Michelangelo Paci

Human induced pluripotent stem cell‐derived versus adult cardiomyocytes: an in silico electrophysiological study on effects of ionic current block

Two new technologies are likely to revolutionize cardiac safety and drug development: in vitro experiments on human‐induced pluripotent stem cell‐derived cardiomyocytes (hiPSC‐CMs) and in silico human adult ventricular cardiomyocyte (hAdultV‐CM) models. Their combination was recently proposed as a potential replacement for the present hERG‐based QT study for pharmacological safety assessments. Here, we systematically compared in silico the effects of selective ionic current block on hiPSC‐CM and hAdultV‐CM action potentials (APs), to identify similarities/differences and to illustrate the potential of computational models as supportive tools for evaluating new in vitro technologies.

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting.

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium.

Aims A fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification. Methods For image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level. Dataset and Results The RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%. Conclusions Here we showed that the developed ensembles of texture descriptors are able to classify the RPE cell maturation stage. Moreover, we proved that preprocessing and region-based decomposition improves many descriptors’ accuracy in biological dataset classification. Finally, we built the first public dataset of stem cell-derived RPE cells, which is publicly available to the scientific community for classification studies. The proposed tool is available at https://www.dei.unipd.it/node/2357 and the RPE dataset at http://www.biomeditech.fi/data/RPE_dataset/. Both are available at https://figshare.com/s/d6fb591f1beb4f8efa6f.

Phenotypic variability in LQT3 human induced pluripotent stem cell−derived cardiomyocytes and their response to antiarrhythmic pharmacologic therapy: An in silico approach

Background Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are in vitro models with the clear advantages of their human origin and suitability for human disease investigations. However, limitations include their incomplete characterization and variability reported in different cell lines and laboratories. Objective The purpose of this study was to investigate in silico ionic mechanisms potentially explaining the phenotypic variability of hiPSC-CMs in long QT syndrome type 3 (LQT3) and their response to antiarrhythmic drugs. Methods Populations of in silico hiPSC-CM models were constructed and calibrated for control (n = 1,463 models) and LQT3 caused by INaL channelopathy (n = 1,401 models), using experimental recordings for late sodium current (INaL) and action potentials (APs). Antiarrhythmic drug therapy was evaluated by simulating mexiletine and ranolazine multichannel effects. Results As in experiments, LQT3 hiPSC-CMs yield prolonged action potential duration at 90% repolarization (APD90) (+34.3% than controls) and large electrophysiological variability. LQT3 hiPSC-CMs with symptomatic APs showed overexpression of ICaL, IK1, and INaL, underexpression of IKr, and increased sensitivity to both drugs compared to asymptomatic LQT3 models. Simulations showed that both mexiletine and ranolazine corrected APD prolongation in the LQT3 population but also highlighted differences in drug response. Mexiletine stops spontaneous APs in more LQT3 hiPSC-CMs models than ranolazine (784/1,401 vs 53/1,401) due to its stronger action on INa. Conclusion In silico simulations demonstrate our ability to recapitulate variability in LQT3 and control hiPSC-CM phenotypes, and the ability of mexiletine and ranolazine to reduce APD prolongation, in agreement with experiments. The in silico models also identify potential ionic mechanisms of phenotypic variability in LQT3 hiPSC-CMs, explaining APD prolongation in symptomatic vs asymptomatic LQT3 hiPSC-CMs.

Histogram-based classification of iPSC colony images using machine learning methods

This paper focuses on induced pluripotent stem cell (iPSC) colony image classification using machine learning methods and different feature sets obtained from the intensity histograms. Intensity histograms are obtained from the whole iPSC colony images and as a baseline for it they are determined only from the iPSC colony area of images. Furthermore, we apply to both of the datasets two simple feature selection methods having altogether four datasets. Altogether, 30 different classification methods are tested and we perform thorough experimental tests. The best accuracy (55%) is obtained for the feature set evaluated from the whole image using Directed Acyclic Graph Support Vector Machines (DAGSVM). DAGSVM is also the best choice when intensity histograms are evaluated only from the iPSC colony area. By this means accuracy of 54% is achieved. The obtained results are promising for further research where, for instance, more sophisticated feature selection and extraction methods and other multi-class extensions of SVM will be examined. However, intensity histograms are not alone adequate for iPSC colony image classification.

Investigating local spatially-enhanced structural and textural descriptors for classification of iPSC colony images

Induced pluripotent stem cells (iPSC) can be derived from fully differentiated cells of adult individuals and used to obtain any other cell type of the human body. This implies numerous prospective applications of iPSCs in regenerative medicine and drug development. In order to obtain valid cell culture, a quality control process must be applied to identify and discard abnormal iPSC colonies. Computer vision systems that analyze visual characteristics of iPSC colony health can be especially useful in automating and improving the quality control process. In this paper, we present an ongoing research that aims at the development of local spatially-enhanced descriptors for classification of iPSC colony images. For this, local oriented edges and local binary patterns are extracted from the detected colony regions and used to represent structural and textural properties of the colonies, respectively. We preliminary tested the proposed descriptors in classifying iPSCs colonies according to the degree of colony abnormality. The tests showed promising results for both, detection of iPSC colony borders and colony classification.

