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New Tech Assigns More Accurate “time of Death” to Cells

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SAN FRANCISCO, CA–December 8, 2021–It’s surprisingly hard to tell when a brain cell is dead. Neurons that appear inactive and fragmented under the microscope can persist in a kind of life-or-death limbo for days, and some suddenly begin signaling again after appearing inert. For researchers who study neurodegeneration, this lack of a precise “time of death” declaration for neurons makes it hard to pin down what factors lead to cell death and to screen drugs that might save aging cells from dying.

SAN FRANCISCO, CA–December 8, 2021–It’s surprisingly hard to tell when a brain cell is dead. Neurons that appear inactive and fragmented under the microscope can persist in a kind of life-or-death limbo for days, and some suddenly begin signaling again after appearing inert. For researchers who study neurodegeneration, this lack of a precise “time of death” declaration for neurons makes it hard to pin down what factors lead to cell death and to screen drugs that might save aging cells from dying.

Now, researchers at Gladstone Institutes have developed a new technology that lets them track thousands of cells at a time and determine the precise moment of death for any cell in the group. The team showed, in a paper published in the journal Nature Communications, that the approach works in rodent and human cells as well as within live zebrafish, and can be used to follow the cells over a period of weeks to months.

“Getting a precise time of death is very important for unraveling cause and effect in neurodegenerative diseases,” says Steve Finkbeiner, MD, PhD, director of the Center for Systems and Therapeutics at Gladstone and senior author of both new studies. “It lets us figure out which factors are directly causing cell death, which are incidental, and which might be coping mechanisms that delay death.”

In a companion paper published in the journal Science Advances, the researchers combined the cell sensor technology with a machine learning approach, teaching a computer how to distinguish live and dead cells 100 times faster and more accurately than a human.

“It took college students months to analyze these kind of data by hand, and our new system is nearly instantaneous–it actually runs faster than we can acquire new images on the microscope,” says Jeremy Linsley, PhD, a scientific program leader in Finkbeiner’s lab and the first author of both new papers.

Teaching an Old Sensor New Tricks

When cells die–whatever the cause or mechanism–they eventually become fragmented and their membranes degenerate. But this degradation process takes time, making it difficult for scientists to distinguish between cells that have long since stopped functioning, those that are sick and dying, and those that are healthy.

Researchers typically use fluorescent tags or dyes to follow diseased cells with a microscope over time and try to diagnose where they are within this degradation process. Many indicator dyes, stains, and labels have been developed to distinguish the already dead cells from those that are still alive, but they often only work over short periods of time before fading and can also be toxic to the cells when they are applied.

“We really wanted an indicator that lasts for a cell’s whole lifetime–not just a few hours–and then gives a clear signal only after the specific moment the cell dies,” says Linsley.

Linsley, Finkbeiner, and their colleagues co-opted calcium sensors, originally designed to track levels of calcium inside a cell. As a cell dies and its membranes become leaky, one side effect is that calcium rushes into the cell’s watery cytosol, which normally has relatively low levels of calcium.

So, Linsley engineered the calcium sensors to reside in the cytosol, where they would fluoresce only when calcium levels increased to a level that indicates cell death. The new sensors, known as genetically encoded death indicator (GEDI, pronounced like Jedi in Star Wars), could be inserted into any type of cell and signal that the cell is alive or dead over the cell’s entire lifetime.

To test the utility of the redesigned sensors, the group placed large groups of neurons–each containing GEDI–under the microscope. After visualizing more than a million cells, in some cases prone to neurodegeneration and in others exposed to toxic compounds, the researchers found that the GEDI sensor was far more accurate than other cell death indicators: there wasn’t a single case where the sensor was activated and a cell remained alive. Moreover, in addition to that accuracy, GEDI also seemed to detect cell death at an earlier stage than previous methods–close to the “point of no return” for cell death.

“This allows you to separate live and dead cells in a way that’s never been possible before,” says Linsley.

