Leukocyte Dynamics Influence Reference Gene Stability in Whole Blood: Data-Driven qRT-PCR Normalization Is a Robust Alternative for Measurement of Transcriptional Biomarkers

Variability in donor leukocyte counts confound the use of common RNA sequencing data normalization strategies in transcriptomic biomarker studies performed with whole blood

Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition.

Rats lacking Ucp1 present a novel translational tool for the investigation of thermogenic adaptation during cold challenge

AIM

Valuable studies have tested the role of UCP1 on body temperature maintenance in mice, and we sought to knockout Ucp1 in rats (Ucp1-/- ) to provide insight into thermogenic mechanisms in larger mammals.

METHODS

We used CRISPR/Cas9 technology to create Ucp1-/- rats. Body weight and adiposity were measured, and rats were subjected to indirect calorimetry. Rats were maintained at room temperature or exposed to 4°C for either 24 h or 14 days. Analyses of brown and white adipose tissue and skeletal muscle were conducted via histology, western blot comparison of oxidative phosphorylation proteins, and qPCR to compare mitochondrial DNA levels and mRNA expression profiles. RNA-seq was performed in skeletal muscle.

RESULTS

Ucp1-/- rats withstood 4°C for 14 days, but core temperature steadily declined. All rats lost body weight after 14 days at 4°C, but controls increased food intake more robustly than Ucp1-/- rats. Brown adipose tissue showed signs of decreased activity in Ucp1-/- rats, while mitochondrial lipid metabolism markers in white adipose tissue and skeletal muscle were increased. Ucp1-/- rats displayed more visible shivering and energy expenditure than controls at 4°C. Skeletal muscle transcriptomics showed more differences between genotypes at 23°C than at 4°C.

CONCLUSION

Room temperature presented sufficient cold stress to rats lacking UCP1 to activate compensatory thermogenic mechanisms in skeletal muscle, which were only activated in control rats following exposure to 4°C. These results provide novel insight into thermogenic responses to UCP1 deficiency; and highlight Ucp1-/- rats as an attractive translational model for the study of thermogenesis.

Polyadenylate-binding protein cytoplasmic 1 mediates alternative splicing events of immune-related genes in gastric cancer cells

Polyadenylate-binding protein cytoplasmic 1 (PABPC1) is dysregulated in malignancies, which is considered as a potential therapeutic target for many cancer types. By alternative splicing (AS) for gastric cancer (GC), we described PABPC1-modulated AS events in this study. PABPC1 expression was analyzed in 408 GC tissues from The Cancer Genome Altas (TCGA) database. Human gastric adenocarcinoma (AGS) cells were transfected with PABPC1-specific small interfering RNA (siPABPC1) with siCtrl as a negative control. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was done for the determination of transcripts. To detect the differentially expressed genes (DEGs) and 10 different types of AS events, RNA sequencing (RNA-seq) was performed. DEGs were analyzed for functional categories including gene ontology, and the Kyoto encyclopedia of genes and genomes pathway were analyzed for DEGs. GC displayed an elevated expression of PABPC1. PABPC1 was efficiently knocked down in AGS cells. Here, we excavated 1234 PABPC1-regulated DEGs, among which 502 were down-regulated and 732 were up-regulated compared to the siCtrl group. A total of 94 DEGs were involved in inflammation and immune response. Results from qRT-PCR validated the up-regulation of 10 immune and inflammation-related DEGs in the siPABPC1 group. PABPC1 deficiency causes 1304 AS events differentially occurred in AGS cells. The most common type of AS events regulated by PABPC2 is alternative 5' splice sites. qRT-PCR confirmed the transcription level of five immune-related genes, in which AS events were detected in the siPABPC1 group. PABPC1 knockdown mediates AS events and thus the transcript level of immune and inflammation-related genes in AGS cells. PABPC1-regulated oncogenic AS events display potential as targets for therapeutic development.

