New imaging tool to track cellular events that may initiate obesity-related conditions

A collaborative team of researchers at Utah State University and the University of Central Florida developed a tool to track cellular events that may lead to obesity-related conditions in people.

The research findings were published Feb. 3 in the Proceedings of the National Academy of Sciences.

The team, led by Anhong Zhou, a professor in USU's Department of Biological Engineering, developed a sensing optical imaging nanoprobe that uses scattered light to provide a structural fingerprint for molecules. The probes can be used to more easily identify and illustrate cell surface receptors that can either prompt or stop cellular responses to certain external stimuli. The probes make it possible to monitor multiple surface receptors on an individual cell and provide researchers an unprecedented view of cellular surface activity. Zhou and his team, including the biological engineering PhD student Wei Zhang, applied these novel nanoprobes to successfully detect the cell receptors that recognize fatty acids at the single living cell level.

The technique represents a major step in developing improved understanding of certain cellular events and could have widespread impact on the study of fat intake and the development of obesity. The new method could also be used as a simple screening technique for testing external stimuli that trigger the surface cell receptors and lead to the linking of fatty acids. This would make for an efficient test to ensure that new drugs accurately prompt the correct cellular activities that lead to obesity and other obesity-related conditions. Zhou and his team's work is increasingly relevant as the prevalence of obesity impacts public health in the United States.

 

Zhou says the research represents an exciting collaboration between researchers and aligns well with his belief that biological engineering is an important frontier in the scientific community.

This is an excellent example that fulfills our long-term goal of applying engineering tools to solve biology-driven problems. In the past several years, we have been thrilled to develop new cell-based assay technologies that potentially benefit human health problems like obesity. We are currently extending this technology for developing a new method for early cancer diagnosis."     Anhong Zhou, professor in USU's Department of Biological Engineering

This work was primarily supported by the National Science Foundation.

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AI can spot depression via sound of your voice

India -- the sixth most depressed country in the world -- has an estimated 56 million people suffering from depression and 38 million from anxiety disorders, according to a recent report by the World Health Organisation (WHO).

To help identify depression early, scientists have now enhanced a technology that uses Artificial Intelligence (AI) to sift through sound of your voice to gauge whether you are depressed or not.

Computing science researchers  have improved technology for identifying depression through vocal cues.

The study builds on past research that suggests that the timbre of our voice contains information about our mood.

Using standard benchmark data sets, the team developed a methodology that combines several Machine Learning (ML) algorithms to recognize depression more accurately using acoustic cues.

A realistic scenario is to have people use an app that will collect voice samples as they speak naturally.

"The app, running on the user's phone, will recognize and track indicators of mood, such as depression, over time. Much like you have a step counter on your phone, you could have a depression indicator based on your voice as you use the phone," he said.

Depression is ranked by WHO as the single largest contributor to global disability. It is also the major contributor to suicide deaths.

The ultimate goal, said researchers, is to develop meaningful applications from this technology.

Such a tool could prove useful to support work with care providers or to help individuals reflect on their own moods over time.

"This work, developing more accurate detection in standard benchmark data sets, is the first step," he added while presenting the paper recently.

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Cancer: new DNA sequencing technique analyses tumours cell by cell to fight disease

A new DNA sequencing technique lets scientists track genetic errors in individual cancer cells. For the first time, they can reconstruct a tumour’s life history and understand how an error in a cell’s DNA led to the uncontrollable growth of a tumour. This new technology will help doctors understand how a particular cancer has evolved and personalise treatments for each patient, to make them more effective and successful.

We are made of billions of cells that work together to build every part of our body. Occasionally, one of these cells acquires an error in its genetic code and this error, or mutation, can sometimes make this abnormal single cell divide and grow faster than the healthy ones, causing a tumour to develop. During this process, cells can continue to evolve and accumulate many more mutations that make it more dangerous than the original one.

Previously, when researchers studied cancer, they used to take a piece of the tumour and analyse it as a whole. Without understanding the life history of each tumour, science could only give us an incomplete picture of the cancer, where the different cells are mixed and averaged, to get an idea of how dangerous the cancer was. But it didn’t tell us anything about how the tumour had evolved and what type of cells it was made of, making it hard for doctors to select the right treatment for each patient. 

This is the reason many cancer treatments don’t work, and when they do, cancer sometimes regrows within a few months or years, coming back a lot more aggressive than the previous one and much more difficult to treat.

Seeing the whole picture

As the entire tumour couldn’t be beaten as a whole, five years ago, researchers started using a different strategy: divide and conquer. They began dividing the tumours into single cells and analysing each cancer cell separately to try and understand which types of cells made up each tumour.
But even with this advance, they still only had two main tools to analyse single cancer cells. One tool allowed them to read the genetic code of a single cell at a time, identifying which cells have genetic mutations. The other tool helped them understand which genes were active in each cancerous cell, and what their role in the cells was. However, neither of these tools revealed the whole picture. Using them, you could either get the genetic errors from each cell, or the genes that are active and functional – but not both. This made it impossible to understand which genes are activated as a result of genetic errors in each cell. 

A team of researchers, developed a new single-cell sequencing technique that allowed them to see the whole picture. It lets scientists analyse the genetic errors that each cell in a tumour has accumulated while also understanding its gene activity and cell function. This will allow researchers to see in fine detail every aspect of the tumour.

In their latest study, they used this new technique, called TARGET-seq, to analyse many thousands of cells from 11 patients whose blood-making cells had become cancerous. Their analysis provided a detailed picture of the cell types that made up blood cancers. Thanks to its high resolution, they could reconstruct the complete life history of each tumour and identify the molecules that were active during the first steps of tumour development. They also found that cells that appeared healthy, as they didn’t have cancerous mutations, were behaving like malignant cells and activating abnormal genes because they were in a tumour environment. 

Scientists are now using TARGET-seq to analyse different types of aggressive leukemias for which there are no effective treatments. They are hoping to understand how to eliminate the cells that started and sustained the tumour, to be able to completely eradicate them. In the future, we hope that this technique will be used by oncologists to determine the exact mixture of cancer cells that makes up each tumour and customise the right treatment for each patient.

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