Subcellular Transcriptomics and Proteomics: A Comparative Methods Review

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.

Subcellular Fractionation and Metaproteogenomic Identification and Validation of Key Differentially Expressed Molecular Targets for Keloid Disease

Keloid disease (KD) is a common connective tissue disorder of unknown aetiopathogenesis with ill-defined treatment. Keloid scars present as exophytic fibroproliferative reticular lesions postcutaneous injury, and even though KD remains neoplastically benign, keloid lesions behave locally aggressive, invasive and expansive. To date, there is limited understanding and validation of biomarkers identified through combined proteomic and genomic evaluation of KD. Therefore, the aim in this study was to identify putative causative candidates in KD by performing a comprehensive proteomics analysis of subcellular fractions as well as the whole cell, coupled with transcriptomics data analysis of normal compared with KD fibroblasts. We then applied novel integrative bioinformatics analysis to demonstrate that NF-kB-p65 (RELA) from the cytosolic fraction and CAPN2 from the whole-cell lysate were statistically significantly upregulated in KD and associated with alterations in relevant key signaling pathways, including apoptosis. Our findings were further confirmed by showing upregulation of both RELA and CAPN2 in KD using flow cytometry and immunohistochemistry. Moreover, functional evaluation using real-time cell analysis and flow cytometry demonstrated that both omeprazole and dexamethasone inhibited the growth of KD fibroblasts by enhancing the rate of apoptosis. In conclusion, subcellular fractionation and metaproteogenomic analyses have identified, to our knowledge, 2 previously unreported biomarkers of significant relevance to keloid diagnostics and therapeutics.

The family of glutathione peroxidase proteins and their role against biotic stress in plants: a systematic review

Introduction

Glutathione peroxidases (GPXs) are extensively studied for their indispensable roles in eliminating reactive oxygen species by catalyzing the reduction of hydrogen peroxide or lipid peroxides to prevent cell damage. However, knowledge of GPXs in plants still has many gaps to be filled. Thus, we present the first systematic review (SR) aimed at examining the function of GPXs and their protective role against cell death in plants subjected to biotic stress.

Methods

To guide the SR and avoid bias, a protocol was developed that contained inclusion and exclusion criteria based on PRISMA guidelines. Three databases (PubMed, Science Direct, and Springer) were used to identify relevant studies for this research were selected.

Results

A total of 28 articles related to the proposed objective. The results highlight the importance of GPXs in plant defense against biotic stress, including their role in protecting against cell death, similar to the anti-apoptotic GPXs in animals. Data from gene expression and protein accumulation studies in plants under various biotic stresses reveal that GPXs can both increase resistance and susceptibility to pathogens. In addition to their antioxidant functions, GPXs act as sensors and transmitters of H2O2 signals, integrating with the ABA signaling pathway during stress.

Discussion

These findings show that GPXs delay senescence or reinforce physical barriers, thereby modulating resistance or susceptibility to pathogens. Additionally, their functions are linked to their cellular localization, which demonstrates an evolutionary relationship between the studied isoforms and their role in plant defense. This information broadens the understanding of molecular strategies involving GPX isoforms and provides a foundation for discussions and actions aimed at controlling necrotrophic and/or hemibiotrophic pathogens.

Global organelle profiling reveals subcellular localization and remodeling at proteome scale

Defining the subcellular distribution of all human proteins and their remodeling across cellular states remains a central goal in cell biology. Here, we present a high-resolution strategy to map subcellular organization using organelle immunocapture coupled to mass spectrometry. We apply this workflow to a cell-wide collection of membranous and membraneless compartments. A graph-based analysis assigns the subcellular localization of over 7,600 proteins, defines spatial networks, and uncovers interconnections between cellular compartments. Our approach can be deployed to comprehensively profile proteome remodeling during cellular perturbation. By characterizing the cellular landscape following HCoV-OC43 viral infection, we discover that many proteins are regulated by changes in their spatial distribution rather than by changes in abundance. Our results establish that proteome-wide analysis of subcellular remodeling provides key insights for elucidating cellular responses, uncovering an essential role for ferroptosis in OC43 infection. Our dataset can be explored at organelles.czbiohub.org.

