Tag: Medicine

Two complementary studies from the Faculty of Medical and Health Sciences at Tel Aviv University, in collaboration with the European Institute of Oncology in Milan, have extensively examined the characteristics of cells with an abnormal number of chromosomes – known as aneuploid cells – and raised findings that may advance new cancer treatments.

Targeting Aneuploid Cancer Cells

According to the researchers: “a significant portion of cancer cells are aneuploid, and this trait distinguishes them from healthy cells. Our work focuses on the vulnerabilities of aneuploid cells, with the aim of promoting new strategies for eliminating cancerous tumors”.

The researchers: “In our studies, we found that aneuploidy increases the sensitivity of cancer cells to certain types of anticancer drugs”.

The studies were led by Prof. Uri Ben-David and doctoral student Johanna Zerbib from the Department of Human Molecular Genetics and Biochemistry at the Faculty of Medical and Health Sciences at Tel Aviv University, in collaboration with Professor Stefano Santaguida and doctoral student Marica Rosaria Ippolito from the University of Milan in Italy, along with researchers from both laboratories. Additional contributors included research teams in Israel, Italy, the USA, and Germany. Two articles based on the research were published in the prestigious journals Cancer Discovery and Nature Communications.

Prof. Ben-David explains: “In the nucleus of a healthy human cell, there are 23 pairs of chromosomes – half from the father and half from the mother, totaling 46. One of the characteristics of cancer cells, which distinguishes them from healthy cells, is an abnormal number of chromosomes, resulting from improper cell division – a phenomenon known as aneuploidy. We believe that if we can identify specific vulnerabilities of aneuploid cells, we can promote new cancer treatments that target these weaknesses and do not harm healthy cells. About three years ago, we published a comprehensive study in the journal Nature, in which we classified approximately 2,000 malignant cells from various cancer types according to their level of aneuploidy, and examined how they respond to various existing treatments. In that study, we found new vulnerabilities in aneuploid cells. However, the study had a limitation: because the cells came from different types of cancer, it was difficult to isolate the impact of aneuploidy itself from the effect of other genetic differences between the tumors”.

Consequently, the researchers chose to conduct a new study using human cell cultures that are all genetically identical (i.e., derived from the same individual). The researchers added a substance to the cultures that disrupts the separation of chromosomes, causing some of them to become aneuploid. Since the cells were genetically identical, the only difference between them after the procedure was the level of aneuploidy – i.e., the number of chromosomes. To thoroughly examine the effects of aneuploidy, the cells underwent various characterization processes: DNA and RNA sequencing, measuring the levels of all the proteins in the cell, assessing the response to 6,000 different drugs, as well as a process known as CRISPR screening – systematically impairing each gene in the genome to identify genes that are essential in the cells. The researchers noted: “In this way, an extensive and unique database of the characteristics of aneuploid cells was established, which can serve as a foundation for future studies, as well as for developing biological markers that predict cancer patients’ responses to specific drugs and treatments”.

How to Exploit Cell Vulnerabilities for Cancer Therapy?

As part of the comprehensive survey, a mechanism called MAPK (mitogen-activated protein kinase) was observed, which is especially crucial for repairing DNA damage in aneuploid cells. The study also showed that this mechanism is relevant for various types of aneuploid cells—among them cancer cells in cultures and in human tumors. Prof. Ben-David: “We found that aneuploid cancer cells increase the activity of DNA repair mechanisms due to the large amount of DNA damage present; and we discovered a mechanism that could allow us to exploit this characteristic to target these cancer cells”.

To test their hypothesis, the researchers disrupted the MAPK pathway in the cells and then examined their sensitivity to chemotherapy. The findings were promising: aneuploid cells in which this mechanism was disrupted were much more sensitive to chemotherapy (which causes DNA damage) compared to cells with a normal number of chromosomes. The researchers then sought to determine whether there is a correlation between this pathway and the clinical response of cancer patients to chemotherapy treatments. For this purpose, they relied on data from clinical treatments and experiments where human tumors were implanted in mice, and the results were clear: the higher the activity of the pathway in the aneuploid tumors, the greater their resistance to chemotherapy.

The comprehensive characterization of aneuploid cells also revealed another significant finding: these cells, which contain more chromosomes than normal cells, also necessarily include a larger amount of DNA, leading to excess production of RNA and proteins. The cell, seeking to compensate for this overproduction, attempts to silence and degrade excess RNA and proteins.

