Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells

A guide to genetically-encoded redox biosensors: state of the art and opportunities

Genetically-encoded redox biosensors have become invaluable tools for monitoring cellular redox processes with high spatiotemporal resolution, coupling the presence of the redox-active analyte with a change in fluorescence signal that can be easily recorded. This review summarizes the available fluorescence recording methods and presents an in-depth classification of the redox biosensors, organized by the analytes they respond to. In addition to the fluorescent protein-based architectures, this review also describes the recent advances on fluorescent, chemigenetic-based redox biosensors and other emerging chemigenetic strategies. This review examines how these biosensors are designed, the biosensors sensing mechanism, and their practical advantages and disadvantages.

Cellular and Biophysical Applications of Genetic Code Expansion

Despite their diverse functions, proteins are inherently constructed from a limited set of building blocks. These compositional constraints pose significant challenges to protein research and its practical applications. Strategically manipulating the cellular protein synthesis system to incorporate novel building blocks has emerged as a critical approach for overcoming these constraints in protein research and application. In the past two decades, the field of genetic code expansion (GCE) has achieved significant advancements, enabling the integration of numerous novel functionalities into proteins across a variety of organisms. This technological evolution has paved the way for the extensive application of genetic code expansion across multiple domains, including protein imaging, the introduction of probes for protein research, analysis of protein-protein interactions, spatiotemporal control of protein function, exploration of proteome changes induced by external stimuli, and the synthesis of proteins endowed with novel functions. In this comprehensive Review, we aim to provide an overview of cellular and biophysical applications that have employed GCE technology over the past two decades.

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

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

High-throughput tagging of endogenous loci for rapid characterization of protein function

To facilitate the interrogation of protein function at scale, we have developed high-throughput insertion of tags across the genome (HITAG). HITAG enables users to rapidly produce libraries of cells, each with a different protein of interest C-terminally tagged. HITAG is based on a modified strategy for performing Cas9-based targeted insertions, coupled with an improved approach for selecting properly tagged lines. Analysis of the resulting clones generated by HITAG reveals high tagging specificity, with most successful tagging events being indel free. Using HITAG, we fuse mCherry to a set of 167 stress granule-associated proteins and elucidate the features that drive a subset of proteins to strongly accumulate within these transient RNA-protein granules.

Pooled multicolour tagging for visualizing subcellular protein dynamics

Imaging-based methods are widely used for studying the subcellular localization of proteins in living cells. While routine for individual proteins, global monitoring of protein dynamics following perturbation typically relies on arrayed panels of fluorescently tagged cell lines, limiting throughput and scalability. Here, we describe a strategy that combines high-throughput microscopy, computer vision and machine learning to detect perturbation-induced changes in multicolour tagged visual proteomics cell (vpCell) pools. We use genome-wide and cancer-focused intron-targeting sgRNA libraries to generate vpCell pools and a large, arrayed collection of clones each expressing two different endogenously tagged fluorescent proteins. Individual clones can be identified in vpCell pools by image analysis using the localization patterns and expression level of the tagged proteins as visual barcodes, enabling simultaneous live-cell monitoring of large sets of proteins. To demonstrate broad applicability and scale, we test the effects of antiproliferative compounds on a pool with cancer-related proteins, on which we identify widespread protein localization changes and new inhibitors of the nuclear import/export machinery. The time-resolved characterization of changes in subcellular localization and abundance of proteins upon perturbation in a pooled format highlights the power of the vpCell approach for drug discovery and mechanism-of-action studies.

Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images

Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions.

Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies

Psoriasis, a complex and recurrent chronic inflammatory skin disease involving various inflammatory cell types, requires effective cell communication to maintain the homeostatic balance of inflammation. However, patterns of communication at the single-cell level have not been systematically investigated. In this study, we employed social network analysis tools, pattern recognition, and manifold learning to compare molecular communication features between psoriasis cells and normal skin cells. Utilizing a process that facilitates the discovery of cell type-specific regulons, we analyzed internal regulatory networks among different cells in psoriasis. Advanced techniques for the quantitative detection of non-targeted proteins in pathological tissue sections were employed to demonstrate protein expression. Our findings revealed a synergistic interplay among the communication signals of immune cells in psoriasis. B-cells were activated, while Langerhans cells shifted into the primary signaling output mode to fulfill antigen presentation, mediating T-cell immunity. In contrast to normal skin cells, psoriasis cells shut down numerous signaling pathways, influencing the balance of skin cell renewal and differentiation. Additionally, we identified a significant number of active cell type-specific regulons of resident immune cells around the hair follicle. This study unveiled the molecular communication features of the hair follicle cell-psoriasis axis, showcasing its potential for therapeutic targeting at the single-cell level. By elucidating the pattern of immune cell communication in psoriasis and identifying new molecular features of the hair follicle cell-psoriasis axis, our findings present innovative strategies for drug targeting to enhance psoriasis treatment.

Quantification of Binding of Small Molecules to Native Proteins Overexpressed in Living Cells

The affinity and selectivity of small molecules for proteins drive drug discovery and development. We report a fluorescent probe cellular binding assay (FPCBA) for determination of these values for native (untagged) proteins overexpressed in living cells. This method uses fluorophores such as Pacific Blue (PB) linked to cell-permeable protein ligands to generate probes that rapidly and reversibly equilibrate with intracellular targets, as established by kinetic assays of cellular uptake and efflux. To analyze binding to untagged proteins, an internal ribosomal entry site (IRES) vector was employed that allows a single mRNA to encode both the protein target and a separate orthogonal fluorescent protein (mVenus). This enabled cellular uptake of the probe to be correlated with protein expression by flow cytometry, allowing measurement of cellular dissociation constants (Kd) of the probe. This approach was validated by studies of the binding of allosteric activators to eight different Protein Kinase C (PKC) isozymes. Full-length PKCs expressed in transiently transfected HEK293T cells were used to measure cellular Kd values of a probe comprising PB linked to the natural product phorbol via a carbamate. These values were further used to determine competitive binding constants (cellular Ki values) of the nonfluorescent phorbol ester PDBu and the anticancer agent bryostatin 1 for each isozyme. For some PKC-small molecule pairs, these cellular Ki values matched known biochemical Ki values, but for others, altered selectivity was observed in cells. This approach can facilitate quantification of interactions of small molecules with physiologically relevant native proteins.

