AI News, Diagnostics of genetic cardiac diseases using stem cell-derived cardiomyocytes

Diagnostics of genetic cardiac diseases using stem cell-derived cardiomyocytes

iPSC-derived cardiomyocytes can be derived from a blood sample or a skin biopsy.

The software is now capable of identifying whether signals are from cells derived from an individual carrying a disease-causing mutation or from a healthy individual.

Currently, genetic diseases are mainly diagnosed by DNA analysis, but in many cases the results do not reveal whether the DNA alteration is the true cause of the disease or whether it is just an innocent variation.

The combination of technologies could also be used in cases of unspecific but severe cardiac findings to identify the specific disease causing the symptoms.

Diagnostics of genetic cardiac diseases using stem cell-derived cardiomyocytes

A new study by Professors Martti Juhola and Katriina Aalto-Setälä of the University of Tampere in Finland demonstrates that with the use of artificial intelligence and machine learning, it is possible not only to accurately sort sick cardiac cell cultures from healthy ones, but also to differentiate between genetic cardiac diseases.

The software is now capable of identifying whether signals are from cells derived from an individual carrying a disease-causing mutation or from a healthy individual.

Currently, genetic diseases are mainly diagnosed by DNA analysis, but in many cases the results do not reveal whether the DNA alteration is the true cause of the disease or whether it is just an innocent variation.

The combination of technologies could also be used in cases of unspecific but severe cardiac findings to identify the specific disease causing the symptoms.

An Electrifying New Role for the Immune System in Heart Health

As their name suggests (from Greek makros = large, and phagein = to eat), macrophages are specialized cells that keep us safe by “eating” foreign material and unwanted cells through a process known as phagocytosis.

Macrophages also help to fight infections by recruiting an alliance of other immune or non-immune cells to the site of injury and by secreting molecules that promote bacterial and viral clearance.

This discovery upends what we know about the heart’s rhythm and opens the door to new potential therapies for diseases in which the heart beats irregularly, like cardiac arrhythmias.

These electrical signals originate in the heart’s physiological pacemaker, the sinoatrial (SA) node, which consists of a group of specialized cells located in the upper right atrium.

The SA node fires regular electrical impulses 60 to 100 times per minute, causing the atria to contract and force blood down into the ventricles.

In particular, previous studies had demonstrated that macrophages increase in number in response to heart attack and heart failure, facilitating cardiac healing after injury.

Everything began when a technician, interested in understanding how macrophages affect the heart, performed an MRI scan on a mouse that lacked macrophages and noticed something odd: the animal’s electrical rhythms were abnormal.

This result was both surprising and intriguing because the protein connexin is one of the major components of gap junctions, which are bridge-like structures that can connect two adjacent cells in order to facilitate their electrical communication.

It didn’t take long for the researchers to put the pieces of the puzzle together: since heart-resident macrophages both resemble and share the same connecting structures as cardiomyocytes, could this mean that the two cell types can actually communicate with each other –

When exposed to light, the macrophages would automatically fire an electrical impulse and, if macrophages and cardiomyocytes were working together, this should result in better electrical communication, and ultimately in a faster contraction rhythm.

Indeed, when macrophages were artificially stimulated by light, the beating of the heart significantly improved, suggesting that macrophages and cardiac muscle cells were working together to transmit electrical currents and promote heart contraction (Figure 2).

Besides describing an exciting new role for macrophages that goes far beyond their well-established roles in host-defense, this study has and will inspire new studies to better our understanding of normal heart function.

Stem Cell Therapy in Heart Diseases: A Review of Selected New Perspectives, Practical Considerations and Clinical Applications

In contrast to adult stem cells, embryonic stem cells (ESCs) have the potential to differentiate into tissue derivatives of all three embryonic germ layers and therefore are termed pluripotent.

A possible strategy for cell-replacement therapy would be to initially allow spontaneous differentiation of ESCs into multiple lineages in vitro followed by selective purification of the cardiomyogenic lineage isolated from embryoid bodies (Fig.

[5] show that transplanted hESC-derived CMCs substitute damaged pacemaker cells in a swine model of atrioventricular block, and are responsible for eliciting an ectopic rhythm compatible with the animal’s survival.