Superhuman Death Detection

Linsley mentioned GEDI to his brother–Drew Linsley, PhD, an assistant professor at Brown University who specializes in applying artificial intelligence to large-scale biological data. His brother suggested that the researchers use the sensor, coupled with a machine learning approach, to teach a computer system to recognize live and dead brain cells based only on the form of the cell.

The team coupled results from the new sensor with standard fluorescence data on the same neurons, and they taught a computer model, called BO-CNN, to recognize the typical fluorescence patterns associated with what dying cells look like. The model, the Linsley brothers showed, was 96 percent accurate and better than what human observers can do, and was more than 100 times faster than previous methods of differentiating live and dead cells.

“For some cell types, it’s extremely difficult for a person to pick up on whether a cell is alive or dead–but our computer model, by learning from GEDI, was able to differentiate them based on parts of the images we had not previously known were helpful in distinguishing live and dead cells,” says Jeremy Linsley.

Both GEDI and BO-CNN will now allow the researchers to carry out new, high-throughput studies to discover when and where brain cells die–a very important endpoint for some of the most important diseases. They can also screen drugs for their ability to delay or avoid cell death in neurodegenerative diseases. Or, in the case of cancer, they can search for drugs that hasten the death of diseased cells.

“These technologies are game changers in our ability to understand where, when, and why death occurs in cells,” says Finkbeiner. “For the first time, we can truly harness the speed and scale provided by advances in robot-assisted microscopy to more accurately detect cell death, and do so well in advance of the moment of death. We hope this can lead to more specific therapeutics for many neurodegenerative diseases that have been so far uncurable.”

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About the Studies

The paper “Genetically encoded cell-death indicators (GEDI) to detect an early irreversible commitment to neurodegeneration” was published by the journal Nature Communications on September 6, 2021. Other authors are Kevan Shah, Nicholas Castello, Michelle Chan, Dominic Haddad, Jay Mancini, Viral Oza, Shijie Wang, and Ashkan Javaherian of Gladstone; and David Kokel of UC San Francisco. The work at Gladstone was supported by the National Institutes of Health (U54 NS191046, R37 NS101996, RF1 AG058476, RF1 AG056151, RF1 AG058447, P01 AG054407, U01 MH115747), the National Library of Medicine (R01 LM013617), the Koret Foundation, the Taube/Koret Center for Neurodegenerative Research, and the National Center for Research Resources (RR18928).

The paper “Superhuman cell death detection with biomarker-optimized neural networks” was published by the journal Science Advances on December 8, 2021. Other authors are Josh Lamstein, Gennadi Ryan, Kevan Shah, Nicholas Castello, Viral Oza, Jaslin Kaira, Shijie Wang, Zachary Tokuno, and Ashkan Javaherian of Gladstone; and Drew Linsley and Thomas Serre of Brown University. The work at Gladstone was supported by the National Institutes of Health (U54 NS191046, R37 NS101996, RF1 AG058476, RF1 AG056151, RF1 AG058447, P01 AG054407, U01 MH115747), the National Library of Medicine (R01 LM013617), the Koret Foundation, the Taube/Koret Center for Neurodegenerative Research, the National Center for Research Resources (RR18928), the Target ALS Foundation, the Amyotrophic Lateral Sclerosis Association Neuro Collaborative, Mike Frumkin, and the Department of Defense (W81XWH-13-ALSRP-TIA).

About Gladstone Institutes

To ensure our work does the greatest good, Gladstone Institutes focuses on conditions with profound medical, economic, and social impact–unsolved diseases. Gladstone is an independent, nonprofit life science research organization that uses visionary science and technology to overcome disease. It has an academic affiliation with the University of California, San Francisco.

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Original Post: bioengineer.org

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Are Researchers One Step Closer to Developing the Theory of Impulse Circuits?

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Computers play an important role in many aspects of life today. Digital computers are the most widely used, while quantum computers are well known. However, the least known computers are the so-called Stochastic Pulse Computers. Their work is based on highly parallel logical operations between trains of electrical pulses, where the pulses occur at random times, as in neurons, the nerve cells in the brains of humans and mammals.