Use of deep artificial neural networks to identify stroke during triage via subtle changes in circulating cell counts

Background

The development of tools that could help emergency department clinicians recognize stroke during triage could reduce treatment delays and improve patient outcomes. Growing evidence suggests that stroke is associated with several changes in circulating cell counts. The aim of this study was to determine whether machine-learning can be used to identify stroke in the emergency department using data available from a routine complete blood count with differential.

Methods

Red blood cell, platelet, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were assessed in admission blood samples collected from 160 stroke patients and 116 stroke mimics recruited from three geographically distinct clinical sites, and an ensemble artificial neural network model was developed and tested for its ability to discriminate between groups.

Results

Several modest but statistically significant differences were observed in cell counts between stroke patients and stroke mimics. The counts of no single cell population alone were adequate to discriminate between groups with high levels of accuracy; however, combined classification using the neural network model resulted in a dramatic and statistically significant improvement in diagnostic performance according to receiver-operating characteristic analysis. Furthermore, the neural network model displayed superior performance as a triage decision making tool compared to symptom-based tools such as the Cincinnati Prehospital Stroke Scale (CPSS) and the National Institutes of Health Stroke Scale (NIHSS) when assessed using decision curve analysis.

Conclusions

Our results suggest that algorithmic analysis of commonly collected hematology data using machine-learning could potentially be used to help emergency department clinicians make better-informed triage decisions in situations where advanced imaging techniques or neurological expertise are not immediately available, or even to electronically flag patients in which stroke should be considered as a diagnosis as part of an automated stroke alert system.

Assessment of cell cycle regulators in human peripheral blood cells as markers of cellular senescence

Cellular senescence has gained increasing interest during recent years, particularly due to causal involvement in the aging process corroborated by multiple experimental findings. Indeed, cellular senescence considered to be one of the hallmarks of aging, is defined as a stable growth arrest predominantly mediated by cell cycle regulators p53, p21 and p16. Senescent cells have frequently been studied in the peripheral blood of humans due to its accessibility. This review summarizes ex vivo studies describing cell cycle regulators as markers of senescence in human peripheral blood cells, along with detection methodologies and associative studies examining demographic and clinical characteristics. The utility of techniques such as the quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), microarray, RNA sequencing and nCounter technologies for detection at the transcriptional level, along with Western blotting, enzyme-linked immunosorbent assay and flow cytometry at the translational level, will be brought up at salient points throughout this review. Notably, housekeeping genes or proteins serving as controls such as GAPDH and β-Actin, were found not to be stably expressed in some contexts. As such, optimization and validation of such genes during experimental design were recommended. In addition, the expression of cell cycle regulators was found to vary not only between different types of blood cells such as T cells and B cells but also between stages of cellular differentiation such as naïve T cells and highly differentiated T cells. On the other hand, the associations of the presence of cell cycle regulators with demographics (age, gender, ethnicity, and socioeconomic status), clinical characteristics (body mass index, specific diseases, disease-related parameters) and lifestyle vary in groups of participants. One envisions that increased understanding and insights into the assessment of cell cycle regulators as markers of senescence in human peripheral blood cells will help inform prognostication and clinical intervention in elderly individuals.

Bioinformatic analysis of brain-specific miRNAs for identification of candidate traumatic brain injury blood biomarkers

BACKGROUND

Detection of brain-specific miRNAs in the peripheral blood could serve as a surrogate marker of traumatic brain injury (TBI). Here, we systematically identified brain-enriched miRNAs, and tested their utility as TBI biomarkers in the acute phase of care.

METHODS

Publically available microarray data generated from 29 postmortem human tissues were used to rank 1,364 miRNAs in terms of their degree of brain-specific expression. Levels of the top six ranked miRNAs were then prospectively measured in serum samples collected from 10 Patients with TBI at hospital admission, as well as from 10 controls.

