Proteomic Biomarker Discovery Research Articles

Correction: Decoding cancer insights: recent progress and strategies in proteomics for biomarker discovery

Correction: Decoding cancer insights: recent progress and strategies in proteomics for biomarker discovery

Plasma proteins and persistent post-surgical pelvic pain among adolescents and young adults with endometriosis

Non-invasive biomarkers that predict surgical treatment response would inform personalized treatments and provide insight into potential biological pathways underlying endometriosis-associated pain and symptom progression. Thus, we evaluated plasma proteins in relation to persistence of pelvic pain following laparoscopic surgery in predominantly adolescents and young adults with endometriosis using a multiplex aptamer-based proteomics biomarker discovery platform. We conducted a prospective analysis including 142 participants with laparoscopically- confirmed endometriosis from the Women's Health Study: From Adolescence to Adulthood (A2A) observational longitudinal cohort with study enrollment from 2012-2018. Biologic samples and patient data were collected with modified World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonization Project (EPHect) tools. In blood collected before laparoscopic ablation or excision of endometriosis, we simultaneously measured 1,305 plasma protein levels including markers for immunity, angiogenesis and inflammation using SomaScan. Worsening or persistent post-surgical pelvic pain was defined as having newly developed, persistent (i.e., stable), or worsening severity, frequency, or persistent life-interference of dysmenorrhea or acyclic pelvic pain at one-year post-surgery compared to pre-surgery. We calculated odds ratios (OR) and 95% confidence intervals (CI) using logistic regression adjusted for age, body mass index, and fasting status and hormone use at blood draw. We applied Ingenuity Pathway Analysis and STRING analysis to identify pathophysiologic pathways and protein interactions. Median age at blood draw was 17 years (interquartile range 15-19), and most participants were white race (90%). All had superficial peritoneal lesions only and were treated by excision or ablation. One-year post-surgery, pelvic pain worsened or persisted for 76 (54%) of these participants with endometriosis, while pelvic pain improved for 66 (46%). We identified 83 proteins associated with worsening or persistent pelvic pain one-year post-surgery (nominal p<0.05). Compared to those with improved pelvic pain one year post-surgery, those with worsening or persistent pelvic pain had higher plasma levels of CD63 antigen (OR=2.98, 95% CI:1.44-6.19) and CD47 (OR=2.68, 95%CI=1.28-5.61), but lower levels of Sonic Hedgehog protein (SHH; OR=0.55, 95%CI=0.36-0.84) in pre-surgical blood. Pathways related to cell migration were upregulated and pathways related to angiogenesis were downregulated in those with worsening/persistent post-surgical pelvic pain compared to those with improved pain. When we examined change in proteins levels from pre- to post-surgery and its subsequent risk of worsening/persistent post-surgical pain at one-year follow-up, we observed increasing levels of SHH from pre- to post-surgery was associated with four-fold increase in risk of post-surgical pain (OR quartile 4 vs. 1=3.86, 1.04-14.33). Using an aptamer-based proteomics platform, we identified plasma proteins and pathways associated with worsening or persistent pelvic pain post-surgical treatment of endometriosis among adolescents and young adults that may aid in risk stratification of individuals with endometriosis.

Induced pluripotent stem cell models as a tool to investigate and test fluid biomarkers in Alzheimer's disease and frontotemporal dementia.

