Gene expression profiles of ischemic stroke clots retrieved by mechanical thrombectomy are associated with disease etiology

Decoding Molecular Mechanisms Underlying Outcomes After Ischemic Stroke Thrombectomy by RNA Sequencing of Retrieved Clots

BACKGROUND

Transcriptomic profiling has emerged as a powerful tool for exploring the molecular landscape of ischemic stroke clots and providing insights into the pathophysiological mechanisms underlying stroke progression and recovery. In this study, we aimed to investigate the relationship between stroke clot transcriptomes and stroke thrombectomy outcome, as measured by early neurological improvement (ENI) 30 (i.e., a 30% reduction in NIHSS at 24 h post-thrombectomy).

HYPOTHESIS

We hypothesized that there exist distinct clot gene expression patterns between good and poor neurological outcomes.

METHODS

Transcriptomic analysis of 32 stroke clots retrieved by mechanical thrombectomy was conducted. Transcriptome data of these clots were analyzed to identify differentially expressed genes (DEGs), defined as those with a log(fold-change) ≥ 1.5 and q < 0.05 between samples with good and poor early neurological outcomes. Gene ontology and bioinformatics analyses were performed on genes with p < 0.01 to identify enriched biological processes and Ingenuity Pathway Analysis canonical pathways. Moreover, AUC analysis assessed the predictive power of DEGs for 90-day function outcome (mRS ≤ 2) and cellular composition of clot was predicted using CIBERSORT. We also assessed whether differential enrichment of immune cell types could indicate patient survival.

RESULTS

A total of 41 DEGs were identified. Bioinformatics showed that enriched biological processes and pathways emphasized the chronic immune response and matrix metalloproteinase inhibition. Moreover, 25 of the DEGs were found to be significant predictors of 90-day mRS. These genes were indicative of monocytes enrichment and neutrophil depletion in patients with poorer outcomes.

CONCLUSION

Our study revealed a distinct gene expression pattern and dysregulated biological pathways associated with ENI. This expression pattern was also predictive of long-term outcome, suggesting a biological link between those ENIs and 90-day mRS.

Hybrid Clot Histomic-Transcriptomic Models Predict Functional Outcome After Mechanical Thrombectomy in Acute Ischemic Stroke

BACKGROUND AND OBJECTIVES

Histologic and transcriptomic analyses of retrieved stroke clots have identified features associated with patient outcomes. Previous studies have demonstrated the predictive capacity of histology or expression features in isolation. Few studies, however, have investigated how paired histologic image features and expression patterns from the retrieved clots can improve understanding of clot pathobiology and our ability to predict long-term prognosis. We hypothesized that computational models trained using clot histomics and mRNA expression can predict early neurological improvement (ENI) and 90-day functional outcome (modified Rankin Scale Score, mRS) better than models developed using histological composition or expression data alone.

METHODS

We performed paired histological and transcriptomic analysis of 32 stroke clots. ENI was defined as a delta-National Institutes of Health Stroke Score/Scale > 4, and a good long-term outcome was defined as mRS ≤2 at 90 days after procedure. Clots were H&E-stained and whole-slide imaged at 40×. An established digital pathology pipeline was used to extract 237 histomic features and to compute clot percent composition (%Comp). When dichotomized by either the ENI or mRS thresholds, differentially expressed genes were identified as those with absolute fold-change >1.5 and q < 0.05. Machine learning with recursive feature elimination (RFE) was used to select clot features and evaluate computational models for outcome prognostication.

RESULTS

For ENI, RFE identified 9 optimal histologic and transcriptomic features for the hybrid model, which achieved an accuracy of 90.8% (area under the curve [AUC] = 0.98 ± 0.08) in testing and outperformed models based on histomics (AUC = 0.94 ± 0.09), transcriptomics (AUC = 0.86 ± 0.16), or %Comp (AUC = 0.70 ± 0.15) alone. For mRS, RFE identified 7 optimal histomic and transcriptomic features for the hybrid model. This model achieved an accuracy of 93.7% (AUC = 0.94 ± 0.09) in testing, also outperforming models based on histomics (AUC = 0.90 ± 0.11), transcriptomics (AUC = 0.55 ± 0.27), or %Comp (AUC = 0.58 ± 0.16) alone.

CONCLUSION

Hybrid models offer improved outcome prognostication for patients with stroke. Identified digital histology and mRNA signatures warrant further investigation as biomarkers of patient functional outcome after thrombectomy.

Multimodal CT imaging of ischemic stroke thrombi identifies scale-invariant radiomic features that reflect clot biology

BACKGROUND

Biological interpretability of ischemic stroke clot imaging remains challenging.

OBJECTIVE

To carry out paired CT/micro-CT imaging of ischemic stroke clots retrieved by thrombectomy with the aim of identifying interpretable image features that are correlated among pretreatment image modalities and post-treatment histopathology.

METHODS

We performed multimodal CT imaging and histology for 10 stroke clots retrieved by mechanical thrombectomy. Clots were manually segmented from co-registered, pretreatment CT angiography (CTA) and non-contrast CT (NCCT). For the same cases, retrieved clots were iodine-stained, and imaged with a ScanCo micro-CT 100 (4.9 µm resolution). Afterwards, clots were subjected to histological processing (hematoxylin and eosin staining) and whole slide scanned (40X). Clot radiomic features (RFs) (n=93 per modality, 279 total) were extracted using PyRadiomics and histological composition was computed using Orbit Image Analysis. Correlation analysis was used to test associations between micro-CT and CTA (or NCCT) RFs as well as between RFs and histological composition. Statistical significance was considered at R≥0.65 and q<0.05.

