Simultaneous Multiplexed Measurement of RNA and Proteins in Single Cells

Spatial proteomics reveals sirtuin 1 to be a determinant of T-cell infiltration in human melanoma

BACKGROUND

The tumour microenvironment significantly influences the clinical response of patients to therapeutic immune checkpoint inhibition (ICI), but a comprehensive understanding of the underlying immune-regulatory proteome is still lacking.

OBJECTIVES

To decipher targetable biologic processes that determine tumour-infiltrating lymphocytes (TiLs) as a cellular equivalent of clinical response to ICI.

METHODS

We mapped the spatial distribution of proteins in TiL-enriched vs. TiL-low compartments in melanoma by combining microscopy, matrix-assisted laser desorption mass spectrometry imaging and liquid chromatography-mass spectrometry, as well as computational data mining. Pharmacological modulation of sirtuin 1 (SIRT1) activity in syngeneic mouse models was used to evaluate the efficacy of pharmacological SIRT1 activation in two syngeneic melanoma mouse models, one known to be α-programmed cell death protein 1 (PD-1) sensitive and the other α-PD-1 resistant.

RESULTS

Spatial proteomics and gene ontology-based enrichment analysis identified > 145 proteins enriched in CD8high tumour compartments, including negative regulators of mammalian target of rapamycin signalling such as SIRT1. Multiplexed immunohistochemistry confirmed that SIRT1 protein was expressed more in CD8high than in CD8low compartments. Further analysis of bulk and single-cell RNA sequencing data from melanoma tissue samples suggested the expression of SIRT1 by different lymphocyte subpopulations (CD8+ T cells, CD4+ T cells and B cells). Furthermore, we showed in vivo that pharmacological SIRT1 activation increased the immunological effect of α-PD-1 ICI against melanoma cells in mice, which was accompanied by an increase in T-cell infiltration and T-cell-related cytokines, including interferon (IFN)-γ, CCL4, CXCL9, CXCL10 and tumour necrosis factor-α. In silico analysis of large transcriptional data cohorts showed that SIRT1 was positively associated with the proinflammatory T-cell chemokines CXCL9, CXCL10 and IFN-γ, and prolonged overall survival of patients with melanoma.

CONCLUSIONS

Our study deciphers the proteomics landscape in human melanoma, providing important information on the tumour microenvironment and identifying SIRT1 as having important prognostic and therapeutic implications.

Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting

Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. Here, we introduce nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells via RNA sequencing and mass spectrometry-based proteomics, respectively. Benchmarking of nanoSPLITS demonstrates high measurement precision with deep proteomic and transcriptomic profiling of single-cells. We apply nanoSPLITS to cyclin-dependent kinase 1 inhibited cells and found phospho-signaling events could be quantified alongside global protein and mRNA measurements, providing insights into cell cycle regulation. We extend nanoSPLITS to primary cells isolated from human pancreatic islets, introducing an efficient approach for facile identification of unknown cell types and their protein markers by mapping transcriptomic data to existing large-scale single-cell RNA sequencing reference databases. Accordingly, we establish nanoSPLITS as a multiomic technology incorporating global proteomics and anticipate the approach will be critical to furthering our understanding of biological systems.

Detection of >400 Cluster of Differentiation Biomarkers and Pathway Proteins in Single Immune Cells by Cyclic Multiplex In Situ Tagging for Single-Cell Proteomic Studies

The identification and characterization of immune cell subpopulations are critical to reveal cell development throughout life and immune responses to environmental factors. Next-generation sequencing technologies have dramatically advanced single-cell genomics and transcriptomics for immune cell classification. However, gene expression is often not correlated with protein expression, and immunotyping is mostly accepted in protein format. Current single-cell proteomic technologies are either limited in multiplex capacity or not sensitive enough to detect the critical functional proteins. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology to simultaneously measure >400 proteins, a scale of >10 times than similar technologies. Such an ultrahigh multiplexity is achieved by reiterative staining of the single cells coupled with a MIST array for detection. This technology has been thoroughly validated through comparison with flow cytometry and fluorescence immunostaining techniques. Both peripheral blood mononuclear cells (PBMCs) and T cells are analyzed by the CycMIST technology, and almost the entire spectrum of cluster of differentiation (CD) surface markers has been measured. The landscape of fluctuation of CD protein expression in single cells has been uncovered by our technology. Further study found T cell activation signatures and protein-protein networks. This study represents the highest multiplexity of single immune cell marker measurement targeting functional proteins. With additional information from intracellular proteins of the same single cells, our technology can potentially facilitate mechanistic studies of immune responses under various disease conditions.

Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease

By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Proteomics mining of cancer hallmarks on a single-cell resolution

Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.

Evaluation of proteome dynamics: Implications for statistical confidence in mass spectrometric determination

Single-cell proteomics is currently far less productive than other approaches. Still, the proteomic community is having trouble adapting to the limitation of having to examine fewer cells than they would like. Studies on a small number of cells should be carefully planned to maximize the chances of success in this situation. This study aims to determine how sample size and measurement speed (slope)/variation affect the accuracy of a protein proteome mass spectrometric determination. The determination accuracy was shown to increase, and the false positive rate was shown to decrease as the sample size increased from 7 to 100 cells and the measurement slope/variation (S/V) ratio increased from 1 to 6. Furthermore, it was discovered that the number of cells in the sample increased the accuracy of this estimate. Thus, for 100 cells, the measurement S/V ratio was typically estimated to be very close to the real-world value, with a standard deviation of 0.35. For sample sizes from 7 to 100 cells, this accuracy was seen when calculating the measurement S/V ratio. The findings can help researchers plan experiments for mass spectroscopic protein proteome determination and other research purposes.

