Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments

Rapid Screening of Biomarkers in KYSE-150 Cells Exposed to Polycyclic Aromatic Hydrocarbons via Inkjet Printing Single-Cell Mass Spectrometry

Single-cell analysis by mass spectrometry (MS) is emerging as a powerful tool that not only contributes to cellular heterogeneity but also offers an unprecedented opportunity to predict pathology onset and facilitates novel biomarker discovery. However, the development of single-cell MS analysis techniques with a focus on sample extraction, separation, and ionization methods for volume-limited samples and complexity of cellular samples are still a big challenge. In this study, we present a high-throughput approach to inkjet drop on demand printing single-cell MS for rapid screening of biomarkers of polycyclic aromatic hydrocarbon (PAH) exposure at the KYSE-150 cell, aiming to elucidate the pathogenesis of PAH-induced esophageal cancer. With an analytical bulk KYSE-150 cell throughput of up to 51 cells per minute, the method provides a new opportunity for simultaneous single-cell analysis of multiple biomarkers. We screened 930 characteristic ions from 3,683 detected peak signals and identified 91 distinctive molecules that exhibited significant differences under various concentrations of PAH exposure. These molecules have potential as clinical diagnostic biomarkers. Additionally, the current study identifies specific biomarkers that behave completely opposite in single-cell and multicell lipidomics as the concentration of PAH changes. These biomarkers potentially subdivide KYSE-150 cells into PAH-sensitive and PAH-insensitive types, providing a basis for revealing PAH toxicity and disease pathogenesis from the heterogeneity of cellular metabolism.

MS2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0

Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS2Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS2Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS2Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS2Rescore offers a powerful solution to boost peptide identifications. MS2Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS2Rescore to be a valuable tool for the wider proteomics community. MS2Rescore is available at https://github.com/compomics/ms2rescore.

Limiting the impact of protein leakage in single-cell proteomics

Limiting artifacts during sample preparation can significantly increase data quality in single-cell proteomics experiments. Towards this goal, we characterize the impact of protein leakage by analyzing thousands of primary single cells that were prepared either fresh immediately after dissociation or cryopreserved and prepared at a later date. We directly identify permeabilized cells and use the data to define a signature for protein leakage. We use this signature to build a classifier for identifying damaged cells that performs accurately across cell types and species.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Picoliter Single-Cell Reactor for Proteome Profiling by In Situ Cell Lysis, Protein Immobilization, Digestion, and Droplet Transfer

Global profiling of single-cell proteomes can reveal cellular heterogeneity, thus benefiting precision medicine. However, current mass spectrometry (MS)-based single-cell proteomic sample processing still faces technical challenges associated with processing efficiency and protein recovery. Herein, we present an innovative sample processing platform based on a picoliter single-cell reactor (picoSCR) for single-cell proteome profiling, which involves in situ protein immobilization and sample transfer. PicoSCR helped minimize surface adsorptive losses by downscaling the processing volume to 400 pL with a contact area of less than 0.4 mm2. Besides, picoSCR reached highly efficient cell lysis and digestion within 30 min, benefiting from optimal reagent and high reactant concentrations. Using the picoSCR-nanoLC-MS system, over 1400 proteins were identified from an individual HeLa cell using data-dependent acquisition mode. Proteins with copy number below 1000 were identified, demonstrating this system with a detection limit of 1.7 zmol. Furthermore, we profiled the proteome of circulating tumor cells (CTCs). Data are available via ProteomeXchange with the identifier PXD051468. Proteins associated with epithelial-mesenchymal transition and neutrophil extracellular traps formation (which are both related to tumor metastasis) were observed in all CTCs. The cellular heterogeneity was revealed by differences in signaling pathways within individual cells. These results highlighted the potential of the picoSCR platform to help discover new biomarkers and explore differences in biological processes between cells.

Boronic Acid-Rich Lanthanide Metal-Organic Frameworks Enable Deep Proteomics with Ultratrace Biological Samples

Label-free proteomics is widely used to identify disease mechanism and potential therapeutic targets. However, deep proteomics with ultratrace clinical specimen remains a major technical challenge due to extensive contact loss during complex sample pretreatment. Here, a hybrid of four boronic acid-rich lanthanide metal-organic frameworks (MOFs) with high protein affinity is introduced to capture proteins in ultratrace samples jointly by nitrogen-boronate complexation, cation-π and ionic interactions. A MOFs Aided Sample Preparation (MASP) workflow that shrinks sample volume and integrates lysis, protein capture, protein digestion and peptide collection steps into a single PCR tube to minimize sample loss caused by non-specific absorption, is proposed further. MASP is validated to quantify ≈1800 proteins in 10 HEK-293T cells. MASP is applied to profile cerebrospinal fluid (CSF) proteome from cerebral stroke and brain damaged patients, and identified ≈3700 proteins in 1 µL CSF. MASP is further demonstrated to detect ≈9600 proteins in as few as 50 µg mouse brain tissues. MASP thus enables deep, scalable, and reproducible proteome on precious clinical samples with low abundant proteins.

