Cell cycle dependent TN-C promoter activity determined by live cell imaging

The extracellular matrix protein tenascin-C plays a critical role in development, wound healing, and cancer progression, but how it is controlled and how it exerts its physiological responses remain unclear. By quantifying the behavior of live cells with phase contrast and fluorescence microscopy, the dynamic regulation of TN-C promoter activity is examined. We employ an NIH 3T3 cell line stably transfected with the TN-C promoter ligated to the gene sequence for destabilized green fluorescent protein (GFP). Fully automated image analysis routines, validated by comparison with data derived from manual segmentation and tracking of single cells, are used to quantify changes in the cellular GFP in hundreds of individual cells throughout their cell cycle during live cell imaging experiments lasting 62 h. We find that individual cells vary substantially in their expression patterns over the cell cycle, but that on average TN-C promoter activity increases during the last 40% of the cell cycle. We also find that the increase in promoter activity is proportional to the activity earlier in the cell cycle. This work illustrates the application of live cell microscopy and automated image analysis of a promoter-driven GFP reporter cell line to identify subtle gene regulatory mechanisms that are difficult to uncover using population averaged measurements.     

A Microscope Automated Fluidic System to Study Bacterial Processes in Real Time

Most time lapse microscopy experiments studying bacterial processes ie growth, progression through the cell cycle and motility have been performed on thin nutrient agar pads. An important limitation of this approach is that dynamic perturbations of the experimental conditions cannot be easily performed. In eukaryotic cell biology, fluidic approaches have been largely used to study the impact of rapid environmental perturbations on live cells and in real time. However, all these approaches are not easily applicable to bacterial cells because the substrata are in all cases specific and also because microfluidics nanotechnology requires a complex lithography for the study of micrometer sized bacterial cells. In fact, in many cases agar is the experimental solid substratum on which bacteria can move or even grow. For these reasons, we designed a novel hybrid micro fluidic device that combines a thin agar pad and a custom flow chamber. By studying several examples, we show that this system allows real time analysis of a broad array of biological processes such as growth, development and motility. Thus, the flow chamber system will be an essential tool to study any process that take place on an agar surface at the single cell level.     

Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution

Phenotypic profiling of natural, engineered or synthetic cells has increasingly become a bottleneck in the mining and engineering of cell factories. Single-cell phenotyping technologies are highly promising for tackling this hurdle, yet ideally they should allow non-invasive live-cell probing, be label-free, provide landscape-like phenotyping capability, distinguish complex functions, operate with high speed, sufficient throughput and low cost, and finally, couple with cell sorting so as to enable downstream omics analysis. This review focuses on recent progress in Ramanome Technology Platform (RTP), which consists of Raman spectroscopy based phenotyping, sorting and sequencing of single cells, and discuss the key challenges and emerging trends. In addition, we propose ramanome, a collection of single-cell Raman spectra (SCRS) acquired from individual cells within a cellular population or consortium, as a new type of biological phenome datatype at the single-cell resolution. By establishing the phenome-genome links in a label-free, single-cell manner, RTP should find wide applications in functional screening and strain development of live microbial, plant and animal cell factories.     

Microfluidic: An innovative tool for efficient cell sorting

At first mostly dedicated to molecular analysis, microfluidic systems are rapidly expanding their range of applications towards cell biology, thanks to their ability to control the mechanical, biological and fluidic environment at the scale of the cells. A number of new concepts based on microfluidics were indeed proposed in the last ten years for cell sorting. For many of these concepts, progress remains to be done regarding automation, standardization, or throughput, but it is now clear that microfluidics will have a major contribution to the field, from fundamental research to point-of-care diagnosis. We present here an overview of cells sorting in microfluidics, with an emphasis on circulating tumor cells. Sorting principles are classified in two main categories, methods based on physical properties of the cells, such as size, deformability, electric or optical properties, and methods based on biomolecular properties, notably specific surface antigens. We document potential applications, discuss the main advantages and limitations of different approaches, and tentatively outline the main remaining challenges in this fast evolving field.     

