Post-transcriptional regulation across human tissues

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.

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.

Proteomic profiling of interferon-responsive reactive astrocytes in rodent and human

Astrocytes are a heterogeneous population of central nervous system glial cells that respond to pathological insults and injury by undergoing a transformation called "reactivity." Reactive astrocytes exhibit distinct and context-dependent cellular, molecular, and functional state changes that can either support or disturb tissue homeostasis. We recently identified a reactive astrocyte sub-state defined by interferon-responsive genes like Igtp, Ifit3, Mx1, and others, called interferon-responsive reactive astrocytes (IRRAs). To further this transcriptomic definition of IRRAs, we wanted to define the proteomic changes that occur in this reactive sub-state. We induced IRRAs in immunopanned rodent astrocytes and human iPSC-differentiated astrocytes using TNF, IL1α, C1Q, and IFNβ and characterized their proteomic profile (both cellular and secreted) using unbiased quantitative proteomics. We identified 2335 unique cellular proteins, including IFIT2/3, IFITM3, OASL1/2, MX1/2/3, and STAT1. We also report that rodent and human IRRAs secrete PAI1, a serine protease inhibitor which may influence reactive states and functions of nearby cells. Finally, we evaluated how IRRAs are distinct from neurotoxic reactive astrocytes (NRAs). While NRAs are described by expression of the complement protein C3, it was not upregulated in IRRAs. Instead, we found ~90 proteins unique to IRRAs not identified in NRAs, including OAS1A, IFIT3, and MX1. Interferon signaling in astrocytes is critical for the antiviral immune response and for regulating synaptic plasticity and glutamate transport mechanisms. How IRRAs contribute to these functions is unknown. This study provides the basis for future experiments to define the functional roles of IRRAs in the context of neurodegenerative disorders.

Comparative Analysis of Molecular Landscape in Mouse Models and Patients Reveals Conserved Inflammation Pathways in Age-Related Macular Degeneration

Purpose

The purpose of this study was to identify key genes and their regulatory networks that are conserved in mouse models of age-related macular degeneration (AMD) and human AMD.

Methods

Retinal RNA-Seq was performed in laser-induced choroidal neovascularization (CNV) mice at day 3 and day 7 after photocoagulation. Mass spectrometry-based proteomic analysis was performed with retinas collected at day 3. Retinal RNA-Seq data was further compared among mouse models of laser-induced CNV and NaIO3-induced retinal degeneration (RD) and a large AMD cohort.

Results

Retinal RNA-Seq revealed upregulated genes and pathways related to innate immunity and inflammation in mice with CNV, with more profound changes at the early stage (day 3). Proteomic analysis further validated these differentially expressed genes and their networks in retinal inflammation during CNV. Notably, the most evident overlap in the retina of mice with laser-induced CNV and NaIO3-induced RD was the upregulation of inflammation-related genes, pointing to a common vital role of retinal inflammation in the early stage for both mouse AMD models. Further comparative transcriptomic analysis of the mouse AMD models and human AMD identified 48 conserved genes mainly involved in inflammation response. Among them, B2M, C3, and SERPING1 were upregulated in all stages of human AMD and the mouse AMD models compared to controls.

Conclusions

Our study demonstrates conserved molecular changes related to retinal inflammation in mouse AMD models and human AMD and provides new insight into the translational application of these mouse models in studying AMD mechanisms and treatments.

iProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis

Large-scale omics studies have generated a wealth of mass spectrometry-based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature-related, and correlation analyses, including protein-protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein-protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.

A High-Throughput PIXUL-Matrix-Based Toolbox to Profile Frozen and Formalin-Fixed Paraffin-Embedded Tissues Multiomes

Large-scale high-dimensional multiomics studies are essential to unravel molecular complexity in health and disease. We developed an integrated system for tissue sampling (CryoGrid), analytes preparation (PIXUL), and downstream multiomic analysis in a 96-well plate format (Matrix), MultiomicsTracks96, which we used to interrogate matched frozen and formalin-fixed paraffin-embedded (FFPE) mouse organs. Using this system, we generated 8-dimensional omics data sets encompassing 4 molecular layers of intracellular organization: epigenome (H3K27Ac, H3K4m3, RNA polymerase II, and 5mC levels), transcriptome (messenger RNA levels), epitranscriptome (m6A levels), and proteome (protein levels) in brain, heart, kidney, and liver. There was a high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles confirmed known organ-specific superenhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic profiles, known to be poorly correlated with transcriptomic data, can be more accurately predicted by the full suite of multiomics data, compared with using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Loss of the DYRK1A Protein Kinase Results in the Reduction in Ribosomal Protein Gene Expression, Ribosome Mass and Reduced Translation

Ribosomal proteins (RPs) are evolutionary conserved proteins that are essential for protein translation. RP expression must be tightly regulated to ensure the appropriate assembly of ribosomes and to respond to the growth demands of cells. The elements regulating the transcription of RP genes (RPGs) have been characterized in yeast and Drosophila, yet how cells regulate the production of RPs in mammals is less well understood. Here, we show that a subset of RPG promoters is characterized by the presence of the palindromic TCTCGCGAGA motif and marked by the recruitment of the protein kinase DYRK1A. The presence of DYRK1A at these promoters is associated with the enhanced binding of the TATA-binding protein, TBP, and it is negatively correlated with the binding of the GABP transcription factor, establishing at least two clusters of RPGs that could be coordinately regulated. However, DYRK1A silencing leads to a global reduction in RPGs mRNAs, pointing at DYRK1A activities beyond those dependent on its chromatin association. Significantly, cells in which DYRK1A is depleted have reduced RP levels, fewer ribosomes, reduced global protein synthesis and a smaller size. We therefore propose a novel role for DYRK1A in coordinating the expression of genes encoding RPs, thereby controlling cell growth in mammals.

