s2fieldsofstudy: Biology Psychology

text:
Epigenetics and cerebral organoids: promising directions in autism spectrum disorders

Autism spectrum disorders (ASD) affect 1 in 68 children in the US according to the Centers for Disease Control and Prevention (CDC). It is characterized by impairments in social interactions and communication, restrictive and repetitive patterns of behaviors, and interests. Owing to disease complexity, only a limited number of treatment options are available mainly for children that alleviate but do not cure the debilitating symptoms. Studies confirm a genetic link, but environmental factors, such as medications, toxins, and maternal infection during pregnancy, as well as birth complications also play a role. Some studies indicate a set of candidate genes with different DNA methylation profiles in ASD compared to healthy individuals. Thus epigenetic alterations could help bridging the gene–environment gap in deciphering the underlying neurobiology of autism. However, epigenome-wide association studies (EWAS) have mainly included a very limited number of postmortem brain samples. Hence, cellular models mimicking brain development in vitro will be of great importance to study the critical epigenetic alterations and when they might happen. This review will give an overview of the state of the art concerning knowledge on epigenetic changes in autism and how new, cutting edge expertise based on three-dimensional (3D) stem cell technology models (brain organoids) can contribute in elucidating the multiple aspects of disease mechanisms.

Introduction
Autism is characterized by a clinical triade of symptoms such as impairment of social interactions and communication as well as restrictive and repetitive patterns of behaviors and interests. Lately, the Diagnostic and Statistical Manual of Mental Disorders 5th edition (American Psychiatric Association, 2013) 1 has classified infantile autism, Asperger's syndrome, childhood disintegrative disorder, and pervasive developmental disorders not otherwise specified (PDD-NOS) as autism spectrum disorder (ASD), usually appearing before the age of 3 years. The term "spectrum" denotes a variety of symptoms and severity from mildly afflicted, highly functioning individuals to extremely afflicted individuals in need of lifelong support and care (World Health Organization). Autism is the psychiatric disorder with the highest heritability rate, the concordance rate for monozygotic twins is >90% 2 , and many susceptibility genes have been identified during the past decade. Moreover, there is evidence of environmental factors, such as, e.g., hypoxia during birth, contributing 3 . There is a high prevalence of autistic symptoms in syndromes with chromosomal aberrations, such as Fragile X syndrome and Tuberous Sclerosis 4 . However, even in those disorders following the classical genetic Mendelian rules, a penetrance of <50% has been reported, indicating that epigenetic factors might play an important role in explaining at least part of the neurobiology of autism 5 .
Epigenetic alterations are defined as non-permanent and potentially heritable changes that regulate the expression of genes through alterations to the shape and configuration of DNA, rather than nucleotide sequence. This leads to changes in the ability of certain genes to be transcribed-meaning, that the chromatin threads carrying the genetic information can unwind, coil more tightly, loop, and interact with other proteins to turn certain genes on or off. Epigenetic control can, besides from direct DNA modification 6 , also take the form of modifications in the three dimensional (3D) structure and packaging of DNA, histones, and noncoding RNA-related factors 7 . Chromatin is a dynamic structure. Its accessibility for transcription factors is determined by certain modifications, including DNA methylation as well as acetylation, phosphorylation, ubiquitination, and methylation of histones. They could modify histone proteins, nucleosome movement, and even larger genomic regions. This change in histone modifications has been pointed to as being the gene-environment interface along with DNA methylation 6,8 . In the case of DNA methylation, a methyl group (-CH3) is added to cytosine, leading to gene silencing. Chromatin remodeling could entail sliding of the nucleosome cores by the disassembling/reassembling of the core and could either induce or repress expression. Histone modifications involves amino acids on the terminal end that can bind to methyl, acetyl, phosphate, or ubiquinone, and the most known effect being that of acetylation of lysine in which chromatin is opened and transcription induced. RNA interference is a process that includes RNA silencing complexes that, e.g., bind to mRNA and blocks the ribosome, thereby promoting gene silencing.
Epigenetic mechanisms are involved in regulating the prenatal development by directing processes, such as cell proliferation and differentiation, as well as tissue specification 9 . Epigenetic alterations are partly due to environmental factors, which thereby affect the phenotype by modulating gene expression 7 . Since the underlying causes of ASD remain elusive, epigenetic alterations take the role of the environment with regards to gene expression into account. Certain environmental conditions or fluctuations can stimulate epigenetic changes of the genome (Fig. 1). Therefore, epigenetic modulations are a promising candidate to explain the complex neurobiology leading to ASD. Research involving rodents showed that rat pups receiving limited maternal care resulted in the change of the expression of stress-related genes. Those alterations were also maintained in future generations 10 . Similarly, the effect of maternal care on gene expression has also been seen in nonhuman primates 11 . Moreover, research on human subjects has revealed that childhood abuse alters DNA methylation patterns in the brain, resulting in epigenetic changes to the gene expression 12 Fig. 1 Prenatal epigenetic programming. Several prenatal factors could lead to epigenetic dysregulation in ASD. In utero development could be affected by maternal environmental conditions, such as maternal toxin exposure, severe infections, placental dysfunction and hormonal disruptions, oxidative stress, maternal nutrition, and metabolic imbalance. This can result in activation of adaptation mechanisms for survival and growth restriction, which in turn leads to alteration in cellular organization through disturbed proliferation/maturation balance; epigenome modification via DNA methylation, histone modification, and RNA interference; and alteration in cellular metabolic pathways involved in neuronal growth, differentiation, proliferation, and survival in the course of neurodevelopment and can also modulate axonal and dendritic outgrowth [13][14][15][16][17][18] . DNA methylation of the brain-derived neurotrophic factor (BDNF) gene has for instance been linked to early life adversity, including autism 13 . Epigenetic processes and the interaction with environmental conditions might help to explain the differences in gene expression patterns in ASD patients. It is still not entirely clear which environmental factors are responsible for the epigenetic changes seen in ASD, but many have been suggested: environmental toxins, parental age (especially of fathers), maternal diet, and chemical exposure [19][20][21][22] . An environmental factor that has been suggested is the maternal use of valproic acid (an anticonvulsant and potent histone deacetylase inhibitor) in early pregnancy 23 . The maternal use of valproic acid during pregnancy could affect the production of GABAergic neurons via the blocking of histone deacetylase 24 . A study also found that severe maternal viral infection in the first trimester of the pregnancy and bacterial infection in the second trimester was associated with a diagnosis of ASD in the child 25 .
Along similar lines to the gene-environment interaction is the investigation of oxidative stress' role in ASD. One recent study (total n = 189) including adults with ASD (n = 92) pointed to increased oxidative stress in the patient's group, in that it found significantly increased levels of superoxide dismutase 1 (although not superoxide dismutase 2 or xanthine oxidase (XO)) 26 . Promising results suggest a major role of oxidative stress in the neurobiology of other neurodevelopmental and psychiatric disorders, which are often comorbid with autism 27-32 such as depression 27,29,30 and schizophrenia 28,31 . There have been several reports connecting depressive disorders and oxidative stress 27 . The pro-oxidant XO has been studied in this regard and been found to be elevated in both schizophrenia 28 and depression 29 . Another study investigated whether oxidative stress could contribute to the brain structural changes seen in patients with recurrent depressive disorder 30 . This study, consisting of seven patients and seven healthy controls, then compared manganese (Mn) and copper/zinc (Cu/Zn) superoxide dismutase (SOD) concentrations in postmortem prefrontal cortex and hippocampal brain tissue. The study found that the concentration of Cu/Zn-SOD was increased in the prefrontal cortex although not in the hippocampus of the patients 30 . Another study investigated impaired oxidative stress defense in schizophrenia spectrum disorder by determining the concentrations of Cu, Zn and MnSOD in postmortem brain tissue 31 . This study found significantly elevated levels of Cu, Zn and MnSOD levels in frontal cortex and substantia innominata of the samples 31 .
Although the epigenetic mechanisms involved in autism are not yet fully understood, there are findings suggestive of genome-wide dysregulation and epigenetic alterations in ASD 33 . These studies point to DNA methylation as a likely contributor in the development of the disorder 34 . There are certain syndromes that have been linked to ASD. DNA methylation in connection to imprinting and X-chromosome inactivation could be relevant to the field of ASD research. X-chromosome inactivation is a process in which one of the copies of X chromosomes is inactivated and this is also achieved through DNA methylation. It might be associated with autism, as inactivation or removal of inactivation could lead to genetic aberrations. An example for this is the formation of tiny "X rings" in females. These rings prevent the inactivation of genes within it resulting in an overexpression and could furthermore end in developmental abnormalities such as those seen in certain Turner syndrome cases-a minority with mental retardation and developmental delay 35 . The deficits seen in this syndrome are most commonly related to social reciprocity and communication; in addition to that, it is also associated with an increase in the risk of autism especially in incidences in which the maternally inherited X chromosome was retained 36,37 . The authors surmised that this was due to an X-linked imprinted gene and the expression of which affecting the risk of autism 36 . DNA methylation is also involved in long-distance chromosomal interactions, in which methylation signals interact with other chromosomes or faraway parts of the same chromosome to alter gene expression. This phenomenon can be seen in chromatin looping, in which gene sequences are brought together by the formation of a loop, enabling long-distance gene activation 38 .
Imprinting is a differential inhibition of gene expression (imprinted genes are not expressed) depending on its parental origin. In this way, imprinting controls the expression of either the paternal or the maternal gene in the offspring 39 . Imprinting's role in ASD has been suggested in several instances 36,37,40,41 , and it is interesting to note that the imprinted domain at 15q11-13 is responsible for Angelman syndrome 42 as well as Prader-Willi syndrome 43 . In the case of Angelman syndrome, children suffering from the syndrome also tend to be diagnosed with ASD 40,41,44 . One of these studies investigated the prevalence of Angelman Syndrome in school children and analyzed its comorbidity with autistic disorder and found that 4 out of the almost 49,000 conformed to a diagnosis of Angelman syndrome and that all 4 also met full behavioral criteria for a diagnosis of autistic disorder 40 . Another study investigated 23 Angelman syndrome patients and made a comorbid diagnosis of ASD in 14 out of the 23 patients (61%) 41 . Angelman syndrome is associated with a maternal deletion in the chromosomal region 15q11-13 affecting UBE3A (related to ubiquitine pathway) expression while the paternal allele is silenced 42 . Prader-Willi syndrome is, on the other hand, caused by the deletion of the parental copies of the SNRPN located in the same region (15q11-13). At the same time, due to imprinting the maternally inherited copies are silenced. This is linked to psychotic episodes in the cases of maternal uniparental disomy or imprinting abnormalities 43 . Also worth mentioning is that the 15q11-13 imprinting region has been associated with the increased incidence of autism when maternally duplicated 45 . This study conducted microsatellite and methylation analyses of three children and their parents, in which two of the children had autism and the third did not. It was found that the two individuals had a maternal inheritance of a 15q11-13 duplication while the unaffected child did not have this duplication. Interestingly, a recent study conducted on mice might have discovered the first proof of principle with regards to epigeneticbased therapy, in particular Prader-Willi syndrome 46 . The study found that two selective inhibitors (UNC0638 and UNC0648) activated imprinted genes on the maternal chromosome of cells usually repressed in those with the disorder. Considering this discovery as a possible epigenetic-based treatment of Prader-Willi 46 , imprinting might yet provide some clues to the etiology and possible treatments for ASD as well.
In summary, epigenetic processes are multifaceted and can be connected to multiple genes or genetic pathways. This could make it difficult to identify the essential targets and pathways involved in epigenetic changes. It is believed that epigenetic processes play a major role in brain development and neurodevelopmental disorders 47 . For all the genetic insights that have been gained, we still do not have a full picture of the etiological basis of ASD. It is only recently that more emphasis has been put on environmental factors and this means that epigenetic mechanisms should also be investigated to further ASD knowledge.