Cell analysis using a multiple internal reflection photonic lab-on-a-chip

Here we present a protocol for analyzing cell cultures using a photonic lab-on-a-chip (PhLoC). By using a broadband light source and a spectrometer, the spectrum of a given cell culture with an arbitrary population is acquired. The PhLoC can work in three different regimes: light scattering (using label-free cells), light scattering plus absorption (using stained cells) and, by subtraction of the two former regimes, absorption (without the scattering band). The acquisition time of the PhLoC is ～30 ms. Hence, it can be used for rapid cell counting, dead/live ratio estimation or multiparametric measurements through the use of different dyes. The PhLoC, including microlenses, micromirrors and microfluidics, is simply fabricated in a single-mask process (by soft lithographic methods) using low-cost materials. Because of its low cost it can easily be implemented for point-of-care applications. From raw substrates to final results, this protocol can be completed in 29 h.     

Bistability versus bimodal distributions in gene regulatory processes from population balance

In recent times, stochastic treatments of gene regulatory processes have appeared in the literature in which a cell exposed to a signaling molecule in its environment triggers the synthesis of a specific protein through a network of intracellular reactions. The stochastic nature of this process leads to a distribution of protein levels in a population of cells as determined by a Fokker-Planck equation. Often instability occurs as a consequence of two (stable) steady state protein levels, one at the low end representing the "off" state, and the other at the high end representing the "on" state for a given concentration of the signaling molecule within a suitable range. A consequence of such bistability has been the appearance of bimodal distributions indicating two different populations, one in the "off" state and the other in the "on" state. The bimodal distribution can come about from stochastic analysis of a single cell. However, the concerted action of the population altering the extracellular concentration in the environment of individual cells and hence their behavior can only be accomplished by an appropriate population balance model which accounts for the reciprocal effects of interaction between the population and its environment. In this study, we show how to formulate a population balance model in which stochastic gene expression in individual cells is incorporated. Interestingly, the simulation of the model shows that bistability is neither sufficient nor necessary for bimodal distributions in a population. The original notion of linking bistability with bimodal distribution from single cell stochastic model is therefore only a special consequence of a population balance model.     

A statistical framework for multiparameter analysis at the single-cell level

Phenotypic characterization of individual cells provides crucial insights into intercellular heterogeneity and enables access to information that is unavailable from ensemble averaged, bulk cell analyses. Single-cell studies have attracted significant interest in recent years and spurred the development of a variety of commercially available and research-grade technologies. To quantify cell-to-cell variability of cell populations, we have developed an experimental platform for real-time measurements of oxygen consumption (OC) kinetics at the single-cell level. Unique challenges inherent to these single-cell measurements arise, and no existing data analysis methodology is available to address them. Here we present a data processing and analysis method that addresses challenges encountered with this unique type of data in order to extract biologically relevant information. We applied the method to analyze OC profiles obtained with single cells of two different cell lines derived from metaplastic and dysplastic human Barrett's esophageal epithelium. In terms of method development, three main challenges were considered for this heterogeneous dynamic system: (i) high levels of noise, (ii) the lack of a priori knowledge of single-cell dynamics, and (iii) the role of intercellular variability within and across cell types. Several strategies and solutions to address each of these three challenges are presented. The features such as slopes, intercepts, breakpoint or change-point were extracted for every OC profile and compared across individual cells and cell types. The results demonstrated that the extracted features facilitated exposition of subtle differences between individual cells and their responses to cell-cell interactions. With minor modifications, this method can be used to process and analyze data from other acquisition and experimental modalities at the single-cell level, providing a valuable statistical framework for single-cell analysis.     