Classification of iPSC colony images using hierarchical strategies with support vector machines

In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for safe and patient specific drug therapy without any ethical problems. However, growing of iPSCs is a sensitive process and abnormalities may occur during the growing process. These abnormalities need to be recognized and the problem returns to image classification. We have a collection of 80 iPSC colony images where each one of the images is prelabeled by an expert to class bad, good or semigood. We use intensity histograms as features for classification and we evaluate histograms from the whole image and the colony area only having two datasets. We perform two feature reduction procedures for both datasets. In classification we examine how different hierarchical constructions effect the classification. We perform thorough evaluation and the best accuracy was around 54% obtained with the linear kernel function. Between different hierarchical structures, in many cases there are no significant changes in results. As a result, intensity histograms are a good baseline for the classification of iPSC colony images but more sophisticated feature extraction and reduction methods together with other classification methods need to be researched in future.

Computational Model of Ca2+ Wave Propagation in Human Retinal Pigment Epithelial ARPE-19 Cells

Objective Computational models of calcium (Ca2+) signaling have been constructed for several cell types. There are, however, no such models for retinal pigment epithelium (RPE). Our aim was to construct a Ca2+ signaling model for RPE based on our experimental data of mechanically induced Ca2+ wave in the in vitro model of RPE, the ARPE-19 monolayer. Methods We combined six essential Ca2+ signaling components into a model: stretch-sensitive Ca2+ channels (SSCCs), P2Y2 receptors, IP3 receptors, ryanodine receptors, Ca2+ pumps, and gap junctions. The cells in our epithelial model are connected to each other to enable transport of signaling molecules. Parameterization was done by tuning the above model components so that the simulated Ca2+ waves reproduced our control experimental data and data where gap junctions were blocked. Results Our model was able to explain Ca2+ signaling in ARPE-19 cells, and the basic mechanism was found to be as follows: 1) Cells near the stimulus site are likely to conduct Ca2+ through plasma membrane SSCCs and gap junctions conduct the Ca2+ and IP3 between cells further away. 2) Most likely the stimulated cell secretes ligand to the extracellular space where the ligand diffusion mediates the Ca2+ signal so that the ligand concentration decreases with distance. 3) The phosphorylation of the IP3 receptor defines the cell’s sensitivity to the extracellular ligand attenuating the Ca2+ signal in the distance. Conclusions The developed model was able to simulate an array of experimental data including drug effects. Furthermore, our simulations predict that suramin may interfere ligand binding on P2Y2 receptors or accelerate P2Y2 receptor phosphorylation, which may partially be the reason for Ca2+ wave attenuation by suramin. Being the first RPE Ca2+ signaling model created based on experimental data on ARPE-19 cell line, the model offers a platform for further modeling of native RPE functions.

An ensemble of visual features for Gaussians of local descriptors and non-binary coding for texture descriptors

improved version of the Gaussians of Local Descriptorswe describe the covariance matrix using a set of visual featuresthe original approach and the new one are combined by sum rule This paper presents an improved version of a recent state-of-the-art texture descriptor called Gaussians of Local Descriptors (GOLD), which is based on a multivariate Gaussian that models the local feature distribution that describes the original image. The full rank covariance matrix, which lies on a Riemannian manifold, is projected on the tangent Euclidean space and concatenated to the mean vector for representing a given image. In this paper, we test the following features for describing the original image: scale-invariant feature transform (SIFT), histogram of gradients (HOG), and webers law descriptor (WLD). To improve the baseline version of GOLD, we describe the covariance matrix using a set of visual features that are fed into a set of Support Vector Machines (SVMs). The SVMs are combined by sum rule. The scores obtained by an SVM trained using the original GOLD approach and the SVMs trained with visual features are then combined by sum rule. Experiments show that our proposed variant outperforms the original GOLD approach. The superior performance of the proposed system is validated across a large set of datasets. Particularly interesting is the performance obtained in two widely used person re-identification datasets, CAVIAR4REID and IAS, where the proposed GOLD variant is coupled with a state-of-the-art ensemble to obtain an improvement of performance on these two datasets. Moreover, we performed further tests that combine GOLD with non-binary features (local ternary/quinary patterns) and deep transfer learning. The fusion among SVMs trained with deep features and the SVMs trained using the ternary/quinary coding ensemble is demonstrated to obtain a very high performance across datasets. The MATLAB code for the ensemble of classifiers and for the extraction of the features will be publicly available11https://www.dei.unipd.it/node/2357 (+Pattern Recognition and Ensemble Classifiers) to other researchers for future comparisons.

Automatic optimization of an in silico model of human iPSC derived cardiomyocytes recapitulating calcium handling abnormalities

The growing importance of human induced pluripotent stem cell-derived cardiomyoyctes (hiPSC-CMs), as patient-specific and disease-specific models for studying cellular cardiac electrophysiology or for preliminary cardiotoxicity tests, generated better understanding of hiPSC-CM biophysical mechanisms and great amount of action potential and calcium transient data. In this paper, we propose a new hiPSC-CM in silico model, with particular attention to Ca2+ handling. We used (i) the hiPSC-CM Paci2013 model as starting point, (ii) a new dataset of Ca2+ transient measurements to tune the parameters of the inward and outward Ca2+ fluxes of sarcoplasmic reticulum, and (iii) an automatic parameter optimization to fit action potentials and Ca2+ transients. The Paci2018 model simulates, together with the typical hiPSC-CM spontaneous action potentials, more refined Ca2+ transients and delayed afterdepolarizations-like abnormalities, which the old Paci2013 was not able to predict due to its mathematical formulation. The Paci2018 model was validated against (i) the same current blocking experiments used to validate the Paci2013 model, and (ii) recently published data about effects of different extracellular ionic concentrations. In conclusion, we present a new and more versatile in silico model, which will provide a platform for modeling the effects of drugs or mutations that affect Ca2+ handling in hiPSC-CMs.