Functionality testing of stem cell grafts to predict infectious complications after allogeneic hematopoietic stem cell transplantation

Nilsson, Jakob; Granrot, I.; Mattsson, Jonas; Omazic, Brigitta; Uhlin, Michael; Thunberg, Sarah

doi:10.1111/vox.12521

Cited by 8 publications

(16 citation statements)

References 35 publications

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“…Further, the median cytokine concentrations were higher in Aspergillus and CMV‐EBV‐Flu‐Tet peptide pool cultures than in tetanus‐toxoid cultures (Figure ). Nilsson et al . stimulated donor T cells with viral antigens and found that IL‐1β and IFN‐γ production was higher in grafts transplanted to recipients who had no post‐alloHSCT infectious complications compared to those who did.…”

Section: Discussionmentioning

confidence: 99%

“…Further, the median cytokine concentrations were higher in Aspergillus and CMV-EBV-Flu-Tet peptide pool cultures than in tetanus-toxoid cultures (Figure 4). Nilsson et al 38 stimulated donor T cells with viral antigens and found that IL-1b and IFN-c production was higher in grafts transplanted to recipients who had no post-alloHSCT infectious complications compared to those who did. While further assessment is required, the data presented indicate that cytokine profiles may have potential in infection risk stratification to individualise antimicrobial prophylaxis post-alloHSCT.…”

Section: Discussionmentioning

confidence: 99%

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Contemporary analysis of functional immune recovery to opportunistic and vaccine‐preventable infections after allogeneic haemopoietic stem cell transplantation

et al. 2018

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ObjectivesInfections are a major cause of mortality after allogeneic haemopoietic stem cell transplantation (alloHSCT), and immune recovery is necessary for prevention. Novel transplant procedures have changed the epidemiology of infections but contemporary data on functional immune recovery are limited. In this pilot study, we aimed to measure immune recovery in the current era of alloHSCT.MethodsTwenty, 13, 11, 9 and 9 alloHSCT recipients had blood collected at baseline (time of conditioning) and 3‐, 6‐, 9‐, and 12‐months post‐alloHSCT, respectively. Clinical data were collected, and immune recovery was measured using immunophenotyping, lymphocyte proliferation, cytokine analysis and antibody isotyping.ResultsMedian absolute T‐ and B‐cell counts were below normal from baseline until 9‐ to 12‐months post‐alloHSCT. Median absolute CD4+ T‐cell counts recovered at 12‐months post‐alloHSCT. Positive proliferative responses to Aspergillus, cytomegalovirus (CMV), Epstein‐Barr virus (EBV), influenza and tetanus antigens were detected from 9 months. IL‐6 was the most abundant cytokine in cell cultures. In cultures stimulated with CMV, EBV, influenza and tetanus peptides, the CD4+ T‐cell count correlated with IL‐1β (P = 0.045) and CD8+ T‐cell count with IFNγ (P = 0.013) and IL‐1β (P = 0.012). The NK‐cell count correlated with IL‐1β (P = 0.02) and IL‐17a (P = 0.03). Median serum levels of IgG1, IgG2 and IgG3 were normal while IgG4 and IgA were below normal range throughout follow‐up.ConclusionsThis pilot study demonstrates that immune recovery can be measured using CD4+ T‐cell counts, in vitro antigen stimulation and selected cytokines (IFNγ, IL‐1β, IL‐4, IL‐6, IL‐17, IL‐21, IL‐31) in alloHSCT recipients. While larger studies are required, monitoring immune recovery may have utility in predicting infection risk post‐alloHSCT.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Contemporary analysis of functional immune recovery to opportunistic and vaccine‐preventable infections after allogeneic haemopoietic stem cell transplantation

et al. 2018

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“…Data set. To evaluate the TopS method, we selected for our analysis an available data set from experiments in immunology using multicolor flow cytometry 10,25 . We have used a publicly available high-dimensional data set where cell population identities are known from the expert manual gating.…”

Section: Methodsmentioning

confidence: 99%

“…The data was manually gated cell population labels as the reference populations. The Nilsson rare data contain rare population from bone marrow cells from healthy donor with 44,140 number of cells, 13 cell-surface markers and 358(0.8%) manually gated cells 10,25 . Data was transformed using arcsinh and TopS method as described in Sardiu et al 6 .…”

Section: Methodsmentioning

confidence: 99%

“…We next investigated the use of X-shift clustering 15,16,25,26 on the Nilsson rare flow cytometry data. X-shift (VorteX) is a standalone application with graphical interface that uses the weighted k-means density estimation 15,16,25,26 . Validation of the k value by elbow point gives an optimal k = 62 for 38 clusters in the case of TopS and k=62 for 30 clusters for the use of the original/transformed data (Supplementary Table 3).…”

Section: Topological Scoring Of Dataset With Machine Learning Approachesmentioning

confidence: 99%

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Identification of Stem Cells from Large Cell Populations with Topological Scoring

Sardiu

Andrew

Haug

et al. 2020

Preprint

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Machine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets. Modern high dimensional flow cytometry data sets share many features with other omics datasets like genomics and proteomics. For example, genomics or proteomics datasets can be sparse and have high dimensionality, and flow cytometry datasets can also share these features.This makes flow cytometry data potentially a suitable candidate for employing machine learning and topological scoring strategies, for example, to gain novel insights into patterns within the data. We have previously developed the Topological Score (TopS) and implemented it for the analysis of quantitative protein interaction network datasets. Here we show that the TopS approach for large scale data analysis is applicable to the analysis of a previously described flow cytometry sorted human hematopoietic stem cell dataset. We demonstrate that TopS is capable of effectively sorting this dataset into cell populations and identify rare cell populations. We demonstrate the utility of TopS when coupled with multiple approaches including topological data analysis, Xshift clustering, and t-Distributed Stochastic Neighbor Embedding (t-SNE). Our results suggest that TopS could be effectively used to analyze large scale flow cytometry datasets to find rare cell populations.

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Identification of stem cells from large cell populations with topological scoring

et al. 2021

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Functionality testing of stem cell grafts to predict infectious complications after allogeneic hematopoietic stem cell transplantation

Abstract: Our results suggest that assaying T-cell function before HSCT could determine individual risks for infectious complications and thus aid in clinical decision-making regarding prophylactic and pre-emptive anti-infective therapy.

Cited by 8 publications

References 35 publications

Contemporary analysis of functional immune recovery to opportunistic and vaccine‐preventable infections after allogeneic haemopoietic stem cell transplantation

Contemporary analysis of functional immune recovery to opportunistic and vaccine‐preventable infections after allogeneic haemopoietic stem cell transplantation

Identification of Stem Cells from Large Cell Populations with Topological Scoring

Identification of stem cells from large cell populations with topological scoring

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