RESULTS

From paired RF correlation analysis, we identified 23 scale-invariant RFs with significant correlation between micro-CT and CTA (18), and micro-CT and NCCT (5). Correlation of unpaired RFs identified 377 positively and 36 negatively correlated RFs between micro-CT and CTA, and 168 positively and 41 negatively correlated RFs between micro-CT and NCCT. Scale-invariant RFs computed from CTA and NCCT demonstrated significant correlation with red blood cell and fibrin-platelet components, while micro-CT RFs were found to be correlated with white blood cell percent composition.

CONCLUSION

Multimodal CT, radiomic, and histological analysis of stroke clots can help to bridge the gap between pretreatment imaging and clot pathobiology.

CT hyperdense cerebral artery sign reflects distinct proteomic composition in acute ischemic stroke thrombus

BACKGROUND

Hyperdense cerebral artery sign (HCAS) is an imaging biomarker in acute ischemic stroke (AIS) that has been shown to be associated with various clinical outcomes and stroke etiology. While prior studies have correlated HCAS with histopathological composition of cerebral thrombus, it is unknown whether and to what extent HCAS is also associated with distinct clot protein composition.

METHODS

Thromboembolic material from 24 patients with AIS were retrieved via mechanical thrombectomy and evaluated with mass spectrometry in order to characterize their proteomic composition. Presence (+) or absence (-) of HCAS on preintervention non-contrast head CT was then determined and correlated with thrombus protein signature with abundance of individual proteins calculated as a function HCAS status.

RESULTS

24 clots with 1797 distinct proteins in total were identified. 14 patients were HCAS(+) and 10 were HCAS(-). HCAS(+) were most significantly differentially abundant in actin cytoskeletal protein (P=0.002, Z=2.82), bleomycin hydrolase (P=0.007, Z=2.44), arachidonate 12-lipoxygenase (P=0.004, Z=2.60), and lysophospholipase D (P=0.007, Z=2.44), among other proteins; HCAS(-) clots were differentially enriched in soluble N-ethylmaleimide-sensitive factor (NSF) attachment protein (P=0.0009, Z=3.11), tyrosine-protein kinase Fyn (P=0.002, Z=2.84), and several complement proteins (P<0.05, Z>1.71 for all), among numerous other proteins. Additionally, HCAS(-) thrombi were enriched in biological processes involved with plasma lipoprotein and protein-lipid remodeling/assembling, and lipoprotein metabolic processes (P<0.001), as well as cellular components including mitochondria (P<0.001).

CONCLUSIONS

HCAS is reflective of distinct proteomic composition in AIS thrombus. These findings suggest that imaging can be used to identify mechanisms of clot formation or maintenance at the protein level, and might inform future research on thrombus biology and imaging characterization.

Bio-Impedance Spectroscopy of Retained Cells Using a Micro-Perforated Sensing Membrane Filtrating Whole Blood Samples under High Flowrate

Blood filtration using micro-fabricated devices is an interdisciplinary topic of research and innovation driven by clinical applications in cytapheresis, cardiovascular disease monitoring, or liquid biopsy. In this paper, we demonstrate that a micro-perforated membrane can be equipped with sensing microelectrodes for detecting, in situ and in real-time, the capture of cellular material during ex vivo filtration of whole blood under high flow rates. This work describes the fabrication process of the sift and detection microdevice. We demonstrate that reliable electrical signals can be measured in whole blood samples flowing inside a fluidic system at typical flow rates, as large as 11.5 mL/min, hence allowing for large-volume sample processing. The in situ monitoring of the electrical impedance of the microelectrodes is shown to characterize the accumulation of living circulating cells retained by the filtrating membrane, opening interesting applications for monitoring blood filtration processes.

Advancing Stroke Research on Cerebral Thrombi with Omic Technologies

Cerebrovascular diseases represent a leading cause of disability, morbidity, and death worldwide. In the last decade, the advances in endovascular procedures have not only improved acute ischemic stroke care but also conceded a thorough analysis of patients' thrombi. Although early anatomopathological and immunohistochemical analyses have provided valuable insights into thrombus composition and its correlation with radiological features, response to reperfusion therapies, and stroke etiology, these results have been inconclusive so far. Recent studies applied single- or multi-omic approaches-such as proteomics, metabolomics, transcriptomics, or a combination of these-to investigate clot composition and stroke mechanisms, showing high predictive power. Particularly, one pilot studies showed that combined deep phenotyping of stroke thrombi may be superior to classic clinical predictors in defining stroke mechanisms. Small sample sizes, varying methodologies, and lack of adjustments for potential confounders still represent roadblocks to generalizing these findings. However, these techniques hold the potential to better investigate stroke-related thrombogenesis and select secondary prevention strategies, and to prompt the discovery of novel biomarkers and therapeutic targets. In this review, we summarize the most recent findings, overview current strengths and limitations, and present future perspectives in the field.