Milk Bottom-Up Proteomics: Method Optimization

Detection of the β-lactoglobulin genotype in zebu cattle (Gangatiri) milk using high-resolution accurate mass spectroscopy

We studied the genetic polymorphism of beta-lactoglobulin (β-Lg) whey protein in Gangatiri zebu cows for this Research Communication. The polymorphic nature of milk protein fractions and their association with milk production traits, composition and quality has attracted several efforts in evaluating the allelic distribution of protein locus as a potential dairy trait marker. Genetic variants of β-Lg have highly significant effects on casein number (B > A) and protein recovery (B > A) and also determine the yield of cheese dry matter (B > A). Molecular techniques of polyacrylamide gel electrophoresis and high-resolution accurate mass-spectroscopy were applied to characterize the β-Lg protein obtained from the Gangatiri breed milk. Sequence analysis of β-Lg showed the presence of variant B having UniProt database accession number P02754, coded on the PAEP gene. Our study can provide reference and guidance for the selection of superior milk (having β-LgB) from this indigenous breed that could potentially give a good yield of β-Lg for industrial applications.

Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection

Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.

Pudding Proteomics: Cyclomaltodextrin Glucanotransferase and Microbial Proteases Can Liquefy Extended Shelf Life Dairy Products

In recent years, a lack of stability of dairy products with extended shelf life (e.g., yoghurt products, UHT desserts) has occurred, with the corresponding products liquefying significantly after days or weeks. This project aimed to identify the enzymes responsible for the liquefaction of the affected products based on differential proteomic analyses. No evidence was found for the presence of starch-degrading bacteria in the affected products. With zymography and proteome analysis, we detected the cause of liquefaction in a pudding by contamination of its aroma component with an engineered amylolytic enzyme, cyclomaltodextrin glucanotransferase (CGTase) from Thermoanaerobacterium thermosulfurigenes. In addition, we detected contamination with Pseudomonas-derived proteolytic ATP-dependent Clp protease in one pudding batch and proteases in technically used amylases, which degraded β-caseins in another batch. Identification of these agents with liquefying properties in dairy products are useful for adjustment of production protocols and/or composition of additives, and thus shelf life extension.

Mining the Wheat Grain Proteome

Bread wheat is the most widely cultivated crop worldwide, used in the production of food products and a feed source for animals. Selection tools that can be applied early in the breeding cycle are needed to accelerate genetic gain for increased wheat production while maintaining or improving grain quality if demand from human population growth is to be fulfilled. Proteomics screening assays of wheat flour can assist breeders to select the best performing breeding lines and discard the worst lines. In this study, we optimised a robust LC–MS shotgun quantitative proteomics method to screen thousands of wheat genotypes. Using 6 cultivars and 4 replicates, we tested 3 resuspension ratios (50, 25, and 17 µL/mg), 2 extraction buffers (with urea or guanidine-hydrochloride), 3 sets of proteases (chymotrypsin, Glu-C, and trypsin/Lys-C), and multiple LC settings. Protein identifications by LC–MS/MS were used to select the best parameters. A total 8738 wheat proteins were identified. The best method was validated on an independent set of 96 cultivars and peptides quantities were normalised using sample weights, an internal standard, and quality controls. Data mining tools found particularly useful to explore the flour proteome are presented (UniProt Retrieve/ID mapping tool, KEGG, AgriGO, REVIGO, and Pathway Tools).

Extraction of protein from food waste: An overview of current status and opportunities

The chief intent of this review is to explain the different extraction techniques and efficiencies for the recovery of protein from food waste (FW) sources. Although FW is not a new concept, increasing concerns about chronic hunger, nutritional deficiency, food security, and sustainability have intensified attention on alternative and sustainable sources of protein for food and feed. Initiatives to extract and utilize protein from FW on a commercial scale have been undertaken, mainly in the developed countries, but they remain largely underutilized and generally suited for low-quality products. The current analysis reveals the extraction of protein from FW is a many-sided (complex) issue, and that identifies for a stronger and extensive integration of diverse extraction perspectives, focusing on nutritional quality, yield, and functionality of the isolated protein as a valued recycled ingredient.

