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Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

Background: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants. Methods: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics). Results: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10 -11 . These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P =3.27×10 -30 ) and MMP-3 (β=-0.60±0.05, P =1.67×10 -32 ), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P =1.34×10 -17 ) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure. Conclusions: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

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Disparities in the data that underlies clinical genomic interpretation is an acknowledged problem but there is a paucity of data demonstrating it. The National Institutes of Health’sAll of UsResearch Program aims to collect whole genome sequences, electronic health record (EHR) data, surveys and physical measurements for over a million participants of diverse ancestry and varied access to healthcare resources. We grouped participants by computed genetic ancestry and summarized the frequency of pathogenic variation within these groups. The European subgroup showed the highest rate of pathogenic variation (2.1%), with other ancestry groups ranging from 1.04% (East Asian) to 1.87% (‘Other’). Pathogenic variants were most frequently observed in genes related to Breast/Ovarian Cancer, Hypercholesterolemia or Hemochromatosis. Variant frequencies were consistent with gnomAD and some notable exceptions were resolved using gnomAD subsets. We additionally use this data to enrich sets of participants for specific genetic findings and to calculate penetrance. Differences in the frequency of pathogenic variants observed between ancestral groups generally indicate biases of ascertainment, but some may indicate differences in disease prevalence. These analyses are available on theAll of UsResearcher Workbench.

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotype’s ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer’s disease.

Self-injurious behavior (SIB) can lead to serious injury and occurs in approximately 1%-4% of the adult population, with higher incidences in adolescent and institutionalized populations, as well as in children with developmental disorders such as Autism. SIB also spontaneously occurs in a low percentage of captive monkeys. Rhesus macaque (Macaca mulatta) monkeys are evolutionarily and physiologically similar to humans, share 93% genetic sequence similarity to humans, and have long been used as testing subjects for vaccine and clinical trials. Previous studies hypothesized that altered endogenous opioid expression occurs in the brains of individuals and animals that self-injure. We examined the regional mRNA expression of opioid signaling genes in sixteen rhesus macaques that exhibited SIB and eight sex- and age- matched controls. The brain regions examined are linked to reward reinforcement and stress adaptation including the hypothalamus, orbital frontal cortex, nucleus accumbens, hippocampus, caudate, and the amygdala. We found decreased μ-opioid receptor (OPRM1) in the amygdala of monkeys with SIB, and reduced prodynorphin (PDYN) in the hypothalamus. Our data suggest dysfunction in the regulation of opioid peptide precursors and calls for further investigation of the endogenous opioid system in SIB.

The internet raises substantial challenges for policy makers in regulating gambling harm. The proliferation of gambling advertising on Twitter is one such challenge. However, the sheer scale renders it extremely difficult to investigate using conventional techniques. In this article, the authors present three U.K. Twitter gambling advertising studies using both big data analytics and manual content analysis to explore the volume and content of gambling ads, the age and engagement of followers, and compliance with U.K. advertising regulations. They analyze 890,000 organic ads from 417 accounts along with data on 620,000 followers and 457,000 engagements (replies and retweets). They find that approximately 41,000 U.K. children follow Twitter gambling accounts, and that two-thirds of gambling advertising tweets fail to fully comply with regulations. Ads for e-sports gambling are markedly different from those for traditional gambling (such as on soccer, casinos, and lotteries) and appear to have strong appeal for children, with 28% of engagements with e-sports gambling ads coming from users under 16 years old. The authors make six policy recommendations: spotlight e-sports gambling advertising, create new social media–specific regulations, revise regulation on content appealing to children, use technology to block users under 18 years from seeing gambling ads, require ad labeling of organic gambling tweets, and deploy better enforcement.

