Protein Quantification Research Articles

Pleomorphic adenoma and carcinoma ex-pleomorphic adenoma tumorigenesis: A proteomic analysis.

To conduct a comprehensive proteomic analysis of normal salivary gland tissue, pleomorphic adenoma (PA), and carcinoma ex-pleomorphic adenoma (CXPA), and validate the proteomic findings using immunohistochemistry. Six normal salivary gland tissues, seven PA and seven CXPA samples underwent laser microdissection followed by liquid chromatography coupled to mass spectrometry. Protein identification and quantification were performed using MaxQuant software. Statistical analysis and functional enrichment were conducted using the Perseus platform and STRING tool, respectively. Immunohistochemistry was used for validation. Comparative proteomic analysis revealed 2680 proteins across the three tissue types, with 799 significantly altered between groups. Translocation protein SEC63 homolog, Annexin A6 and Biglycan were up-regulated in CXPA compared to PA. Decorin was markedly up-regulated in both PA and CXPA compared to normal salivary gland (log2 fold changes of 7.58 and 7.38, respectively). Validation confirmed elevated levels of Biglycan and Decorin in the extracellular matrix of CXPA compared to PA. Proteomic analysis identified differential protein expression patterns associated with malignant transformation of PA into CXPA. Findings indicate a crucial role for extracellular matrix proteins, specifically Biglycan and Decorin, in the tumorigenic progression of PA and CXPA.

A quantitative method for aquaporin-1 protein using magnetic preconcentration and probe-based immunoassay coupling to inductively coupled plasma mass spectrometry in urine analysis

BackgroundAquaporin-1 (AQP1) protein plays a crucial role in intracellular and extracellular water homeostasis and fluid transport in organs and tissues associated with diverse life activities and is extremely abundant in the kidney. Accurate detection of AQP1 in urine can be applied as screening of early-stage disease. Application of magnetic preconcentration and probe-based signal amplification strategy coupling to inductively coupled plasma mass spectrometry (ICP-MS) is a more accurate, sensitive and specific detection method for AQP1 in complex biological samples compared to conventional methods. ResultsWe described an element-labelling strategy based on magnetic preconcentration and probe-based immunoassay coupling to ICP-MS detection. The magnetic beads (MBs) modified with epoxy groups were capable of enriching AQP1 proteins and separating them from complex matrices. The probe constructed by conjugating anti-AQP1 antibody molecules on the surface of gold nanoparticles could specifically recognize AQP1 proteins attached on MBs and be analyzed by ICP-MS. The concentration of AQP1 protein could be precisely quantified and amplified by 14,000 times through the corresponding signal of Au atoms. This assay for AQP1 protein quantification achieved a detection limit down to 0.023 ng mL−1, a broad linear calibration curve between 0.3 ng mL−1 and 30 ng mL−1, as well as outstanding specificity. SignificanceThe proposed method was successfully applied to detect AQP1 protein in human urine samples, showing the potential for its applications concerning accurate AQP1 quantification. It can also screen a wide range of proteins provided the antibodies specific to these target proteins are available.

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP.

Single-cell proteomics by mass spectrometry (MS) allows the quantification of proteins with high specificity and sensitivity. To increase its throughput, we developed nano-proteomic sample preparation (nPOP), a method for parallel preparation of thousands of single cells in nanoliter-volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation using the plexDIA MS multiplexing method, which uses non-isobaric mass tags to barcode peptides from different samples for data-independent acquisition, demonstrates accurate quantification of ~3,000-3,700 proteins per human cell. A separate implementation with isobaric mass tags and prioritized data acquisition demonstrates analysis of 1,827 single cells at a rate of >1,000 single cells per day at a depth of 800-1,200 proteins per human cell. The protocol is implemented by using a cell-dispensing and liquid-handling robot-the CellenONE instrument-and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 d to prepare >3,000 single cells. We provide metrics and software (the QuantQC R package) for quality control and data exploration. QuantQC supports the robust scaling of nPOP to higher plex reagents for achieving reliable and scalable single-cell proteomics.