Analyzing Fcγ-Receptor Interactions on Monocytes with the Proximity Ligation Assay (PLA)

Proximity ligation assays (PLA) enable the detection and characterization of protein interactions independent of protein abundance or genetic modifications. This technique exploits both antibody and DNA-binding features, providing high selectivity and sensitivity for protein recognition and visualization of single-protein molecules with high spatial accuracy. Here, we describe the general procedure for a direct PLA on splenic monocytes to analyze FcγRIIb homodimerization. However, this method can be applied to other cells and receptors of interest.

Single-Cell Plasmonic Immunosandwich Assay Reveals the Modulation of Nucleocytoplasmic Localization Fluctuation of ABL1 on Cell Migration

Cell migration is an essential process of cancer metastasis. The spatiotemporal dynamics of signaling molecules influences cellular phenotypic outcomes. It has been increasingly documented that the Abelson (ABL) family kinases play critical roles in solid tumors. However, ABL1's shuttling dynamics in cell migration still remains unexplored. This is mainly because tools permitting the investigation of translocation dynamics of proteins in single living cells are lacking. Herein, to bridge this gap, we developed a unique multifunctional integrated single-cell analysis method that enables long-term observation of cell migration behavior and monitoring of signaling proteins and complexes at the subcellular level. We found that the shuttling of ABL1's to the cytoplasm results in a higher migration speed, while its trafficking back to the nucleus leads to a lower one. Furthermore, our results indicated that fluctuant protein-protein interactions between 14-3-3 and ABL1 modulate ABL1's nucleocytoplasmic fluctuation and eventually affect the cell speed. Importantly, based on these new insights, we demonstrated that disturbing ABL1's nuclear export traffic and 14-3-3-ABL1 complexes formation can effectively suppress cell migration. Thus, our method opens up a new possibility for simultaneous tracking of internal molecular mechanisms and cell behavior, providing a promising tool for the in-depth study of cancer.

30 years of nanobodies - an ongoing success story of small binders in biological research

A milestone in the field of recombinant binding molecules was achieved 30 years ago with the discovery of single-domain antibodies from which antigen-binding variable domains, better known as nanobodies (Nbs), can be derived. Being only one tenth the size of conventional antibodies, Nbs feature high affinity and specificity, while being highly stable and soluble. In addition, they display accessibility to cryptic sites, low off-target accumulation and deep tissue penetration. Efficient selection methods, such as (semi-)synthetic/naïve or immunized cDNA libraries and display technologies, have facilitated the isolation of Nbs against diverse targets, and their single-gene format enables easy functionalization and high-yield production. This Review highlights recent advances in Nb applications in various areas of biological research, including structural biology, proteomics and high-resolution and in vivo imaging. In addition, we provide insights into intracellular applications of Nbs, such as live-cell imaging, biosensors and targeted protein degradation.

Recent methodological advances towards single-cell proteomics

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

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

Pervasive mislocalization of pathogenic coding variants underlying human disorders

Widespread sequencing has yielded thousands of missense variants predicted or confirmed as disease-causing. This creates a new bottleneck: determining the functional impact of each variant - largely a painstaking, customized process undertaken one or a few genes or variants at a time. Here, we established a high-throughput imaging platform to assay the impact of coding variation on protein localization, evaluating 3,547 missense variants of over 1,000 genes and phenotypes. We discovered that mislocalization is a common consequence of coding variation, affecting about one-sixth of all pathogenic missense variants, all cellular compartments, and recessive and dominant disorders alike. Mislocalization is primarily driven by effects on protein stability and membrane insertion rather than disruptions of trafficking signals or specific interactions. Furthermore, mislocalization patterns help explain pleiotropy and disease severity and provide insights on variants of unknown significance. Our publicly available resource will likely accelerate the understanding of coding variation in human diseases.

Development of a V5-tag-directed nanobody and its implementation as an intracellular biosensor of GPCR signaling

Protein-protein interactions (PPIs) form the foundation of any cell signaling network. Considering that PPIs are highly dynamic processes, cellular assays are often essential for their study because they closely mimic the biological complexities of cellular environments. However, incongruity may be observed across different PPI assays when investigating a protein partner of interest; these discrepancies can be partially attributed to the fusion of different large functional moieties, such as fluorescent proteins or enzymes, which can yield disparate perturbations to the protein's stability, subcellular localization, and interaction partners depending on the given cellular assay. Owing to their smaller size, epitope tags may exhibit a diminished susceptibility to instigate such perturbations. However, while they have been widely used for detecting or manipulating proteins in vitro, epitope tags lack the in vivo traceability and functionality needed for intracellular biosensors. Herein, we develop NbV5, an intracellular nanobody binding the V5-tag, which is suitable for use in cellular assays commonly used to study PPIs such as BRET, NanoBiT, and Tango. The NbV5:V5 tag system has been applied to interrogate G protein-coupled receptor signaling, specifically by replacing larger functional moieties attached to the protein interactors, such as fluorescent or luminescent proteins (∼30 kDa), by the significantly smaller V5-tag peptide (1.4 kDa), and for microscopy imaging which is successfully detected by NbV5-based biosensors. Therefore, the NbV5:V5 tag system presents itself as a versatile tool for live-cell imaging and a befitting adaptation to existing cellular assays dedicated to probing PPIs.