RESULTS

The top six miRNAs identified in our analysis (miR-124-3p, miR-219a-5p, miR-9-5p, miR-9-3p, miR-137, and miR-128-3p) were enriched 70 to 320-fold in brain relative to other tissues, and exhibited dramatically greater brain specificity compared to several miRNAs previously proposed as biomarkers. Furthermore, their levels were elevated in serum from patients with TBI compared to controls, and could collectively discriminate between groups with 90% sensitivity and 100% specificity. Interestingly, subsequent informatic pathway analysis revealed that their target transcripts were enriched for components of signaling pathways active in peripheral organs involved in common post-TBI complications.

CONCLUSIONS

The six candidate miRNAs identified in this preliminary study have promise as blood biomarkers of TBI, and could also be molecular contributors to systemic physiologic changes commonly observed post-injury.

Diagnosis of ischemic stroke using circulating levels of brain-specific proteins measured via high-sensitivity digital ELISA

Limited lower detection ranges associated with traditional immunoassay techniques have prevented the use of brain-specific proteins as blood biomarkers of stroke in the acute phase of care, as these proteins are often only present in circulation at low concentrations. Digital ELISA is a newly developed technique with allows for quantification of proteins in biofluids with up to 1000 times greater sensitivity than conventional ELISA techniques. The purpose of this study was to determine whether the extended lower limits of detection associated with digital ELISA could enable the use of brain-specific proteins as blood biomarkers of ischemic stroke during triage. Blood was sampled from ischemic stroke patients (n = 14) at emergency department admission, as well as from neurologically normal controls matched in terms of risk factors for cardiovascular disease (n = 33). Plasma levels of two brain-specific axonal proteins, neurofilament light chain (NfL) and tau, were measured via digital ELISA, and receiver-operating characteristic analysis was used to determine their ability to discriminate between groups. Plasma levels of NfL and tau were both significantly elevated in stroke patients versus controls, and could respectively discriminate between groups with 92.9% sensitivity / 84.9% specificity, and 85.7% sensitivity / 54.6% specificity. Furthermore, adjustment of measured NfL and Tau levels according to the lower-limits of detection associated with commercially-available conventional ELISA assays resulted in a dramatic and statistically significant decrease in diagnostic performance. Collectively, our results suggest that the increased analytical sensitivity of digital ELISA could enable the use of brain-specific proteins as blood biomarkers of ischemic stroke during triage.

Use of high-sensitivity digital ELISA improves the diagnostic performance of circulating brain-specific proteins for detection of traumatic brain injury during triage

Background: Historically, limited sensitivity associated with traditional immunoassay methods has prevented the use of brain-specific proteins as blood biomarkers of traumatic brain injury (TBI) during triage, as these proteins exhibit low circulating concentrations. Digital ELISA is a newly-developed technique that is up to 1000 times more sensitive than conventional ELISA methods. The purpose of this study was to determine whether the use of digital ELISA over conventional ELISA improves the performance of brain-specific proteins as blood biomarkers of TBI during triage.Methods: Blood was sampled from TBI patients (n = 13) at emergency department admission, as well as from neurologically normal controls (n = 72). Serum levels of two brain-specific proteins, neurofilament light chain (NfL) and Tau, were measured via digital ELISA. Estimated conventional ELISA measures were generated by adjusting values according to the lower limits of detection achievable with commercially available conventional ELISA assays, and receiver operating characteristic (ROC) analysis was used to compare the diagnostic performance of digital ELISA measures to estimated conventional ELISA measures in terms of their ability to discriminate between TBI patients and controls.Results: Used in combination, digital ELISA measures of NfL and Tau could discriminate between groups with 100% sensitivity and 91.7% specificity. Estimated conventional ELISA measures could only discriminate between groups with 7.7% sensitivity and 94.4% specificity. This difference in diagnostic performance was statistically significant when comparing areas under ROC curves.Conclusions: The use of digital ELISA over conventional ELISA methods improves the diagnostic performance of circulating brain-specific proteins for detection of TBI during triage.