Absolute membrane protein abundance of P-glycoprotein, breast cancer resistance protein, and multidrug resistance proteins in term human placenta tissue and commonly used cell systems: Application in physiologically based pharmacokinetic modeling of placental drug disposition

The placenta acts as a barrier, excluding noxious substances while actively transferring nutrients to the fetus, mediated by various transporters. This study quantified the expression of key placental transporters in term human placenta (n = 5) and BeWo, BeWo b30, and JEG-3 placenta cell lines. Combining these results with pregnancy physiologically based pharmacokinetic (PBPK) modeling, we demonstrate the utility of proteomic analysis for predicting placental drug disposition and fetal exposure. Using targeted proteomics with quantification concatemer standards, we found significant expression of P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), multidrug resistance protein (MRP) 2, MRP4, and MRP6 in the human placenta (0.05-0.25 pmol/mg membrane protein) with only regional differences observed for P-gp. Unexpectedly, both P-gp and BCRP were below the limit of quantification in the regularly used BeWo cells, indicating that this cell line may not be suitable for the study of placental P-gp and BCRP-mediated transport. In cellular and vesicular overexpression systems, P-gp and BCRP were detectable as expected. Vesicle batches showed consistent P-gp expression correlating with functional activity (N-methyl-quinidine transport). However, BCRP activity (estrone 3-sulfate transport) did not consistently align with expression levels. Incorporating in vitro transporter kinetic data, along with placental transporter abundance, into a PBPK model enabled the evaluation of fetal exposure. Simulation with a hypothetical drug indicated that estimating fetal exposure relies on the intrinsic clearances of relevant transporters. To minimize interlaboratory discrepancies, expression data was generated using consistent proteomic methodologies in the same lab. Integration of this data in pregnancy PBPK modeling offers a promising tool to investigate maternal, placental, and fetal drug exposure. SIGNIFICANCE STATEMENT: This study quantified the expression of key placental transporters in human placenta and various placental cell lines, revealing significant expression variations. By integrating these data with physiologically based pharmacokinetic modeling, the study highlights the importance of transporter abundance data in understanding and predicting placental drug disposition, essential for maternal and fetal health during pregnancy.

SPOT: spatial proteomics through on-site tissue-protein-labeling

BACKGROUND

Spatial proteomics seeks to understand the spatial organization of proteins in tissues or at different subcellular localization in their native environment. However, capturing the spatial organization of proteins is challenging. Here, we present an innovative approach termed Spatial Proteomics through On-site Tissue-protein-labeling (SPOT), which combines the direct labeling of tissue proteins in situ on a slide and quantitative mass spectrometry for the profiling of spatially-resolved proteomics.

MATERIALS AND METHODS

Efficacy of direct TMT labeling was investigated using seven types of sagittal mouse brain slides, including frozen tissues without staining, formalin-fixed paraffin-embedded (FFPE) tissues without staining, deparaffinized FFPE tissues, deparaffinized and decrosslinked FFPE tissues, and tissues with hematoxylin & eosin (H&E) staining, hematoxylin (H) staining, eosin (E) staining. The ability of SPOT to profile proteomes at a spatial resolution was further evaluated on a horizontal mouse brain slide with direct TMT labeling at eight different mouse brain regions. Finally, SPOT was applied to human prostate cancer tissues as well as a tissue microarray (TMA), where TMT tags were meticulously applied to confined regions based on the pathological annotations. After on-site direct tissue-protein-labeling, tissues were scraped off the slides and subject to standard TMT-based quantitative proteomics analysis.

RESULTS

Tissue proteins on different types of mouse brain slides could be directly labeled with TMT tags. Moreover, the versatility of our direct-labeling approach extended to discerning specific mouse brain regions based on quantitative outcomes. The SPOT was further applied on both frozen tissues on slides and FFPE tissues on TMAs from prostate cancer tissues, where a distinct proteomic profile was observed among the regions with different Gleason scores.

CONCLUSIONS

SPOT is a robust and versatile technique that allows comprehensive profiling of spatially-resolved proteomics across diverse types of tissue slides to advance our understanding of intricate molecular landscapes.

Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations

Our ability to understand the cellular complexity of tissues has been revolutionized in recent years with significant advances in proteogenomic technologies including those enabling spatial analyses. This has led to numerous consortium efforts, such as the human cell atlas initiative which aims to profile all cells in the human body in healthy and diseased contexts. The availability of such information will subsequently lead to the identification of novel biomarkers of disease and of course therapeutic avenues. However, before such an atlas of any given healthy or diseased tissue can be generated, several factors should be considered including which specific techniques are optimal for the biological question at hand. In this review, we aim to highlight some of the considerations we believe to be important in the experimental design and analysis process, with the goal of helping to navigate the rapidly changing landscape of technologies available.

Dissecting human adipose tissue heterogeneity using single-cell omics technologies

Single-cell omics technologies that profile genes (genomic and epigenomic) and determine the abundance of mRNA (transcriptomic), protein (proteomic and secretomic), lipids (lipidomic), and extracellular matrix (matrisomic) support the dissection of adipose tissue heterogeneity at unprecedented resolution in a temporally and spatially defined manner. In particular, cell omics technologies may provide innovative biomarkers for the identification of rare specific progenitor cell subpopulations, assess transcriptional and proteomic changes affecting cell proliferation and immunomodulatory potential, and accurately define the lineage hierarchy and differentiation status of progenitor cells. Unraveling adipose tissue complexity may also provide for the precise assessment of a dysfunctional state, which has been associated with cancer, as cancer-associated adipocytes play an important role in shaping the tumor microenvironment supporting tumor progression and metastasis, obesity, metabolic syndrome, and type 2 diabetes mellitus. The information collected by single-cell omics has relevant implications for regenerative medicine because adipose tissue is an accessible source of multipotent cells; alternative cell-free approaches, including the use of adipose tissue stromal cell-conditioned medium, extracellular vesicles, or decellularized extracellular matrix, are clinically valid options. Subcutaneous white adipose tissue, which is generally harvested via liposuction, is highly heterogeneous because of intrinsic biological variability and extrinsic inconsistencies in the harvesting and processing procedures. The current limited understanding of adipose tissue heterogeneity impinges on the definition of quality standards appropriate for clinical translation, which requires consistency and uniformity of the administered product. We review the methods used for dissecting adipose tissue heterogeneity and provide an overview of advances in omics technology that may contribute to the exploration of heterogeneity and dynamics of adipose tissue at the single-cell level.

Targeting of human cancer stem cells predicts efficacy and toxicity of FDA-approved oncology drugs

Cancer remains the leading cause of death worldwide with approved oncology drugs continuing to have heterogenous patient responses and accompanied adverse effects (AEs) that limits effectiveness. Here, we examined >100 FDA-approved oncology drugs in the context of stemness using a surrogate model of transformed human pluripotent cancer stem cells (CSCs) vs. healthy stem cells (hSCs) capable of distinguishing abnormal self-renewal and differentiation. Although a proportion of these drugs had no effects (inactive), a larger portion affected CSCs (active), and a unique subset preferentially affected CSCs over hSCs (selective). Single cell gene expression and protein profiling of each drug's FDA recognized target provided a molecular correlation of responses in CSCs vs. hSCs. Uniquely, drugs selective for CSCs demonstrated clinical efficacy, measured by overall survival, and reduced AEs. Our findings reveal that while unintentional, half of anticancer drugs are active against CSCs and associated with improved clinical outcomes. Based on these findings, we suggest ability to target CSC targeting should be included as a property of early onco-therapeutic development.

Proximitomics by Reactive Species

The proximitome is defined as the entire collection of biomolecules spatially in the proximity of a biomolecule of interest. More broadly, the concept of the proximitome can be extended to the totality of cells proximal to a specific cell type. Since the spatial organization of biomolecules and cells is essential for almost all biological processes, proximitomics has recently emerged as an active area of scientific research. One of the growing strategies for proximitomics leverages reactive species-which are generated in situ and spatially confined, to chemically tag and capture proximal biomolecules and cells for systematic analysis. In this Outlook, we summarize different types of reactive species that have been exploited for proximitomics and discuss their pros and cons for specific applications. In addition, we discuss the current challenges and future directions of this exciting field.

Proteomics Impact on Cell Biology to Resolve Cell Structure and Function

The acceleration of advances in proteomics has enabled integration with imaging at the EM and light microscopy levels, cryo-EM of protein structures, and artificial intelligence with proteins comprehensively and accurately resolved for cell structures at nanometer to subnanometer resolution. Proteomics continues to outpace experimentally based structural imaging, but their ultimate integration is a path toward the goal of a compendium of all proteins to understand mechanistically cell structure and function.