Single-cell Technologies Provide Novel Insights into Liver Physiology and Pathology

The liver is the largest glandular organ in the body and has a unique distribution of cells and biomolecules. However, the treatment outcome of end-stage liver disease is extremely poor. Single-cell sequencing is a new advanced and powerful technique for identifying rare cell populations and biomolecules by analyzing the characteristics of gene expression between individual cells. These cells and biomolecules might be used as potential targets for immunotherapy of liver diseases and contribute to the development of precise individualized treatment. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) or other single-cell histological techniques have solved the problem of cell population heterogeneity and characterize molecular changes associated with liver diseases with higher accuracy and resolution. In this review, we comprehensively summarized single-cell approaches including transcriptomic, spatial transcriptomic, immunomic, proteomic, epigenomic, and multiomic technologies, and described their application in liver physiology and pathology. We also discussed advanced techniques and recent studies in the field of single-cell; our review might provide new insights into the pathophysiological mechanisms of the liver to achieve precise and individualized treatment of liver diseases.

The functional role of CST1 and CCL26 in asthma development

BACKGROUND

Asthma is the most common chronic disease in children with an increasing prevalence. Its development is caused by genetic and environmental factors and allergic sensitization is a known trigger. Dog allergens affect up to 30% of all children and dog dander-sensitized children show increased expression of cystatin-1 (CST1) and eotaxin-3 (CCL26) in nasal epithelium. The aim of our study was to investigate the functional mechanism of CST1 and CCL26 in the alveolar basal epithelial cell line A549.

METHODS

A549 cells were transfected with individual overexpression vectors for CST1 and CCL26 and RNA sequencing was performed to examine the transcriptomics. edgeR was used to identify differentially expressed genes (= DEG, |log2 FC | ≥ 2, FDR < 0.01). The protein expression levels of A549 cells overexpressing CST1 and CCL26 were analyzed using the Target 96 inflammation panel from OLINK (antibody-mediated proximity extension-based assay; OLINK Proteomics). Differentially expressed proteins were considered with a |log2 FC| ≥ 1, p < .05.

RESULTS

The overexpression of CST1 resulted in a total of 27 DEG (1 upregulated and 26 downregulated) and the overexpression of CCL26 in a total of 137 DEG (0 upregulated and 137 downregulated). The gene ontology enrichment analysis showed a significant downregulation of type I and III interferon signaling pathway genes as well as interferon-stimulated genes. At the protein level, overexpression of CST1 induced a significantly increased expression of CCL3, whereas CCL26 overexpression led to increased expression of HGF, and a decrease of CXCL11, CCL20, CCL3 and CXCL10.

CONCLUSION

Our results indicate that an overexpression of CST1 and CCL26 cause a downregulation of interferon related genes and inflammatory proteins. It might cause a higher disease susceptibility, mainly for allergic asthma, as CCL26 is an agonist for CCR-3-carrying cells, such as eosinophils and Th2 lymphocytes, mostly active in allergic asthma.

Modeling and interpretation of single-cell proteogenomic data

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

The technological landscape and applications of single-cell multi-omics

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data

Single-cell sequencing technology enables the simultaneous capture of multiomic data from multiple cells. The captured data can be represented by tensors, i.e. the higher-rank matrices. However, the existing analysis tools often take the data as a collection of two-order matrices, renouncing the correspondences among the features. Consequently, we propose a probabilistic tensor decomposition framework, SCOIT, to extract embeddings from single-cell multiomic data. SCOIT incorporates various distributions, including Gaussian, Poisson, and negative binomial distributions, to deal with sparse, noisy, and heterogeneous single-cell data. Our framework can decompose a multiomic tensor into a cell embedding matrix, a gene embedding matrix, and an omic embedding matrix, allowing for various downstream analyses. We applied SCOIT to eight single-cell multiomic datasets from different sequencing protocols. With cell embeddings, SCOIT achieves superior performance for cell clustering compared to nine state-of-the-art tools under various metrics, demonstrating its ability to dissect cellular heterogeneity. With the gene embeddings, SCOIT enables cross-omics gene expression analysis and integrative gene regulatory network study. Furthermore, the embeddings allow cross-omics imputation simultaneously, outperforming current imputation methods with the Pearson correlation coefficient increased by 3.38-39.26%; moreover, SCOIT accommodates the scenario that subsets of the cells are with merely one omic profile available.

A Fast And Versatile Method for Simultaneous HCR, Immunohistochemistry And Edu Labeling (SHInE)

Access to newer, fast, and cheap sequencing techniques, particularly on the single-cell level, have made transcriptomic data of tissues or single cells accessible to many researchers. As a consequence, there is an increased need for in situ visualization of gene expression or encoded proteins to validate, localize, or help interpret such sequencing data, as well as put them in context with cellular proliferation. A particular challenge for labeling and imaging transcripts are complex tissues that are often opaque and/or pigmented, preventing easy visual inspection. Here, we introduce a versatile protocol that combines in situ hybridization chain reaction, immunohistochemistry, and proliferative cell labeling using 5-ethynyl-2'-deoxyuridine, and demonstrate its compatibility with tissue clearing. As a proof-of-concept, we show that our protocol allows for the parallel analysis of cell proliferation, gene expression, and protein localization in bristleworm heads and trunks.