Intestinal Dysbiosis, Tight Junction Proteins, and Inflammation in Rheumatoid Arthritis Patients: A Cross-Sectional Study.

Recent studies point to intestinal permeability as an important factor in the establishment and development of rheumatoid arthritis (RA). Tight junctions (TJs) play a major role in intestinal homeostasis. The alteration of this homeostasis is related to RA. Furthermore, RA patients present dysbiosis and a lower microbiota diversity compared to healthy individuals. A cross-sectional study including RA patients and sex- and age-matched healthy controls was performed. The quantification of TJ proteins was carried out by ELISA. Gut microbiota was evaluated by NGS platform Ion Torrent S. The inflammatory variables included were DAS28, CRP, inflammatory cytokines (IL-6, IL-1, TNF-α) and oxidised LDL. Claudin-1 levels showed significant differences between groups. Results evidenced a correlation between claudin-1 values and age (r: -0.293; p < 0.05), IL6 (r: -0.290; p < 0.05) and CRP (r: -0.327; p < 0.05), and between zonulin values and both age (r: 0.267; p < 0.05) and TNFα (r: 0.266; p < 0.05). Moreover, claudin-1 and CRP levels are related in RA patients (β: -0.619; p: 0.045), and in patients with high inflammatory activity, the abundance of the genus Veillonella is positively associated with claudin-1 levels (β: 39.000; p: 0.004).

Determination of Fertility Hormones and Adipokines by LC-MRM/MS Analysis.

Due to the increase in the rate of male and female infertility, assisted fertilization practices are currently adopted as valid support for couples unable to get pregnant. Analytical approaches for fertility hormone dosages are constantly being developed, following the technological progress of fertilization methods that have evolved for more than a century. Indeed, the analysis of fertility hormones in serum samples is a common clinical practice to check the fertility state, but absolute quantification of these hormones is a great challenge due to biological variability and low serum concentrations. Currently, ELISA (enzyme-linked immunosorbent assay) based methods are the most used analytical techniques to quantify hormones in blood in clinical settings. The current Article discusses the development of a liquid chromatography-tandem mass spectrometry method (LC-MS/MS) to monitor multiple fertility hormones of a protein nature in a single chromatographic run, i.e., LH (luteinizing hormone), FSH (follicle-stimulating hormone), TSH (thyroid-stimulating hormone), AMH (anti-Müllerian hormone), adiponectin, ghrelin, leptin, glucagon, and obestatin. Particular attention has been paid to the AMH hormone, whose ELISA-based quantification is known to be controversial due to the poor reproducibility between the various kits used. For AMH, the internal standard method was used for the quantitative determination to compare mass spectrometry data to the ELISA assays performed by an accredited analysis laboratory on a cohort of samples from women aged between 18 and 60 years. The ability to monitor multiple transitions by LC-MRM/MS ensured both high specificity and high selectivity, which is necessary for the quantification of protein and steroid hormones, besides improvements in data reproducibility and reduced analysis times and costs.

Mass Spectrometry Acquisition and Fractionation Recommendations for TMT11 and TMT16 Labeled Samples.

Proteome coverage and accurate protein quantification are both important for evaluating biological systems; however, compromises between quantification, coverage, and mass spectrometry (MS) resources are often necessary. Consequently, experimental parameters that impact coverage and quantification must be adjusted, depending on experimental goals. Among these parameters is offline prefractionation, which is utilized in MS-based proteomics to decrease sample complexity resulting in higher overall proteome coverage upon MS analysis. Prefractionation leads to increases in required MS analysis time, although this is often mitigated by isobaric labeling using tandem-mass tags (TMT), which allow samples to be multiplexed. Here we evaluate common prefractionation schemes, TMT variants, and MS acquisition methods and their impact on protein quantification and coverage. Furthermore, we provide recommendations for experimental design depending on the experimental goals.