Unveiling spatial complexity in solid tumor immune microenvironments through multiplexed imaging

Deciphering cellular components and the spatial interaction network of the tumor immune microenvironment (TIME) of solid tumors is pivotal for understanding biologically relevant cross-talks and, ultimately, advancing therapies. Multiplexed tissue imaging provides a powerful tool to elucidate spatial complexity in a holistic manner. We established and cross-validated a comprehensive immunophenotyping panel comprising over 121 markers for multiplexed tissue imaging using MACSima™ imaging cyclic staining (MICS) alongside an end-to-end analysis workflow. Applying this panel and workflow to primary cancer tissues, we characterized tumor heterogeneity, investigated potential therapeutical targets, conducted in-depth profiling of cell types and states, sub-phenotyped T cells within the TIME, and scrutinized cellular neighborhoods of diverse T cell subsets. Our findings highlight the advantage of spatial profiling, revealing immunosuppressive molecular signatures of tumor-associated myeloid cells interacting with neighboring exhausted, PD1high T cells in the TIME of hepatocellular carcinoma (HCC). This study establishes a robust framework for spatial exploration of TIMEs in solid tumors and underscores the potency of multiplexed tissue imaging and ultra-deep cell phenotyping in unraveling clinically relevant tumor components.

System-wide analysis of RNA and protein subcellular localization dynamics

Although the subcellular dynamics of RNA and proteins are key determinants of cell homeostasis, their characterization is still challenging. Here we present an integrative framework to simultaneously interrogate the dynamics of the transcriptome and proteome at subcellular resolution by combining two methods: localization of RNA (LoRNA) and a streamlined density-based localization of proteins by isotope tagging (dLOPIT) to map RNA and protein to organelles (nucleus, endoplasmic reticulum and mitochondria) and membraneless compartments (cytosol, nucleolus and cytosolic granules). Interrogating all RNA subcellular locations at once enables system-wide quantification of the proportional distribution of RNA. We obtain a cell-wide overview of localization dynamics for 31,839 transcripts and 5,314 proteins during the unfolded protein response, revealing that endoplasmic reticulum-localized transcripts are more efficiently recruited to cytosolic granules than cytosolic RNAs, and that the translation initiation factor eIF3d is key to sustaining cytoskeletal function. Overall, we provide the most comprehensive overview so far of RNA and protein subcellular localization dynamics.

Integrative transcriptomics and proteomics profiling of Arabidopsis thaliana elucidates novel mechanisms underlying spaceflight adaptation

Spaceflight presents a unique environment with complex stressors, including microgravity and radiation, that can influence plant physiology at molecular levels. Combining transcriptomics and proteomics approaches, this research gives insights into the coordination of transcriptome and proteome in Arabidopsis' molecular and physiological responses to Spaceflight environmental stress. Arabidopsis seedlings were germinated and grown in microgravity (µg) aboard the International Space Station (ISS) in NASA Biological Research in Canisters - Light Emitting Diode (BRIC LED) hardware, with the ground control established on Earth. At 10 days old, seedlings were frozen in RNA-later and returned to Earth. RNA-seq transcriptomics and TMT-labeled LC-MS/MS proteomic analysis of cellular fractionates from the plant tissues suggest the alteration of the photosynthetic machinery (PSII and PSI) in spaceflight, with the plant shifting photosystem core-regulatory proteins in an organ-specific manner to adapt to the microgravity environment. An overview of the ribosome, spliceosome, and proteasome activities in spaceflight revealed a significant abundance of transcripts and proteins involved in protease binding, nuclease activities, and mRNA binding in spaceflight, while those involved in tRNA binding, exoribonuclease activity, and RNA helicase activity were less abundant in spaceflight. CELLULOSE SYNTHASES (CESA1, CESA3, CESA5, CESA7) and CELLULOSE-LIKE PROTEINS (CSLE1, CSLG3), involved in cellulose deposition and TUBULIN COFACTOR B (TFCB) had reduced abundance in spaceflight. This contrasts with the increased expression of UDP-ARABINOPYRANOSE MUTASEs, involved in the biosynthesis of cell wall non-cellulosic polysaccharides, in spaceflight. Both transcripts and proteome suggested an altered polar auxin redistribution, lipid, and ionic intracellular transportation in spaceflight. Analyses also suggest an increased metabolic energy requirement for plants in Space than on Earth, hence, the activation of several shunt metabolic pathways. This study provides novel insights, based on integrated RNA and protein data, on how plants adapt to the spaceflight environment and it is a step further at achieving sustainable crop production in Space.

Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence

Protein is one of the primary biochemical macromolecular regulators in the compartmental cellular structure, and the subcellular locations of proteins can therefore provide information on the function of subcellular structures and physiological environments. Recently, data-driven systems have been developed to predict the subcellular location of proteins based on protein sequence, immunohistochemistry (IHC) images, or immunofluorescence (IF) images. However, the research on the fusion of multiple protein signals has received little attention. In this study, we developed a dual-signal computational protocol by incorporating IHC images into protein sequences to learn protein subcellular localization. Three major steps can be summarized as follows in this protocol: first, a benchmark database that includes 281 proteins sorted out from 4722 proteins of the Human Protein Atlas (HPA) and Swiss-Prot database, which is involved in the endoplasmic reticulum (ER), Golgi apparatus, cytosol, and nucleoplasm; second, discriminative feature operators were first employed to quantitate protein image-sequence samples that include IHC images and protein sequence; finally, the feature subspace of different protein signals is absorbed to construct multiple sub-classifiers via dimensionality reduction and binary relevance (BR), and multiple confidence derived from multiple sub-classifiers is adopted to decide subcellular location by the centralized voting mechanism at the decision layer. The experimental results indicated that the dual-signal model embedded IHC images and protein sequences outperformed the single-signal models with accuracy, precision, and recall of 75.41%, 80.38%, and 74.38%, respectively. It is enlightening for further research on protein subcellular location prediction under multi-signal fusion of protein.

Mass Spectrometry-Based Proteomic Technology and Its Application to Study Skeletal Muscle Cell Biology

Voluntary striated muscles are characterized by a highly complex and dynamic proteome that efficiently adapts to changed physiological demands or alters considerably during pathophysiological dysfunction. The skeletal muscle proteome has been extensively studied in relation to myogenesis, fiber type specification, muscle transitions, the effects of physical exercise, disuse atrophy, neuromuscular disorders, muscle co-morbidities and sarcopenia of old age. Since muscle tissue accounts for approximately 40% of body mass in humans, alterations in the skeletal muscle proteome have considerable influence on whole-body physiology. This review outlines the main bioanalytical avenues taken in the proteomic characterization of skeletal muscle tissues, including top-down proteomics focusing on the characterization of intact proteoforms and their post-translational modifications, bottom-up proteomics, which is a peptide-centric method concerned with the large-scale detection of proteins in complex mixtures, and subproteomics that examines the protein composition of distinct subcellular fractions. Mass spectrometric studies over the last two decades have decisively improved our general cell biological understanding of protein diversity and the heterogeneous composition of individual myofibers in skeletal muscles. This detailed proteomic knowledge can now be integrated with findings from other omics-type methodologies to establish a systems biological view of skeletal muscle function.

Deep spatial-omics analysis of Head & Neck carcinomas provides alternative therapeutic targets and rationale for treatment failure

Immune checkpoint inhibitor (ICI) therapy has had limited success (<30%) in treating metastatic recurrent Head and Neck Oropharyngeal Squamous Cell Carcinomas (OPSCCs). We postulate that spatial determinants in the tumor play a critical role in cancer therapy outcomes. Here, we describe the case of a male patient diagnosed with p16+ OPSCC and extensive lung metastatic disease who failed Nivolumab and Pembrolizumab/Lenvatinib therapies. Using advanced integrative spatial proteogenomic analysis on the patient's recurrent OPSCC tumors we demonstrate that: (i) unbiased tissue clustering based on spatial transcriptomics (ST) successfully detected tumor cells and enabled the investigation of phenotypic traits such as proliferation or drug-resistance genes in the tumor's leading-edge and core; (ii) spatial proteomic imagining used in conjunction with ST (SpiCi, Spatial Proteomics inferred Cell identification) can resolve the profiling of tumor infiltrating immune cells, (iii) ST data allows for the discovery and ranking of clinically relevant alternative medicines based on their interaction with their matching ligand-receptor. Importantly, when the spatial profiles of ICI pre- and post-failure OPSCC tumors were compared, they exhibited highly similar PD-1/PD-L1low and VEGFAhigh expression, suggesting that these new tumors were not the product of ICI resistance but rather of Lenvatinib dose reduction due to complications. Our work establishes a path for incorporating spatial-omics in clinical settings to facilitate treatment personalization.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Characterization of Xenobiotic and Steroid Disposition Potential of Human Placental Tissue and Cell Lines (BeWo, JEG-3, JAR, and HTR-8/SVneo) by Quantitative Proteomics