Dynamic thresholding and tissue dissociation optimization for CITE-seq identifies differential surface protein abundance in metastatic melanoma

Multi-omics profiling by CITE-seq bridges the RNA-protein gap in single-cell analysis but has been largely applied to liquid biopsies. Applying CITE-seq to clinically relevant solid biopsies to characterize healthy tissue and the tumor microenvironment is an essential next step in single-cell translational studies. In this study, gating of cell populations based on their transcriptome signatures for use in cell type-specific ridge plots allowed identification of positive antibody signals and setting of manual thresholds. Next, we compare five skin dissociation protocols by taking into account dissociation efficiency, captured cell type heterogeneity and recovered surface proteome. To assess the effect of enzymatic digestion on transcriptome and epitope expression in immune cell populations, we analyze peripheral blood mononuclear cells (PBMCs) with and without dissociation. To further assess the RNA-protein gap, RNA-protein we perform codetection and correlation analyses on thresholded protein values. Finally, in a proof-of-concept study, using protein abundance analysis on selected surface markers in a cohort of healthy skin, primary, and metastatic melanoma we identify CD56 surface marker expression on metastatic melanoma cells, which was further confirmed by multiplex immunohistochemistry. This work provides practical guidelines for processing and analysis of clinically relevant solid tissue biopsies for biomarker discovery.

Single-Cell Multiomics

Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.

Fine tuning of neurointensive care in aneurysmal subarachnoid hemorrhage: From one-size-fits-all towards individualized care

Aneurysmal subarachnoid hemorrhage (aSAH) is a severe type of acute brain injury with high mortality and burden of neurological sequelae. General management aims at early aneurysm occlusion to prevent re-bleeding, cerebrospinal fluid drainage in case of increased intracranial pressure and/or acute hydrocephalus, and cerebral blood flow augmentation in case of delayed ischemic neurological deficits. In addition, the brain is vulnerable to physiological insults in the acute phase and neurointensive care (NIC) is important to optimize the cerebral physiology to avoid secondary brain injury. NIC has led to significantly better neurological recovery following aSAH, but there is still great room for further improvements. First, current aSAH NIC management protocols are to some extent extrapolated from those in traumatic brain injury, notwithstanding important disease-specific differences. Second, the same NIC management protocols are applied to all aSAH patients, despite great patient heterogeneity. Third, the main variables of interest, intracranial pressure and cerebral perfusion pressure, may be too superficial to fully detect and treat several important pathomechanisms. Fourth, there is a lack of understanding not only regarding physiological, but also cellular and molecular pathomechanisms and there is a need to better monitor and treat these processes. This narrative review aims to discuss current state-of-the-art NIC of aSAH, knowledge gaps in the field, and future directions towards a more individualized care in the future.

Omics-based approaches for the systematic profiling of mitochondrial biology

Mitochondria are essential for cellular functions such as metabolism and apoptosis. They dynamically adapt to the changing environmental demands by adjusting their protein, nucleic acid, metabolite, and lipid contents. In addition, the mitochondrial components are modulated on different levels in response to changes, including abundance, activity, and interaction. A wide range of omics-based approaches has been developed to be able to explore mitochondrial adaptation and how mitochondrial function is compromised in disease contexts. Here, we provide an overview of the omics methods that allow us to systematically investigate the different aspects of mitochondrial biology. In addition, we show examples of how these methods have provided new biological insights. The emerging use of these toolboxes provides a more comprehensive understanding of the processes underlying mitochondrial function.

Single-cell proteomics enabled by next-generation sequencing or mass spectrometry

In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies.

Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution

The clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic screening has been demonstrated as a powerful approach for unbiased functional genomics research. Single-cell CRISPR screening (scCRISPR) techniques, which result from the combination of single-cell toolkits and CRISPR screening, allow dissecting regulatory networks in complex biological systems at unprecedented resolution. These methods allow cells to be perturbed en masse using a pooled CRISPR library, followed by high-content phenotyping. This is technically accomplished by annotating cells with sgRNA-specific barcodes or directly detectable sgRNAs. According to the integration of distinct single-cell technologies, these methods principally fall into four categories: scCRISPR with RNA-seq, scCRISPR with ATAC-seq, scCRISPR with proteome probing, and imaging-based scCRISPR. scCRISPR has deciphered genotype-phenotype relationships, genetic regulations, tumor biological issues, and neuropathological mechanisms. This review provides insight into the technical breakthrough of scCRISPR by systematically summarizing the advancements of various scCRISPR methodologies and analyzing their merits and limitations. In addition, an application-oriented approach guide is offered to meet researchers' individualized demands.

A Dedicated 21-Plex Proximity Extension Assay Panel for High-Sensitivity Protein Biomarker Detection Using Microdialysis in Severe Traumatic Brain Injury: The Next Step in Precision Medicine?

