Reduce Sample Heterogeneity Research Articles

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders.

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

Improved selection of participants in genetic longevity studies: family scores revisited

BackgroundAlthough human longevity tends to cluster within families, genetic studies on longevity have had limited success in identifying longevity loci. One of the main causes of this limited success is the selection of participants. Studies generally include sporadically long-lived individuals, i.e. individuals with the longevity phenotype but without a genetic predisposition for longevity. The inclusion of these individuals causes phenotype heterogeneity which results in power reduction and bias. A way to avoid sporadically long-lived individuals and reduce sample heterogeneity is to include family history of longevity as selection criterion using a longevity family score. A main challenge when developing family scores are the large differences in family size, because of real differences in sibship sizes or because of missing data.MethodsWe discussed the statistical properties of two existing longevity family scores: the Family Longevity Selection Score (FLoSS) and the Longevity Relatives Count (LRC) score and we evaluated their performance dealing with differential family size. We proposed a new longevity family score, the mLRC score, an extension of the LRC based on random effects modeling, which is robust for family size and missing values. The performance of the new mLRC as selection tool was evaluated in an intensive simulation study and illustrated in a large real dataset, the Historical Sample of the Netherlands (HSN).ResultsEmpirical scores such as the FLOSS and LRC cannot properly deal with differential family size and missing data. Our simulation study showed that mLRC is not affected by family size and provides more accurate selections of long-lived families. The analysis of 1105 sibships of the Historical Sample of the Netherlands showed that the selection of long-lived individuals based on the mLRC score predicts excess survival in the validation set better than the selection based on the LRC score .ConclusionsModel-based score systems such as the mLRC score help to reduce heterogeneity in the selection of long-lived families. The power of future studies into the genetics of longevity can likely be improved and their bias reduced, by selecting long-lived cases using the mLRC.

Multimodal genome‐wide meta‐analysis of brain amyloidosis reveals heterogeneity across CSF, PET, and pathological amyloid measures

AbstractBackgroundAmyloidosis in Alzheimer’s disease (AD) is a common feature that can be measured via cerebrospinal fluid (CSF), positron emission tomography (PET), and autopsy measures of neuritic plaques. While individual genome‐wide association studies (GWAS) have identified more than 40 loci as potential biological drivers of AD, fewer drivers of amyloidosis have been identified due to the small sample sizes in endophenotype analyses. Combining unimodal measures of amyloid into a larger multi‐modal analysis could provide new insight into mechanisms driving amyloidosis. The goal of this study was to conduct a large meta‐analysis of CSF‐, PET‐, and pathology‐derived metrics of amyloid positivity.Method11,941 non‐Hispanic white participants (CSF = 2505, PET = 3976, Autopsy = 5460) from 14 cohorts were analyzed. To facilitate cross‐modality harmonization, a binarized amyloid status (negative/positive) was determined on an individual cohort basis using two approaches: a Gaussian mixture model clustering algorithm for CSF/PET measurements and CERAD thresholds (CERAD>sparse or CERAD ≤ sparse) for autopsy measures of amyloid pathology. In total, our analysis included 5,634 amyloid‐negative and 6,307 amyloid‐positive individuals. GWAS using a logistic regression model covarying for age and sex were performed in each of the 14 cohorts, and a meta‐analysis was performed within each modality. These meta‐analyses were followed with a multi‐modal meta‐analysis. The standard genome‐wide threshold of statistical significance (p < 5 × 10−8) was applied. Additionally, we performed candidate analysis for the 39 previously published clinical AD or amyloid risk loci.ResultAside from variants within the APOE region, our multi‐modal meta‐analysis did not identify any genome‐wide loci (Figure 1). A lack of convergent GWAS signals was also observed when stratifying by age, sex, and APOE carrier status. However, our candidate gene analysis demonstrated some consistency across modalities. Two prior loci were significant in 2/3 modalities, including ABCA7 (rs4147929; p = 1.98 × 10−4) and CLU (rs4236671; p = 0.001).ConclusionIn the largest GWAS of brain amyloidosis, we found a lack of convergence across modalities, suggesting substantial heterogeneity across measures and cohorts. Candidate analyses highlight some consistent signals in ABCA7 and CLU. Results suggest continued emphasis on increasing sample sizes for endophenotype analyses and on standardized methodologies to reduce sample heterogeneity to improve statistical power.

Glutamine-walking: Creating reactive substrates for transglutaminase-mediated protein labeling.

Chemically modified proteins are increasingly being tested and approved as therapeutic products. Batch-to-batch homogeneity is crucial to ensure safety and quality of therapeutic products. Highly selective protein modification may be achieved using enzymatic routes. Microbial transglutaminase (mTG) is a robust, easy to use and well-established enzyme that is used at a very large scale in the food industry such that its efficacy and its safety for human consumption are well established. In the context of therapeutic protein modification, mTG should crosslink one or more glutamines on the target protein with an aminated moiety such as a solubilizer, a tracer or a cytotoxic moiety. mTG has the advantage of being unreactive toward the majority of surface-exposed glutamines on most proteins, reducing sample heterogeneity. The caveat is that there may be no reactive glutamine on the target protein, or else a reactive glutamine may be found in a location where its modification compromises function of the target protein. Here we describe the glutamine-walk (Gln-walk), a straightforward method to create a glutamine-substrate site that is reactive to mTG in a target protein. Iterative substitution of single amino acids to a glutamine is followed by facile identification of reactivity with mTG, where covalent labeling of the target with an aminated fluorophore allows visualization of the most reactive modified targets. The approach is empirical; knowledge of the target protein structure and functional regions facilitates application of the method.