The placenta is a fetal organ that performs critical functions to maintain pregnancy and support fetal development, including metabolism and transport of xenobiotics and steroids between the maternal-fetal unit. In vitro placenta models are used to study xenobiotic and steroid disposition, but how well these models recapitulate the human placenta is not well understood. We first characterized the abundance of proteins involved in xenobiotic and steroid disposition in human placental tissue. In pooled human placenta, the following xenobiotic and steroid disposition proteins were detected (highest to lowest), 1) enzymes: glutathione S-transferase P, carbonyl reductase 1, aldo-keto reductase 1B1, hydroxysteroid dehydrogenases (HSD3B1 and HSD11B1), aromatase, epoxide hydrolase 1 (EPHX1) and steryl-sulfatase, and 2) transporters: monocarboxylate transporters (MCT1 and 4), organic anion transporting polypeptide 2B1, organic anion transporter 4, and breast cancer resistance protein (BCRP). Then, the tissue proteomics data were compared with four placental cell lines (BeWo, JEG-3, JAR, and HTR-8/SVneo). The differential global proteomics analysis revealed that the tissue and cell lines shared 1420 cytosolic and 1186 membrane proteins. Although extravillous trophoblast and cytotrophoblast marker proteins were detected in all cell lines, only BeWo and JEG-3 cells expressed the syncytiotrophoblast marker, chorionic somatomammotropin hormone 1. BeWo and JEG-3 cells expressed most target proteins including aromatase, HSDs, EPHX1, MCT1, and BCRP. JEG-3 cells treated with commonly detected phthalates in human biofluids showed dysregulation of steroid pathways. The data presented here show that BeWo and JEG-3 cells are closer to the placental tissue for studying xenobiotic and steroid disposition. SIGNIFICANCE STATEMENT: This is the first study to compare proteomics data of human placental tissue and cell lines (BeWo, JAR, JEG-3, and HTR-8/SVneo). The placental cell line and tissue proteomes are vastly different, but BeWo and JEG-3 cells showed greater resemblance to the tissue in the expression of xenobiotic and steroid disposition proteins. These data will assist researchers to select an optimum cell model for mechanistic investigations on xenobiotic and steroid disposition in the placenta.

A revised central dogma for the 21st century:all biology is cognitive information processing

Crick's Central Dogma has been a foundational aspect of 20th century biology, describing an implicit relationship governing the flow of information in biological systems in biomolecular terms. Accumulating scientific discoveries support the need for a revised Central Dogma to buttress evolutionary biology's still-fledgling migration from a Neodarwinian canon. A reformulated Central Dogma to meet contemporary biology is proposed: all biology is cognitive information processing. Central to this contention is the recognition that life is the self-referential state, instantiated within the cellular form. Self-referential cells act to sustain themselves and to do so, cells must be in consistent harmony with their environment. That consonance is achieved by the continuous assimilation of environmental cues and stresses as information to self-referential observers. All received cellular information must be analyzed to be deployed as cellular problem-solving to maintain homeorhetic equipoise. However, the effective implementation of information is definitively a function of orderly information management. Consequently, effective cellular problem-solving is information processing and management. The epicenter of that cellular information processing is its self-referential internal measurement. All further biological self-organization initiates from this obligate activity. As the internal measurement by cells of information is self-referential by definition, self-reference is biological self-organization, underpinning 21st century Cognition-Based Biology.

