Blood-based protein biomarkers for the diagnosis of acute stroke: A discovery-based SWATH-MS proteomic approach

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Blood-based protein biomarkers for the diagnosis of acute stroke: A discovery-based SWATH-MS proteomic approach

Shubham Misra¹, Praveen Singh², Manabesh Nath¹, Divya Bhalla¹, Shantanu Sengupta², Amit Kumar¹, Awadh K. Pandit¹, Praveen Aggarwal³, Achal K. Srivastava¹, Dheeraj Mohania⁴, Kameshwar Prasad^1,5 and Deepti Vibha^1*

• ¹Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
• ²CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
• ³Department of Emergency Medicine, All India Institute of Medical Sciences, New Delhi, India
• ⁴Dr. R.P. Centre, All India Institute of Medical Sciences, New Delhi, India
• ⁵Department of Neurology, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India

Background and purposes: Recent developments in high-throughput proteomic approach have shown the potential to discover biomarkers for diagnosing acute stroke and to elucidate the pathomechanisms specific to different stroke subtypes. We aimed to determine blood-based protein biomarkers to diagnose total stroke (IS+ICH) from healthy controls, ischemic stroke (IS) from healthy controls, and intracerebral hemorrhage (ICH) from healthy control subjects within 24 h using a discovery-based SWATH-MS proteomic approach.

Methods: In this discovery phase study, serum samples were collected within 24 h from acute stroke (IS & ICH) patients and healthy controls and were subjected to SWATH-MS-based untargeted proteomics. For protein identification, a high-pH fractionated peptide library for human serum proteins (obtained from SCIEX) comprising of 465 proteins was used. Significantly differentially expressed (SDE) proteins were selected using the following criteria: >1.5-fold change for upregulated, <0.67 for downregulated, p-value <0.05, and confirmed/tentative selection using Boruta random forest. Protein–protein interaction network analysis and the functional enrichment analysis were conducted using STRING 11 online tool, g:Profiler tool and Cytoscape 3.9.0 software. The statistical analyses were conducted in R version 3.6.2.

Results: Our study included 40 stroke cases (20 IS, 20 ICH) within 24 h and 40 age-, sex-, hypertension-, and diabetes-matched healthy controls. We quantified 375 proteins between the stroke cases and control groups through SWATH-MS analysis. We observed 31 SDE proteins between total stroke and controls, 16 SDE proteins between IS and controls, and 41 SDE proteins between ICH and controls within 24 h. Four proteins [ceruloplasmin, alpha-1-antitrypsin (SERPINA1), von Willebrand factor (vWF), and coagulation factor XIII B chain (F13B)] commonly differentiated total stroke, IS, and ICH from healthy control subjects. The most common significant pathways in stroke cases involved complement and coagulation cascades, platelet degranulation, immune-related processes, acute phase response, lipid-related processes, and pathways related to extracellular space and matrix.

Conclusion: Our discovery phase study identified potential protein biomarker candidates for the diagnosis of acute stroke and highlighted significant pathways associated with different stroke subtypes. These potential biomarker candidates warrant further validation in future studies with a large cohort of stroke patients to investigate their diagnostic performance.

Introduction

Stroke is a medical emergency in which brain cells die rapidly post its onset. It is broadly classified based on its etiology into two types: (1) ischemic stroke (IS)—occlusion of the artery supplying oxygen-rich blood to the brain resulting in brain cell or tissue death within minutes; and (2) intracerebral hemorrhage (ICH)—rupturing of the blood vessel that bleeds into the surrounding brain leading to further brain damage (1). Despite the two stroke subtypes sharing a similar risk profile (2), they exhibit distinct molecular mechanisms in the acute phase (3–6). Thus, an efficient and rapid diagnosis of stroke is warranted within the first few hours of symptom onset for the effective treatment strategies to be implemented to prevent adverse outcomes. Due to the unavailability of neuroimaging facilities in most developing nations and time-sensitive nature of revascularization therapies, blood biomarkers are needed to aid clinical decision-making. Biomarkers detected in the blood may also help in elucidating the molecular mechanisms underlying the two stroke subtypes.

Recent developments in high-throughput proteomic approaches have shown the potential to discover biomarkers for diagnosing acute stroke and to elucidate the pathomechanisms specific to different stroke subtypes. The label-free approach using data-independent acquisition (DIA) method acquires superior peptide peaks compared to conventional proteomic data-dependent acquisition (DDA) methods and allows screening of a broad range of protein biomarkers with high reproducibility and efficiency.

Few studies in the past have utilized the high-throughput proteomic approaches for blood biomarker identification in stroke (7–11). However, these studies were conducted beyond the 24-h time window and failed to identify the expression pattern of proteins in the acute phase of stroke. Majority of these studies pooled their samples for proteomic analysis, which might lead to false-positive or false-negative results as pooled samples do not reflect the diseased/non-diseased state of a single person (10, 11). Therefore, our exploratory study aimed to determine blood-based protein biomarkers related to the pathogenesis of stroke in the acute phase of onset. Our goal was to provide a list of candidate protein markers that can diagnose and differentiate total stroke (IS + ICH) from healthy controls, IS from healthy controls, and ICH from healthy control subjects within 24 h of symptom onset using a discovery-based SWATH-MS proteomic approach without pooling any sample. We used an age-, sex-, and risk factor- (hypertension and diabetes) matched healthy control group, to identify biomarker expression pattern specific to stroke pathophysiology.

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