Ronald Kahn, MD Chief Academic Officer, Joslin Diabetes Center Mary K. Iacocca Professor of Medicine Harvard Medical School

RNA Profiling Identifies Distinct Classes of White Fat Cells

MedicalResearch.com Interview with:

Ronald Kahn, MD Chief Academic Officer, Joslin Diabetes Center Mary K. Iacocca Professor of Medicine Harvard Medical School

Dr. Kahn

Ronald Kahn, MD
Chief Academic Officer, Joslin Diabetes Center
Mary K. Iacocca Professor of Medicine
Harvard Medical School

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: Adipose tissue is a heterogeneous organ and composed of several cell types, including mature adipocytes, preadipocytes, stem cells, endothelial cells, and various blood cells.

Different adipose depots have distinct physiological functions associated with their anatomical location and cell composition. For example, accumulation of intra-abdominal (visceral) white adipose tissue is associated with insulin resistance and metabolic syndrome, whereas accumulation of subcutaneous adipose tissue is not metabolically detrimental and may be even associated with increased insulin sensitivity.

Determining the mechanisms for these phenotypic differences could lead to development of novel therapies for diabetes, obesity, and their associated morbidities.

A central challenging question in research of metabolic disease is whether disease risk for diabetes and metabolic syndrome is driven by a subset of fat cells that may interact with environmental stresses in disease pathogenesis in a way different from other fat cells. Indeed, previous studies from the Kahn lab have shown different fat cells in a single depot from the mouse may exhibit developmental heterogeneity.

In this new study, we attempted to address this question for human white fat using a synergistic application of several methodologies:

1) single cell transcriptional profiling of human white fat during differentiation,

2) analysis of individual clones of white fat cells taken from humans at surgery,

3) novel computer based network analysis and

4) integration of the gene signatures across experimental models. Single-cell RNA sequencing is an ideal technique to profile gene expression of heterogeneous cell populations obtained from a single tissue, including fat tissue.

MedicalResearch.com: What should readers take away from your report?

Response: By analyzing single cell RNA-seq profiles of human preadipocytes during adipogenesis in vitro, we have identified at least two distinct classes of subcutaneous white adipocytes.

These differences in gene expression are separate from the process of browning and beiging, and could also be observed in an independent set of clonally-expanded human white preadipocyte cell lines, previously obtained in studies by the Tseng lab.

Using a novel systems biology approach, we have also identified and experimentally tested a previously uncharacterized network of zinc finger proteins that are expressed in one class of preadipocytes and is potentially involved in regulating adipogenesis.

Our findings indicate that the synergy of network biology, single cell sequencing and data integration is a promising approach to gain a deeper understanding of both the heterogeneity of white adipocytes; their link to normal metabolism and disease; and ultimately new targets for the therapy of obesity-related complications, such as type 2 diabetes and metabolic syndrome.

Citation:

Ramirez, A.K., Dankel, S.N., Rastegarpanah, B. et al. Single-cell transcriptional networks in differentiating preadipocytes suggest drivers associated with tissue heterogeneity. Nat Commun 11, 2117 (2020). https://doi.org/10.1038/s41467-020-16019-9

The information on MedicalResearch.com is provided for educational purposes only, and is in no way intended to diagnose, cure, or treat any medical or other condition. Always seek the advice of your physician or other qualified health and ask your doctor any questions you may have regarding a medical condition. In addition to all other limitations and disclaimers in this agreement, service provider and its third party providers disclaim any liability or loss in connection with the content provided on this website.

 

Last Updated on May 5, 2020 by Marie Benz MD FAAD