Single-cell proteomics reveals decreased abundance of proteostasis and meiosis proteins in advanced maternal age oocytes

Advanced maternal age is associated with a decline in oocyte quality, which often leads to reproductive failure in humans. However, the mechanisms behind this age-related decline remain unclear. To gain insights into this phenomenon, we applied plexDIA, a multiplexed data-independent acquisition, single-cell mass spectrometry method, to analyze the proteome of oocytes from both young women and women of advanced maternal age. Our findings primarily revealed distinct proteomic profiles between immature fully grown germinal vesicle and mature metaphase II oocytes. Importantly, we further show that a woman's age is associated with changes in her oocyte proteome. Specifically, when compared to oocytes obtained from young women, advanced maternal age oocytes exhibited lower levels of the proteasome and TRiC complex, as well as other key regulators of proteostasis and meiosis. This suggests that aging adversely affects the proteostasis and meiosis networks in human oocytes. The proteins identified in this study hold potential as targets for improving oocyte quality and may guide future studies into the molecular processes underlying oocyte aging.

Single cell proteomics by mass spectrometry reveals deep epigenetic insight into the actions of an orphan histone deacetylase inhibitor

Epigenetic programming has been shown to play a role in nearly every human system and disease where anyone has thought to look. However, the levels of heterogeneity at which epigenetic or epiproteomic modifications occur at single cell resolution across a population remains elusive. While recent advances in sequencing technology have allowed between 1 and 3 histone post-translational modifications to be analyzed in each single cell, over twenty separate chemical PTMs are known to exist, allowing thousands of possible combinations. Single cell proteomics by mass spectrometry (SCP) is an emerging technology in which hundreds or thousands of proteins can be directly quantified in typical human cells. As the proteins detected and quantified by SCP are heavily biased toward proteins of highest abundance, chromatin proteins are an attractive target for analysis. To this end, I applied SCP to the analysis of cancer cells treated with mocetinostat, a class specific histone deacetylase inhibitor. I find that 16 PTMs can be confidently identified and localized with high site specificity in single cells. In addition, the high abundance of histone proteins allows higher throughput methods to be utilized for SCP than previously described. While quantitative accuracy suffers when analyzing more than 700 cells per day, 9 histone proteins can be measured in single cells analyzed at even 3,500 cells per day, a throughput 10-fold greater than any previous report. In addition, the unbiased global approach utilized herein identifies a previously uncharacterized response to this drug through the S100-A8/S100-A9 protein complex partners. This response is observed in nearly every cell of the over 1,000 analyzed in this study, regardless of the relative throughput of the method utilized. While limitations exist in the methods described herein, current technologies can easily improve upon the results presented here to allow comprehensive analysis of histone PTMs to be performed in any mass spectrometry lab. All raw and processed data described in this study has been made publicly available through the ProteomeXchange/MASSIVE repository system as MSV000093434.

Single-nucleus proteomics identifies regulators of protein transport

The physiological response of a cell to stimulation depends on its proteome configuration. Therefore, the abundance variation of regulatory proteins across unstimulated single cells can be associatively linked with their response to stimulation. Here we developed an approach that leverages this association across individual cells and nuclei to systematically identify potential regulators of biological processes, followed by targeted validation. Specifically, we applied this approach to identify regulators of nucleocytoplasmic protein transport in macrophages stimulated with lipopolysaccharide (LPS). To this end, we quantified the proteomes of 3,412 individual nuclei, sampling the dynamic response to LPS treatment, and linking functional variability to proteomic variability. Minutes after the stimulation, the protein transport in individual nuclei correlated strongly with the abundance of known protein transport regulators, thus revealing the impact of natural protein variability on functional cellular response. We found that simple biophysical constraints, such as the quantity of nuclear pores, partially explain the variability in LPS-induced nucleocytoplasmic transport. Among the many proteins newly identified to be associated with the response, we selected 16 for targeted validation by knockdown. The knockdown phenotypes confirmed the inferences derived from natural protein and functional variation of single nuclei, thus demonstrating the potential of (sub-)single-cell proteomics to infer functional regulation. We expect this approach to generalize to broad applications and enhance the functional interpretability of single-cell omics data.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Acetic acid is a superior ion pairing modifier for sub-nanogram and single cell proteomics

A recent study demonstrated a substantial increase in peptide signal and corresponding proteome coverage when employing 0.5% acetic acid (AA) as the ion pairing modifier in place of the 0.1% formic acid traditionally used in shotgun proteomics. In this study, we investigated the effect of modifier in the context of sub-nanogram and single cell proteomics (SCP). We first evaluated a tryptic digest standard down to 20 picograms total load on column on a TIMSTOF SCP system. In line with the previous results, we observed a signal increase when using AA, leading to increased proteome coverage at every peptide load assessed. Relative improvements were more apparent at lower concentrations, with a 20 picogram peptide digest demonstrating a striking 1.8-fold increase to over 2,000 protein groups identified in a 30 minute analysis. Furthermore, we find that this increase in signal can be leveraged to reduce ramp times, leading to 1.7x more scans across each peak and improvements in quantification as measured by %CVs. When evaluating single cancer cells, approximately 13% more peptide groups were identified on average when employing AA in the place of FA. All vendor raw and processed data are available through ProteomeXchange as PXD046002 and PXD051590.

