The new era of quantitative cell imaging-challenges and opportunities.

Quantitative optical microscopy-an emerging, transformative approach to single-cell biology-has seen dramatic methodological advancements over the past few years. However, its impact has been hampered by challenges in the areas of data generation, management, and analysis. Here we outline these technical and cultural challenges and provide our perspective on the trajectory of this field, ushering in a new era of quantitative, data-driven microscopy. We also contrast it to the three decades of enormous advances in the field of genomics that have significantly enhanced the reproducibility and wider adoption of a plethora of genomic approaches.

How measurement science can improve confidence in research results.

The current push for rigor and reproducibility is driven by a desire for confidence in research results. Here, we suggest a framework for a systematic process, based on consensus principles of measurement science, to guide researchers and reviewers in assessing, documenting, and mitigating the sources of uncertainty in a study. All study results have associated ambiguities that are not always clarified by simply establishing reproducibility. By explicitly considering sources of uncertainty, noting aspects of the experimental system that are difficult to characterize quantitatively, and proposing alternative interpretations, the researcher provides information that enhances comparability and reproducibility.

Information Thermodynamics and Reducibility of Large Gene Networks.

Gene regulatory networks (GRNs) control biological processes like pluripotency, differentiation, and apoptosis. Omics methods can identify a large number of putative network components (on the order of hundreds or thousands) but it is possible that in many cases a small subset of genes control the state of GRNs. Here, we explore how the topology of the interactions between network components may indicate whether the effective state of a GRN can be represented by a small subset of genes. We use methods from information theory to model the regulatory interactions in GRNs as cascading and superposing information channels. We propose an information loss function that enables identification of the conditions by which a small set of genes can represent the state of all the other genes in the network. This information-theoretic analysis extends to a measure of free energy change due to communication within the network, which provides a new perspective on the reducibility of GRNs. Both the information loss and relative free energy depend on the density of interactions and edge communication error in a network. Therefore, this work indicates that a loss in mutual information between genes in a GRN is directly coupled to a thermodynamic cost, i.e., a reduction of relative free energy, of the system.

Standards for Cell Line Authentication and Beyond.

Different genomic technologies have been applied to cell line authentication, but only one method (short tandem repeat [STR] profiling) has been the subject of a comprehensive and definitive standard (ASN-0002). Here we discuss the power of this document and why standards such as this are so critical for establishing the consensus technical criteria and practices that can enable progress in the fields of research that use cell lines. We also examine other methods that could be used for authentication and discuss how a combination of methods could be used in a holistic fashion to assess various critical aspects of the quality of cell lines.

Improving Reproducibility in Research: The Role of Measurement Science.

We report on a workshop held 1-3 May 2018 at the National Physical Laboratory, Teddington, U.K., in which the focus was how the world's national metrology institutes might help to address the challenges of reproducibility of research.The workshop brought together experts from the measurement and wider research communities in physical sciences, data analytics, life sciences, engineering, and geological science. The workshop involved 63 participants from metrology laboratories (38), academia (16), industry (5), funding agencies (2), and publishers (2). The participants came from the U.K., the United States, Korea, France, Germany, Australia, Bosnia and Herzegovina, Canada, Turkey, and Singapore.Topics explored how good measurement practice and principles could foster confidence in research findings and how to manage the challenges of increasing volume of data in both industry and research.

A novel measure and significance testing in data analysis of cell image segmentation.

BACKGROUND: Cell image segmentation (CIS) is an essential part of quantitative imaging of biological cells. Designing a performance measure and conducting significance testing are critical for evaluating and comparing the CIS algorithms for image-based cell assays in cytometry. Many measures and methods have been proposed and implemented to evaluate segmentation methods. However, computing the standard errors (SE) of the measures and their correlation coefficient is not described, and thus the statistical significance of performance differences between CIS algorithms cannot be assessed. RESULTS: We propose the total error rate (TER), a novel performance measure for segmenting all cells in the supervised evaluation. The TER statistically aggregates all misclassification error rates (MER) by taking cell sizes as weights. The MERs are for segmenting each single cell in the population. The TER is fully supported by the pairwise comparisons of MERs using 106 manually segmented ground-truth cells with different sizes and seven CIS algorithms taken from ImageJ. Further, the SE and 95% confidence interval (CI) of TER are computed based on the SE of MER that is calculated using the bootstrap method. An algorithm for computing the correlation coefficient of TERs between two CIS algorithms is also provided. Hence, the 95% CI error bars can be used to classify CIS algorithms. The SEs of TERs and their correlation coefficient can be employed to conduct the hypothesis testing, while the CIs overlap, to determine the statistical significance of the performance differences between CIS algorithms. CONCLUSIONS: A novel measure TER of CIS is proposed. The TER's SEs and correlation coefficient are computed. Thereafter, CIS algorithms can be evaluated and compared statistically by conducting the significance testing.