Impact of Peptide Sequence on Functional siRNA Delivery and Gene Knockdown with Cyclic Amphipathic Peptide Delivery Agents

Short-interfering RNA (siRNA) oligonucleotide therapeutics that modify gene expression by accessing RNA-interference (RNAi) pathways have great promise for the treatment of a range of disorders; however, their application in clinical settings has been limited by significant challenges in cellular delivery. Herein, we report a structure-function study using a series of modified cyclic amphipathic cell-penetrating peptides (CAPs) to determine the impact of peptide sequence on (1) siRNA-binding efficiency, (2) cellular delivery and knockdown efficiency, and (3) the endocytic uptake mechanism. Nine cyclic peptides of the general sequence Ac-C[XZ]4CG-NH2 in which X residues are hydrophobic/aromatic (Phe, Tyr, Trp, or Leu) and Z residues are charged/hydrophilic (Arg, Lys, Ser, or Glu) are assessed along with one acyclic peptide, Ac-(WR)4G-NH2. Cyclization is enforced by intramolecular disulfide bond formation between the flanking Cys residues. Binding analyses indicate that strong cationic character and the presence of aromatic residues that are competent to participate in CH-π interactions lead to CAP sequences that most effectively interact with siRNA. CAP-siRNA binding increases in the following order as a function of CAP hydrophobic/aromatic content: His < Phe < Tyr < Trp. Both cationic charge and disulfide-constrained cyclization of CAPs improve uptake of siRNA in vitro. Net neutral CAPs and an acyclic peptide demonstrate less-efficient siRNA translocation compared to the cyclic, cationic CAPs tested. All CAPs tested facilitated efficient siRNA target gene knockdown of at least 50% (as effective as a lipofectamine control), with the best CAPs enabling >80% knockdown. Significantly, gene knockdown efficiency does not strongly correlate with CAP-siRNA internalization efficiency but moderately correlates with CAP-siRNA-binding affinity. Finally, utilization of small-molecule inhibitors and targeted knockdown of essential endocytic pathway proteins indicate that most CAP-siRNA nanoparticles facilitate siRNA delivery through clathrin- and caveolin-mediated endocytosis. These results provide insight into the design principles for CAPs to facilitate siRNA delivery and the mechanisms by which these peptides translocate siRNA into cells. These studies also demonstrate the nature of the relationships between peptide-siRNA binding, cellular delivery of siRNA cargo, and functional gene knockdown. Strong correlations between these properties are not always observed, which illustrates the complexity in the design of optimal next-generation materials for oligonucleotide delivery.

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

An interferon gamma response signature links myocardial aging and immunosenescence

AIMS

Aging entails profound immunological transformations that can impact myocardial homeostasis and predispose to heart failure. However, preclinical research in the immune-cardiology field is mostly conducted in young healthy animals, which potentially weakens its translational relevance. Herein, we sought to investigate how the aging T-cell compartment associates with changes in myocardial cell biology in aged mice.

METHODS AND RESULTS

We phenotyped the antigen-experienced effector/memory T cells purified from heart-draining lymph nodes of 2-, 6-, 12-, and 18-month-old C57BL/6J mice using single-cell RNA/T cell receptor sequencing. Simultaneously, we profiled all non-cardiomyocyte cell subsets purified from 2- to 18-month-old hearts and integrated our data with publicly available cardiomyocyte single-cell sequencing datasets. Some of these findings were confirmed at the protein level by flow cytometry. With aging, the heart-draining lymph node and myocardial T cells underwent clonal expansion and exhibited an up-regulated pro-inflammatory transcription signature, marked by an increased interferon-γ (IFN-γ) production. In parallel, all major myocardial cell populations showed increased IFN-γ responsive signature with aging. In the aged cardiomyocytes, a stronger IFN-γ response signature was paralleled by the dampening of expression levels of transcripts related to most metabolic pathways, especially oxidative phosphorylation. Likewise, induced pluripotent stem cells-derived cardiomyocytes exposed to chronic, low grade IFN-γ treatment showed a similar inhibition of metabolic activity.

CONCLUSIONS

By investigating the paired age-related alterations in the T cells found in the heart and its draining lymph nodes, we provide evidence for increased myocardial IFN-γ signaling with age, which is associated with inflammatory and metabolic shifts typically seen in heart failure.

Workability of mRNA Sequencing for Predicting Protein Abundance

Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome-proteome levels for different tissues does not exceed 0.3-0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction.

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.

Gene expression flux analysis reveals specific regulatory modalities of gene expression

The level of a given protein is determined by the synthesis and degradation rates of its mRNA and protein. While several studies have quantified the contribution of different gene expression steps in regulating protein levels, these are limited by using equilibrium approximations in out-of-equilibrium biological systems. Here, we introduce gene expression flux analysis to quantitatively dissect the dynamics of the expression level for specific proteins and use it to analyze published transcriptomics and proteomics datasets. Our analysis reveals distinct regulatory modalities shared by sets of genes with clear functional signatures. We also find that protein degradation plays a stronger role than expected in the adaptation of protein levels. These findings suggest that shared regulatory strategies can lead to versatile responses at the protein level and highlight the importance of going beyond equilibrium approximations to dissect the quantitative contribution of different steps of gene expression to protein dynamics.

An integrated proteome and transcriptome of B cell maturation defines poised activation states of transitional and mature B cells

During B cell maturation, transitional and mature B cells acquire cell-intrinsic features that determine their ability to exit quiescence and mount effective immune responses. Here we use label-free proteomics to quantify the proteome of B cell subsets from the mouse spleen and map the differential expression of environmental sensing, transcription, and translation initiation factors that define cellular identity and function. Cross-examination of the full-length transcriptome and proteome identifies mRNAs related to B cell activation and antibody secretion that are not accompanied by detection of the encoded proteins. In addition, proteomic data further suggests that the translational repressor PDCD4 restrains B cell responses, in particular those from marginal zone B cells, to a T-cell independent antigen. In summary, our molecular characterization of B cell maturation presents a valuable resource to further explore the mechanisms underpinning the specialized functions of B cell subsets, and suggest the presence of 'poised' mRNAs that enable expedited B cell responses.