Increasing Proteome Coverage Through a Reduction in Analyte Complexity in Single-Cell Equivalent Samples

The advancement of sophisticated instrumentation in mass spectrometry has catalyzed an in-depth exploration of complex proteomes. This exploration necessitates a nuanced balance in experimental design, particularly between quantitative precision and the enumeration of analytes detected. In bottom-up proteomics, a key challenge is that oversampling of abundant proteins can adversely affect the identification of a diverse array of unique proteins. This issue is especially pronounced in samples with limited analytes, such as small tissue biopsies or single-cell samples. Methods such as depletion and fractionation are suboptimal to reduce oversampling in single cell samples, and other improvements on LC and mass spectrometry technologies and methods have been developed to address the trade-off between precision and enumeration. We demonstrate that by using a monosubstrate protease for proteomic analysis of single-cell equivalent digest samples, an improvement in quantitative accuracy can be achieved, while maintaining high proteome coverage established by trypsin. This improvement is particularly vital for the field of single-cell proteomics, where single-cell samples with limited number of protein copies, especially in the context of low-abundance proteins, can benefit from considering analyte complexity. Considerations about analyte complexity, alongside chromatographic complexity, integration with data acquisition methods, and other factors such as those involving enzyme kinetics, will be crucial in the design of future single-cell workflows.

Large-scale neurophysiology and single-cell profiling in human neuroscience

Advances in large-scale single-unit human neurophysiology, single-cell RNA sequencing, spatial transcriptomics and long-term ex vivo tissue culture of surgically resected human brain tissue have provided an unprecedented opportunity to study human neuroscience. In this Perspective, we describe the development of these paradigms, including Neuropixels and recent brain-cell atlas efforts, and discuss how their convergence will further investigations into the cellular underpinnings of network-level activity in the human brain. Specifically, we introduce a workflow in which functionally mapped samples of human brain tissue resected during awake brain surgery can be cultured ex vivo for multi-modal cellular and functional profiling. We then explore how advances in human neuroscience will affect clinical practice, and conclude by discussing societal and ethical implications to consider. Potential findings from the field of human neuroscience will be vast, ranging from insights into human neurodiversity and evolution to providing cell-type-specific access to study and manipulate diseased circuits in pathology. This Perspective aims to provide a unifying framework for the field of human neuroscience as we welcome an exciting era for understanding the functional cytoarchitecture of the human brain.

High-throughput analysis of hazards in novel food based on the density functional theory and multimodal deep learning

The emergence of cultured meat presents the potential for personalized food additive manufacturing, offering a solution to address future food resource scarcity. Processing raw materials and products in synthetic food products poses challenges in identifying hazards, impacting the entire industrial chain during the industry's further evolution. It is crucial to examine the correlation of biological information at different levels and to reveal the temporal dynamics jointly. Proposed active prevention method includes four aspects: (i) Investigating the molecular-level mechanism underlying the binding and dissociation of hazards with proteins represents a novel approach to mitigate matrix effect. (ii) Identifying distinct fragments is a pivotal advancement toward developing a novel screening strategy for hazards throughout the food chain. (iii) Designing an artificial intelligence model-based approach to acquire multi-dimensional histology data also holds significant potential for various applications. (iv) Integrating multimodal data is a practical approach to enhance evaluation and feedback control accuracy.

AM-DMF-SCP: Integrated Single-Cell Proteomics Analysis on an Active Matrix Digital Microfluidic Chip

Single-cell proteomics offers unparalleled insights into cellular diversity and molecular mechanisms, enabling a deeper understanding of complex biological processes at the individual cell level. Here, we develop an integrated sample processing on an active-matrix digital microfluidic chip for single-cell proteomics (AM-DMF-SCP). Employing the AM-DMF-SCP approach and data-independent acquisition (DIA), we identify an average of 2258 protein groups in single HeLa cells within 15 min of the liquid chromatography gradient. We performed comparative analyses of three tumor cell lines: HeLa, A549, and HepG2, and machine learning was utilized to identify the unique features of these cell lines. Applying the AM-DMF-SCP to characterize the proteomes of a third-generation EGFR inhibitor, ASK120067-resistant cells (67R) and their parental NCI-H1975 cells, we observed a potential correlation between elevated VIM expression and 67R resistance, which is consistent with the findings from bulk sample analyses. These results suggest that AM-DMF-SCP is an automated, robust, and sensitive platform for single-cell proteomics and demonstrate the potential for providing valuable insights into cellular mechanisms.

Quantification Quality Control Emerges as a Crucial Factor to Enhance Single-Cell Proteomics Data Analysis

Mass spectrometry (MS)-based single-cell proteomics (SCP) provides us the opportunity to unbiasedly explore biological variability within cells without the limitation of antibody availability. This field is rapidly developed with the main focuses on instrument advancement, sample preparation refinement, and signal boosting methods; however, the optimal data processing and analysis are rarely investigated which holds an arduous challenge because of the high proportion of missing values and batch effect. Here, we introduced a quantification quality control to intensify the identification of differentially expressed proteins (DEPs) by considering both within and across SCP data. Combining quantification quality control with isobaric matching between runs (IMBR) and PSM-level normalization, an additional 12% and 19% of proteins and peptides, with more than 90% of proteins/peptides containing valid values, were quantified. Clearly, quantification quality control was able to reduce quantification variations and q-values with the more apparent cell type separations. In addition, we found that PSM-level normalization performed similar to other protein-level normalizations but kept the original data profiles without the additional requirement of data manipulation. In proof of concept of our refined pipeline, six uniquely identified DEPs exhibiting varied fold-changes and playing critical roles for melanoma and monocyte functionalities were selected for validation using immunoblotting. Five out of six validated DEPs showed an identical trend with the SCP dataset, emphasizing the feasibility of combining the IMBR, cell quality control, and PSM-level normalization in SCP analysis, which is beneficial for future SCP studies.