Epigenome-wide analysis and candidate genes in ASD
Several studies have focused on DNA methylation levels on a genome-wide scale (or EWAS) and one very recent study has investigated histone acetylation (see Table 1).
These EWAS studies have identified potential biomarkers that could be useful in future studies and implicated certain brain regions with signs of epigenetic dysregulation. Regarding proline-rich transmembrane protein (PRRT1), a study found lower methylation of a differentially methylated region (DMR) in the regions of the temporal cortex, cerebellum, and prefrontal cortex 48 . Similarly, a region related to zinc finger protein 52 (ZFP52) showed higher methylation of a DMR in the temporal cortex 48 , as well as higher methylation of the cingulate cortex 49 . Tetraspanin 32 gene/chromosome 11 open reading frame 21(TSPAN32/C11ORF21) has also been shown to have lower methylation levels in a DMR in the temporal cortex and cerebral cortex 48 , along with lower methylation in the anterior cingulate gyrus and prefrontal cortex 49 . Olfactory receptor family 2 subfamily L member 13 (OR2L13) is a special case having shown both significantly increased and decreased methylation in studies 50,51 . Although they have provided insights, there are also some associated issues with the EWAS studies conducted on postmortem brain samples. The most fundamental issue is the uncertainty in how many conclusions can be drawn from postmortem samples to a disorder that arises through the course of development 52 . The dependency on bio-banks also limits the number of samples one can attain. All of the aforementioned studies had fewer than 100 samples and should optimally include more for increased power of the study. There are only a limited number of samples obtainable from bio-banks. Therefore, cell culture systems could offer a viable alternative and will be discussed later on.
Even as genome-wide approaches seem to be the focus in most studies, candidate genes (see Table 2) and their methylation status could still offer valuable insights into the neurobiology of ASD. Although certain genes appear to be involved in the disorder, investigating epigenetic alterations will contribute to get a better understanding of the complex mechanisms involved. Differences in methylation have been observed in different areas of the brain 47, 53-59 , and the CpG island (genomic regions >200 bp in length with high CpG sites-in which a cytosine nucleotide is followed by a guanine nucleotide in the linear sequence of bases) known to regulate the oxytocin receptor (OXTRA) has presented increases in the DNA methylation status in the temporal cortex as well as in blood (plasma) 47 . Further studies of, e.g., methylation status in blood and other more easily obtained peripheral tissues could possibly result in finding helpful biomarkers for the disorder. The biomarkers could assist the diagnostic process and improve the prognosis, as well as possibly identifying individuals at risk of ASD eventually leading to preventive measures. Other epigenetic mechanisms, aside from DNA methylation itself, have not been investigated nearly as closely and could be involved in the pathogenesis as well.

Induced pluripotent stem cells and cerebral organoids: models for studying epigenetics in vitro
The neurobiology leading to ASD might already start early in the prenatal stages. Therefore, mimicking the prenatal development in vitro can give extremely valuable insight into the etiology of autism. In addition to that, the challenges in research using postmortem brain tissue warrants the utilization of other viable disease models, such as cell cultures, and in vivo recapitulation of brain development and related pathology on the basis of new, cutting edge technology involving induced pluripotent stem cells (iPSCs). IPSCs refer to somatic cells that have been reprogrammed to a pluripotent stage and then can be triggered to differentiate into all cell types in the human body, including neurons. The technology is relatively new; for the first time, it became clear about a decade ago that iPSCs could be generated via a set of transcription factors introduced to fibroblasts in vitro 60 . Other somatic cells have also been used in the generation of iPSCs, for example: keratinocytes 61 , epithelial bladder cells derived from urine 62 as well as from hair follicles 63 . IPSCs and derived differentiated cells retain the genetic signature of the individual that they have been taken from and thus they represent a relevant disease model. IPSCs have also been utilized in ASD research 64 . The respective study found increased rates of proliferation of neural progenitor cells and neuron numbers in the ASD individual IPSCs who also had an increase of brain volume as seen on magnetic resonnce imaging. The increase in proliferation was due to dysregulation of the beta-catenin/ BRN2 transcriptional cascade, and it seemed that the  Inability to define methylation and X-inactivation borders 56 Associated with Rett syndrome Unlcear X-inactivation role 56,124,125 Other: MECP2 regulation of other genes via epigenetics: recruitment of co-repressors, chromatin looping 126,127 OXTR oxytocin receptor, GABRB3 gamma-aminobutyric acid-A receptor, UBE3A ubiquitin-protein ligase E3A gene, GAD1 glutamate decarboxylase, EN engrailed 2, RELN Reelin, MECP2 methyl CpG-binding protein 2 defects in neuronal networks could be rescued by insulin growth factor 1 64 . Along with the advantages, there are also some additional challenges regarding iPSCs. Since it is a new technique, there is limited availability of "bio-banks". Additionally, the cells themselves may present different phenotypes in two lines derived from the same person 65 . A possible solution to this issue could be the generation of several clones of each patient iPSC line but at additional time and cost. Reprogramming iPSCs has also been found to introduce mutations of the genomic DNA as well as exogenous reprogramming genes 66 . iPSCs sustain an epigenetic memory, essentially maintaining residual DNA methylation from the tissue of origin 67 . Most iPSCs are derived from differentiated somatic cells, namely, fibroblasts. This should be taken into account in the epigenetic studies of central nervous system in which iPSCs are used as a model and transdifferentiation is considered preferable to eliminate this challenge. There have also been reports on prolonged culturing affecting the transcriptomes and the phenotypes of cells 68 . Throughout the course of life, somatic cells (fibroblasts) accumulate somatic mutations. Individual clones from the same person could host different somatic mutations, as in the case of mitochondrial DNA in iPSC neurons 69 . The study reports that somatic mutations could randomly occur in individual cells and those mutations of mitochondrial DNA in iPSCs had a tendency to increase along with the age of the donor. Another study found that iPSC genomes potentially exhibit imperfectly rewired 3D folding linked to inaccurately reprogrammed gene expression 70 . How much this chromatin architecture is reorganized in the process of reprogramming remains uncertain. As it was mentioned before, a possible way around the disadvantages could be achieved by removing the iPSC step as the midway, i.e., transdifferentiation 71,72 . In this method, fibroblasts are directly converted into induced neural progenitor cells by way of timely restricted expression of the following genes: Sox2, Klf4, and c-Myc and limited and very strictly controlled expression of Oct4 73 . Another method based on forced expression of neurogenin 2 (Ngn2) turns skin fibroblasts into functional neurons 74 . Treatment of Ngn2-transduced fibroblasts with a mix of small molecules including SB431542 (a transforming growth factor-β inhibitor), CHIR99021 (a GSK3β inhibitor), and Noggin (a protein inhibitor of bone morphogenic protein signaling) increase conversion efficiency up to 85% 75 .
Two-dimensional (2D) cell cultures of iPSC and derived neurons offer easier environmental control, cell manipulation, and imaging; it is homogenic and has fair reproducibility 76,77 . Along with the advantages, there are also a set of disadvantages, including an inability to depict traits exhibited in vivo (e.g., gene expression), less compatibility with in vivo settings, as well as increased drug sensitivity (due to monolayer) and exposed surface.
The development of stem cell technology gave rise to the possibility of growing and differentiating iPSCs into small 3D structures called organoids. Cerebral organoids follow the in vivo timeline and can recapitulate the early (8-10 gestation week) to mid-fetal (22-24/35 gestation week) of human brain development. Depending on the presence or absence of growth factors and morphogens, different region-specific organoids can be formed such as the forebrain, midbrain, cortex, and hypothalamus. Moreover, brain organoids can exhibit a well-defined sub ventricular zone (SVZ) and six-layered cortical architecture typical for the human brain. However, the brain cortex and SVZ in the mid-fetal development are the main targets for modeling ASD. This makes cerebral organoids a valuable new tool for neurodevelopmental disorders. The organoid gene expression program is highly similar to human fetal tissue [78][79][80][81][82][83][84] .
The organoids more closely resemble the cells' natural environment, demonstrating cellular variety and intercellular communication, cell-matrix interactions, and complex transport systems of nutrients [85][86][87][88][89][90][91] . 2D monolayer cultures are not relevant in this aspect, leading them to be poorer determinants in drug and toxicity studies [92][93][94][95] . 3D cultures are a relevant model of cell migration, differentiation, and growth [86][87][88][89][90][91]96 . Furthermore, 3D cell cultures have a different gene expression compared to those grown in 2D 83,87,97 . There seems to be a dysregulation of morphology, response to environmental stress as well as cellular regulation in 2D cultures compared to 3D cultures 98 . This could limit the use of 2D cell culture in the search for epigenetic regulation in ASD since comparisons would be harder to make at the interface of gene-environment.
The 3D cell cultures offer several advantages such as: the cytoarchitecture is similar to that found in vivo, variety of cellular populations, organ specific functions, and niche-like environment. Carrying the specific genetic signature from the individual the cells were taken from, they can be utilized to compare the early brain structure and composition with ASD to that of their healthy family members as well as healthy controls. This means that researchers to a certain extent are able to model neurodevelopment in vitro and re-create the brain pathology related to ASD. This could also aid in elucidating the interface of gene expression levels and epigenetic changes [99][100][101] . The disadvantages related to this method include: diffusional transport limitations (oxygen, nutrients), culture-dependent alterations in gene expression, technical challenges in manipulation and imaging, standardization and reproducibility issues, as well as being time and labor consuming.
It should also be noted that the method still has an issue of "batch syndrome" in which different batches of organoids demonstrate significant variability in quality and brain regions they produce 102 . A very recent study analyzed 80,000 individual cells isolated from 31 human brain organoids. In addition to finding that organoids could generate a broad diversity of cells, they could also be developed over an extended period of time leading to the formation of dendritic spines as well as spontaneously active neuronal networks. Furthermore, this study also quantified variability among organoids and pointed to bioreactor-based batch effects as part of the explanation for the organoid-to-organoid variability 86 .
A recent study has conducted the first transcriptome and epigenome-wide sequencing of cerebral organoids by comparing them to human fetal brain, using genomewide, base resolution DNA methylome and transcriptome sequencing 83 . Particularly, organoid differentiation was able to recapitulate the transcriptomic dynamics of early/ middle fetal development. DNA methylation of cerebral organoids found a new kind of mCH enrichment (cytosine DNA methylation in non-CG contexts) that was indicative of transcriptional repression of the later brain in vivo.  Fig. 2 Schematic presentation of a brain organoid-focused study, with groups consisting of familial ASD cases, idiopathic cases, and a control group. Somatic cells (for example, fibroblasts) can be obtained from the participants and reprogrammed into iPSC and furthermore into brain organoids specific for each individual. This could enable an in vitro multi-omics studies (genomics, transcriptomics, proteomics, epigenomics, metabolomics), oxidative stress, cellular organization, etc., hopefully, unveiling a molecular mechanism of the disorder that in the future can lead to better tools in diagnosis, prevention, and treatment

Cellular organizaƟon and CommunicaƟons
Hypermethylation of DNA methylation valleys, regions of low methylation of at least 5 kb in length (DMVs), served as a robust epigenomic signature of forebrain specification. Pervasive demethylation of pericentromeric repeats were found in human neural cultures that are not observed during fetal brain development. The last aspect points to the importance of genomic/epigenomic methods in the evaluation of in vitro-derived neuronal tissues vs. in vivo. The study came to the conclusion that the remodeling of mCG and mCH during the organoid differentiation resulted in an epigenomic state very similar to the human fetal brain 83 . It is for the moment not clear how epigenetic patterns of brain organoids could be verified by comparison with a human ES line and this should warrant further studies and be addressed. Studies concerned with iPSC epigenetic signatures could aid in the investigation of alterations in epigenetic regulation of gene expression in brain development. There is also added hope that this technique will in the future aid in drug discovery research (see Fig. 2).