基于液滴微流控的细胞凝胶微球研究进展

液滴微流控技术在微纳米尺度上对多种流体的流动进行精确控制,从而能够以高通量的方式生成结构可调和成分可控的微纳米液滴。通过结合合适的水凝胶材料和制造方法,可以将单个或多个细胞高效地封装进水凝胶中,制备细胞凝胶微球。细胞凝胶微球可以为细胞的增殖、分化等提供一个三维的、相对独立可控的微环境,在三维细胞培养、组织工程与再生医学、干细胞研究和单细胞研究等生命科学领域具有重要价值。本文主要综述了基于液滴微流控技术的细胞凝胶微球的制备及其在生物医学领域的应用,并对未来的研究工作提出了展望。     

Microfluidic formation of single cell array for parallel analysis of Ca release-activated Ca (CRAC) channel activation and inhibition

High-throughput single cell analysis is required for understanding and predicting the complex stochastic responses of individual cells in changing environments. We have designed a microfluidic device consisting of parallel, independent channels with cell-docking structures for the formation of an array of individual cells. The microfluidic cell array was used to quantify single cell responses and the distribution of response patterns of calcium channels among a population of individual cells. In this device, 15 cell-docking units in each channel were fabricated with each unit containing 5 sandbag structures, such that an array of individual cells was formed in 8 independent channels. Single cell responses to different treatments in different channels were monitored in parallel to study the effects of the specific activator and inhibitor of the Ca²⁺ release-activated Ca²⁺ (CRAC) channels. Multichannel detection was performed to obtain the response patterns of the population of cells within this single cell array. The results demonstrate that it is possible to acquire single cell features in multichannels simultaneously with passive structural control, which provides an opportunity for high-throughput single cell response analysis in a microfluidic chip.     

Real Time Assays for Quantifying Cytotoxicity with Single Cell Resolution

A new live cell-based assay platform has been developed for the determination of complement dependent cytotoxicity (CDC), antibody dependent cellular cytotoxicity (ADCC), and overall cytotoxicity in human whole blood. In these assays, the targeted tumor cell populations are first labeled with fluorescent Cell Tracker dyes and immobilized using a DNA-based adhesion technique. This allows the facile generation of live cell arrays that are arranged arbitrarily or in ordered rectilinear patterns. Following the addition of antibodies in combination with serum, PBMCs, or whole blood, cell death within the targeted population can be assessed by the addition of propidium iodide (PI) as a viability probe. The array is then analyzed with an automated microscopic imager. The extent of cytotoxicity can be quantified accurately by comparing the number of surviving target cells to the number of dead cells labeled with both Cell Tracker and PI. Excellent batch-to-batch reproducibility has been achieved using this method. In addition to allowing cytotoxicity analysis to be conducted in real time on a single cell basis, this new assay overcomes the need for hazardous radiochemicals. Fluorescently-labeled antibodies can be used to identify individual cells that bear the targeted receptors, but yet resist the CDC and ADCC mechanisms. This new approach also allows the use of whole blood in cytotoxicity assays, providing an assessment of antibody efficacy in a highly relevant biological mixture. Given the rapid development of new antibody-based therapeutic agents, this convenient assay platform is well-poised to streamline the drug discovery process significantly.     

Addressing heterogeneity of individual blood cancers: the need for single cell analysis

Cancer heterogeneity is a significant factor in response to treatment and escape leading to relapse. Within an individual cancer, especially blood cancers, there exists multiple subclones as well as distinct clonal expansions unrelated to the clinically detected, dominant clone. Over time, multiple subclones and clones undergo emergence, expansion, and extinction. Although sometimes this intra-clonal and inter-clonal heterogeneity can be detected and/or quantified in tests that measure aggregate populations of cells, frequently, such heterogeneity can only be detected using single cell analysis to determine its frequency and to detect minor clones that may subsequently emerge to become drug resistant and dominant. Most genetic/genomic tests look at the pooled tumor population as a whole rather than at its individual cellular components. Yet, minor clones and cancer stem cells are unlikely to be detected against the background of expanded major clones. Because selective pressures are likely to govern much of what is seen clinically, single cell analysis allows identification of otherwise cryptic compartments of the malignancy that may ultimately mediate progression and relapse. Single cell analysis can track intra- or inter-clonal heterogeneity and provide useful clinical information, often before changes in the disease are detectable in the clinic. To a very limited extent, single cell analysis has already found roles in clinical care. Because inter- and intra-clonal heterogeneity likely occurs more frequently than can be currently appreciated on a clinical level, future use of single cell analysis is likely to have profound clinical utility.     