Astrocytic connexin 43‐hemichannels aggravate seizures by modulating blood‐brain barrier permeability in temporal lobe epilepsy mice

AbstractThe involvement of astrocytic connexin 43 (Cx43) in epileptogenesis has been extensively studied through various approaches, yet the underlying mechanism remains enigmatic. In this study, we explored whether astrocytic Cx43 forms hemichannels (HCs) that contribute to seizure progression in temporal lobe epilepsy (TLE) in mice. We focused on how these HCs influence the permeability of the blood‐brain barrier (BBB), a crucial factor in the pathophysiology of epilepsy. Immunofluorescence staining and western blot analysis were employed to assess Cx43 expression in kainic acid‐induced TLE mice, while BBB permeability was evaluated in TLE mice and those treated with TAT‐Gap19 (an astrocytic Cx43 HC inhibitor) using Evans Blue permeation, serum S100β protein quantification, ZO‐1 expression, and albumin extravasation into brain parenchyma via western blotting. Furthermore, seizure burden was monitored continuously using telemetric electroencephalography (EEG) and video monitoring in epileptic and TAT‐Gap19‐treated mice. Results demonstrated a significant increase in Cx43 content in hippocampal tissue in the TLE group, with a pronounced expression around blood vessels. TAT‐GAP19 treatment alleviated EEG seizures and BBB permeability in TLE mice. These findings suggest that astrocytic Cx43 HCs in the hippocampus play a crucial role in epileptogenesis and seizure progression by regulating BBB permeability. Targeting Cx43‐formed HCs distributed around the neurovascular unit may offer a novel therapeutic approach for epilepsy.

IL-1R Mediated Activation of Renal Sensory Nerves in DOCA-Salt Hypertension.

Clinical trials of renal denervation for the treatment of hypertension have shown a variety of off-target improvements in conditions associated with sympathetic overactivity. This may be due to the ablation of sympathoexcitatory afferent renal nerves, which are overactive under conditions of renal inflammation. Renal IL (interleukin)-1β is elevated in the deoxycorticosterone acetate-salt model of hypertension, and its activity may be responsible for the elevation in afferent renal nerve activity and arterial pressure. Continuous blood pressure recording of deoxycorticosterone acetate-salt mice with IL-1R (IL-1 receptor) knockout or antagonism was used individually and combined with afferent renal denervation (ARDN) to assess mechanistic overlap. Protein quantification and histological analysis of kidneys were performed to characterize renal inflammation. ARDN attenuated deoxycorticosterone acetate-salt hypertension (-20±2-Δmm Hg mean arterial pressure [MAP] relative to control at study end) to a similar degree as total renal denervation (-21±2-Δmm Hg MAP), IL-1R knockout (-16±4-Δmm Hg MAP), or IL-1R antagonism (-20±3-Δmm Hg MAP). The combination of ARDN with knockout (-18±2-Δmm Hg MAP) or antagonism (-19±4-Δmm Hg MAP) did not attenuate hypertension any further than ARDN alone. IL-1R antagonism was found to have an acute depressor effect (-15±3-Δmm Hg MAP, day 10) in animals with intact renal nerves but not those with ARDN. These findings suggest that IL-1R signaling is partially responsible for the elevated afferent renal nerve activity, which stimulates central sympathetic outflow to drive deoxycorticosterone acetate-salt hypertension.

Investigating the Antimicrobial Potential of 560 Compounds from the Pandemic Response Box and COVID Box against Resistant Gram-Negative Bacteria.

Antimicrobial resistance (AMR) has emerged as a significant threat to public health, particularly in infections caused by critically important Gram-negative bacteria. The development of novel antibiotics has its limitations, and therefore it is crucial to explore alternative strategies to effectively combat infections with resistant pathogens. In this context, the present study investigated the antibacterial potency of 560 compounds against the multidrug-resistant (MDR) strains of Klebsiella pneumoniae and Serratia marcescens. The evaluated compounds were selected from the Pandemic Response Box (PRB) and COVID Box (CB) and subjected to assays to determine the inhibitory concentration (IC), minimum bactericidal concentration (MBC), and biofilm formation. Further, the effects of these compounds on membrane integrity were assessed through protein quantification. Several of the evaluated compounds, including fusidic acid, MMV1580853, and MMV1634399, exhibited a significant reduction in biofilm formation and growth in K. pneumoniae. Trimethoprim exhibited potential against S. marcescens. The IC values of the compounds indicated significant microbial growth inhibition at various concentrations. These findings underscore the potency of the existing antibiotics and novel compounds in combating the MDR strains of bacteria. The importance of reconsidering the known antibiotics and utilizing drug repositioning strategies to address the increasing risk of AMR is highlighted.