Simultaneous measurement of nascent transcriptome and translatome using 4-thiouridine metabolic RNA labeling and translating ribosome affinity purification

Regulation of gene expression in response to various biological processes, including extracellular stimulation and environmental adaptation requires nascent RNA synthesis and translation. Analysis of the coordinated regulation of dynamic RNA synthesis and translation is required to determine functional protein production. However, reliable methods for the simultaneous measurement of nascent RNA synthesis and translation at the gene level are limited. Here, we developed a novel method for the simultaneous assessment of nascent RNA synthesis and translation by combining 4-thiouridine (4sU) metabolic RNA labeling and translating ribosome affinity purification (TRAP) using a monoclonal antibody against evolutionarily conserved ribosomal P-stalk proteins. The P-stalk-mediated TRAP (P-TRAP) technique recovered endogenous translating ribosomes, allowing easy translatome analysis of various eukaryotes. We validated this method in mammalian cells by demonstrating that acute unfolded protein response (UPR) in the endoplasmic reticulum (ER) induces dynamic reprogramming of nascent RNA synthesis and translation. Our nascent P-TRAP (nP-TRAP) method may serve as a simple and powerful tool for analyzing the coordinated regulation of transcription and translation of individual genes in various eukaryotes.

On the Decoupling of Evolutionary Changes in mRNA and Protein Levels

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here, we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level; this observation held true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and the translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic data.

Antibody reliability influences observed mRNA-protein correlations in tumour samples

Reverse phase protein arrays (RPPA) have been used to quantify the abundance of hundreds of proteins across thousands of tumour samples in the Cancer Genome Atlas. By number of samples, this is the largest tumour proteomic dataset available and it provides an opportunity to systematically assess the correlation between mRNA and protein abundances. However, the RPPA approach is highly dependent on antibody reliability and approximately one-quarter of the antibodies used in the the Cancer Genome Atlas are deemed to be somewhat less reliable. Here, we assess the impact of antibody reliability on observed mRNA-protein correlations. We find that, in general, proteins measured with less reliable antibodies have lower observed mRNA-protein correlations. This is not true of the same proteins when measured using mass spectrometry. Furthermore, in cell lines, we find that when the same protein is quantified by both mass spectrometry and RPPA, the overall correlation between the two measurements is lower for proteins measured with less reliable antibodies. Overall our results reinforce the need for caution in using RPPA measurements from less reliable antibodies.

Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-iid) Data

Natural science datasets frequently violate assumptions of independence. Samples may be clustered (e.g., by study site, subject, or experimental batch), leading to spurious associations, poor model fitting, and confounded analyses. While largely unaddressed in deep learning, this problem has been handled in the statistics community through mixed effects models, which separate cluster-invariant fixed effects from cluster-specific random effects. We propose a general-purpose framework for Adversarially-Regularized Mixed Effects Deep learning (ARMED) models through non-intrusive additions to existing neural networks: 1) an adversarial classifier constraining the original model to learn only cluster-invariant features, 2) a random effects subnetwork capturing cluster-specific features, and 3) an approach to apply random effects to clusters unseen during training. We apply ARMED to dense, convolutional, and autoencoder neural networks on 4 datasets including simulated nonlinear data, dementia prognosis and diagnosis, and live-cell image analysis. Compared to prior techniques, ARMED models better distinguish confounded from true associations in simulations and learn more biologically plausible features in clinical applications. They can also quantify inter-cluster variance and visualize cluster effects in data. Finally, ARMED matches or improves performance on data from clusters seen during training (5-28% relative improvement) and generalization to unseen clusters (2-9% relative improvement) versus conventional models.

Synthetic Adrenocorticotropic Peptides Modulate the Expression Pattern of Immune Genes in Rat Brain following the Early Post-Stroke Period

Ischemic stroke is an acute local decrease in cerebral blood flow due to a thrombus or embolus. Of particular importance is the study of the genetic systems that determine the mechanisms underlying the formation and maintenance of a therapeutic window (a time interval of up to 6 h after a stroke) when effective treatment can be provided. Here, we used a transient middle cerebral artery occlusion (tMCAO) model in rats to study two synthetic derivatives of adrenocorticotropic hormone (ACTH). The first was ACTH(4-7)PGP, which is known as Semax. It is actively used as a neuroprotective drug. The second was the ACTH(6-9)PGP peptide, which is elucidated as a prospective agent only. Using RNA-Seq analysis, we revealed hundreds of ischemia-related differentially expressed genes (DEGs), as well as 131 and 322 DEGs related to the first and second peptide at 4.5 h after tMCAO, respectively, in dorsolateral areas of the frontal cortex of rats. Furthermore, we showed that both Semax and ACTH(6-9)PGP can partially prevent changes in the immune- and neurosignaling-related gene expression profiles disturbed by the action of ischemia at 4.5 h after tMCAO. However, their different actions with regard to predominantly immune-related genes were also revealed. This study gives insight into how the transcriptome depends on the variation in the structure of the related peptides, and it is valuable from the standpoint of the development of measures for early post-stroke therapy.

Unveiling the complexity of transcription factor networks in hematopoietic stem cells: implications for cell therapy and hematological malignancies

The functionality and longevity of hematopoietic tissue is ensured by a tightly controlled balance between self-renewal, quiescence, and differentiation of hematopoietic stem cells (HSCs) into the many different blood lineages. Cell fate determination in HSCs is influenced by signals from extrinsic factors (e.g., cytokines, irradiation, reactive oxygen species, O2 concentration) that are translated and integrated by intrinsic factors such as Transcription Factors (TFs) to establish specific gene regulatory programs. TFs also play a central role in the establishment and/or maintenance of hematological malignancies, highlighting the need to understand their functions in multiple contexts. TFs bind to specific DNA sequences and interact with each other to form transcriptional complexes that directly or indirectly control the expression of multiple genes. Over the past decades, significant research efforts have unraveled molecular programs that control HSC function. This, in turn, led to the identification of more than 50 TF proteins that influence HSC fate. However, much remains to be learned about how these proteins interact to form molecular networks in combination with cofactors (e.g. epigenetics factors) and how they control differentiation, expansion, and maintenance of cellular identity. Understanding these processes is critical for future applications particularly in the field of cell therapy, as this would allow for manipulation of cell fate and induction of expansion, differentiation, or reprogramming of HSCs using specific cocktails of TFs. Here, we review recent findings that have unraveled the complexity of molecular networks controlled by TFs in HSCs and point towards possible applications to obtain functional HSCs ex vivo for therapeutic purposes including hematological malignancies. Furthermore, we discuss the challenges and prospects for the derivation and expansion of functional adult HSCs in the near future.