The Role of Poly(ADP-ribose) Polymerase 1 in Nuclear and Mitochondrial Base Excision Repair

Poly(ADP-ribose) (PAR) Polymerase 1 (PARP-1), also known as ADP-ribosyl transferase with diphtheria toxin homology 1 (ARTD-1), is a critical player in DNA damage repair, during which it catalyzes the ADP ribosylation of self and target enzymes. While the nuclear localization of PARP-1 has been well established, recent studies also suggest its mitochondrial localization. In this review, we summarize the differences between mitochondrial and nuclear Base Excision Repair (BER) pathways, the involvement of PARP-1 in mitochondrial and nuclear BER, and its functional interplay with other BER enzymes.

Biochemical, molecular and anti-tumor characterization of L-methionine gamma lyase produced by local Pseudomonas sp. in Egypt

A soil inhabiting Pseudomonas sp. has been examined for producing L- methionine gamma-lyase enzyme. The identity of the tested bacteria was verified by VITEK2, and MALDI-TOF analysis in addition to molecular confirmation by 16S rDNA sequence and submitted in Genbank under accession number ON993898.1. Production of the targeted enzyme was done using a commercial medium including L-methionine, as the main substrate. This obtained enzyme was precipitated using acetone (1:1v/v) followed by purification with Sephadex G100 and sepharose columns. The specific activity of the purified enzyme (105.8 µmol/ mg/min) increased by 1.89 folds after the purification steps. The peptide fingerprint of the native MGL was verified from the proteomics analysis, with identical conserved active site domains with database-deposited MGLs. The molecular mass of the pure MGL denatured subunit was (>40 kDa) and that of the native enzyme was (>150 kDa) ensuring their homotetrameric identity. The purified enzyme showed absorption spectra at 280 nm and 420 nm for the apo-MGL and PLP coenzyme, respectively. Amino acids suicide analogues analysis by DTNB, hydroxylamine, iodoacetate, MBTH, mercaptoethanol and guanidine thiocyanate reduced the relative activity of purified MGL. From the kinetic properties, the catalytic effectiveness (K_cat/k_m) of Pseudomonas sp. MGL was 10.8 mM ^-1 S^-1 for methionine and 5.51 mM ^-1 S^-1 for cysteine, respectively. The purified MGL showed highly significant antiproliferative activity towards the liver carcinoma cell line (HEPG-2) and breast carcinoma cell line (MCF-7) with half inhibitory concentration values (IC₅₀) 7.23 U/ml and 21.14 U/ml, respectively. No obvious signs of toxicity on liver and kidney functions in the examined animal models were observed.

DeepSP: A Deep Learning Framework for Spatial Proteomics

The study of protein subcellular localization (PSL) is a fundamental step toward understanding the mechanism of protein function. The recent development of mass spectrometry (MS)-based spatial proteomics to quantify the distribution of proteins across subcellular fractions provides us a high-throughput approach to predict unknown PSLs based on known PSLs. However, the accuracy of PSL annotations in spatial proteomics is limited by the performance of existing PSL predictors based on traditional machine learning algorithms. In this study, we present a novel deep learning framework named DeepSP for PSL prediction of an MS-based spatial proteomics data set. DeepSP constructs the new feature map of a difference matrix by capturing detailed changes between different subcellular fractions of protein occupancy profiles and uses the convolutional block attention module to improve the prediction performance of PSL. DeepSP achieved significant improvement in accuracy and robustness for PSL prediction in independent test sets and unknown PSL prediction compared to current state-of-the-art machine learning predictors. As an efficient and robust framework for PSL prediction, DeepSP is expected to facilitate spatial proteomics studies and contributes to the elucidation of protein functions and the regulation of biological processes.

Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning

With the improvement of single-cell measurement techniques, there is a growing awareness that individual differences exist among cells, and protein expression distribution can vary across cells in the same tissue or cell line. Pinpointing the protein subcellular locations in single cells is crucial for mapping functional specificity of proteins and studying related diseases. Currently, research about single-cell protein location is still in its infancy, and most studies and databases do not annotate proteins at the cell level. For example, in the human protein atlas database, an immunofluorescence image stained for a particular protein shows multiple cells, but the subcellular location annotation is for the whole image, ignoring intercellular difference. In this study, we used large-scale immunofluorescence images and image-level subcellular locations to develop a deep-learning-based pipeline that could accurately recognize protein localizations in single cells. The pipeline consisted of two deep learning models, i.e. an image-based model and a cell-based model. The former used a multi-instance learning framework to comprehensively model protein distribution in multiple cells in each image, and could give both image-level and cell-level predictions. The latter firstly used clustering and heuristics algorithms to assign pseudo-labels of subcellular locations to the segmented cell images, and then used the pseudo-labels to train a classification model. Finally, the image-based model was fused with the cell-based model at the decision level to obtain the final ensemble model for single-cell prediction. Our experimental results showed that the ensemble model could achieve higher accuracy and robustness on independent test sets than state-of-the-art methods.

Astragaloside IV attenuates IL-1β-induced intervertebral disc degeneration through inhibition of the NF-κB pathway

BACKGROUND

Intervertebral disc degeneration (IDD) is the main cause of low back pain. Patients with low back pain may experience significant socio-economic burdens and decreased productivity. Previous studies have shown that inflammation is one of the main causes of IDD. Astragaloside IV (AS IV), a traditional Chinese medicine, has been reported to have therapeutic effects on many inflammation-related diseases; however, the effectiveness of AS IV as the treatment for IDD has not been studied.

METHODS

Nucleus pulposus (NP) cells from patients with IDD were used for the experiments. Cell counting kit 8 (CCK8) was used to evaluate the effect of AS IV on the viability of NP cells (NPCs). To mimic IDD in vitro, NPCs were divided into the following groups: control group, interleukin 1β (IL-1β) group, and AS IV + IL-1β group. To analyse the effect of AS IV on IL-1β-induced IDD, Western blotting, RT-qPCR, flow cytometry, and immunofluorescence assays were performed. To evaluate the effect of AS IV in vivo, a rat model of puncture-induced IDD was established.