Influence of Dry Period Length of Swedish Dairy Cows on the Proteome of Colostrum

The aim of this study was to evaluate the influence of applying a 4-week instead of an 8-week dry period to dairy cows on the proteome of colostrum (first sample) and of transition milk (the fifth postpartum milk sample). Individual milk serum samples of colostrum and transition milk were analysed from 12 Swedish Holstein (SH) and 12 Swedish Red (SR) cows. Samples were prepared by filter-aided sample preparation and dimethyl labelling and analysed by liquid chromatography tandem mass spectrometry. Shortening the dry period resulted in upregulation of 18 proteins in colostrum and transition milk of SR, whereas no statistical differences were found for SH colostrum and transition milk. These upregulated proteins may reflect a specific immune response in the SR samples that was reflected in colostrum as well as in transition milk. Upregulated proteins in colostrum seemed to reflect increased mammary epithelial cell proliferation in the periparturient period when a 4-week dry period was applied. The proteome data indicate that a dry period of 4 weeks to SR cows may not be sufficient for complete regeneration of the mammary epithelium.

Urinary proteins detected using modern proteomics intervene in early type 2 diabetic kidney disease – a pilot study

Aim: An advanced proteomics platform for protein biomarker discovery in diabetic chronic kidney disease (DKD) was developed, validated and implemented. Materials & methods: Three Type 2 diabetes mellitus patients and three control subjects were enrolled. Urinary peptides were extracted, samples were analyzed on a hybrid LTQ-Orbitrap Velos Pro instrument. Raw data were searched using the SEQUEST algorithm and integrated into Proteome Discoverer platform. Results & discussion: Unique peptide sequences, resulted sequence coverage, scoring of peptide spectrum matches were reported to albuminuria and databases. Five proteins that can be associated with early DKD were found: apolipoprotein AI, neutrophil gelatinase-associated lipocalin, cytidine deaminase, S100-A8 and hemoglobin subunit delta. Conclusion: Urinary proteome analysis could be used to evaluate mechanisms of pathogenesis of DKD.

High-Resolution Mass Spectrometer-Based Ultra-Deep Profile of Milk Whey Proteome in Indian Zebu (Sahiwal) Cattle

Milk serves as a mode of protection to neonate through transferring the host defense proteins from mother to offspring. It also guards the mammary gland against various types of infections. Along with the presence of six vital proteins, bovine milk (whey) contains a massive class of minor proteins, not all of which have been comprehensively reported. In this study, we performed an LC-MS/MS-based ultra-deep identification of the milk whey proteome of Indian zebu (Sahiwal) cattle. Three independent search engines that are Comet, Tandem, and Mascot-based analysis resulted in the discovery of over 6,210 non-redundant proteins commonly identified. Genome-wise mapping revealed that chromosome 1 showed a minimum expression of 14 proteins, whereas chromosome 19 expressed 250 maximum proteins in milk whey. These results demonstrate that milk proteome in Sahiwal cattle is quite complicated, and minor milk fractions play a significant role in host defense.

Generation of High Affinity Anti-Peptide Polyclonal Antibodies Recognizing Goat αs1-Casein

The chemical, technological and allergy properties of goat's milk are significantly affected by the level of αs1-casein. Detection and quantification of αs1-casein requires high-specificity methods to overcome high-sequence similarity between this protein and others in the casein family. Unavailability of antibodies with high affinity and specificity towards goat αs1-casein hinders the development of immuno-based analytical methods such as enzyme-linked immunosorbent assay (ELISA) and biosensors. Here, we report the generation of polyclonal antibodies (or immunoglobulins, IgGs) raised towards goat αs1-casein N- (Nter) and C-terminal (Cter) peptide sequences. The Nter and Cter peptides of goat αs1-casein were immunized in rabbits for the generation of antisera, which were purified using protein G affinity chromatography. The binding affinity of the antisera and purified IgGs were tested and compared using indirect ELISA, where peptide-BSA conjugates and goat αs1-casein were used as the coating antigens. The Nter antiserum displayed higher titer than Cter antiserum, at 1/64,000 and 1/32,000 dilutions, respectively. The purification step further yielded 0.5 mg/mL of purified IgGs from 3 mL of antisera. The purified Nter IgG showed a significantly (p < 0.05) higher binding affinity towards peptide-BSA and goat αs1-casein, with lower Kd value at 5.063 × 10-3 μM compared to 9.046 × 10-3 μM for the Cter IgG. A cross-reactivity test showed that there was no binding in neither Nter nor Cter IgGs towards protein extracts from the milk of cow, buffalo, horse and camel. High-quality antibodies generated will allow further development of immuno-based analytical methods and future in vitro studies to be conducted on goat αs1-casein.