Prebiotics Mitigate the Detrimental Effects of High-Fat Diet on memory, anxiety and microglia functionality in Ageing Mice

Ageing is characterised by a progressive increase in systemic inflammation and especially neuroinflammation. Neuroinflammation is associated with altered brain states that affect behaviour, such as an increased level of anxiety with a concomitant decline in cognitive abilities. Although multiple factors play a role in the development of neuroinflammation, microglia have emerged as a crucial target. Microglia are the only macrophage population in the CNS parenchyma that plays a crucial role in maintaining homeostasis and in the immune response, which depends on the activation and subsequent deactivation of microglia. Therefore, microglial dysfunction has a major impact on neuroinflammation. The gut microbiota has been shown to significantly influence microglia from birth to adulthood in terms of development, proliferation, and function. Diet is a key modulating factor that influences the composition of the gut microbiota, along with prebiotics that support the growth of beneficial gut bacteria. Although the role of diet in neuroinflammation and behaviour has been well established, its relationship with microglia functionality is less explored. This article establishes a link between diet, animal behaviour and the functionality of microglia. The results of this research stem from experiments on mouse behaviour, i.e., memory, anxiety, and studies on microglia functionality, i.e., cytochemistry (phagocytosis, cellular senescence, and ROS assays), gene expression and protein quantification. In addition, shotgun sequencing was performed to identify specific bacterial families that may play a crucial role in the brain function. The results showed negative effects of long-term consumption of a high fat diet on ageing mice, epitomised by increased body weight, glucose intolerance, anxiety, cognitive impairment and microglia dysfunction compared to ageing mice on a control diet. These effects were a consequence of the changes in gut microbiota modulated by the diet. However, by adding the prebiotics fructo- and galacto-oligosaccharides, we were able to mitigate the deleterious effects of a long-term high-fat diet.

B-BIND: Biophysical Bayesian Inference for Neurodegenerative Dynamics.

Throughout an organism's life, a multitude of complex and interdependent biological systems transition through biophysical processes that serve as indicators of the underlying biological states. Inferring these latent, unobserved states is a goal of modern biology and neuroscience. However, in many experimental setups, we can at best obtain discrete snapshots of the system at different times and for different individuals. This challenge is particularly relevant in the study of Alzheimer's Disease (AD) progression, where we observe the aggregation of pathology in brain donors, but the underlying disease state is unknown. This paper proposes a biophysically motivated Bayesian framework (B-BIND: Biophysical Bayesian Inference for Neurodegenerative Dynamics), where the disease state is modeled and continuously inferred from observed quantifications of multiple AD pathological proteins. Inspired by biophysical models, we describe pathological burden as an exponential process. The progression of AD is modeled by assigning a latent score, termed pseudotime, to each pathological state, creating a pseudotemporal order of donors based on their pathological burden. We study the theoretical properties of the model using linearization to reveal convergence and identifiability properties. We provide Markov chain Monte Carlo estimation algorithms, illustrating the effectiveness of our approach with multiple simulation studies across various data conditions. Applying this methodology to data from the Seattle Alzheimer's Disease Brain Cell Atlas, we infer the pseudotime ordering of donors. Finally, we analyze the information within each pathological feature to refine the model, focusing on the most informative pathologies. This framework lays the groundwork for continuous pseudotime modeling in the analysis of neurodegenerative diseases.