Challenges and best practices in omics benchmarking

Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.

Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition

Physiological processes, such as the epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within a cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in the cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism, and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and, thus, reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offers a window into protein regulation during physiological transitions.

Dynamics of single-cell protein covariation during epithelial-mesenchymal transition

Physiological processes, such as epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and thus reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offer a window into protein regulation during physiological transitions.

scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding

Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.

Proteomics applications in next generation induced pluripotent stem cell models

INTRODUCTION

Induced pluripotent stem (iPS) cell technology has transformed biomedical research. New opportunities now exist to create new organoids, microtissues, and body-on-a-chip systems for basic biology investigations and clinical translations.

AREAS COVERED

We discuss the utility of proteomics for attaining an unbiased view into protein expression changes during iPS cell differentiation, cell maturation, and tissue generation. The ability to discover cell-type specific protein markers during the differentiation and maturation of iPS-derived cells has led to new strategies to improve cell production yield and fidelity. In parallel, proteomic characterization of iPS-derived organoids is helping to realize the goal of bridging in vitro and in vivo systems.

EXPERT OPINIONS

We discuss some current challenges of proteomics in iPS cell research and future directions, including the integration of proteomic and transcriptomic data for systems-level analysis.

Sex differences in single neuron function and proteomics profiles examined by patch-clamp and mass spectrometry in the locus coeruleus of the adult mouse

AIMS

This study aimed to characterize the properties of locus coeruleus (LC) noradrenergic neurons in male and female mice. We also sought to investigate sex-specific differences in membrane properties, action potential generation, and protein expression profiles to understand the mechanisms underlying neuronal excitability variations.

METHODS

Utilizing a genetic mouse model by crossing Dbhcre knock-in mice with tdTomato Ai14 transgenic mice, LC neurons were identified using fluorescence microscopy. Neuronal functional properties were assessed using patch-clamp recordings. Proteomic analyses of individual LC neuron soma was conducted using mass spectrometry to discern protein expression profiles. Data are available via ProteomeXchange with identifier PXD045844.

RESULTS

Female LC noradrenergic neurons displayed greater membrane capacitance than those in male mice. Male LC neurons demonstrated greater spontaneous and evoked action potential generation compared to females. Male LC neurons exhibited a lower rheobase and achieved higher peak frequencies with similar current injections. Proteomic analysis revealed differences in protein expression profiles between sexes, with male mice displaying a notably larger unique protein set compared to females. Notably, pathways pertinent to protein synthesis, degradation, and recycling, such as EIF2 and glucocorticoid receptor signaling, showed reduced expression in females.

CONCLUSIONS

Male LC noradrenergic neurons exhibit higher intrinsic excitability compared to those from females. The discernible sex-based differences in excitability could be ascribed to varying protein expression profiles, especially within pathways that regulate protein synthesis and degradation. This study lays the groundwork for future studies focusing on the interplay between proteomics and neuronal function examined in individual cells.

scPerturb: harmonized single-cell perturbation data

Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.

The 15-min (Sub)Cellular Proteome

Single-cell mass spectrometry (MS) opens a proteomic window onto the inner workings of cells. Here, we report the discovery characterization of the subcellular proteome of single, identified embryonic cells in record speed and molecular coverage. We integrated subcellular capillary microsampling, fast capillary electrophoresis (CE), high-efficiency nano-flow electrospray ionization, and orbitrap tandem MS. In proof-of-principle tests, we found shorter separation times to hinder proteome detection using DDA, but not DIA. Within a 15-min effective separation window, CE data-independent acquisition (DIA) was able to identify 1,161 proteins from single HeLa-cell-equivalent (∼200 pg) proteome digests vs. 401 proteins by the reference data-dependent acquisition (DDA) on the same platform. The approach measured 1,242 proteins from subcellular niches in an identified cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. CE-MS with DIA enables fast, sensitive, and deep profiling of the (sub)cellular proteome, expanding the bioanalytical toolbox of cell biology.

Authorship Contributions

P.N. and B.S. designed the study. L.R.P. collected the X. laevis cell aspirates. B.S. prepared and measured the samples. B.S. and P.N. analyzed the data and interpreted the results. P.N. and B.S. wrote the manuscript. All the authors commented on the manuscript.

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

Analyzing Posttranslational Modifications in Single Cells

With the rapid expansion of capabilities in the analysis of proteins in single cells, we can now identify multiple classes of protein posttranslational modifications on some of these proteins. Each new technology that has increased the number of proteins measured per cell has likewise increased our ability to identify and quantify modified peptides. In this chapter, I will discuss our current capabilities, concerns, and challenges specific to this emerging field of study and the inevitable demand for services, providing a general review of concepts that should be considered.

