A combined approach for single-cell mRNA and intracellular protein expression analysis

From single cell to spatial multi-omics: unveiling molecular mechanisms in dynamic and heterogeneous systems

Single-cell multi-omics and spatial technology have been widely applied to biomedical studies and recently to environmental studies. The cell size detected by single-cell omics ranges from ∼2 µm (e.g., Bacillus subtilis) to ∼120 µm (e.g., human oocytes). Simultaneous detection of single-cell multi-omics is available to human and plant tissues while limited to microbial samples. Spatial technology enables mapping the detected biomolecules in situ. The recent advances in Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging and Micro/Nanodroplet Processing in One Pot for Trace Samples for the first time allow the application of spatial multi-omics in highly heterogeneous environmental samples composed of plants, fungi, and bacteria. We envision that these technologies will continue to advance our understanding of unique cell types, their developmental trajectory, and the intercellular signaling and interaction within biological samples.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Transfer RNA levels are tuned to support differentiation during Drosophila neurogenesis

Neural differentiation requires a multifaceted program to alter gene expression along the proliferation to differentiation axis. While critical changes occur at the level of transcription, post-transcriptional mechanisms allow fine-tuning of protein output. We investigated the role of tRNAs in regulating gene expression during neural differentiation by quantifying tRNA abundance in neural progenitor-biased and neuron-biased Drosophila larval brains. We found that tRNA profiles are largely consistent between progenitor-biased and neuron-biased brains but significant variation occurs for 10 cytoplasmic isodecoders (individual tRNA genes) and this establishes differential tRNA levels for 8 anticodon groups. We used these tRNA data to investigate relationships between tRNA abundance, codon optimality-mediated mRNA decay, and translation efficiency in progenitors and neurons. Our data reveal that tRNA levels strongly correlate with codon optimality-mediated mRNA decay within each cell type but generally do not explain differences in stabilizing versus destabilizing codons between cell types. Regarding translation efficiency, we found that tRNA expression in neural progenitors preferentially supports translation of mRNAs whose products are in high demand in progenitors, such as those associated with protein synthesis. In neurons, tRNA expression shifts to disfavor translation of proliferation-related transcripts and preferentially support translation of transcripts tied to neuron-specific functions like axon pathfinding and synapse formation. Overall, our analyses reveal that changes in tRNA levels along the neural differentiation axis support optimal gene expression in progenitors and neurons.

Detection of low-copy proteins in proteomic studies: issues and solutions

Detection of low-copy proteins in complex biological samples is one of the most important issues of modern proteomics. The main reason for inefficient detection of low protein concentrations is the insufficient sensitivity of mass spectrometric detectors and the high dynamic range of protein concentrations. In this study we have investigated the possibilities and limitations of a targeted mass spectrometric analysis using the reconstructed system of standard proteins UPS1 (Universal Proteomic Standard 1) as an example. The study has shown that the sensitivity of the method is affected by the concentration of target proteins of the UPS1 system, as well as by a high level of biological noise modelled by proteins of whole E. coli cell lysate. The limitations of the method have been overcome by concentrating and pre-fractionating the sample peptides in a reversed phase chromatographic system under alkaline elution conditions. Proteomic analysis of the biological sample (proteins of the human hepatocellular carcinoma cell line HepG2 encoded by genes of human chromosome 18) showed an increase in the sensitivity of the method as compared to the standard targeted mass spectrometric analysis. This culminated in registration of 94 proteins encoded by genes located on human chromosome18.

Integration of single-cell RNA sequencing of endothelial cells and proteomics to unravel the role of ICAM1-PTGS2 communication in apical periodontitis: A laboratory investigation

AIM

Endothelial cells (EDs) play a key role in angiogenesis and are associated with granulomatous lesions in patients with chronic apical periodontitis (CAP). This study aimed to investigate the diversity of EDs using single-cell ribonucleic acid sequencing (scRNA-seq) and to evaluate the regulation of intercellular adhesion molecule 1 (ICAM1) on the ferroptosis-related protein, prostaglandin-endoperoxide synthase 2 (PTGS2), in CAP.

METHODOLOGY

EDs from the uploaded scRNA-seq data of five CAP samples (GSE181688 and GSE197680) were categorized using distinct marker genes. The interactions between vein EDs (veinEndo) and other cell types were analysed using CellPhoneDB. Differentially expressed proteins in the proteomics of human umbilical vein EDs (HUVECs) and THP-1-derived macrophages infected with Porphyromonas gingivalis were compared with the differentially expressed genes (DEGs) of VeinEndo in scRNA-seq of CAP versus healthy control periodontal tissues. The protein-protein interaction of ICAM1-PTGS2 in macrophages and HUVECs was validated by adding recombinant ICAM1, ICAM1 inhibitor and PTGS2 inhibitor using real-time polymerase chain reaction (PCR), western blotting, and immunofluorescence staining.

RESULTS

EDs in patients with CAP were divided into eight subclusters: five vein ED, capillaries, arterials and EC (PLA). There were 29 mutually upregulated DEGs and two mutually downregulated DEGs in vein cells in the scRNA-seq data, as well as differentially expressed proteins in the proteomics of HUVECs. Real-time PCR and immunofluorescence staining showed that ICAM1 and PTGS2 were highly expressed in CAP, infected HUVECs, and macrophages. Recombinant protein ICAM1 may improve PTGS2 expression, reactive oxygen species (ROS), and Fe2+ levels and decrease glutathione peroxidase 4 (GPX4) and SLC7A11 protein levels. ICAM1 inhibitor may inverse the above changes.