Rapid prototyping of thermoplastic microfluidic devices via SLA 3D printing

Microfluidic devices have immense potential for widespread community use, but a current bottleneck is the transition from research prototyping into mass production because the gold standard prototyping strategy is too costly and labor intensive when scaling up fabrication throughput. For increased throughput, it is common to mold devices out of thermoplastics due to low per-unit costs at high volumes. However, conventional fabrication methods have high upfront development expenses with slow mold fabrication methods that limit the speed of design evolution for expedited marketability. To overcome this limitation, we propose a rapid prototyping protocol to fabricate thermoplastic devices from a stereolithography (SLA) 3D printed template through intermediate steps akin to those employed in soft lithography. We apply this process towards the design of self-operating capillaric circuits, well suited for deployment as low-cost decentralized assays. Rapid development of these geometry- and material-dependent devices benefits from prototyping with thermoplastics. We validated the constructed capillaric circuits by performing an autonomous, pre-programmed, bead-based immunofluorescent assay for protein quantification. Overall, this prototyping method provides a valuable means for quickly iterating and refining microfluidic devices, paving the way for future scaling of production.

A Trapping-Micro-LC-FAIMS/dCV-MS Strategy for Ultrasensitive and Robust Targeted Quantification of Protein Drugs and Biomarkers.

The sensitivity of LC-MS in quantifying target proteins in plasma/tissues is significantly hindered by coeluted matrix interferences. While antibody-based immuno-enrichment effectively reduces interferences, developing and optimizing antibodies are often time-consuming and costly. Here, by leveraging the orthogonal separation capability of Field Asymmetric Ion Mobility Spectrometry (FAIMS), we developed a FAIMS/differential-compensation-voltage (FAIMS/dCV) method for antibody-free, robust, and ultrasensitive quantification of target proteins directly from plasma/tissue digests. By comparing the intensity-CV profiles of the target vs coeluted endogenous interferences, the FAIMS/dCV approach identifies the optimal CV for quantification of each target protein, thus maximizing the signal-to-noise ratio (S/N). Compared to quantification without FAIMS, this technique dramatically reduces endogenous interferences, showing a median improvement of the S/N by 14.8-fold for the quantification of 17 representative protein drugs and biomarkers in plasma or tissues and a 5.2-fold median increase in S/N over conventional FAIMS approach, which uses the peak CV of each target. We also discovered that the established CV parameters remain consistent over months and are matrix-independent, affirming the robustness of the developed FAIMS/dCV method and the transferability of the method across matrices. The developed method was successfully demonstrated in three applications: the quantification of monoclonal antibodies with subng/mL LOQ in plasma, an investigation of the time courses of evolocumab and its target PCSK9 in a preclinical setting, and a clinical investigation of low abundance obesity-related biomarkers. This innovative and easy-to-use method has extensive potential in clinical and pharmaceutical research, particularly where sensitive and high-throughput quantification of protein drugs and biomarkers is required.

Establishing the capacity to monitor proteins relevant to the study of drug exposure and response using liver-derived extracellular vesicles.

Drug exposure and response is determined by pharmacokinetic (PK) and pharmacodynamic (PD) profiles. Interindividual differences in abundance of drug metabolizing enzymes (DMEs) and drug target proteins underpin PK and PD variability and impact treatment efficacy and tolerability. Extracellular vesicles (EVs) carry protein cargo inherited from originating cells and may be useful for defining differences in key proteins related to hepatic drug metabolism and the treatment of metabolic-associated fatty liver disease (MAFLD). We sought to quantify these proteins in liver-derived EVs and establish the profile relative to paired tissue. EVs were recovered from human liver tissue samples (LT-EV, n = 11). Targeted liquid chromatography with tandem mass spectrometry (LC-MS/MS) assays were employed for absolute quantification of proteins in EV isolates and matched liver tissue. DMEs and MAFLD drug targets were readily detected and quantified in LT-EVs. Twelve of 15 DMEs exhibited moderate to strong correlation (Spearman ⍴ = 0.618-0.973) between tissue and EVs. Correlation in protein abundance was influenced by the extent of extra-hepatic expression of the target. This study provides evidence that key proteins related to PK and PD profiles can be measured in liver-derived EVs and abundance of liver-enriched DMEs are robustly correlated between paired tissue and EVs. The robust detection of protein markers related to drug PD profile in MAFLD opens the possibility to track within-subject changes in MAFLD and lays the foundation for future development of a liver-derived EV liquid biopsy to assess markers of drug exposure and response in vivo.

