Background: Subarachnoid Hemorrhage (SAH) accounts for 2-7% of strokes and has high mortality and morbidity. We sought to identify peripheral blood transcriptome changes in the acute SAH phase that associated with 90-day outcome, as measured by the modified Rankin Scale (mRS), to gain insights about potential mechanisms contributing to long term outcome. Methods: We sequenced the peripheral blood transcriptome of SAH patients within 3 days post ictus and stratified the patients into patients with Good (mRS≤2, n=37) and Poor (mRS≥3, n=23) outcomes at 90-day follow up. We generated the co-expression networks using the Weighted Gene Co-Expression Network Analysis (WGCNA) package to determine modules (groups of co-expressed genes) associated with 90-day SAH outcome. The outcome-significant modules (p<0.05) were further analyzed for their biological relevance using pathway analysis. Results: We identified two outcome-significant modules, the Pink module and the Purple module. The Pink module was enriched (corrected p<0.05) in Monocyte- and Granulocyte-specific genes, while the Purple module and its hubs were enriched in Monocyte-specific genes. The hub genes are the most interconnected genes in each module, which are therefore potential master regulators. The Pink module was enriched (corrected p<0.05) in Neutrophil Degranulation, an inflammatory pathway which was predicted to be activated in patients with worse outcome, in Histone Modification Signaling, which is involved in epigenetic control of gene expression, and in SUMOylation of transcriptional cofactors, which confers post-transcriptional modifications of these cofactors. The Purple module was enriched in 45 canonical biological pathways, including numerous inflammatory pathways predicted to be activated in SAH patients with worse outcomes, such as Neutrophil Degranulation, Phagosome Formation, IL-8 Signaling, and Macrophage Classical Activation Signaling. Conclusions: Early peripheral blood changes in the transcriptome architecture following SAH are associated with long-term outcome. The identified genes and networks may guide the search for potential biomarkers of outcome and novel treatment targets.

Peripheral blood gene expression profiles can distinguish ischemic stroke from intracerebral hemorrhage and controls. However, it can be difficult to clinically distinguish “mimics” of transient ischemic attacks (TIA) and minor strokes from true TIAs and minor strokes. Even with imaging this can be a challenging differential diagnosis in the ED and other acute settings. Hence, there is a need for defining a molecular profile from blood that could guide triage of TIA mimics to reduce ED burden. This multi-site project collected peripheral blood from the participants and generated gene-level data from RNA sequencing. The cohort was composed of patients with a) TIA mimic presentation (n= 142 TIA mimics: an acute onset of neurological symptoms lasting <24h, that can be explained by some identifiable process other than cerebral ischemia including migraine, seizure, peripheral vestibular disease, brain tumors, syncope, root or peripheral nerve disease, and others); b) patients with transient ischemic attacks (n= 181 TIA: acute onset of neurological symptoms and signs that last <24h with a likely underlying cause of either large vessel atherosclerosis or cardioembolic origin but with a negative brain DWI-MRI); c) controls (n= 172 VRFC: no acute brain event, usually with one or more vascular risk factors including diabetes, hypertension or hypercholesterolemia). Differential expression (DE) analyses (fold change >

OBJECTIVE:Approximately half of ischemic strokes (IS) in cancer patients are cryptogenic, with many presumed cardioembolic. We evaluated whether there were specific miRNA and mRNA transcriptome architectures in peripheral blood of IS patients with and without comorbid cancer, and between cardioembolic versus noncardioembolic IS etiologies in comorbid cancer. METHODS:We studied patients with cancer and IS (CS; n = 42), stroke only (SO; n = 41), and cancer only (n = 28), and vascular risk factor-matched controls (n = 30). mRNA-Seq and miRNA-Seq data, analyzed with linear regression models, identified differentially expressed genes in CS versus SO and in cardioembolic versus noncardioembolic CS, and miRNA-mRNA regulatory pairs. Network-level analyses identified stroke etiology-specific responses in CS. RESULTS:A total of 2,085 mRNAs and 31 miRNAs were differentially expressed between CS and SO. In CS, 122 and 35 miRNA-mRNA regulatory pairs, and 5 and 3 coexpressed gene modules, were associated with cardioembolic and noncardioembolic CS, respectively. Complement, growth factor, and immune/inflammatory pathways showed differences between IS etiologies in CS. A 15-gene biomarker panel assembled from a derivation cohort (n = 50) correctly classified 81% of CS and 71% of SO participants in a validation cohort (n = 33). Another 15-gene panel correctly identified etiologies for 13 of 13 CS-cardioembolic and 11 of 11 CS-noncardioembolic participants upon cross-validation; 11 of 16 CS-cryptogenic participants were predicted cardioembolic. INTERPRETATION:We discovered unique mRNA and miRNA transcriptome architecture in CS and SO, and in CS with different IS etiologies. Cardioembolic and noncardioembolic etiologies in CS showed unique coexpression networks and potential master regulators. These may help distinguish CS from SO and identify IS etiology in cryptogenic CS patients. ANN NEUROL 2024;96:565-581.

Gene expression changes in peripheral leukocytes display distinctive profiles after intracerebral hemorrhage (ICH) and ischemic stroke (IS), differentiating both conditions at the molecular level. The breadth of data produced by high-throughput transcriptomic analyses can identify groups of genes and main gene expression drivers that have meaningful functional associations with the disease. This can help prioritize the investigation of key genes for diagnosis and treatment. Thus, we performed whole transcriptome analyses on ICH and IS samples and constructed gene networks from a genome-wide perspective. RNA-seq was performed on peripheral blood (WB) and isolated monocytes (MON) and neutrophils (NEU) (n=6 ICH, n=33 IS and n=9 vascular risk factors control (VRFC) subjects). Gene expression results were used to construct separate co-expression networks for all datasets analyzed (ICH + VRFCs, and IS + VRFCs, for MON, NEU and WB) using Weighted Gene Co-expression Network Analysis. Modules of genes significantly associated with ICH in the ICH + VRFCs network, and with IS in the IS + VRFCs network, were identified. The most highly interconnected genes in each of these modules were identified, representing hub genes that are potential master regulators. Functional annotation of the modules and hubs were done using gene ontology. From the significantly associated modules for ICH and IS in all sample types analyzed, there was little overlap in genes between diagnoses (≤2% in MON, ≤21 % in NEU and ≤16% in WB), and no overlap of ICH or IS hub genes from MON and NEU. In WB, ≤2% of the hubs were common to ICH and IS. It is plausible that these potential master regulators drive diagnosis-specific gene expression profiles. ICH hubs were associated with RNA splicing and mRNA processing (MON), cell adhesion (NEU) and NF-κβ signaling (WB). IS hubs were associated with cell migration (MON), T cell chemotaxis (NEU) and transcription factor activity (WB). In addition, most hubs in IS MON were noncoding RNA. The gene networks and their respective hub genes provide novel cell-specific pathophysiological insights and could represent potential key pharmacological targets and biomarkers.

Background This study identified early immune gene responses in peripheral blood associated with 90-day ischemic stroke (IS) outcomes. Methods Peripheral blood samples from the CLEAR trial IS patients at ≤ 3 h, 5 h, and 24 h after stroke were compared to vascular risk factor matched controls. Whole-transcriptome analyses identified genes and networks associated with 90-day IS outcome assessed using the modified Rankin Scale (mRS) and the NIH Stroke Scale (NIHSS). Results The expression of 467, 526, and 571 genes measured at ≤ 3, 5 and 24 h after IS, respectively, were associated with poor 90-day mRS outcome (mRS ≥ 3), while 49, 100 and 35 genes at ≤ 3, 5 and 24 h after IS were associated with good mRS 90-day outcome (mRS ≤ 2). Poor outcomes were associated with up-regulated genes or pathways such as IL-6, IL-7, IL-1, STAT3, S100A12 , acute phase response, P38/MAPK, FGF, TGFA , MMP9 , NF-kB, Toll-like receptor, iNOS, and PI3K/AKT. There were 94 probe sets shared for poor outcomes vs. controls at all three time-points that correlated with 90-day mRS; 13 probe sets were shared for good outcomes vs. controls at all three time-points; and 46 probe sets were shared for poor vs. good outcomes at all three time-points that correlated with 90-day mRS. Weighted Gene Co-Expression Network Analysis (WGCNA) revealed modules significantly associated with 90-day outcome for mRS and NIHSS. Poor outcome modules were enriched with up-regulated neutrophil genes and with down-regulated T cell, B cell and monocyte-specific genes; and good outcome modules were associated with erythroblasts and megakaryocytes. Finally, genes identified by genome-wide association studies (GWAS) to contain significant stroke risk loci or loci associated with stroke outcome including ATP2B , GRK5 , SH3PXD2A , CENPQ , HOXC4, HDAC9, BNC2 , PTPN11 , PIK3CG , CDK6, and PDE4DIP were significantly differentially expressed as a function of stroke outcome in the current study. Conclusions This study suggests the immune response after stroke may impact functional outcomes and that some of the early post-stroke gene expression markers associated with outcome could be useful for predicting outcomes and could be targets for improving outcomes.