We show that the `optimal' use of the parallel composition theorem corresponds to finding the size of the largest subset of queries that `overlap' on the data domain, a quantity we call the \emph{maximum overlap} of the queries. It has previously been shown that a certain instance of this problem, formulated in terms of determining the sensitivity of the queries, is NP-hard, but also that it is possible to use graph-theoretic algorithms, such as finding the maximum clique, to approximate query sensitivity. In this paper, we consider a significant generalization of the aforementioned instance which encompasses both a wider range of differentially private mechanisms and a broader class of queries. We show that for a particular class of predicate queries, determining if they are disjoint can be done in time polynomial in the number of attributes. For this class, we show that the maximum overlap problem remains NP-hard as a function of the number of queries. However, we show that efficient approximate solutions exist by relating maximum overlap to the clique and chromatic numbers of a certain graph determined by the queries. The link to chromatic number allows us to use more efficient approximate algorithms, which cannot be done for the clique number as it may underestimate the privacy budget. Our approach is defined in the general setting of $f$-differential privacy, which subsumes standard pure differential privacy and Gaussian differential privacy. We prove the parallel composition theorem for $f$-differential privacy. We evaluate our approach on synthetic and real-world data sets of queries. We show that the approach can scale to large domain sizes (up to $10^{20000}$), and that its application can reduce the noise added to query answers by up to 60\%., This is the full version of the paper with the same title to appear in the proceedings on the 22nd Privacy Enhancing Technologies Symposium (PETS 2022)

BACKGROUND: Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma. METHODS: WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician’s diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes. RESULTS: A genome-wide significant association was identified between baseline FEV(1)/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10(−8) in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10(−6)). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV(1) (P = 3.3 × 10(−3)), postbronchodilator (PB) FEV(1) (7.3 × 10(−3)), and PB FEV(1)/FVC ratio (P = 2.7 × 10(−3)). The identified baseline FEV(1)/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR. CONCLUSIONS: These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.

Peritonsillar abscess (PTA) is a difficult diagnosis to make clinically, with clinical examination of even otolaryngologists showing poor sensitivity and specificity. Machine learning is a form of artificial intelligence that "learns" from data to make predictions. We developed a machine learning classifier to predict the diagnosis of PTA based on patient symptoms. We retrospectively collected clinical data and symptomatology from 916 patients who underwent attempted needle aspiration for PTA. Machine learning classifiers were trained on a subset of the data to predict the presence or absence of purulence on attempted aspiration. The performance of the model was evaluated on a holdout set. The accuracy of the top-performing algorithm, the artificial neural network, was 72.3%. Artificial neural networks can use patient symptoms to exceed human ability to predict PTA in patients with clinical suspicion for PTA. Similar models can assist medical decision making for clinicians who have suspicion of PTA.

The All of Us Research Program (AoURP, ‘the program’) is an initiative, sponsored by the National Institutes of Health (NIH), that aims to enroll one million people (or more) across the United States. Through repeated engagement of participants, a research resource is being created to enable a variety of future observational and interventional studies. The program has also committed to genomic data generation and returning important health-related information to participants. To do so, whole genome sequencing (WGS), variant calling processes, data interpretation, and return-of-results procedures had to be created and receive an Investigational Device Exemption (IDE) from the United States Food and Drug Administration (FDA). The performance of the entire workflow was assessed through the largest known cross-center, WGS-based, validation activity that was refined iteratively through interactions with the FDA over many months. The accuracy and precision of the WGS process as a device for the return of certain health-related genomic results was determined to be sufficient, and an IDE was granted. We present here both the process of navigating the IDE application process with the FDA and the results of the validation study as a guide to future projects which may need to follow a similar path. Future supplements to the IDE will be submitted to support additional variant classes, sample types, and any expansion to the reportable regions.

Protein bound uremic toxins (PBUTs), a series of chemicals that remain a challenge for removal strategies used on patients suffering with chronic kidney disease, could be strong candidates for MD study in order to better understand the interactions and time scales associated with binding mode transitions. Currently, traditional dialysis methods cannot satisfactorily remove PBUTs from the bloodstream. This is at least partly due to these toxin's high level of affinity for protein binding sites, particularly the prominent human serum albumin (HSA) and two of its drug binding sites (Sudlow site I and II). We investigate the dynamics of binding site transitions and interactions by MD simulations targeting four well-known toxins: indoxyl sulfate (IS), p-cresyl sulfate (PCS), indole-3-acetic acid (IAA), and hippurate acid (HIP). Long-time scale dynamics are obtained by the use of time-structure independent component analysis (tICA) for dimensionality reduction followed by spectral analysis of a Markov state model (MSM) scored using the generalized matrix Rayleigh quotient (GMRQ). Our results add new insights to prior findings related to the key role of charge-pairing in governing toxin-protein interactions. We find that IAA, the bulkiest hydrophobic toxin studied, observes the slowest process of at least 3 times slower than the smaller, less hydrophobic toxins. In general, we find that the processes slower than 15 ns are correlated with a transition from dominantly hydrophobic interactions deep in the binding pocket to a gain in hydrogen bonding partners near the mouth of the pocket. Our results indicate that aromatic residues such as PHE play a part in a type of toxin stabilization akin to π-stacking. In conclusion, this work presents mechanistic descriptions of interactions/transitions for a set of important PBUTs that bind Sudlow site II on time scales relevant to the underlying binding kinetics of most interest.