Improved reproducibility by assuring confidence in measurements in biomedical research.

'Irreproducibility' is symptomatic of a broader challenge in measurement in biomedical research. From the US National Institute of Standards and Technology (NIST) perspective of rigorous metrology, reproducibility is only one aspect of establishing confidence in measurements. Appropriate controls, reference materials, statistics and informatics are required for a robust measurement process. Research is required to establish these tools for biological measurements, which will lead to greater confidence in research results.

Evaluating the quality of a cell counting measurement process via a dilution series experimental design.

BACKGROUND AIMS: Cell counting measurements are critical in the research, development and manufacturing of cell-based products, yet determining cell quantity with accuracy and precision remains a challenge. Validating and evaluating a cell counting measurement process can be difficult because of the lack of appropriate reference material. Here we describe an experimental design and statistical analysis approach to evaluate the quality of a cell counting measurement process in the absence of appropriate reference materials or reference methods. METHODS: The experimental design is based on a dilution series study with replicate samples and observations as well as measurement process controls. The statistical analysis evaluates the precision and proportionality of the cell counting measurement process and can be used to compare the quality of two or more counting methods. As an illustration of this approach, cell counting measurement processes (automated and manual methods) were compared for a human mesenchymal stromal cell (hMSC) preparation. RESULTS: For the hMSC preparation investigated, results indicated that the automated method performed better than the manual counting methods in terms of precision and proportionality. DISCUSSION: By conducting well controlled dilution series experimental designs coupled with appropriate statistical analysis, quantitative indicators of repeatability and proportionality can be calculated to provide an assessment of cell counting measurement quality. This approach does not rely on the use of a reference material or comparison to "gold standard" methods known to have limited assurance of accuracy and precision. The approach presented here may help the selection, optimization, and/or validation of a cell counting measurement process.

Biological imaging software tools.

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.

Predicting rates of cell state change caused by stochastic fluctuations using a data-driven landscape model.

We develop a potential landscape approach to quantitatively describe experimental data from a fibroblast cell line that exhibits a wide range of GFP expression levels under the control of the promoter for tenascin-C. Time-lapse live-cell microscopy provides data about short-term fluctuations in promoter activity, and flow cytometry measurements provide data about the long-term kinetics, because isolated subpopulations of cells relax from a relatively narrow distribution of GFP expression back to the original broad distribution of responses. The landscape is obtained from the steady state distribution of GFP expression and connected to a potential-like function using a stochastic differential equation description (Langevin/Fokker-Planck). The range of cell states is constrained by a force that is proportional to the gradient of the potential, and biochemical noise causes movement of cells within the landscape. Analyzing the mean square displacement of GFP intensity changes in live cells indicates that these fluctuations are described by a single diffusion constant in log GFP space. This finding allows application of the Kramers' model to calculate rates of switching between two attractor states and enables an accurate simulation of the dynamics of relaxation back to the steady state with no adjustable parameters. With this approach, it is possible to use the steady state distribution of phenotypes and a quantitative description of the short-term fluctuations in individual cells to accurately predict the rates at which different phenotypes will arise from an isolated subpopulation of cells.

High resolution surface plasmon resonance imaging for single cells.