BACKGROUND:After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post-IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy.METHODS:The transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNA-seq as a function of time and etiology after stroke. Differential expression analyses were performed at 0-24 h, 24-48 h, and >48 h following stroke.RESULTS:Unique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils, and whole blood with enrichment of interleukin signaling pathways for different time points and stroke etiologies. Compared to control subjects, gene expression was generally upregulated in neutrophils and generally downregulated in monocytes over all times for cardioembolic, large vessel, and small vessel strokes. Self-organizing maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co-expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood.CONCLUSIONS:Altogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets.

MicroRNA (miRNA) expression is altered following ischemic stroke. Advances in RNA sequencing and bioinformatic tools allow study of isomiRs, isoforms of miRNA, which can have altered regulatory actions from canonical parent miRNA due to base modifications within the molecule or on the flanking regions. Here, we characterize isomiRs in the blood of ischemic stroke patients compared to vascular risk factor controls (VRFCs) and elucidate diversity of the post-stroke miRNA environment. Total RNA was isolated from peripheral blood of 47 ischemic stroke patients and 31 VRFCs using PAXgene protocol. Small RNA libraries were prepared using QIAseq miRNA Library Kit and sequenced to 14±2 M 1x75 bp reads. Raw sequence reads were trimmed, de-duplicated, and profiled using isomiRmap , which separates isomiRs from canonical miRNA sequences based on 3’ and 5’ miRNA modifications. Analysis of differentially expressed isomiRs was performed with DESeq2. A total of 74,562 unique isomiRs were identified, the majority expressed at very low level. Filtering for low abundance left 2,133 isomiRs (from 335 canonical miRNA) expressed in all subjects. Of these, 505 isomiRs (from 129 canonical miRNA) were differentially expressed (LFC > |0.5|, p adj < 0.05) between stroke and VRFCs. These isomiRs represent significant elevations in 3’ modifications (68.3% of DE isomiRs), 5’ modifications (20.2%) and non-template additions (34.3%). miRNA previously implicated in stroke had diverse isomiR profiles including cases where all isomiRs within a miRNA group were differentially expressed (e.g. let-7i) and cases where multiple isomiRs were present but not differentially expressed (e.g. miR-363). Four of 28 differentially expressed let-7i isomiRs have changes to the seed region that interacts with mRNA. Thus, miRNA-mRNA interactions distinct from the canonical let-7i seed may exist and play an altered role in stroke pathology and neuroinflammation. Analysis of small noncoding RNA sensitive to base-specific variations detects many differentially expressed isomiRs and highlights the complexity of the post-stroke miRNA environment. Further study of these isomiRs within mRNA regulatory networks will deepen our understanding of stroke related neuroinflammation and neural repair.

We postulate that myelin injury contributes to cholesterol release from myelin and cholesterol dysmetabolism which contributes to Abeta dysmetabolism, and combined with genetic and AD risk factors, leads to increased Abeta and amyloid plaques. Increased Abeta damages myelin to form a vicious injury cycle. Thus, white matter injury, cholesterol dysmetabolism and Abeta dysmetabolism interact to produce or worsen AD neuropathology. The amyloid cascade is the leading hypothesis for the cause of Alzheimer’s disease (AD). The failure of clinical trials based on this hypothesis has raised other possibilities. Even with a possible new success (Lecanemab), it is not clear whether this is a cause or a result of the disease. With the discovery in 1993 that the apolipoprotein E type 4 allele (APOE4) was the major risk factor for sporadic, late-onset AD (LOAD), there has been increasing interest in cholesterol in AD since APOE is a major cholesterol transporter. Recent studies show that cholesterol metabolism is intricately involved with Abeta (Aβ)/amyloid transport and metabolism, with cholesterol down-regulating the Aβ LRP1 transporter and upregulating the Aβ RAGE receptor, both of which would increase brain Aβ. Moreover, manipulating cholesterol transport and metabolism in rodent AD models can ameliorate pathology and cognitive deficits, or worsen them depending upon the manipulation. Though white matter (WM) injury has been noted in AD brain since Alzheimer’s initial observations, recent studies have shown abnormal white matter in every AD brain. Moreover, there is age-related WM injury in normal individuals that occurs earlier and is worse with the APOE4 genotype. Moreover, WM injury precedes formation of plaques and tangles in human Familial Alzheimer’s disease (FAD) and precedes plaque formation in rodent AD models. Restoring WM in rodent AD models improves cognition without affecting AD pathology. Thus, we postulate that the amyloid cascade, cholesterol dysmetabolism and white matter injury interact to produce and/or worsen AD pathology. We further postulate that the primary initiating event could be related to any of the three, with age a major factor for WM injury, diet and APOE4 and other genes a factor for cholesterol dysmetabolism, and FAD and other genes for Abeta dysmetabolism.