The volumetric oxygen mass transfer coefficient ($$k_{l} a$$ k l a ) is an essential parameter in aerobic high-cell density fermentation where the availability of oxygen to growing microorganisms is a limiting factor. Bioprocess teams looking to scale-up/down between the Eppendorf BioBLU 0.3f single-use vessel and the BioFlo® 320 reusable vessel bioreactors may find it challenging using a matched $$k_{l} a$$ k l a . The maximum $$k_{l} a$$ k l a of the BioFlo® 320 reusable bioreactor was 109 h−1, which was approximately twice that of the BioBLU 0.3f single-use vessel. The results here show no overlap in $$k_{l} a$$ k l a values when both bioreactors were compared and thus conclude that scalability based on $$k_{l} a$$ k l a is not viable. The maximum $$k_{l} a$$ k l a of the Eppendorf BioBLU 0.3f single-use reported here was 47 h−1 compared to that of the manufacturer’s value of 2500 h−1, indicating a 53-fold difference. This discrepancy was attributed to the incompatible sulfite addition method used by the manufacturer for estimation.

Objectives\ud The aim of the present study is to explore whether there is a temporal association between anti-Islamic online and offline hate, and if so in which direction.\ud \ud Methods\ud We used data on hateful Twitter content, and hate incidents and hate crimes/offences recorded by the Metropolitan Police Service in the United Kingdom to analyse this association, using time-series analysis. This study is unique in its use of newly developed technology to undertake big data analysis with recent, disaggregated online and offline hate data.\ud \ud Results\ud Our study examined the ‘everyday’ incidents of (online and offline) hate that affect communities throughout the United Kingdom and we found that anti-Islamic hate speech followed rather than preceded Islamophobic hate offline.\ud \ud Conclusions\ud Our findings likely point to what we have referred to as compound retaliation, which suggests that media and social media dissemination about offline acts of hate compound already tense intergroup hostilities, providing further permission for those to express hatred online. Such a situation represents the compounding of hate and hostility through offline and online networks that are likely to be reinforcing.

Objective (1) To examine the prevalence of college-aged students on campus who had a history of concussion(s) or traumatic brain injury (TBI) and associated symptoms and (2) to evaluate the rate of campus service utilization among students who had a TBI. Setting A representative sampling, randomly selected campus-wide e-mail was sent out with a Qualtrics online survey. Participants College-aged students (N = 466, n = 429) at a northeastern US university responded and consented to participate in the online survey. Design Descriptive study using an online survey to evaluate the prevalence of TBI and subsequent utility of campus services. Main outcome measures The survey assessed concussion history and the diagnosing clinician, activity in which it occurred, level of consciousness, associated symptoms, and utilization of campus services for academic accommodation. Results Prevalence of concussion/TBI in the study population (51.7%) is notably higher than previous reporting in adult and college populations (∼10%-25%). Campus disability and accommodative services were underutilized (10%-13%) and were accessed more readily by the female population. In addition, there were reportable increases in symptom prevalence and service utilization with a greater number of previous concussions. Conclusions Awareness and utilization of accommodative services, especially with a history of concussions, need to be improved in order to ensure academic and occupational success of students.

