Computational imaging in cell biology

Microscopy of cells has changed dramatically since its early days in the mid-seventeenth century. Image analysis has concurrently evolved from measurements of hand drawings and still photographs to computational methods that (semi-) automatically quantify objects, distances, concentrations, and velocities of cells and subcellular structures. Today's imaging technologies generate a wealth of data that requires visualization and multi-dimensional and quantitative image analysis as prerequisites to turning qualitative data into quantitative values. Such quantitative data provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks that, in turn, open the way toward a quantitative view of cell biology. Here, we will review technologies for analyzing and reconstructing dynamic structures and processes in the living cell. We will present live-cell studies that would have been impossible without computational imaging. These applications illustrate the potential of computational imaging to enhance our knowledge of the dynamics of cellular structures and processes.     

Dark proteins: effect of inclusion body formation on quantification of protein expression

Plasmid-borne gene expression systems have found wide application in the emerging fields of systems biology and synthetic biology, where plasmids are used to implement simple network architectures, either to test systems biology hypotheses about issues such as gene expression noise or as a means of exerting artificial control over a cell's dynamics. In both these cases, fluorescent proteins are commonly applied as a means of monitoring the expression of genes in the living cell, and efforts have been made to quantify protein expression levels through fluorescence intensity calibration and by monitoring the partitioning of proteins among the two daughter cells after division; such quantification is important in formulating the predictive models desired in systems and synthetic biology research. A potential pitfall of using plasmid-based gene expression systems is that the high protein levels associated with expression from plasmids can lead to the formation of inclusion bodies, insoluble aggregates of misfolded, nonfunctional proteins that will not generate fluorescence output; proteins caught in these inclusion bodies are thus "dark" to fluorescence-based detection methods. If significant numbers of proteins are incorporated into inclusion bodies rather than becoming biologically active, quantitative results obtained by fluorescent measurements will be skewed; we investigate this phenomenon here. We have created two plasmid constructs with differing average copy numbers, both incorporating an unregulated promoter (P(LtetO-1) in the absence of TetR) expressing the GFP derivative enhanced green fluorescent protein (EGFP), and inserted them into Escherichia coli bacterial cells (a common model organism for work on the dynamics of prokaryotic gene expression). We extracted the inclusion bodies, denatured them, and refolded them to render them active, obtaining a measurement of the average number of EGFP per cell locked into these aggregates; at the same time, we used calibrated fluorescent intensity measurements to determine the average number of active EGFP present per cell. Both measurements were carried out as a function of cellular doubling time, over a range of 45-75 min. We found that the ratio of inclusion body EGFP to active EGFP varied strongly as a function of the cellular growth rate, and that the number of "dark" proteins in the aggregates could in fact be substantial, reaching ratios as high as approximately five proteins locked into inclusion bodies for every active protein (at the fastest growth rate), and dropping to ratios well below 1 (for the slowest growth rate). Our results suggest that efforts to compare computational models to protein numbers derived from fluorescence measurements should take inclusion body loss into account, especially when working with rapidly growing cells.     

Biomolecular interactions measured by atomic force microscopy

Atomic force microscopy (AFM) is nowadays frequently applied to determine interaction forces between biological molecules. Starting with the detection of the first discrete unbinding forces between ligands and receptors by AFM only several years ago, measurements have become more and more quantitative. At the same time, theories have been developed to describe and understand the dynamics of the unbinding process and experimental techniques have been refined to verify this theory. In addition, the detection of molecular recognition forces has been exploited to map and image the location of binding sites. In this review we discuss the important contributions that have led to the development of this field. In addition, we emphasize the potential of chemically well-defined surface modification techniques to further improve reproducible measurements by AFM. This increased reproducibility will pave the way for a better understanding of molecular interactions in cell biology.     

Chlamydomonas reinhardtii proteomics

Proteomics, based on the expanding genomic resources, has begun to reveal new details of Chlamydomonas reinhardtii biology. In particular, analyses focusing on subproteomes have already provided new insight into the dynamics and composition of the photosynthetic apparatus, the chloroplast ribosome, the oxidative phosphorylation machinery of the mitochondria, and the flagellum. It assisted to discovered putative new components of the circadian clockwork as well as shed a light on thioredoxin protein-protein interactions. In the future, quantitative techniques may allow large scale comparison of protein expression levels. Advances in software algorithms will likely improve the use of genomic databases for mass spectrometry (MS) based protein identification and validation of gene models that have been predicted from the genomic DNA sequences. Although proteomics has only been recently applied for exploring C. reinhardtii biology, it will likely be utilized extensively in the near future due to the already existing genetic, genomic, and biochemical tools.     