PAT-H-MS coupled with laser microdissection to study histone post-translational modifications in selected cell populations from pathology samples

BackgroundAberrations in histone post-translational modifications (hPTMs) have been linked with various pathologies, including cancer, and could not only represent useful biomarkers but also suggest possible targetable epigenetic mechanisms. We have recently developed an approach, termed pathology tissue analysis of histones by mass spectrometry (PAT-H-MS), that allows performing a comprehensive and quantitative analysis of histone PTMs from formalin-fixed paraffin-embedded pathology samples. Despite its great potential, the application of this technique is limited by tissue heterogeneity.MethodsIn this study, we further implemented the PAT-H-MS approach by coupling it with techniques aimed at reducing sample heterogeneity and selecting specific portions or cell populations within the samples, such as manual macrodissection and laser microdissection (LMD).ResultsWhen applied to the analysis of a small set of breast cancer samples, LMD-PAT-H-MS allowed detecting more marked changes between luminal A-like and triple negative patients as compared with the classical approach. These changes included not only the already known H3 K27me3 and K9me3 marks, but also H3 K36me1, which was found increased in triple negative samples and validated on a larger cohort of patients, and could represent a potential novel marker distinguishing breast cancer subtypes.ConclusionsThese results show the feasibility of applying techniques to reduce sample heterogeneity, including laser microdissection, to the PAT-H-MS protocol, providing new tools in clinical epigenetics and opening new avenues for the comprehensive analysis of histone post-translational modifications in selected cell populations.

Phenomics: expanding the role of clinical evaluation in genomic studies.

With advances in high-throughput genotyping technologies, the rate-limiting step of large-scale genetic investigations has become the collection of sensitive and specific phenotype information in large samples of study participants. Clinicians play a pivotal role for successful genetic studies because sound clinical acumen can substantially increase study power by reducing measurement error and improving diagnostic precision for translational research. Phenomics is the systematic measurement and analysis of qualitative and quantitative traits, including clinical, biochemical, and imaging methods, for the refinement and characterization of a phenotype. Phenomics requires deep phenotyping, the collection of a wide breadth of phenotypes with fine resolution, and phenomic analysis, composed of constructing heat maps, cluster analysis, text mining, and pathway analysis. In this article, we review the components of phenomics and provide examples of their application to genomic studies, specifically for implicating novel disease processes, reducing sample heterogeneity, hypothesis generation, integration of multiple types of data, and as an extension of Mendelian randomization studies.

Single-cell technologies in environmental omics

Environmental studies are primarily done by culturing isolated microorganisms or by amplifying and sequencing conserved genes. Difficulties understanding the complexity of large numbers of various microorganisms in an environment led to the development of techniques to enrich specific microorganisms for upstream analysis, ultimately leading to single-cell isolation and analyses. We discuss the significance of single-cell technologies in omics studies with focus on metagenomics and metatranscriptomics. We propose that by reducing sample heterogeneity using single-cell genomics, metaomic studies can be simplified.

Familial aggregation of quantitative autistic traits in multiplex versus simplex autism

Recent research has suggested that the mode of inheritance for simplex autism (SA, one individual in the family affected) may be distinct from that for multiplex autism (MA, two or more individuals affected). Since sub clinical autistic traits have been observed in "unaffected" relatives of children with autism, we explored whether the distributions of such traits in families supported differential modes of genetic transmission for SA and MA autism. We measured patterns of familial aggregation of quantitative autistic traits (QAT) in children and parents in 80 SA families and 210 MA families, using the Social Responsiveness Scale. When considering all SA and MA siblings who scored below a uniform quantitative (clinical-level) severity threshold, MA brothers exhibited a distinct pathological shift in the distribution, compared to SA brothers (P < 0.0001). Such aggregation of QAT was also observed in fathers but not among females in MA families. Significant spousal correlations for QAT-suggestive of assortative mating-were observed in both SA and MA families, but neither group was characterized by a greater-than-chance level of concordant elevation among spousal pairs in this volunteer sample. Among male first degree relatives, there exist distinct patterns of QAT manifestation for simplex versus multiplex autism. These findings are consistent with the results of molecular genetic studies that have suggested differential modes of intergenerational transmission for SA and MA. Characterization of QAT and other endophenotypes among close relatives may be useful for reducing sample heterogeneity in future genetic and neurobiologic studies of autism.

Improving dementia assessment by reducing sample heterogeneity

Outcome assessment for clinical dementia trials could be enhanced by using the Mini-mental State Examination (MMSE) and the Hopkins Verbal Learning Test (HVLT), in addition to imaging and genetic screening. The statistical power of trials could be increased if heterogeneity of the sample were reduced by the administration of a risk factor inventory which could be used for subject selection or selection of drug responders.

Microwave-assisted decomposition of polyacrylamide gels containing metalloproteins using mini-vials: An auxiliary strategy for metallomics studies

The majority of samples submitted for elemental analysis are solids that require some kind of treatment for their conversions to aqueous solutions prior to analysis. The main strategy is to reduce sample heterogeneity at a molecular level [1,2]. Although such conversion nowadays is attained successfully using microwave technology [1–3], some inherent problems related to errors due to sample handling (e.g., transfers of volume and mass, drying, contamination) continue to be the “Achilles’ heel” of sample preparation procedures. In addition, when small sample amounts having low analyte concentrations are determined, these errors can determine the success or failure of the analysis. Such problems can be even more relevant when quantitative metalloprotein analysis is considered because all analytical information is taken from a sample localized in a twodimensional polyacrylamide gel electrophoresis (2D– PAGE) spot (only a few milligrams) [4,5]. Although laser ablation may be used for metalloprotein analysis [6,7], this technique is not readily available in most laboratories. Thus, a single vial concept using mini-vials in sample preparation procedures can aid in reducing errors [8,9]. This strategy allows working with low sample quantities and minimizes sample manipulation (e.g., sample weighting, reagent addition, dilution) applying the single

Linkage analysis for autism in a subset families with obsessive-compulsive behaviors: evidence for an autism susceptibility gene on chromosome 1 and further support for susceptibility genes on chromosome 6 and 19.

Although there is considerable evidence for a strong genetic component to idiopathic autism, several genome-wide screens for susceptibility genes have been carried out with limited concordance of linked loci, reflecting numerous genes of weak effect and/or sample heterogeneity. In the current study, linkage analysis was carried out in a sample of 62 autism-affected relative pairs with more severe obsessive-compulsive behaviors, selected from a larger (n=115) set of autism-affected relative pairs as a means of reducing sample heterogeneity. Obsessive-compulsive behaviors were assessed using the Autism Diagnostic Interview-Revised (ADI-R). In the sample with more severe obsessive-compulsive behaviors, multipoint NPL scores above 2 were observed on chromosomes 1, 4, 5, 6, 10, 11 and 19, with the strongest evidence for linkage on chromosome 1 at the marker D1S1656, where the multipoint NPL score was 3.06, and the two-point NPL score was 3.21. In follow-up analyses, analyzing the subset of families (n=35) where the patients had the most severe obsessive-compulsive behaviors generated a multipoint NPL score of 2.76, and a two-point NPL score of 2.79, indicating that the bulk of evidence for linkage was derived from the families most severely affected with obsessive-compulsive behaviors. The data suggest that there is an autism susceptibility gene on chromosome 1 and provide further support for the presence of autism susceptibility genes on chromosomes 6 and 19.

Finding susceptibility genes for developmental disorders of speech: the long and winding road

Finding susceptibility genes for complex disorders is the next major challenge facing genetics researchers. The purpose of this paper is to stimulate creative thinking about the gene-finding process for developmental speech disorders (DSDs), specifically disorders of articulation/phonology and stuttering. The paper will begin with a review of existing behavioral genetic studies of these phenotypes. This will be followed by a discussion of roadblocks that may impede the molecular study of DSDs, research that is in very early stages of development. As a third objective, the small number of molecular genetic studies of DSDs that have been published or presented will be described. The paper concludes with a discussion of research strategies that may maximize the success of molecular studies of speech phenotypes. It will be argued that progress will most likely be enhanced if theories about biological systems and processes can be used to narrow the search for candidate susceptibility genes. Learning outcomes The reader will be introduced to findings and conceptual issues that relate to the behavioral and molecular genetic investigation of DSDs. After completing this paper, readers should be able to (a) identify key epidemiological findings for the three speech phenotypes that were discussed ( DAS, speech delay, and stuttering); (b) summarize the findings of the behavioral genetic studies of speech disorders that were presented; (c) identify four specific challenges that may impede future molecular genetic studies of these phenotypes; (d) describe the methodological sequence that led to the discovery of the FOXP2 gene; and (e) summarize the two research strategies that were presented to potentially reduce sample heterogeneity for future molecular genetics research.

Drug and psychosocial therapy in schizophrenia: current status and research needs.

The author assesses the current status of research on psychopharmacological and psychosocial interventions with schizophrenic patients. Among the issues discussed are the needs to validate the diagnosis of schizoprenia; to reduce sample heterogeneity by defining subgroups based on meaningful response to drug treatment, long-term course, family history, neurologic soft signs, etc.; to characterize the subgroup of patients who do not respond to or do not require neuroleptic treatment; to determine the prevalence of tardive dyskinesia on a national and regional basis; to use blood levels of neuroleptic drugs as indices of whether individual patients are receiving adequate dosages; to investigate further the question of drug compliance and optimal duration of maintenance therapy; and finally to demonstrate how drug treatment and social skills training interact with each other.