Standardized Workflow for Mass-Spectrometry-Based Single-Cell Proteomics Data Processing and Analysis Using the scp Package

Mass-spectrometry (MS)-based single-cell proteomics (SCP) explores cellular heterogeneity by focusing on the functional effectors of the cells-proteins. However, extracting meaningful biological information from MS data is far from trivial, especially with single cells. Currently, data analysis workflows are substantially different from one research team to another. Moreover, it is difficult to evaluate pipelines as ground truths are missing. Our team has developed the R/Bioconductor package called scp to provide a standardized framework for SCP data analysis. It relies on the widely used QFeatures and SingleCellExperiment data structures. In addition, we used a design containing cell lines mixed in known proportions to generate controlled variability for data analysis benchmarking. In this chapter, we provide a flexible data analysis protocol for SCP data using the scp package together with comprehensive explanations at each step of the processing. Our main steps are quality control on the feature and cell level, aggregation of the raw data into peptides and proteins, normalization, and batch correction. We validate our workflow using our ground truth data set. We illustrate how to use this modular, standardized framework and highlight some crucial steps.

A Multiplexed Single-Cell Proteomic Workflow Applicable to Drug Treatment Studies

It is now well accepted that individual cells within a population will respond to treatment of the same drug in a heterogenous manner. Recent advances have allowed, for the first time, the quantitative analysis of the proteomes of single human cells by mass spectrometry. A major focus of many groups, including our own, has been to use this emerging technology to rapidly identify subpopulations of cells with unique drug response and adaptation methods. While the technology in single-cell proteomics today is progressing at a truly staggering rate, we will detail our current methods for applying highly multiplexed single-cell proteomics to drug treatment studies.

Simple Tool for Rapidly Assessing the Quality of Multiplexed Single Cell Proteomics Data

Recent advances in the sensitivity and speed of mass spectrometers coupled with improved sample preparation methods have enabled the field of single cell proteomics to proliferate. While heavy development is occurring in the label free space, dramatic improvements in throughput are provided by multiplexing with tandem mass tags. Hundreds or thousands of single cells can be analyzed with this method, yielding large data sets which may contain poor data arising from loss of material during cell sorting or poor digestion, labeling, and lysis. To date, no tools have been described that can assess data quality prior to data processing. We present herein a lightweight python script and accompanying graphic user interface that can rapidly quantify reporter ion peaks within each MS/MS spectrum in a file. With simple summary reports, we can identify single cell samples that fail to pass a set quality threshold, thus reducing analysis time waste. In addition, this tool, Diagnostic Ion Data Analysis Reduction (DIDAR), will create reduced MGF files containing only spectra possessing a user-specified number of single cell reporter ions. By reducing the number of spectra that have excessive zero values, we can speed up sample processing with little loss in data completeness as these spectra are removed in later stages in data processing workflows. DIDAR and the DIDAR GUI are compatible with all modern operating systems and are available at: https://github.com/orsburn/DIDARSCPQC. All files described in this study are available at www.massive.ucsd.edu as accession MSV000088887.

Metabolomic, Proteomic, and Single-Cell Proteomic Analysis of Cancer Cells Treated with the KRASG12D Inhibitor MRTX1133

Mutations in KRAS are common drivers of human cancers and are often those with the poorest overall prognosis for patients. A recently developed compound, MRTX1133, has shown promise in inhibiting the activity of KRASG12D mutant proteins, which is one of the main drivers of pancreatic cancer. To better understand the mechanism of action of this compound, I performed both proteomics and metabolomics on four KRASG12D mutant pancreatic cancer cell lines. To obtain increased granularity in the proteomic observations, single-cell proteomics was successfully performed on two of these lines. Following quality filtering, a total of 1498 single cells were analyzed. From these cells, 3140 total proteins were identified with approximately 953 proteins quantified per cell. At 48 h of treatment, two distinct populations of cells can be observed based on the level of effectiveness of the drug in decreasing the total abundance of the KRAS protein in each respective cell, with results that are effectively masked in the bulk cell analysis. All mass spectrometry data and processed results are publicly available at www.massive.ucsd.edu at accessions PXD039597, PXD039601, and PXD039600.

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

Single-cell proteomics by mass spectrometry (MS) allows quantifying proteins with high specificity and sensitivity. To increase its throughput, we developed 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 with plexDIA demonstrates accurate quantification of about 3,000 - 3,700 proteins per human cell. The protocol is implemented on 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 days to prepare over 3,000 single cells. We provide metrics and software for quality control that can support the robust scaling of nPOP to higher plex reagents for achieving reliable high-throughput single-cell protein analysis.

An integrated method for single cell proteomics with simultaneous measurements of intracellular drug concentration implicates new mechanisms for adaptation to KRASG12D inhibitors

It is well established that a population of single human cells will often respond to the same drug treatment in a heterogeneous manner. In the context of chemotherapeutics, these diverse responses may lead to individual adaptation mechanisms and ultimately multiple distinct methods of resistance. The obvious question from a pharmacology perspective is how intracellular concentrations of active drug varies between individual cells, and what role does that variation play in drug response heterogeneity? To date, no integrated methods for rapidly measuring intracellular drug levels while simultaneously measuring drug responses have been described. This study describes a method for single cell preparation that allows proteins to be extracted and digested from single cells while maintaining conditions for small molecules to be simultaneously measured. The method as described allows up to 40 cells to be analyzed per instrument per day. When applied to a KRASG12D small molecule inhibitor I observe a wide degree of intracellular levels of the drug, and that proteomic responses largely stratify based on the concentration of drug within each single cell. Further work is in progress to develop and standardize this method and - more importantly - to normalize drug measurements against direct measurements of cell volume. However, these preliminary results appear promising for the identification of single cells with unique drug response mechanisms. All data described in this study has been made publicly available through the ProteomeXchange consortium under accession PXD046002.