Transcriptomic and Proteomic Analyses of Myzus persicae Carrying Brassica Yellows Virus

Viruses in the genus Polerovirus infect a wide range of crop plants and cause severe economic crop losses. BrYV belongs to the genus Polerovirus and is transmitted by Myzus persicae. However, the changes in transcriptome and proteome profiles of M. persicae during viral infection are unclear. Here, RNA-Seq and TMT-based quantitative proteomic analysis were performed to compare the differences between viruliferous and nonviruliferous aphids. In total, 1266 DEGs were identified at the level of transcription with 980 DEGs being upregulated and 286 downregulated in viruliferous aphids. At the protein level, among the 18 DEPs identified, the number of upregulated proteins in viruliferous aphids was twice that of the downregulated DEPs. Enrichment analysis indicated that these DEGs and DEPs were mainly involved in epidermal protein synthesis, phosphorylation, and various metabolic processes. Interestingly, the expressions of a number of cuticle proteins and tubulins were upregulated in viruliferous aphids. Taken together, our study revealed the complex regulatory network between BrYV and its vector M. persicae from the perspective of omics. These findings should be of great benefit to screening key factors involved in the process of virus circulation in aphids and provide new insights for BrYV prevention via vector control in the field.

Integration of a multi-omics stem cell differentiation dataset using a dynamical model

Stem cell differentiation is a highly dynamic process involving pervasive changes in gene expression. The large majority of existing studies has characterized differentiation at the level of individual molecular profiles, such as the transcriptome or the proteome. To obtain a more comprehensive view, we measured protein, mRNA and microRNA abundance during retinoic acid-driven differentiation of mouse embryonic stem cells. We found that mRNA and protein abundance are typically only weakly correlated across time. To understand this finding, we developed a hierarchical dynamical model that allowed us to integrate all data sets. This model was able to explain mRNA-protein discordance for most genes and identified instances of potential microRNA-mediated regulation. Overexpression or depletion of microRNAs identified by the model, followed by RNA sequencing and protein quantification, were used to follow up on the predictions of the model. Overall, our study shows how multi-omics integration by a dynamical model could be used to nominate candidate regulators.

Discovery of Oogenesis Biomarkers from Mouse Oocytes Using a Single-Cell Proteomics Approach

We established an efficient and simplified single-cell proteomics (ES-SCP) workflow to realize proteomics profiling at the single-oocyte level. With the ES-SCP workflow, we constructed a deep coverage proteome library during oocyte maturation, which contained more than 6000 protein groups, and identified and quantified more than 4000 protein groups from a pool of only 15 oocytes at germinal vesicle (GV), GV breakdown (GVBD), and metaphase II (MII) stages. More than 1500 protein groups can be identified from single oocytes. We found that marker proteins including maternal factors and mRNA regulators, such as ZAR1, TLE6, and BTG4, showed significant variations in abundance during oocyte maturation, and it was discovered that maternal mRNA degradation was indispensable during oocyte maturation. Proteomics analysis from single oocytes revealed that changes in antioxidant factors, maternal factors, mRNA stabilization, and energy metabolism were the factors that affect the oocyte quality during ovary aging. Our data laid the foundation for future innovations in assisted reproduction.

Decoupling of evolutionary changes in mRNA and protein levels

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level, which is true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and its translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic studies.

MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes

Background

The multiome is an integrated assembly of distinct classes of molecules and molecular properties, or "omes," measured in the same biospecimen. Freezing and formalin-fixed paraffin-embedding (FFPE) are two common ways to store tissues, and these practices have generated vast biospecimen repositories. However, these biospecimens have been underutilized for multi-omic analysis due to the low throughput of current analytical technologies that impede large-scale studies.

Methods

Tissue sampling, preparation, and downstream analysis were integrated into a 96-well format multi-omics workflow, MultiomicsTracks96. Frozen mouse organs were sampled using the CryoGrid system, and matched FFPE samples were processed using a microtome. The 96-well format sonicator, PIXUL, was adapted to extract DNA, RNA, chromatin, and protein from tissues. The 96-well format analytical platform, Matrix, was used for chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), methylated RNA immunoprecipitation (MeRIP), and RNA reverse transcription (RT) assays followed by qPCR and sequencing. LC-MS/MS was used for protein analysis. The Segway genome segmentation algorithm was used to identify functional genomic regions, and linear regressors based on the multi-omics data were trained to predict protein expression.

Results

MultiomicsTracks96 was used to generate 8-dimensional datasets including RNA-seq measurements of mRNA expression; MeRIP-seq measurements of m6A and m5C; ChIP-seq measurements of H3K27Ac, H3K4m3, and Pol II; MeDIP-seq measurements of 5mC; and LC-MS/MS measurements of proteins. We observed high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles (ChIP-seq: H3K27Ac, H3K4m3, Pol II; MeDIP-seq: 5mC) was able to recapitulate and predict organ-specific super-enhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic expression profiles can be more accurately predicted by the full suite of multi-omics data, compared to using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Conclusions

The MultiomicsTracks96 workflow is well suited for high dimensional multi-omics studies - for instance, multiorgan animal models of disease, drug toxicities, environmental exposure, and aging as well as large-scale clinical investigations involving the use of biospecimens from existing tissue repositories.

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

Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .

Using protein-per-mRNA differences among human tissues in codon optimization

BACKGROUND

Codon usage and nucleotide composition of coding sequences have profound effects on protein expression. However, while it is recognized that different tissues have distinct tRNA profiles and codon usages in their transcriptomes, the effect of tissue-specific codon optimality on protein synthesis remains elusive.