BACKGROUND: Surface plasmon resonance imaging (SPRI) is a label-free technique that can image refractive index changes at an interface. We have previously used SPRI to study the dynamics of cell-substratum interactions. However, characterization of spatial resolution in 3 dimensions is necessary to quantitatively interpret SPR images. Spatial resolution is complicated by the asymmetric propagation length of surface plasmons in the x and y dimensions leading to image degradation in one direction. Inferring the distance of intracellular organelles and other subcellular features from the interface by SPRI is complicated by uncertainties regarding the detection of the evanescent wave decay into cells. This study provides an experimental basis for characterizing the resolution of an SPR imaging system in the lateral and distal dimensions and demonstrates a novel approach for resolving sub-micrometer cellular structures by SPRI. The SPRI resolution here is distinct in its ability to visualize subcellular structures that are in proximity to a surface, which is comparable with that of total internal reflection fluorescence (TIRF) microscopy but has the advantage of no fluorescent labels. RESULTS: An SPR imaging system was designed that uses a high numerical aperture objective lens to image cells and a digital light projector to pattern the angle of the incident excitation on the sample. Cellular components such as focal adhesions, nucleus, and cellular secretions are visualized. The point spread function of polymeric nanoparticle beads indicates near-diffraction limited spatial resolution. To characterize the z-axis response, we used micrometer scale polymeric beads with a refractive index similar to cells as reference materials to determine the detection limit of the SPR field as a function of distance from the substrate. Multi-wavelength measurements of these microspheres show that it is possible to tailor the effective depth of penetration of the evanescent wave into the cellular environment. CONCLUSION: We describe how the use of patterned incident light provides SPRI at high spatial resolution, and we characterize a finite limit of detection for penetration depth. We demonstrate the application of a novel technique that allows unprecedented subcellular detail for SPRI, and enables a quantitative interpretation of SPRI for subcellular imaging.

An automated protocol for performance benchmarking a widefield fluorescence microscope.

Widefield fluorescence microscopy is a highly used tool for visually assessing biological samples and for quantifying cell responses. Despite its widespread use in high content analysis and other imaging applications, few published methods exist for evaluating and benchmarking the analytical performance of a microscope. Easy-to-use benchmarking methods would facilitate the use of fluorescence imaging as a quantitative analytical tool in research applications, and would aid the determination of instrumental method validation for commercial product development applications. We describe and evaluate an automated method to characterize a fluorescence imaging system's performance by benchmarking the detection threshold, saturation, and linear dynamic range to a reference material. The benchmarking procedure is demonstrated using two different materials as the reference material, uranyl-ion-doped glass and Schott 475 GG filter glass. Both are suitable candidate reference materials that are homogeneously fluorescent and highly photostable, and the Schott 475 GG filter glass is currently commercially available. In addition to benchmarking the analytical performance, we also demonstrate that the reference materials provide for accurate day to day intensity calibration. Published 2014 Wiley Periodicals Inc.

High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies.

To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to assure the reliability of the training. The model is generalizable in that it performs well on different datasets with an average F1 score of 0.94, on cells at different densities, and on cells from different pluripotent cell lines. The method allows us to assess, in a non-invasive manner, rates of mitosis and cell division which serve as indicators of cell state and cell health. We assess these parameters in up to hundreds of thousands of cells in culture for more than 36 hours, at different locations in the colonies, and as a function of excitation light exposure.

Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data.

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

Cell spreading and proliferation in response to the composition and mechanics of engineered fibrillar extracellular matrices.

The extracellular matrix (ECM) consists of a complex mixture of biochemical and physical stimuli that together regulate cell behavior. In this study, we engineer a model ECM consisting of fibrillar Type-1 collagen plus fibronectin that allows systematic examination of the effects of matrix composition and mechanics on cells. On this combined protein matrix, cells exhibit intermediate degrees of spreading and proliferation compared to their responses on collagen or fibronectin alone. Adhesion to the combination matrix could be blocked by peptides containing the sequence arginine-glycine-aspartic acid (RGD) and by antibodies against α1 integrin, suggesting cell-matrix engagement was mediated by a combination of integrin receptors that recognize fibronectin and collagen. Regardless of integrin engagement, cells were sensitive to the mechanical properties of the combination ECM, suggesting that cells could process biochemical and mechanical cues simultaneously and independently.

Contemporaneous sample data tracking for the generation of genome edited cell lines.

