Biological imaging, combined with molecular insights into genes and proteins, holds immense promise for deepening our understanding of complex cellular systems and accelerating the development of predictive, personalized therapies for human health. To fully realize this potential at scale-and harness the power of AI/ML to extract novel biological insights and therapeutic interventions-it is necessary to transition from siloed datasets to globally shared, rigorously annotated, and computationally ready image data. This demands systematic harmonization of multidimensional bioimaging data, where interoperable formats, and standardized context-rich annotation, quality controls, and analytical pipelines transform scattered observations into a coherent knowledge base ripe for computational mining. Only this machine-actionable aggregation can provide the substrate for AI/ML to extract mechanistic insights into fundamental biological mechanisms, novel diagnostic biomarkers and intervention targets. Enabling seamless image data sharing in the life sciences requires addressing two key areas. The first is outlined in an accompanying publication, Enabling Global Image Data Sharing in the Life Sciences, which focuses on the publicly available repositories needed to share digital array data 1. This White Paper details a comprehensive set of requirements for integrated image data and metadata management - from acquisition through dissemination - ensuring the contextual information necessary for assessing quality, interpreting scientific validity, and enabling meaningful reuse remains intrinsically linked to the data throughout its lifecycle. Critically, it recognizes that generating harmonized, well-annotated publicly available corpora of FAIR bioimage data requires these datasets to be "FAIR-from-the-start" - an objective that can only be achieved by enabling experimental scientists to manage, organize, and analyze their data according to community standards from the very first experiment. Building on recent progress made by the bioimaging field towards establishing shared practices for bioimaging Quality Control (QC) and metadata capture, we present actionable recommendations to advance these efforts through embedding researcher-friendly integrated software infrastructure directly into pre-publication workflows, thus transforming disorganized data capture into structured, shareable resources ready for aggregation and reuse. Our ultimate goal is to expand the use of streamlined tools and practices thus transforming how researchers capture, annotate, analyze and eventually publish bioimaging data thus laying the foundation for a new era of data-driven discovery.

Repeatability of measurements from image analytics is difficult, due to the heterogeneity and complexity of cell samples, exact microscope stage positioning, and slide thickness. We present a method to define and use a reference focal plane that provides repeatable measurements with very high accuracy, by relying on control beads as reference material and a convolutional neural network focused on the control bead images. Previously we defined a reference effective focal plane (REFP) based on the image gradient of bead edges and three specific bead image features. This paper both generalizes and improves on this previous work. First, we refine the definition of the REFP by fitting a cubic spline to describe the relationship between the distance from a bead's center and pixel intensity and by sharing information across experiments, exposures, and fields of view. Second, we remove our reliance on image features that behave differently from one instrument to another. Instead, we apply a convolutional regression neural network (ResNet 18) trained on cropped bead images that is generalizable to multiple microscopes. Our ResNet 18 network predicts the location of the REFP with only a single inferenced image acquisition that can be taken across a wide range of focal planes and exposure times. We illustrate the different strategies and hyperparameter optimization of the ResNet 18 to achieve a high prediction accuracy with an uncertainty for every image tested coming within the microscope repeatability measure of 7.5 mu m from the desired focal plane. We demonstrate the generalizability of this methodology by applying it to two different optical systems and show that this level of accuracy can be achieved using only 6 beads per image.

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.

In order to predict cell population behavior, it is important to understand the dynamic characteristics of individual cells. Individual induced pluripotent stem (iPS) cells in colonies have been difficult to track over long times, both because segmentation is challenging due to close proximity of cells and because cell morphology at the time of cell division does not change dramatically in phase contrast images; image features do not provide sufficient discrimination for 2D neural network models of label-free images. However, these cells do not move significantly during division, and they display a distinct temporal pattern of morphologies. As a result, we can detect cell division with images overlaid in time. Using a combination of a 3D neural network applied over time-lapse data to find regions of cell division activity, followed by a 2D neural network for images in these selected regions to find individual dividing cells, we developed a robust detector of iPS cell division. We created an initial 3D neural network to find 3D image regions in (x,y,t) in which identified cell divisions occurred, then used semi-supervised training with additional stacks of images to create a more refined 3D model. These regions were then inferenced with our 2D neural network to find the location and time immediately before cells divide when they contain two sets of chromatin, information needed to track the cells after division. False positives from the 3D inferenced results were identified and removed with the addition of the 2D model. We successfully identified 37 of the 38 cell division events in our manually annotated test image stack, and specified the time and (x,y) location of each cell just before division within an accuracy of 10 pixels.

