Peripheral blood lymphocyte subpopulations as a marker of severe mechanical soft tissue injury in rats: an immunocytochemical study and ROC analysis

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Abstract

BACKGROUND: Injuries and burns are accompanied by changes in lymphocyte subpopulations, reflecting the immune system's response. ROC (receiver operating characteristic) analysis is a classification method that uses ROC curves. An ROC curve is a graph that evaluates the quality of binary classification; it shows the relationship between the proportion of true positives (sensitivity) and the proportion of false positives (1 – specificity). Using an ROC curve, one can identify the main diagnostic criteria for trauma severity and determine the post-traumatic stage based on the total lymphocyte count and the percentage of various subpopulations (CD3+, CD4+, CD8+, CD19+, CD56+). This approach enables prediction of the main pathogenetic mechanisms of wound healing.

AIM: To study, using immunocytochemical methods, the responses of different peripheral blood lymphocyte types (T cells, B cells, and NK cells) to mechanical soft tissue injury of the thigh in rats.

METHODS: The study was performed on outbred male white rats weighing 180–200 g. Using a special device (under ether anesthesia), a mechanical thigh injury comparable in kinetic energy to that caused by a 5.6 mm bullet was induced in the animals. The animals were euthanized by decapitation at 3 and 14 days after injury in the morning; blood was collected from the tail vein beforehand. Quantitative assessment of peripheral blood lymphocyte subpopulations was performed using a panel of monoclonal antibodies; the relative and absolute counts of the following subpopulations were determined: CD3+ (all T lymphocytes); CD4+ (T-helpers); CD8+ (cytotoxic T lymphocytes); CD19+ (B lymphocytes); and CD56+ (NK cells). Statistical analysis (ROC analysis) was performed using STATISTICA v.7.0 (StatSoft Inc., USA).

RESULTS: ROC analysis accurately evaluates the quality of binary classification – the effect of a given factor on the state of the components of the system under study. Here, using ROC analysis, we demonstrated the effect (yes/no) of high-kinetic soft tissue injury of the thigh and the effect of time (3 and 14 days after injury) on the relative counts of individual lymphocyte subpopulations in the peripheral blood of rats.

CONCLUSION: On day 3 post-injury, the relative counts of all lymphocyte subpopulations had changed in response to the injury. By day 14, changes in ROC curves were observed only for B lymphocytes and NK cells. These data indicate that the addition of the time factor primarily affects B lymphocytes and NK cells but does not affect T lymphocytes.

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About the authors

Albina Kh. Lanicheva

Bashkir State Medical University

Author for correspondence.
Email: lanichevaa@mail.ru
ORCID iD: 0009-0004-0550-2760
SPIN-code: 9390-2689

MD, Cand. Sci. (Medicine), Assistant Professor

Russian Federation, Ufa

Valery V. Semchenko

Omsk State Agrarian University

Email: ivm_omgau_gistology@mail.ru
ORCID iD: 0009-0006-8933-9618
SPIN-code: 8269-3077