Investigating the short and long-term signaling mechanisms via small extracellular vesicles (sEVs) in peripheral blood following human Ischemic Stroke (IS) and Intracerebral Hemorrhage (ICH) is of great interest. The sEV’s cargo can induce distant responses which can be used to derive potential novel therapeutic targets and biomarkers to differentiate the two brain pathologies. Thus, we sequenced the miRNA cargo derived from peripheral blood sEVs in IS (n=3), ICH (n=3), and Vascular Risk Factor-matched Control (VRFC; n=3) subjects. Subjects were divided into contrast groups (IS vs VRFC, ICH vs VRFC, ICH vs IS), and log2 transformed expression underwent Kruskal-Wallis tests to identify differentially expressed (DE; p<0.05) miRNAs. We found 55 DE miRNAs in IS vs VRFC, 38 in ICH vs VRFC, and 45 in ICH vs IS ( Fig. 1A ). The combination of these miRNAs differentiated the three groups on Principal Components Analysis ( Fig. 1B ). IS associated miRNA included miR-30a, miR-30b, and miR-144. miR-30a is involved in hematopoietic stem cell self-renewal and can impair B cell differentiation. miR-30b may be an immune suppressor via Notch1. In male mice, miR-144 is protective against atherosclerosis. ICH associated miRNA included miR-195, miR-1-3p, and miR-20b-5p. miR-195 can inhibit the pro-inflammatory roles of macrophages. miR-1-3p is involved in cardiomyocyte development, can target TLR1 (Toll-Like Receptor 1), and may regulate autophagy. miR-20b-5p reduces Amyloid Precursor Protein (APP) mRNA and protein levels; vascular accumulation of APP is one cause of Lobar ICH. We show differential expression of sEV-derived miRNAs in peripheral blood of human IS and ICH patients that are involved in relevant signaling processes. sEV cargo profiles pose a largely underexplored intercellular signaling mechanism in IS and ICH with the potential to better characterize long distance signaling from injured brain to peripheral blood leukocytes in these brain disorders.

Objective: Identifying the molecular underpinnings associated with long-term functional outcome after ischemic stroke (IS) could guide search for treatments for improved outcome. Thus, we identified early peripheral immune gene expression (GE) responses associated with 90-day IS outcome, as measured by the Barthel Index (BI). Methods: RNA from 108 samples from three peripheral blood draws (≤3h – before thrombolytic treatment; 5h and 24h - post-treatment) from 36 CLEAR-trial subjects was analyzed on Affymetrix arrays. We performed Analysis of Covariance accounting for treatment (tPA or tPA+eptifibatide), hypercholesterolemia, hypertension, diabetes, age, sex and BI. Genes whose partial correlation with BI was significant ( P <0.005) were identified. Results: There were 206, 865, and 209 genes that significantly correlated with 90-day BI at ≤3h, 5h and 24h post ictus , respectively. The gene lists at all three time-points were enriched in B-cell specific genes (hypergeometric probability p<0.05), while the 5h gene list was also enriched in neutrophil-specific genes, and the 24h gene list was also enriched in monocyte-specific genes. B Cell Receptor Signaling pathway was predicted activated in subjects with better 90-day outcome at the ≤3h time-point, while ICOS-ICOSL Signaling in T Helper Cells and Role of NFAT in Regulation of the Immune Response were predicted activated at the 24h time-point.Immune-related pathways were enriched at three time-points including B cell-related, IL-7, and T cell signaling pathways. Apoptosis was suppressed in patients with better 90-day outcome at 3h and 24h post ictus . Among the genes associated with the 90-day BI were genes important for stroke and repair after stroke, such as KCNG1 , Potassium voltage-gated channel subfamily G member 1, which like other potassium channels could modulate stroke outcome. KCNG1 expression was positively correlated with 90-day BI at ≤3h and 5h. TERML4 , implicated in inflammatory response and human coronary arterial calcification had expression that positively correlated with BI at all three time-points. Conclusion: The findings expand our understanding of the early molecular biology associated with long-term stroke outcome and may serve as potential targets to improve outcome.