Modeling and interpretation of single-cell proteogenomic data

Biological functions stem from coordinated interactions among proteins, nucleic acids and small molecules. Mass spectrometry technologies for reliable, high throughput single-cell proteomics will add a new modality to genomics and enable data-driven modeling of the molecular mechanisms coordinating proteins and nucleic acids at single-cell resolution. This promising potential requires estimating the reliability of measurements and computational analysis so that models can distinguish biological regulation from technical artifacts. We highlight different measurement modes that can support single-cell proteogenomic analysis and how to estimate their reliability. We then discuss approaches for developing both abstract and mechanistic models that aim to biologically interpret the measured differences across modalities, including specific applications to directed stem cell differentiation and to inferring protein interactions in cancer cells from the buffing of DNA copy-number variations. Single-cell proteogenomic data will support mechanistic models of direct molecular interactions that will provide generalizable and predictive representations of biological systems.

50 shades of oxidative stress: A state-specific cysteine redox pattern hypothesis

Oxidative stress is biochemically complex. Like primary colours, specific reactive oxygen species (ROS) and antioxidant inputs can be mixed to create unique "shades" of oxidative stress. Even a minimal redox module comprised of just 12 (ROS & antioxidant) inputs and 3 outputs (oxidative damage, cysteine-dependent redox-regulation, or both) yields over half a million "shades" of oxidative stress. The present paper proposes the novel hypothesis that: state-specific shades of oxidative stress, such as a discrete disease, are associated with distinct tell-tale cysteine oxidation patterns. The patterns are encoded by many parameters, from the identity of the oxidised proteins, the cysteine oxidation type, and magnitude. The hypothesis is conceptually grounded in distinct ROS and antioxidant inputs coalescing to produce unique cysteine oxidation outputs. And considers the potential biological significance of the holistic cysteine oxidation outputs. The literature supports the existence of state-specific cysteine oxidation patterns. Measuring and manipulating these patterns offer promising avenues for advancing oxidative stress research. The pattern inspired hypothesis provides a framework for understanding the complex biochemical nature of state-specific oxidative stress.

Identification and functional analysis of senescent cells in the cardiovascular system using omics approaches

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and senescent cells have emerged as key contributors to its pathogenesis. Senescent cells exhibit cell cycle arrest and secrete a range of proinflammatory factors, termed the senescence-associated secretory phenotype (SASP), which promotes tissue dysfunction and exacerbates CVD progression. Omics technologies, specifically transcriptomics and proteomics, offer powerful tools to uncover and define the molecular signatures of senescent cells in cardiovascular tissue. By analyzing the comprehensive molecular profiles of senescent cells, omics approaches can identify specific genetic alterations, gene expression patterns, protein abundances, and metabolite levels associated with senescence in CVD. These omics-based discoveries provide insights into the mechanisms underlying senescence-induced cardiovascular damage, facilitating the development of novel diagnostic biomarkers and therapeutic targets. Furthermore, integration of multiple omics data sets enables a systems-level understanding of senescence in CVD, paving the way for precision medicine approaches to prevent or treat cardiovascular aging and its associated complications.

A review of the current state of single-cell proteomics and future perspective

Single-cell methodologies and technologies have started a revolution in biology which until recently has primarily been limited to deep sequencing and imaging modalities. With the advent and subsequent torrid development of single-cell proteomics over the last 5 years, despite the fact that proteins cannot be amplified like transcripts, it has now become abundantly clear that it is a worthy complement to single-cell transcriptomics. In this review, we engage in an assessment of the current state of the art of single-cell proteomics including workflow, sample preparation techniques, instrumentation, and biological applications. We investigate the challenges associated with working with very small sample volumes and the acute need for robust statistical methods for data interpretation. We delve into what we believe is a promising future for biological research at single-cell resolution and highlight some of the exciting discoveries that already have been made using single-cell proteomics, including the identification of rare cell types, characterization of cellular heterogeneity, and investigation of signaling pathways and disease mechanisms. Finally, we acknowledge that there are a number of outstanding and pressing problems that the scientific community vested in advancing this technology needs to resolve. Of prime importance is the need to set standards so that this technology becomes widely accessible allowing novel discoveries to be easily verifiable. We conclude with a plea to solve these problems rapidly so that single-cell proteomics can be part of a robust, high-throughput, and scalable single-cell multi-omics platform that can be ubiquitously applied to elucidating deep biological insights into the diagnosis and treatment of all diseases that afflict us.

Recent methodological advances towards single-cell proteomics

Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and provide an outlook on this research field.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.

The impact of the immune system on lung injury and regeneration in COPD

COPD is a devastating respiratory condition that manifests via persistent inflammation, emphysema development and small airway remodelling. Lung regeneration is defined as the ability of the lung to repair itself after injury by the proliferation and differentiation of progenitor cell populations, and becomes impaired in the COPD lung as a consequence of cell intrinsic epithelial stem cell defects and signals from the micro-environment. Although the loss of structural integrity and lung regenerative capacity are critical for disease progression, our understanding of the cellular players and molecular pathways that hamper regeneration in COPD remains limited. Intriguingly, despite being a key driver of COPD pathogenesis, the role of the immune system in regulating lung regenerative mechanisms is understudied. In this review, we summarise recent evidence on the contribution of immune cells to lung injury and regeneration. We focus on four main axes: 1) the mechanisms via which myeloid cells cause alveolar degradation; 2) the formation of tertiary lymphoid structures and the production of autoreactive antibodies; 3) the consequences of inefficient apoptotic cell removal; and 4) the effects of innate and adaptive immune cell signalling on alveolar epithelial proliferation and differentiation. We finally provide insight on how recent technological advances in omics technologies and human ex vivo lung models can delineate immune cell-epithelium cross-talk and expedite precision pro-regenerative approaches toward reprogramming the alveolar immune niche to treat COPD.

Metabolomic, proteomic and single cell proteomic analysis of cancer cells treated with the KRASG12D inhibitor MRTX1133

Mutations in KRAS are common drivers of human cancers and are often those with the poorest overall prognosis for patients. A recently developed compound, MRTX1133, has shown promise in inhibiting the activity of KRASG12D mutant proteins, one of the main drivers in pancreatic cancer. To better understand the mechanism of action of this compound I performed both proteomics and metabolomics on four KRASG12D mutant pancreatic cancer cell lines. To obtain increased granularity in the proteomic observations, single cell proteomics was successfully performed on two of these lines. Following quality filtering, a total of 1,498 single cells were analyzed. From these cells 3,140 total proteins were identified with approximately 953 proteins quantified per cell. At 48 hours of treatment, two distinct populations of cells can be observed based on the level of effectiveness of the drug in decreasing total abundance of the KRAS protein in each respective cell, results that are effectively masked in the bulk cell analysis. All mass spectrometry data and processed results are publicly available at the www.massive.ucsd.edu at accessions PXD039597, PXD039601 and PXD039600.

Assessing the Role of Trypsin in Quantitative Plasma and Single-Cell Proteomics toward Clinical Application

Mass spectrometry-based bottom-up proteomics is rapidly evolving and routinely applied in large-scale biomedical studies. Proteases are a central component of every bottom-up proteomics experiment, digesting proteins into peptides. Trypsin has been the most widely applied protease in proteomics due to its characteristics. With ever-larger cohort sizes and possible future clinical application of mass spectrometry-based proteomics, the technical impact of trypsin becomes increasingly relevant. To assess possible biases introduced by trypsin digestion, we evaluated the impact of eight commercially available trypsins in a variety of bottom-up proteomics experiments and across a range of protease concentrations and storage times. To investigate the universal impact of these technical attributes, we included bulk HeLa cell lysate, human plasma, and single HEK293 cells, which were analyzed over a range of selected reaction monitoring (SRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) instrument methods on three LC-MS instruments. The quantification methods employed encompassed both label-free approaches and absolute quantification utilizing spike-in heavy-labeled recombinant protein fragment standards. Based on this extensive data set, we report variations between commercial trypsins, their source, and their concentration. Furthermore, we provide suggestions on the handling of trypsin in large-scale studies.

Oit3, a promising hallmark gene for targeting liver sinusoidal endothelial cells

Liver sinusoidal endothelial cells (LSECs) play a pivotal role in maintaining liver homeostasis and influencing the pathological processes of various liver diseases. However, neither LSEC-specific hallmark genes nor a LSEC promoter-driven Cre mouse line has been introduced before, which largely restricts the study of liver diseases with vascular disorders. To explore LSEC-specific hallmark genes, we compared the top 50 marker genes between liver endothelial cells (ECs) and liver capillary ECs and identified 18 overlapping genes. After excluding globally expressed genes and those with low expression percentages, we narrowed our focus to two final candidates: Oit3 and Dnase1l3. Through single-cell RNA sequencing (scRNA-seq) and analysis of the NCBI database, we confirmed the extrahepatic expression of Dnase1l3. The paired-cell sequencing data further demonstrated that Oit3 was predominantly expressed in the midlobular liver ECs. Subsequently, we constructed inducible Oit3-CreERT2 transgenic mice, which were further crossed with ROSA26-tdTomato mice. Microscopy validated that the established Oit3-CreERT2-tdTomato mice exhibited significant fluorescence in the liver rather than in other organs. The staining analysis confirmed the colocalization of tdTomato and EC markers. Ex-vivo experiments further confirmed that isolated tdTomato+ cells exhibited well-differentiated fenestrae and highly expressed EC markers, confirming their identity as LSECs. Overall, Oit3 is a promising hallmark gene for tracing LSECs. The establishment of Oit3-CreERT2-tdTomato mice provides a valuable model for studying the complexities of LSECs in liver diseases.

Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics

Missing values are a notable challenge when analyzing mass spectrometry-based proteomics data. While the field is still actively debating the best practices, the challenge increased with the emergence of mass spectrometry-based single-cell proteomics and the dramatic increase in missing values. A popular approach to deal with missing values is to perform imputation. Imputation has several drawbacks for which alternatives exist, but currently, imputation is still a practical solution widely adopted in single-cell proteomics data analysis. This perspective discusses the advantages and drawbacks of imputation. We also highlight 5 main challenges linked to missing value management in single-cell proteomics. Future developments should aim to solve these challenges, whether it is through imputation or data modeling. The perspective concludes with recommendations for reporting missing values, for reporting methods that deal with missing values, and for proper encoding of missing values.