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Omsk

References

  1. Suhoparova EP, Zinoviev EV, Khrustaleva IE, Kniaazeva ES. Prediction of the complicated wound process in overweight patients depending on the composition of the intestinal microbiota. Journal of Experimental and Clinical Surgery. 2023;16(1(58)):10–17. doi: 10.18499/2070-478X-2023-16-1-10-17 EDN: AFGZGJ
  2. Lord JM, Midwinter MJ, Chen YF, et al. The systemic immune response to trauma: an overview of pathophysiology and treatment. Lancet. 2014;384(9952):1455–1465. doi: 10.1016/S0140-6736(14)60687-5
  3. Anderson EN, Gochenaur L, Singh A, et al. Traumatic injury induces stress granule formation and enhances motor dysfunctions in ALS/FTD models. Hum Mol Genet. 2018;27(8):1366–1381. doi: 10.1093/hmg/ddy047
  4. Hotchkiss RS, Colston E, Yende S, et al. Immune checkpoint inhibition in sepsis: a phase 1b randomized, placebo-controlled, single ascending dose study of Antiprogrammed Cell Death-Ligand 1 antibody (BMS-936559). Crit Care Med. 2019;47(5):632–642. doi: 10.1097/CCM.0000000000003685
  5. Huber-Lang MS, Ignatius A, Köhl J, et al. Complement in trauma-Traumatised complement? Br J Pharmacol. 2021;178(14):2863–2879. doi: 10.1111/bph.15245 EDN: GXAKPC
  6. Cheadle WG, Pemberton RM, Robinson D, et al. Lymphocyte subset responses to trauma and sepsis. J Trauma. 1993;35(6):844–849. doi: 10.1097/00005373-199312000-00007
  7. Saxena A, Dagur PK, Biancotto A. Multiparametric flow cytometry analysis of naïve, memory, and effector T cells. Methods Mol Biol. 2019;2032:129–140. doi: 10.1007/978-1-4939-9650-6_8
  8. Binkowska AM, Michalak G, Słotwiński R. Current views on the mechanisms of immune responses to trauma and infection. Cent Eur J Immunol. 2015;40(2):206–216. doi: 10.5114/ceji.2015.52835
  9. Portale F, Di Mitri D. NK cells in cancer: mechanisms of dysfunction and therapeutic potential. Int J Mol Sci. 2023;24(11):9521. doi: 10.3390/ijms24119521 EDN: BGVQPC
  10. Deng Z, Zhang M, Zhu T, et al. Dynamic changes in peripheral blood lymphocyte subsets in adult patients with COVID-19. Int J Infect Dis. 2020;98:353–358. doi: 10.1016/j.ijid.2020.07.003 EDN: GLXTBV
  11. Kovach MA, Standiford TJ. The function of neutrophils in sepsis. Curr Opin Infect Dis. 2012;25(3):321–327. doi: 10.1097/QCO.0b013e3283528c9b
  12. Burback L, Brémault-Phillips S, Nijdam MJ, et al. Treatment of posttraumatic stress disorder: a state-of-the-art review. Curr Neuropharmacol. 2024;22(4):557–635. doi: 10.2174/1570159X21666230428091433 EDN: TUSMMA
  13. Spahn DR, Bouillon B, Cerny V, et al. The European guideline on management of major bleeding and coagulopathy following trauma: fifth edition. Crit Care. 2019;23(1):98. doi: 10.1186/s13054-019-2347-3 EDN: OEXUUO
  14. Patent RUS № 2807925/ 21.11.23. Byul. № 33. Lanicheva AKh, Semchenko VV, Murzabaev KhKh, Imaeva AK. A device for simulating mechanical trauma in adult laboratory rats, comparable to a gunshot wound from a 5.6 mm caliber bullet. Available from: https://patentimages.storage.googleapis.com/66/c6/cd/e7525fdc63c260/RU2807925C1.pdf (In Russ.)
  15. Sanchez AM, Yang Y. The role of natural regulatory T cells in infection. Immunol Res. 2011;49(1–3):124–134. doi: 10.1007/s12026-010-8176-8 EDN: TPORPW
  16. Grigoryev SG, Lobzin YuV, Skripchenko NV. The role and place of logistic regression and ROC analysis in solving medical diagnostic task. Journal Infectology. 2016;8(4):36–45. doi: 10.22625/2072-6732-2016-8-4-36-45 EDN: XFWBJT
  17. Hanczar B, Hua J, Sima C, et al. Small-sample precision of ROC-related estimates. Bioinformatics. 2010;26(6):822–830. doi: 10.1093/bioinformatics/btq037 EDN: NZTSEP
  18. Zhao J, Zhang S, Liu Y, et al. Single-cell RNA sequencing reveals the heterogeneity of liver-resident immune cells in human. Cell Discov. 2020;6:22. doi: 10.1038/s41421-020-0157-z EDN: MNEHEQ
  19. Maheshwari S, Dwyer LJ, Sîrbulescu RF. Inflammation and immunomodulation in central nervous system injury - B cells as a novel therapeutic opportunity. Neurobiol Dis. 2023;180:106077. doi: 10.1016/j.nbd.2023.106077 EDN: QVETBJ
  20. Ruiz-Argüelles A, Pérez-Romano B. Immunophenotypic analysis of peripheral blood lymphocytes. Curr Protoc Cytom. 2001;Chapter 6. doi: 10.1002/0471142956.cy0605s11
  21. Mousset CM, Hobo W, Woestenenk R, et a. Comprehensive phenotyping of T cells using flow cytometry. Cytometry A. 2019;95(6):647–654. doi: 10.1002/cyto.a.23724
  22. Wendel P, Reindl LM, Bexte T, et al. Arming immune cells for battle: a brief journey through the advancements of T and NK cell immunotherapy. Cancers (Basel). 2021;13(6):1481. doi: 10.3390/cancers13061481 EDN: HZKCAQ
  23. Herman P, Stein A, Gibbs K, et al. Persons with chronic spinal cord injury have decreased natural killer cell and increased Toll-like receptor/inflammatory gene expression. J Neurotrauma. 2018;35(15):1819–1829. doi: 10.1089/neu.2017.5519
  24. Maestre L, García-García JF, Jiménez S, et al. High-mobility group box (TOX) antibody a useful tool for the identification of B and T cell subpopulations. PLoS One. 2020;15(2):e0229743. doi: 10.1371/journal.pone.0229743 EDN: GUCRSR
  25. Telegin GB, Chernov AS, Konovalov NA, et al. Cytokine profile as a marker cellu demage and immune disfunction after spinal cord injuriy. Acta Naturae. 2020;12(3(46)):92–101. doi: 10.32607/actanaturae.11096 EDN: MXUULW
  26. Oelsner S, Wagner J, Friede ME, et al. Chimeric antigen receptor-engineered cytokine-induced killer cells overcome treatment resistance of pre-B-cell acute lymphoblastic leukemia and enhance survival. Int J Cancer. 2016;139(8):1799–1809. doi: 10.1002/ijc.30217 EDN: WSAXTR
  27. Rodríguez-Perea AL, Arcia ED, Rueda CM, Velilla PA. Phenotypical characterization of regulatory T cells in humans and rodents. Clin Exp Immunol. 2016;185(3):281–291. doi: 10.1111/cei.12804
  28. Boiko AA, Malanchuk VA, Myroshnychenko MS, et al. Expression features of T-lymphocytes, B-lymphocytes and macrophages in the post-traumatic regenerate of the mandible rats under conditions of filling a bone defect with hydroxyapatite-containing osteotropic material and thymalin injecting the surrounding soft tissues. Pol Merkur Lekarski. 2024;52(2):171–177. doi: 10.36740/Merkur202402105 EDN: WBAHNJ