Optimization of Protein Extraction Method for 2DE Proteomics of Goat's Milk

Two-dimensional electrophoretic (2DE)-based proteomics remains a powerful tool for allergenomic analysis of goat’s milk but requires effective extraction of proteins to accurately profile the overall causative allergens. However, there are several current issues with goat’s milk allergenomic analysis, and among these are the absence of established standardized extraction method for goat’s milk proteomes and the complexity of goat’s milk matrix that may hamper the efficacy of protein extraction. This study aimed to evaluate the efficacies of three different protein extraction methods, qualitatively and quantitatively, for the 2DE-proteomics, using milk from two commercial dairy goats in Malaysia, Saanen, and Jamnapari. Goat’s milk samples from both breeds were extracted by using three different methods: a milk dilution in urea/thiourea based buffer (Method A), a triphasic separation protocol in methanol/chloroform solution (Method B), and a dilution in sulfite-based buffer (Method C). The efficacies of the extraction methods were assessed further by performing the protein concentration assay and 1D and 2D SDS-PAGE profiling, as well as identifying proteins by MALDI-TOF/TOF MS/MS. The results showed that method A recovered the highest amount of proteins (72.68% for Saanen and 71.25% for Jamnapari) and produced the highest number of protein spots (199 ± 16.1 and 267 ± 10.6 total spots for Saanen and Jamnapari, respectively) with superior gel resolution and minimal streaking. Six milk protein spots from both breeds were identified based on the positive peptide mass fingerprinting matches with ruminant milk proteins from public databases, using the Mascot software. These results attest to the fitness of the optimized protein extraction protocol, method A, for 2DE proteomic and future allergenomic analysis of the goat’s milk.

Detection of milk powder in liquid whole milk using hydrolyzed peptide and intact protein mass spectral fingerprints coupled with data fusion technologies

Detection of the presence of milk powder in liquid whole milk is challenging due to their similar chemical components. In this study, a sensitive and robust approach has been developed and tested for potential utilization in discriminating adulterated milk from liquid whole milk by analyzing the intact protein and hydrolyzed peptide using ultra‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometer (UPLC‐QTOF‐MS) fingerprints combined with data fusion. Two different datasets from intact protein and peptide fingerprints were fused to improve the discriminating ability of principle component analysis (PCA). Furthermore, the midlevel data fusion coupled with PCA could completely distinguish liquid whole milk from the milk. The limit of detection of milk powder in liquid whole milk was 0.5% (based on the total protein equivalence). These results suggested that fused data from intact protein and peptide fingerprints created greater synergic effect in detecting milk quality, and the combination of data fusion and PCA analysis could be used for the detection of adulterated milk.

A Multiple Protease Strategy to Optimise the Shotgun Proteomics of Mature Medicinal Cannabis Buds

Earlier this year we published a method article aimed at optimising protein extraction from mature buds of medicinal cannabis for trypsin-based shotgun proteomics (Vincent, D., et al. Molecules2019, 24, 659). We then developed a top-down proteomics (TDP) method (Vincent, D., et al. Proteomes2019, 7, 33). This follow-up study aims at optimising the digestion of medicinal cannabis proteins for identification purposes by bottom-up and middle-down proteomics (BUP and MDP). Four proteases, namely a mixture of trypsin/LysC, GluC, and chymotrypsin, which target different amino acids (AAs) and therefore are orthogonal and cleave proteins more or less frequently, were tested both on their own as well as sequentially or pooled, followed by nLC-MS/MS analyses of the peptide digests. Bovine serum albumin (BSA, 66 kDa) was used as a control of digestion efficiency. With this multiple protease strategy, BSA was reproducibly 97% sequenced, with peptides ranging from 0.7 to 6.4 kD containing 5 to 54 AA residues with 0 to 6 miscleavages. The proteome of mature apical buds from medicinal cannabis was explored more in depth with the identification of 27,123 peptides matching 494 unique accessions corresponding to 229 unique proteins from Cannabis sativa and close relatives, including 130 (57%) additional annotations when the list is compared to that of our previous BUP study (Vincent, D., et al. Molecules2019, 24, 659). Almost half of the medicinal cannabis proteins were identified with 100% sequence coverage, with peptides composed of 7 to 91 AA residues with up to 9 miscleavages and ranging from 0.6 to 10 kDa, thus falling into the MDP domain. Many post-translational modifications (PTMs) were identified, such as oxidation, phosphorylations, and N-terminus acetylations. This method will pave the way for deeper proteome exploration of the reproductive organs of medicinal cannabis, and therefore for molecular phenotyping within breeding programs.

