A framework for quality control in quantitative proteomics.

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.

Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome.

Membrane-bound particles in plasma are composed of exosomes, microvesicles, and apoptotic bodies and represent ~1-2% of the total protein composition. Proteomic interrogation of this subset of plasma proteins augments the representation of tissue-specific proteins, representing a "liquid biopsy," while enabling the detection of proteins that would otherwise be beyond the dynamic range of liquid chromatography-tandem mass spectrometry of unfractionated plasma. We have developed an enrichment strategy (Mag-Net) using hyper-porous strong-anion exchange magnetic microparticles to sieve membrane-bound particles from plasma. The Mag-Net method is robust, reproducible, inexpensive, and requires <100 µL plasma input. Coupled to a quantitative data-independent mass spectrometry analytical strategy, we demonstrate that we can collect results for >37,000 peptides from >4,000 plasma proteins with high precision. Using this analytical pipeline on a small cohort of patients with neurodegenerative disease and healthy age-matched controls, we discovered 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer's disease dementia (ADD) from those without ADD. Our method also discovered 310 proteins that were different between Parkinson's disease and those with either ADD or healthy cognitively normal individuals. Using machine learning we were able to distinguish between ADD and not ADD with a mean ROC AUC = 0.98 ± 0.06.

The multi-tissue landscape of somatic mtDNA mutations indicates tissue-specific accumulation and removal in aging.

Accumulation of somatic mutations in the mitochondrial genome (mtDNA) has long been proposed as a possible mechanism of mitochondrial and tissue dysfunction that occurs during aging. A thorough characterization of age-associated mtDNA somatic mutations has been hampered by the limited ability to detect low-frequency mutations. Here, we used Duplex Sequencing on eight tissues of an aged mouse cohort to detect >89,000 independent somatic mtDNA mutations and show significant tissue-specific increases during aging across all tissues examined which did not correlate with mitochondrial content and tissue function. G→A/C→T substitutions, indicative of replication errors and/or cytidine deamination, were the predominant mutation type across all tissues and increased with age, whereas G→T/C→A substitutions, indicative of oxidative damage, were the second most common mutation type, but did not increase with age regardless of tissue. We also show that clonal expansions of mtDNA mutations with age is tissue- and mutation type-dependent. Unexpectedly, mutations associated with oxidative damage rarely formed clones in any tissue and were significantly reduced in the hearts and kidneys of aged mice treated at late age with elamipretide or nicotinamide mononucleotide. Thus, the lack of accumulation of oxidative damage-linked mutations with age suggests a life-long dynamic clearance of either the oxidative lesions or mtDNA genomes harboring oxidative damage.

An Analysis of Metabolic Changes in the Retina and Retinal Pigment Epithelium of Aging Mice.

Purpose: The purpose of this study was to present our hypothesis that aging alters metabolic function in ocular tissues. We tested the hypothesis by measuring metabolism in aged murine tissues alongside retinal responses to light. Methods: Scotopic and photopic electroretinogram (ERG) responses in young (3-6 months) and aged (23-26 months) C57Bl/6J mice were recorded. Metabolic flux in retina and eyecup explants was quantified using U-13C-glucose or U-13C-glutamine with gas chromatography-mass spectrometry (GC-MS), O2 consumption rate (OCR) in a perifusion apparatus, and quantifying adenosine triphosphatase (ATP) with a bioluminescence assay. Results: Scotopic and photopic ERG responses were reduced in aged mice. Glucose metabolism, glutamine metabolism, OCR, and ATP pools in retinal explants were mostly unaffected in aged mice. In eyecups, glutamine usage in the Krebs Cycle decreased while glucose metabolism, OCR, and ATP pools remained stable. Conclusions: Our examination of metabolism showed negligible impact of age on retina and an impairment of glutamine anaplerosis in eyecups. The metabolic stability of these tissues ex vivo suggests age-related metabolic alterations may not be intrinsic. Future experiments should focus on determining whether external factors including nutrient supply, oxygen availability, or structural changes influence ocular metabolism in vivo.

A replication-linked mutational gradient drives somatic mutation accumulation and influences germline polymorphisms and genome composition in mitochondrial DNA.

