Label-free macrophage phenotype classification using machine learning methods

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.

Unraveling the potential of 3D bioprinted immunomodulatory materials for regulating macrophage polarization: State-of-the-art in bone and associated tissue regeneration

“…In this context, the development of novel and stimuli-responsive materials should be required for studying the immunomodulation of macrophages in the future for a better understanding of the effects of biophysical stimulation. Recently, the phenotypic classification of macrophage is migrated towards smart-computer-based identification techniques for better interpretation of immune cells [ [345] , [346] , [347] ]. Conventional identification procedures of macrophages include the use of fluorescence (FL) microscopy, quantitative real-time PCR (qRT-PCR), and western blotting (WB), which usually takes a longer time to perform the experiments [ [348] , [349] , [350] , [351] , [352] ].…”

Two new clades recovered at high temperatures provide novel phylogenetic and genomic insights into Candidatus Accumulibacter

“…As a laterally derived gene which was considered to have played a key role in the emergence of the polyphosphate-accumulating trait of Ca. Accumulibacter [ 38 ], ppk 2-1 seems to show increased resolution in differentiating different clades with higher intra-clade conservativity ( Fig. 3 ), implying a special standing of the ppk 2 gene.…”

“…To overcome the disadvantage of single-gene classification, modern evolutionary studies employ multiple genes and the whole-genome data for more robust and accurate clade identification [ 37 ]. Machine learning is particularly strong in analyzing complex data and identifying trends and patterns that can easily be overlooked by traditional measures, showing promising power in classifying bacteria [ 38–40 ]. The combination of machine learning and whole-genome data has the potential to achieve a more rapid and accurate classification.…”

Effects of bone surface topography and chemistry on macrophage polarization

“…On the other hand, M2b macrophages are elongated and granular, whereas M2c macrophages are smaller and more circular. Finally, M2d macrophages are identified by their flattened and elongated morphology 91 – 93 . Macrophages on unmodified membranes resembled M2a macrophages, displaying an elongated and narrowed shape.…”