Cerebral protein profiling in traumatic brain injury (TBI) is needed to better comprehend secondary injury pathways. Cerebral microdialysis (CMD), in combination with the proximity extension assay (PEA) technique, has great potential in this field. By using PEA, we have previously screened >500 proteins from CMD samples collected from TBI patients. In this study, we customized a PEA panel prototype of 21 selected candidate protein biomarkers, involved in inflammation (13), neuroplasticity/-repair (six), and axonal injury (two). The aim was to study their temporal dynamics and relation to age, structural injury, and clinical outcome. Ten patients with severe TBI and CMD monitoring, who were treated in the Neurointensive Care Unit, Uppsala University Hospital, Sweden, were included. Hourly CMD samples were collected for up to 7 days after trauma and analyzed with the 21-plex PEA panel. Seventeen of the 21 proteins from the CMD sample analyses showed significantly different mean levels between days. Early peaks (within 48 h) were noted with interleukin (IL)-1β, IL-6, IL-8, granulocyte colony-stimulating factor, transforming growth factor alpha, brevican, junctional adhesion molecule B, and neurocan. C-X-C motif chemokine ligand 10 peaked after 3 days. Late peaks (>5 days) were noted with interleukin-1 receptor antagonist (IL-1ra), monocyte chemoattractant protein (MCP)-2, MCP-3, urokinase-type plasminogen activator, Dickkopf-related protein 1, and DRAXIN. IL-8, neurofilament heavy chain, and TAU were biphasic. Age (above/below 22 years) interacted with the temporal dynamics of IL-6, IL-1ra, vascular endothelial growth factor, MCP-3, and TAU. There was no association between radiological injury (Marshall grade) or clinical outcome (Extended Glasgow Outcome Scale) with the protein expression pattern. The PEA method is a highly sensitive molecular tool for protein profiling from cerebral tissue in TBI. The novel TBI dedicated 21-plex panel showed marked regulation of proteins belonging to the inflammation, plasticity/repair, and axonal injury families. The method may enable important insights into complex injury processes on a molecular level that may be of value in future efforts to tailor pharmacological TBI trials to better address specific disease processes and optimize timing of treatments.

Divergent Directionality of Immune Cell-Specific Protein Expression between Bipolar Lithium Responders and Non-Responders Revealed by Enhanced Flow Cytometry

Background and Objectives: There is no biomarker to predict lithium response. This study used CellPrint™ enhanced flow cytometry to study 28 proteins representing a spectrum of cellular pathways in monocytes and CD4⁺ lymphocytes before and after lithium treatment in patients with bipolar disorder (BD). Materials and Methods: Symptomatic patients with BD type I or II received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks. Patients were assessed with standard rating scales and divided into two groups, responders (≥50% improvement from baseline) and non-responders. Twenty-eight intracellular proteins in CD4⁺ lymphocytes and monocytes were analyzed with CellPrint™, an enhanced flow cytometry procedure. Data were analyzed for differences in protein expression levels. Results: The intent-to-treat sample included 13 lithium-responders (12 blood samples before treatment and 9 after treatment) and 11 lithium-non-responders (11 blood samples before treatment and 4 after treatment). No significant differences in expression between the groups was observed prior to lithium treatment. After treatment, the majority of analytes increased expression in responders and decreased expression in non-responders. Significant increases were seen for PDEB4 and NR3C1 in responders. A significant decrease was seen for NR3C1 in non-responders. Conclusions: Lithium induced divergent directionality of protein expression depending on the whether the patient was a responder or non-responder, elucidating molecular characteristics of lithium responsiveness. A subsequent study with a larger sample size is warranted.

Into the multi-omics era: Progress of T cells profiling in the context of solid organ transplantation

T cells are the common type of lymphocyte to mediate allograft rejection, remaining long-term allograft survival impeditive. However, the heterogeneity of T cells, in terms of differentiation and activation status, the effector function, and highly diverse T cell receptors (TCRs) have thus precluded us from tracking these T cells and thereby comprehending their fate in recipients due to the limitations of traditional detection approaches. Recently, with the widespread development of single-cell techniques, the identification and characterization of T cells have been performed at single-cell resolution, which has contributed to a deeper comprehension of T cell heterogeneity by relevant detections in a single cell - such as gene expression, DNA methylation, chromatin accessibility, surface proteins, and TCR. Although these approaches can provide valuable insights into an individual cell independently, a comprehensive understanding can be obtained when applied joint analysis. Multi-omics techniques have been implemented in characterizing T cells in health and disease, including transplantation. This review focuses on the thesis, challenges, and advances in these technologies and highlights their application to the study of alloreactive T cells to improve the understanding of T cell heterogeneity in solid organ transplantation.

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics

Gene expression in mammalian cells is highly variable and episodic, resulting in a series of discontinuous bursts of mRNAs. A challenge is to understand how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this challenge, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpson's paradoxes, i.e. genome-wide burst kinetics exhibit different characteristics in two cases without and with distinguishing feedback regulations. We also showed that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.

Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation

Quantitative stochastic models of gene regulatory networks are important tools for studying cellular regulation. Such models can be formulated at many different levels of fidelity. A practical challenge is to determine what model fidelity to use in order to get accurate and representative results. The choice is important, because models of successively higher fidelity come at a rapidly increasing computational cost. In some situations, the level of detail is clearly motivated by the question under study. In many situations however, many model options could qualitatively agree with available data, depending on the amount of data and the nature of the observations. Here, an important distinction is whether we are interested in inferring the true (but unknown) physical parameters of the model or if it is sufficient to be able to capture and explain available data. The situation becomes complicated from a computational perspective because inference needs to be approximate. Most often it is based on likelihood-free Approximate Bayesian Computation (ABC) and here determining which summary statistics to use, as well as how much data is needed to reach the desired level of accuracy, are difficult tasks. Ultimately, all of these aspects-the model fidelity, the available data, and the numerical choices for inference-interplay in a complex manner. In this paper we develop a computational pipeline designed to systematically evaluate inference accuracy for a wide range of true known parameters. We then use it to explore inference settings for negative feedback gene regulation. In particular, we compare a detailed spatial stochastic model, a coarse-grained compartment-based multiscale model, and the standard well-mixed model, across several data-scenarios and for multiple numerical options for parameter inference. Practically speaking, this pipeline can be used as a preliminary step to guide modelers prior to gathering experimental data. By training Gaussian processes to approximate the distance function values, we are able to substantially reduce the computational cost of running the pipeline.

Quantification of extracellular proteins, protein complexes and mRNAs in single cells by proximity sequencing

We present proximity sequencing (Prox-seq) for simultaneous measurement of proteins, protein complexes and mRNAs in thousands of single cells. Prox-seq combines proximity ligation assay with single-cell sequencing to measure proteins and their complexes from all pairwise combinations of targeted proteins, providing quadratically scaled multiplexing. We validate Prox-seq and analyze a mixture of T cells and B cells to show that it accurately identifies these cell types and detects well-known protein complexes. Next, by studying human peripheral blood mononuclear cells, we discover that naïve CD8+ T cells display the protein complex CD8-CD9. Finally, we study protein interactions during Toll-like receptor (TLR) signaling in human macrophages. We observe the formation of signal-specific protein complexes, find CD36 co-receptor activity and additive signal integration under lipopolysaccharide (TLR4) and Pam2CSK4 (TLR2) stimulation, and show that quantification of protein complexes identifies signaling inputs received by macrophages. Prox-seq provides access to an untapped measurement modality for single-cell phenotyping and can discover uncharacterized protein interactions in different cell types.

Droplet microfluidics for functional temporal analysis and cell recovery on demand using microvalves: application in immunotherapies for cancer

Most common methods of cellular analysis employ the top-down approach (investigating proteomics or genomics directly), thereby destroying the cell, which does not allow the possibility of using the same cell to correlate genomics with functional assays. Herein we describe an approach for single-cell tools that serve as a bottom-up approach. Our technology allows functional phenotyping to be conducted by observing the cytotoxicity of cells and then probe the underlying biology. We have developed a droplet microfluidic device capable of trapping droplets in the array and releasing the droplet of interest selectively using microvalves. Each droplet in the array encapsulates natural killer cells (NK cells) and tumour cells for real-time monitoring of burst kinetics and spatial coordination during killing by single NK cells. Finally, we use the microvalve actuation to selectively release droplets with the desired functional phenotype such as for fast and serial killing of target tumour cells by NK cells. From this perspective, our device allows for investigating first interactions and real-time monitoring of kinetics and later cell recovery on demand for single-cell omic analysis such as single-cell RNA sequencing (scRNA), which to date, is primarily based on in-depth analyses of the entire transcriptome of a relatively low number of cells.

Future of Digital Assays to Resolve Clinical Heterogeneity of Single Extracellular Vesicles

Extracellular vesicles (EVs) are complex lipid membrane vehicles with variable expressions of molecular cargo, composed of diverse subpopulations that participate in the intercellular signaling of biological responses in disease. EV-based liquid biopsies demonstrate invaluable clinical potential for overhauling current practices of disease management. Yet, EV heterogeneity is a major needle-in-a-haystack challenge to translate their use into clinical practice. In this review, existing digital assays will be discussed to analyze EVs at a single vesicle resolution, and future opportunities to optimize the throughput, multiplexing, and sensitivity of current digital EV assays will be highlighted. Furthermore, this review will outline the challenges and opportunities that impact the clinical translation of single EV technologies for disease diagnostics and treatment monitoring.

Quantifying the phenotypic information in mRNA abundance

Quantifying the dependency between mRNA abundance and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single-cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual genes and gene combinations to a given phenotype. Using an information theory approach, we analyzed multimodal data of the expression of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca2+ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. Using different heuristics, we estimated the dependency between the size of a gene set and its information content, revealing that on average, a set of 53 genes contains 54% of the information about Ca2+ signaling. Our results provide the first direct quantification of information content about complex cellular phenotype that exists in mRNA abundance measurements.

Into the multiverse: advances in single-cell multiomic profiling

Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease.

Frontiers in single cell analysis: multimodal technologies and their clinical perspectives

Single cell multimodal analysis is at the frontier of single cell research: it defines the roles and functions of distinct cell types through simultaneous analysis to provide unprecedented insight into cellular processes. Current single cell approaches are rapidly moving toward multimodal characterizations. It replaces one-dimensional single cell analysis, for example by allowing for simultaneous measurement of transcription and post-transcriptional regulation, epigenetic modifications and/or surface protein expression. By providing deeper insights into single cell processes, multimodal single cell analyses paves the way to new understandings in various cellular processes such as cell fate decisions, physiological heterogeneity or genotype-phenotype linkages. At the forefront of this, microfluidics is key for high-throughput single cell analysis. Here, we present an overview of the recent multimodal microfluidic platforms having a potential in biomedical research, with a specific focus on their potential clinical applications.