Conclusions
Several findings suggest that epigenetic alterations play a role in the development of ASD and further research is therefore warranted. This review points out that there is a necessity to carry out further EWAS studies. These should include larger numbers of patients who should be clinically well characterized, as well as investigate other, different epigenetic mechanisms as the majority of research has been focused on DNA methylation. The potential biomarkers, e.g., C11ORF21 and PRRT1, identified in these studies should also be replicated. In addition, iPSCs and cerebral organoids could aid in bypassing the issue of obtaining sufficient postmortem brains, enabling a "disorder in a dish-setting". Moreover, brain organoids are patient specific and can be utilized in the development of targeted personalized treatments. There are, however, some issues regarding this as well including, but not limited to, epigenetic memory of the tissue of origin in addition to issues regarding mutations and differing phenotypes from the same sample. For all the challenges posed, there are new findings that offer some valuable clues and that might guide the field forward, the most recent advance being the first epigenomic sequencing of cerebral organoids and a newly acquired understanding of how closely they could resemble the human brain. Studying organoids and the epigenetic alterations in the different brain structures during embryonal development might open up new avenues for new treatments targeting specific brain structures.


extfieldsofstudy:
Computer Science
Medicine

text:
Current applications of antibody microarrays

The concept of antibody microarrays is one of the most versatile approaches within multiplexed immunoassay technologies. These types of arrays have increasingly become an attractive tool for the exploratory detection and study of protein abundance, function, pathways, and potential drug targets. Due to the properties of the antibody microarrays and their potential use in basic research and clinical analytics, various types of antibody microarrays have already been developed. In spite of the growing number of studies utilizing this technique, few reviews about antibody microarray technology have been presented to reflect the quality and future uses of the generated data. In this review, we provide a summary of the recent applications of antibody microarray techniques in basic biology and clinical studies, providing insights into the current trends and future of protein analysis. Electronic supplementary material The online version of this article (10.1186/s12014-018-9184-2) contains supplementary material, which is available to authorized users.

Background
Antibody microarrays are built on immobilizing antibodies for a parallel analysis of multiple targets in a given sample [1]. Today's antibody and affinity reagentengineering methods have helped to advance the methodology [2,3]. Antibodies and a variety of antibody derivatives have been used to build arrays, including nanobodies, single-chain variable fragments (scFvs) and fragment antigen-binding (Fab)-fragments [4]. In addition, phage display [5] and ribosome display [6], combined with advanced materials and bioinformatics development have being driving forces in recent years [7].
The typical workflow of an antibody microarray is depicted in Fig. 1. Briefly, antibodies are immobilized onto a chemically functionalized or otherwise modified surface. After blocking the reactive groups of the surface, a sample containing soluble proteins of interest is incubated on the array, and the targeted proteins from the sample are captured by the antibodies. The resulting binding events are reported directly by fluorescent labelling of the sample or by the addition of a secondary detection reagent.
The attractiveness of antibody microarrays is that they can be used to study a diverse number of biological processes [8] and have been used to investigate protein-protein interactions [9], signal pathway analysis [10], studies of post-translation modifications [11], and detection of toxins [12]. In the clinical context, arrays have enabled opportunities to identify novel disease biomarkers [13] as well as generating unique proteome signature by comparing healthy and disease states. This information will be of great value in the future, enabling better disease management through improved diagnostics and the ability to track disease status and therapeutic efficacy.
Antibody microarrays have demonstrated a number of advantages compared to traditional, single analyte methods of protein analysis, such as, enzyme-linked immunosorbent assays (ELISA) and Western blotting. Microarrays are high throughput, highly sensitivity, require small sample volumes, and more recently have become more standardized and user friendly experimental procedures. Compared with mainstream proteomics strategies, especially mass spectrometry (MS), the process of antibody microarray assays is fast and takes less

Open Access
Clinical Proteomics  than 24 h from sample preparation to data interpretation. Detailed comparison is shown Fig. 2.
In theory, like DNA microarrays, antibody microarrays can be designed to host a few to thousands, or even ten-thousands, of features. Currently, high features have been achieved by immobilizing proteins [14] or lysates [15,16], and antibody microarrays are under active technological development and to-date operate at a few hundred features [17][18][19]. The arrays can be constructed either host many features per sample or be designed to compartmentalize the array into sets of arrays that allow many samples to be investigated simultaneously. Generally, the latter is more common, particularly if a large number of clinical samples are analysed in a given study. For the analysis of a large amount of samples, bead based systems have become attractive, as they can be processed in microtiter plates and by liquid handling devices using cytometry for instant data availability [20,21]. Importantly, antibody microarrays generate high dimensional data that can be processed using already well-established DNA microarray software, since the images scanned from planar antibody microarrays are similar to those of DNA microarrays.
Antibody microarray experiments are most often used as the initial tool during the biomarker discovery process. These results continue down the validation pipeline using additional antibody-based assays, such as ELISA and Western blotting, or immune-capture MS to validate the findings clinical/pathophysiological relevance [22]. However, the cross-reactivity to off-target proteins that are more abundant than the target of interest is still a challenge for the single binder version of the assay. Thus, undesired reactions with other target proteins represent one of the main obstacles in establishing high performance and high specificity assays. The scope of this review is to outline the antibody microarray techniques that are being used today for biology studies and clinical research.

Recent applications (since 2011)
Due to unique capabilities of the antibody microarray and its applicability in a range of biomedical projects, a series of different antibody microarrays have been developed, of which some have become commercially available. Herein, we have collected some of the representative studies in the last 7 years (Table 1), and organized our overview into two parts depending on the focus of the projects: basic biology-driven studies and clinical research. As it remains practically impossible to cover all the recent applications of antibody microarray, we focus on those applications which we deemed are of high interest and findings of important clinical significance.

Basic research Signalling pathway related
Changes in signalling pathways is a hallmark of many disease states, including cancer [23,24], diabetes [25,26] and neurodegenerative disorders [27]. Building antibody arrays using capture reagents targeting proteins in the signalling cascade of interest has enabled researchers to investigate changes in protein profiles and modifications in signalling pathways in normal biological processes and disease states. Researchers have applied this concept to a range of biological sample materials and preparations, including cell lysates [28], tissue extracts [29], and plasma [30].
Calbindin-D28 k (CB), an important calcium-binding protein that acts as a calcium buffer and is found to be expressed at lower levels in the brains of mice and humans with Alzheimer's disease but it is not known if these changes contribute to AD-related dysfunction (AD). Kook et al. generated a CB-deficient Alzheimer's transgenic mouse (CBKOTg) to investigate in CB's contribution to signaling pathways in AD [23]. Utilizing antibody microarrays to examine mouse brain tissue, they identified significant alterations in cell death pathways, synaptic transmission and MAPK signalling pathways upon CB deletion. These findings were verified by immunohistochemistry which showed increased apoptotic markers and increased neuronal death, demonstrating that antibody microarrays can provide novel information, enhancing our understanding of the role CB and its implication in AD pathophysiology.
Antibody microarrays have been used to uncover the physiological role in highly conserved proteins, including elongation factor 4. Gao et al. dissected the mechanism of quality-control factor mitochondrial Elongation factor 4 (mtEF4) in translation using a phospho-explorer antibody microarray [31]. By comparing cell lysates between mtEF4 knock-out and WT mice testis tissues, they found that the fold enrichment of mTOR was the highest among different signalling pathways. The authors showed that with mtEF4 deletion, the major feedback signal from the somatic cytoplasm is mTOR upregulation. This was accompanied by an increased cytoplasmic translation, thus indicating that mTOR plays a crucial role as a downstream effector compensating for mitochondrial translation deficiency [32]. Furthermore, the study linked previously undescribed cross-talk between mtEF4-dependent quality control in the mitochondria and the mTOR pathway in the cytoplasm.

