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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Cerenkov Luminescence Imaging of Interscapular Brown Adipose Tissue
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BATLAS: Deconvoluting Brown Adipose Tissue.

Aliki Perdikari1, Germán Gastón Leparc2, Miroslav Balaz1

  • 1Institute of Food, Nutrition and Health, ETH Zurich, Schwerzenbach, Switzerland.

Cell Reports
|October 18, 2018
PubMed
Summary
This summary is machine-generated.

Thermogenic adipocytes, crucial for metabolic control, are challenging to analyze. This study identifies a gene signature and develops an algorithm to quantify brown adipose tissue (BAT) content, linking it to energy expenditure and weight loss.

Keywords:
BAT contentdeconvolutiongene signaturepure adipocyte populations

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Area of Science:

  • Metabolic Research
  • Adipose Tissue Biology
  • Genomics

Background:

  • Thermogenic adipocytes (brown and brite) are key targets for improving metabolic health.
  • Analyzing these cells in complex adipose tissue biopsies presents significant challenges.
  • Transcriptome analysis offers a powerful approach to differentiate adipocyte subtypes.

Purpose of the Study:

  • To identify a reliable gene signature for classifying brown and white adipocytes across species.
  • To develop a computational tool for quantifying brown adipocyte content in mixed adipose tissue samples.
  • To investigate the association between brown adipose tissue (BAT) content and metabolic parameters in humans.

Main Methods:

  • In-depth transcriptome analysis of isolated mouse and human brown, brite, and white adipocytes.
  • Integration of mouse and human transcriptome data to identify conserved gene signatures.
  • Development of a machine-learning-based cell deconvolution algorithm for adipose tissue analysis.
  • Application of the algorithm to human weight loss study data.

Main Results:

  • A robust gene signature capable of classifying brown and white adipocytes in both mice and humans was identified.
  • A novel algorithm was developed to accurately calculate brown adipocyte content in complex human and mouse adipose tissue biopsies.
  • In a human cohort, higher brown adipose tissue (BAT) content correlated significantly with increased energy expenditure and a greater propensity for weight loss.
  • The developed algorithm is publicly available for researchers.

Conclusions:

  • The identified gene signature and developed algorithm provide a valuable tool for characterizing brown and white adipocytes.
  • Quantifying BAT content can offer insights into individual metabolic differences and weight management potential.
  • This research supports the development of therapeutic strategies aimed at increasing human energy expenditure through targeting BAT.