SCP Viz - A universal graphical user interface for single protein analysis in single cell proteomics datasets

Single cell proteomics (SCP) requires the analysis of dozens to thousands of single human cells to draw biological conclusions. However, assessing of the abundance of single proteins in output data presents a considerable challenge, and no simple universal solutions currently exist. To address this, we developed SCP Viz, a statistical package with a graphical user interface that can handle small and large scale SCP output from any instrument or data processing software. In this software, the abundance of individual proteins can be plotted in a variety of ways, using either unadjusted or normalized outputs. These outputs can also be transformed or imputed within the software. SCP Viz offers a variety of plotting options which can help identify significantly altered proteins between groups, both before and after quantitative transformations. Upon the discovery of subpopulations of single cells, users can easily regroup the cells of interest using straightforward text-based filters. When used in this way, SCP Viz allows users to visualize proteomic heterogeneity at the level of individual proteins, cells, or identified subcellular populations. SCP Viz is compatible with output files from MaxQuant, FragPipe, SpectroNaut, and Proteome Discoverer, and should work equally well with other formats. SCP Viz is publicly available at https://github.com/orsburn/SCPViz. For demonstrations, users can download our test data from GitHub and use an online version that accepts user input for analysis at https://orsburnlab.shinyapps.io/SCPViz/.

An adult clock component links circadian rhythms to pancreatic β-cell maturation

How ubiquitous circadian clocks orchestrate tissue-specific outputs is not well understood. Pancreatic β cell-autonomous clocks attune insulin secretion to daily energy cycles, and desynchrony from genetic or behavioral disruptions raises type 2 diabetes risk. We show that the transcription factor DEC1, a clock component induced in adult β cells, coordinates their glucose responsiveness by synchronizing energy metabolism and secretory gene oscillations. Dec1-ablated mice develop lifelong hypo-insulinemic diabetes, despite normal islet formation and intact circadian Clock and Bmal1 activators. DEC1, but not CLOCK/BMAL1, binds maturity-linked genes that mediate respiratory metabolism and insulin exocytosis, and Dec1 loss disrupts their transcription synchrony. Accordingly, β-cell Dec1 ablation causes hypo-insulinemia due to immature glucose responsiveness, dampening insulin rhythms. Thus, Dec1 links circadian clockwork to the β-cell maturation process, aligning metabolism to diurnal energy cycles.

Considerations for reproducible omics in aging research

Technical advancements over the past two decades have enabled the measurement of the panoply of molecules of cells and tissues including transcriptomes, epigenomes, metabolomes and proteomes at unprecedented resolution. Unbiased profiling of these molecular landscapes in the context of aging can reveal important details about mechanisms underlying age-related functional decline and age-related diseases. However, the high-throughput nature of these experiments creates unique analytical and design demands for robustness and reproducibility. In addition, 'omic' experiments are generally onerous, making it crucial to effectively design them to eliminate as many spurious sources of variation as possible as well as account for any biological or technical parameter that may influence such measures. In this Perspective, we provide general guidelines on best practices in the design and analysis of omic experiments in aging research from experimental design to data analysis and considerations for long-term reproducibility and validation of such studies.

Reverse-ChIP Techniques for Identifying Locus-Specific Proteomes: A Key Tool in Unlocking the Cancer Regulome

A phenotypic hallmark of cancer is aberrant transcriptional regulation. Transcriptional regulation is controlled by a complicated array of molecular factors, including the presence of transcription factors, the deposition of histone post-translational modifications, and long-range DNA interactions. Determining the molecular identity and function of these various factors is necessary to understand specific aspects of cancer biology and reveal potential therapeutic targets. Regulation of the genome by specific factors is typically studied using chromatin immunoprecipitation followed by sequencing (ChIP-Seq) that identifies genome-wide binding interactions through the use of factor-specific antibodies. A long-standing goal in many laboratories has been the development of a 'reverse-ChIP' approach to identify unknown binding partners at loci of interest. A variety of strategies have been employed to enable the selective biochemical purification of sequence-defined chromatin regions, including single-copy loci, and the subsequent analytical detection of associated proteins. This review covers mass spectrometry techniques that enable quantitative proteomics before providing a survey of approaches toward the development of strategies for the purification of sequence-specific chromatin as a 'reverse-ChIP' technique. A fully realized reverse-ChIP technique holds great potential for identifying cancer-specific targets and the development of personalized therapeutic regimens.

Single-cell proteomics: quantifying post-transcriptional regulation during development with mass-spectrometry

Many developmental processes are regulated post-transcriptionally. Such post-transcriptional regulatory mechanisms can now be analyzed by robust single-cell mass spectrometry methods that allow accurate quantification of proteins and their modification in single cells. These methods can enable quantitative exploration of protein synthesis and degradation mechanisms that contribute to developmental cell fate specification. Furthermore, they may support functional analysis of protein conformations and activities in single cells, and thus link protein functions to developmental processes. This Spotlight provides an accessible introduction to single-cell mass spectrometry methods and suggests initial biological questions that are ripe for investigation.