Pushing the Isotopic Envelope: When carrier channels pollute their neighbors' signals.

Individual cells are the foundational unit of biology, and understanding their functions and interactions is critical to advancing our understanding of health and disease. Single cell proteomics has seen intense interest from mass spectrometrists, with a goal of quantifying the proteome of single cells by adapting current techniques used in bulk samples. To date, most method optimizations research has worked towards increasing the proteome coverage of single cells. One prominent technique multiplexes many individual cells into a single data acquisition event using isobaric labels. Accompanying the single cells, one label is typically used for a mixed set of many cells, called a carrier or boost channel. Although this improves peptide identification rates, several groups have examined the impact on quantitative accuracy as more cells are included in the carrier channel, e.g. 100x or 500x. This manuscript explores how impurities in the multiplexing reagent can lead to inaccurate quantification observed as a measurable signal in the wrong channel. We discover that the severe abundance differential between carrier and single cell, combined with the reagent impurities, can overshadow several channels typically used for single cells. For carrier amounts 100x and above, this contamination can be as abundant as true signal from a single cell. Therefore, we suggest limiting the carrier channel to a minimal amount and balance the goals of identification and quantification.

Human tear film protein sampling using soft contact lenses.

BACKGROUND: Human tear protein biomarkers are useful for detecting ocular and systemic diseases. Unfortunately, existing tear film sampling methods (Schirmer strip; SS and microcapillary tube; MCT) have significant drawbacks, such as pain, risk of injury, sampling difficulty, and proteomic disparities between methods. Here, we present an alternative tear protein sampling method using soft contact lenses (SCLs). RESULTS: We optimized the SCL protein sampling in vitro and performed in vivo studies in 6 subjects. Using Etafilcon A SCLs and 4M guanidine-HCl for protein removal, we sampled an average of 60 ± 31 µg of protein per eye. We also performed objective and subjective assessments of all sampling methods. Signs of irritation post-sampling were observed with SS but not with MCT and SCLs. Proteomic analysis by mass spectrometry (MS) revealed that all sampling methods resulted in the detection of abundant tear proteins. However, smaller subsets of unique and shared proteins were identified, particularly for SS and MCT. Additionally, there was no significant intrasubject variation between MCT and SCL sampling. CONCLUSIONS: These experiments demonstrate that SCLs are an accessible tear-sampling method with the potential to surpass current methods in sampling basal tears.

TDP-43-stratified single-cell proteomics of postmortem human spinal motor neurons reveals protein dynamics in amyotrophic lateral sclerosis.

A limitation of conventional bulk-tissue proteome studies in amyotrophic lateral sclerosis (ALS) is the confounding of motor neuron (MN) signals by admixed non-MN proteins. Here, we leverage laser capture microdissection and nanoPOTS single-cell mass spectrometry-based proteomics to query changes in protein expression in single MNs from postmortem ALS and control tissues. In a follow-up analysis, we examine the impact of stratification of MNs based on cytoplasmic transactive response DNA-binding protein 43 (TDP-43)+ inclusion pathology on the profiles of 2,238 proteins. We report extensive overlap in differentially abundant proteins identified in ALS MNs with or without overt TDP-43 pathology, suggesting early and sustained dysregulation of cellular respiration, mRNA splicing, translation, and vesicular transport in ALS. Together, these data provide insights into proteome-level changes associated with TDP-43 proteinopathy and begin to demonstrate the utility of pathology-stratified trace sample proteomics for understanding single-cell protein dynamics in human neurologic diseases.

A proteomic meta-analysis refinement of plasma extracellular vesicles.

Extracellular vesicles play major roles in cell-to-cell communication and are excellent biomarker candidates. However, studying plasma extracellular vesicles is challenging due to contaminants. Here, we performed a proteomics meta-analysis of public data to refine the plasma EV composition by separating EV proteins and contaminants into different clusters. We obtained two clusters with a total of 1717 proteins that were depleted of known contaminants and enriched in EV markers with independently validated 71% true-positive. These clusters had 133 clusters of differentiation (CD) antigens and were enriched with proteins from cell-to-cell communication and signaling. We compared our data with the proteins deposited in PeptideAtlas, making our refined EV protein list a resource for mechanistic and biomarker studies. As a use case example for this resource, we validated the type 1 diabetes biomarker proplatelet basic protein in EVs and showed that it regulates apoptosis of ß cells and macrophages, two key players in the disease development. Our approach provides a refinement of the EV composition and a resource for the scientific community.

Evaluating a large language model's ability to solve programming exercises from an introductory bioinformatics course.

