A quantitative image cytometry technique for time series or population analyses of signaling networks

Protocol for automated multivariate quantitative-image-based cytometry analysis by fluorescence microscopy of asynchronous adherent cells

Here, we present a protocol for multivariate quantitative-image-based cytometry (QIBC) analysis by fluorescence microscopy of asynchronous adherent cells. We describe steps for the preparation, treatment, and fixation of cells, sample staining, and imaging for QIBC. We then detail image analysis with our open source Fiji script developed for QIBC and present multiparametric data visualization. Our QIBC Fiji script integrates modern artificial-intelligence-based tools, applying deep learning, for robust automated nuclei segmentation with minimal user adjustments, a major asset for efficient QIBC analysis. For complete details on the use and execution of this protocol, please refer to Besse et al. (2023).1.

Imaging-assisted hydrogel formation for single cell isolation

We report a flexible single-cell isolation method by imaging-assisted hydrogel formation. Our approach consists of imaging-aided selective capture of cells of interest by encasing them into a polymeric hydrogel, followed by removal of unwanted cells and subsequent release of isolated cells by enzymatic hydrogel degradation, thus offering an opportunity for further analysis or cultivation of selected cells. We achieved high sorting efficiency and observed excellent viability rates (>98%) for NIH/3T3 fibroblasts and A549 carcinoma cells isolated using this procedure. The method presented here offers a mask-free, cost-efficient and easy-to-use alternative to many currently existing surface-based cell-sorting techniques, and has the potential to impact the field of cell culturing and isolation, e.g. single cell genomics and proteomics, investigation of cellular heterogeneity and isolation of best performing mutants for developing new cell lines.

Application of information theory in systems biology

Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work.

Cracking the context-specific PI3K signaling code

Specificity in signal transduction is determined by the ability of cells to "encode" and subsequently "decode" different environmental signals. Akin to computer software, this "signaling code" governs context-dependent execution of cellular programs through modulation of signaling dynamics and can be corrupted by disease-causing mutations. Class IA phosphoinositide 3-kinase (PI3K) signaling is critical for normal growth and development and is dysregulated in human disorders such as benign overgrowth syndromes, cancer, primary immune deficiency, and metabolic syndrome. Despite decades of PI3K research, understanding of context-dependent regulation of the PI3K pathway and of the underlying signaling code remains rudimentary. Here, we review current knowledge on context-specific PI3K signaling and how technological advances now make it possible to move from a qualitative to quantitative understanding of this pathway. Insight into how cellular PI3K signaling is encoded or decoded may open new avenues for rational pharmacological targeting of PI3K-associated diseases. The principles of PI3K context-dependent signal encoding and decoding described here are likely applicable to most, if not all, major cell signaling pathways.

System identification of signaling dependent gene expression with different time-scale data

Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system identification method with such different time scales exists. Here we used compressed sensing technology and developed a system identification method using data of different time scales by recovering signals of missing time points. We measured phosphorylation of ERK and CREB, immediate early gene expression products, and mRNAs of decoder genes for neurite elongation in PC12 cell differentiation and performed system identification, revealing the input-output relationships between signaling and gene expression with sensitivity such as graded or switch-like response and with time delay and gain, representing signal transfer efficiency. We predicted and validated the identified system using pharmacological perturbation. Thus, we provide a versatile method for system identification using data with different time scales.

Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements

High-throughput imaging with single-cell resolution has enabled remarkable discoveries in cell physiology and Systems Biology investigations. A common, and often the most challenging step in all such imaging implementations, is the ability to segment multiple images to regions that correspond to individual cells. Here, a robust segmentation strategy for microbial cells using Quantitative Phase Imaging is reported. The proposed method enables a greater than 99% yeast cell segmentation success rate, without any computationally-intensive, post-acquisition processing. We also detail how the method can be expanded to bacterial cell segmentation with 98% success rates with substantially reduced processing requirements in comparison to existing methods. We attribute this improved performance to the remarkably uniform background, elimination of cell-to-cell and intracellular optical artifacts, and enhanced signal-to-background ratio-all innate properties of imaging in the optical-phase domain. © 2017 International Society for Advancement of Cytometry.

Large population cell characterization using quantitative phase cytometer

A major challenge in cellular analysis is the phenotypic characterization of large cell populations within a short period of time. Among various parameters for cell characterization, the cell dry mass is often used to describe cell size but is difficult to be measured directly with traditional techniques. Here, we propose an interferometric approach based on line-focused beam illumination for high-content precision dry mass measurements of adherent cells in a non-invasive fashion-we call it quantitative phase cytometry (QPC). Besides dry mass, abundant cellular morphological features such as projected area, sphericity, and phase skewness can be readily extracted from the QPC interferometric data. To validate the utility of our technique, we demonstrate characterizing a large population of ∼10⁴ HeLa cells. Our reported QPC system is envisioned as a promising quantitative tool for label-free characterization of a large cell count at single cell resolution. © 2017 International Society for Advancement of Cytometry.

