Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker

MinDong Sung; Jong Hyun Kim; Hyun-Seok Min; Sooyoung Jang; JaeSeong Hong; Bo Kyu Choi; JuHye Shin; Kyung Soo Chung; Yu Rang Park

doi:10.1038/s41377-023-01309-w

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Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker

Original Articles

- Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker
- Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker
- LSA 2023年12卷第12期页码：2565-2576
- Affiliations：
  
  1.Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
  2.Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
  3.Tomocube, Inc, 155 Sinseong-ro, Shinsung-dong, Yuseong-gu, Daejeon, Republic of Korea
- Author bio：
  
  Kyung Soo Chung (chungks@yuhs.ac)
  Yu Rang Park (yurangpark@yuhs.ac)
- Funds：
- DOI：10.1038/s41377-023-01309-w
  中图分类号：
- 纸质出版日期：2023-12-31，
  
  网络出版日期：2023-11-07，
  
  收稿日期：2023-05-19，
  
  修回日期：2023-09-20，
  
  录用日期：2023-10-13
- Accepted：
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Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker[J]. LSA, 2023,12(12):2565-2576.

Sung, M. D. et al. Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker. Light: Science & Applications, 12, 2565-2576 (2023).
Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker[J]. LSA, 2023,12(12):2565-2576. DOI： 10.1038/s41377-023-01309-w.

Sung, M. D. et al. Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker. Light: Science & Applications, 12, 2565-2576 (2023). DOI： 10.1038/s41377-023-01309-w.

摘要

Abstract

Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate. The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management. We aimed to investigate the potential

of three-dimensional label-free CD8 + T cell morphology as a biomarker for sepsis. This study included three-time points in the sepsis recovery cohort (

= 8) and healthy controls (

= 20). Morphological features and spatial distribution within cells were compared among the patients' statuses. We developed a deep learning model to predict the diagnosis and prognosis of sepsis using the internal cell morphology. Correlation between the morphological features and clinical indices were analysed. Cell morphological features and spatial distribution differed significantly between patients with sepsis and healthy controls and between the survival and non-survival groups. The model for predicting the diagnosis and prognosis of sepsis showed an area under the receiver operating characteristic curve of nearly 100% with only a few cells

and a strong correlation between the morphological features and clinical indices was observed. Our study highlights the potential of three-dimensional label-free CD8 + T cell morphology as a promising biomarker for sepsis. This approach is rapid

requires a minimum amount of blood samples

and has the potential to provide valuable information for the early diagnosis and prognosis of sepsis.

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references

Seymour, C. W. et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (Sepsis-3).JAMA315, 762–774 (2016)..

Chan, H. K. et al. Updated estimates of sepsis hospitalizations at United States academic medical centers.J. Am. Coll. Emerg. Physicians Open3, e12782 (2022)..

Rhee, C. et al. Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014.JAMA318, 1241–1249 (2017)..

Leligdowicz, A., Harhay, M. O.&Calfee, C. S. Immune modulation in sepsis, ARDS, and Covid-19 — The road traveled and the road ahead.NEJM Evid.1, EVIDra2200118 (2022)..

Seymour, C. W. et al. Time to treatment and mortality during mandated emergency care for sepsis.N. Engl. J. Med.376, 2235–2244 (2017)..

Kim, M. H.&Choi, J. H. An update on sepsis biomarkers.Infect. Chemother.52, 1–18 (2020)..

Barichello, T. et al. More than just fever and leukocytosis-a narrative review.Crit. Care26, 14 (2022)..

Kweon, O. J. et al. Usefulness of presepsin (sCD14 subtype) measurements as a new marker for the diagnosis and prediction of disease severity of sepsis in the Korean population.J. Crit. Care29, 965–970 (2014)..

Lu, B. et al. The utility of presepsin in diagnosis and risk stratification for the emergency patients with sepsis.Am. J. Emerg. Med.36, 1341–1345 (2018)..

Aksaray, S. et al. Diagnostic value of sTREM-1 and procalcitonin levels in the early diagnosis of sepsis.North. Clin. Istanb.3, 175–182 (2016)..

Brenner, T. et al.Soluble TREM-1 as a diagnostic and prognostic biomarker in patients with septic shock: an observational clinical study.Biomarkers22, 63–69 (2017)..

Niehues, T. C-reactive protein and other biomarkers—the sense and non-sense of using inflammation biomarkers for the diagnosis of severe bacterial infection.LymphoSign J.5, 35–47 (2018)..

Pfäfflin, A.&Schleicher, E. Inflammation markers in point-of-care testing (POCT).Anal. Bioanal. Chem.393, 1473–1480 (2009)..

Reinhart, K. et al. New approaches to sepsis: molecular diagnostics and biomarkers.Clin. Microbiol. Rev.25, 609–634 (2012)..

McElvaney, O. J. et al. Interleukin-6: obstacles to targeting a complex cytokine in critical illness.Lancet Respiratory Med.9, 643–654 (2021)..

Yu, R. J. et al. Identification of potential biomarkers and pathways for sepsis using RNA sequencing technology and bioinformatic analysis.Heliyon9, e15034 (2023)..

Zhou, J. et al. Identification of Nine mRNA signatures for sepsis using random forest.Computational Math. Methods Med.2022, 5650024 (2022)..

Reyes, M. et al. An immune-cell signature of bacterial sepsis.Nat. Med.26, 333–340 (2020)..

Wake, H. et al. Histidine-rich glycoprotein prevents septic lethality through regulation of immunothrombosis and inflammation.EBioMedicine9, 180–194 (2016)..

Nishibori, M. Novel aspects of sepsis pathophysiology: NETs, plasma glycoproteins, endotheliopathy and COVID-19.J. Pharmacol. Sci.150, 9–20 (2022)..

Lipcsey, M. et al. The time course of calprotectin liberation from human neutrophil granulocytes afterEscherichia coliand endotoxin challenge.Innate Immun.25, 369–373 (2019)..

Lu, B. et al. Molecular mechanism and therapeutic modulation of high mobility group box 1 release and action: an updated review.Expert Rev. Clin. Immunol.10, 713–727 (2014)..

Bradley, Z.&Bhalla, N. Point-of-care diagnostics for sepsis using clinical biomarkers and microfluidic technology.Biosens. Bioelectron.227, 115181 (2023)..

German, Y. et al. Morphological profiling of human T and NK lymphocytes by high-content cell imaging.Cell Rep.36, 109318 (2021)..

Severin, Y. et al. Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes.Sci. Adv.8, eabn5631 (2022)..

Vom Werth, K. L. et al. Investigating morphological changes of T-lymphocytes after exposure with bacterial determinants for early detection of septic conditions.Microorganisms10, 391 (2022)..

Singh, R. et al. Quantitative assessment of morphology and sub-cellular changes in macrophages and trophoblasts during inflammation.Biomed. Opt. Express11, 3733–3752 (2020)..

Wäldchen, S. et al. Light-induced cell damage in live-cell super-resolution microscopy.Sci. Rep.5, 15348 (2015)..

Khodjakov, A.&Rieder, C. L. Imaging the division process in living tissue culture cells.Methods38, 2–16 (2006)..

Schroeder, T. Long-term single-cell imaging of mammalian stem cells.Nat. Methods8, S30–S35 (2011)..

Vasilenko, I. et al.Opportunities of QPI in the epigenetic diagnostics and assessment of therapeutic efficacy. Proceedings of SPIE 10074, Quantitative Phase Imaging III. San Francisco, CA, USA: SPIE, (2017).

Nguyen, T. L. et al. Quantitative phase imaging: recent advances and expanding potential in biomedicine.ACS Nano16, 11516–11544 (2022)..

Zhao, Z. Q. et al. Object detection with deep learning: A review.IEEE Trans. Neural Netw. Learn. Syst.30, 3212–3232 (2019)..

Chen, X. N. et al.Neural feature search: a neural architecture for automated feature engineering. 2019IEEE International Conference on Data Mining (ICDM). Beijing, China: IEEE, (2019).

Islam, M. M. et al. Prediction of sepsis patients using machine learning approach: a meta-analysis.Computer Methods Prog. Biomed.170, 1–9 (2019)..

Goh, K. H. et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.Nat. Commun.12, 711 (2021)..

Hu, C. et al. Interpretable machine learning for early prediction of prognosis in sepsis: A discovery and validation study.Infect. Dis. Ther.11, 1117–1132 (2022)..

Gupta, S. et al. Developmental heterogeneity in DNA packaging patterns influences T-cell activation and transmigration.PLoS One7, e43718 (2012)..

Wilde, J. G.&Metcalf, W. K. Changes in the lymphocyte cytoplasmic refractive index following typhoid vaccination.Ann. Clin. Lab. Sci.5, 23–26 (1975)..

Keohane, K. W.&Metcalf, W. K. Changes in lymphocytes during antibody production.Nature183, 195 (1959)..

Singer, M. et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3).JAMA315, 801–810 (2016)..

Park, Y., Depeursinge, C.&Popescu, G. Quantitative phase imaging in biomedicine.Nat. Photonics12, 578–589 (2018)..

Amiad-Pavlov, D. et al. Live imaging of chromatin distribution reveals novel principles of nuclear architecture and chromatin compartmentalization.Sci. Adv.7, eabf6251 (2021)..

Onofrey, J. A. et al. Generalizable multi-site training and testing of deep neural networks using image normalization. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Venice, Italy: IEEE, (2019).

Kim, G. et al. Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network.Light : Sci. Appl.11, 190 (2022)..

Israelsson, P. et al. Cytokine mRNA and protein expression by cell cultures of epithelial ovarian cancer-Methodological considerations on the choice of analytical method for cytokine analyses.Am. J. Reprod. Immunol.84, e13249 (2020)..

Selvaraju, R. R. et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization.Int. J. Computer Vis.128, 336–359 (2020)..

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