RESULTS

AS IV effectively alleviated IL-1β-induced inflammation, apoptosis, and extracellular matrix degeneration in NPCs. We also observed that AS IV decreased the IL-1β-induced phosphorylation of inhibitor of kappa B-alpha (p-IκBα) in the cytosol, and reduced nuclear translocation of NF-κB p65, indicating that AS IV inhibited the NF-κB pathway. Using the puncture-induced rat IDD model, our results showed that AS IV had a protective effect against the progression of IDD, suggesting that AS IV could alleviate IDD in vivo.

CONCLUSIONS

Our results demonstrated that AS IV effectively alleviated IDD in vivo and in vitro, indicating that it could be used as a therapeutic to treat IDD.

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

Mitochondrial PARP1 regulates NAD+-dependent poly ADP-ribosylation of mitochondrial nucleoids

PARPs play fundamental roles in multiple DNA damage recognition and repair pathways. Persistent nuclear PARP activation causes cellular NAD⁺ depletion and exacerbates cellular aging. However, very little is known about mitochondrial PARP (mtPARP) and poly ADP-ribosylation (PARylation). The existence of mtPARP is controversial, and the biological roles of mtPARP-induced mitochondrial PARylation are unclear. Here, we demonstrate the presence of PARP1 and PARylation in purified mitochondria. The addition of the PARP1 substrate NAD⁺ to isolated mitochondria induced PARylation, which was suppressed by treatment with the inhibitor olaparib. Mitochondrial PARylation was also evaluated by enzymatic labeling of terminal ADP-ribose (ELTA). To further confirm the presence of mtPARP1, we evaluated mitochondrial nucleoid PARylation by ADP ribose-chromatin affinity purification (ADPr-ChAP) and PARP1 chromatin immunoprecipitation (ChIP). We observed that NAD⁺ stimulated PARylation and TFAM occupancy on the mtDNA regulatory region D-loop, inducing mtDNA transcription. These findings suggest that PARP1 is integrally involved in mitochondrial PARylation and that NAD⁺-dependent mtPARP1 activity contributes to mtDNA transcriptional regulation.

Phasor Analysis of Fluorescence Lifetime Enables Quantitative Multiplexed Molecular Imaging of Three Probes

The excited-state lifetime is an intrinsic property of fluorescent molecules that can be leveraged for multiplexed imaging. An advantage of fluorescence lifetime-based multiplexing is that signals from multiple probes can be gathered simultaneously, whereas traditional spectral fluorescence imaging typically requires multiple images at different excitation and emission wavelengths. Additionally, lifetime and spectra could both be utilized to expand the multiplexing capacity of fluorescence. However, resolving exogenous molecular probes based exclusively on the fluorescence lifetime has been limited by technical challenges in analyzing lifetime data. The phasor approach to lifetime analysis offers a simple, graphical solution that has increasingly been used to assess endogenous cellular autofluorescence to quantify metabolic factors. In this study, we employed the phasor analysis of FLIM to quantitatively resolve three exogenous, antibody-targeted fluorescent probes with similar spectral properties based on lifetime information alone. First, we demonstrated that three biomarkers that were spatially restricted to the cell membrane, cytosol, or nucleus could be accurately distinguished using FLIM and phasor analysis. Next, we successfully resolved and quantified three probes that were all targeted to cell surface biomarkers. Finally, we demonstrated that lifetime-based quantitation accuracy can be improved through intensity matching of various probe-biomarker combinations, which will expand the utility of this technique. Importantly, we reconstructed images for each individual probe, as well as an overlay of all three probes, from a single FLIM image. Our results demonstrate that FLIM and phasor analysis can be leveraged as a powerful tool for simultaneous detection of multiple biomarkers with high sensitivity and accuracy.

Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition

While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas - Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images. The particular challenges of this competition include class imbalance, weak labels and multi-label classification, prompting competitors to apply a wide range of approaches in their solutions. The winning models serve as the first subcellular omics tools that can annotate single-cell locations, extract single-cell features and capture cellular dynamics.

ENKD1 promotes epidermal stratification by regulating spindle orientation in basal keratinocytes

Stratification of the epidermis is essential for the barrier function of the skin. However, the molecular mechanisms governing epidermal stratification are not fully understood. Herein, we demonstrate that enkurin domain-containing protein 1 (ENKD1) contributes to epidermal stratification by modulating the cell-division orientation of basal keratinocytes. The epidermis of Enkd1 knockout mice is thinner than that of wild-type mice due to reduced generation of suprabasal cells from basal keratinocytes through asymmetric division. Depletion of ENKD1 impairs proper orientation of the mitotic spindle and delays mitotic progression in cultured cells. Mechanistic investigation further reveals that ENKD1 is a novel microtubule-binding protein that promotes the stability of astral microtubules. Introduction of the microtubule-binding domain of ENKD1 can largely rescue the spindle orientation defects in ENKD1-depleted cells. These findings establish ENKD1 as a critical regulator of astral microtubule stability and spindle orientation that stimulates epidermal stratification in mammalian cells.

ENKD1 is a centrosomal and ciliary microtubule-associated protein important for primary cilium content regulation

Centrioles and cilia are conserved, microtubule-based structures critical for cell function and development. Their dysfunction causes cancer and developmental disorders. How microtubules are organized into ordered structures by microtubule-associated proteins (MAPs) and tubulin modifications is best understood during mitosis but is largely unexplored for the centrioles and the ciliary axoneme, which are composed of stable microtubules that maintain their length at a steady-state. In particular, we know little about the identity of the centriolar and ciliary MAPs and how they work together during the assembly and maintenance of the cilium and centriole. Here, we identified the Enkurin domain containing 1 (ENKD1) as a component of the centriole wall and the axoneme in mammalian cells and showed that it has extensive proximity interactions with these compartments and MAPs. Using in vitro and cellular assays, we found that ENKD1 is a new MAP that regulates microtubule organization and stability. Consistently, we observed an increase in tubulin polymerization and microtubule stability, as well as disrupted microtubule organization in ENKD1 overexpression. Cells depleted for ENKD1 were defective in ciliary length and content regulation and failed to respond to Hedgehog pathway activation. Together, our results advance our understanding of the functional and regulatory relationship between MAPs and the primary cilium.