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2. Fig. 1. Histograms showing the distribution of the main types of lymphocytes in the peripheral blood of rats following soft tissue injury to the thigh: the X-axis shows the relative cell content per field of view (%), and the Y-axis shows the proportion of fields of view with the corresponding cell content; solid line — control, dotted line — 3 days after injury, solid line — 14 days after injury.

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3. Fig. 2. Histograms showing the distribution of T-lymphocytes in the peripheral blood of white rats following soft-tissue injury to the thigh: the X-axis shows the relative cell content per field of view (%), and the Y-axis shows the proportion of fields of view with the corresponding cell content; solid line — control, dotted line — 3 days after injury, solid line — 14 days after injury.

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4. Fig. 3. ROC curves for the main lymphocyte subpopulations in the peripheral blood of rats 3 and 14 days after soft tissue injury to the thigh — effect of the presence of injury: on the X-axis — 100-specificity (proportion of false-positive results in %), on the Y-axis — sensitivity (%); T-cytotox — cytotoxic T-lymphocytes (CD8+); AUC — area under the curve; Ч — sensitivity; С — specificity.

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5. Fig. 4. ROC curves for the main lymphocyte subpopulations in the peripheral blood of rats following soft tissue injury to the thigh — effect of the duration of the post-injury period: on the X-axis — 100-specificity (proportion of false-positive results, %), on the Y-axis — sensitivity (%); T-cytotox — cytotoxic T-lymphocytes (CD8+); AUC — area under the curve; Ч — sensitivity; С — specificity.

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