Paving the Way for Future Treatments

Johanna Zerbib noted: “Here we found another vulnerability of aneuploid cells, based on our hypothesis that these cells are more sensitive to existing drugs that inhibit protein degradation. To validate this hypothesis, we exposed cell cultures to such drugs and analyzed clinical data from patients treated with a drug that inhibits protein degradation in the cells. The findings supported the hypothesis – that aneuploidy increases the sensitivity of cancer cells to these drugs”.

Prof. Ben-David concluded: “In our research, we identified two significant vulnerabilities characterizing aneuploid cells – cells with chromosomal changes, commonly found in cancer cells. The first is a mechanism essential for repairing DNA damage, where impairment significantly increases the sensitivity of aneuploid cells to chemotherapy; the second is the increased degradation of excess RNA and proteins, which can be targeted, among other things, with inhibitors that are already in clinical use. We also created an extensive database of characteristics of aneuploid cells that can serve to predict cancer patients’ responses to various drugs and treatments. We believe that our research findings will benefit many researchers, oncologists, and patients in the years to come”.

Researchers at Tel Aviv University cooperated with three major Israeli medical centers to develop a new method for detecting protein aggregation in cells – a hallmark of Parkinson’s disease. The technology can enable diagnosis up to 20 years before the first motor symptoms appear, facilitating treatment or even prevention of the severe disease which is currently incurable. The novel approach is based on super-resolution microscopy combined with computational analysis, allowing for precise mapping of the aggregates’ molecules and structures. The researchers: “Our method can be used to identify early signs and enable preventive treatment in young people at risk for developing Parkinson’s later on in their lives. In the future, the technology may also be adapted for early diagnosis of other neurodegenerative diseases, including Alzheimer’s”.

The study was piloted by researchers from the School of Neurobiology, Biochemistry & Biophysics at the Wise Faculty of Life Sciences, the Sagol School of Neuroscience and the Faculty of Medical and Health Sciences at Tel Aviv University, led by Prof. Uri Ashery and PhD candidate Ofir Sade. Other participants included: Prof. Anat Mirelman, Prof. Avner Thaler, Prof. Nir Giladi, Prof. Roy Alcalay, Prof. Sharon Hassin, Prof. Nirit Lev, Dr. Irit Gottfried, Dr. Dana Bar-On, Dr. Meir Kestenbaum, Dr. Saar Anis, Dr. Shimon Shahar, Daphna Fischel, Dr. Noa Barak-Broner, Shir Halevi, and Dr. Aviv Gour – all from Tel Aviv University, with some also affiliated with the Tel Aviv Sourasky (Ichilov), Sheba, or Meir Medical Centers. Researchers from Germany and the USA also contributed to the study. The paper was published in Frontiers in Molecular Neuroscience.

Spotting Parkinson’s Before Symptoms Appear

Prof. Ashery: “Parkinson’s disease is the second most prevalent neurodegenerative disease in the world after Alzheimer’s – with about 8.5 million people with Parkinson’s living worldwide today, and 1,200 new sufferers diagnosed annually in Israel. The debilitating disease is characterized by the destruction of dopaminergic (dopamine-producing) neurons in the brain’s Substantia Nigra area. Today, diagnosis of Parkinson’s disease is based mainly on clinical symptoms such as tremors or gait dysfunctions, alongside relevant questionnaires. However, these symptoms usually appear at a relatively advanced stage of the disease, when over 50% and up to 80% of the dopaminergic neurons in the Substantia Nigra are already dead. Consequently, available treatments are quite limited in their effect and usually address only motor problems. In this study, we began to develop a research tool to enable diagnosis of Parkinson’s at a much earlier stage, when it is still treatable, and deterioration can be prevented”.

Ofir Sade: “One known feature of Parkinson’s is cell death resulting from aggregates of the alpha-synuclein protein. The protein begins to aggregate about 15 years before symptoms appear, and cells begin to die 5-10 years before diagnosis is possible with the means available today. This means that we have an extensive time window of up to 20 years for diagnosis and prevention before symptoms appear. If we can identify the process at an early stage, in people who are 30, 40, or 50 years old, we may be able to prevent further protein aggregation and cell death”. Past studies have shown that alpha-synuclein aggregates form in other parts of the body as well, such as the skin and digestive system. In the current work, the researchers examined skin biopsies from 7 people with and 7 without Parkinson’s disease, received from the Sheba, Ichilov, and Meir Medical Centers. 