Drug mechanism
With the help of antibody microarray assays, researchers can investigate drug mechanisms in a systematic and efficient manner. Among many possible applications, tumor-induced angiogenesis has been studied as it plays a pivotal role in cancer progression [33]. P11, a novel peptide ligand containing a PDZ-binding motif (Ser-Asp-Val) with high affinity to integrin α v β 3, was identified from a hexapeptide library (PS-SPCL) using an antibody microarray [34]. The pharmacological mechanism of P11, was elucidated using a specifically designed pharmacoproteomic microarray approach containing 48 cancer related antibodies [35]. The study revealed that P11 inhibits bFGF-induced human umbilical vein endothelial cell proliferation via mitogen-activated protein kinase and extracellular-signal regulated kinase inhibition. In addition, the microarray revealed P11 caused the upregulation of apoptotic marker p53, resulted in apoptosis induction via activation of the caspases system, which indicates P11 may play a key role in preventing tumor progression. The link between P11 and p53 presents clinical and basic cancer researchers will help to elucidate further clues about the role of P11 and its inhibitory target.
Lovastatin, a natural product derived from Aspergillus terreus or Monascus ruber, has been widely used as a cholesterol-lowing drug in the clinic. Yang et al. have used an antibody microarray containing 656 antibodies with a focus on different functional cell pathways, which suggested that lovastatin also has anti-cancer properties, through poorly defined mechanisms. There is a strong need to find effective cancer treatments and to understand their mechanisms of action. Using a microarray, breast cancer cells were studied under hypoxic conditions to investigate the molecular mechanisms through Lovastin exherts its effect. They showed 17 up-regulated proteins and 20 down-regulated proteins for lovastatin treated breast cancer cells, compared to the control, and the results were subsequently validated by real-time PCR [36]. The protein signature included proteins involved in apoptosis, cell proliferation and tumour metastasis, linking modulation of these pathways to the pharmacological action of lovastin.
The type 2 diabetes drug metformin, has also proposed to have anti-cancer properties. Lee et al. investigated the potency of novel metformin derivatives using an antibody microarray built to study cell cycle-related molecules. The authors showed that metformin-butyrate (MFB) had better anti-tumor efficacies than metformin-HCl, through greater neoplastic activity and better efficiency at impairing cell cycle progression during S and G2/M phases in addition to appearing to have preferential cytotoxic effects on breast cancer stem cell populations [37]. These findings highlight how antibody arrays can provide proteomic evidence supporting development of drugs with better efficacy. For basic biology research, antibody microarrays can serve in detecting signalling proteins and proteins associated to a particular phenotype. Arrays with parallel protein detection create an opportunity to investigate multiple signalling pathways in a single experiment, providing insights into novel mechanisms, such as the study of disease progression, drug interaction, and response to infections.

Clinical research
Analysis of clinical samples at the protein level will enable improvement in diagnostics markers, and directing treatment options. Since proteins are involved in the majority of cellular processes, protein analysis may provide a biological disease signature, generating detailed insights about the current state of a cell, organ or system. Antibody microarrays can provide a real-time guide of current changes in biological processes during health and disease. In this section, we will exemplify how antibody microarrays have been used to investigate clinical cohorts, primarily analysing serum and plasma samples. It is not possible to cover all the aspects of antibody microarrays for clinical research, herein, we only focus on several of the most important diseases, i.e. autoimmune diseases, infectious diseases, cancer and neurodegenerative diseases.

Autoimmune diseases
The diagnosis of systemic lupus erythematosus (SLE) is challenging due to its heterogeneous clinical presentation and the lack of robust biomarkers to distinguish it from active and inactive disease as well as from other autoimmune diseases. Lin et al. [38] performed a DotScan ™ antibody microarray screening of peripheral blood mononuclear cells from 60 SLE patients of varying disease activity, 25 rheumatoid arthritis patients, 28 other autoimmune disease samples, and 24 healthy controls. The antibody microarray profiles could distinguish active SLE patients from healthy controls. Using a leukocyte capture array improved the discriminative ability of conventional SLE diagnostics, by verifying serum anti-dsDNA, complements C3 and C4, the microarrays increased the capability of discrimination of semi-active and active SLE, information which will assist better disease management. In another study, Carlsson et al. [39] constructed 135 human recombinant single-chain fragment variables (scFv) targeting immune proteins to build an inhouse antibody microarray. In this study, they examined patients with systemic sclerosis (SSc), SLE and 15 healthy volunteers. SSc is an autoimmune disorder affecting connecting tissue, which can be challenging to differentially diagnose from SLE. The array identified 40 differentially expressed proteins creating a candidate proteome signature to delineate SLE and its severity from SSc. This protein signature was a better disease classification than single or even combinations of conventional clinical parameters, including, ANA, anti-DNA, SLEDAI-2 k, C1q, C3, C4 and CRP, illustrating the potential use for antibody microarrays to create new disease-signatures which will add clinical value to disease management.

Infectious diseases
Infections activate the immune system differently, and these mechanisms can be investigated using antibody arrays on a proteome-wide level. Helicobacter pylori is a pathogen that colonizes roughly one half of the world's population which causes chronic gastritis. Sukri et al. [40] employed the DotScan ™ antibody microarray to determine the tolerance of immune system toward tumor cells in gastric cancer using 144 CD antibodies to profile the distribution of CD markers between Helicobacter pylori infected and un-infected gastric adenocarcinoma cells. Interestingly, they found gastric adenocarcinoma cell line AGS infected by cagA + H. pylori showed increased CD27 expression, which is essential for maintenance of T cell population, and increased CD markers were also detected in H. pylori-infected gastric cancer patients. This study suggests not only the tolerance of the immune system toward gastric cancer, but also the immune response variations exploited by different H. pylori strains.
Furthermore, antibody arrays can be useful in tracking a continuing change in the physiological environment, including disease status and response to therapies and interventions. After liver transplantation, 94% of patients were found with hepatitis C virus (HCV) induced histological damage, with some patients suffering severe disease recurrence [41]. There is an urgent need for predictive biomarkers to identify recurrent disease severity. Using a CD antibody microarray, peripheral blood from patients defined as pre-transplant, early, mid, and late-transplant groups were investigated to predict the severity of HCV recurrence after transplantation [42]. Serial blood samples taken from patients before and after liver transplant enabled tracking of the disease milieu, with its own internal controls. Five CD markers (CD27, CD182, CD260, CD41, and CD34) were significantly increased in severe recurrence compared to mild recurrence. This result shows that antibody arrays may assist in assessing recurrent HCV disease severity after liver transplantation.
Ellmark et al. [43] devised a single framework recombinant antibody (SinFabs) microarray containing 127 different antibodies against immune-regulatory antigens selected from the n-CoDeR library on gastric adenocarcinoma. They elucidated that distinct tumor-as well as infection-associated protein expression signatures, such as IL-9, IL-11, and MCP-4, could be identified from the plasma proteomes and serve as potential biomarkers. These findings may aid to improve the understanding of H-pylori induced cancer and may pave the way for refined diagnostics in the future.
Bead based antibody arrays have been performed to investigate differential protein profiles in plasma in children suffering from malaria and malaria-related complications [44]. From 1000 proteins screened, 41 proteins had differential expression between malaria infected children and community controls. Thirteen further proteins were linked to malaria-disease severity. These findings indicate the involvement of inflammation as well as unbalanced metabolism of glucose in severe forms of the disease and the biggest changes were seen in two of muscle proteins (carbonic anhydrase 3 and creatine kinase) pointed at muscle damage and lesions for children with cerebral malaria. These findings may aid the development of simple tests for classifying malaria-infected children into group showing a higher risk for the severe form of the disease.