Neurodegenerative diseases are increasing in prevalence and comprise a large socioeconomic burden on patients and their caretakers. The need for effective therapies and avenues for disease prevention and monitoring is of paramount importance. Fluid biomarkers for neurodegenerative diseases have gained a variety of uses, including informing participant selection for clinical trials, lending confidence to clinical diagnosis and disease staging, determining prognosis, and monitoring therapeutic response. Their role is expected to grow as disease-modifying therapies start to be available to a broader range of patients and as prevention strategies become established. Many of the underlying molecular mechanisms of currently used biomarkers are incompletely understood. Animal models and in vitro systems using cell lines have been extensively employed but face important translatability limitations. Induced pluripotent stem cell (iPSC) technology, where a theoretically unlimited range of celltypes can be reprogrammed from peripheral cells sampled from patients or healthy individuals, has gained prominence over the last decade. It is a promising avenue to study physiological and pathological biomarker function and response to experimental therapeutics. Such systems are amenable to high-throughput drug screening or multiomics readouts such as transcriptomics, lipidomics, and proteomics for biomarker discovery, investigation, and validation. The present review describes the current state of biomarkers in the clinical context of neurodegenerative diseases, with a focus on Alzheimer's disease and frontotemporal dementia. We include a discussion of how iPSC models have been used to investigate and test biomarkers such as amyloid-β, phosphorylated tau, neurofilament light chain or complement proteins, and even nominate novel biomarkers. We discuss the limitations of current iPSC methods, mentioning alternatives such as coculture systems and three-dimensional organoids which address some of these concerns. Finally, we propose exciting prospects for stem cell transplantation paradigms using animal models as a preclinical tool to study biomarkers in the in vivo context.

Feasibility demonstration of a device for vitreous liquid biopsy incidental to intravitreal injection.

VitreoDx is an experimental device enabling push-button collection of a neat vitreous liquid biopsy incidental to an intravitreal injection. We explored the ability of the device to collect a sample usable for proteomic biomarker discovery and testing. Pilot study using ex vivo human eyes. Non-vitrectomized, human eyes from nine donors 75-91 years of age were refrigerated in BSS and used within 5 days of death. Four VitreoDx devices fitted with 25G needles, and four staked needle insulin syringes with 30G needles, were inserted at equal intervals through the pars plana of each eye and held in place by a fixture. The sampling mode of each VitreoDx device was triggered to attempt to acquire a liquid biopsy up to 70 μL. The plunger of each insulin syringe was retracted to attempt to obtain a liquid biopsy with a maximum volume of 50 μL. Samples acquired with the VitreoDx were extracted to polypropylene cryovials, refrigerated to -80 ºC, and sent for offsite proteomic analysis by proximity extension assay with a focus on panels containing approved and pipelined drug targets for neovascular disease and inflammatory factors. Of the attempted liquid biopsies with the novel 25G VitreoDx, 92% (66 of 72) resulted in successful acquisition (>25 μL) while 89% (64 of 72) attempted by a traditional 30G needle resulted in a successful acquisition. Sample volume sufficient for proteomics array analysis was acquired by the VitreoDx for every eye. Detectable protein was found for 151 of 166 unique proteins assayed in at least 25% of eyes sampled by VitreoDx. The high acquisition rate achieved by the prototype was similar to that achieved in previous clinical studies where a standard syringe was used with a 25G needle to biopsy vitreous fluid directly prior to standard intravitreal injection. Successful aspiration rates were likewise high for 30G needles. Together, these suggest that it is possible to routinely acquire liquid vitreous biopsies from patients who typically receive intravitreal injections with an injection device using a standard size needle without a vitreous cutter. Protein analysis shows that proteins of interest survive the sampling mechanism and may have potential to direct care in the future.

Proteomic profiling and biomarker discovery for predicting the response to PD-1 inhibitor immunotherapy in gastric cancer patients.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment; however, a significant proportion of gastric cancer (GC) patients do not respond to this therapy. Consequently, there is an urgent need to elucidate the mechanisms underlying resistance to ICIs and identify robust biomarkers capable of predicting the response to ICIs at treatment initiation. Methods: In this study, we collected GC tissues from 28 patients prior to the administration of anti-programmed death 1 (PD-1) immunotherapy and conducted protein quantification using high-resolution mass spectrometry (MS). Subsequently, we analyzed differences in protein expression, pathways, and the tumor microenvironment (TME) between responders and non-responders. Furthermore, we explored the potential of these differences as predictive indicators. Finally, using machine learning algorithms, we screened for biomarkers and constructed a predictive model. Results: Our proteomics-based analysis revealed that low activity in the complement and coagulation cascades pathway (CCCP) and a high abundance of activated CD8 T cells are positive signals corresponding to ICIs. By using machine learning, we successfully identified a set of 10 protein biomarkers, and the constructed model demonstrated excellent performance in predicting the response in an independent validation set (N = 14; area under the curve [AUC] = 0.959). Conclusion: In summary, our proteomic analyses unveiled unique potential biomarkers for predicting the response to PD-1 inhibitor immunotherapy in GC patients, which may provide the impetus for precision immunotherapy.

Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis

The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package (https://github.com/InfectionMedicineProteomics/BINN).

#2749 QUANTIFICATION OF URINE PROTEINS FOR DISCOVERY OF BIOMARKERS FOR KIDNEY INJURIES BY DIA-PROTEOMICS

Abstract Background and Aims Recent advances in proteomics have enabled to quantify proteins comprehensively. We aimed to establish a platform of urine quantitative proteomics for selection of the biomarkers and assessment of health conditions. Since plasma proteins of varying amounts of were present in urine especially from proteinuric patients, we subtracted the plasma protein conteents from total urine proteomes after the quantitative proteomics for discovery of biomarkers for kidney injuries. On the other hand, urine proteins excreted from plasma may be indicators of pathophysiological conditions in the body, suggesting that the urine proteome profiles figure out health conditions and early discovery of any diseases in the future. Method More than 120,000 urine samples (∼10 ml each) have been collected from ∼15,000 patients with various diseases and healthy volunteers (HV) and stored in -20C freezers. In this study, urine samples were selected from HV, diabetic patients (DM) with or without microalbuminuria and other CKD patients. Proteins were precipitated by a methanol/chloroform precipitation method from 1 ml urine. The protein fractions were digested with trypsin and the tryptic peptides were purified by C18 column chromatography. The tryptic peptides of 200 ng each were analyzed by LC-MS (tims-TOFpro, Bruker) by quantitative Data-independent acquisition (DIA) proteomics and proteins were quantified by the DIA-nn tool. Major top 30 plasma proteins were removed from the quantitative proteomics data and kidney- and other urinary tract organ-derived proteins were normalized to compare their proportions in urine. Results Approximately 2,500 proteins were quantified by the DIA analysis. In urine from HV, proportions of plasma proteins were small, however, in urine from DM patients with microalbuminuria or CKD patients, the plasma protein contents were increased, resulting in relative decreases of urine proportions of other proteins derived from the kidney or other urinary tract organs. By subtraction of the plasma proteins from total urine proteins, protein proportions were comparable to select candidates of biomarkers for kidney injuries. Comparison of total urine protein profiles between HV and DM elucidated their differences, which might indicate changes of pathophysiological conditions. Conclusion By recent quantitative proteomics urine proteins were quantified comprehensively, facilitating discovery of biomarkers for kidney injuries and also evaluation of health conditions in the body.

Generating Detailed Spectral Libraries for Canine Proteomes Obtained from Serum and Urine

Domestic dogs (Canis lupus familiaris) are popular companion animals. Increase in medical expenses associated with them and demand for extending their lifespan in a healthy manner has created the need to develop new diagnostic technology. Companion dogs also serve as important animal models for non-clinical research as they can provide various biological phenotypes. Proteomics have been increasingly used on dogs and humans to identify novel biomarkers of various diseases. Despite the growing applications of proteomics in liquid biopsy in veterinary medicine, no publicly available spectral assay libraries have been created for the proteome of canine serum and urine. In this study, we generated spectral assay libraries for the two-representative liquid-biopsy samples using mid-pH fractionation that allows in-depth understanding of proteome coverage. The resultant canine serum and urine spectral assay libraries include 1,132 and 4,749 protein groups and 5,483 and 25,228 peptides, respectively. We built these complimentary accessible resources for proteomic biomarker discovery studies through ProteomeXchange with the identifier PXD034770.