Single-cell sequencing: expansion, integration and translation

With the rapid advancement in sequencing technologies, the concept of omics has revolutionized our understanding of cellular behaviors. Conventional omics investigation approaches measure the averaged behaviors of multiple cells, which may easily hide signals represented by a small-cell cohort, urging for the development of techniques with enhanced resolution. Single-cell RNA sequencing, investigating cell transcriptomics at the resolution of a single cell, has been rapidly expanded to investigate other omics such as genomics, proteomics and metabolomics since its invention. The requirement for comprehensive understanding of complex cellular behavior has led to the integration of multi-omics and single-cell sequencing data with other layers of information such as spatial data and the CRISPR screening technique towards gained knowledge or innovative functionalities. The development of single-cell sequencing in both dimensions has rendered it a unique field that offers us a versatile toolbox to delineate complex diseases, including cancers.

From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes?

Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.

Comparative single-cell RNA-sequencing profiling of BMP4-treated primary glioma cultures reveals therapeutic markers

BACKGROUND

Glioblastoma (GBM) is the most aggressive primary brain tumor. Its cellular composition is very heterogeneous, with cells exhibiting stem-cell characteristics (GSCs) that co-determine therapy resistance and tumor recurrence. Bone Morphogenetic Protein (BMP)-4 promotes astroglial and suppresses oligodendrocyte differentiation in GSCs, processes associated with superior patient prognosis. We characterized variability in cell viability of patient-derived GBM cultures in response to BMP4 and, based on single-cell transcriptome profiling, propose predictive positive and early-response markers for sensitivity to BMP4.

METHODS

Cell viability was assessed in 17 BMP4-treated patient-derived GBM cultures. In two cultures, one highly-sensitive to BMP4 (high therapeutic efficacy) and one with low-sensitivity, response to treatment with BMP4 was characterized. We applied single-cell RNA-sequencing, analyzed the relative abundance of cell clusters, searched for and identified the aforementioned two marker types, and validated these results in all 17 cultures.

RESULTS

High variation in cell viability was observed after treatment with BMP4. In three cultures with highest sensitivity for BMP4, a substantial new cell subpopulation formed. These cells displayed decreased cell proliferation and increased apoptosis. Neuronal differentiation was reduced most in cultures with little sensitivity for BMP4. OLIG1/2 levels were found predictive for high sensitivity to BMP4. Activation of ribosomal translation (RPL27A, RPS27) was up-regulated within one day in cultures that were very sensitive to BMP4.

CONCLUSION

The changes in composition of patient-derived GBM cultures obtained after treatment with BMP4 correlate with treatment efficacy. OLIG1/2 expression can predict this efficacy, and upregulation of RPL27A and RPS27 are useful early-response markers.

The Intriguing Landscape of Single-Cell Protein Analysis

Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

Simultaneous expansion microscopy imaging of proteins and mRNAs via dual-ExM

Simultaneous nanoscale imaging of mRNAs and proteins of the same specimen can provide better information on the translational regulation, molecular trafficking, and molecular interaction of both normal and diseased biological systems. Expansion microscopy (ExM) is an attractive option to achieve such imaging; however, simultaneous ExM imaging of proteins and mRNAs has not been demonstrated. Here, a technique for simultaneous ExM imaging of proteins and mRNAs in cultured cells and tissue slices, which we termed dual-expansion microscopy (dual-ExM), is demonstrated. First, we verified a protocol for the simultaneous labeling of proteins and mRNAs. Second, we combined the simultaneous labeling protocol with ExM to enable the simultaneous ExM imaging of proteins and mRNAs in cultured cells and mouse brain slices and quantitatively study the degree of signal retention after expansion. After expansion, both proteins and mRNAs can be visualized with a resolution beyond the diffraction limit of light in three dimensions. Dual-ExM is a versatile tool to study complex biological systems, such as the brain or tumor microenvironments, at a nanoscale resolution.

Protein Biomarkers in Monocytes and CD4+ Lymphocytes for Predicting Lithium Treatment Response of Bipolar Disorder: a Feasibility Study with Tyramine-Based Signal-Amplified Flow Cytometry

Purpose

To determine if enhanced flow cytometry (CellPrint™) can identify intracellular proteins of lithium responsiveness in monocytes and CD4⁺ lymphocytes from patients with bipolar disorder.

Methods

Eligible bipolar I or II patients were openly treated with lithium for 16-weeks. Baseline levels of Bcl2, BDNF, calmodulin, Fyn, phospho-Fyn/phospho-Yes, GSK3β, phospho-GSK3αβ, HMGB1, iNOS, IRS2, mTor, NLPR3, PGM1, PKA C-α, PPAR-γ, phospho-RelA, and TPH1 in monocytes and CD4⁺ lymphocytes of lithium responders and non-responders were measured with CellPrint™. Their utility of discriminating responders from non-responders was explored. Protein-protein network and pathway enrichment analyses were conducted.

Results

Of the 24 intent-to-treat patients, 12 patients completed the 16-week study. Eleven of 13 responders and 8 of 11 non-responders were available for this analysis. The levels of the majority of analytes in lithium responders were lower than non-responders in both cell types, but only the level of GSK3β in monocytes was significantly different (p = 0.034). The combination of GSK3β and phospho-GSK3αβ levels in monocytes correctly classified 11/11 responders and 5/8 non-responders. Combination of GSK3β, phospho-RelA, TPH1 and PGM1 correctly classified 10/11 responders and 6/7 non-responders, both with a likelihood of ≥ 85%. Prolactin, leptin, BDNF, neurotrophin, and epidermal growth factor/epidermal growth factor receptor signaling pathways are involved in the lithium treatment response. GSK3β and RelA genes are involved in 4 of 5 these pathways.