SubCellBarCode: integrated workflow for robust spatial proteomics by mass spectrometry

The molecular functions of a protein are defined by its inherent properties in relation to its environment and interaction network. Within a cell, this environment and network are defined by the subcellular location of the protein. Consequently, it is crucial to know the localization of a protein to fully understand its functions. Recently, we have developed a mass spectrometry- (MS) and bioinformatics-based pipeline to generate a proteome-wide resource for protein subcellular localization across multiple human cancer cell lines ( www.subcellbarcode.org ). Here, we present a detailed wet-lab protocol spanning from subcellular fractionation to MS-sample preparation and analysis. A key feature of this protocol is that it includes all generated cell fractions without discarding any material during the fractionation process. We also describe the subsequent quantitative MS-data analysis, machine learning-based classification, differential localization analysis and visualization of the output. For broad applicability, we evaluated the pipeline by using MS data generated by two different peptide pre-fractionation approaches, namely high-resolution isoelectric focusing and high-pH reverse-phase fractionation, as well as direct analysis without pre-fractionation by using long-gradient liquid chromatography-MS. Moreover, an R package covering the dry-lab part of the method was developed and made available through Bioconductor. The method is straightforward and robust, and the entire protocol, from cell harvest to classification output, can be performed within 1-2 weeks. The protocol enables accurate classification of proteins to 15 compartments and 4 neighborhoods, visualization of the output data and differential localization analysis including treatment-induced protein relocalization, condition-dependent localization or cell type-specific localization. The SubCellBarCode package is freely available at https://bioconductor.org/packages/devel/bioc/html/SubCellBarCode.html .

Force Mapping Reveals the Spatial Distribution of Individual Proteins in a Neuron

Conventional methods for studying the spatial distribution and expression level of proteins within neurons have primarily relied on immunolabeling and/or signal amplification. Here, we present an atomic force microscopy (AFM)-based nanoscale force mapping method, where Anti-LIMK1-tethered AFM probes were used to visualize individual LIMK1 proteins in cultured neurons directly through force measurements. We observed that the number density of LIMK1 decreased in neuronal somas after the cells were depolarized. We also elucidated the spatial distribution of LIMK1 in single spine areas and found that the protein predominantly locates at heads of spines rather than dendritic shafts. The study demonstrates that our method enables unveiling of the abundance and spatial distribution of a protein of interest in neurons without signal amplification or labeling. We expected that this approach should facilitate the studies of protein expression phenomena in depth in a wide range of biological systems.

Chemokine (C-X-C motif) ligand 1/chemokine (C-X-C motif) receptor 2 autocrine loop contributes to cellular proliferation, migration and apoptosis in cervical cancer

Cervical cancer is the most common malignant tumor in gynecology with high mortality rate, so novel approaches for cervical cancer treatment are urgently needed. In this study, we analyzed the gene expression data and clinicopathological data of The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Project (GTEx) downloaded from University of California Santa Cruz (UCSC) Xena database. Chemokine (C-X-C motif) ligand 1 (CXCL1) was screened out as a key prognostic gene for cervical cancer. Revealed by the results of ELISA and Western blot, the expression of CXCL1 and chemokine (C-X-C motif) receptor 2 (CXCR2) in cervical cancer cell lines (HeLa and C33A) was significantly higher than that in the primary cervical epithelial cells. Cellular immunofluorescence was used in this study to observe CXCR2 localization. Through CCK8, clone formation assay, wound healing assay and Annexin V/PI staining, it was found that down-regulation of CXCL1 expression or treatment with CXCR2 antagonist (SB 225002) could reduce the cell viability, affect the proliferation, weaken the migration ability, and promote the apoptosis of cervical cancer cells; however, the effect of CXCR2 antagonist was improved after over-expressed CXCL1. CXCL1/CXCR2 chemokine system regulates the proliferation, migration, and apoptosis of cervical cancer cells in the form of an autocrine loop, thus affecting the development of cervical cancer. This study provides a theoretical basis for researching the molecular mechanism of cervical cancer deterioration and development, and brings forward a new idea for the prevention and treatment of cervical cancer.

Pseudouridine synthases modify human pre-mRNA co-transcriptionally and affect pre-mRNA processing

Pseudouridine is a modified nucleotide that is prevalent in human mRNAs and is dynamically regulated. Here, we investigate when in their life cycle mRNAs become pseudouridylated to illuminate the potential regulatory functions of endogenous mRNA pseudouridylation. Using single-nucleotide resolution pseudouridine profiling on chromatin-associated RNA from human cells, we identified pseudouridines in nascent pre-mRNA at locations associated with alternatively spliced regions, enriched near splice sites, and overlapping hundreds of binding sites for RNA-binding proteins. In vitro splicing assays establish a direct effect of individual endogenous pre-mRNA pseudouridines on splicing efficiency. We validate hundreds of pre-mRNA sites as direct targets of distinct pseudouridine synthases and show that PUS1, PUS7, and RPUSD4-three pre-mRNA-modifying pseudouridine synthases with tissue-specific expression-control widespread changes in alternative pre-mRNA splicing and 3' end processing. Our results establish a vast potential for cotranscriptional pre-mRNA pseudouridylation to regulate human gene expression via alternative pre-mRNA processing.