Proximity Labeling in Plants

Proteins are workhorses in the cell; they form stable and more often dynamic, transient protein-protein interactions, assemblies, and networks and have an intimate interplay with DNA and RNA. These network interactions underlie fundamental biological processes and play essential roles in cellular function. The proximity-dependent biotinylation labeling approach combined with mass spectrometry (PL-MS) has recently emerged as a powerful technique to dissect the complex cellular network at the molecular level. In PL-MS, by fusing a genetically encoded proximity-labeling (PL) enzyme to a protein or a localization signal peptide, the enzyme is targeted to a protein complex of interest or to an organelle, allowing labeling of proximity proteins within a zoom radius. These biotinylated proteins can then be captured by streptavidin beads and identified and quantified by mass spectrometry. Recently engineered PL enzymes such as TurboID have a much-improved enzymatic activity, enabling spatiotemporal mapping with a dramatically increased signal-to-noise ratio. PL-MS has revolutionized the way we perform proteomics by overcoming several hurdles imposed by traditional technology, such as biochemical fractionation and affinity purification mass spectrometry. In this review, we focus on biotin ligase-based PL-MS applications that have been, or are likely to be, adopted by the plant field. We discuss the experimental designs and review the different choices for engineered biotin ligases, enrichment, and quantification strategies. Lastly, we review the validation and discuss future perspectives.

Clinical Proteomics: Liquid Chromatography-Mass Spectrometry (LC-MS) Purification Systems

Liquid chromatography/mass spectrometry (LC/MS) has become a routine powerful technology in clinical proteomic studies for protein identification, protein characterization, and the discovery of biomarkers. In this chapter, we describe two protocol methods to analyze clinical patient samples using a resin-based depletion column followed by either protein In-Gel enzymatic digestion or protein In-Solution enzymatic digestion using a simple kit-based approach (i.e., using the PreOmics iST sample preparation kit), followed by analysis using one-dimensional reverse-phase chromatography (RPC) or high pH reversed-phase peptide fractionation.

Labeling of Phospho-Specific Antibodies with oYo-Link® Epitope Tags for Multiplex Immunostaining

Spatial proteomics has recently garnered significant interest, as it offers to provide unprecedented insight into biological processes in both health and disease, by connecting protein expression patterns from the subcellular level to the tissue or even organism level. These high-content approaches generally rely on a high degree of multiplexing, whereby multiple proteins can be detected simultaneously. The most versatile multiplexing approaches utilize antibodies to confer specificity for various intracellular proteins of interest. Therefore, these methods must be able to differentiate many antibodies at once. In this chapter, we describe a simple and rapid approach to labeling antibodies with distinct epitope tags in a site-specific manner. This allows multiple antibodies, even from the same host species, to be uniquely identified and detected and offers a simple approach for spatial proteomic applications.

CoLoC-seq probes the global topology of organelle transcriptomes

Proper RNA localisation is essential for physiological gene expression. Various kinds of genome-wide approaches permit to comprehensively profile subcellular transcriptomes. Among them, cell fractionation methods, that couple RNase treatment of isolated organelles to the sequencing of protected transcripts, remain most widely used, mainly because they do not require genetic modification of the studied system and can be easily implemented in any cells or tissues, including in non-model species. However, they suffer from numerous false-positives since incompletely digested contaminant RNAs can still be captured and erroneously identified as resident transcripts. Here we introduce Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq) as a new subcellular transcriptomics approach that efficiently bypasses this caveat. CoLoC-seq leverages classical enzymatic kinetics and tracks the depletion dynamics of transcripts in a gradient of an exogenously added RNase, with or without organellar membranes. By means of straightforward mathematical modelling, CoLoC-seq infers the localisation topology of RNAs and robustly distinguishes between genuinely resident, luminal transcripts and merely abundant surface-attached contaminants. Our generic approach performed well on human mitochondria and is in principle applicable to other membrane-bounded organelles, including plastids, compartments of the vacuolar system, extracellular vesicles, and viral particles.

Progress in kidney transplantation: The role for systems immunology

The development of systems biology represents an immense breakthrough in our ability to perform translational research and deliver personalized and precision medicine. A multidisciplinary approach in combination with use of novel techniques allows for the extraction and analysis of vast quantities of data even from the volume and source limited samples that can be obtained from human subjects. Continued advances in microfluidics, scalability and affordability of sequencing technologies, and development of data analysis tools have made the application of a multi-omics, or systems, approach more accessible for use outside of specialized centers. The study of alloimmune and protective immune responses after solid organ transplant offers innumerable opportunities for a multi-omics approach, however, transplant immunology labs are only just beginning to adopt the systems methodology. In this review, we focus on advances in biological techniques and how they are improving our understanding of the immune system and its interactions, highlighting potential applications in transplant immunology. First, we describe the techniques that are available, with emphasis on major advances that allow for increased scalability. Then, we review initial applications in the field of transplantation with a focus on topics that are nearing clinical integration. Finally, we examine major barriers to adapting these methods and discuss potential future developments.