On this issue, recent high-profile reports of the derivation of human embryonic stem cells from human blastocysts produced by somatic cell nuclear transfer (SCNT) [1,33,34] have highlighted the possibility of making autologous cell lines specific to individual patients.

Furthermore, with the advent of other techniques such as xeno-free [36,37] and direct differentiation of resident cells to cardiomyoyctes [38] may offer additional and exciting avenues for autologous cell therapy in the future.

As discussed by Authors [41,42], an ES-derived teratoma is not in essence malignant, but its natural propensity to grow makes it potentially dangerous when implanted into an individual and, as such, a crippling obstacle on the path to ES cell therapeutics.

[44] showed that ES cells implanted allogenically into a non-human primate fetus in utero formed a teratoma when developing in a natural cavity, but conversely integrated normally in tissues when implanted within various organs.

Other safeguards proposed to purify cardiomyocytes such as flow cytometry cell sorting using cardiomyocyte-specific fluorescent dye [45] or cardiac plasma membrane surface marker [46] and other strategies reviewed elsewhere [47] would further enhance the safety profile of these exogenously derived cardiomyocytes.

Detection of genetic cardiac diseases by Ca2+ transient profiles using machine learning methods

The iPSC modelling of human cardiac disorders enables the study of disease pathophysiology and the development of therapies, but it can also, as shown in this study, offer a tool for disease diagnostics.

Some computational machine learning methods, particularly random forests and the least square support vector machine with an RBF kernel, including the computation of Ca2+ transient peak variable values, were shown to be a powerful tool to accurately separate the Ca2+ transient signals of the three diseases – including LQT1, HCM, and CPVT – from each other and from control WT iPSC-CMs with high classification accuracies (79–88%).

This strongly indicates the possibility of discriminating between genetic cardiac diseases using Ca2+ transient profiles recorded from iPSC-CMs with signal analysis and machine learning classification methods.

By combining the normal and abnormal signals for each disease class and showing the high classification accuracy, it was demonstrated as a proof of principle that, in the future, prior Ca2+ signal analysis will not be required in diagnostic practice, but all cells can be pooled and analyzed.

Since the differences between the average peak variable values and the value distributions computed from the three cardiac diseases and control data were considerable, this provided a good opportunity for classification.

Our results indicated that, subject to high classification accuracies, the peak data computed from the Ca2+ transient signals of iPSC-CMs enable the reliable classification of such signals into these four classes.

Several different machine learning methods have been developed since the 1960s: from nearest neighbor searching23 as one of the earliest to the “newest” support vector machines in the 1990s24 and random forest in the early 2000s25.

Although these machine learning methods were developed several years or even decades ago, they have in recent years become highly useful with the innumerable data sources that have arisen and along with global digitalization and the development of various measurement and data collecting technologies.

However, sudden cardiac death at a relatively young age is still often the only clinical observation in the family, and genetic tests employing current genetic analysis methods can only suggest potentially disease-causing variation or variations of unknown significance.

Thus, it can be stated that diseased and control iPSC-CMs differ slightly more in abnormal Ca2+ transient signals than in normal signals, which is an expected observation, since abnormal signals are thought to represent the phenotype of the diseased CMs.

Genetically encoded indicators offer photo stability, excellent signal-to-noise ratio and minimal cellular-toxicity39 but their well-known limitation is their slow response time because of the slow on and off kinetics of calcium binding40.

It can be concluded that the ideal and optimal calcium indicator is still missing but so far, the iPSC disease modeling studies using well-known chemical indicators have been able to recapitulate well clinical phenotypes of the diseased patients.

Earlier numerous studies have demonstrated that single iPSC-CMs display physiologically relevant characteristics and patient-derived iPSC-CMs recapitulate aspects of patient cardiac pathology/phenotype in vitro2,3,4,6,7,8,9,10,11,12,13,14,15,16 as well as clinically-relevant drug responsiveness9.

In this study we chose to analyze single cardiomyocytes and their Ca2+ transients in diseased and healthy state and these machine learning algorithms presented here are currently based on analyzing single cardiomyocytes.

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