Single cell transcriptomics based-MacSpectrum reveals novel macrophage activation signatures in diseases.
JCI Insight
; 52019 04 16.
Article
em En
| MEDLINE
| ID: mdl-30990466
Adipose tissue macrophages (ATM) are crucial for maintaining adipose tissue homeostasis and mediating obesity-induced metabolic abnormalities, including prediabetic conditions and type 2 diabetes mellitus. Despite their key functions in regulating adipose tissue metabolic and immunologic homeostasis under normal and obese conditions, a high-resolution transcriptome annotation system that can capture ATM multifaceted activation profiles has not yet been developed. This is primarily attributed to the complexity of their differentiation/activation process in adipose tissue and their diverse activation profiles in response to microenvironmental cues. Although the concept of multifaceted macrophage action is well-accepted, no current model precisely depicts their dynamically regulated in vivo features. To address this knowledge gap, we generated single-cell transcriptome data from primary bone marrow-derived macrophages under polarizing and non-polarizing conditions to develop new high-resolution algorithms. The outcome was creation of a two-index platform, MacSpectrum (https://macspectrum.uconn.edu), that enables comprehensive high-resolution mapping of macrophage activation states from diverse mixed cell populations. MacSpectrum captured dynamic transitions of macrophage subpopulations under both in vitro and in vivo conditions. Importantly, MacSpectrum revealed unique "signature" gene sets in ATMs and circulating monocytes that displayed significant correlation with BMI and homeostasis model assessment of insulin resistance (HOMA-IR) in obese human patients. Thus, MacSpectrum provides unprecedented resolution to decode macrophage heterogeneity and will open new areas of clinical translation.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Tecido Adiposo
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Transcriptoma
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Ativação de Macrófagos
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Macrófagos
Limite:
Animals
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Humans
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Male
Idioma:
En
Revista:
JCI Insight
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos