High-throughput roll-to-roll production of polymer biochips for multiplexed DNA detection in point-of-care diagnostics

Roll-to-roll UV nanoimprint lithography has superior advantages for high-throughput manufacturing of micro- or nano-structures on flexible polymer foils with various geometries and configurations. Our pilot line provides large-scale structure imprinting for cost-effective polymer biochips (4500 biochips/hour), enabling rapid and multiplexed detections. A complete high-volume process chain of the technology for producing structures like μ-sized, triangular optical out-couplers or capillary channels (width: from 1 μm to 2 mm, height: from 200 nm up to 100 μm) to obtain biochips (width: 25 mm, length: 75 mm, height: 100 μm to 1.5 mm) was described. The imprinting process was performed with custom-developed resins on polymer foils with resin thicknesses ranging between 125–190 μm. The produced chips were tested in a commercial point-of-care diagnostic system for multiplexed DNA analysis of methicillin resistant Staphylococcus aureus (e.g., mecA, mecC gene detections). Specific target DNA capturing was based on hybridisation between surface bound DNA probes and biotinylated targets from the sample. The immobilised biotinylated targets subsequently bind streptavidin–horseradish peroxidase conjugates, which in turn generate light upon incubation with a chemiluminescent substrate. To enhance the light out-coupling thus to improve the system performance, optical structures were integrated into the design. The limits-of-detection of mecA (25 bp) for chips with and without structures were calculated as 0.06 and 0.07 μM, respectively. Further, foil-based chips with fluidic channels were DNA functionalised in our roll-to-roll micro-array spotter following the imprinting. This straightforward approach of sequential imprinting and multiplexed DNA functionalisation on a single foil was also realised for the first time. The corresponding foil-based chips were able to detect mecA gene DNA sequences down to a 0.25 μM concentration.This research was supported by R2R BIOFLUIDICS project (http://www.r2r-biofluidics.eu/) under Horizon 2020 European Union (EU) Research and Innovation Programme with grant agreement no 646260. The research was also partially supported by NextGenMicrofluidics project (https:// www. nextgenmicrofluidics.eu/) under HORIZON2020 with grant agreement no 862092. The authors cordially thank Gerburg Schider & Gerhard Mohr, Markus Postl, Paul Patter and Alexander Wheeldon (JOANNEUM RESEARCH – Materials, Weiz, Austria) for revising the manuscript, preparing all the chip and R2R pilot line illustrations, taking the photographs and providing technical support, respectively. The authors are also grateful to Christian Wolf and Johannes Götz (JOANNEUM RESEARCH – Materials, Weiz, Austria) for their supports in the fluidic design and R2R UV-NIL structuring, respectively. We further kindly thank Alba Simon Munoz and Robert Fay (SCIENION AG, Berlin, Germany) for providing the illustration of the R2R micro-spotting line. PT specially thanks Ege Ozgun (NANOTAM, Bilkent University, Ankara, Turkey) for critically reading the manuscript

The IMAGEN study: a decade of imaging genetics in adolescents

Imaging genetics offers the possibility of detecting associations between genotype and brain structure as well as function, with effect sizes potentially exceeding correlations between genotype and behavior. However, study results are often limited due to small sample sizes and methodological differences, thus reducing the reliability of findings. The IMAGEN cohort with 2000 young adolescents assessed from the age of 14 onwards tries to eliminate some of these limitations by offering a longitudinal approach and sufficient sample size for analyzing gene-environment interactions on brain structure and function. Here, we give a systematic review of IMAGEN publications since the start of the consortium. We then focus on the specific phenotype ‘drug use’ to illustrate the potential of the IMAGEN approach. We describe findings with respect to frontocortical, limbic and striatal brain volume, functional activation elicited by reward anticipation, behavioral inhibition, and affective faces, and their respective associations with drug intake. In addition to describing its strengths, we also discuss limitations of the IMAGEN study. Because of the longitudinal design and related attrition, analyses are underpowered for (epi-) genome-wide approaches due to the limited sample size. Estimating the generalizability of results requires replications in independent samples. However, such densely phenotyped longitudinal studies are still rare and alternative internal cross-validation methods (e.g., leave-one out, split-half) are also warranted. In conclusion, the IMAGEN cohort is a unique, very well characterized longitudinal sample, which helped to elucidate neurobiological mechanisms involved in complex behavior and offers the possibility to further disentangle genotype × phenotype interactions

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Neuropsychosocial profiles of current and future adolescent alcohol misusers

A comprehensive account of the causes of alcohol misuse must accommodate individual differences in biology, psychology and environment, and must disentangle cause and effect. Animal models1 can demonstrate the effects of neurotoxic substances; however, they provide limited insight into the psycho-social and higher cognitive factors involved in the initiation of substance use and progression to misuse. One can search for pre-existing risk factors by testing for endophenotypic biomarkers2 in non-using relatives; however, these relatives may have personality or neural resilience factors that protect them from developing dependence3. A longitudinal study has potential to identify predictors of adolescent substance misuse, particularly if it can incorporate a wide range of potential causal factors, both proximal and distal, and their influence on numerous social, psychological and biological mechanisms4. Here we apply machine learning to a wide range of data from a large sample of adolescents (n = 692) to generate models of current and future adolescent alcohol misuse that incorporate brain structure and function, individual personality and cognitive differences, environmental factors (including gestational cigarette and alcohol exposure), life experiences, and candidate genes. These models were accurate and generalized to novel data, and point to life experiences, neurobiological differences and personality as important antecedents of binge drinking. By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions