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High dimensional data moves from bench to bedside with focus on individualized care


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3 minutes

High dimensional genomic data are moving out of the lab and into clinical practice. One example: Manoj Bhasin, PhD, MS, is using genomic findings to tailor treatments to achieve better outcomes with fewer adverse events in the heterogeneous population of people with diabetes.

Manoj Bhasin, PhD, MS
Manoj Bhasin, PhD, MS

“High dimensional, genome-level data analytics is really helping us to understand the mechanism of complex diseases like diabetes,” said Dr. Bhasin, Associate Professor of Pediatrics and Biomedical Informatics, and Scientific Director, Bioinformatics and Systems Biology Shared Resource, at the Emory University Winship Cancer Institute. “The high throughput, single-cell approaches have helped us to identify a unique subtype of inflammatory fibroblasts that are key for healing of chronic diabetic wounds and might be useful in developing new therapies for diabetic foot ulcers.”

Dr. Bhasin will unveil some of the latest developments in multi-dimensional and spatial single-cell omics during the session High Dimensional Data in Epidemiologic Research—Gentle Introduction to Methods and Survey of Strengths and Limitations, which will begin at 8:00 a.m. ET on Tuesday, June 29.

Kristi L. Hoffman, PhD, MPH, Baylor College of Medicine, and James B. Meigs, MD, MPH, Professor of Medicine at Harvard Medical School and Co-Director of the MGH Clinical Research Program’s Clinical Effectiveness Research Group, will also discuss high dimensional data during the session.

High dimensional data is an umbrella term that covers a variety of technologies and techniques to analyze data at the genomic level, most often by sequencing single cells, explained Dr. Bhasin, who is also Director of Bioinformatics and Systems Biology and Director of the Single Cell Biology Program at Aflac Cancer & Blood Disorders Center at Children Healthcare of Atlanta. A growing array of single-cell transcriptomics, epigenomics, proteomics, and other novel omics widely used in diabetes research are beginning to move into clinical settings.

But obtaining and analyzing high dimensional data is not a simple task. The typical human genome contains about 33,000 genes. Most gene expression is affected by other genes and also by environmental and host factors. Current approaches allow for genomic analysis of thousands of individual cells to provide a clearer, unbiased view of gene expression and function in individual cells and in individual patients, Dr. Bhasin said.

For years, researchers have been using these higher dimensional approaches to help identify new biomarkers that may help diagnose and treat diabetes and its many complications. It is now possible to use these same techniques to assess individual patients, their disease, and potential treatment approaches.

“We all know that not every person who has diabetes is the same,” Dr. Bhasin said. “High dimensional analytics is telling us very precisely how every individual is different and how they can be treated with different approaches that match their genomic landscape. That is what we are doing with diabetic foot ulcers.”

High dimensional data analysis is also disrupting the long-established progression from preclinical to clinical research to translation into new clinical applications. The current approach to high dimensional data starts with patient-level genomic and biosamples that move to the bench for manipulation and experimentation, then back to the patient for clinical application.

“The high dimensional analytics approach brings a fresher perspective than more traditional approaches,” Dr. Bhasin said. “It offers a much broader assessment of disease that is also very focused on the individual. Single-cell techniques help us understand the practical effects of disease heterogeneity in diabetes and the heterogeneous approaches we need to treat it.”


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