Computer programming is a fundamental tool for life scientists, allowing them to carry out essential research tasks. However, despite various educational efforts, learning to write code can be a challenging endeavor for students and researchers in life-sciences disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists' efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such tool-OpenAI's ChatGPT-could successfully complete programming tasks. ChatGPT solved 139 (75.5%) of the exercises on its first attempt. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have implications for life-sciences education and research. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public. For some programming tasks, researchers may be able to work in collaboration with machine-learning models to produce functional code.

Pan-cancer proteogenomics connects oncogenic drivers to functional states.

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin.

DNA methylation plays a critical role in establishing and maintaining cellular identity. However, it is frequently dysregulated during tumor development and is closely intertwined with other genetic alterations. Here, we leveraged multi-omic profiling of 687 tumors and matched non-involved adjacent tissues from the kidney, brain, pancreas, lung, head and neck, and endometrium to identify aberrant methylation associated with RNA and protein abundance changes and build a Pan-Cancer catalog. We uncovered lineage-specific epigenetic drivers including hypomethylated FGFR2 in endometrial cancer. We showed that hypermethylated STAT5A is associated with pervasive regulon downregulation and immune cell depletion, suggesting that epigenetic regulation of STAT5A expression constitutes a molecular switch for immunosuppression in squamous tumors. We further demonstrated that methylation subtype-enrichment information can explain cell-of-origin, intra-tumor heterogeneity, and tumor phenotypes. Overall, we identified cis-acting DNA methylation events that drive transcriptional and translational changes, shedding light on the tumor's epigenetic landscape and the role of its cell-of-origin.

Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation.

Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology in both normal and cancer cells. Advances in mass spectrometry enable high-throughput, accurate, and sensitive measurement of PTM levels to better understand their role, prevalence, and crosstalk. Here, we analyze the largest collection of proteogenomics data from 1,110 patients with PTM profiles across 11 cancer types (10 from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns of changes in protein acetylation and phosphorylation involved in hallmark cancer processes. These patterns revealed subsets of tumors, from different cancer types, including those with dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated with immune response driven by acetylation, affected kinase specificity by crosstalk between acetylation and phosphorylation, and modified histone regulation. Overall, this resource highlights the rich biology governed by PTMs and exposes potential new therapeutic avenues.

TDP-43-stratified single-cell proteomic profiling of postmortem human spinal motor neurons reveals protein dynamics in amyotrophic lateral sclerosis.

Unbiased proteomics has been employed to interrogate central nervous system (CNS) tissues (brain, spinal cord) and fluid matrices (CSF, plasma) from amyotrophic lateral sclerosis (ALS) patients; yet, a limitation of conventional bulk tissue studies is that motor neuron (MN) proteome signals may be confounded by admixed non-MN proteins. Recent advances in trace sample proteomics have enabled quantitative protein abundance datasets from single human MNs (Cong et al., 2020b). In this study, we leveraged laser capture microdissection (LCM) and nanoPOTS (Zhu et al., 2018c) single-cell mass spectrometry (MS)-based proteomics to query changes in protein expression in single MNs from postmortem ALS and control donor spinal cord tissues, leading to the identification of 2515 proteins across MNs samples (>900 per single MN) and quantitative comparison of 1870 proteins between disease groups. Furthermore, we studied the impact of enriching/stratifying MN proteome samples based on the presence and extent of immunoreactive, cytoplasmic TDP-43 inclusions, allowing identification of 3368 proteins across MNs samples and profiling of 2238 proteins across TDP-43 strata. We found extensive overlap in differential protein abundance profiles between MNs with or without obvious TDP-43 cytoplasmic inclusions that together point to early and sustained dysregulation of oxidative phosphorylation, mRNA splicing and translation, and retromer-mediated vesicular transport in ALS. Our data are the first unbiased quantification of single MN protein abundance changes associated with TDP-43 proteinopathy and begin to demonstrate the utility of pathology-stratified trace sample proteomics for understanding single-cell protein abundance changes in human neurologic diseases.

Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics.

We combined efficient sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in rapid label-free analyses. WWA employs large isolation windows to intentionally co-isolate and co-fragment adjacent precursors along with the selected precursor. Optimized WWA increased the number of MS2-identified proteins by ≈40 % relative to standard data-dependent acquisition. For a 40-min LC gradient operated at ≈15 nL/min, we identified an average of 3,524 proteins per single-cell-sized aliquot of protein digest. Reducing the active gradient to 20 min resulted in a modest 10 % decrease in proteome coverage. Using this platform, we compared protein expression between single HeLa cells having an essential autophagy gene, atg9a, knocked out, with their isogenic WT parental line. Similar proteome coverage was observed, and 268 proteins were significantly up- or downregulated. Protein upregulation primarily related to innate immunity, vesicle trafficking and protein degradation.

HyperSCP: Combining Isotopic and Isobaric Labeling for Higher Throughput Single-Cell Proteomics.

