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SBU News

Study authors Liwei Yang, left, and Jun Wang, in the Wang laboratory by the microscope that incorporates the single-cell cyclic multiplex in situ tagging (CycMIST) technology to analyze proteins on single cells. Photo provided by Jun Wang

A new biomedical research tool that enables scientists to measure hundreds of functional proteins in a single cell could offer new insights into cell machinery. Led by Jun Wang, Associate Professor of Biomedical Engineering at Stony Brook University, this microchip assay — called the single-cell cyclic multiplex in situ tagging (CycMIST) technology – may help to advance fields such as molecular diagnostics and drug discovery. Details about the cyclic microchip assay method are published in  Nature Communications.

While newer technologies of single-cell omics (ie, genomics, transcriptomics, etc.) are revolutionizing the study of complex biological and cellular systems and scientists can analyze genome-wide sequences of individual cells, these technologies do not apply to proteins because they are not amplifiable like DNAs. Thus, protein analysis in single cells has not reached large-scale experimentation. Because proteins represent cell functions and biomarkers for cell types and disease diagnosis, further analysis on a single-cell basis is needed.

“The CycMIST assay enables comprehensive evaluation of cellular functions and physiological status by examining 100 times more protein types than conventional immunofluorescence staining, which is a distinctive feature not achievable by any other similar technology,” explains Liwei Yang, lead author of the study and a postdoctoral scholar within the Wang research team and Multiplex Biotechnology Laboratory.

Wang, who is affiliated with the Renaissance School of Medicine and Stony Brook Cancer Center, and colleagues demonstrated CycMIST by detecting 182 proteins that include surface markers, neuron function proteins, neurodegeneration markers, signaling pathway proteins and transcription factors. They used a model of Alzheimer’s Disease (AD) in mice to validate the technology and method.

By analyzing the 182 proteins with CycMIST, they were able to perform a functional protein analysis that revealed the deep heterogeneity of brain cells, distinguished AD markers, and identified AD pathogenesis mechanisms.

With this detailed way to unravel proteins in the AD model, the team suggests that such functional protein analysis could be promising for new drug targets for AD, for which there is not yet an effective treatment. And they provide a landscape of potential drug targets at the cellular level from the CycMIST protein analysis.

The authors believe that CycMIST could also have enormous potential for commercialization.

They say that before this study model with CycMIST, researchers could only measure and know a tip of protein types in a cell. But this new approach enables scientists to identify and know the actions of each aspect of a cell, and therefore they can potentially identify if a cell is in a disease status or not – the first step in a possible way to diagnose disease by analyzing a single protein cell. And compared with standard approaches like flow cytometry, their approach with CycMIST can analyze 10 times the amount of proteins and on a single-cell level.

The researchers also suggest that the cyclic microchip assay is portable, inexpensive, and could be adapted to any existing fluorescence microscope, which are additional reasons for its marketability if it proves to be effective with subsequent experimentation.

Much of the research for this study was supported by the National Institutes of Health’s National Institute of Aging (grant # R21AG072076), other NIH grants, and a Memorial Sloan Kettering Cancer Center Support Grant.

This visual presentation shows the words and phrases used on Facebook posts from individuals in the study considered either high- or low-risk for excessive drinking. Credit: Rupa Jose and Andrew Schwartz

Alcoholism can be a difficult condition to diagnose, especially in cases where individuals’ drinking habits are not noticed and physical symptoms have not yet manifested. In a new study, published in Alcoholism: Clinical & Experimental Research, co-author H. Andrew Schwartz, PhD, of the Department of Computer Science at Stony Brook University, and colleagues determined that the language people used in Facebook posts can identify those at risk for hazardous drinking habits and alcohol use disorders.

Collaborating with Schwartz working on The Data Science for Unhealthy Drinking Project is Stony Brook University doctoral candidate Matthew Matero, and Rupa Jose, PhD, lead author and Postdoctoral Researcher at the University of Pennsylvania.

Key to the research was the use of Facebook content analyzed with “contextual embeddings,” a new artificial intelligence application that interprets language in context. The contextual embedding model, say Schwartz, Jose and colleagues, had a 75 percent chance of correctly identifying individuals as high- or low-risk drinkers from their Facebook posts. This rate at identifying at risk people for excessive drinking is higher than other more traditional models that identify high-risk drinkers and those vulnerable to alcoholism.

“What people write on social media and online offers a window into psychological mechanisms that are difficult to capture in research or medicine otherwise,” says Schwartz, commenting on the unique aspect of the study.

“Our findings imply that drinking is not only an individually motivated behavior but a contextual one; with social activities and group membership helping set the tone when it comes to encouraging or discouraging drinking,” summarizes Jose.

Investigators used data from more than 3,600 adults recruited online — average age 43, mostly White — who consented to sharing their Facebook data. The participants filled out surveys on demographics, their drinking behaviors, and their own perceived stress  — a risk factor for problematic alcohol use. Researchers then used a diagnostic scale to organize participants — based on their self-reported alcohol use — into high-risk drinkers (27 percent) and low-risk drinkers (73 percent).

The Facebook language and topics associated with high-risk drinking included more frequent references to going out and/or drinking (e.g., “party,” “beer”), more swearing, more informality and slang (“lmao”), and more references to negative emotions (“miss,” “hate,” “lost,” and “hell”). These may reflect factors associated with high-risk drinking, including neighborhood access to bars, and personality traits such as impulsivity.

Low-risk drinking status was associated with religious language (“prayer,” “Jesus”), references to relationships (“family,” “those who”), and future-oriented verbs (“will,” “hope”). These may reflect meaningful support networks that encourage drinking moderation and the presence of future goals, both of which are protective against dangerous drinking.

Overall, the authors conclude that “social media data serves as a readily available, rich, and under-tapped resource to understand important public health problems, including excessive alcohol use…(The) study findings support the use of Facebook language to help identify probable alcohol vulnerable populations in need of follow-up assessments or interventions, and note multiple language markers that describe individuals in high/low alcohol risk groups.”