Conclusion

CellPrint™ flow cytometry was able to detect differences in multiple proteins in monocytes and CD4⁺ lymphocytes between lithium responders and non-responders. A large study is warranted to confirm or refute these findings.

SM-Omics is an automated platform for high-throughput spatial multi-omics

The spatial organization of cells and molecules plays a key role in tissue function in homeostasis and disease. Spatial transcriptomics has recently emerged as a key technique to capture and positionally barcode RNAs directly in tissues. Here, we advance the application of spatial transcriptomics at scale, by presenting Spatial Multi-Omics (SM-Omics) as a fully automated, high-throughput all-sequencing based platform for combined and spatially resolved transcriptomics and antibody-based protein measurements. SM-Omics uses DNA-barcoded antibodies, immunofluorescence or a combination thereof, to scale and combine spatial transcriptomics and spatial antibody-based multiplex protein detection. SM-Omics allows processing of up to 64 in situ spatial reactions or up to 96 sequencing-ready libraries, of high complexity, in a ~2 days process. We demonstrate SM-Omics in the mouse brain, spleen and colorectal cancer model, showing its broad utility as a high-throughput platform for spatial multi-omics.

Computational Methods for Single-Cell Imaging and Omics Data Integration

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

SETMAR Shorter Isoform: A New Prognostic Factor in Glioblastoma

Recent evidence suggests that the chimeric protein SETMAR is a factor of interest in cancer, especially in glioblastoma. However, little is known about the expression of this protein in glioblastoma tissues, and no study has been done to assess if SETMAR could be a prognostic and/or diagnostic marker of glioblastoma. We analyzed protein extracts of 47 glioblastoma samples coming from a local and a national cohort of patients. From the local cohort, we obtained localized biopsies from the central necrosis area, the tumor, and the perilesional brain. From the French Glioblastoma Biobank (FGB), we obtained three types of samples: from the same tumors before and after treatment, from long survivors, and from very short survivors. We studied the correlations between SETMAR amounts, clinical profiles of patients and other associated proteins (PTN, snRNP70 and OLIG2). In glioblastoma tissues, the shorter isoform of SETMAR (S-SETMAR) was predominant over the full-length isoform (FL-SETMAR), and the expression of both SETMAR variants was higher in the tumor compared to the perilesional tissues. Data from the FGB showed that SETMAR amounts were not different between the initial tumors and tumor relapses after treatment. These data also showed a trend toward higher amounts of S-SETMAR in long survivors. In localized biopsies, we found a positive correlation between good prognosis and large amounts of S-SETMAR in the perilesional area. This is the main result presented here: survival in Glioblastoma is correlated with amounts of S-SETMAR in perilesional brain, which should be considered as a new relevant prognosis marker.

Application of omics beyond the central dogma in coronary heart disease research: A bibliometric study and literature review

Despite remarkable progress in disease diagnosis and treatment, coronary heart disease (CHD) remains the number one leading cause of death worldwide. Many practical challenges still faced in clinical settings necessitates the pursuit of omics studies to identify alternative/orthogonal biomarkers, as well as to discover novel insights into disease mechanisms. Albeit relatively nascent as compared to the omics frontrunners (genomics, transcriptomics, and proteomics), omics beyond the central dogma (OBCD; e.g., metabolomics, lipidomics, glycomics, and metallomics) have undeniable contributions and prospects in CHD research. In this bibliometric study, we characterised the global trends in publication/citation outputs, collaborations, and research hotspots concerning OBCD-CHD, with a focus on the more prolific fields of metabolomics and lipidomics. As for glycomics and metallomics, there were insufficient publication records on their applications in CHD research for quantitative bibliometrics analysis. Thus, we reviewed their applications in health/disease research in general, discussed and justified their potential in CHD research, and suggested important/promising research avenues. By summarising evidence obtained both quantitatively and qualitatively, this study offers a first and comprehensive picture of OBCD applications in CHD, facilitating the establishment of future research directions.

Microglia in Alzheimer's Disease: a Key Player in the Transition Between Homeostasis and Pathogenesis

Immune activation accompanies the development of proteinopathy in the brains of Alzheimer's dementia patients. Evolving from the long-held viewpoint that immune activation triggers the pathological trajectory in Alzheimer's disease, there is accumulating evidence now that microglial activation is neither pro-amyloidogenic nor just a simple reactive process to the proteinopathy. Preclinical studies highlight an interesting aspect of immunity, i.e., spurring immune system activity may be beneficial under certain circumstances. Indeed, a dynamic evolving relationship between different activation states of the immune system and its neuronal neighbors is thought to regulate overall brain organ health in both healthy aging and progression of Alzheimer's dementia. A new premise evolving from genome, transcriptome, and proteome data is that there might be at least two major phases of immune activation that accompany the pathological trajectory in Alzheimer's disease. Though activation on a chronic scale will certainly lead to neurodegeneration, this emerging knowledge of a potential beneficial aspect of immune activation allows us to form holistic insights into when, where, and how much immune system activity would need to be tuned to impact the Alzheimer's neurodegenerative cascade. Even with the trove of recently emerging -omics data from patients and preclinical models, how microglial phenotypes are functionally related to the transition of a healthy aging brain towards progressive degenerative state remains unknown. A deeper understanding of the synergism between microglial functional states and brain organ health could help us discover newer interventions and therapies that enable us to address the current paucity of disease-modifying therapies in Alzheimer's disease.