Multilocation proteins in organelle communication: Based on protein-protein interactions

Protein-protein interaction (PPI) plays a crucial role in most biological processes, including signal transduction and cell apoptosis. Importantly, the knowledge of PPIs can be useful for identification of multimeric protein complexes and elucidation of uncharacterized protein functions. Arabidopsis thaliana, the best-characterized dicotyledonous plant, the steadily increasing amount of information on the levels of its proteome and signaling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated. In this study, we performed an overview analysis of the 10 major organelles and their associated proteins of the dicotyledonous model plant Arabidopsis thaliana via PPI network, and found that PPI may play an important role in organelle communication. Further, multilocation proteins, especially phosphorylation-related multilocation proteins, can function as a "needle and thread" via PPIs and play an important role in organelle communication. Similar results were obtained in a monocotyledonous model crop, rice. Furthermore, we provide a research strategy for multilocation proteins by LOPIT technique, proteomics, and bioinformatics analysis and also describe their potential role in the field of plant science. The results provide a new view that the phosphorylation-related multilocation proteins play an important role in organelle communication and provide new insight into PPIs and novel directions for proteomic research. The research of phosphorylation-related multilocation proteins may promote the development of organelle communication and provide an important theoretical basis for plant responses to external stress.

DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features

MOTIVATION

Knowledge of subcellular locations of proteins is of great significance for understanding their functions. The multi-label proteins that simultaneously reside in or move between more than one subcellular structure usually involve with complex cellular processes. Currently, the subcellular location annotations of proteins in most studies and databases are descriptive terms, which fail to capture the protein amount or fractions across different locations. This highly limits the understanding of complex spatial distribution and functional mechanism of multi-label proteins. Thus, quantitatively analyzing the multiplex location patterns of proteins is an urgent and challenging task.

RESULTS

In this study, we developed a deep-learning-based pattern unmixing pipeline for protein subcellular localization (DULoc) to quantitatively estimate the fractions of proteins localizing in different subcellular compartments from immunofluorescence images. This model used a deep convolutional neural network to construct feature representations, and combined multiple nonlinear decomposing algorithms as the pattern unmixing method. Our experimental results showed that the DULoc can achieve over 0.93 correlation between estimated and true fractions on both real and synthetic datasets. In addition, we applied the DULoc method on the images in the human protein atlas database on a large scale, and showed that 70.52% of proteins can achieve consistent location orders with the database annotations.

AVAILABILITY AND IMPLEMENTATION

The datasets and code are available at: https://github.com/PRBioimages/DULoc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and networks

Location proteomics seeks to provide automated high-resolution descriptions of protein location patterns within cells. Many efforts have been undertaken in location proteomics over the past decades, thereby producing plenty of automated predictors for protein subcellular localization. However, most of these predictors are trained solely from high-throughput microscopic images or protein amino acid sequences alone. Unifying heterogeneous protein data sources has yet to be exploited. In this paper, we present a pipeline called sequence, image, network-based protein subcellular locator (SIN-Locator) that constructs a multi-view description of proteins by integrating multiple data types including images of protein expression in cells or tissues, amino acid sequences and protein-protein interaction networks, to classify the patterns of protein subcellular locations. Proteins were encoded by both handcrafted features and deep learning features, and multiple combining methods were implemented. Our experimental results indicated that optimal integrations can considerately enhance the classification accuracy, and the utility of SIN-Locator has been demonstrated through applying to new released proteins in the human protein atlas. Furthermore, we also investigate the contribution of different data sources and influence of partial absence of data. This work is anticipated to provide clues for reconciliation and combination of multi-source data for protein location analysis.

Fluorescence Microscopy-An Outline of Hardware, Biological Handling, and Fluorophore Considerations

Fluorescence microscopy has become a critical tool for researchers to understand biological processes at the cellular level. Micrographs from fixed and live-cell imaging procedures feature in a plethora of scientific articles for the field of cell biology, but the complexities of fluorescence microscopy as an imaging tool can sometimes be overlooked or misunderstood. This review seeks to cover the three fundamental considerations when designing fluorescence microscopy experiments: (1) hardware availability; (2) amenability of biological models to fluorescence microscopy; and (3) suitability of imaging agents for intended applications. This review will help equip the reader to make judicious decisions when designing fluorescence microscopy experiments that deliver high-resolution and informative images for cell biology.

SUBCELLULAR TRANSCRIPTOMICS & PROTEOMICS: A COMPARATIVE METHODS REVIEW

The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics.

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Subcellular information of protein and RNA give insights into molecular function.

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This review discusses strategies available to measure subcellular information.

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Hybridization of methods shows promise for exploring the composition of organelles.

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Advances are aiding understanding of the organisation and dynamics of cells.

Nanobodies - Little helpers unravelling intracellular signaling

The identification of diagnostic and therapeutic targets requires a comprehensive understanding of cellular processes, for which advanced technologies in biomedical research are needed. The emergence of nanobodies (Nbs) derived from antibody fragments of camelid heavy chain-only antibodies as intracellular research tools offers new possibilities to study and modulate target antigens in living cells. Here we summarize this rapidly changing field, beginning with a brief introduction of Nbs, followed by an overview of how target-specific Nbs can be generated, and introduce the selection of intrabodies as research tools. Intrabodies, by definition, are intracellular functional Nbs that target ectopic or endogenous intracellular antigens within living cells. Such binders can be applied in various formats, e.g. as chromobodies for live cell microscopy or as biosensors to decipher complex intracellular signaling pathways. In addition, protein knockouts can be achieved by target-specific Nbs, while modulating Nbs have the potential as future therapeutics. The development of fine-tunable and switchable Nb-based systems that simultaneously provide spatial and temporal control has recently taken the application of these binders to the next level.

Proximity-dependent biotinylation approaches to study apicomplexan biology

In the last 10 years, proximity-dependent biotinylation (PDB) techniques greatly expanded the ability to study protein environments in the living cell that range from specific protein complexes to entire compartments. This is achieved by using enzymes such as BirA* and APEX that are fused to proteins of interest and biotinylate proteins in their proximity. PDB techniques are now also increasingly used in apicomplexan parasites. In this review, we first give an overview of the main PDB approaches and how they compare with other techniques that address similar questions. PDB is particularly valuable to detect weak or transient protein associations under physiological conditions and to study cellular structures that are difficult to purify or have a poorly understood protein composition. We also highlight new developments such as novel smaller or faster-acting enzyme variants and conditional PDB approaches, providing improvements in both temporal and spatial resolution which may offer broader application possibilities useful in apicomplexan research. In the second part, we review work using PDB techniques in apicomplexan parasites and how this expanded our knowledge about these medically important parasites.