She continues: “We examined the samples under a unique microscope, applying an innovative technique called super-resolution imaging, combined with advanced computational analysis – enabling us to map the aggregates and distribution of alpha-synuclein molecules.  As expected, we found more protein aggregates in people with Parkinson’s compared to people without the disease. We also identified damage to nerve cells in the skin, in areas with a large concentration of the pathological protein”.

Parkinson’s Detection Boosted by AI

With proof of concept obtained through the study, the researchers now plan to expand their work, supported by the Michael J. Fox Foundation for Parkinson’s Research. In the next phase, they will increase the number of samples to 90 – 45 from healthy subjects and 45 from people without Parkinson’s disease – to identify differences between the two groups. Ofir Sade: “We intend to pinpoint the exact juncture at which a normal quantity of proteins turns into a pathological aggregate. In addition, we will collaborate with Prof. Lior Wolf of TAU’s School of Computer Science to develop a machine learning algorithm that will identify correlations between the results of motor and cognitive tests and our findings under the microscope. Using this algorithm, we will be able to predict the future development and severity of various pathologies”.

Prof. Ashery: “In this study, we identified differences between tissues taken from people with and without Parkinson’s disease, using super-resolution microscopy and computational analysis. In future studies, we will increase the number of samples and develop a machine-learning algorithm to spot relatively young individuals at risk for Parkinson’s. Our main target population is relatives of Parkinson’s patients who carry mutations that increase the risk for the disease. Specifically, we emphasize two mutations known to be widespread among Ashkenazi Jews. A clinical trial is already underway to test a drug expected to hinder the formation of the aggregates that cause Parkinson’s disease. We hope that in the coming years, it will be possible to offer preventive treatments while tracking the effects of medications under the microscope. It is important to note that the method we’ve developed can also be suitable for early diagnosis of other neurodegenerative diseases associated with protein aggregates in neurons, including Alzheimer’s”.

A new technological development allows for the first time to monitor changes in pupil size and gaze direction behind closed eyes using touchless infrared imaging. In the future, tracking changes in pupil size will help identify a state of wakefulness in sleep, anesthesia, and intensive care and help track the depth of sedation, detect seizures and nightmares, and recognize pain or responsiveness that may occur after trauma and in intensive care departments. The investigators anticipate that this technology has strong potential to become an important tool in clinical care.

The breakthrough was achieved by a team of investigators from Tel Aviv University led by doctoral student Omer Ben Barak-Dror, under the joint supervision of Prof. Yuval Nir from the Department of Physiology and Pharmacology, Faculty of Medical and Health Sciences, Sagol School of Neuroscience, and the Department of Biomedical Engineering; and Prof. Israel Gannot from the Department of Biomedical Engineering. Other team members include Dr. Michal Tepper, Dr. Barak Hadad, Dr. Hani Barhum, and David Haggiag. The research was published in the journal Communications Medicine.

An Eye-Opening Discovery

Prof. Nir explains: “It is often said that the eyes are the windows to the soul”. Indeed, pupil size changes constantly, dilating or contracting to regulate the amount of incoming light, while providing valuable clinical information. We all know that our pupils get smaller in bright light and larger in darkness, but this is only one reason why pupils change size. They also dilate when we’re stimulated, for example when we react to a sudden event or when we are in pain. In such cases, our autonomic nervous system serves as an alarm and prepares us to take action. Tracking pupil size and eye movements can be critical in many clinical situations. However, until now this has been limited to open-eye scenarios. No method allowed anyone to do this when their eyes were closed.

The new research describes innovative technology that combines short-wave infrared (SWIR) imaging with deep learning algorithms to perform touchless pupillometry and eye tracking behind closed eyelids. “To establish and validate our technology, we focused on the pupillary light reflex (PLR) when the pupil constricts in response to a sudden flash of light, and then dilates back to normal. This is a basic reflex that occurs symmetrically across the two eyes in healthy people. We performed experiments testing our technology on the closed eye while comparing the results to the open-eye data,” said Omer Ben Barak-Dror, lead author of the study at Tel Aviv University.

Eye Spy: Monitoring Pupils While You Sleep

Profs. Nir and Gannot add: “Our method can successfully track the precise dynamics of the pupillary light reflex in closed-eye conditions, revealing the changes in pupil size following each light flash in individual subjects, and also accurately estimating where the eye gaze is directed to, within a few degrees accuracy. The system operates at wavelengths where light has its maximum depth of penetration in biological tissue, and by analyzing the data using deep learning algorithms, we can go beyond what is typically possible with standard methods of near-infrared imaging”. Dr. Tepper adds that the information collected using continuous touchless monitoring is a critical element of the patient’s electronic medical record (EMR) and helps with decisions concerning optimal medical treatment.