Mutations in mitochondrial DNA (mtDNA) cause maternally inherited diseases, while somatic mutations are linked to common diseases of aging. Although mtDNA mutations impact health, the processes that give rise to them are under considerable debate. To investigate the mechanism by which de novo mutations arise, we analyzed the distribution of naturally occurring somatic mutations across the mouse and human mtDNA obtained by Duplex Sequencing. We observe distinct mutational gradients in G→A and T→C transitions delimited by the light-strand origin and the mitochondrial Control Region (mCR). The gradient increases unequally across the mtDNA with age and is lost in the absence of DNA polymerase γ proofreading activity. In addition, high-resolution analysis of the mCR shows that important regulatory elements exhibit considerable variability in mutation frequency, consistent with them being mutational 'hot-spots' or 'cold-spots'. Collectively, these patterns support genome replication via a deamination prone asymmetric strand-displacement mechanism as the fundamental driver of mutagenesis in mammalian DNA. Moreover, the distribution of mtDNA single nucleotide polymorphisms in humans and the distribution of bases in the mtDNA across vertebrate species mirror this gradient, indicating that replication-linked mutations are likely the primary source of inherited polymorphisms that, over evolutionary timescales, influences genome composition during speciation.

Shotgun proteomics coupled to nanoparticle-based biomarker enrichment reveals a novel panel of extracellular matrix proteins as candidate serum protein biomarkers for early-stage breast cancer detection.

BACKGROUND: The lack of specificity and high degree of false positive and false negative rates when using mammographic screening for detecting early-stage breast cancer is a critical issue. Blood-based molecular assays that could be used in adjunct with mammography for increased specificity and sensitivity could have profound clinical impact. Our objective was to discover and independently verify a panel of candidate blood-based biomarkers that could identify the earliest stages of breast cancer and complement current mammographic screening approaches. METHODS: We used affinity hydrogel nanoparticles coupled with LC-MS/MS analysis to enrich and analyze low-abundance proteins in serum samples from 20 patients with invasive ductal carcinoma (IDC) breast cancer and 20 female control individuals with positive mammograms and benign pathology at biopsy. We compared these results to those obtained from five cohorts of individuals diagnosed with cancer in organs other than breast (ovarian, lung, prostate, and colon cancer, as well as melanoma) to establish IDC-specific protein signatures. Twenty-four IDC candidate biomarkers were then verified by multiple reaction monitoring (LC-MRM) in an independent validation cohort of 60 serum samples specifically including earliest-stage breast cancer and benign controls (19 early-stage (T1a) IDC and 41 controls). RESULTS: In our discovery set, 56 proteins were increased in the serum samples from IDC patients, and 32 of these proteins were specific to IDC. Verification of a subset of these proteins in an independent cohort of early-stage T1a breast cancer yielded a panel of 4 proteins, ITGA2B (integrin subunit alpha IIb), FLNA (Filamin A), RAP1A (Ras-associated protein-1A), and TLN-1 (Talin-1), which classified breast cancer patients with 100% sensitivity and 85% specificity (AUC of 0.93). CONCLUSIONS: Using a nanoparticle-based protein enrichment technology, we identified and verified a highly specific and sensitive protein signature indicative of early-stage breast cancer with no false positives when assessing benign and inflammatory controls. These markers have been previously reported in cell-ECM interaction and tumor microenvironment biology. Further studies with larger cohorts are needed to evaluate whether this biomarker panel improves the positive predictive value of mammography for breast cancer detection.

Daily mitochondrial dynamics in cone photoreceptors.

Cone photoreceptors in the retina are exposed to intense daylight and have higher energy demands in darkness. Cones produce energy using a large cluster of mitochondria. Mitochondria are susceptible to oxidative damage, and healthy mitochondrial populations are maintained by regular turnover. Daily cycles of light exposure and energy consumption suggest that mitochondrial turnover is important for cone health. We investigated the three-dimensional (3D) ultrastructure and metabolic function of zebrafish cone mitochondria throughout the day. At night retinas undergo a mitochondrial biogenesis event, corresponding to an increase in the number of smaller, simpler mitochondria and increased metabolic activity in cones. In the daytime, endoplasmic reticula (ER) and autophagosomes associate more with mitochondria, and mitochondrial size distribution across the cluster changes. We also report dense material shared between cone mitochondria that is extruded from the cell at night, sometimes forming extracellular structures. Our findings reveal an elaborate set of daily changes to cone mitochondrial structure and function.