Isolation of CD133+ Liver Stem Cells for Clonal Expansion

Liver stem cell, or oval cells, proliferate during chronic liver injury, and are proposed to differentiate into both hepatocytes and cholangiocytes. In addition, liver stem cells are hypothesized to be the precursors for a subset of liver cancer, Hepatocellular carcinoma. One of the primary challenges to stem cell work in any solid organ like the liver is the isolation of a rare population of cells for detailed analysis. For example, the vast majority of cells in the liver are hepatocytes (parenchymal fraction), which are significantly larger than non-parenchymal cells. By enriching the specific cellular compartments of the liver (i.e. parenchymal and non-parenchymal fractions), and selecting for CD45 negative cells, we are able to enrich the starting population of stem cells by over 600-fold.The proceduresdetailed in this report allow for a relatively rare population of cells from a solid organ to be sorted efficiently. This process can be utilized to isolateliver stem cells from normal murine liver as well as chronic liver injury models, which demonstrate increased liver stem cell proliferation. This method has clear advantages over standard immunohistochemistry of frozen or formalin fixed liver as functional studies using live cells can be performed after initial co-localization experiments. To accomplish the procedure outlined in this report, a working relationship with a research based flow-cytometry core is strongly encouraged as the details of FACS isolation are highly dependent on specialized instrumentation and a strong working knowledge of basic flow-cytometry procedures. The specific goal of this process is to isolate a population of liver stem cells that can be clonally expanded in vitro.     

SCOPE-Seq: a scalable technology for linking live cell imaging and single-cell RNA sequencing

Optically decodable beads link the identity of a sample to a measurement through an optical barcode, enabling libraries of biomolecules to be captured on beads in solution and decoded by fluorescence. This approach has been foundational to microarray, sequencing, and flow-based expression profiling technologies. We combine microfluidics with optically decodable beads and show that phenotypic analysis of living cells can be linked to single-cell sequencing. As a proof-of-concept, we demonstrate the accuracy and scalability of our tool called Single Cell Optical Phenotyping and Expression sequencing (SCOPE-Seq) to combine live cell imaging with single-cell RNA sequencing.     

Cell motility measurements with an automated microscope system

The motility of 3T3 cells has been studied using a newly developed automated microscope system which is capable of recognizing live unstained cells growing in tissue culture. A large number of individual cells can be rapidly identified and characterized and their precise positions recorded. All cells can be revisited automatically every few minutes, and the new cell positions can be determined. Quantitative data from up to 1 000 cells can then be obtained, and cell movement parameters like cell speed, distance travelled, direction of movement, etc., can be measured for individual cells and for the whole cell population. In addition, for any number of chosen cells, high-resolution digitized images can be taken for further morphological studies, including acquisition of images of individual cells.     

Single-cell protein analysis

Heterogeneity of cellular systems has been widely recognized but only recently have tools become available that allow probing of genes and proteins in single cells to understand it. While the advancement in single cell genomic analysis has been greatly aided by the power of amplification techniques (e.g. PCR), analysis of proteins in single cells has proven to be more challenging. However, recent advances in multi-parameter flow cytometry, microscopy, microfluidics and other techniques have made it possible to measure wide variety of proteins in single cells. In this review, we highlight key recent developments in analysis of proteins in a single cell (excluding imaging-based methods), and discuss their significance in biological research.