RESULTS

We leverage existing state-of-the-art transcriptomics and proteomics datasets from the GTEx project and the Human Protein Atlas to compute the protein-to-mRNA ratios of 36 human tissues. Using this as a proxy of translational efficiency, we build a machine learning model that identifies codons enriched or depleted in specific tissues. We detect two clusters of tissues with an opposite pattern of codon preferences. We then use these identified patterns for the development of CUSTOM, a codon optimizer algorithm which suggests a synonymous codon design in order to optimize protein production in a tissue-specific manner. In human cell-line models, we provide evidence that codon optimization should take into account particularities of the translational machinery of the tissues in which the target proteins are expressed and that our approach can design genes with tissue-optimized expression profiles.

CONCLUSIONS

We provide proof-of-concept evidence that codon preferences exist in tissue-specific protein synthesis and demonstrate its application to synthetic gene design. We show that CUSTOM can be of benefit in biological and biotechnological applications, such as in the design of tissue-targeted therapies and vaccines.

Increasing the throughput of sensitive proteomics by plexDIA

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

Exploring functional protein covariation across single cells using nPOP

BACKGROUND

Many biological processes, such as cell division cycle and drug resistance, are reflected in protein covariation across single cells. This covariation can be quantified and interpreted by single-cell mass spectrometry with sufficiently high throughput and accuracy.

RESULTS

Here, we describe nPOP, a method that enables simultaneous sample preparation of thousands of single cells, including lysing, digesting, and labeling individual cells in volumes of 8-20 nl. nPOP uses piezo acoustic dispensing to isolate individual cells in 300 pl volumes and performs all subsequent sample preparation steps in small droplets on a fluorocarbon-coated glass slide. Protein covariation analysis identifies cell cycle dynamics that are similar and dynamics that differ between cell types, even within subpopulations of melanoma cells delineated by markers for drug resistance priming. Melanoma cells expressing these markers accumulate in the G1 phase of the cell cycle, display distinct protein covariation across the cell cycle, accumulate glycogen, and have lower abundance of glycolytic enzymes. The non-primed melanoma cells exhibit gradients of protein abundance, suggesting transition states. Within this subpopulation, proteins functioning in oxidative phosphorylation covary with each other and inversely with proteins functioning in glycolysis. This protein covariation suggests divergent reliance on energy sources and its association with other biological functions. These results are validated by different mass spectrometry methods.

CONCLUSIONS

nPOP enables flexible, automated, and highly parallelized sample preparation for single-cell proteomics. This allows for quantifying protein covariation across thousands of single cells and revealing functionally concerted biological differences between closely related cell states. Support for nPOP is available at https://scp.slavovlab.net/nPOP .

Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier

Single-cell proteomics analysis requires sensitive, quantitatively accurate, widely accessible, and robust methods. To meet these requirements, the Single-Cell ProtEomics (SCoPE2) protocol was developed as a second-generation method for quantifying hundreds to thousands of proteins from limited samples, down to the level of a single cell. Experiments using this method have achieved quantifying over 3,000 proteins across 1,500 single mammalian cells (500-1,000 proteins per cell) in 10 days of mass spectrometer instrument time. SCoPE2 leverages a freeze-heat cycle for cell lysis, obviating the need for clean-up of single cells and consequently reducing sample losses, while expediting sample preparation and simplifying its automation. Additionally, the method uses an isobaric carrier, which aids protein identification and reduces sample losses. This video protocol provides detailed guidance to enable the adoption of automated single-cell protein analysis using only equipment and reagents that are widely accessible. We demonstrate critical steps in the procedure of preparing single cells for proteomic analysis, from harvesting up to injection to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Additionally, viewers are guided through the principles of experimental design with the isobaric carrier, quality control for both isobaric carrier and single-cell preparations, and representative results with a discussion of limitations of the approach.

Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners

Protein and mRNA levels correlate only moderately. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. Here we examined the contributions of input features in protein abundance prediction models. Using large proteogenomics data from 8 cancer types within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set, we trained models to predict the abundance of over 13,000 proteins using matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and compared predictive performances across algorithms, data set sizes, and input features. Over one-third of proteins (4,648) showed relatively poor predictability (elastic net r ≤ 0.3) from their cognate transcripts. Moreover, we found widespread occurrences where the abundance of a protein is considerably less well explained by its own cognate transcript level than that of one or more trans locus transcripts. The incorporation of additional trans-locus transcript abundance data as input features increasingly improved the ability to predict sample protein abundance. Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known or predicted interaction partners of the protein of interest, including not only large multi-protein complexes as previously shown, but also small stable complexes in the proteome with only one or few stable interacting partners. Network analysis further shows a complex proteome-wide interdependency of protein abundance on the transcript levels of multiple interacting partners. The predictive model analysis here therefore supports that protein-protein interaction including in small protein complexes exert post-transcriptional influence on proteome compositions more broadly than previously recognized. Moreover, the results suggest mRNA and protein co-expression analysis may have utility for finding gene interactions and predicting expression changes in biological systems.

Proteogenomic analysis of cancer aneuploidy and normal tissues reveals divergent modes of gene regulation across cellular pathways

How cells control gene expression is a fundamental question. The relative contribution of protein-level and RNA-level regulation to this process remains unclear. Here, we perform a proteogenomic analysis of tumors and untransformed cells containing somatic copy number alterations (SCNAs). By revealing how cells regulate RNA and protein abundances of genes with SCNAs, we provide insights into the rules of gene regulation. Protein complex genes have a strong protein-level regulation while non-complex genes have a strong RNA-level regulation. Notable exceptions are plasma membrane protein complex genes, which show a weak protein-level regulation and a stronger RNA-level regulation. Strikingly, we find a strong negative association between the degree of RNA-level and protein-level regulation across genes and cellular pathways. Moreover, genes participating in the same pathway show a similar degree of RNA- and protein-level regulation. Pathways including translation, splicing, RNA processing, and mitochondrial function show a stronger protein-level regulation while cell adhesion and migration pathways show a stronger RNA-level regulation. These results suggest that the evolution of gene regulation is shaped by functional constraints and that many cellular pathways tend to evolve one predominant mechanism of gene regulation at the protein level or at the RNA level.

Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles

Large-scale studies of human proteomes have revealed only a moderate correlation between mRNA and protein abundances. It is unclear to what extent this moderate correlation reflects post-transcriptional regulation and to what extent it reflects measurement error. Here, by analyzing replicate profiles of tumors and cell lines, we show that there is considerable variation in the reproducibility of measurements of transcripts and proteins from individual genes. Proteins with more reproducible measurements tend to have a higher mRNA-protein correlation, suggesting that measurement reproducibility accounts for a substantial fraction of the unexplained variation between mRNA and protein abundances. The reproducibility of individual proteins is somewhat consistent across studies, and we exploit this to develop an aggregate reproducibility score that explains a substantial amount of the variation in mRNA-protein correlations across multiple studies. Finally, we show that pathways previously reported to have a higher-than-average mRNA-protein correlation may simply contain members that can be more reproducibly quantified.

Absolute protein quantitation of the mouse macrophage Toll-like receptor and chemotaxis pathways

The Toll-like receptor (TLR) and chemotaxis pathways are key components of the innate immune system. Subtle variation in the concentration, timing, and molecular structure of the ligands are known to affect downstream signaling and the resulting immune response. Computational modeling and simulation at the molecular interaction level can be used to study complex biological pathways, but such simulations require protein concentration values as model parameters. Here we report the development and application of targeted mass spectrometry assays to measure the absolute abundance of proteins of the mouse macrophage Toll-like receptor 4 (TLR4) and chemotaxis pathways. Two peptides per protein were quantified, if possible. The protein abundance values ranged from 1,332 to 227,000,000 copies per cell. They moderately correlated with transcript abundance values from a previously published mouse macrophage RNA-seq dataset, and these two datasets were combined to make proteome-wide abundance estimates. The datasets produced during this investigation can be used for pathway modeling and simulation, as well as for other studies of the TLR and chemotaxis pathways.

Measurement(s)	molecules per cell
Technology Type(s)	nanoflow high-performance liquid chromatography-electrospray ionisation tandem mass spectrometry
Sample Characteristic - Organism	Mus musculus

A Proteomic Approach to Study the Biological Role of Hepatitis C Virus Protein Core+1/ARFP

Hepatitis C virus is the major cause of chronic liver diseases and the only cytoplasmic RNA virus known to be oncogenic in humans. The viral genome gives rise to ten mature proteins and to additional proteins, which are the products of alternative translation initiation mechanisms. A protein-known as ARFP (alternative reading frame protein) or Core+1 protein-is synthesized by an open reading frame overlapping the HCV Core coding region in the (+1) frame of genotype 1a. Almost 20 years after its discovery, we still know little of the biological role of the ARFP/Core+1 protein. Here, our differential proteomic analysis of stable hepatoma cell lines expressing the Core+1/Long isoform of HCV-1a relates the expression of the Core+1/Long isoform with the progression of the pathology of HCV liver disease to cancer.

Age Evolution of Lipid Accretion Rate in Boars Selected for Lean Meat and Duroc Barrows

Fatty acid (FA) deposition in growing–fattening pigs is mainly based on endogenous lipid synthesis, but also direct FA incorporation from the diet. To evaluate the direct fat incorporation rates and the endogenous desaturation action of the stearoyl-CoA desaturase (SCD) enzyme, a deuterium (D)-labeled saturated FA (d35-C18:0) was added to the diet. Sixteen three-way (3W) crossbred boars, and thirty-two purebred Duroc barrows homozygous for the SCD single nucleotide polymorphism rs80912566 (16 CC/16 TT), were used. Half of the animals of each genotype belonged to the growing and fattening phases. The fractional incorporation rate (FIR) of dietary fat in growing pigs was generally higher in adipose tissues, whereas in fattening pigs it was higher in the liver. Duroc pigs exhibited lower FIRs than 3W pigs, suggesting lower rates of endogenous synthesis by 3W pigs. Real fractional unsaturation rates (FURs) increased with age by the higher FIRs in 3W pigs and the de novo synthesis pathway in Duroc genotypes. Moreover, pigs carrying the SCD_T allele showed more enhanced oleic acid biosynthesis than Duroc CC pigs. In conclusion, suitable feeding protocols should be designed for each pig type to optimize production traits, considering that the metabolic pathway of FA for its deposition may differ.

Comparative Use of Contralateral and Sham-Operated Controls Reveals Traces of a Bilateral Genetic Response in the Rat Brain after Focal Stroke

Ischemic stroke is a multifactorial disease with a complex etiology and global consequences. Model animals are widely used in stroke studies. Various controls, either brain samples from sham-operated (SO) animals or symmetrically located brain samples from the opposite (contralateral) hemisphere (CH), are often used to analyze the processes in the damaged (ipsilateral) hemisphere (IH) after focal stroke. However, previously, it was shown that focal ischemia can lead to metabolic and transcriptomic changes not only in the IH but also in the CH. Here, using a transient middle cerebral artery occlusion (tMCAO) model and genome-wide RNA sequencing, we identified 1941 overlapping differentially expressed genes (DEGs) with a cutoff value >1.5 and Padj < 0.05 that reflected the general transcriptome response of IH subcortical cells at 24 h after tMCAO using both SO and CH controls. Concomitantly, 861 genes were differentially expressed in IH vs. SO, whereas they were not vs. the CH control. Furthermore, they were associated with apoptosis, the cell cycle, and neurotransmitter responses. In turn, we identified 221 DEGs in IH vs. CH, which were non-DEGs vs. the SO control. Moreover, they were predominantly associated with immune-related response. We believe that both sets of non-overlapping genes recorded transcriptome changes in IH cells associated with transhemispheric differences after focal cerebral ischemia. Thus, the specific response of the CH transcriptome should be considered when using it as a control in studies of target brain regions in diseases that induce a global bilateral genetic response, such as stroke.