MALDImID: Spatialomics R package and Shiny app for more specific identification of MALDI imaging proteolytic peaks using LC-MS/MS-based proteomic biomarker discovery data.

Matrix-assisted laser desorption/ionization (MALDI) imaging of proteolytic peptides from formalin-fixed paraffin embedded (FFPE) tissue sections could be integrated in the portfolio of molecular pathologists for protein localization and tissue classification. However, protein identification can be very tedious using MALDI-time-of-flight (TOF) and post-source decay (PSD)-based fragmentation. Hereby, we implemented an R package and Shiny app to exploit liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic biomarker discovery data for more specific identification of peaks observed in bottom-up MALDI imaging data. The package is made available under the GPL 3 license. The Shiny app can directly be used at the following address: https://biosciences.shinyapps.io/Maldimid.

Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning.

The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the current screening procedures for early diagnosis. Familial pancreatic cancer (FPC) describes the cases of PDAC that are present in two or more individuals within a circle of first-degree relatives. Using innovative high-throughput proteomics, we were able to quantify the protein profiles of individuals at risk from FPC families in different potential pre-cancer stages. However, the high-dimensional proteomics data structure challenges the use of traditional statistical analysis tools. Hence, we applied advanced statistical learning methods to enhance the analysis and improve the results' interpretability. We applied model-based gradient boosting and adaptive lasso to deal with the small, unbalanced study design via simultaneous variable selection and model fitting. In addition, we used stability selection to identify a stable subset of selected biomarkers and, as a result, obtain even more interpretable results. In each step, we compared the performance of the different analytical pipelines and validated our approaches via simulation scenarios. In the simulation study, model-based gradient boosting showed a more accurate prediction performance in the small, unbalanced, and high-dimensional datasets than adaptive lasso and could identify more relevant variables. Furthermore, using model-based gradient boosting, we discovered a subset of promising serum biomarkers that may potentially improve the current screening procedure of FPC. Advanced statistical learning methods helped us overcome the shortcomings of an unbalanced study design in a valuable clinical dataset. The discovered serum biomarkers provide us with a clear direction for further investigations and more precise clinical hypotheses regarding the development of FPC and optimal strategies for its early detection.

Proteome Analysis of Whole Blood Collected by Volumetric Absorptive Microsampling.

Proteomic biomarker discovery and analysis from human biofluids using liquid chromatography-mass spectrometry (LC-MS) is an area of intense biomedical research. There is a growing interest to analyze microsampled patient blood specimens as this is potentially more patient-friendly enabling at-home and bedside self-collection of small blood volumes. However, there are limited studies applying LC-MS proteomic analysis of whole blood as it is dominated by red blood cell proteins such as hemoglobin which suppresses the detection of other less abundant proteins. Volumetric absorptive microsampling (VAMS) devices overcome this issue in part by providing a trapping matrix which allows depletion of abundant blood cell proteins through washing, prior to proteolysis and LC-MS. This approach allows the analysis of proteins from erythrocytes, leukocytes, and plasma and leads to deeper proteomic coverage compared to conventional plasma proteomics, increasing the prospects to discover novel biomarker proteins.

Cancer Serum Atlas-Supported Precise Pan-Targeted Proteomics Enable Multicancer Detection.

The wide dynamic range of serum proteome restrained discovery of clinically interested proteins in large cohort studies. Herein, we presented a high-sensitivity, high-throughput, and precise pan-targeted serum proteomic strategy for highly efficient cancer serum proteomic research and biomarker discovery. We constructed a resource of over 2000 cancer-secreted proteins, and the standard MS assays and spectra of at least one synthetic unique peptide per protein were acquired and documented (Cancer Serum Atlas, www.cancerserumatlas.com). Then, the standard peptide-anchored parallel reaction monitoring (SPA-PRM) method was developed with support of the Cancer Serum Atlas, achieving precise quantification of cancer-secreted proteins with high throughput and sensitivity. We directly quantified 325 cancer-related serum proteins in 288 serums of four cancer types (liver, stomach, lung, breast) and controls with the pan-targeted strategy and discovered considerable potential biomarker benefits for early detection of cancer. Finally, a proteomic-based multicancer detection model was built, demonstrating high sensitivity (87.2%) and specificity (100%), with 73.8% localization accuracy for an independent test set. In conclusion, the Cancer Serum Atlas provides a wide range of potential biomarkers that serve as targets and standard assays for systematic and highly efficient serological studies of cancer. The Cancer Serum Atlas-supported pan-targeted proteomic strategy enables highly efficient biomarker discovery and multicancer detection and thus can be a powerful tool for liquid biopsy.

A PROSPECTIVE STUDY OF PLASMA PROTEIN MARKERS ASSOCIATED WITH RISK OF LAPAROSCOPICALLY CONFIRMED ENDOMETRIOSIS IN THE NURSES’ HEALTH STUDY II

To identify plasma proteins associated with risk of laparoscopically confirmed endometriosis among premenopausal women using a multiplex aptamer-based proteomics biomarker discovery platform.

Mass Spectrometry-Based Approaches for Clinical Biomarker Discovery in Traumatic Brain Injury.

Precision treatments to address the multifaceted pathophysiology of traumatic brain injury (TBI) are desperately needed, which has led to the intense study of fluid-based protein biomarkers in TBI. Mass Spectrometry (MS) is increasingly being applied to biomarker discovery and quantification in neurological disease to explore the proteome, allowing for more flexibility in biomarker discovery than commonly encountered antibody-based assays. In this narrative review, we will provide specific examples of how MS technology has advanced translational research in traumatic brain injury (TBI) focusing on clinical studies, and looking ahead to promising emerging applications of MS to the field of Neurocritical Care. Proteomic biomarker discovery using MS technology in human subjects has included the full range of injury severity in TBI, though critically ill patients can offer more options to biofluids given the need for invasive monitoring. Blood, urine, cerebrospinal fluid, brain specimens, and cerebral extracellular fluid have all been sources for analysis. Emerging evidence suggests there are distinct proteomic profiles in radiographic TBI subtypes, and that biomarkers may be used to distinguish patients sustaining TBI from healthy controls. Metabolomics may offer a window into the perturbations of ongoing cerebral insults in critically ill patients after severe TBI. Emerging MS technologies may offer biomarker discovery and validation opportunities not afforded by conventional means due to its ability to handle the complexities associated with the proteome. While MS techniques are relatively early in development in the neurosciences space, the potential applications to TBI and neurocritical care are likely to accelerate in the coming decade.

Proteomic Profiling and Biomarker Discovery in Colorectal Liver Metastases.

Colorectal liver metastases (CRLM) are the leading cause of death among patients with metastatic colorectal cancer (CRC). As part of multimodal therapy, liver resection is the mainstay of curative-intent treatment for select patients with CRLM. However, effective treatment of CRLM remains challenging as recurrence occurs in most patients after liver resection. Proposed clinicopathologic factors for predicting recurrence are inconsistent and lose prognostic significance over time. The rapid development of next-generation sequencing technologies and decreasing DNA sequencing costs have accelerated the genomic profiling of various cancers. The characterisation of genomic alterations in CRC has significantly improved our understanding of its carcinogenesis. However, the functional context at the protein level has not been established for most of this genomic information. Furthermore, genomic alterations do not always result in predicted changes in the corresponding proteins and cancer phenotype, while post-transcriptional and post-translational regulation may alter synthesised protein levels, affecting phenotypes. More recent advancements in mass spectrometry-based technology enable accurate protein quantitation and comprehensive proteomic profiling of cancers. Several studies have explored proteomic biomarkers for predicting CRLM after oncologic resection of primary CRC and recurrence after curative-intent resection of CRLM. The current review aims to rationalise the proteomic complexity of CRC and explore the potential applications of proteomic biomarkers in CRLM.

Proteomics in Biomarker Discovery for Tuberculosis: Current Status and Future Perspectives.

Tuberculosis (TB) continues to threaten many peoples’ health worldwide, regardless of their country of residence or age. The current diagnosis of TB still uses mainly traditional, time-consuming, and/or culture-based techniques. Efforts have focused on discovering new biomarkers with higher efficiency and accuracy for TB diagnosis. Proteomics—the systematic study of protein diversity—is being applied to the discovery of novel protein biomarkers for different types of diseases. Mass spectrometry (MS) technology plays a revolutionary role in proteomics, and its applicability benefits from the development of other technologies, such as matrix-based and immune-based methods. MS and derivative strategies continuously contribute to disease-related discoveries, and some promising proteomic biomarkers for efficient TB diagnosis have been identified, but challenges still exist. For example, there are discrepancies in the biomarkers identified among different reports and the diagnostic accuracy of clinically applied proteomic biomarkers. The present review summarizes the current status and future perspectives of proteomics in the field of TB biomarker discovery and aims to elicit more promising findings for rapid and accurate TB diagnosis.

Biomarkers and biosensors for the diagnosis of noncompliant pH, dark cutting beef predisposition, and welfare in cattle.

Meat quality can be affected by stress, exhaustion, feed composition, and other physical and environmental conditions. These stressors can alter the pH in postmortem muscle, leading to high pH and low-quality dark cutting (DC) beef, resulting in considerable economic loss. Moreover, the dark cutting prediction may equally provide a measure for animal welfare since it is directly related to animal stress. There are two needs to advance on-site detection of dark cutters: (1) a clear indication that biomarker (signature compounds) levels in cattle correlate with stress and DC outcome; and (2) measuring these biomarkers rapidly and accurately on-farm or the abattoir, depending on the objectives. This critical review assesses which small molecules and proteins have been identified as potential biomarkers of stress and dark cutting in cattle. We discuss the potential of promising small molecule biomarkers, including catecholamine/cortisol metabolites, lactate, succinate, inosine, glucose, and β-hydroxybutyrate, and we identify a clear research gap for proteomic biomarker discovery in live cattle. We also explore the potential of chemical-sensing and biosensing technologies, including direct electrochemical detection improved through nanotechnology (e.g., carbon and gold nanostructures), surface-enhanced Raman spectroscopy in combination with chemometrics, and commercial hand-held devices for small molecule detection. No current strategy exists to rapidly detect predictive meat quality biomarkers due to the need to further validate biomarkers and the fact that different biosensor types are needed to optimally detect different molecules. Nonetheless, several biomarker/biosensor combinations reported herein show excellent potential to enable the measurement of DC potential in live cattle.

Rapid sample preparation workflow based on enzymatic nanoreactors for potential serum biomarker discovery in pancreatic cancer

Mass spectrometry (MS)-based proteomics have been extensively applied in clinical practice to discover potential protein and peptide biomarkers. However, the traditional sample pretreatment workflow remains labor-intensive and time-consuming, which limits the application of MS-based proteomic biomarker discovery studies in a high throughput manner. In the current work, we improved the previously reported procedure of the simple and rapid sample preparation methods (RSP) by introducing macroporous ordered siliceous foams (MOSF), namely RSP-MOSF. With the aid of MOSF, we further reduced the digestion time to 10 min, facilitating the whole sample handling process within 30 min. Combining with 30 min direct data independent acquisition (DIA) of LC-MS/MS, we accomplished a serum sample analysis in 1 h. Comparing with the RSP method, the performance of protein and peptide identification, quantitation, as well as the reproducibility of RSP-MOSF is comparable or even outperformed the RSP method. We further applied this workflow to analyze serum samples for potential candidate biomarker discovery of pancreatic cancer. Overall, 576 serum proteins were detected with 41 proteins significantly changed, which could serve as potential biomarkers for pancreatic cancer. Additionally, we evaluated the performance of RSP-MOSF method in a 96-well plate format which demonstrated an excellent reproducibility of the analysis. These results indicated that RSP-MOSF method had the potential to be applied on an automatic platform for further scaled analysis.