RNALocate v2.0: an updated resource for RNA subcellular localization with increased coverage and annotation

Resolving the spatial distribution of the transcriptome at a subcellular level can increase our understanding of biology and diseases. To facilitate studies of biological functions and molecular mechanisms in the transcriptome, we updated RNALocate, a resource for RNA subcellular localization analysis that is freely accessible at http://www.rnalocate.org/ or http://www.rna-society.org/rnalocate/. Compared to RNALocate v1.0, the new features in version 2.0 include (i) expansion of the data sources and the coverage of species; (ii) incorporation and integration of RNA-seq datasets containing information about subcellular localization; (iii) addition and reorganization of RNA information (RNA subcellular localization conditions and descriptive figures for method, RNA homology information, RNA interaction and ncRNA disease information) and (iv) three additional prediction tools: DM3Loc, iLoc-lncRNA and iLoc-mRNA. Overall, RNALocate v2.0 provides a comprehensive RNA subcellular localization resource for researchers to deconvolute the highly complex architecture of the cell.

Substantial restoration of night vision in adult mice with congenital stationary night blindness

Complete congenital stationary night blindness (cCSNB) due to mutations in TRPM1, GRM6, GPR179, NYX, or leucine-rich repeat immunoglobulin-like transmembrane domain 3 (LRIT3) is an incurable inherited retinal disorder characterized by an ON-bipolar cell (ON-BC) defect. Since the disease is non-degenerative and stable, treatment could theoretically be administrated at any time in life, making it a promising target for gene therapy. Until now, adeno-associated virus (AAV)-mediated therapies lead to significant functional improvements only in newborn cCSNB mice. Here we aimed to restore protein localization and function in adult Lrit3 ^{-/ -} mice. LRIT3 localizes in the outer plexiform layer and is crucial for TRPM1 localization at the dendritic tips of ON-BCs and the electroretinogram (ERG)-b-wave. AAV2-7m8-Lrit3 intravitreal injections were performed targeting either ON-BCs, photoreceptors (PRs), or both. Protein localization of LRIT3 and TRPM1 at the rod-to-rod BC synapse, functional rescue of scotopic responses, and ON-responses detection at the ganglion cell level were achieved in a few mice when ON-BCs alone or both PRs and ON-BCs, were targeted. More importantly, a significant number of treated adult Lrit3 ^-/- mice revealed an ERG b-wave recovery under scotopic conditions, improved optomotor responses, and on-time ON-responses at the ganglion cell level when PRs were targeted. Functional rescue was maintained for at least 4 months after treatment.

Wnt Signaling and Cadherin Expressions in Different Staging of Colorectal Cancer as Biomarkers for Metastasis: Study of SW480, COLO 320DM, and HCT116 Cellines

BACKGROUND: Early metastases is still unresolved problem in cancer management, eventually in colorectal cancer (CRC). In addition, many markers are useful just only in the late stage of CRC. AIM: This study evaluates the differences in the expression intensity of nuclear β-catenin, cytoplasmic β-catenin, E-cadherin, and N-cadherin between CRC SW480 cell line as control group and COLO320DM and HCT116 cell lines as case groups. MATERIALS AND METHODS: This study applied experimental research design with the different test methods. Culture growing and subcultures manufacturing for the CRC cell line models were done initially and followed by the immunofluorescence method by administering antibodies on β-catenin, E-cadherin, and N-cadherin, and continued with staining process using fluorescein-5-isothiocyanate and 4’, 6-diamidino-2-phenylindole. Observations were done using an immunofluorescence microscope. Calculation of area density in each cell to perceive the expressions of cytoplasmic and nuclear β-catenin, E-cadherin, and N-cadherin was conducted using ImageJ software, resulted in mean fluorescence intensity. RESULTS: There are significant differences in the expressions of cytoplasmic β-catenin, nuclear β-catenin, E-cadherin, and N-cadherin among SW480, COLO320DM, and HCT116 cell lines (p < 0.05). Despite no significant differences in cytoplasmic and nuclear β-catenin expressions between SW480 and HCT116 cell lines, and in E-cadherin and N-cadherin expressions between COLO320DM and HCT116 cell lines (p > 0.05). SW480 cell line has a higher expression of nuclear β-catenin than the cytoplasm (p < 0.05). CONCLUSION: This study reveals differences in the expression of nucleic and cytoplasmic β-catenin, E-cadherin, and N-cadherin in three stages of CRC (Duke B, C, and D) refer to different activation invasion, migration, and metastatic processes. Furthermore, the high expression of nuclear β-catenin and N-cadherin in the early stage of CRC indicate there is a metastatic process in that stage, so nuclear β-catenin and cadherin can be considered as potential biomarkers in the early stage of this cancer.

Excited-State Proton Transfer Dynamics in LSSmOrange Studied by Time-Resolved Impulsive Stimulated Raman Spectroscopy

LSSmOrange is a fluorescent protein that exhibits a large energy gap between absorption and emission, which makes it a useful tool for multicolor bioimaging. This characteristic of LSSmOrange originates from excited-state proton transfer (ESPT): The neutral chromophore is predominantly present in the ground state while the bright fluorescence is emitted from the anionic excited state after ESPT. Interestingly, it was reported that this ESPT process follows bimodal dynamics, but its origin has not clearly been understood. We investigate ESPT of LSSmOrange using time-resolved impulsive stimulated Raman spectroscopy (TR-ISRS) that provides femtosecond time-resolved Raman spectra. The results indicate that the bimodal ESPT dynamics originates from the structural heterogeneity of the chromophore. Species-associated Raman spectra obtained by spectral analysis based on singular value decomposition (SVD) suggest that cis and trans chromophores coexist in the ground state. It is considered that these two forms are photoexcited and undergo ESPT in parallel, resulting in the bimodal dynamics of ESPT in LSSmOrange.