Computers play an important role in many aspects of life today. Digital computers are the most widely used, while quantum computers are well known. However, the least known computers are the so-called Stochastic Pulse Computers. Their work is based on highly parallel logical operations between trains of electrical pulses, where the pulses occur at random times, as in neurons, the nerve cells in the brains of humans and mammals.

The main motivation for the growing interest in research on RPC computers over the past decade is the hope that they could solve faster and with less energy consumption tasks that are normally easy for living beings, but difficult for digital computers, such as instantaneous responses to stimuli, pattern recognition, robustness to errors and damage in the system, learning, and autonomy.

In a recent study published in Scientific Reports, researchers from the Croatian Centre of Excellence for Advanced Materials and Sensors, Dr Mario Stipčević of the Ruđer Bošković Institute (RBI) and Mateja Batelić, a student at the Faculty of Science at the University of Zagreb (FS), Croatia, describe new or improved versions of RPC circuits that use quantum randomness for the first time, but also go a significant step further and lay the first foundation for RPC circuit theory.

Namely, while circuits for processing information in a digital computer can be assembled from logic circuits as building blocks based on the well-known Boolean theory, a similar theory for RPC circuits does not yet exist. Therefore, the synthesis of circuits for an RPC is limited to trial and error through experimentation or simulation.

‘’The central part of our paper is the formulation and proof of the so-called entropy budget theorem, which can be used to easily verify whether a given mathematical (or logical) operation can be performed or “calculated” by any physical circuit, and if so, how much excess entropy must be available to a circuit in order to perform the given operation.

In this paper, we demonstrate the theorem using several examples of mathematical operations. Perhaps the most interesting proof is the existence of a deterministic half-sum circuit (a + b) / 2. However, this circuit is not yet known, and finding it is a challenge for further research,” says Mario Stipčević, head of the Laboratory of Photonics and Quantum Optics at the Ruđer Bošković Institute.

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PSMA PET Validates EAU Classification System to Determine Risk of Prostate Cancer Recurrence

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Reston, VA (January 20, 2022)—New research has confirmed the accuracy of the novel European Association of Urology (EAU) risk classification system that groups prostate cancer patients based on their risk of recurrence. Prostate-specific membrane antigen (PSMA) PET imaging of men with prostate cancer validated the EAU groupings and provided insights that could further refine risk assessment for patients. This study was published in the January issue of The Journal of Nuclear Medicine.

Credit: Justin Ferdinandus, Wolfgang P. Fendler, Andrea Farolfi, et al.

Reston, VA (January 20, 2022)—New research has confirmed the accuracy of the novel European Association of Urology (EAU) risk classification system that groups prostate cancer patients based on their risk of recurrence. Prostate-specific membrane antigen (PSMA) PET imaging of men with prostate cancer validated the EAU groupings and provided insights that could further refine risk assessment for patients. This study was published in the January issue of The Journal of Nuclear Medicine.

The diagnostic workup of prostate cancer has changed rapidly over the past few years. Recently, the EAU introduced a clinical system separating patients with rising PSA values after first-line therapy (prostate surgery or radiation) into groups of those with high risk and those with low risk for development of metastases. Shortly after this, the U.S. Food and Drug Administration approved 68Ga-PSMA-11 as the first PET drug to target the PSMA for men with prostate cancer.

“Given the growing availability of PSMA-directed PET imaging, our study sought to assess disease in patients based on the EAU classifications while using PSMA PET to identify subgroups of patients, such as those with undetectable, locoregional or distant metastatic disease,” said Justin Ferdinandus, MD, nuclear medicine physician at University Hospital in Essen, Germany.

The multicenter, international study analyzed PSMA PET scans of nearly 2,000 patients with prostate cancer and rising PSA levels. Patterns of disease spread on PSMA PET imaging were used to classify prostate cancer patients into both low- and high-risk groups. High-risk groups were found to have higher rates of metastatic disease on PSMA PET compared to low-risk groups. However, PSMA PET also found metastatic disease in low-risk and no disease in high-risk patients.

