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Updated: Jul 13, 2025
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In Vitro and In Vivo Assessment of T, B and Myeloid Cells Suppressive Activity and Humoral Responses from Transplant Recipients
Published on: August 12, 2017
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Exploring Perturbations in Peripheral B Cell Memory Subpopulations Early after Kidney Transplantation Using
Ariadni Fouza1, Anneta Tagkouta2,3, Maria Daoudaki2
1Department of Transplant Surgery, Medical School, Aristotle University of Thessaloniki, General Hospital "Hippokratio", 54642 Thessaloniki, Greece.
Journal of Clinical Medicine
|October 14, 2023
Summary
Kidney transplant patients show distinct B cell patterns post-transplant. Machine learning identified two patient clusters based on naïve B cells (NBCs) and memory B cells (MBCs), correlating with kidney function (eGFR).
Area of Science:
- Immunology
- Transplantation Science
- Bioinformatics
Background:
- B cells play a crucial role in transplant outcomes.
- This study investigates memory B cell (MBC) and naïve B cell (NBC) phenotypes post-kidney transplantation.
- Unsupervised machine learning is employed to link cellular phenotypes with renal function.
Purpose of the Study:
- To analyze the distribution of MBC subpopulations and NBCs in kidney transplant recipients.
- To determine the association between B cell phenotypes and estimated glomerular filtration rate (eGFR).
- To stratify patients based on B cell profiles and renal function using machine learning.
Main Methods:
- Flow cytometry was used to characterize MBCs and NBCs in 47 stable renal transplant recipients at baseline (T0) and 6 months post-transplant (T6).
- Patient clusters with similar T6 cellular phenotypic profiles were identified using unsupervised machine learning.
- Estimated glomerular filtration rate (eGFR) was compared between identified patient clusters.
Main Results:
- A significant increase in NBC frequency was observed from T0 to T6, while MBC subpopulations remained stable.
- Cluster 1 exhibited a predominance of NBCs with fewer MBCs, whereas Cluster 2 showed a high frequency of MBCs and fewer NBCs.
- Cluster 1 demonstrated a higher eGFR compared to Cluster 2, indicating better renal function.
Conclusions:
- Kidney transplant recipients can be stratified into distinct clusters based on B cell phenotypes (MBCs and NBCs) and eGFR.
- Unsupervised machine learning effectively identifies patient subgroups with differing B cell compositions and renal function.
- This stratification may offer insights into post-transplant outcomes and personalized management strategies.