Laguerre Filter Analysis with Partial Least Square Regression Reveals a Priming Effect of ERK and CREB on c-FOS Induction

Signaling networks are made up of limited numbers of molecules and yet can code information that controls different cellular states through temporal patterns and a combination of signaling molecules. In this study, we used a data-driven modeling approach, the Laguerre filter with partial least square regression, to describe how temporal and combinatorial patterns of signaling molecules are decoded by their downstream targets. The Laguerre filter is a time series model used to represent a nonlinear system based on Volterra series expansion. Furthermore, with this approach, each component of the Volterra series expansion is expanded by Laguerre basis functions. We combined two approaches, application of a Laguerre filter and partial least squares (PLS) regression, and applied the combined approach to analysis of a signal transduction network. We applied the Laguerre filter with PLS regression to identify input and output (IO) relationships between MAP kinases and the products of immediate early genes (IEGs). We found that Laguerre filter with PLS regression performs better than Laguerre filter with ordinary regression for the reproduction of a time series of IEGs. Analysis of the nonlinear characteristics extracted using the Laguerre filter revealed a priming effect of ERK and CREB on c-FOS induction. Specifically, we found that the effects of a first pulse of ERK enhance the subsequent effects on c-FOS induction of treatment with a second pulse of ERK, a finding consistent with prior molecular biological knowledge. The variable importance of projections and output loadings in PLS regression predicted the upstream dependency of each IEG. Thus, a Laguerre filter with partial least square regression approach appears to be a powerful method to find the processing mechanism of temporal patterns and combination of signaling molecules by their downstream gene expression.

Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling

Cellular signaling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signaling systems remain poorly understood. Here, we measure the dynamics of phosphorylated MEK and ERK across cell populations and quantify the levels of population heterogeneity over time using high-throughput image cytometry. We use a statistical modeling framework to show that extrinsic noise, particularly that from upstream MEK, is the dominant factor causing cell-to-cell variability in ERK phosphorylation, rather than stochasticity in the phosphorylation/dephosphorylation of ERK. We furthermore show that without extrinsic noise in the core module, variable (including noisy) signals would be faithfully reproduced downstream, but the within-module extrinsic variability distorts these signals and leads to a drastic reduction in the mutual information between incoming signal and ERK activity.

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Active MEK and ERK levels differ profoundly among genetically identical cells

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A statistical framework is developed to identify the causes of this variability

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Analysis shows that extrinsic noise upstream MEK-ERK module causes cell variability

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Within-module extrinsic variability distorts signals

Application of an Image Cytometry Protocol for Cellular and Mitochondrial Phenotyping on Fibroblasts from Patients with Inherited Disorders

Cellular phenotyping of human dermal fibroblasts (HDFs) from patients with inherited diseases provides invaluable information for diagnosis, disease aetiology, prognosis and assessing of treatment options. Here we present a cell phenotyping protocol using image cytometry that combines measurements of crucial cellular and mitochondrial parameters: (1) cell number and viability, (2) thiol redox status (TRS), (3) mitochondrial membrane potential (MMP) and (4) mitochondrial superoxide levels (MSLs). With our protocol, cell viability, TRS and MMP can be measured in one small cell sample and MSL on a parallel one. We analysed HDFs from healthy individuals after treatment with various concentrations of hydrogen peroxide (H2O2) for different intervals, to mimic the physiological effects of oxidative stress. Our results show that cell number, viability, TRS and MMP decreased, while MSL increased both in a time- and concentration-dependent manner. To assess the use of our protocol for analysis of HDFs from patients with inherited diseases, we analysed HDFs from two patients with very long-chain acyl-CoA dehydrogenase (VLCAD) deficiency (VLCADD), one with a severe clinical phenotype and one with a mild one. HDFs from both patients displayed increased MSL without H2O2 treatment. Treatment with H2O2 revealed significant differences in MMP and MSL between HDFs from the mild and the severe patient. Our results establish the capacity of our protocol for fast analysis of cellular and mitochondrial parameters by image cytometry in HDFs from patients with inherited metabolic diseases.

Photostick: a method for selective isolation of target cells from culture

Sorting of target cells from a heterogeneous pool is technically difficult when the selection criterion is complex, e.g. a dynamic response, a morphological feature, or a combination of multiple parameters. At present, mammalian cell selections are typically performed either via static fluorescence (e.g. fluorescence activated cell sorter), via survival (e.g. antibiotic resistance), or via serial operations (flow cytometry, laser capture microdissection). Here we present a simple protocol for selecting cells based on any static or dynamic property that can be identified by video microscopy and image processing. The "photostick" technique uses a cell-impermeant photochemical crosslinker and digital micromirror array-based patterned illumination to immobilize selected cells on the culture dish. Other cells are washed away with mild protease treatment. The crosslinker also labels the selected cells with a fluorescent dye and a biotin for later identification. The photostick protocol preserves cell viability, permits genetic profiling of selected cells, and can be performed with complex functional selection criteria such as neuronal firing patterns.