We demonstrate the use of a Generative Adversarial Network (GAN), trained from a set of over 400,000 light and heavy chain human antibody sequences, to learn the rules of human antibody formation. The resulting model surpasses common in silico techniques by capturing residue diversity throughout the variable region, and is capable of generating extremely large, diverse libraries of novel antibodies that mimic somatically hypermutated human repertoire response. This method permits us to rationally design de novo humanoid antibody libraries with explicit control over various properties of our discovery library. Through transfer learning, we are able to bias the GAN to generate molecules with key properties of interest such as improved stability and developability, lower predicted MHC Class II binding, and specific complementarity-determining region (CDR) characteristics. These approaches also provide a mechanism to better study the complex relationships between antibody sequence and molecular behavior, both in vitro and in vivo. We validate our method by successfully expressing a proof-of-concept library of nearly 100,000 GAN-generated antibodies via phage display. We present the sequences and homology-model structures of example generated antibodies expressed in stable CHO pools and evaluated across multiple biophysical properties. The creation of discovery libraries using our in silico approach allows for the control of pharmaceutical properties such that these therapeutic antibodies can provide a more rapid and cost-effective response to biological threats.

Protein bound uremic toxins (PBUTs) are known to bind strongly with the primary drug carrying sites of human serum albumin (HSA), Sudlow site I and Sudlow site II. A detailed energetic and structural description of PBUT interactions with these binding sites would provide useful insight into the design of materials that specifically displace and capture PBUTs. In this work, we used molecular dynamics (MD) simulations to study in atomistic detail 4 PBUTs bound in Sudlow site II. Specifically, we used the experimentally resolved X-ray structure of simulated indoxyl sulfate (IS) bound to Sudlow site II (PBD ID: 2BXH) to generate initial binding poses for p-cresyl sulfate (pCS), indole-3-acetic acid (IAA), and hippuric acid (HA). We calculated the interaction energy between toxin and protein in MD simulations and performed mean shift clustering on the collection of molecular structures from MD to identify the primary binding modes of each toxin. We find that all 4 toxins are primarily stabilized by electrostatic interactions between their anionic moiety and the hydrophilic residues in Sudlow site II. We observed transience in the strongest toxin-protein interaction, a charge-pairing with the positively charged R410 residue. We confirm the finding that the primary binding pose of IS in Sudlow site II is stabilized by a hydrogen bond with the carbonyl oxygen of L430, and find that this is also true for IAA. We provide insight into the chemical functional groups that might be incorporated to improve the specificity of synthetic materials for PBUT capture. This work represents a next step toward the de novo design of solutions to the problem of PBUT management in CKD patients.Significance StatementIn spite of their implication in poor clinical outcomes, surprisingly little information is available about the structure and mechanisms that govern the binding of protein bound uremic toxins to their primary carrier human serum albumin. To date, only the structure of indoxyl sulfate has been determined by experiment. This paper describes a comprehensive characterization of four toxins that are known to bind Sudlow site II using molecular dynamics simulations. Based on the experimental structure of indoxyl sulfate bound to HSA, the binding mode within Sudlow site II of three additional PBUTs was determined. The structures, energetic and mechanistic analysis provide substantial new information for the nephrology community about these toxins as well as new protocols to aid future studies of PBUTs.

OBJECTIVE Peritonsillar abscess (PTA) is a common infectious complication of pharyngeal infection managed by otolaryngologists and emergency room physicians. Streptococcus and Fusobacterium (e.g., Fusobacterium necrophorum, FN) species are commonly isolated pathogens. The aim of this study was to determine the implication of culture results on abscess recurrence following drainage. METHODS Single-institution retrospective review of patients treated at the University of Michigan between 2000 and 2017. Demographic and clinical outcome data were analyzed, including treatment details, culture data, and recurrence. RESULTS One hundred fifty-six of the 990 patients in our study developed recurrence of their abscess (16%). The age ranges most susceptible to recurrence included adolescent (22.9%) and young adult groups (17.1%). Recurrent patients were more likely to have experienced acute progression of symptoms (79% vs. 71%, P = 0.03), trismus (67% vs. 55%, P = 0.006), voice changes (65% vs. 57%, P = 0.04), and dysphagia (72% vs. 61%, P = 0.01) compared to nonrecurrent patients. They were also more likely to have clinical lymphadenopathy noted on initial examination (67% vs. 56%, P = 0.009). Culture data was sent for 852 patients (86%). The presence of FN was significantly more prevalent in the recurrent group (P