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.

Cell-based therapies are an exciting branch of modern medicine.By modifying certain cells taken from a patient or donor, researchers have recently discovered new ways to treat diseases, ranging from neurological conditions to certain forms of cancer.Currently, however, many cellbased treatments remain in their early stages and must overcome several key challenges before becoming widespread in practice.To gauge how populations of cells will respond as therapies, developers and manufacturers must determine their 'phenotypes' or critical quality attributes, that describe the sets of measurable traits which are expressed by their underlying genetic code.

Trypan blue dye exclusion-based cell viability measurements are highly dependent upon image quality and consistency. In order to make measurements repeatable, one must be able to reliably capture images at a consistent focal plane, and with signal-to-noise ratio within appropriate limits to support proper execution of image analysis routines. Imaging chambers and imaging systems used for trypan blue analysis can be inconsistent or can drift over time, leading to a need to assure the acquisition of images prior to automated image analysis. Although cell-based autofocus techniques can be applied, the heterogeneity and complexity of the cell samples can make it difficult to assure the effectiveness, repeatability and accuracy of the routine for each measurement. Instead of auto-focusing on cells in our images, we add control beads to the images, and use them to repeatedly return to a reference focal plane. We use bead image features that have stable profiles across a wide range of focal values and exposure levels. We created a predictive model based on image quality features computed over reference datasets. Because the beads have little variation, we can determine the reference plane from bead image features computed over a single-shot image and can reproducibly return to that reference plane with each sample. The achieved accuracy (over 95%) is within the limits of the actuator repeatability. We demonstrate that a small number of beads (less than 3 beads per image) is needed to achieve this accuracy. We have also developed an open-source Graphical User Interface called Bead Benchmarking-Focus And Intensity Tool (BB-FAIT) to implement these methods for a semi-automated cell viability analyser.

We quantify the robustness of the semantic segmentation model U-Net, applied to single cell nuclei detection, with respect to the following factors: (1) automated vs manual training annotations, (2) quantity of training data, and (3) microscope image focus. The difficulty of obtaining sufficient volumes of accurate manually annotated training data to create an accurate Convolutional Neural Networks (CNN) model is overcome by the temporary use of fluorescent labels to automate the creation of training datasets using traditional image processing algorithms. The accuracy measurement is computed with respect to manually annotated masks which were also created to evaluate the effectiveness of using automated training set generation via the fluorescent images. The metric to compute the accuracy is the false positive/negative rate of cell nuclei detection. The goal is to maximize the true positive rate while minimizing the false positive rate. We found that automated segmentation of fluorescently labeled nuclei provides viable training data without the need for manual segmentation. A training dataset size of four large stitched images with medium cell density was enough to reach a true positive rate above 88% and a false positive rate below 20%.

Optical imaging is a pillar in biomedical research from super-resolution microscopy to whole-body imaging in animal models. Optical imaging covers such a large field that it is difficult to decide what was the first medical application .Could it be said that the introduction of spectacles in 1270, in Florence, Italy was the first use of optical imaging in medicine? Or the introduction in 1590 of the compound microscope by the father and son team of Hans and Zacharias Janssen in the Netherlands? It is more likely that optical imaging (as understood by the general scientific community) and the application of biomedical optics to diagnose various diseases, came to prominence with the discovery of optical coherence tomography (OCT) in the 1990s, although there had been a variety of fluorescence and other simple optical imaging techniques being sporadically explored for many years earlier. Now there are many sophisticated optical imaging methodologies being studied and explored for diagnosis, such as in vivo confocal microscopy, optical frequency domain imaging , diffuse optical imaging , fluorescence tomography , Brillouin microscopy , Cerenkov imaging , polarization sensitive techniques , photoacoustic techniques , and so on. In the present book, fifteen typical literatures about advances in optical imaging technique published on international authoritative journals were selected to introduce the worldwide newest progress, which contains reviews or original researches on optical imaging technique, optical coherence tomography and sophisticated optical imaging methodologies, etc. We hope this book can demonstrate advances in optical imaging technique as well as give references to the researchers, students and other related people.

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 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.