Universal Nanoparticle Counting Platform for Tetraplexed Biomarkers by Integrating Immunorecognition and Nucleic Acid Hybridization in One Assay

The development of a simple and universal strategy for simultaneous quantification of proteins and nucleic acid biomarkers in one assay is valuable, particularly for disease diagnosis and pathogenesis studies. Herein, a universal and amplification-free quantum dot-doped nanoparticle counting platform was developed by integrating immunorecognition and nucleic acid hybridization in one assay. The assay can be performed at room temperature, which is friendly for routine analysis. Multiplexed biomarkers associated with Alzheimer's disease (AD) including proteins and nucleic acids were detected. For simultaneous detection of tetraplex biomarkers, the assay for amyloid β 1-42 (Aβ42), tau protein, miR-146a, and miR-138 presented limit of detection values of 250 pg/mL, 55.7 pg/mL, 52.5 pM, and 0.62 pM, respectively. By spiking all the above four biomarkers in one artificial cerebrospinal fluid sample, the recoveries were found to be 94.7-117.2%. Using tau protein as the model, four measurements in 88 days presented a coefficient of variance of 7.5%. The proposed platform for the multiplexed assay of proteins and nucleic acids presents the universality, reasonable sensitivity, and repeatability, which may open a new door for early diagnosis and pathogenesis research for AD and other diseases.

Multiplexed single-cell proteomics using SCoPE2

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

Ultra-Sensitive Quantification of Protein and mRNA in Single Mammalian Cells with Digital PLA

Digital proximity ligation assay (PLA) detects single protein molecules with a pair of oligonucleotide-conjugated antibodies and digital PCR (dPCR) readout, which allows absolute quantitation of proteins in single cells with high sensitivity. The pipeline also allows simultaneous measurement of protein and mRNA from the same single cell. The sensitivity of the assay has been further improved with implementation of the assay on a microfluidic system, which enables quantitation of rare protein species, with expression level as low as ~3000 protein molecules per cell.

Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects

Single-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.

High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip

Global quantification of protein abundances in single cells could provide direct information on cellular phenotypes and complement transcriptomics measurements. However, single-cell proteomics is still immature and confronts many technical challenges. Herein we describe a nested nanoPOTS (N2) chip to improve protein recovery, operation robustness, and processing throughput for isobaric-labeling-based scProteomics workflow. The N2 chip reduces reaction volume to <30 nL and increases capacity to >240 single cells on a single microchip. The tandem mass tag (TMT) pooling step is simplified by adding a microliter droplet on the nested nanowells to combine labeled single-cell samples. In the analysis of ~100 individual cells from three different cell lines, we demonstrate that the N2 chip-based scProteomics platform can robustly quantify ~1500 proteins and reveal membrane protein markers. Our analyses also reveal low protein abundance variations, suggesting the single-cell proteome profiles are highly stable for the cells cultured under identical conditions.

Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis

Understanding the dynamics of the human proteome is crucial for developing biomarkers to be used as measurable indicators for disease severity and progression, patient stratification, and drug development. The Proximity Extension Assay (PEA) is a technology that translates protein information into actionable knowledge by linking protein-specific antibodies to DNA-encoded tags. In this report we demonstrate how we have combined the unique PEA technology with an innovative and automated sample preparation and high-throughput sequencing readout enabling parallel measurement of nearly 1500 proteins in 96 samples generating close to 150,000 data points per run. This advancement will have a major impact on the discovery of new biomarkers for disease prediction and prognosis and contribute to the development of the rapidly evolving fields of wellness monitoring and precision medicine.

Argininosuccinate lyase is a metabolic vulnerability in breast development and cancer

Epithelial-to-mesenchymal transition (EMT) is fundamental to both normal tissue development and cancer progression. We hypothesized that EMT plasticity defines a range of metabolic phenotypes and that individual breast epithelial metabolic phenotypes are likely to fall within this phenotypic landscape. To determine EMT metabolic phenotypes, the metabolism of EMT was described within genome-scale metabolic models (GSMMs) using either transcriptomic or proteomic data from the breast epithelial EMT cell culture model D492. The ability of the different data types to describe breast epithelial metabolism was assessed using constraint-based modeling which was subsequently verified using ¹³C isotope tracer analysis. The application of proteomic data to GSMMs provided relatively higher accuracy in flux predictions compared to the transcriptomic data. Furthermore, the proteomic GSMMs predicted altered cholesterol metabolism and increased dependency on argininosuccinate lyase (ASL) following EMT which were confirmed in vitro using drug assays and siRNA knockdown experiments. The successful verification of the proteomic GSMMs afforded iBreast2886, a breast GSMM that encompasses the metabolic plasticity of EMT as defined by the D492 EMT cell culture model. Analysis of breast tumor proteomic data using iBreast2886 identified vulnerabilities within arginine metabolism that allowed prognostic discrimination of breast cancer patients on a subtype-specific level. Taken together, we demonstrate that the metabolic reconstruction iBreast2886 formalizes the metabolism of breast epithelial cell development and can be utilized as a tool for the functional interpretation of high throughput clinical data.