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle that contains apolipoprotein(a), encoded by LPA, and is a highly heritable, causal risk factor for cardiovascular diseases that varies in concentrations across ancestries. Here, we use deep-coverage whole genome sequencing in 8392 individuals of European and African ancestry to discover and interpret both single-nucleotide variants and copy number (CN) variation associated with Lp(a). We observe that genetic determinants between Europeans and Africans have several unique determinants. The common variant rs12740374 associated with Lp(a) cholesterol is an eQTL for SORT1 and independent of LDL cholesterol. Observed associations of aggregates of rare non-coding variants are largely explained by LPA structural variation, namely the LPA kringle IV 2 (KIV2)-CN. Finally, we find that LPA risk genotypes confer greater relative risk for incident atherosclerotic cardiovascular diseases compared to directly measured Lp(a), and are significantly associated with measures of subclinical atherosclerosis in African Americans., Circulating lipoprotein(a) is an important risk factor for cardiovascular disease and shows variability between different ethnic groups. Here, Zekavat et al. perform whole-genome sequencing in individuals of European and African ancestries and find ancestry-specific genetic determinants for lipoprotein(a) levels.

Glycosylation is a prominent co- and post-translational modification which contributes to a variety of important biological functions. Protein glycosylation characteristics, particularly N-glycosylation, are influenced by changes in one's pathological state, such as through the presence of disease, and as such, there is great interest in N-glycans as potential disease biomarkers. Human serum is an attractive source for N-glycan based biomarker studies as circulatory proteins are representative of one's physiology, with many serum proteins containing N-glycosylation. The difficulty in comprehensively characterizing the serum N-glycome arises from the absence of a biosynthetic template resulting in great structural heterogeneity and complexity. To help overcome these challenges we developed a 2-dimensional liquid chromatography platform which utilizes offline weak anion exchange (WAX) chromatography in the first dimension and hydrophilic interaction liquid chromatography (HILIC) in the second dimension to separate N-glycans by charge, corresponding to degree of sialylation, and size, respectively. Performing these separations offline enables subsequent derivatization with 4-(4,6-Dimethoxy-1,3,5-triazin-2-yl)-4-methylmorpholinium chloride (DMT-MM) for sialic acid linkage determination and the identification of sialic acid linkage isomers. Subsequent tandem mass spectrometry analysis revealed the identification of 212 complete and partial N-glycan structures including low abundant N-glycans containing acetyl and sulphate modifications. The identifications obtained through this platform were then applied to N-glycans released from a set of stage 3 gastric cancer serum samples obtained from patients before (pre-op) and after (post-op) tumour resection to investigate how the serum N-glycome can facilitate differentiation between the two pathological states.

The therapeutic potential of protein drugs has been hindered by difficulties with long-term stability and rapid clearance from the body. Recombinant fusion proteins provide a scalable platform for engineered biologics, whereby a polypeptide domain is appended to alter the physical characteristics of a therapeutic protein and enhance its pharmaceutical viability. Two simple design principles for recombinant fusion proteins, based on the physical properties of the polypeptide domain, have been separately applied to address issues with the stability and delivery of biologics. "Conformationally disordered" peptides, exemplified by the homo amino acid peptide polyG, have been shown to increase the circulation half-life and bioactivity of protein therapeutics

Post-translational modification of proteins by the attachment of glycans is governed by a variety of highly specific enzymes and is associated with fundamental impacts on the parent protein's physical, chemical and biological properties. The inherent connection between cellular physiology and specific glycosylation patterns has been shown to offer potential for diagnostic and prognostic monitoring of altered glycosylation in the disease state. Conversely, glycoprotein based biopharmaceuticals have emerged as dominant therapeutic strategies in the treatment of intricate diseases. Glycosylation present on these biopharmaceuticals represents a major critical quality attribute with impacts on both pharmacokinetics and pharmacodynamics. The structural variety of glycans, based upon their non-template driven assembly, poses a significant analytical challenge for both qualitative and quantitative analysis. Labile monosaccharide constituents, isomeric species and often low sample availability from biological sources necessitates meticulous sample handling, ultra-high-resolution analytical separation and sensitive detection techniques, respectively. In this article a critical review of analytical quantitation approaches using liquid phase separations coupled to mass spectrometry for released glycans of biopharmaceutical and biomedical significance is presented. Considerations associated with sample derivatisation strategies, ionisation, relative quantitation through isotopic as well as isobaric labelling, metabolic/enzymatic incorporation and targeted analysis are all thoroughly discussed.