AKIRIN1: A Potential New Reference Gene in Human Natural Killer Cells and Granulocytes in Sepsis

Timely and reliable distinction of sepsis from non-infectious systemic inflammatory response syndrome (SIRS) supports adequate antimicrobial therapy and saves lives but is clinically challenging. Blood transcriptional profiling promises to deliver insights into the pathomechanisms of SIRS and sepsis and to accelerate the discovery of urgently sought sepsis biomarkers. However, suitable reference genes for normalizing gene expression in these disease conditions are lacking. In addition, variability in blood leukocyte subtype composition complicates gene profile interpretation. Here, we aimed to identify potential reference genes in natural killer (NK) cells and granulocytes from patients with SIRS and sepsis on intensive care unit (ICU) admission. Discovery by a two-step probabilistic selection from microarray data followed by validation through branched DNA assays in independent patients revealed several candidate reference genes in NK cells including AKIRIN1, PPP6R3, TAX1BP1, and ADRBK1. Initially, no candidate genes could be validated in patient granulocytes. However, we determined highly similar AKIRIN1 expression also in SIRS and sepsis granulocytes and no change by in vitro LPS challenge in granulocytes from healthy donors. Inspection of external neutrophil transcriptome datasets further support unchanged AKIRIN1 expression in human systemic inflammation. As a potential new reference gene in NK cells and granulocytes in infectious and inflammatory diseases, AKIRIN1 may improve our pathomechanistic understanding of SIRS and sepsis and help identifying new sepsis biomarkers.

Analysis of Early Stroke-induced Changes in Circulating Leukocyte Counts using Transcriptomic Deconvolution

Growing evidence suggests that stroke alters the phenotype of the peripheral immune system; better characterization of this response could provide new insights into stroke pathophysiology. In this investigation, we employed a deconvolution approach to informatically infer the cellular composition of the circulating leukocyte pool at multiple timepoints following stroke onset based on whole blood mRNA expression. Microarray data generated from the peripheral blood of 23 cardiovascular disease controls and 23 ischemic stroke patients at 3, 5, and 24 hours post-symptom onset were obtained from a public repository. Transcriptomic deconvolution was used to estimate the relative counts of nine leukocyte populations based on the expression of cell-specific transcripts, and cell counts were compared between groups across timepoints. Inferred counts of lymphoid cell populations including B-cells, CD4+ T-cells, CD8+ T-cells, γδ T-cells, and NK-cells were significantly lower in stroke samples relative to control samples. With respect to myeloid cell populations, inferred counts of neutrophils and monocytes were significantly higher in stroke samples compared to control samples, however inferred counts of eosinophils and dendritic cells were significantly lower. These collective differences were most dramatic in samples collected at 5 and 24 hours post-symptom onset. Findings were subsequently confirmed in a second dataset generated from an independent population of 24 controls and 39 ischemic stroke patients. Collectively, these results offer a comprehensive picture of the early stroke-induced changes to the complexion of the circulating leukocyte pool, and provide some of the first evidence that stroke triggers an acute decrease in eosinophil counts.

Free Circulating miRNAs Measurement in Clinical Settings: The Still Unsolved Issue of the Normalization

Circulating molecules that are released into the circulation in response to specific stimuli are considered potential biomarkers for physiological or pathological processes. Their effective usefulness as biomarkers resides in their stability and high availability in all the biological fluids, combined with the limited invasiveness of intervention. Among the circulating molecules, miRNAs represent a novel class of biomarkers as they possess all the required characteristics such as sensitivity, predictivity, specificity, robustness, translatability, and noninvasiveness.

miRNAs are small non-coding RNAs, that act as inhibitors of protein translation, and intervene in the complex network of the post-transcriptional mechanisms finely regulating gene expression.

