ArticleScientific reports2023
Label-free macrophage phenotype classification using machine learning methods.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Illuminating Immunity: A Systematic Review of Immune Cell Autofluorescence.Journal of biophotonics · 2025Pooled it
- Article
- Uncovering the Potential of 3D Spheroids for Modeling the Cellular Crosstalk Within the Tumor Microenvironment in HNSCC.Biotechnology journal · 2026Review
- Systematic establishment of approaches to the detection of equine macrophage polarization and their application in pathogenic infection.Microbiology spectrum · 2026Article
- Morpho-functional plasticity of tumour-associated macrophages: linking cell shape, ultrastructure, and immune regulation in cancer.Clinical and experimental medicine · 2026Review
- Radical-Mediated, Substrate-Independent Fabrication of Hybrid Solid-Hydrogel Materials With Tunable Crosslinking: An Initiator- and Crosslinker-Free Approach.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- CD44-targeted immunoliposomes for IL-1β knockdown modulate macrophage-mediated inflammation.Communications biology · 2026Article
- Cell-Based Immuno-Biosensors Using Microfluidics.Sensors (Basel, Switzerland) · 2026Review
- Predicting targeted- and immunotherapeutic response outcomes in melanoma with single-cell Raman spectroscopy and AI.bioRxiv : the preprint server for biology · 2026Article
- Carnosic acid-loaded stimuli-responsive hydrogel promotes wound healing through PGP-1 mediated immune remodeling.Journal of nanobiotechnology · 2026Article
- Lassa virus circumvents macrophage and dendritic cell antiviral defences in its natural reservoir, the Natal multimammate mouse (Mastomys natalensis).Npj viruses · 2026Article
- Macrophage morphology in the tumor microenvironment predicts metachronous liver metastasis in gastric cancer: establishment and validation of a predictive model.Frontiers in immunology · 2026Article
- A versatile distance-based approach for gene expression selection across diverse biological systems.Frontiers in immunology · 2026Article
- Proteomic and morphologic characterization of ovine macrophage differentiation and polarization.Scientific reports · 2025Article
- CaMKK2 Regulates Macrophage Polarization Induced by Matrix Stiffness: Implications for Shaping the Immune Response in Stiffened Tissues.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Multispectral autofluorescence for label free classification of immune cell type and activation/polarization status.Scandinavian journal of immunology · 2025Article
- Innate Immune Memory is Stimulus Specific.bioRxiv : the preprint server for biology · 2025Article
- The mechanisms of specialized pro-resolving mediators in pain relief: neuro-immune and neuroglial regulations.Frontiers in immunology · 2025Review
- A broadly adaptable protocol for isolating Kupffer cells from non-model species: application toFrontiers in immunology · 2025Article
- A data-driven model of macrophage polarization states reveals an IFN macrophage signature in active Crohn's disease.Frontiers in immunology · 2025Article
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Authors and funding
8 authors.
Funding
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Abstract
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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