Quantitative phase imaging (QPI) provides an approach for monitoring the dry mass of individual cells by measuring the optical pathlength of visible light as it passes through cells. A distinct advantage of QPI is that the measurements result in optical path length quantities that are, in principle, instrument independent. Reference materials that induce a well-defined optical pathlength shift and are compatible with QPI imaging systems will be valuable in assuring the accuracy of such measurements on different instruments. In this study, we evaluate seven combinations of microspheres embedded in index refraction matching media as candidate reference materials for benchmarking the performance of a QPI system and as calibration standards for the optical pathlength measurement. Poly(methyl metharylate) microspheres and mineral oil were used to evaluate the range of illumination apertures, signal-to-noise ratios, and focus positions that allow an accurate quantitative optical pathlength measurement. The microsphere-based reference material can be used to verify settings on an instrument that are suitable for obtaining an accurate pathlength measurement from biological cells. The microsphere/media reference material is applied to QPI-based dry mass measurements of a population of HEK293 cells to benchmark and provide evidence that the QPI image data are accurate.

ABSTRACTThe 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. 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. We conjecture that there is an upper limit to the rate of dissipative heat produced by a biological system, and we show that the dissipative heat has a lower bound. The magnitudes of the landscape gradients and the dynamic correlated fluctuations of network variables are experimentally accessible, and through an analysis that we refer to as Thermo-Fokker-Planck (Thermo-FP), provide 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.

The visual inspection of pluripotent stem cell colonies by microscopy is widely used as a primary method to assess the quality of the preparations and degree of pluripotency. The lack of ground truth and the possible inconsistency of evaluations from multiple experts within and between stem cell laboratories are sources of uncertainty about the state of the cells, the reproducibility of preparations, and the efficiency of expansion protocols. To examine how to evaluate the level of confidence one has in disparate rating from experts, we explored a statistical method for assessing the differences in ratings of pluripotent stem cells by two different experts. Two experts rated phase contrast microscope images of human embryonic stem cell (hESC) colonies on a scale of 1 (poor) to 5 (maximum pluripotency character) but agreed with one another only 48% of the time. To assess whether experts used similar criteria to rate colonies, we developed custom image feature algorithms based on the stated visual criteria provided by the experts for selection of colonies. These features, plus others, were then used to develop pluripotency scoring algorithms trained to reflect ratings of both experts. We treated expert ratings as inexact indicators of a continuous pluripotency score and considered the inconsistency between expert ratings in developing our models. The model suggests that the two experts use somewhat different scales for discriminating between colony quality. Covariance analysis indicated that both experts use features that are not included in the model. Two image features, colony perimeter and a feature based on texture, were the most important for both experts for predicting the ratings. Interestingly, colony perimeter was not one of the expert-provided criteria for rating colonies, showing that this modeling approach allowed identification of features that the experts were not aware they were using. A linear model based on both experts identified each expert’s top-rated colonies as well as, or better than, the ratings of the other expert, as indicated by receiver operator characteristic curve analysis. By providing an understanding of the differences and similarities in disparate sets of expert ratings, this analysis helps to establish confidence in the ratings and the criteria for ratings, even when the experts disagree.

Induced pluripotent stem cells (iPSCs) are reprogrammed cells that can have heterogeneous biological potential. Quality assurance metrics of reprogrammed iPSCs will be critical to ensure reliable use in cell therapies and personalized diagnostic tests. We present a quantitative phase imaging (QPI) workflow which includes acquisition, processing, and stitching multiple adjacent image tiles across a large field of view (LFOV) of a culture vessel. Low magnification image tiles (10x) were acquired with a Phasics SID4BIO camera on a Zeiss microscope. iPSC cultures were maintained using a custom stage incubator on an automated stage. We implement an image acquisition strategy that compensates for non-flat illumination wavefronts to enable imaging of an entire well plate, including the meniscus region normally obscured in Zernike phase contrast imaging. Polynomial fitting and background mode correction was implemented to enable comparability and stitching between multiple tiles. LFOV imaging of reference materials indicated that image acquisition and processing strategies did not affect quantitative phase measurements across the LFOV. Analysis of iPSC colony images demonstrated mass doubling time was significantly different than area doubling time. These measurements were benchmarked with prototype microsphere beads and etched-glass gratings with specified spatial dimensions designed to be QPI reference materials with optical pathlength shifts suitable for cell microscopy. This QPI workflow and the use of reference materials can provide non-destructive traceable imaging method for novel iPSC heterogeneity characterization.

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.