The research was led by Sahar Hajyahia and Mor Taub, students in the laboratory of Prof. Yossi Yovel of Tel Aviv University’s School of Zoology in the Wise Faculty of Life Sciences and the Sagol School of Neuroscience. The study was published in the scientific journal iScience.

Prof. Yossi Yovel.

Professor Yossi Yovel explains: “During the study, the bats crawled up a vertical maze while moving backward as we recorded their movement using an advanced tracking system. The bats used their tails like a blind walking cane, swinging them from side to side to detect obstacles and climb more safely and efficiently. In contrast, when the tail was numbed, the climbing time increased by an average of 10%, and the bats made more lateral movements instead of moving upward, apparently trying to find their way”.

The researchers also noted that the bats demonstrated a remarkable ability to distinguish between different textures using their tails. They were able to differentiate between a fine wooden grid (1 cm intervals) and a sparser grid (1.5 cm), highlighting the tail's complex ability to serve as an exceptionally sensitive tactile sensor.

Greater Mouse-Tailed Bat (Photo courtesy of Jens Rydell).

Professor Yossi Yovel concludes: “In most bats, the tail is very short and integrated into the wing membrane. In Rhinopoma, however, the tail remains long and free, and to the best of our knowledge, they are the only bats that use it to sense their immediate surroundings. This is another example of how evolution adapts animals’ senses to meet specific needs—in this case, moving backward in dark places around obstacles and other bats. Many bats crawl backward on dark walls and cannot use their frontal senses like vision and sonar to ‘see’ behind them. One can think of the tail as a sort of reverse sensor for the Rhinopoma. This discovery opens the door to further research on tail usage as a sensor in other animal species. Additionally, the findings could inspire the development of new sensory technologies inspired by nature, such as robotic navigation systems for complex environments”.

Researchers from Tel Aviv University have developed an innovative method that can help to understand better how cells behave in changing biological environments, such as those found within a cancerous tumor. The new system, called scNET, combines information on gene expression at the single-cell level with information on gene interactions, enabling the identification of important biological patterns such as responses to drug treatments. The scientific article published in the Nature Methods journal explains how scNET may improve medical research and assist in the development of treatments for diseases. The research was led by PhD student Ron Sheinin under Prof. Asaf Madi, from the Faculty of Medical and Health Sciences and Prof. Roded Sharan, head of the School of Computer Science and AI at Tel Aviv University.

From noisy data to clear insights

Today, advanced sequencing technologies allow the measurement of gene expression at the single-cell level and, for the first time, researchers can investigate the gene expression profiles of different cell populations within a biological sample and discover their effects on the functional behavior of each cell type. One fascinating example is understanding the impact of cancer treatments – not only on the cancer cells themselves but also on the pro-cancer supporting cells or anti-cancer cell populations, such as some cells of the immune system surrounding the tumor.

Despite the amazing resolution, these measurements are characterized by high levels of noise, which makes it difficult to identify precise changes in genetic programs that underlie vital cellular functions. This is where scNET comes into play.

A social network for genes

Ron Sheinin: "scNET integrates single-cell sequencing data with networks that describe possible gene interactions, much like a social network, providing a map of how different genes might influence and interact with each other. scNET enables more accurate identification of existing cell populations in the sample. Thus, it is possible to investigate the common behavior of genes under different conditions and to expose the complex mechanisms that characterize the healthy state or response to treatments".

Prof. Asaf Madi: "In this research, we focused on a population of T cells, immune cells known for their power to fight cancerous tumors. scNET revealed the effects of treatments on these T cells and how they became more active in their cytotoxic activity against the tumor, something that was not possible to discover before due to the high level of noise in the original data".

Prof. Roded Sharan: "This is an excellent example of how artificial intelligence tools can help decipher biological and medical data, allowing us to gain new and significant insights. The idea is to provide biomedical researchers with computational tools that will aid in understanding how the body's cells function, thereby identifying new ways to improve our health".

In conclusion, scNET demonstrates how combining AI with biomedical research could lead to the development of new therapeutic approaches, reveal hidden mechanisms in diseases, and propose new treatment options.

The study was led by Dr. Tomer Itkin from the Faculty of Medical and Health Sciences and the Sagol Center for Regenerative Medicine at Tel Aviv University, and the Neufeld Cardiac Research Institute at Sheba Medical Center, Tel Hashomer. The research also included contributions from leading medical institutions worldwide, including Weill Cornell Medical College and Hospital in New York, the Memorial Sloan Kettering Cancer Center (MSKCC), Mount Sinai Hospital, the University of Toronto Medical Center, and the Fred Hutchinson Cancer Research Center in Seattle.

Dr. Tomer Itkin.

Switching On Stem Cells

In the study, which is based on a comprehensive big data analysis of RNA sequencing and epigenetic DNA sequencing, the researchers identified a key protein—the Fli-1 transcription factor—that activates stem cells of the immune and blood system. These stem cells are highly active when sourced from umbilical cord blood but remain in a "dormant" and inactive state when obtained from adult bone marrow donors. Using modified mRNA technology—the same technology used to develop COVID-19 vaccines—the researchers successfully "awakened" the adult stem cells, allowing them to divide in a controlled manner without cancer risk. The activated cells were expanded on endothelial cells, which mimic the blood vessels that support stem cells in the bone marrow environment, demonstrating an enhanced ability to integrate and restore blood production under transplant conditions.

According to Dr. Itkin, "This new method significantly expands the available pool of stem cells for transplantation without relying on rare bone marrow donors. Additionally, the method can be used to treat patients whose stem cells have undergone genetic correction, such as those with thalassemia and hereditary anemia, as well as patients who have undergone multiple rounds of chemotherapy and have an insufficient number of stem cells for autologous transplantation".

The key takeaway from the study is that activating stem cells through molecular programming, rather than arbitrary cell transplantation, substantially improves the success rates of regenerative treatments. The next stage of research involves testing the method in clinical trials to bring this groundbreaking technology into widespread therapeutic use. Furthermore, the researchers plan to apply the same therapeutic approach to regenerate additional tissues, including those without existing adult stem cells, such as the heart.