The emerging role of miRNAs as potential biomarkers for clinical applications (e.g., cancer and cardiovascular diseases diagnosis and prediction, musculoskeletal disease diagnosis and bone fracture risk prediction), however, requires the standardization of miRNA processing, from sample collection and sample storage, to RNA isolation, RNA reverse-transcription, and data analyses. Normalization is one of the most controversial issues related to quantitative Real-Time PCR data analysis since no universally accepted normalization strategies and reference genes exist, even more importantly, for circulating miRNA quantification. As it is widely demonstrated that the choice of different normalization strategies influences the results of gene expression analysis, it is important to select the most appropriate normalizers for each experimental set. This review discloses on the different strategies adopted in RT-qPCR miRNA normalization and the concerning issues to highlight on the need of a universally accepted methodology to make comparable the results produced by different studies.

Shifts in Leukocyte Counts Drive the Differential Expression of Transcriptional Stroke Biomarkers in Whole Blood

Our group recently identified a panel of ten genes whose RNA expression levels in whole blood have utility for detection of stroke. The purpose of this study was to determine the mechanisms by which these genes become differentially expressed during stroke pathology. First, we assessed the transcriptional distribution of the ten genes across the peripheral immune system by measuring their expression levels on isolated neutrophils, monocytes, B-lymphocytes, CD-4+ T-lymphocytes, CD-8+ T-lymphocytes, and NK-cells generated from the blood of healthy donors (n = 3). Then, we examined the relationship between the whole-blood expression levels of the ten genes and white blood cell counts in a cohort of acute ischemic stroke patients (n = 36) and acute stroke mimics (n = 15) recruited at emergency department admission. All ten genes displayed strong patterns of lineage-specific expression in our analysis of isolated leukocytes, and their whole-blood expression levels were correlated with white blood cell differential across the total patient population, suggesting that many of them are likely differentially expressed in whole blood during stroke as an artifact of stroke-induced shifts in leukocyte counts. Specifically, factor analysis inferred that over 50% of the collective variance in their whole-blood expression levels across the patient population was driven by underlying variance in white blood cell counts alone. However, the cumulative expression levels of the ten genes displayed a superior ability to discriminate between stroke patients and stroke mimics relative to white blood cell differential, suggesting that additional less prominent factors influence their expression levels which add to their diagnostic utility. These findings not only provide insight regarding this particular panel of ten genes, but also into the results of prior stroke transcriptomics studies performed in whole blood.

Monocyte-lymphocyte cross-communication via soluble CD163 directly links innate immune system activation and adaptive immune system suppression following ischemic stroke

CD163 is a scavenger receptor expressed on innate immune cell populations which can be shed from the plasma membrane via the metalloprotease ADAM17 to generate a soluble peptide with lympho-inhibitory properties. The purpose of this study was to investigate CD163 as a possible effector of stroke-induced adaptive immune system suppression. Liquid biopsies were collected from ischemic stroke patients (n = 39), neurologically asymptomatic controls (n = 20), and stroke mimics (n = 20) within 24 hours of symptom onset. Peripheral blood ADAM17 activity and soluble CD163 levels were elevated in stroke patients relative to non-stroke control groups, and negatively associated with post-stroke lymphocyte counts. Subsequent in vitro experiments suggested that this stroke-induced elevation in circulating soluble CD163 likely originates from activated monocytic cells, as serum from stroke patients stimulated ADAM17-dependant CD163 shedding from healthy donor-derived monocytes. Additional in vitro experiments demonstrated that stroke-induced elevations in circulating soluble CD163 can elicit direct suppressive effects on the adaptive immune system, as serum from stroke patients inhibited the proliferation of healthy donor-derived lymphocytes, an effect which was attenuated following serum CD163 depletion. Collectively, these observations provide novel evidence that the innate immune system employs protective mechanisms aimed at mitigating the risk of post-stroke autoimmune complications driven by adaptive immune system overactivation, and that CD163 is key mediator of this phenomenon.