MicroID2: A Novel Biotin Ligase Enables Rapid Proximity Dependent Proteomics

Identifying protein-protein and other proximal interactions is central to dissecting signaling and regulatory processes in cells. BioID is a proximity dependent biotinylation method that uses an "abortive" biotin ligase to detect proximal interactions in cells in a highly reproducible manner. Recent advancements in proximity dependent biotinylation tools have improved efficiency and timing of labeling, allowing for measurement of interactions on a cellular timescale. However, issues of size, stability, and background labeling of these constructs persist. Here we modified the structure of BioID2, derived from A. aeolicus BirA, to create a smaller, highly active, biotin ligase that we named MicroID2. Truncation of the c-terminus of BioID2 and addition of mutations to alleviate blockage of biotin/ATP binding at the active site of BioID2 resulted in a smaller, highly active construct with lower background labeling. Several additional point mutations improved the function of our modified MicroID2 construct compared to BioID2 and other biotin ligases, including TurboID and miniTurbo. MicroID2 is the smallest biotin ligase reported so far (180 amino acids, for MicroID2 vs. 257 AA for miniTurbo and 338 AA for TurboID), yet it demonstrates only slightly less labeling activity than TurboID and outperforms miniTurbo. MicroID2 also had lower background labeling than TurboID. For experiments where precise temporal control of labeling is essential, we additionally developed a MicroID2 mutant, termed lbMicroID2, that has lower labeling efficiency, but significantly reduced biotin scavenging compared to BioID2. Finally, we demonstrate utility of MicroID2 in mass spectrometry experiments by localizing MicroID2 constructs to subcellular organelles and measuring proximal interactions.

Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.

AktAR and Akt-STOPS: Genetically Encodable Molecular Tools to Visualize and Perturb Akt Kinase Activity at Different Subcellular Locations in Living Cells

The serine/threonine protein kinase Akt integrates diverse upstream inputs to regulate cell survival, growth, metabolism, migration, and differentiation. Mounting evidence suggests that Akt activity is differentially regulated depending on its subcellular location, which can include the plasma membrane, endomembrane, and nuclear compartment. This spatial control of Akt activity is critical for achieving signaling specificity and proper physiological functions, and deregulation of compartment-specific Akt signaling is implicated in various diseases, including cancer and diabetes. Understanding the spatial coordination of the signaling network centered around this key kinase and the underlying regulatory mechanisms requires precise tracking of Akt activity at distinct subcellular compartments within its native biological contexts. To address this challenge, new molecular tools are being developed, enabling us to directly interrogate the spatiotemporal regulation of Akt in living cells. These include, for instance, the newly developed genetically encodable fluorescent-protein-based Akt kinase activity reporter (AktAR2), which serves as a substrate surrogate of Akt kinase and translates Akt-specific phosphorylation into a quantifiable change in Förster resonance energy transfer (FRET). In addition, we developed the Akt substrate tandem occupancy peptide sponge (Akt-STOPS), which allows biochemical perturbation of subcellular Akt activity. Both molecular tools can be readily targeted to distinct subcellular localizations. Here, we describe a workflow to study Akt kinase activity at different subcellular locations in living cells. We provide a protocol for using genetically targeted AktAR2 and Akt-STOPS, along with fluorescence imaging in living NIH3T3 cells, to visualize and perturb, respectively, the activity of endogenous Akt kinase at different subcellular compartments. We further describe a protocol for using chemically inducible dimerization (CID) to control the plasma membrane-specific inhibition of Akt activity in real time. Lastly, we describe a protocol for maintaining NIH3T3 cells in culture, a cell line known to exhibit robust Akt activity. In all, this approach enables interrogation of spatiotemporal regulation and functions of Akt, as well as the intricate signaling networks in which it is embedded, at specific subcellular locations. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Visualizing and perturbing subcellular Akt kinase activity using AktAR and Akt-STOPS Basic Protocol 2: Using chemically inducible dimerization (CID) to control inhibition of Akt at the plasma membrane Support Protocol: Maintaining NIH3T3 cells in culture.