Mass spectrometry-based draft of the mouse proteome

The laboratory mouse ranks among the most important experimental systems for biomedical research and molecular reference maps of such models are essential informational tools. Here, we present a quantitative draft of the mouse proteome and phosphoproteome constructed from 41 healthy tissues and several lines of analyses exemplify which insights can be gleaned from the data. For instance, tissue- and cell-type resolved profiles provide protein evidence for the expression of 17,000 genes, thousands of isoforms and 50,000 phosphorylation sites in vivo. Proteogenomic comparison of mouse, human and Arabidopsis reveal common and distinct mechanisms of gene expression regulation and, despite many similarities, numerous differentially abundant orthologs that likely serve species-specific functions. We leverage the mouse proteome by integrating phenotypic drug (n > 400) and radiation response data with the proteomes of 66 pancreatic ductal adenocarcinoma (PDAC) cell lines to reveal molecular markers for sensitivity and resistance. This unique atlas complements other molecular resources for the mouse and can be explored online via ProteomicsDB and PACiFIC.

Single-Cell Proteomics: The Critical Role of Nanotechnology

In single-cell analysis, biological variability can be attributed to individual cells, their specific state, and the ability to respond to external stimuli, which are determined by protein abundance and their relative alterations. Mass spectrometry (MS)-based proteomics (e.g., SCoPE-MS and SCoPE2) can be used as a non-targeted method to detect molecules across hundreds of individual cells. To achieve high-throughput investigation, novel approaches in Single-Cell Proteomics (SCP) are needed to identify and quantify proteins as accurately as possible. Controlling sample preparation prior to LC-MS analysis is critical, as it influences sensitivity, robustness, and reproducibility. Several nanotechnological approaches have been developed for the removal of cellular debris, salts, and detergents, and to facilitate systematic sample processing at the nano- and microfluidic scale. In addition, nanotechnology has enabled high-throughput proteomics analysis, which have required the improvement of software tools, such as DART-ID or DO-MS, which are also fundamental for addressing key biological questions. Single-cell proteomics has many applications in nanomedicine and biomedical research, including advanced cancer immunotherapies or biomarker characterization, among others; and novel methods allow the quantification of more than a thousand proteins while analyzing hundreds of single cells.

Bap1/SMN axis in Dpp4+ skeletal muscle mesenchymal cells regulates the neuromuscular system

The survival of motor neuron (SMN) protein is a major component of the pre-mRNA splicing machinery and is required for RNA metabolism. Although SMN has been considered a fundamental gene for the central nervous system, due to its relationship with neuromuscular diseases, such as spinal muscular atrophy, recent studies have also revealed the requirement of SMN in non-neuronal cells in the peripheral regions. Here, we report that the fibro-adipogenic progenitor subpopulation expressing Dpp4 (Dpp4⁺ FAPs) is required for the neuromuscular system. Furthermore, we also reveal that BRCA1-associated protein-1 (Bap1) is crucial for the stabilization of SMN in FAPs by preventing its ubiquitination-dependent degradation. Inactivation of Bap1 in FAPs decreased SMN levels and accompanied degeneration of the neuromuscular junction, leading to loss of motor neurons and muscle atrophy. Overexpression of the ubiquitination-resistant SMN variant, SMN^K186R, in Bap1-null FAPs completely prevented neuromuscular degeneration. In addition, transplantation of Dpp4⁺ FAPs, but not Dpp4^– FAPs, completely rescued neuromuscular defects. Our data reveal the crucial role of Bap1-mediated SMN stabilization in Dpp4⁺ FAPs for the neuromuscular system and provide the possibility of cell-based therapeutics to treat neuromuscular diseases.

Molecular Quantitative Trait Locus Mapping in Human Complex Diseases

Mapping quantitative trait loci (QTLs) for molecular traits from chromatin to metabolites (i.e., xQTLs) provides insight into the locations and effect modes of genetic variants that influence these molecular phenotypes and the propagation of functional consequences of each variant. xQTL studies indirectly interrogate the functional landscape of the molecular basis of complex diseases, including the impact of non-coding regulatory variants, the tissue specificity of regulatory elements, and their contribution to disease by integrating with genome-wide association studies (GWAS). We summarize a variety of molecular xQTL studies in human tissues and cells. In addition, using the Alzheimer's Disease Sequencing Project (ADSP) as an example, we describe the ADSP xQTL project, a collaborative effort across the ADSP Functional Genomics Consortium (ADSP-FGC). The project's ultimate goal is a reference map of Alzheimer's-related QTLs using existing datasets from multiple omics layers to help us study the consequences of genetic variants identified in the ADSP. xQTL studies enable the identification of the causal genes and pathways in GWAS loci, which will likely aid in the discovery of novel biomarkers and therapeutic targets for complex diseases. © 2022 Wiley Periodicals LLC.

Growth-rate-dependent and nutrient-specific gene expression resource allocation in fission yeast

Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Here, we determined the importance of the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombe grown on non-limiting nitrogen sources. Although expression of half of fission yeast genes was significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production apart from RNA polymerase II-dependent transcription. Negatively correlated genes belonged mainly to the environmental stress response programme. Critically, metabolic enzymes, which represent ∼55-70% of the proteome by mass, showed mostly condition-specific regulation. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate-dependent and nutrient-specific gene expression.

CRYPTOCHROMES promote daily protein homeostasis

The daily organisation of most mammalian cellular functions is attributed to circadian regulation of clock-controlled protein expression, driven by daily cycles of CRYPTOCHROME-dependent transcriptional feedback repression. To test this, we used quantitative mass spectrometry to compare wild-type and CRY-deficient fibroblasts under constant conditions. In CRY-deficient cells, we found that temporal variation in protein, phosphopeptide, and K+ abundance was at least as great as wild-type controls. Most strikingly, the extent of temporal variation within either genotype was much smaller than overall differences in proteome composition between WT and CRY-deficient cells. This proteome imbalance in CRY-deficient cells and tissues was associated with increased susceptibility to proteotoxic stress, which impairs circadian robustness, and may contribute to the wide-ranging phenotypes of CRY-deficient mice. Rather than generating large-scale daily variation in proteome composition, we suggest it is plausible that the various transcriptional and post-translational functions of CRY proteins ultimately act to maintain protein and osmotic homeostasis against daily perturbation.