Cancer
Antibody microarrays have been applied for cancer research mainly to study cancer progression and candidate proteins that may serve as diagnostic biomarkers [45,46]. Pancreatic cancer is an aggressive disease with poor prognosis, and disease-specific biomarkers that offer early and accurate diagnosis are in urgent need. Using an in-house developed recombinant antibody microarray platform, Wingren et al. [47] screened sera from 148 patients with pancreatic cancer, chronic pancreatitis, autoimmune pancreatitis (AIP), and healthy controls. They identified a panel of 25 protein targets, including IL-2, IL-11, IL-12, TNF-, which contributed to distinguishing pancreatic cancer from healthy controls. This 25 protein signature exhibits a high diagnostic potential (AUC of 0.88). In addition, the group has recently extended their analysis of their panel to additional sample sets, including lymphoma [48], prostate cancer [49], and breast cancer in cell lysates [50] and plasma [51]. These findings are useful to aid the development of novel and multivariate diagnostics methods.
One of the most important questions in cancer biology, is determining the invasiveness of a tumor. One study developed a customized an antibody microarray platform containing 4096 features to interrogate plasma samples spanning pre-invasive and invasive diseases from a mouse model of pancreatic ductal adenocarcinoma (PDA) [18]. They found a protein signature, comprising of the differential expression of three proteins, ERBB2, TNC and ESR1, which could be used to improve the AUC from 0.86 (95% confidence interval [CI] 0.76-0.96) to 0.97 (95% CI 0.92-1.0) when the PDA marker CA19-9 included.
The majority of newly diagnosed cases of bladder cancer are low-stage, low-grade, non-muscle-invasive [52]. After standard transurethral resection, 50-70% of tumors recur, however 10-30% of the tumors will progress to muscle-invasive disease [53,54]. Understanding the mechanism of tumor progression could provide more useful information to clinical scopes. Srinivasan et al. studied cell lysates with an antibody microarray containing 810 cancer related antibodies constructed by the Hoheisel lab [55]. The authors built a multivariate classifier containing 20 proteins, which facilitatated the prediction of recurrence with a sensitivity of 80% and a specificity of 100%. Interestingly, they found repression of the TGF-β signaling pathway in recurrent cancer. The signaling factors IFNG, TNF-α, and THBS1 were expressed less and the abundance of the inhibitor MAPK3 (also known as ERK1) was higher, while SMAD2, SMAD3, and SMAD4 were again significantly underrepresented [56]. The data indicated that TGF-β signaling pathway inhibitors may reduce bladder cancer recurrence [57].
Puig-Costa et al. [58] employed antibody microarrays to discover biomarkers for gastric cancer (GC). Notably, the antibody microarray contained antibodies targeting different well-known functional proteins that play a crucial role in cancer progression, including 120 cytokines, 43 angiogenic factors, 41 growth factors, 40 inflammatory factors and 10 metalloproteinases. Ingenuity pathway analysis confirmed that some biomarkers, such as ICAM-1 and angiogenin, indicated high inflammatory response in GC patients. Cellular movement and immune cell trafficking targets such as monocyte chemoattractant protein (MCP)-1 was found to be overrepresented within GC patients. The positive predictive and negative predictive values in this validation cohort were 75% (95% CI 53-90) and 80% (95% CI 56-94), respectively. Finally, antibody microarray analyses of the GC-associated inflammatory proteome identified a 21-protein inflammatory protein-driven gastric cancer signature (INPROGAS) that accurately discriminated GC from noncancerous gastric mucosa, and may provide new leads for the analysis of cancer progression.
In prostate cancer, Schwenk et al. [59] used antibody arrays on suspension bead arrays to compare plasma levels of proteins between different groups in order to find additional biomarkers alongside prostate-specific antigen (PSA). Besides classifying the patients based on PSA, they identified decreased plasma CNDP1 levels and in a subsequent larger studies this was shown to association in more aggressive forms of the disease. A sandwich immunoassay was developed to validate these findings in more than 1200 patients, and the association of the decreased CNDP1 to lymph node metastasis was further elucidated [60]. This demonstrates one of the very few studies that have successfully validated an initial indication through the antibody microarray pipeline and the development of a targeted assay. In a subsequent investigation of cancer cachexia, plasma levels of CNDP1 was found to be decreased in cachexic patients, hence pointing at a metabolic role of changes in CNDP1 levels [61].
In a study of small intestine neuroendocrine tumours, Darmanis et al. [62] investigated the plasma levels of two independent study set (77 and 132 samples) and a targeted bead array for 124 unique proteins. They could achieve classification accuracy of up to 85% using a panel of proteins and concluded with proposing novel candidates for classifying the tumors. Of the shortlisted candidates, such as for IGFBP2 and IGF1, the group performed ELISA assays to confirm the indications. These findings indicate a metabolic association of plasma proteins with cancer and support to consider these targets in future cancer studies.

Neurodegenerative diseases
Analysis of protein expression provides a possibility to extend the current knowledge on neurodegenerative pathophysiology [63]. Recent advances within the field of proteomics have offered the potential to search for novel biomarkers using antibody microarrays [64]. Advancement in protocol development have allowed researchers to study not only blood, but also cerebrospinal fluid (CSF) [69].
Within neuroproteomics, the Nilsson lab has conducted several studies by using antibodies form the Human Protein Atlas [65] together with the suspension bead array assays. Profiling CSF from patients with Multiple Sclerosis (MS), they found GAP43, a cytoplasmic protein involved in the formation, and regeneration of neurons, hence a promising biomarker diseases of the brain [66]. More recently, Remnestål et al. [67] compared protein levels in 441 CSF samples collected from different neurodegenerative disease sample sets as well as CSF collected post-mortem. Among 376 antibodies, the synaptic proteins GAP43 and NRGN were found to be associated to AD patients compared with controls.
Using plasma samples, CSF and brain tissue from patients suffering from MS, a large-scale screening was conducted that started from utilizing 4500 antibodies on bead arrays [68]. A set of proteins was found to be associated with MS subtypes in CSF and plasma. Utilizing some of the candidate antibodies raised against IRF8, IL7, and METTL14 also for immuno-fluorescence analysis of brain tissue showed staining of neurons in proximity of MS lesions. This indicates that antibodies selected from array-based assays for analysis of body fluids can also provide further evidence at the affected tissue.
Lastly, the bead arrays were used to profile plasma from patients suffering from amyotrophic lateral sclerosis (ALS) [69], where 367 ALS patients and 101 controls were analyzed for 278 proteins. The study concludes with proposing neurofilament medium polypeptide (NEFM), solute carrier family 25 (SLC25A20), and regulator of G-protein signalling 18 (RGS18) as valuable proteins because they are involved in processes related to disease pathophysiology, which warrant further validation in independent sample sets.

