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.

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.

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.

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.

Challenges for Computational Stem Cell Biology: A Discussion for the Field.

The first meetup for Computational Stem Cell Biologists was held at the 2020 annual meeting of the International Society for Stem Cell Research. The discussions highlighted opportunities and barriers to computational stem cell research that require coordinated action across the stem cell sector.

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.

Probing pluripotency gene regulatory networks with quantitative live cell imaging.

Live cell imaging uniquely enables the measurement of dynamic events in single cells, but it has not been used often in the study of gene regulatory networks. Network components can be examined in relation to one another by quantitative live cell imaging of fluorescent protein reporter cell lines that simultaneously report on more than one network component. A series of dual-reporter cell lines would allow different combinations of network components to be examined in individual cells. Dynamical information about interacting network components in individual cells is critical to predictive modeling of gene regulatory networks, and such information is not accessible through omics and other end point techniques. Achieving this requires that gene-edited cell lines are appropriately designed and adequately characterized to assure the validity of the biological conclusions derived from the expression of the reporters. In this brief review we discuss what is known about the importance of dynamics to network modeling and review some recent advances in optical microscopy methods and image analysis approaches that are making the use of quantitative live cell imaging for network analysis possible. We also discuss how strategies for genetic engineering of reporter cell lines can influence the biological relevance of the data.

The role of fluctuations in determining cellular network thermodynamics.

The steady state distributions of phenotypic responses within an isogenic population of cells result from both deterministic and stochastic characteristics of biochemical networks. A biochemical network can be characterized by a multidimensional potential landscape based on the distribution of responses and a diffusion matrix of the correlated dynamic fluctuations between N-numbers of intracellular network variables. In this work, we develop a thermodynamic description of biological networks at the level of microscopic interactions between network variables. The Boltzmann H-function defines the rate of free energy dissipation of a network system and provides a framework for determining the heat associated with the nonequilibrium steady state and its network components. The magnitudes of the landscape gradients and the dynamic correlated fluctuations of network variables are experimentally accessible. We describe the use of Fokker-Planck dynamics to calculate housekeeping heat from the experimental data by a method that we refer to as Thermo-FP. The method provides insight into the composition of the network and the relative thermodynamic contributions from network components. We surmise that these thermodynamic quantities allow determination of the relative importance of network components to overall network control. We conjecture that there is an upper limit to the rate of dissipative heat produced by a biological system that is associated with system size or modularity, and we show that the dissipative heat has a lower bound.

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.

Mass Measurements of Focal Adhesions in Single Cells Using High Resolution Surface Plasmon Resonance Microscopy.

Surface plasmon resonance microscopy (SPRM) is a powerful label-free imaging technique with spatial resolution approaching the optical diffraction limit. The high sensitivity of SPRM to small changes in index of refraction at an interface allows imaging of dynamic protein structures within a cell. Visualization of subcellular features, such as focal adhesions (FAs), can be performed on live cells using a high numerical aperture objective lens with a digital light projector to precisely position the incident angle of the excitation light. Within the cell-substrate region of the SPRM image, punctate regions of high contrast are putatively identified as the cellular FAs. Optical parameter analysis is achieved by application of the Fresnel model to the SPRM data and resulting refractive index measurements are used to calculate protein density and mass. FAs are known to be regions of high protein density that reside at the cell-substratum interface. Comparing SPRM with fluorescence images of antibody stained for vinculin, a component in FAs, reveals similar measurements of FA size. In addition, a positive correlation between FA size and protein density is revealed by SPRM. Comparing SPRM images for two cell types reveals a distinct difference in the protein density and mass of their respective FAs. Application of SPRM to quantify mass can greatly aid monitoring basic processes that control FA mass and growth and contribute to accurate models that describe cell-extracellular interactions.

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.

Opportunities and Challenges in Implementation of Multiparameter Single Cell Analysis Platforms for Clinical Translation.

The high-content interrogation of single cells with platforms optimized for the multiparameter characterization of cells in liquid and solid biopsy samples can enable characterization of heterogeneous populations of cells ex vivo. Doing so will advance the diagnosis, prognosis, and treatment of cancer and other diseases. However, it is important to understand the unique issues in resolving heterogeneity and variability at the single cell level before navigating the validation and regulatory requirements in order for these technologies to impact patient care. Since 2013, leading experts representing industry, academia, and government have been brought together as part of the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium to foster the potential of high-content data integration for clinical translation.

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.

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.

Surface plasmon resonance microscopy: Achieving a quantitative optical response.

Surface plasmon resonance (SPR) imaging allows real-time label-free imaging based on index of refraction and changes in index of refraction at an interface. Optical parameter analysis is achieved by application of the Fresnel model to SPR data typically taken by an instrument in a prism based figuration. We carry out SPR imaging on a microscope by launching light into a sample and collecting reflected light through a high numerical aperture microscope objective. The SPR microscope enables spatial resolution that approaches the diffraction limit and has a dynamic range that allows detection of subnanometer to submicrometer changes in thickness of biological material at a surface. However, unambiguous quantitative interpretation of SPR changes using the microscope system could not be achieved using the Fresnel model because of polarization dependent attenuation and optical aberration that occurs in the high numerical aperture objective. To overcome this problem, we demonstrate a model to correct for polarization diattenuation and optical aberrations in the SPR data and develop a procedure to calibrate reflectivity to index of refraction values. The calibration and correction strategy for quantitative analysis was validated by comparing the known indices of refraction of bulk materials with corrected SPR data interpreted with the Fresnel model. Subsequently, we applied our SPR microscopy method to evaluate the index of refraction for a series of polymer microspheres in aqueous media and validated the quality of the measurement with quantitative phase microscopy.

Strategies for Achieving Measurement Assurance for Cell Therapy Products.

UNLABELLED: The cell therapy industry has identified the inability to reliably characterize cells as possibly its greatest challenge and has called for standards and reference materials to provide assurance for measurements of cell properties. The challenges in characterization of cell therapy products can be largely addressed with systematic approaches for assessing sources of uncertainty and improving confidence in key measurements. This article presents the many strategies that can be used to ensure measurement confidence and discusses them in terms of how they can be applied to characterization of cell therapy products. SIGNIFICANCE: Application of these strategies to cell measurements will help to establish qualified assays for cell characterization, which may help streamline regulatory approval and enable more efficient development of cell therapy products.

Comparability: manufacturing, characterization and controls, report of a UK Regenerative Medicine Platform Pluripotent Stem Cell Platform Workshop, Trinity Hall, Cambridge, 14-15 September 2015.

This paper summarizes the proceedings of a workshop held at Trinity Hall, Cambridge to discuss comparability and includes additional information and references to related information added subsequently to the workshop. Comparability is the need to demonstrate equivalence of product after a process change; a recent publication states that this 'may be difficult for cell-based medicinal products'. Therefore a well-managed change process is required which needs access to good science and regulatory advice and developers are encouraged to seek help early. The workshop shared current thinking and best practice and allowed the definition of key research questions. The intent of this report is to summarize the key issues and the consensus reached on each of these by the expert delegates.

Large-scale time-lapse microscopy of Oct4 expression in human embryonic stem cell colonies.

Identification and quantification of the characteristics of stem cell preparations is critical for understanding stem cell biology and for the development and manufacturing of stem cell based therapies. We have developed image analysis and visualization software that allows effective use of time-lapse microscopy to provide spatial and dynamic information from large numbers of human embryonic stem cell colonies. To achieve statistically relevant sampling, we examined >680 colonies from 3 different preparations of cells over 5days each, generating a total experimental dataset of 0.9 terabyte (TB). The 0.5 Giga-pixel images at each time point were represented by multi-resolution pyramids and visualized using the Deep Zoom Javascript library extended to support viewing Giga-pixel images over time and extracting data on individual colonies. We present a methodology that enables quantification of variations in nominally-identical preparations and between colonies, correlation of colony characteristics with Oct4 expression, and identification of rare events.