“Our study underscores the utility of the EAU risk groups to determine risk of metastasis in biochemically recurrent prostate cancer. But not every high-risk patient has metastases and not every low-risk patient has locoregional or no disease,” said Wolfgang Fendler, MD, nuclear medicine physician at University Hospital in Essen.

He continued, “The ultimate aim of imaging is to provide the right treatment for each patient. As evidenced in this research, the accuracy of PSMA PET is essential to improve stratification and potentially outcomes both in low-risk and high-risk settings.” 

The authors of “PSMA PET validates higher rates of metastatic disease for European Association of Urology Biochemical Recurrence Risk Groups: an international multicenter study” include Justin Ferdinandus, Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany; Wolfgang P. Fendler and Ken Hermann, Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany, and Ahmanson Translational Imaging Division, Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California; Andrea Farolfi, Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany, and Division of Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy; Samuel Washington, Department of Urology, University of California San Francisco, San Francisco, California, and Department of Epidemiology and Statistics, University of California San Francisco, San Francisco, California; Osama Mohamad, Department of Radiation Oncology, University of California San Francisco, San Francisco, California; Miguel H. Pampaloni and Thomas A. Hope, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California; Peter J.H. Scott, Melissa Rodnick, Benjamin L. Viglianti and Morand Piert, Department of Radiology, University of Michigan, Ann Arbor, Michigan; Matthias Eiber, Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany; and Johannes Czernin, Wesley R. Armstrong and Jeremie Calais, Ahmanson Translational Imaging Division, Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California.

Visit JNM’s new website for the latest research, and follow our new Twitter and Facebook pages @JournalofNucMed.

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Please visit the SNMMI Media Center for more information about molecular imaging and precision imaging. To schedule an interview with the researchers, please contact Rebecca Maxey at (703) 652-6772 or [email protected].
 

About JNM and the Society of Nuclear Medicine and Molecular Imaging
The Journal of Nuclear Medicine (JNM) is the world’s leading nuclear medicine, molecular imaging and theranostics journal, accessed more than 13 million times each year by practitioners around the globe, providing them with the information they need to advance this rapidly expanding field. Current and past issues of The Journal of Nuclear Medicine can be found online at http://jnm.snmjournals.org.

JNM is published by the Society of Nuclear Medicine and Molecular Imaging (SNMMI), an international scientific and medical organization dedicated to advancing nuclear medicine and molecular imaging—precision medicine that allows diagnosis and treatment to be tailored to individual patients in order to achieve the best possible outcomes. For more information, visit www.snmmi.org.

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UCLA Researchers Develop Novel Microscopic Picoshell Particles

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Production of high-energy fats by microalgae may provide a sustainable, renewable energy source that can help tackle climate change. However, microalgae engineered to produce lipids rapidly usually grow slowly themselves, making it difficult to increase overall yields. 

Production of high-energy fats by microalgae may provide a sustainable, renewable energy source that can help tackle climate change. However, microalgae engineered to produce lipids rapidly usually grow slowly themselves, making it difficult to increase overall yields. 

UCLA bioengineers have created a new type of petri dish in the form of microscopic, permeable particles that can dramatically speed up research and development (R&D) timelines of biological products, such as fatty acids for biofuels. Dubbed PicoShells, the picoliter (trillionth of a liter), porous, hydrogel particles can enable more than one million individual cells to be compartmentalized, cultured in production-relevant environments, and selected based on growth and biomass accumulation traits using standard cell-processing equipment. 

Proceedings of the National Academy of Sciences recently published a study detailing how PicoShells work and their potential applications.

PicoShells consist of a hollow inner cavity where cells are encapsulated and a porous outer shell that allows for continuous solution exchange with the external environment so that nutrients, cell-communication molecules and cytotoxic cellular byproducts can transport freely in and out of the inner cavity. The shell also keeps the small groups of growing cells penned in, allowing researchers to study and compare their behaviors — what they do, how fast they grow, what they produce — to those of other groups inside various PicoShells. 

This new class of lab tool allows researchers to grow living, single-cell microorganisms — including algae, fungi and bacteria — under the same industrial-production conditions, such as in a bioreactor filled with wastewater or an outdoor cultivation pond. 

“PicoShells are like very tiny mesh balloons. The growing cells inside them are effectively fenced in but not sealed off,” said study leader Dino Di Carlo, UCLA’s Armond and Elena Hairapetian Professor in Engineering and Medicine at the UCLA Samueli School of Engineering. “With this new tool, we can now study the individual behaviors of millions of living cells in the relevant environment. This could shorten R&D-to-commercial production timelines for bioproducts from a few years to a few months. PicoShells could also be a valuable tool for fundamental biology studies.” 

PicoShells’ permeability can bring the lab to the industrial environment, allowing testing at a sectioned-off area of a working facility. Growth can occur more quickly and cell strains that perform well can be identified and selected for further screening. 

According to the researchers, another advantage of this new tool is that the analysis of millions of PicoShells is automated since they are also compatible with standard lab equipment used for high-volume cell processing.

Massive groups of cells, up to 10 million in one day, can be sorted and organized by certain characteristics. Continuous analysis could result in ideal sets of cells — ones that are already performing well in the environment with suitable temperature, nutrient composition and other properties that could be used in mass production — in just a few days rather than the several months it would take using current technologies.

The shells can be engineered to burst when the cells inside have divided and grown beyond their peak volume. Those free cells are still viable and can be recaptured for continued research or further selection. The researchers can also create shells with chemical groups that break down when exposed to biocompatible reagent, enabling a multifaceted approach to release selected cells.

“If we want to zero in on algae that are the best at producing biofuels, we can use PicoShells to organize, grow and process millions of single algal cells,” said lead author Mark van Zee, a bioengineering graduate student at UCLA Samueli. “And we can do that in machines that sort them using fluorescent tags that light up to signify fuel levels.”

Currently, cultivating and comparing such microorganisms are done mostly using traditional lab tools, such as microwell plates — cartons that hold several dozen small test tube-like volumes. However, these methods are slow and it is difficult to quantify their effectiveness because it can take weeks or months to grow large colonies for study. Other approaches, such as water-in-oil droplet emulsions, can be used to analyze cells in smaller volumes, but surrounding oils prevent the free exchange of medium into the water drops. Even cells or microorganisms that perform well in lab conditions may not do as well once they are placed in industrial environments, such as bioreactors or outdoor cultivation farms. As a result, cell strains that are developed in the lab often do not exhibit the same beneficial characteristic behavior when transferred to industrial production. 

Microwell plates also are limited in the number of experiments that can be performed, resulting in a great deal of trial and error in finding cell strains that work sufficiently well for mass production.

The researchers demonstrated the new tool by growing colonies of algae and yeast, comparing their growth and viability against other colonies grown in water-in-oil emulsions. For the algae, the team found that PicoShell colonies accumulated biomass rapidly while algae did not grow at all in water-in-oil emulsions. Similar results were found in their yeast experiments. By selecting the top growing algae in PicoShells, the researchers could increase the production of chlorophyll biomass by 8% after only a single cycle. 

The authors said PicoShells could offer a faster alternative to develop new algae and yeast strains, leading to improved biofuels, plastics, carbon-capture materials and even food products and alcoholic beverages. Further refinements to the technology, such as coating the shells with antibodies, could also lead to developing new types of protein-based medicines.

Di Carlo, van Zee and study co-author Joseph de Rutte Ph.D. ’20, a former member of Di Carlo’s research group, are named inventors on a patent application filed by the UCLA Technology Development Group.

The other UCLA authors on the paper are Rose Rumyan, Cayden Williamson, Trevor Burnes, Andrew Sonico Eugenio, Sara Badih, Dong-Hyun Lee and Maani Archang. Randor Radakovits from Synthetic Genomics of San Diego is also an author.

The study was supported by the Presidential Early Career Award for Scientists and Engineers and a planning award from the California NanoSystems Institute (CNSI) at UCLA.

Di Carlo holds faculty appointments in bioengineering, and mechanical and aerospace engineering at UCLA Samueli. He is a member of CNSI and the Jonsson Comprehensive Cancer Center at UCLA.

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