The investigators concluded: “Our technology, backed by a patent application, paves the way for developing devices with wide-ranging clinical and commercial applications in domains ranging from sleep medicine, through monitoring sedation level and intraoperative awareness in anesthesia, to assessing pain and reactivity in unresponsive patients or neurology intensive care and trauma wards”.

The study was supported by grants from the Zimin Foundation and the breakthrough technology program of the Israeli Ministry of Science and Technology.

Researchers at TAU’s Faculty of Medical & Health Sciences invited the international community of machine learning researchers to participate in a contest devised to advance their study and assist neurologists: developing a machine learning model to support a wearable sensor for continuous, automated monitoring and quantification of FOG (freezing of gate) episodes in people with Parkinson’s disease. Close to 25,000 solutions were submitted, and the best algorithms were incorporated into the novel technology.

The study was led by Prof. Jeff Hausdorff from the Department of Physical Therapy at the Faculty of Medical & Health Sciences and the Sagol School of Neuroscience at Tel Aviv University, and the Center for the Study of Movement, Cognition, and Mobility at the Tel Aviv Medical Center, together with Amit Salomon and Eran Gazit from the Tel Aviv Medical Center. Other investigators included researchers from Belgium, France, and Harvard University. The paper was published in Nature Communications and featured in the Editors’ Highlights.

Prof. Hausdorff, an expert in the fields of gait, aging, and Parkinson’s disease, explains: “FOG is a debilitating and so far unexplained phenomenon, affecting 38-65% of Parkinson’s sufferers. A FOG episode can last from a few seconds to more than a minute, during which the patient’s feet are suddenly ‘glued’ to the floor, and the person cannot begin or continue walking. FOG can seriously impair the mobility, independence, and quality of life of people with Parkinson’s disease, causing great frustration, and frequently leading to falls and injuries”.

Amit Salomon adds: “Today the diagnosis and tracking of FOG are usually based on self-report questionnaires and visual observation by clinicians, as well as frame-by-frame analysis of videos of patients in motion. This last method, currently the prevailing gold standard, is reliable and accurate. Still, it has some serious drawbacks: it is time-consuming, requires the involvement of at least two experts, and is impracticable for long-term monitoring in the home and daily living environment. Researchers worldwide are trying to use wearable sensors to track and quantify patients’ daily functioning. So far, however, successful trials have all relied on a very small number of subjects”.

TAU’s AI Challenge Advances FOG Tracking

In the current study, the researchers collected data from several existing studies, relating to over 100 patients and about 5,000 FOG episodes. All data were uploaded to the Kaggle platform, a Google company that conducts international machine learning competitions. Members of the worldwide machine learning community were invited to develop models that would be incorporated into wearable sensors to quantify various FOG parameters (e.g. duration, frequency, and severity of episodes). A prize of $100,000, funded by Kaggle and the Michael J. Fox Foundation for Parkinson’s Research, was offered for the best solutions. 1,379 groups from 83 countries rose to the challenge, ultimately submitting a total of 24,862 solutions. The results of the best models were very close to those obtained through the video analysis method, and significantly better than previous experiments relying on a single wearable sensor.

Moreover, the models led to a discovery: an interesting relationship between FOG frequency and the time of day. Co-author Eran Gazit notes: “We observed, for the first time, a recurring daily pattern, with peaks of FOG episodes at certain hours of the day, that may be associated with clinical phenomena such as fatigue, or effects of medications. These findings are significant for both clinical treatment and continued research about FOG”.

Prof. Hausdorff: “Wearable sensors supported by machine learning models can continuously monitor and quantify FOG episodes, as well as the patient’s general functioning in daily life. This gives the clinician an accurate picture of the patient’s condition at all times: has the illness improved or deteriorated? Does it respond to prescribed drugs? The informed clinician can respond promptly, while data collected through this technology can support the development of new treatments. In addition, our study demonstrates the power of machine learning contests in advancing medical research. The contest we initiated brought together capable, dynamic teams all over the world, who enjoyed a friendly atmosphere of learning and competition for a good cause. Rapid improvement was gained in the effective and precise quantification of FOG data. Moreover, the study laid the foundations for the next stage: long-term 24/7 FOG monitoring in the patient’s home and real-world environment”.