Signal amplification by cyclic extension enables high-sensitivity single-cell mass cytometry.

Mass cytometry uses metal-isotope-tagged antibodies to label targets of interest, which enables simultaneous measurements of ~50 proteins or protein modifications in millions of single cells, but its sensitivity is limited. Here, we present a signal amplification technology, termed Amplification by Cyclic Extension (ACE), implementing thermal-cycling-based DNA in situ concatenation in combination with 3-cyanovinylcarbazole phosphoramidite-based DNA crosslinking to enable signal amplification simultaneously on >30 protein epitopes. We demonstrate the utility of ACE in low-abundance protein quantification with suspension mass cytometry to characterize molecular reprogramming during the epithelial-to-mesenchymal transition as well as the mesenchymal-to-epithelial transition. We show the capability of ACE to quantify the dynamics of signaling network responses in human T lymphocytes. We further present the application of ACE in imaging mass cytometry-based multiparametric tissue imaging to identify tissue compartments and profile spatial aspects related to pathological states in polycystic kidney tissues.

Concatemer Assisted Stoichiometry Analysis (CASA): targeted mass spectrometry for protein quantification.

Large multi-protein machines are central to multiple biological processes. However, stoichiometric determination of protein complex subunits in their native states presents a significant challenge. This study addresses the limitations of current tools in accuracy and precision by introducing concatemer-assisted stoichiometry analysis (CASA). CASA leverages stable isotope-labeled concatemers and liquid chromatography parallel reaction monitoring mass spectrometry (LC-PRM-MS) to achieve robust quantification of proteins with sub-femtomole sensitivity. As a proof-of-concept, CASA was applied to study budding yeast kinetochores. Stoichiometries were determined for ex vivo reconstituted kinetochore components, including the canonical H3 nucleosomes, centromeric (Cse4CENP-A) nucleosomes, centromere proximal factors (Cbf1 and CBF3 complex), inner kinetochore proteins (Mif2CENP-C, Ctf19CCAN complex), and outer kinetochore proteins (KMN network). Absolute quantification by CASA revealed Cse4CENP-A as a cell-cycle controlled limiting factor for kinetochore assembly. These findings demonstrate that CASA is applicable for stoichiometry analysis of multi-protein assemblies.

Quality control and validation of extracellular vesicles isolated from cultured human breast cancer cells

ObjectiveExtracellular vesicles (EVs) have been shown to play a critical role in promoting tumorigenesis. As EV research grows, it is of importance to have standardization of isolation, quality control, characterization and validation methods across studies along with reliable references to explore troubleshooting solutions. Therefore, our objective with this Research Note was to isolate EVs from multiple breast cancer cell lines and to describe and perform protocols for validation as outlined by the list of minimal information for studies of EVs (MISEV) from the International Society for Extracellular Vesicles.ResultsTo isolate EVs, two techniques were employed: ultracentrifugation and size exclusion chromatography. Ultracentrifugation yielded better recovery of EVs in our hands and was therefore used for further validation. In order to satisfy the MISEV requirements, protein quantification, immunoblotting of positive (CD9, CD63, TSG101) and negative (TGFβ1, β-tubulin) markers, nanoflow cytometry and electron microscopy was performed. With these experiments, we demonstrate that yield of validated EVs varied between different breast cancer cell lines. Protocols were optimized to accommodate for low levels of EVs, and various technical and troubleshooting suggestions are included for potential application to other cell types that may provide benefit to investigators interested in future EV studies.

