide was used to vary the mean decay time of indole or of the tryptophan fluorescence from melittin. Anisotropy decays were obtained from the frequency-response of the polarized emission at frequencies from 4 to 2,000 MHz. Quenching increases the fraction of the total emission, which occurs on the subnanosecond timescale, and thereby provides increased information on picosecond rotational motions or local motions in proteins. For monoexponential subnanosecond anisotropy decays, enhanced resolution is obtained by measurement of the most highly quenched samples. For complex anisotropy decays, such as those due to both local motions and overall protein rotational diffusion, superior resolution is obtained by simultaneous analysis of data from quenched and unquenched samples. We demonstrate that measurement of quenched samples greatly reduces the uncertainty of the 50-ps correlation time of indole in water at 20 degrees C, and allows resolution of the anisotropic rotation of indole with correlation times of 140 and 720 ps. The method was applied to melittin in the monomeric and tetrameric forms. With increased quenching, the anisotropy data showed decreasing contributions from overall protein rotation and increased contribution from picosecond tryptophan motions. The tryptophan residues in both the monomeric and the tetrameric forms of melittin displayed substantial local motions with correlation times near 0.16 and 0.06 ns, respectively. The amplitude of the local motion is twofold less in the tetramer. These highly resolved anisotropy decays should be valuable for comparison with molecular dynamics simulations of melittin.     

A General Framework for Longitudinal Data through Marked Point Processes

This paper reviews a general framework for the modelling of longitudinal data with random measurement times based on marked point processes and presents a worked example. We construct a quite general regression models for longitudinal data, which may in particular include censoring that only depend on the past and outside random variation, and dependencies between measurement times and measurements. The modelling also generalises statistical counting process models. We review a non-parametric Nadarya-Watson kernel estimator of the regression function, and a parametric analysis that is based on a conditional least squares (CLS) criterion. The parametric analysis presented, is a conditional version of the generalised estimation equations of LIANG and ZEGER (1986). We conclude that the usual nonparametric and parametric regression modelling can be applied to this general set-up, with some modifications. The presented framework provides an easily implemented and powerful tool for model building for repeated measurements.     

The ethology of inter-individual differences

In recent times psychologists have shown a growing interest in ethological methods of data collection. At the same time ethologists are showing a growing interest in the methods of data processing as developed in personality psychology. These methods of data processing appear to be most useful to ethological research when investigating differences between individuals. Using factor analysis of aggressive behaviour as an example, it is argued that an ethological approach which focusses on individual differences may add substantial information to the models derived from a (classical) ethological approach which only concerns general pictures of species. The advantages of the proposed research procedures are discussed, especially in relation to behavioural genetic and ontogenetic research.Without the help and assistance of staff members of the departments of ethology and personality psychology of Groningen University this text would never have reached its present form. The suggestions of Ference Bodnár, Ciska Feekes, No Molenaar, Nance Vodegel and Annemarie van der Molen are gratefully acknowledged. Translation from Dutch into English was taken care of by Margi and Talib Rothengatter. The drawings were prepared by Agnes de Graaf.     

Rapid Global Fitting of Large Fluorescence Lifetime Imaging Microscopy Datasets

Fluorescence lifetime imaging (FLIM) is widely applied to obtain quantitative information from fluorescence signals, particularly using Förster Resonant Energy Transfer (FRET) measurements to map, for example, protein-protein interactions. Extracting FRET efficiencies or population fractions typically entails fitting data to complex fluorescence decay models but such experiments are frequently photon constrained, particularly for live cell or in vivo imaging, and this leads to unacceptable errors when analysing data on a pixel-wise basis. Lifetimes and population fractions may, however, be more robustly extracted using global analysis to simultaneously fit the fluorescence decay data of all pixels in an image or dataset to a multi-exponential model under the assumption that the lifetime components are invariant across the image (dataset). This approach is often considered to be prohibitively slow and/or computationally expensive but we present here a computationally efficient global analysis algorithm for the analysis of time-correlated single photon counting (TCSPC) or time-gated FLIM data based on variable projection. It makes efficient use of both computer processor and memory resources, requiring less than a minute to analyse time series and multiwell plate datasets with hundreds of FLIM images on standard personal computers. This lifetime analysis takes account of repetitive excitation, including fluorescence photons excited by earlier pulses contributing to the fit, and is able to accommodate time-varying backgrounds and instrument response functions. We demonstrate that this global approach allows us to readily fit time-resolved fluorescence data to complex models including a four-exponential model of a FRET system, for which the FRET efficiencies of the two species of a bi-exponential donor are linked, and polarisation-resolved lifetime data, where a fluorescence intensity and bi-exponential anisotropy decay model is applied to the analysis of live cell homo-FRET data. A software package implementing this algorithm, FLIMfit, is available under an open source licence through the Open Microscopy Environment.     

Merging the Idiographic Filter With Dynamic Factor Analysis to Model Process

To model processes we propose merging idiographic filter measurement with dynamic factor analysis. This involves testing whether or not the same latent dynamics (concurrent and lagged factor interrelations) can describe different individuals' observed multivariate time series. The methodology allows fitting, across different individuals, dynamic factor models that are invariant with respect to the latent dynamics, but not necessarily the factor loadings (measurement model). This methodology allows the same latent process to manifest differently from one individual to another, thus recognizing that the process is general but its realization in a given person is to some degree idiosyncratic. The approach is illustrated with empirical data.