The peripheral immune system response to Intracerebral Hemorrhage (ICH) may differ with ICH in different brain locations. Thus, we investigated peripheral blood mRNA expression of Deep ICH, Lobar ICH, and vascular risk factor-matched control subjects (n = 59). Deep ICH subjects usually had hypertension. Some Lobar ICH subjects had cerebral amyloid angiopathy (CAA). Genes and gene networks in Deep ICH and Lobar ICH were compared to controls. We found 774 differentially expressed genes (DEGs) and 2 co-expressed gene modules associated with Deep ICH, and 441 DEGs and 5 modules associated with Lobar ICH. Pathway enrichment showed some common immune/inflammatory responses between locations including Autophagy, T Cell Receptor, Inflammasome, and Neuroinflammation Signaling. Th2, Interferon, GP6, and BEX2 Signaling were unique to Deep ICH. Necroptosis Signaling, Protein Ubiquitination, Amyloid Processing, and various RNA Processing terms were unique to Lobar ICH. Finding amyloid processing pathways in blood of Lobar ICH patients suggests peripheral immune cells may participate in processes leading to perivascular/vascular amyloid in CAA vessels and/or are involved in its removal. This study identifies distinct peripheral blood transcriptome architectures in Deep and Lobar ICH, emphasizes the need for considering location in ICH studies/clinical trials, and presents potential location-specific treatment targets.

This study identified early immune gene responses in peripheral blood associated with 90-day ischemic stroke (IS) outcomes and an early gene profile that predicted 90-day outcomes. Peripheral blood from the CLEAR trial IS patients was compared to vascular risk factor matched controls. Whole-transcriptome analyses identified genes and networks associated with 90-day IS outcome (NIHSS-NIH Stroke Scale, mRS-modified Rankin Scale). The expression of 467, 526, and 571 genes measured at ≤3, 5 and 24 hours after IS, respectively, were associated with poor 90-day mRS outcome (mRS=3-6), while 49, 100 and 35 associated with good mRS 90-day outcome (mRS=0-2). Poor outcomes were associated with up-regulated MMP9 , S100A12 , interleukin-related and STAT3 pathways. Weighted Gene Co-Expression Network Analysis (WGCNA) revealed modules significantly associated with 90-day outcome. Poor outcome modules were enriched in down-regulated T cell and monocyte-specific genes plus up-regulated neutrophil genes and good outcome modules were associated with erythroblasts and megakaryocytes. Using the difference in gene expression between 3 and 24 hours, 10 genes correctly predicted 100% of patients with Good 90-day mRS outcome and 67% with Poor mRS outcome (AUC=0.88) in a validation set. The predictors included AVPR1A , which mediates platelet aggregation, release of coagulation factors and exacerbates the brain inflammatory response; and KCNK1 ( TWIK-1 ), a member of a two-pore potassium channel family, which like other potassium channels likely modulates stroke outcomes. This study suggests the immune response after stroke impacts long-term functional outcomes. Furthermore, early post-stroke gene expression may predict stroke outcomes and outcome-associated genes could be targets for improving outcomes.

During the first hours after stroke onset, neurological deficits can be highly unstable: some patients rapidly improve, while others deteriorate. This early neurological instability has a major impact on long-term outcome. Here, we aimed to determine the genetic architecture of early neurological instability measured by the difference between the National Institutes of Health Stroke Scale (NIHSS) within 6 h of stroke onset and NIHSS at 24 h. A total of 5876 individuals from seven countries (Spain, Finland, Poland, USA, Costa Rica, Mexico and Korea) were studied using a multi-ancestry meta-analyses. We found that 8.7% of NIHSS at 24 h of variance was explained by common genetic variations, and also that early neurological instability has a different genetic architecture from that of stroke risk. Eight loci (1p21.1, 1q42.2, 2p25.1, 2q31.2, 2q33.3, 5q33.2, 7p21.2 and 13q31.1) were genome-wide significant and explained 1.8% of the variability suggesting that additional variants influence early change in neurological deficits. We used functional genomics and bioinformatic annotation to identify the genes driving the association from each locus. Expression quantitative trait loci mapping and summary data-based Mendelian randomization indicate that ADAM23 (log Bayes factor = 5.41) was driving the association for 2q33.3. Gene-based analyses suggested that GRIA1 (log Bayes factor = 5.19), which is predominantly expressed in the brain, is the gene driving the association for the 5q33.2 locus. These analyses also nominated GNPAT (log Bayes factor = 7.64) ABCB5 (log Bayes factor = 5.97) for the 1p21.1 and 7p21.1 loci. Human brain single-nuclei RNA-sequencing indicates that the gene expression of ADAM23 and GRIA1 is enriched in neurons. ADAM23, a presynaptic protein and GRIA1, a protein subunit of the AMPA receptor, are part of a synaptic protein complex that modulates neuronal excitability. These data provide the first genetic evidence in humans that excitotoxicity may contribute to early neurological instability after acute ischaemic stroke.