Understanding proteome quantification in an interactive learning module on Google Cloud Platform.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on protein quantification in an interactive format that uses appropriate cloud resources for data access and analyses. Quantitative proteomics is a rapidly growing discipline due to the cutting-edge technologies of high resolution mass spectrometry. There are many data types to consider for proteome quantification including data dependent acquisition, data independent acquisition, multiplexing with Tandem Mass Tag reporter ions, spectral counts, and more. As part of the NIH NIGMS Sandbox effort, we developed a learning module to introduce students to mass spectrometry terminology, normalization methods, statistical designs, and basics of R programming. By utilizing the Google Cloud environment, the learning module is easily accessible without the need for complex installation procedures. The proteome quantification module demonstrates the analysis using a provided TMT10plex data set using MS3 reporter ion intensity quantitative values in a Jupyter notebook with an R kernel. The learning module begins with the raw intensities, performs normalization, and differential abundance analysis using limma models, and is designed for researchers with a basic understanding of mass spectrometry and R programming language. Learners walk away with a better understanding of how to navigate Google Cloud Platform for proteomic research, and with the basics of mass spectrometry data analysis at the command line. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Integration analysis of pituitary proteome and transcriptome reveals fertility-related biomarkers in FecB mutant Small Tail Han sheep.

The Booroola fecundity mutation (FecB) in Small Tail Han sheep has been shown to enhance ovulation rates and litter sizes by affecting the hypothalamic-pituitary-gonadal (HPG) axis. Despite the pituitary's role in reproductive regulation, its involvement in FecB-induced ovulation remains understudied. Our study aimed to fill this gap by analyzing pituitary tissues from FecB homozygous (BB) and wild-type (WW) ewes during luteal and follicular phases using tandem mass tag-based protein quantification and the DIABLO framework for proteomic and transcriptomic data integration. Significant differences in 277 proteins were observed across estrus periods, with network analysis highlighting the voltage-dependent calcium channel L-type alpha-1C as a key convergence point in oxytocin signaling and GnRH secretion pathways. The DIABLO method revealed a strong correlation (0.98) between proteomic and transcriptomic datasets, indicating a coordinated response in FecB ewes. Notably, higher expression levels of Follicle Stimulating Hormone Subunit Beta (FSHB) and Luteinizing Hormone Subunit Beta (LHB) were found in BB ewes during the follicular phase, potentially due to elevated E2 concentrations. Furthermore, our analysis identified genes related to the Gamma-aminobutyric acid type A receptor family (GABRA2, GABRG1, GABRB1) in the pituitary, with GABRB1 showing higher expression in BB ewes. This suggests a role for GABA in modulating GnRH and gonadotropin feedback loops, potentially contributing to the FecB mutation's effect on ovulation. This study provides novel insights into the pituitary's role in fertility among FecB sheep, identifying GABA as a potential regulatory factor within the HPG axis. The findings also open avenues for discovering new biomarkers in pituitary endocrinology for sheep breeding purposes.

In Situ Measurement of Nanoparticle-Blood Protein Adsorption and Its Heterogeneity with Single-Nanoparticle Resolution via Dual Fluorescence Quantification.

The formation of a protein corona gives nanomedicines a distinct biological identity, profoundly influencing their fate in the body. Nonspecific nanoparticle-protein interactions are typically highly heterogeneous, which can lead to unique biological behaviors and in vivo fates for individual nanoparticles that remain underexplored. To address this, we have established an in situ approach that allows quantitative examination of nanoparticle-protein adsorption at the individual nanoparticle level. This method integrates dual fluorescence quantification techniques, wherein the nanoparticles are first individually analyzed via nanoflow cytometry to detect fluorescent signals from adsorbed proteins. The obtained fluorescence intensity is then translated into protein quantities through calibration with microplate reader quantification. Consequently, this approach enables analysis of interparticle heterogeneity of nano-protein interactions, as well as in situ monitoring of protein adsorption kinetics and nanoparticle aggregation status in blood serum, preconditioning for a comprehensive understanding of nano-bio interactions, and predicting in vivo fate of nanomedicines.