It is difficult to capture the large numbers of steps and details that often characterize research in the biomedical sciences. We present an approach that is based on commercial spreadsheet software so it is easily adaptable by the experimentalist. The approach is designed to be compatible with an experimentalist's workflow and allows the capture in real time of detailed information associated, in this use case, with laboratory actions involved in the process of editing, enriching and isolating clonal gene-edited pluripotent stem cell (PSC) lines. Intuitive features and flexibility allow an experimentalist without extensive programming knowledge to modify spreadsheets in response to changes in protocols and to perform simple queries. The experimental details are collated in a table format from which they can be exported in open standard formats (e.g., Extensible Markup Language (XML) or Comma Separated Values (CSV) for ingestion into a data repository supporting interoperability with other applications. We demonstrate a sample- and file-naming convention that enables the automated creation of file directory folders with human readable semantic titles within a local file system. These operations facilitate the local organization of documentation and data for each cell line derived from each transfection in designated folder/file locations. This approach is generalizable to experimental applications beyond this use case.

New concepts for building vocabulary for cell image ontologies.

BACKGROUND: There are significant challenges associated with the building of ontologies for cell biology experiments including the large numbers of terms and their synonyms. These challenges make it difficult to simultaneously query data from multiple experiments or ontologies. If vocabulary terms were consistently used and reused across and within ontologies, queries would be possible through shared terms. One approach to achieving this is to strictly control the terms used in ontologies in the form of a pre-defined schema, but this approach limits the individual researcher's ability to create new terms when needed to describe new experiments. RESULTS: Here, we propose the use of a limited number of highly reusable common root terms, and rules for an experimentalist to locally expand terms by adding more specific terms under more general root terms to form specific new vocabulary hierarchies that can be used to build ontologies. We illustrate the application of the method to build vocabularies and a prototype database for cell images that uses a visual data-tree of terms to facilitate sophisticated queries based on a experimental parameters. We demonstrate how the terminology might be extended by adding new vocabulary terms into the hierarchy of terms in an evolving process. In this approach, image data and metadata are handled separately, so we also describe a robust file-naming scheme to unambiguously identify image and other files associated with each metadata value. The prototype database http://sbd.nist.gov/ consists of more than 2000 images of cells and benchmark materials, and 163 metadata terms that describe experimental details, including many details about cell culture and handling. Image files of interest can be retrieved, and their data can be compared, by choosing one or more relevant metadata values as search terms. Metadata values for any dataset can be compared with corresponding values of another dataset through logical operations. CONCLUSIONS: Organizing metadata for cell imaging experiments under a framework of rules that include highly reused root terms will facilitate the addition of new terms into a vocabulary hierarchy and encourage the reuse of terms. These vocabulary hierarchies can be converted into XML schema or RDF graphs for displaying and querying, but this is not necessary for using it to annotate cell images. Vocabulary data trees from multiple experiments or laboratories can be aligned at the root terms to facilitate query development. This approach of developing vocabularies is compatible with the major advances in database technology and could be used for building the Semantic Web.

Reproducibility and robustness of a real-time microfluidic cell toxicity assay.

Numerous opportunities exist to apply microfluidic technology to high-throughput and high-content cell-based assays. However, maximizing the value of microfluidic assays for applications such as drug discovery, screening, or toxicity evaluation will require assurance of within-device repeatability, day-to-day reproducibility, and robustness to variations in conditions that might occur from laboratory to laboratory. This report describes a study of the performance and variability of a cell-based toxicity assay in microfluidic devices made of poly(dimethylsiloxane) (PDMS). The assay involves expression of destabilized green fluorescent protein (GFP) as a reporter of intracellular protein synthesis and degradation. Reduction in cellular GFP due to inhibition of ribosome activity by cycloheximide (CHX) was quantified with real-time quantitative fluorescence imaging. Assay repeatability was measured within a 64-chamber microfluidic device. Assay performance across a range of cell loading densities within a single device was assessed, as was replication of measurements in microfluidic devices prepared on different days. Assay robustness was tested using different fluorescence illumination sources and reservoir-to-device tubing choices. Both microfluidic and larger scale assay conditions showed comparable GFP decay rates upon CHX exposure, but the microfluidic data provided the higher level of confidence.

Comparison of segmentation algorithms for fluorescence microscopy images of cells.

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability.

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.