Current issues and solutions
We list commercial antibody microarrays in Table 2, and further details about these products can be found as supplementary informtion (Additional file 1: Table S1). Despite the rapid technological advances in recent years, there are still technical issues that need to be overcome to ensure high-specificity and reproducibility of antibody arrays, to ensure high impact data and meaningful conclusions.
There is a need for validated antibodies for antibody microarray applications and respective sample types. The data from such validation efforts should consequently be provided. The quality of validated antibodies, the standardization of data analysis, and data storage and sharing are three important challenges to be considered.
Proteomic investigations using antibody microarrays have provided valuable data that reveal the pathophysiological background of a disease [70]. But there are also issues to address. Besides the overall still relatively small number of studies, the number of samples analysed in each study is usually limited to less than a hundred. Typically, these experiments are performed in an "exploratory cohort" first, examining preferably 1000's of different features. These microarray results need to be validated with an independent set of preferably larger number of specimens and by other methods.
Moreover, samples prepared from frozen or fresh samples may provide different profiles depending on the specific set and that could be referred to a "study markers". Another challenge of applying antibody microarrays for further studies is the heterozygosity of specific diseases (e.g. relapse remitting multiple sclerosis versus non-relapse multiple sclerosis) requesting careful study designs, in-depth knowledge about the sample itself and the disease phenotype of the subject it was obtained from. Some of these considerations concerning the design of discovery assays for the analysis of plasma proteome have been recently addressed elsewhere [71][72][73].
Finally, for a better study applying antibody microarray. Control should be carefully taken into consideration. Sample controls, not only internalize control like betaactin or GAPDH for each sample, but also positive (or negative) control that already confirmed by other method would help to set cut-off for further analysis.

The reproducibility challenge of the antibody microarray
One challenge for data derived from antibody microarrays is often linked to a lack of reproducibility. There are several experimental factors that may contribute to these problems, such as the surface chemistry and the mode of antibody immobilization, lack of sufficiently stringent processes for generation and validation of antibodies, as well as a bias from sample preparation, labelling, and batch effects from analysing samples across several slides or plates. We next focus on how to use general and novel methods to reduce systematic errors and potentially improve the reproducibility of antibody microarray experiment.
In the single-color approach, labels can be introduced at different efficacy in different samples due to chemistry dye binding stronger to certain amino acids. This might expand since the detection of proteins may reveal different fluorescent signal even though their quantity is the same. The samples could be labelled by a single fluorescent reporter dye, e.g. Cy3, Cy5, then probed one by one on antibody microarrays. In this circumstance, the slide-to-slide variation is unavoidable, thus reducing the reliability of the final microarray data and introducing systematic error. This systemic error can be attenuated by employing a dual-color labeling strategy inspired by DNA microarrays. Schroder et al. [74] reported an antibody microarray containing 741 cancer-related antibodies, employing dual-color mode. This approach has greatly improved the reproducibility, as reported in the study. The coefficients of variation (CV) of 89% features between 20 slides derived from the five production batches were under 15%, the CV of 96% features were under 20%. This dual-color mode can be applied in many other protein microarray experiments as long as they are comparing paired samples. This dual-color strategy will not only enhance the reproducibility of antibody microarrays, but may also improve the robustness of the assay.

The cross-reactivity of antibody microarray
The DNA microarray has rapidly been scaled up from 256 to 6.5 million features, but to date sensitive antibody microarray assays have only been scaled up to thousands of targets [75]. Cross-reactivity, commonly arising when multiple detection antibodies are mixed, is a known weakness of these assays that is mitigated by lengthy optimization [76].
In antibody co-localization microarray (ACM) described by Pla-Roca et al. [77], both capture and detection antibodies were physically spotted onto the same two-dimensional coordinate, which reduced crossreactivity and improved antibody reproducibility. Specifically, after spotting of the capture antibodies, the chip was removed from the arrayer, incubated with the sample, and then placed back onto the arrayer to be spotted with detection antibodies. After 3 years, the same group developed the next generation ACM, employing an array containing 50 capture antibodies. Instead of spotting, they used another perfect matched array with pre-spotted detection antibodies to transfer detection antibodies onto the assay slide. This method employs a pair of antibodies to detect a single protein similar to ELISA, thus improving the accuracy of the test that sets the path for an improved reproducibility.

The availability of validated antibodies for antibody microarray
The primary power of antibody microarrays based on single binder assays lies in the capability of multiple protein detection. Theoretically, the power is only limited to the number of antibodies on the microarray. However, unlike DNA microarrays where the probes can easily be synthesized at large scale either off-chip or on-chip, antibodies have to be produced and validated one-by-one. Thus, the number of qualified antibodies for antibody microarrays is still limited and remains a challenge. To address this, high-throughput screening strategies such as phage display may hold the solution to this problem.
In addition, binding molecules of non-protein scaffolds have been described [78], yet the number remains low when compared to almost 3 million commercially available antibodies (http://www.antibodypedia.com/). However, the size of synthetic binder libraries can further be increased by introducing additional modifications to the scaffolds, such as aptamers [79]. To maximize the use and impact of affinity reagents, the EU FP7 programme AFFI-NOMICS (http://www.affinomics.org), together with the preceding EU programmes ProteomeBinders and Affini-tyProteome, aims to extend affinity proteomics research by generating a large-scale resource of validated proteinbinding molecules for characterization of the human proteome [80].
Despite antibody use, a comprehensive scientific framework for the validation antibodies across research applications (i.e. immunohistochemistry, immuno-precipitation) is essential for the success of the antibody microarray platform. Uhlen et al. [81] have developed a five 'pillars' strategy for antibody validation: (1) genetic strategies, (2) orthogonal strategies, (3) independent antibody strategies, (4) expression of tagged proteins, and (5) immunocapture followed by MS for antibody validation. Immunoreagents, such as polyclonal, monoclonal antibodies, and other recombinant or synthetic binders are also suitable for this proposal. It will highly improve the quality and reproducibility for methods, such as antibody microarrays, given that the binders are validated in the intended application and for the sample type of interest.
For antibody microarrays, immuno-capture mass spectrometry or alternative assays such as dual-capture assays [82] offer similar assay formats for the target enrichment, as both require the immobilization of the binder. In the dual-capture system, antibodies are used to enrich their target proteins from a solution (e.g. plasma) and after labelling the proteins on the capture bead surface, the eluted proteins are detected by a multiplexed array consisting of several antibodies of interest. This allows two antibodies to bind a common target, hence presenting a sandwich assay based on consecutive binding. In comparison to MS, the antibodies used for read-out in affinity based assays need to be selected in relation to the target of the primary enrichment. Affinity assays are often still more sensitive and require less antibody and sample material then MS, however an MS based read-out will provide a wider view on which proteins have been enriched from a give sample, such as cell lysates [83] and more recently also plasma [84]. The breadth of MS data generated from such immuno-enrichment assessments requires though in order to carefully define specific peptides of interest from those that appear as unspecific and common contaminants [85].