Top-Down Proteomics of Medicinal Cannabis

The revised legislation on medicinal cannabis has triggered a surge of research studies in this space. Yet, cannabis proteomics is lagging. In a previous study, we optimised the protein extraction of mature buds for bottom-up proteomics. In this follow-up study, we developed a top-down mass spectrometry (MS) proteomics strategy to identify intact denatured protein from cannabis apical buds. After testing different source-induced dissociation (SID), collision-induced dissociation (CID), higher-energy collisional dissociation (HCD), and electron transfer dissociation (ETD) parameters on infused known protein standards, we devised three LC-MS/MS methods for top-down sequencing of cannabis proteins. Different MS/MS modes produced distinct spectra, albeit greatly overlapping between SID, CID, and HCD. The number of fragments increased with the energy applied; however, this did not necessarily translate into greater sequence coverage. Some precursors were more amenable to fragmentation than others. Sequence coverage decreased as the mass of the protein increased. Combining all MS/MS data maximised amino acid (AA) sequence coverage, achieving 73% for myoglobin. In this experiment, most cannabis proteins were smaller than 30 kD. A total of 46 cannabis proteins were identified with 136 proteoforms bearing different post-translational modifications (PTMs), including the excision of N-terminal M, the N-terminal acetylation, methylation, and acetylation of K resides, and phosphorylation. Most identified proteins are involved in photosynthesis, translation, and ATP production. Only one protein belongs to the phytocannabinoid biosynthesis, olivetolic acid cyclase.

Milk proteome from in silico data aggregation allows the identification of putative biomarkers of negative energy balance in dairy cows

A better knowledge of the bovine milk proteome and its main drivers is a prerequisite for the modulation of bioactive proteins in milk for human nutrition, as well as for the discovery of biomarkers that are useful in husbandry and veterinary medicine. Milk composition is affected by lactation stage and reflects, in part, the energy balance of dairy cows. We aggregated the cow milk proteins reported in 20 recent proteomics publications to produce an atlas of 4654 unique proteins. A multistep assessment was applied to the milk proteome datasets according to lactation stages and milk fractions, including annotations, pathway analysis and literature mining. Fifty-nine proteins were exclusively detected in milk from early lactation. Among them, we propose six milk proteins as putative biomarkers of negative energy balance based on their implication in metabolic adaptative pathways. These proteins are PCK2, which is a gluconeogenic enzyme; ACAT1 and IVD, which are involved in ketone metabolism; SDHA and UQCRC1, which are related to mitochondrial oxidative metabolism; and LRRC59, which is linked to mammary gland cell proliferation. The cellular origin of these proteins warrants more in-depth research but may constitute part of a molecular signature for metabolic adaptations typical of early lactation.