Recent developments in mass spectrometry-based single-cell proteomics (SCP) have resulted in dramatically improved sensitivity, yet the relatively low measurement throughput remains a limitation. Isobaric and isotopic labeling methods have been separately applied to SCP to increase throughput through multiplexing. Here we combined both forms of labeling to achieve multiplicative scaling for higher throughput. Two-plex stable isotope labeling of amino acids in cell culture (SILAC) and isobaric tandem mass tag (TMT) labeling enabled up to 28 single cells to be analyzed in a single liquid chromatography-mass spectrometry (LC-MS) analysis, in addition to carrier, reference, and negative control channels. A custom nested nanowell chip was used for nanoliter sample processing to minimize sample losses. Using a 145-min total LC-MS cycle time, ∼280 single cells were analyzed per day. This measurement throughput could be increased to ∼700 samples per day with a high-duty-cycle multicolumn LC system producing the same active gradient. The labeling efficiency and achievable proteome coverage were characterized for multiple analysis conditions.

Short-term Satisfaction, Psychosocial Impact, and Complication Profile of Reduction Mammaplasty During Adolescence.

BACKGROUND: Reduction mammaplasty is an effective and safe treatment option for adults with symptomatic macromastia, but there are few data regarding outcomes in adolescents. OBJECTIVES: The purpose of this study was to determine the short-term psychosocial impact, satisfaction, and safety of reduction mammaplasty when performed during adolescence. METHODS: A retrospective review was performed of a single pediatric plastic surgeon's experience with reduction mammaplasty from 2018 to 2021 in patients aged ≤18 years. Patients completed the preoperative and postoperative "Satisfaction with Breasts" and "Psychosocial Well-being" sections of the BREAST-Q survey. Clinical variables gathered included age, weight, BMI, complication profile, specimen resection weight, and follow-up duration. RESULTS: In total, 41 patients met inclusion criteria. The mean converted Rasch scores for BREAST-Q "Satisfaction with Breasts" and "Psychosocial Well-being" increased significantly following reduction mammaplasty ("Satisfaction with Breasts": preoperative, 24.1 vs postoperative, 92.6; "Psychosocial Well-being": preoperative, 37.7 vs postoperative, 90.4; P < .001). Obesity (BMI ≥ 30 kg/m2) was associated with lower preoperative "Psychosocial Well-being" scores (obese, 29.7 vs nonobese, 43.3; P < .001) but a greater improvement in score following surgery (obese, +63.9 vs nonobese, +44.9; P < .001). Specimen weight ≥1000 grams was also associated with greater improvement in score on the "Psychosocial Well-being" section (≥1000 grams, +58 vs <1000 grams, +49.7; P = .046). Overall complication rate was 31.7% while the major complication rate was 2.4%. Mean specimen resection weight was higher in patients who experienced complications (1141.3 grams vs 836.8 grams, P = .008). CONCLUSIONS: Reduction mammaplasty during adolescence predictably improves both short-term satisfaction with breasts and psychosocial well-being while demonstrating a favorable short-term complication profile.

Toward an Integrated Machine Learning Model of a Proteomics Experiment.

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

Safety and Efficacy of Liposomal Bupivacaine Supraclavicular Nerve Blocks in Open Treatment of Distal Radius Fractures: A Perioperative Pain Management Protocol.

INTRODUCTION: Distal radius fractures (DRFs) are common fractures requiring surgical fixation. The literature varies regarding opioid prescribing habits, opioid consumption, and postoperative pain scores. We hypothesized that the preoperative administration of a liposomal bupivacaine (LB) supraclavicular nerve block would be safe and effective in controlling postoperative pain. METHODS: A standardized pain management protocol was implemented at a single institution from July 2021 to March 2022 for patients undergoing open reduction internal fixation of DRF. Protocol elements included a preoperative LB supraclavicular nerve block and a multimodal postoperative pain regimen. Primary clinical outcomes included postoperative pain scores and number of opioid tablets consumed. RESULTS: Twenty patients underwent a newly implemented protocol. The average age was 56 years. Mean number of oxycodone 5-mg tablets consumed was 4.1 (median, 2.5), and mean visual analog scale pain score at first postoperative appointment was 2.8. There were no incidences of missed acute carpal tunnel postoperatively. When compared with an institutional historical control (n = 189), number of opioid pills prescribed was reduced by 60% (21.4 vs 8.6 tablets, P < 0.0001), and no patients had unscheduled health care contact because of uncontrolled pain (22% vs 0%, P < 0.016). CONCLUSIONS: Liposomal bupivacaine supraclavicular nerve blocks are safe and effective in the treatment of postoperative pain after open reduction internal fixation of DRF. Patients consumed <5 oxycodone tablets on average, which is less than many recommend prescribed quantities (>20-30 tablets). Patients had low pain scores (2.8/10) at the first postoperative follow-up. To our knowledge, this is the first study demonstrating the utility of LB in this clinical setting.

Challenges and Opportunities for Single-cell Computational Proteomics.

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics.

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

Mandibular Distraction in Neonatal Pierre Robin Sequence: Is Immediate Extubation Both Feasible and Safe?

BACKGROUND: The timing of extubation following placement of mandibular distractors in the setting of Pierre Robin sequence is variable across institutional algorithms. Postoperative maintenance of intubation allows for an improvement in airway dimension and tongue positioning before extubation, theoretically decreasing the impact of postoperative airway edema. Maintenance of intubation, however, is not without risk. The authors analyze their institutional experience with neonatal mandibular distraction followed by immediate extubation to assess feasibility and safety profiles. METHODS: A 4-year retrospective review of patients diagnosed with Pierre Robin sequence who underwent mandibular distraction within the first 3 months of life was performed. Patients intubated preoperatively were excluded. RESULTS: Fifty-two patients met inclusion criteria. Thirty-eight patients (73 percent) were extubated immediately, whereas 14 patients (27 percent) remained intubated. No differences between these groups were found when comorbidities, cleft pathology, preoperative respiratory support, or grade of view on direct laryngoscopy were analyzed. Case duration greater than 120 minutes, operation start time after 3 pm, and the subjective designation of a difficult airway by the anesthesiologist were associated with maintaining intubation (p < 0.05). Eight patients (21 percent) in the extubated group required an increase in respiratory support in the postoperative interval. Four of these patients (11 percent) required reintubation. Increased postoperative respiratory support was more likely in patients with certain comorbidities and higher preoperative respiratory support requirements (p < 0.05). CONCLUSIONS: The authors' data suggest that immediate extubation following neonatal mandibular distraction is feasible in patients who are not intubated preoperatively. Careful consideration should be given to patients who require significant respiratory support preoperatively and in those with certain comorbidities. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.

Skin Reducing Mastectomy and Immediate Tissue Expander Reconstruction: A Critical Analysis.

BACKGROUND: Complications of implant-based reconstruction have been shown to be related to increasing body mass index (BMI) and breast size. The impact of skin reducing mastectomy (SRM) with a dermal flap is examined. METHODS: A retrospective review of a single surgeon's experience with immediate submuscular tissue expander (TE) reconstruction from 2011 to 2019 was performed. The outcomes of SRM were compared with those of skin sparing mastectomy (SSM). RESULTS: A total of 162 patients (292 breasts) were identified. Mastectomy types were as follows: SRM, 73 (136 breasts) and SSM, 89 (156 breasts). Acellular dermal matrix (ADM) was used to supplement TE coverage in 65.4% of SRM cases. Mean BMI was 29.2 among SRM patients and 25.9 in SSM patients (P < 0.001). Obesity (BMI ≥ 30) was more prevalent in the SRM group (SRM, 38.4% vs SSM, 22.5%; P = 0.03). Mean mastectomy weight was higher in the SRM group (SRM, 833.6 g vs SSM, 425.6 g; P < 0.001). Mean BMI and mastectomy weight were lower in SRM patients who were reconstructed with ADM (ADM, 28.1 vs no ADM, 30.8; P = 0.01; ADM, 746.1 g vs no ADM, 1006.3 g; P < 0.001). Minor complications were more prevalent in the SRM group (SRM, 22.8% vs SSM, 4.5%; P < 0.001). Mastectomy skin flap necrosis (MSFN) was more common in the SRM group (SRM, 22.8% vs SSM, 7.7%; P < 0.001), but MSFN necessitating operative debridement was similarly low in both groups (SRM: 1.9% vs SSM: 4.5%). Major complication rates (SRM 11.0% vs SSM 10.9%) and reconstructive failure rates (SRM 5.9% vs SSM 5.1%) were similar between groups. Mastectomy weight 800 g or higher and BMI of 30 or higher were found to be risk factors for complications on analysis of the SRM cohort (P < 0.05). CONCLUSIONS: Mastectomy weight and BMI were positive predictors of complications after immediate TE reconstruction. Mastectomy skin flap necrosis is more common after SRM than SSM. The use of SRM with a dermal flap has a similar major complication rate as SSM despite its use in obese, large-breasted women. The dermal flap provides soft tissue coverage, which prevents implant exposure and seroma. The use of ADM does not adversely affect the complication rate of SRM.

A Preprocessing Tool for Enhanced Ion Mobility-Mass Spectrometry-Based Omics Workflows.

The ability to improve the data quality of ion mobility-mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.