Data processing, analysis and storage
Unlike the DNA microarray community, which has established standards for data submission and storage, such as minimum information about a microarray experiment (MIAME) [86] these guidelines have not yet been implemented for antibody microarrays. Most of the analysis tools in use for proteome-analysis have been adopted from the field of DNA microarrays. The data formats are similar between antibody microarrays and traditional DNA microarrays, the data analysis can also be subdivided into steps of image capture, data preprocessing, differential expression detection, clustering. There are now several R packages available to support some of these steps, such as Limma [87], Clusterprofiler [88], Qlucore Omics Explorer (http://www.qlucore.com) and IPA (http://www.ingenuity.com). DNA microarray-specific MIAME standards have been applied in Gene Expression Omnibus (GEO), which includes some data sets from antibody microarrays. Since diversity of antibody microarrays exceeds those of DNA microarrays due to diversified applications, a classification scheme that can include different types of antibody microarray data is needed.
In data acquisition and analysis, Ensink et al. [89] provided an advanced approach for locating signals in microarray image data, called segment and fit thresholding (SFT). This approach was optimized based on the initial settings by locating background and signal on the acquired antibody microarray image and immunofluorescence data. It was found that SFT performed well over multiple, diverse image characteristics without readjustment of settings, promoting full automation in microarray image analysis.
Image acquisition parameters such as the laser power and photomultiplier tube gain (PMT) during scanning can influence the readout of fluorescent intensities and thus may affect data quantitation. Gu et al. demonstrated an experimental approach using two fluorescent dyes to determine optimal settings of scan parameters for microarray experiments. Their efforts may facilitate the improvement of the accuracy of quantitative outcome in antibody microarray experiments [90].
Several main technical features and assay procedures remain to be improved. The handling of protein microarray data, i.e. the biostatistics parts, is one of the key features. Delfani et al. [91] have standardized the analytical workflow of their in-house designed recombinant antibody microarray platform and Hong et al. [92] have described an approach of handling data generated from different assay batches from suspension bead array assays. This is an important aspect when aiming to generate data for a larger number of samples addressing the remaining technical issues such as: antibody quality, array production, sample labelling, and selected assay conditions and biostatistics subjects.
Finally, to address the database issue, Xu et al. [93] constructed the protein microarray database (PMD), which is specifically designed for archiving and analyzing protein microarray data including antibody microarray data. In PMD, users can easily browse and search the entire database by experimental name, protein microarray type, and sample information. Additionally, PMD integrates several data analysis tools and provides an automated data analysis pipeline for users, who can obtain a comprehensive analysis report for their protein microarray data. Making antibody array data available to the community will be one of the important aspects to assess data quality and eventually improve the acceptance of microarray assay data.

Antibody and affinity reagent libraries
Beyond antibodies, other types of affinity reagents may unlock the bright future of microarrays [78]. Fridy et al. have showed a robust, fast pipeline to produce nanobodies. Inspired by their previous studies of neutralizing HIV antibodies in human, they have developed a strategy for rapid discovery of nanobodies through computation. The nanobody discovery is based on MS identification of affinity-purified heavy-chain antibodies isolated from an individual llama, using a DNA sequence database generated from the same animal. Notably, the authors have provided an advanced technology to improve nanobody's affinity by conforming dimerized nanobodies to specific antigens. Utilizing this method, phage display could be used to screen for interesting proteins and develop novel nanobody microarrays [94].
Aptamers are single-stranded oligonucleotides, utilized as affinity reagents. Depending on their sequence, the temperature, pH and the presence of certain ions, the aptamers fold into defined three-dimensional (3D) structures [95]. Properly folded aptamers are able to bind other molecules with high affinity and specificity [96]. High-throughput DNA engineering methods can be applied to generate high-diversity libraries to screen for target proteins, which can be utilized in microarray applications. The use of aptamers selected for a slow offrate has indeed lead to building high-throughput assays for plasma profiling [97], nowadays expanding into the analysis of the plasma proteome in thousands of samples [98]. Recent studies point at an extended use of this platform for studying thousands of proteins and samples. Other affinity reagents, such as designed ankyrin repeat proteins (DARPins) [99] and single-chain variable fragment (scFV) [100,101] also hold the promise to further boost the advancement and application of affinity reagent microarrays [102].

Combine with other technologies Flow cytometry
There are further developments and novel applications of antibody microarrays. Sharivkin et al. [103] showed a novel method of combining antibody microarrays with flow cytometry to isolate specific cell types from differentiating stem cell populations. An array of antibodies against cell-surface antigens was printed on a hydrogel coated glass slide. Live cells isolated by different markers using flow cytometry. And then captured on specific antibody spots by interaction between their surface antigens and the printed antibodies. This allowed identifying additional markers that further refined different subpopulation from flow sorting [104].

CRISPR/Cas9
The bacterial clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system for genome editing has greatly expanded the toolbox for mammalian genetics, enabling the rapid generation of isogenic cell lines and mice with modified alleles. Wang et al. [105] generated a knockout pool for two human cell lines, and this loss-of-function genetic screening approach was suitable for both positive and negative selection. With 1000's of genes to manipulate in one experiment, the antibody microarray has a great opportunity to be combined with this pool and the CRISPR/Cas9 technology holds promise to create a whole human proteome knock out library, which had the potential to revolutionise how we study proteins and their interactions in health and disease.

Advance materials
Chinnasamy et al. presented a novel lateral flow microarray-based device using a novel dually labelled gold nanoparticle-strategy for rapid and sensitive detection of clinical serum samples. Each gold nanoparticle was conjugated to an optimized ratio of HRP and anti-IgE, allowing a significant improvement of assay sensitivity as compared to commercially available detection reagents. Also, due to the rapid and simple procedure, inexpensive materials and read-out by means of a consumer flatbed scanner, the presented assay may provide a sensitive and low-cost platform for multiple fast testing which could facilitate antibody microarray in translation medicine area [106].
Antibody microarrays have been established alongside the advancement of materials. Hu et al. [107] developed a hierarchically nanostructured organic-inorganic hybrid substrate, comprising of randomly oriented ZnO nanorods on glass slides with coaxially tethered dense polymer brush, which highly improved the limit of detection (LOD) to 100 fg/mL. Huang et al. [108] have invented a glycol-gold nanoparticle-based antibody microarray, with potential to amplify a signal that could be detected by the naked eye.
A perspective on the future applications of antibody microarrays 1. With currently ongoing discussions about the validation of antibodies used for the generation of affinity proteomics assays data, a key element for a continuous use of antibody microarrays will relate to how experimental results from these multiplexed systems can be validated and confirmed by targeted assays. Validation refers to confirming and certifying specificity and reproducibility of the assay across batches of experiments, as well as to describe possible sources of interference and variance. With more and more affinity reagents on the market-currently almost 3,000,000 antibody products can be purchased covering 94% of all human genes (19,155 genes) according to Anitbodypedia (http://www.antibodypedia.org)not all of these will be functional for antibody microarray assays. Hence, careful upfront qualification and selection of binding molecules is required. This process will require additional, orthogonal and multiplexed assays and read-out systems that resemble the conditions of the array-based assays. An important readout with be the correlation with data from mass spectrometry, as this technology is capable of delivering absolute identifications of target molecules. 2. Analytical sensitivity and degrees of multiplexing are important areas of improvement but these are already common (popular) features that have been addressed ever since. Advances made through interactions with alternative assay concepts and read-out systems are often driven by other areas of life science and engineering. These have and will continue to advance array technologies and its derivate systems. This will also lead to new content being integrated into the current and coming platforms. 3. Lastly, disseminating the method's utility and data will be key for a continued awareness about the technology. Making antibody array data and assays more available will subsequently enable its use beyond those labs developing technologies to meet and even larger group of users. The success of antibody arrays will then be dependent on how robust and translatable the generated information is. Only if the data provides new leads to advance research projects or supports clinical decision making for an improved precision, then we will see a continued use of antibody arrays.

Conclusion
In summary, various forms of antibody microarrays have gradually evolved as analytical tools for proteomics research. These microarrays have facilitated advances in basic biology and discoveries towards potential tools of clinical use. With the progressive development, standardization of the experimental workflow and data interpretation, increasing numbers of binding reagents as well as innovative concepts are arising and will provide further advances in the foreseeable future. Antibody microarrays and concepts based upon, hold the promises to contribute to the advancement of research and even diagnostic applications, but there is a strong need to apply the appropriate validation schemes and follow-up studies to confirm the presented indications for a possible clinical decision making.