Scaling Up Single-Cell Proteomics

Single-cell tandem MS has enabled analyzing hundreds of single cells per day and quantifying thousands of proteins across the cells. The broad dissemination of these capabilities can empower the dissection of pathophysiological mechanisms in heterogeneous tissues. Key requirements for achieving this goal include robust protocols performed on widely accessible hardware, robust quality controls, community standards, and automated data analysis pipelines that can pinpoint analytical problems and facilitate their timely resolution. Toward meeting these requirements, this perspective outlines both existing resources and outstanding opportunities, such as parallelization, for catalyzing the wide dissemination of quantitative single-cell proteomics analysis that can be scaled up to tens of thousands of single cells. Indeed, simultaneous parallelization of the analysis of peptides and single cells is a promising approach for multiplicative increase in the speed of performing deep and quantitative single-cell proteomics. The community is ready to begin a virtuous cycle of increased adoption fueling the development of more technology and resources for single-cell proteomics that in turn drive broader adoption, scientific discoveries, and clinical applications.

Resveratrol enhances A1 and hinders A2A adenosine receptors signaling in both HeLa and SH-SY5Y cells: Potential mechanism of its antitumoral action

Despite great efforts, effective treatment against cancer has not yet been found. However, natural compounds such as the polyphenol resveratrol have emerged as promising preventive agent in cancer therapy. The mode of action of resveratrol is still poorly understood, but it can modulate many signaling pathways related to the initiation and progression of cancer. Adenosinergic signaling may be involved in the antitumoral action of resveratrol since resveratrol binds to the orthosteric binding site of adenosine A_2A receptors and acts as a non-selective agonist for adenosine receptors. In the present study, we measured the impact of resveratrol treatment on different adenosinergic pathway components (i.e. adenosine receptors levels, 5'-nucleotidase, adenosine deaminase, and adenylyl cyclase activities, protein kinase A levels, intracellular adenosine and other related metabolites levels) and cell viability and proliferation in HeLa and SH-SY5Y cell lines. Results revealed changes leading to turning off cAMP signaling such as decreased levels of A_2A receptors and reduced adenylyl cyclase activation, increased levels of A₁ receptors and increased adenylyl cyclase inhibition, and lower levels of PKA. All these changes could contribute to the antitumoral action of resveratrol. Interestingly, these effects were almost identical in HeLa and SH-SY5Y cells suggesting that resveratrol enhances A₁ and hinders A_2A adenosine receptors signaling as part of a potential mechanism of antitumoral action.

Accurate Prediction of Protein Sequences for Proteogenomics Data Integration

This book chapter discusses proteogenomics data integration and provides an overview into the different omics layer involved in defining the proteome of a living organism. Various aspects of genome variability affecting either the sequence or abundance level of proteins are discussed in this book chapter, such as the effect of single-nucleotide variants or larger genomic structural variants on the proteome. Next, various sequencing technologies are introduced and discussed from a proteogenomics data integration perspective such as those providing short- and long-read sequencing and listing their respective advantages and shortcomings for accurate protein variant prediction using genomic/transcriptomics sequencing data. Finally, the various bioinformatics tools used to process and analyze DNA/RNA sequencing data are discussed with the ultimate goal of obtaining accurately predicted sample-specific protein sequences that can be used as a drop-in replacement in existing approaches for peptide and protein identification using popular database search engines such as MSFragger, SearchGUI/PeptideShaker.

Expression of transport proteins in the rete mirabile of european silver and yellow eel

BACKGROUND

In physoclist fishes filling of the swimbladder requires acid secretion of gas gland cells to switch on the Root effect and subsequent countercurrent concentration of the initial gas partial pressure increase by back-diffusion of gas molecules in the rete mirabile. It is generally assumed that the rete mirabile functions as a passive exchanger, but a detailed analysis of lactate and water movements in the rete mirabile of the eel revealed that lactate is diffusing back in the rete. In the present study we therefore test the hypothesis that expression of transport proteins in rete capillaries allows for back-diffusion of ions and metabolites, which would support the countercurrent concentrating capacity of the rete mirabile. It is also assumed that in silver eels, the migratory stage of the eel, the expression of transport proteins would be enhanced.

RESULTS

Analysis of the transcriptome and of the proteome of rete mirabile tissue of the European eel revealed the expression of a large number of membrane ion and metabolite transport proteins, including monocarboxylate and glucose transport proteins. In addition, ion channel proteins, Ca2+-ATPase, Na+/K+-ATPase and also F1F0-ATP synthase were detected. In contrast to our expectation in silver eels the expression of these transport proteins was not elevated as compared to yellow eels. A remarkable number of enzymes degrading reactive oxygen species (ROS) was detected in rete capillaries.

CONCLUSIONS

Our results reveal the expression of a large number of transport proteins in rete capillaries, so that the back diffusion of ions and metabolites, in particular lactate, may significantly enhance the countercurrent concentrating ability of the rete. Metabolic pathways allowing for aerobic generation of ATP supporting secondary active transport mechanisms are established. Rete tissue appears to be equipped with a high ROS defense capacity, preventing damage of the tissue due to the high oxygen partial pressures generated in the countercurrent system.

Multiplexed single-cell proteomics using SCoPE2

Many biological systems are composed of diverse single cells. This diversity necessitates functional and molecular single-cell analysis. Single-cell protein analysis has long relied on affinity reagents, but emerging mass-spectrometry methods (either label-free or multiplexed) have enabled quantifying >1,000 proteins per cell while simultaneously increasing the specificity of protein quantification. Here we describe the Single Cell ProtEomics (SCoPE2) protocol, which uses an isobaric carrier to enhance peptide sequence identification. Single cells are isolated by FACS or CellenONE into multiwell plates and lysed by Minimal ProteOmic sample Preparation (mPOP), and their peptides labeled by isobaric mass tags (TMT or TMTpro) for multiplexed analysis. SCoPE2 affords a cost-effective single-cell protein quantification that can be fully automated using widely available equipment and scaled to thousands of single cells. SCoPE2 uses inexpensive reagents and is applicable to any sample that can be processed to a single-cell suspension. The SCoPE2 workflow allows analyzing ~200 single cells per 24 h using only standard commercial equipment. We emphasize experimental steps and benchmarks required for achieving quantitative protein analysis.