Virtual biomarkers: predicting immune status using label-free holotomography of individual human monocytes and machine learning analysis
Abstract Sepsis is an abnormally dysregulated immune response against infection in which the human immune system ranges from a hyper-inflammatory phase to an immune-suppressive phase. Current assessment methods are limiting owing to time-consuming and laborious sample preparation protocols. We propose a rapid label-free imaging-based technique to assess the immune status of individual human monocytes. High-resolution intracellular compositions of individual monocytes are quantitatively measured in terms of the three-dimensional distribution of refractive index values using holotomography, which are then analyzed using machine-learning algorithms to train for the classification into three distinct immune states: normal, hyper-inflammation, and immune suppression. The immune status prediction accuracy of the machine-learning holotomography classifier was 83.7% and 99.9% for one and six cell measurements, respectively. Our results suggested that this technique can provide a rapid deterministic method for the real-time evaluation of the immune status of an individual..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 18. Sept. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lee, Mahn Jae [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.09.12.557503 |
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| PPN (Katalog-ID): |
XBI040853306 |
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| 245 | 1 | 0 | |a Virtual biomarkers: predicting immune status using label-free holotomography of individual human monocytes and machine learning analysis |
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| 520 | |a Abstract Sepsis is an abnormally dysregulated immune response against infection in which the human immune system ranges from a hyper-inflammatory phase to an immune-suppressive phase. Current assessment methods are limiting owing to time-consuming and laborious sample preparation protocols. We propose a rapid label-free imaging-based technique to assess the immune status of individual human monocytes. High-resolution intracellular compositions of individual monocytes are quantitatively measured in terms of the three-dimensional distribution of refractive index values using holotomography, which are then analyzed using machine-learning algorithms to train for the classification into three distinct immune states: normal, hyper-inflammation, and immune suppression. The immune status prediction accuracy of the machine-learning holotomography classifier was 83.7% and 99.9% for one and six cell measurements, respectively. Our results suggested that this technique can provide a rapid deterministic method for the real-time evaluation of the immune status of an individual. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Kim, Geon |4 aut | |
| 700 | 1 | |a Lee, Moo Sung |4 aut | |
| 700 | 1 | |a Shin, Jeong Won |4 aut | |
| 700 | 1 | |a Lee, Joong Ho |4 aut | |
| 700 | 1 | |a Ryu, Dong Hun |4 aut | |
| 700 | 1 | |a Kim, Young Seo |4 aut | |
| 700 | 1 | |a Chung, YoonJae |4 aut | |
| 700 | 1 | |a Kim, Kyu Seok |4 aut | |
| 700 | 1 | |a Park, YongKeun |4 aut | |
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