Optimisation of Protein Extraction from Medicinal Cannabis Mature Buds for Bottom-Up Proteomics

Medicinal cannabis is used to relieve the symptoms of certain medical conditions, such as epilepsy. Cannabis is a controlled substance and until recently was illegal in many jurisdictions. Consequently, the study of this plant has been restricted. Proteomics studies on Cannabis sativa reported so far have been primarily based on plant organs and tissues other than buds, such as roots, hypocotyl, leaves, hempseeds and flour. As far as we know, no optimisation of protein extraction from cannabis reproductive tissues has been attempted. Therefore, we set out to assess different protein extraction methods followed by mass spectrometry-based proteomics to recover, separate and identify the proteins of the reproductive organs of medicinal cannabis, apical buds and isolated trichomes. Database search following shotgun proteomics was limited to protein sequences from C. sativa and closely related species available from UniprotKB. Our results demonstrate that a buffer containing the chaotrope reagent guanidine hydrochloride recovers many more proteins than a urea-based buffer. In combination with a precipitation with trichloroacetic acid, such buffer proved optimum to identify proteins using a trypsin digestion followed by nano-liquid chromatography tandem mass spectrometry (nLC-MS/MS) analyses. This is validated by focusing on enzymes involved in the phytocannabinoid pathway.

Symposium review: Characterization of the bovine milk protein profile using proteomic techniques

Identification and characterization of the comprehensive bovine milk proteome has historically been limited due to the dichotomy of protein abundances within milk. The high abundance of a select few proteins, including caseins, α-lactalbumin, β-lactoglobulin, and serum albumin, has hindered intensive identification and characterization of the vast array of low-abundance proteins in milk due to limitations in separation techniques and protein labeling capacity. In more recent years, the development and advancement of proteomics techniques have yielded valuable tools for characterization of the protein profile in bovine milk. More extensive fractionation and enrichment techniques, including the use of combinations of precipitation techniques, immunosorption, gel electrophoresis, chromatography, ultracentrifugation, and hexapeptide-based binding enrichment, have allowed for better isolation of lower abundance proteins for further downstream liquid chromatography-tandem mass spectrometry approaches. The different milk subfractions isolated during these processes can also be analyzed as individual entities to assess the protein profile unique to the different fractions-for instance, investigation of the skim milk-associated proteome versus the milk fat globule membrane-associated proteome. Updates to high-throughput methods, equipment, and software have also allowed for greater interpretation and visualization of the data. For instance, labeling techniques have enabled analysis of multiplexed samples and more accurate comparison of specific protein abundances and quantities across samples, and integration of gene ontology analysis has allowed for a more in-depth and visual representation of potential relationships between identified proteins. Inclusively, these developments in proteomic techniques have allowed for a rapid increase in the number of milk-associated proteins identified and a better grasp of the relationships and potential functionality of the proteins within the milk proteome.

Developmental, genetic and environmental variations of global DNA methylation in the first leaves emerging from the shoot apical meristem in poplar trees

In the context of climate changes, clarifying the causes underlying tree phenotypic plasticity and adaptation is crucial. Studies suggest a role of epigenetic mechanisms in response to external stimuli, raising the question whether such processes can promote acclimation of trees exposed to adverse climate conditions. Recently, we revealed an environmental epigenetic footprint in the shoot apical meristem (SAM) which could partially be transmitted mitotically, for several months, up until the winter-dormant bud in field conditions. Here, we extended our previous analysis to the leaves of the same P. deltoides×P. nigra clones. We aimed at estimating the range of developmentally, genetically, and environmentally induced variations on DNA methylation. We showed that only the first leaves emerging from the SAM displayed variations of DNA methylation under changing water conditions. We also found that these variations are genotype- and pedoclimatic site-dependent. Altogether, our data raised questions and perspectives on the direct acquisition, the maintenance of environmentally induced DNA methylation changes, and their mitotic transmission from the SAM to the first emerging leaves.

Optimisation of Milk Protein Top-Down Sequencing Using In-Source Collision-Induced Dissociation in the Maxis Quadrupole Time-of-Flight Mass Spectrometer

Top-down sequencing in proteomics has come of age owing to continuous progress in LC-MS. With their high resolution and broad mass range, Quadrupole Time-of-Flight (Q-ToF) hybrid mass spectrometers equipped with electrospray ionisation source and tandem MS capability by collision-induced dissociation (CID) can be employed to analyse intact proteins and retrieve primary sequence information. To our knowledge, top-down proteomics methods with Q-ToF have only been evaluated using samples of relatively low complexity. Furthermore, the in-source CID (IS-CID) capability of Q-ToF instruments has been under-utilised. This study aimed at optimising top-down sequencing of intact milk proteins to achieve the greatest sequence coverage possible from samples of increasing complexity, assessed using nine known proteins. Eleven MS/MS methods varying in their IS-CID and conventional CID parameters were tested on individual and mixed protein standards as well as raw milk samples. Top-down sequencing results from the nine most abundant proteoforms of caseins, alpha-lactalbumin and beta-lactoglubulins were compared. Nine MS/MS methods achieved more than 70% sequence coverage overall to distinguish between allelic proteoforms, varying only by one or two amino acids. The optimal methods utilised IS-CID at low energy. This experiment demonstrates the utility of Q-ToF systems for top-down proteomics and that IS-CID could be more frequently employed.