Advances in Cell‐Free Biosensors: Principle,Mechanism, and Applications

Synthetic biology has promoted the development of biosensors as tools for detecting trace substances. In the past, biosensors based on synthetic biology have been designed on living cells, but the development of cell biosensors has been greatly limited by defects such as genetically modified organism problem and the obstruction of cell membrane. However, the advent of cell‐free synthetic biology addresses these limitations. Biosensors based on the cell‐free protein synthesis system have the advantages of higher safety, higher sensitivity, and faster response time over cell biosensors, which make cell‐free biosensors have a broader application prospect. This review summarizes the workflow of various cell‐free biosensors, including the identification of analytes and signal output. The detection range of cell‐free biosensors is greatly enlarged by different recognition mechanisms and output methods. In addition, the review also discusses the applications of cell‐free biosensors in environmental monitoring and health diagnosis, as well as existing deficiencies and aspects that should be improved. In the future, through continuous improvement and optimization, the potential of cell‐free biosensors will be stimulated, and their application fields will be expanded.     

Tracking microdomain dynamics in cell membranes

Studies of the diffusion of proteins and lipids in the plasma membrane of cells have long pointed to the presence of membrane domains. A major challenge in the field of membrane biology has been to characterize the various cellular structures and mechanisms that impede free diffusion in cell membranes and determine the consequences that membrane compartmentalization has on cellular biology. In this review, we will provide a brief summary of the classes of domains that have been characterized to date, focusing on recent efforts to identify the properties of lipid rafts in cells through measurements of protein and lipid diffusion.     

Cell cycle: proteomics gives it a spin

The eukaryotic cell division cycle has been studied at the molecular level for over 30 years, most fruitfully in model organisms. In the past 5 years, developments in mass spectrometry-based proteomics have been applied to the study of protein interactions and post-translational modifications involving key cell cycle regulators such as cyclin-dependent kinases and the anaphase-promoting complex, as well as effectors such as centrosomes, the kinetochore and DNA replication forks. In addition, innovations in chemical biology, functional proteomics and bioinformatics have been employed to study the cell cycle at the proteome level. This review surveys the contributions of proteomics to cell cycle research. The near future should see the application of more quantitative proteomic approaches to probe the dynamic aspects of the molecular system that underlie the cell cycle in model organisms and in human cells.     

Dynamics of protein synthesis and degradation through the cell cycle

Protein expression levels depend on the balance between their synthesis and degradation rates. Even quiescent (G₀) cells display a continuous turnover of proteins, despite protein levels remaining largely constant over time. In cycling cells, global protein levels need to be precisely doubled at each cell division in order to maintain cellular homeostasis, but we still lack a quantitative understanding of how this is achieved. Recent studies have shed light on cell cycle-dependent changes in protein synthesis and degradation rates. Here we discuss current population-based and single cell approaches used to assess protein synthesis and degradation, and review the insights they have provided into the dynamics of protein turnover in different cell cycle phases.     

Challenges in quantitative single molecule localization microscopy

Single molecule localization microscopy (SMLM), which can provide up to an order of magnitude improvement in spatial resolution over conventional fluorescence microscopy, has the potential to be a highly useful tool for quantitative biological experiments. It has already been used for this purpose in varied fields in biology, ranging from molecular biology to neuroscience. In this review article, we briefly review the applications of SMLM in quantitative biology, and also the challenges involved and some of the solutions that have been proposed. Due to its advantages in labeling specificity and the relatively low overcounting caused by photoblinking when photo-activable fluorescent proteins (PA-FPs) are used as labels, we focus specifically on Photo-Activated Localization Microscopy (PALM), even though the ideas presented might be applicable to SMLM in general. Also, we focus on the following three quantitative measurements: single molecule counting, analysis of protein spatial distribution heterogeneity and co-localization analysis.     

Peroxisystem: Harnessing systems cell biology to study peroxisomes

In recent years, high‐throughput experimentation with quantitative analysis and modelling of cells, recently dubbed systems cell biology, has been harnessed to study the organisation and dynamics of simple biological systems. Here, we suggest that the peroxisome, a fascinating dynamic organelle, can be used as a good candidate for studying a complete biological system. We discuss several aspects of peroxisomes that can be studied using high‐throughput systematic approaches and be integrated into a predictive model. Such approaches can be used in the future to study and understand how a more complex biological system, like a cell and maybe even ultimately a whole organism, works.     

In vivo protein trapping in Drosophila.

The deciphering of complete genome sequences has opened a post-genomic proteomics era. While the sequence of many proteins is now known, attention will increasingly focus on the complex interaction networks that regulate their activity, and the analysis of protein distribution in the cell will be crucial to elucidating their function. A new generation of gene trapping devices, protein trap transposons, offers a way of analysing In vivo the dynamics of sub-cellular distribution of a large number of proteins. Many transgenic lines have already been established and are available. Screens focusing on particular cell compartments can be devised.     