Gene expression changes in peripheral blood reflect injury and repair processes occurring post ischemic stroke (IS). Our study explored the dynamic time-dependent expression of key genes involved in the immune response after IS to better understand the biology and to identify specific diagnostic biomarkers. Using RNA-sequencing, we analyzed gene expression profiles of 38 IS patients and 18 controls with at least one vascular risk factor (VRFC) including diabetes and/or hypertension and/or hypercholesterolemia in isolated monocytes, neutrophils and whole blood. We used two approaches: Weighted Gene Co-expression Network Analysis (WGCNA) with respect to time after stroke onset; and differential expression analyses with subject samples split into time points (TPs) from stroke onset (TP0=VRFC; TP1=0-24 h; TP2=24-48 h; and TP3≥48 h). In WGCNA, highly interconnected “hub” genes were identified for modules significant to time (p<0.05). Differentially expressed genes (DEGs) with diagnosisхTP p<0.02 and fold-change>

OMICs-based technologies prove the opportunity to assess multiple nucleic acids, proteins, lipids, and metabolites associated with stroke. They could prove useful in understanding the pathophysiology of ischemic stroke (IS) and intracerebral hemorrhage (ICH) as well as potentially providing novel biomarkers for diagnosis, prognosis, treatment selection, cause of IS and ICH, and discovery of novel subgroups. In this chapter we review OMICs-based approaches in stroke including epigenomics, transcriptomics, proteomics, metabolomics, and lipidomics. Epigenetics is DNA methylation, histone modifications, transcription factors, microRNAs, and long intervening noncoding RNAs. Transcriptomics refers to the entire transcriptome of ∼20,000 genes which are alternatively spliced into ∼250,000 alternatively spliced, unique mRNA transcripts. RNAseq, arrays, and other technologies are used to study the transcriptome. Proteomics refers to all of the proteins found within a cell or biofluid (like serum or plasma), with >250,000 proteins from their mRNA transcripts. Mass spectrometry, antibody arrays, and other approaches are used to study the proteome. Metabolomics refers to the study of all metabolites in a cell or biological fluid, and similarly lipidomics is the study of all lipids in a cell or fluid. Nuclear magnetic resonance spectroscopy and mass spectrometry are used to study the metabolome and lipidome. To date, there are emerging studies providing proof of principle that OMICs approaches might eventually be used to diagnose and differentiate IS or ICH, identify the causes of IS and ICH, predict the cause of cryptogenic stroke, differentiate transient ischemic attacks (TIAs) from TIA mimics, predict patients likely to develop stroke, predict prognosis of IS and ICH, identify patients at greatest risk for hemorrhagic transformation, and potentially stratify patients for different types of treatments. While no OMICs-based test is currently used in practice, ongoing studies over the next decade will likely identify precision markers to aid clinicians in the diagnosis, treatment decisions, and risk classification of patients with stroke.

The mechanisms of cognitive decline after intraventricular hemorrhage (IVH) in some patients continue to be poorly understood. Multiple rodent models of intraventricular or subarachnoid hemorrhage have only shown mild or even no cognitive impairment on subsequent behavioral testing. In this study, we show that intraventricular hemorrhage only leads to a significant spatial memory deficit in the Morris water maze if it occurs in the setting of an elevated intracranial pressure (ICP). Histopathological analysis of these IVH + ICP animals did not show evidence of neuronal degeneration in the hippocampal formation after 2 weeks but instead showed significant microglial activation measured by lacunarity and fractal dimensions. RNA sequencing of the hippocampus showed distinct enrichment of genes in the IVH + ICP group but not in IVH alone having activated microglial signaling pathways. The most significantly activated signaling pathway was the classical complement pathway, which is used by microglia to remove synapses, followed by activation of the Fc receptor and DAP12 pathways. Thus, our study lays the groundwork for identifying signaling pathways that could be targeted to ameliorate behavioral deficits after IVH.