Autophagy-an underestimated coordinator of construction and destruction during plant root ontogeny

MAIN CONCLUSION

Autophagy is a key but undervalued process in root ontogeny, ensuring both the proper development of root tissues as well as the senescence of the entire organ. Autophagy is a process which occurs during plant adaptation to changing environmental conditions as well as during plant ontogeny. Autophagy is also engaged in plant root development, however, the limitations of belowground studies make it challenging to understand the entirety of the developmental processes. We summarize and discuss the current data pertaining to autophagy in the roots of higher plants during their formation and degradation, from the beginning of root tissue differentiation and maturation; all the way to the aging of the entire organ. During root growth, autophagy participates in the processes of central vacuole formation in cortical tissue development, as well as vascular tissue differentiation and root senescence. At present, several key issues are still not entirely understood and remain to be addressed in future studies. The major challenge lies in the portrayal of the mechanisms of autophagy on subcellular events in belowground plant organs during the programmed control of cellular degradation pathways in roots. Given the wide range of technical areas of inquiry where root-related research can be applied, including cutting-edge cell biological methods to track, sort and screen cells from different root tissues and zones of growth, the identification of several lines of evidence pertaining to autophagy during root developmental processes is the most urgent challenge. Consequently, a substantial effort must be made to ensure whether the analyzed process is autophagy-dependent or not.

Strategies for Site-Specific Labeling of Receptor Proteins on the Surfaces of Living Cells by Using Genetically Encoded Peptide Tags

Fluorescence microscopy imaging enables receptor proteins to be investigated within their biological context. A key challenge is to site-specifically incorporate reporter moieties into proteins without interfering with biological functions or cellular networks. Small peptide tags offer the opportunity to combine inducible labeling with small tag sizes that avoid receptor perturbation. Herein, we review the current state of live-cell labeling of peptide-tagged cell-surface proteins. Considering their importance as targets in medicinal chemistry, we focus on membrane receptors such as G protein-coupled receptors (GPCRs) and receptor tyrosine kinases (RTKs). We discuss peptide tags that i) are subject to enzyme-mediated modification reactions, ii) guide the complementation of reporter proteins, iii) form coiled-coil complexes, and iv) interact with metal complexes. Given our own contributions in the field, we place emphasis on peptide-templated labeling chemistry.

Subcellular proteomics

The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.

Absolute Quantification of Protein Copy Number in Single Cells With Immunofluorescence Microscopy Calibrated Using Single-Molecule Microarrays

Great strides toward routine single-cell analyses have been made over the last decade, particularly in the field of transcriptomics. For proteomics, amplification is not currently possible and has necessitated the development of ultrasensitive platforms capable of performing such analyses on single cells. These platforms are improving in terms of throughput and multiplexability but still fall short in relation to more established methods such as fluorescence microscopy. However, microscopy methods rely on fluorescence intensity as a proxy for protein abundance and are not currently capable of reporting this in terms of an absolute copy number. Here, a microfluidic implementation of single-molecule microarrays for single-cell analysis is assessed in its ability to calibrate fluorescence microscopy data. We show that the equivalence of measurements of the steady-state distribution of protein abundance to single-molecule microarray data can be exploited to pave the way for absolute quantitation by fluorescence and immunofluorescence microscopy. The methods presented have been developed using GFP but are extendable to other proteins and other biomolecules of interest.

Re-expression of glucuronyl C5-epimerase in the mutant MEF cells increases heparan sulfate epimerization but has no influence on the Golgi localization and enzymatic activity of 2-O-sulfotransferase

Heparan sulfate (HS) is a linear and complex polysaccharide that modulates the biological activities through protein recognition and interaction. Evidence indicates that protein-binding properties of HS are largely dependent on distinctive sulfation and epimerization patterns that are modified by a series of Golgi-localized enzymes. In particular, the glucuronyl C5-epimerase (Hsepi) converts D-glucuronic acid residues to L-iduronic acid, and 2-O-sulfotransferase (2OST) catalyzes sulfation at C2 position of IdoA and rarely GlcA residues. Mice lacking both Hsepi and 2OST display multiple development defects, indicating the importance of IdoA in HS. Here, to gain greater insights of HS structure-function relationships, as well as a better understanding of the regulatory mechanisms of Hsepi and 2OST, the fine structure and cellular signaling functions of HS were investigated after restoration of Hsepi in the mutant mouse embryonic fibroblast cells. Introduction of Hsepi into the Hsepi mutant MEF cells led to robustly increased proportion of IdoA residues, which rescued the cell signaling in responding to FGF2. However, we found that Hsepi knockout had no influence on either cellular transportation or enzymatic activity of 2OST in the MEF cells, which is not in accord with the findings reported, suggesting that the enzymatic activity and cellular transportation of 2OST and Hsepi might be differently regulated.

Challenges facing quantitative large-scale optical super-resolution, and some simple solutions

Optical super-resolution microscopy (SRM) has enabled biologists to visualize cellular structures with near-molecular resolution, giving unprecedented access to details about the amounts, sizes, and spatial distributions of macromolecules in the cell. Precisely quantifying these molecular details requires large datasets of high-quality, reproducible SRM images. In this review, we discuss the unique set of challenges facing quantitative SRM, giving particular attention to the shortcomings of conventional specimen preparation techniques and the necessity for optimal labeling of molecular targets. We further discuss the obstacles to scaling SRM methods, such as lengthy image acquisition and complex SRM data analysis. For each of these challenges, we review the recent advances in the field that circumvent these pitfalls and provide practical advice to biologists for optimizing SRM experiments.