Cognitive, Neuroanatomical, and Genetic Predictors of Executive Function in Healthy Children and Adolescents

The Dimensional Change Card Sort (DCCS) is a measure of cognitive flexibility for children, which requires rule-use and shifting. Demographic, cognitive, regional cortical thickness, and genetic variables, including those related to language and executive function, were used to build predictive models of DCCS scores in 556 healthy pediatric participants. Gender, age, frontal, and temporal lobe regions of interest, and measures of sustained attention, inhibition, and word reading were selected as the best predictors of DCCS performance. Results indicated that DCCS performance is related to a broad range of cognitive functions and anatomic regions associated with various levels of cognitive function.

Quantitation and Identification of Intact Major Milk Proteins for High-Throughput LC-ESI-Q-TOF MS Analyses

Cow's milk is an important source of proteins in human nutrition. On average, cow's milk contains 3.5% protein. The most abundant proteins in bovine milk are caseins and some of the whey proteins, namely beta-lactoglobulin, alpha-lactalbumin, and serum albumin. A number of allelic variants and post-translationally modified forms of these proteins have been identified. Their occurrence varies with breed, individuality, stage of lactation, and health and nutritional status of the animal. It is therefore essential to have reliable methods of detection and quantitation of these proteins. Traditionally, major milk proteins are quantified using liquid chromatography (LC) and ultra violet detection method. However, as these protein variants co-elute to some degree, another dimension of separation is beneficial to accurately measure their amounts. Mass spectrometry (MS) offers such a tool. In this study, we tested several RP-HPLC and MS parameters to optimise the analysis of intact bovine proteins from milk. From our tests, we developed an optimum method that includes a 20-28-40% phase B gradient with 0.02% TFA in both mobile phases, at 0.2 mL/min flow rate, using 75°C for the C8 column temperature, scanning every 3 sec over a 600-3000 m/z window. The optimisations were performed using external standards commercially purchased for which ionisation efficiency, linearity of calibration, LOD, LOQ, sensitivity, selectivity, precision, reproducibility, and mass accuracy were demonstrated. From the MS analysis, we can use extracted ion chromatograms (EICs) of specific ion series of known proteins and integrate peaks at defined retention time (RT) window for quantitation purposes. This optimum quantitative method was successfully applied to two bulk milk samples from different breeds, Holstein-Friesian and Jersey, to assess differences in protein variant levels.

The development of high-content screening (HCS) technology and its importance to drug discovery

INTRODUCTION

High-content screening (HCS) was introduced about twenty years ago as a promising analytical approach to facilitate some critical aspects of drug discovery. Its application has spread progressively within the pharmaceutical industry and academia to the point that it today represents a fundamental tool in supporting drug discovery and development.

AREAS COVERED

Here, the authors review some of significant progress in the HCS field in terms of biological models and assay readouts. They highlight the importance of high-content screening in drug discovery, as testified by its numerous applications in a variety of therapeutic areas: oncology, infective diseases, cardiovascular and neurodegenerative diseases. They also dissect the role of HCS technology in different phases of the drug discovery pipeline: target identification, primary compound screening, secondary assays, mechanism of action studies and in vitro toxicology.

EXPERT OPINION

Recent advances in cellular assay technologies, such as the introduction of three-dimensional (3D) cultures, induced pluripotent stem cells (iPSCs) and genome editing technologies (e.g., CRISPR/Cas9), have tremendously expanded the potential of high-content assays to contribute to the drug discovery process. Increasingly predictive cellular models and readouts, together with the development of more sophisticated and affordable HCS readers, will further consolidate the role of HCS technology in drug discovery.