Proteomic characterization of four subtypes of M2 macrophages derived from human THP-1 cells

Li, Pengfei; Ma, Chen; Li, Jing; You, Shanshan; Dang, Liuyi; Wu, Jingyu; Zou, Hao; Li, Jun; Zhi, Yuan; Chen, Lin; Sun, Shisheng

doi:10.1631/jzus.b2100930

“…Overall, the lowest confusion was noted between M0, M2b, M1, and M2d, while the highest confusion was between M0, M2c, and M2a. A similarity between M0, M2a, and M2c comparing to M1 were previously reported in gene expression and proteomics studies 51 – 53 . Our findings suggest that macrophage phenotypes have a detectable unique autofluorescence which allows label-free macrophage identification, however, it should be expected that when simultaneously comparing six phenotypes, some phenotypes are more distinguishable than the others possibly due to the differences in the biological characteristics.…”

Section: Resultssupporting

confidence: 80%

Label-free macrophage phenotype classification using machine learning methods

Hourani

¹

,

Perez‐Gonzalez

²

,

Khoshmanesh

³

et al. 2023

Sci Rep

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Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functions, where the identification of the different phenotypes is of an utmost importance in modelling immune response. While expressed markers are the most used signature to classify phenotypes, multiple reports indicate that macrophage morphology and autofluorescence are also valuable clues that can be used in the identification process. In this work, we investigated macrophage autofluorescence as a distinct feature for classifying six different macrophage phenotypes, namely: M0, M1, M2a, M2b, M2c, and M2d. The identification was based on extracted signals from multi-channel/multi-wavelength flow cytometer. To achieve the identification, we constructed a dataset containing 152,438 cell events each having a response vector of 45 optical signals fingerprint. Based on this dataset, we applied different supervised machine learning methods to detect phenotype specific fingerprint from the response vector, where the fully connected neural network architecture provided the highest classification accuracy of 75.8% for the six phenotypes compared simultaneously. Furthermore, by restricting the number of phenotypes in the experiment, the proposed framework produces higher classification accuracies, averaging 92.0%, 91.9%, 84.2%, and 80.4% for a pool of two, three, four, five phenotypes, respectively. These results indicate the potential of the intrinsic autofluorescence for classifying macrophage phenotypes, with the proposed method being quick, simple, and cost-effective way to accelerate the discovery of macrophage phenotypical diversity.

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“…Overall, the lowest confusion was noted between M0, M2b, M1, and M2d, while the highest confusion was between M0, M2c, and M2a. A similarity between M0, M2a, and M2c comparing to M1 were previously reported in gene expression and proteomics studies [49][50][51] . Our ndings suggest that macrophage phenotypes have a detectable unique auto uorescence which allows label-free macrophage identi cation, however, it should be expected that when simultaneously comparing six phenotypes, some phenotypes are more distinguishable than the others possibly due to the differences in the biological characteristics.…”

Section: Macrophage Classi Cation Accuracy Applying Machine Learningsupporting

confidence: 79%

Label-free Macrophage Phenotype Classification Using Machine Learning Methods

Hourani

¹

,

Perez‐Gonzalez

²

,

Khoshmanesh

³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functions, where the identification of the different phenotypes is of an utmost importance in modelling immune response. While expressed markers are the most used signature to classify phenotypes, multiple reports indicate that macrophage morphology and autofluorescence are also valuable clues that can be used in the identification process. In this work, we investigated macrophage autofluorescence as a distinct feature for classifying six different macrophage phenotypes, namely: M0, M1, M2a, M2b, M2c, and M2d. The identification was based on extracted signals from multi-channel/multi-wavelength flow cytometer. To achieve the identification, we constructed a dataset containing 152,438 cell events each having a response vector of 45 optical signals fingerprint. Based on this dataset, we applied different supervised machine learning methods to detect phenotype specific fingerprint from the response vector, where the fully connected neural network architecture provided the highest classification accuracy of 75.8% for the six phenotypes compared simultaneously. Furthermore, by restricting the number of phenotypes in the experiment, the proposed framework produces higher classification accuracies, averaging 92.0%, 91.9%, 84.2%, and 80.4% for a pool of two, three, four, five phenotypes, respectively. These results indicate the potential of the intrinsic autofluorescence for classifying macrophage phenotypes, with the proposed method being quick, simple, and cost-effective way to accelerate the discovery of macrophage phenotypical diversity.

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“…These differences may influence intracellular drug concentrations between these two cell subsets. In another study, ABCB1 expression levels were elevated in M2a subtype macrophages, which participate in the role of phospholipids in phagocytosis and retinoic acid-mediated signal transduction [23]. In addition, ABCC1 (MRP1) was shown to be upregulated in M1 macrophages, whereas ABCG2 (BCRP) expression was upregulated in M2 polarized macrophages [24].…”

Section: The Innate Immune Systemmentioning

confidence: 94%

Clinical Significance of Lipid Transport Function of ABC Transporters in the Innate Immune System

Kotlyarov

¹

,

Kotlyarova

²

2022

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ABC transporters are a large family of proteins that transport a variety of substrates across cell plasma membranes. Because of this, they are involved in many physiological processes. It is of interest to note that many ABC transporters are involved in the transport of various lipids. In addition, this function may be related to the innate immune system. The evidence that ABC transporters are involved in the regulation of the innate immune system through the transport of various substances greatly enhances the understanding of their clinical significance. ABC transporters are involved in the cellular homeostasis of cholesterol as well as in the regulation of its content in lipid rafts. Through these mechanisms, they can regulate the function of membrane proteins, including receptors of the innate immune system. By regulating lipid transport, some members of ABC transporters are involved in phagocytosis. In addition, ABC transporters are involved in the transport of lipopolysaccharide, lipid mediators of inflammation, and perform other functions in the innate immune system.

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Proteomic characterization of four subtypes of M2 macrophages derived from human THP-1 cells

Cited by 34 publications

References 59 publications

Label-free macrophage phenotype classification using machine learning methods

Label-free macrophage phenotype classification using machine learning methods

Label-free Macrophage Phenotype Classification Using Machine Learning Methods

Clinical Significance of Lipid Transport Function of ABC Transporters in the Innate Immune System

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