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A recently developed matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) method to spatially profile the location and distribution of multiple N-linked glycan species in frozen tissues has been extended and improved for the direct analysis of glycans in clinically derived formalin-fixed paraffin-embedded (FFPE) tissues. Formalin-fixed tissues from normal mouse kidney, human pancreatic and prostate cancers, and a human hepatocellular carcinoma tissue microarray were processed by antigen retrieval followed by on-tissue digestion with peptide N-glycosidase F. The released N-glycans were detected by MALDI-IMS analysis, and the structural composition of a subset of glycans could be verified directly by on-tissue collision-induced fragmentation. Other structural assignments were confirmed by off-tissue permethylation analysis combined with multiple database comparisons. Imaging of mouse kidney tissue sections demonstrates specific tissue distributions of major cellular N-linked glycoforms in the cortex and medulla. Differential tissue distribution of N-linked glycoforms was also observed in the other tissue types. The efficacy of using MALDI-IMS glycan profiling to distinguish tumor from non-tumor tissues in a tumor microarray format is also demonstrated. This MALDI-IMS workflow has the potential to be applied to any FFPE tissue block or tissue microarray to enable higher throughput analysis of the global changes in N-glycosylation associated with cancers.

Biomarkers for acute kidney injury (AKI) have been used to predict the progression of AKI, but a systematic comparison of the prognostic ability of each biomarker alone or in combination has not been performed. In order to assess this, we measured the concentration of 32 candidate biomarkers in the urine of 95 patients with AKIN stage 1 after cardiac surgery. Urine markers were divided into eight groups based on the putative pathophysiological mechanism they reflect. We then compared the ability of the markers alone or in combination to predict the primary outcome of worsening AKI or death (23 patients) and the secondary outcome of AKIN stage 3 or death (13 patients). IL-18 was the best predictor of both outcomes (AUC of 0.74 and 0.89). L-FABP (AUC of 0.67 and 0.85), NGAL (AUC of 0.72 and 0.83), and KIM-1 (AUC of 0.73 and 0.81) were also good predictors. Correlation between most of the markers was generally related to their predictive ability, but KIM-1 had a relatively weak correlation with other markers. The combination of IL-18 and KIM-1 had a very good predictive value with an AUC of 0.93 to predict AKIN 3 or death. Thus, a combination of IL-18 and KIM-1 would result in improved identification of high-risk patients for enrollment in clinical trials.

The circulating blood proteome comprises soluble and cellular components that reflect physiological and pathological states across tissues. Advances in mass spectrometry and affinity-based proteomics have improved sensitivity and throughput, enabling the generation of public blood proteomics resources. However, comprehensive assessments of these datasets remain limited. This work reviews the cellular and molecular complexity of publicly available blood proteomics data, recent methodological developments, and the complementarity of diverse data sources across the abundance range, while outlining remaining challenges for translating blood proteomics into personalized medicine.

Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.).

Bats are increasingly studied for their ability to coexist with diverse viruses of human health importance. While this focus has yielded insights into host–pathogen dynamics, baseline physiological data from healthy bats remain limited, constraining comparative and mechanistic understanding. Serum proteomics offers a direct window into circulating proteins that underpin immune regulation, cellular maintenance, and metabolism. Here, we characterize the serum proteome of clinically healthy, captive Egyptian rousette bats ( Rousettus aegyptiacus ), the only known natural reservoir host species of Marburg virus. Using untargeted proteomic profiling, we identified and ranked over 400 proteins across major functional categories. Prominent findings included extensive representation of complement components spanning all activation pathways, high levels of interferon-responsive proteins, and abundant proteasome subunits, including immunoproteasome components. The dataset further revealed a robust profile of oxidoreductases and antioxidant enzymes, consistent with mechanisms of redox balance and iron regulation, alongside an apolipoprotein profile suggestive of dietary specialization. An unexpected finding was the unusually high abundance of type XX collagen, potentially linked to tissue remodeling demands of flight. Exploratory sex-based analyses suggested trends in stress response and immune-related proteins, although sample size limited statistical resolution. This work provides the first-look of the Egyptian rousette bat serum proteome, providing a reference point for cross-species comparisons and future studies of bat immunity, metabolism, and longevity. By identifying constitutive molecular features in healthy individuals, these findings expand the use of proteomics for understanding non-traditional model organisms and set the stage for future targeted functional investigations.

Domoic acid toxicosis (DAT) in California sea lions (Zalophus californianus) is caused by exposure to the marine biotoxin domoic acid and has been linked to massive stranding events and mortality. Diagnosis is based on clinical signs in addition to the presence of domoic acid in body fluids. Chronic DAT further is characterized by reoccurring seizures progressing to status epilepticus. Diagnosis of chronic DAT is often slow and problematic, and minimally invasive tests for DAT have been the focus of numerous recent biomarker studies. The goal of this study was to retrospectively profile plasma proteins in a population of sea lions with chronic DAT and those without DAT using two dimensional gel electrophoresis to discover whether individual, multiple, or combinations of protein and clinical data could be utilized to identify sea lions with DAT. Using a training set of 32 sea lion sera, 20 proteins and their isoforms were identified that were significantly different between the two groups (p<0.05). Interestingly, 11 apolipoprotein E (ApoE) charge forms were decreased in DAT samples, indicating that ApoE charge form distributions may be important in the progression of DAT. In order to develop a classifier of chronic DAT, an independent blinded test set of 20 sea lions, seven with chronic DAT, was used to validate models utilizing ApoE charge forms and eosinophil counts. The resulting support vector machine had high sensitivity (85.7% with 92.3% negative predictive value) and high specificity (92.3% with 85.7% positive predictive value). These results suggest that ApoE and eosinophil counts along with machine learning can perform as a robust and accurate tool to diagnose chronic DAT. Although this analysis is specifically focused on blood biomarkers and routine clinical data, the results demonstrate promise for future studies combining additional variables in multidimensional space to create robust classifiers.

Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics. A network science framework for understanding and predicting human and animal susceptibility to viral infections is proposed.

The last decade has witnessed dramatic improvements in whole-genome sequencing capabilities coupled to drastically decreased costs, leading to an inundation of high-quality de novo genomes. For this reason, the continued development of genome quality metrics is imperative. Using the 2016 Atlantic bottlenose dolphin NCBI RefSeq annotation and mass spectrometry-based proteomic analysis of six tissues, we confirmed 10,402 proteins from 4711 protein groups, constituting nearly one-third of the possible predicted proteins. Since the identification of larger proteins with more identified peptides implies reduced database fragmentation and improved gene annotation accuracy, we propose the metric NP10, which attempts to capture this quality improvement. The NP10 metric is calculated by first stratifying proteomic results by identifying the top decile (or 10th 10-quantile) of identified proteins based on the number of peptides per protein and then returns the median molecular weight of the resulting proteins. When using the 2016 versus 2012 Tursiops truncatus genome annotation to search this proteomic data set, there was a 21% improvement in NP10. This metric was further demonstrated by using a publicly available proteomic data set to compare human genome annotations from 2004, 2013 and 2016, which showed a 33% improvement in NP10. These results demonstrate that proteomics may be a useful metrological tool to benchmark genome accuracy, though there is a need for reference proteomic datasets across species to facilitate the evaluation of new de novo and existing genome.

Diabetic nephropathy is the leading cause of end-stage renal disease. The urinary albumin to creatinine ratio is used as a predictor for the development of nephropathy but it is neither sensitive nor specific. Here we used liquid chromatography/mass spectrometry on urine of eight normoalbuminuric patients with type 2 diabetes from the VA Diabetes Trial to identify candidate markers for loss of renal function. Initial verification of seven markers (agrin, haptoglobin, mannan-binding lectin serine protease 2, LAMP-2, angiotensinogen, NGAL, and uromodulin) in the urine of an additional 30 patients showed that haptoglobin was the best predictor of early renal functional decline. We then measured this in the urine of 204 patients with type 2 diabetes who did not yet have significant kidney disease (estimated glomerular filtration rate stage 2 or better and an albumin to creatinine ratio <300mg/g). In comparing the highest to lowest tertiles, the odds ratio for having early renal function decline was 2.70 (CI: 1.15, 6.32) using the haptoglobin to creatinine ratio compared with 2.50 (CI 1.14, 5.48) using the albumin to creatinine ratio after adjusting for treatment group and use of ACE inhibitors. Addition of the haptoglobin to creatinine ratio to a model using the albumin to creatinine ratio to predict early renal function decline resulted in improved predictive performance. Thus, the haptoglobin to creatinine ratio may be useful to predict patients with type 2 diabetes at risk of nephropathy before the development of macroalbuminuria or reduced glomerular filtration rate.

Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices. AKqcSWMLpN1xDKxGK6XHvB Video Abstract