Circulating neutrophil transcriptome may reveal intracranial aneurysm signature

Tutino, Vincent M.; Poppenberg, Kerry E.; Jiang, Kaiyu; Jarvis, James N.; Sun, Yijun; Sonig, Ashish; Siddiqui, Adnan H.; Snyder, Kenneth V.; Levy, Elad I.; Kolega, John

doi:10.1371/journal.pone.0191407

Cited by 29 publications

(62 citation statements)

References 42 publications

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“…Fortythree of these samples had been sequenced as a part of our previous studies. 16,17 In all cases, IA diagnosis was confirmed by DSA images. Patient medical record data was also collected to study demographics and comorbidities.…”

Section: Study Enrollmentmentioning

confidence: 78%

“…Neutrophil RNA was extracted as described previously 16 using TRIzol, according to the manufacturer's instructions. Trace DNA was removed by DNase I (Life Technologies, Carlsbad, CA) treatment.…”

Section: Rna Preparationmentioning

confidence: 99%

“…Samples were subjected to 50-cycle, single-read sequencing in a HiSeq2500 system (Illumina) and demultiplexed using Bcl2Fastq. To increase sample size, we combined reads from these new samples with reads from our previous samples 16,17 that were sequenced in the same manner, but for which libraries were constructed using the Illumina TruSeq RNA library Prep Kit V2 (Illumina, San Diego, CA). For all data, per-cycle base-call (BCL) files generated by the Illumina HiSeq2500 were converted to per-read FASTQ files using bcl2fastq version 2.20.0.422 using default parameters.…”

Section: Rna Sequencingmentioning

confidence: 99%

“…12−15 In a small, proof-of-concept study, we previously performed differential expression analysis in casecontrolled cohorts (n = 11 IA, n = 11 control) and found an 82-gene signature that distinguishes IAs from controls. 16 Bioinformatics analyses broadly reflected peripheral neutrophil activation in patients with IA, as genes with elevated expression in the IA group were associated with leukocyte activation, cell activation, and defense response. 16 In a follow-up study, we next utilized machine learning to determine whether an algorithm could predict the presence of unruptured IA using differential gene expression.…”

Section: Introductionmentioning

confidence: 99%

“…16 Bioinformatics analyses broadly reflected peripheral neutrophil activation in patients with IA, as genes with elevated expression in the IA group were associated with leukocyte activation, cell activation, and defense response. 16 In a follow-up study, we next utilized machine learning to determine whether an algorithm could predict the presence of unruptured IA using differential gene expression. 17 In an unmatched cohort (n = 30), 26 highly-informative neutrophil transcripts (FDR < 0.05, abs[fold-change] ≥ 1.5) were used to construct a diagonal Linear Discriminant Analysis model, which predicted the presence of IA with an accuracy of 90% in a small independent cohort (n = 10).…”

Section: Introductionmentioning

confidence: 99%

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Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Poppenberg

Tutino

Li³

et al. 2020

Preprint

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Background Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. Methods Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n=94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n=40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 10 IA-associated genes was used to verify gene expression in a subset of 50 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. Results Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC)=0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 8 of 10 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. Conclusions We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

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Section: Study Enrollmentmentioning

confidence: 78%

Section: Rna Preparationmentioning

confidence: 99%

Section: Rna Sequencingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Poppenberg

Tutino

Li³

et al. 2020

Preprint

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Metabolic alterations of peripheral blood immune cells and heterogeneity of neutrophil in intracranial aneurysms patients

Ya,

Ma,

Liu

et al. 2024

Clinical & Translational Med

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BackgroundIntracranial aneurysms (IAs) represent a severe cerebrovascular disease that can potentially lead to subarachnoid haemorrhage. Previous studies have demonstrated the involvement of peripheral immune cells in the formation and progression of IAs. Nevertheless, the impact of metabolic alterations in peripheral immune cells and changes in neutrophil heterogeneity on the occurrence and progression of IAs remains uncertain.MethodsSingle‐cell Cytometry by Time‐of‐Flight (CyTOF) technology was employed to profile the single‐cell atlas of peripheral blood mononuclear cells (PBMCs) and polymorphonuclear cells (PMNs) in 72 patients with IAs. In a matched cohort, metabolic shifts in PBMC subsets of IA patients were investigated by contrasting the expression levels of key metabolic enzymes with their respective counterparts in the healthy control group. Simultaneously, compositional differences in peripheral blood PMNs subsets between the two groups were analysed to explore the impact of altered heterogeneity in neutrophils on the initiation and progression of IAs. Furthermore, integrating immune features based on CyTOF analysis and clinical characteristics, we constructed an aneurysm occurrence model and an aneurysm growth model using the random forest method in conjunction with LASSO regression.ResultsDifferent subsets exhibited distinct metabolic characteristics. Overall, PBMCs from patients elevated CD98 expression and increased proliferation. Conversely, CD36 was up‐regulated in T cells, B cells and monocytes from the controls but down‐regulated in NK and NKT cells. The comparison also revealed differences in the metabolism and function of specific subsets between the two groups. In terms of PMNs, the neutrophil landscape within patients group revealed a pronounced shift towards heightened complexity. Various neutrophil subsets from the IA group generally exhibited lower expression levels of anti‐inflammatory functional molecules (IL‐4 and IL‐10). By integrating clinical and immune features, the constructed aneurysm occurrence model could precisely identify patients with IAs with high prediction accuracy (AUC = 0.987). Furthermore, the aneurysm growth model also exhibited superiority over ELAPSS scores in predicting aneurysm growth (lower prediction errors and out‐of‐bag errors).ConclusionThese findings enhanced our understanding of peripheral immune cell participation in aneurysm formation and growth from the perspectives of immune metabolism and neutrophil heterogeneity. Moreover, the predictive model based on CyTOF features holds the potential to aid in diagnosing and monitoring the progression of human IAs.

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Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

Stumpo

Staartjes

Esposito

et al. 2021

Acta Neurochirurgica Supplement

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Circulating neutrophil transcriptome may reveal intracranial aneurysm signature

Cited by 29 publications

References 42 publications

Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Metabolic alterations of peripheral blood immune cells and heterogeneity of neutrophil in intracranial aneurysms patients

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

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