The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

Robustness and compensation of information transmission of signaling pathways

Robust transmission of information despite the presence of variation is a fundamental problem in cellular functions. However, the capability and characteristics of information transmission in signaling pathways remain poorly understood. We describe robustness and compensation of information transmission of signaling pathways at the cell population level. We calculated the mutual information transmitted through signaling pathways for the growth factor-mediated gene expression. Growth factors appeared to carry only information sufficient for a binary decision. Information transmission was generally more robust than average signal intensity despite pharmacological perturbations, and compensation of information transmission occurred. Information transmission to the biological output of neurite extension appeared robust. Cells may use information entropy as information so that messages can be robustly transmitted despite variation in molecular activities among individual cells.

Controlled cell adhesion using a biocompatible anchor for membrane-conjugated bovine serum albumin/bovine serum albumin mixed layer

We report here a method for controlling cell adhesion, allowing simple yet accurate cell detachment from the substrate, which is required for the establishment of new cytometry-based cell processing and analyzing methods. A biocompatible anchor for membrane (BAM) was conjugated with bovine serum albumin (BSA) to produce a cell-anchoring agent (BAM-BSA). By coating polystyrene substrates with a mixture of BAM-BSA and BSA, controlled suppression of the substrate's adhesive properties was achieved. Hook-shaped nanoneedles were used to pick up cells from the substrate, while recording the cell-substrate adhesion force, using an atomic force microscope (AFM). Due to the lipid bilayer targeting property of BAM, the coated surface showed constant adhesion forces for various cell lines, and controlling the BAM-BSA/BSA ratio enabled tuning of the adhesion force, ranging from several tens of nano-Newtons down to several nano-Newtons. Optimized tuning of the adhesion force also enabled the detachment of cells from BAM-BSA/BSA-coated dishes, using a shear flow. Moreover, the method was shown to be noncell type specific and similar results were observed using four different cell types, including nonadherent cells. The attenuation of cell adhesion was also used to enable the collection of single cells by capillary aspiration. Thus, this versatile and relatively simple method can be used to control the adhesion of various cell types to substrates.

Temporal decoding of MAP kinase and CREB phosphorylation by selective immediate early gene expression

A wide range of growth factors encode information into specific temporal patterns of MAP kinase (MAPK) and CREB phosphorylation, which are further decoded by expression of immediate early gene products (IEGs) to exert biological functions. However, the IEG decoding system remain unknown. We built a data-driven based on time courses of MAPK and CREB phosphorylation and IEG expression in response to various growth factors to identify how signal is processed. We found that IEG expression uses common decoding systems regardless of growth factors and expression of each IEG differs in upstream dependency, switch-like response, and linear temporal filters. Pulsatile ERK phosphorylation was selectively decoded by expression of EGR1 rather than c-FOS. Conjunctive NGF and PACAP stimulation was selectively decoded by synergistic JUNB expression through switch-like response to c-FOS. Thus, specific temporal patterns and combinations of MAPKs and CREB phosphorylation can be decoded by selective IEG expression via distinct temporal filters and switch-like responses. The data-driven modeling is versatile for analysis of signal processing and does not require detailed prior knowledge of pathways.

Elucidating the in vivo phosphorylation dynamics of the ERK MAP kinase using quantitative proteomics data and Bayesian model selection

Ever since reversible protein phosphorylation was discovered, it has been clear that it plays a key role in the regulation of cellular processes. Proteins often undergo double phosphorylation, which can occur through two possible mechanisms: distributive or processive. Which phosphorylation mechanism is chosen for a particular cellular regulation bears biological significance, and it is therefore in our interest to understand these mechanisms. In this paper we study dynamics of the MEK/ERK phosphorylation. We employ a model selection algorithm based on approximate Bayesian computation to elucidate phosphorylation dynamics from quantitative time course data obtained from PC12 cells in vivo. The algorithm infers the posterior distribution over four proposed models for phosphorylation and dephosphorylation dynamics, and this distribution indicates the amount of support given to each model. We evaluate the robustness of our inferential framework by systematically exploring different ways of parameterizing the models and for different prior specifications. The models with the highest inferred posterior probability are the two models employing distributive dephosphorylation, whereas we are unable to choose decisively between the processive and distributive phosphorylation mechanisms.

Imaging flow cytometry: coping with heterogeneity in biological systems

Imaging flow cytometry (IFC) platforms combine features of flow cytometry and fluorescent microscopy with advances in data-processing algorithms. IFC allows multiparametric fluorescent and morphological analysis of thousands of cellular events and has the unique capability of identifying collected events by their real images. IFC allows the analysis of heterogeneous cell populations, where one of the cellular components has low expression (<0.03%) and can be described by Poisson distribution. With the help of IFC, one can address a critical question of statistical analysis of subcellular distribution of proteins in a cell. Here the authors review advantages of IFC in comparison with more traditional technologies, such as Western blotting and flow cytometry (FC), as well as new high-throughput fluorescent microscopy (HTFM), and discuss further developments of this novel analytical technique.