Origins of regulated cell-to-cell variability

Single-cell measurements and lineage-tracing experiments are revealing that phenotypic cell-to-cell variability is often the result of deterministic processes, despite the existence of intrinsic noise in molecular networks. In most cases, this determinism represents largely uncharacterized molecular regulatory mechanisms, which places the study of cell-to-cell variability in the realm of molecular cell biology. Further research in the field will be important to advance quantitative cell biology because it will provide new insights into the mechanisms by which cells coordinate their intracellular activities in the spatiotemporal context of the multicellular environment.     

Cell wall traits that influence plant development,immunity, and bioconversion

The architecture of the plant cell wall is highly dynamic, being substantially re‐modeled during growth and development. Cell walls determine the size and shape of cells and contribute to the functional specialization of tissues and organs. Beyond the physiological dynamics, the wall structure undergoes changes upon biotic or abiotic stresses. In this review several cell wall traits, mainly related to pectin, one of the major matrix components, will be discussed in relation to plant development, immunity and industrial bioconversion of biomass, especially for energy production. Plant cell walls are a source of oligosaccharide fragments with a signaling function for both development and immunity. Sensing cell wall damage, sometimes through the perception of released damage‐associated molecular patterns (DAMPs), is crucial for some developmental and immunity responses. Methodological advances that are expected to deepen our knowledge of cell wall (CW) biology will also be presented.     

Cell-to-cell variability in cell death: can systems biology help us make sense of it all?

One of the most common observations in cell death assays is that not all cells die at the same time, or at the same treatment dose. Here, using the perspective of the systems biology of apoptosis and the context of cancer treatment, we discuss possible sources of this cell-to-cell variability as well as its implications for quantitative measurements and computational models of cell death. Many different factors, both within and outside of the apoptosis signaling networks, have been correlated with the variable responses to various death-inducing treatments. Systems biology models offer us the opportunity to take a more synoptic view of the cell death process to identify multifactorial determinants of the cell death decision. Finally, with an eye toward ‘systems pharmacology'', we discuss how leveraging this new understanding should help us develop combination treatment strategies to compel cancer cells toward apoptosis by manipulating either the biochemical state of cancer cells or the dynamics of signal transduction.     

Anchored cell analysis and sorting (ACAS) system]

Advanced biology and recent technology have provided sophisticated and objective method for analyzing biological characteristics on cells. Following that, many new instruments have developed. Diagnostic immunocytochemistry has become an accepted diagnostic tool in cell biology. In recent years, remarkable advances in technology provide a method for quantitative and objective analyses of cell characteristics. The newly developed computer assisted laser cytometer (ACAS 570) can be applied in clinical basis as well as in research laboratory. Fluorescent intensities of ancharage-dependent cells can be automatically analyzed and make it possible to separate a subpopulation of cells. This computer controlled system principally consists of argon ion laser, phase contrast microscope. Quantitative fluorescence measurements and computer graphic images can be obtained. The present paper demonstrates multiple applications of laser cytometer for evaluation of cell biology.     

Stem cell plasticity, cell fusion, and transdifferentiation

One of the most contentious issues in biology today concerns the existence of stem cell plasticity. The term "plasticity" refers to the capacity of tissue-derived stem cells to exhibit a phenotypic potential that extends beyond the differentiated cell phenotypes of their resident tissue. Although evidence of stem cell plasticity has been reported by multiple laboratories, other scientists have not found the data persuasive and have remained skeptical about these new findings. This review will provide an overview of the stem cell plasticity controversy. We will examine many of the major objections that have been made to challenge the stem cell plasticity data. This controversy will be placed in the context of the traditional view of stem cell potential and cell phenotypic diversification. What the implications of cell plasticity are, and how its existence may modulate our present understanding of stem cell biology, will be explored. In addition, we will examine a topic that is usually not included within a discussion of stem cell biology--the direct conversion of one differentiated cell type into another. We believe that these observations on the transdifferentiation of differentiated cells have direct bearing on the issue of stem cell plasticity, and may provide insights into how cell phenotypic diversification is realized in the adult and into the origin of cell phenotypes during evolution.     

Modeling population dynamics in a microbial consortium under control of a synthetic pheromone‐mediated communication system

Microbial consortia can be used to catalyze complex biotransformations. Tools to control the behavior of these consortia in a technical environment are currently lacking. In the present study, a synthetic biology approach was used to build a model consortium of two Saccharomyces cerevisiae strains where growth and expression of the fluorescent marker protein EGFP by the receiver strain is controlled by the concentration of α‐factor pheromone, which is produced by the emitter strain. We have developed a quantitative experimental and theoretical framework to describe population dynamics in the model consortium. We measured biomass growth and metabolite production in controlled bioreactor experiments, and used flow cytometry to monitor changes of the subpopulations and protein expression under different cultivation conditions. This dataset was used to parameterize a segregated mathematical model, which took into account fundamental growth processes, pheromone‐induced growth arrest and EGFP production, as well as pheromone desensitization after extended exposure. The model was able to predict the growth dynamics of single‐strain cultures and the consortium quantitatively and provides a basis for using this approach in actual biotransformations.