Evaluation of Disposable Trap Column nanoLC-FAIMS-MS/MS for the Proteomic Analysis of FFPE Tissue.

Proteomic biomarker discovery using formalin-fixed paraffin-embedded (FFPE) tissue requires robust workflows to support the analysis of large cohorts of patient samples. It also requires finding a reasonable balance between achieving a high proteomic depth and limiting the overall analysis time. To this end, we evaluated the merits of online coupling of single-use disposable trap column nanoflow liquid chromatography, high-field asymmetric-waveform ion-mobility spectrometry (FAIMS), and tandem mass spectrometry (nLC-FAIMS-MS/MS). The data show that ≤600 ng of peptide digest should be loaded onto the chromatographic part of the system. Careful characterization of the FAIMS settings enabled the choice of optimal combinations of compensation voltages (CVs) as a function of the employed LC gradient time. We found nLC-FAIMS-MS/MS to be on par with StageTip-based off-line basic pH reversed-phase fractionation in terms of proteomic depth and reproducibility of protein quantification (coefficient of variation ≤15% for 90% of all proteins) but requiring 50% less sample and substantially reducing sample handling. Using FFPE materials from the lymph node, lung, and prostate tissue as examples, we show that nLC-FAIMS-MS/MS can identify 5000-6000 proteins from the respective tissue within a total of 3 h of analysis time.

Discovery of Novel Proteomic Biomarkers for the Prediction of Kidney Recovery from Dialysis-Dependent AKI Patients.

AKI requiring dialysis (AKI-D) is associated with prolonged hospitalization, mortality, and progressive CKD among survivors. Previous studies have examined only select urine or serum biomarkers for predicting kidney recovery from AKI. Serum samples collected on day 8 of randomized RRT from 72 patients enrolled in the Veteran's Affairs/National Institutes of Health Acute Renal Failure Trial Network study were analyzed by the SOMAscan proteomic platform to profile 1305 proteins in each sample. Of these patients, 38 recovered kidney function and dialysis was discontinued, whereas another 34 patients remained on dialysis by day 28. Differential serum levels of 119 proteins, with 53 higher and 66 lower, were detected in samples from patients who discontinued dialysis, compared with patients who remained on dialysis by day 28. Patients were classified into tertiles on the basis of SOMAscan protein measurements for the 25 proteins most differentially expressed. The association of serum levels of each protein with kidney recovery was further evaluated using logistic regression analysis. Higher serum levels of CXCL11, CXCL2/CXCL3, CD86, Wnt-7a, BTK, c-Myc, TIMP-3, CCL5, ghrelin, PDGF-C, survivin, CA2, IL-9, EGF, and neuregulin-1, and lower levels of soluble CXCL16, IL1RL1, stanniocalcin-1, IL-6, and FGF23 when classified in tertiles were significantly associated with better kidney recovery. This significant association persisted for each of these proteins after adjusting for potential confounding risk factors including age, sex, cardiovascular SOFA score, congestive heart failure, diabetes, modality of intensive dialysis treatment, cause of AKI, baseline serum creatinine, day 8 urine volume, and estimated 60-day mortality risk. These results suggest concerted changes between survival-related proteins and immune-regulatory chemokines in regulating angiogenesis, endothelial and epithelial remodeling, and kidney cell regeneration, illustrating potential mechanisms of kidney recovery. Thus, this study identifies potential novel predictive biomarkers of kidney recovery in patients with AKI-D.