CONCLUSIONS

scRNA-seq revealed the diversity of EDs in CAP and identified the possible regulation of ICAM1 by the ferroptosis-related protein, PTGS2, in infected HUVECs and macrophages, thus providing a basis for therapeutic approaches that target the inflammatory microenvironment of CAP.

Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization.

Cellular control of protein levels: A systems biology perspective

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

Fetal brain response to maternal inflammation requires microglia

In utero infection and maternal inflammation can adversely impact fetal brain development. Maternal systemic illness, even in the absence of direct fetal brain infection, is associated with an increased risk of neuropsychiatric disorders in affected offspring. The cell types mediating the fetal brain response to maternal inflammation are largely unknown, hindering the development of novel treatment strategies. Here, we show that microglia, the resident phagocytes of the brain, highly express receptors for relevant pathogens and cytokines throughout embryonic development. Using a rodent maternal immune activation (MIA) model in which polyinosinic:polycytidylic acid is injected into pregnant mice, we demonstrate long-lasting transcriptional changes in fetal microglia that persist into postnatal life. We find that MIA induces widespread gene expression changes in neuronal and non-neuronal cells; importantly, these responses are abolished by selective genetic deletion of microglia, indicating that microglia are required for the transcriptional response of other cortical cell types to MIA. These findings demonstrate that microglia play a crucial durable role in the fetal response to maternal inflammation, and should be explored as potential therapeutic cell targets.

Comparative analysis of whole plant, flower and root extracts of Chamomilla recutita L. and characteristic pure compounds reveals differential anti-inflammatory effects on human T cells

Introduction

Chronic inflammation is a hallmark of chronic wounds and inflammatory skin diseases. Due to a hyperactive and prolonged inflammation triggered by proinflammatory immune cells, transitioning to the repair and healing phase is halted. T cells may exacerbate the proinflammatory milieu by secreting proinflammatory cytokines. Chamomilla recutita L. (chamomile) has been suggested for use in several inflammatory diseases, implying a capability to modulate T cells. Here, we have characterized and compared the effects of differently prepared chamomile extracts and characteristic pure compounds on the T cell redox milieu as well as on the migration, activation, proliferation, and cytokine production of primary human T cells.

Methods

Phytochemical analysis of the extracts was carried out by LC-MS/MS. Primary human T cells from peripheral blood (PBTs) were pretreated with aqueous or hydroethanolic chamomile extracts or pure compounds. Subsequently, the effects on intracellular ROS levels, SDF-1α induced T cell migration, T cell activation, proliferation, and cytokine production after TCR/CD3 and CD28 costimulation were determined. Gene expression profiling was performed using nCounter analysis, followed by ingenuity pathway analysis, and validation at protein levels.

Results

The tested chamomile extracts and pure compounds differentially affected intracellular ROS levels, migration, and activation of T cells. Three out of five differently prepared extracts and two out of three pure compounds diminished T cell proliferation. In line with these findings, LC-MS/MS analysis revealed high heterogeneity of phytochemicals among the different extracts. nCounter based gene expression profiling identified several genes related to T cell functions associated with activation and differentiation to be downregulated. Most prominently, apigenin significantly reduced granzyme B induction and cytotoxic T cell activity.

Conclusion

Our results demonstrate an anti-inflammatory effect of chamomile- derived products on primary human T cells. These findings provide molecular explanations for the observed anti-inflammatory action of chamomile and imply a broader use of chamomile extracts in T cell driven chronic inflammatory diseases such as chronic wounds and inflammatory skin diseases. Importantly, the mode of extract preparation needs to be considered as the resulting different phytochemicals can result in differential effects on T cells.

Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues

Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and dissecting transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins, as in cells of the immune system. Cellular Indexing of Transcriptomes and Epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell type annotation requires a classifier that integrates multimodal data. Here, we describe MultiModal Classifier Hierarchy (MMoCHi), a marker-based approach for classification, reconciling gene and protein expression without reliance on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal novel subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.

Effect of Curcuma longa extract on reproduction function in mice and testosterone production in Leydig cells

Curcuma longa, best known for its culinary application as the main constituent of curry powder, has shown potential impact on the reproductive system. This study aimed to investigate the efficacy of Curcuma longa extract (CLE) on Kidney-Yang deficiency mice induced by hydrocortisone and the possible roles in testosterone secretion in Leydig cells. We evaluated male sexual behaviour, reproductive organ weight, testosterone levels, and histological tissue changes in hydrocortisone-induced mice. CLE effectively reversed hydrocortisone-induced Kidney-Yang deficiency syndrome by improving sexual behaviour, testis and epididymis weight, testosterone levels and reducing pathological damage. Our in vitro study further indicated that CLE stimulated testosterone production via upregulating the mRNA and protein expression of steroidogenic enzymes in Leydig cells. It significantly improved H89-inhibited protein expression of StAR and cAMP-response element-binding (CREB), as well as melatonin-suppressed StAR protein expression. The data obtained from this study suggest that CLE could alleviate Kidney-Yang deficiency symptoms and stimulate testosterone production by upregulating the steroidogenic pathway. This research identifies CLE as a potential nutraceutical option for addressing testosterone deficiency diseases.

Single-cell analytical technologies: uncovering the mechanisms behind variations in immune responses

The immune landscape varies among individuals. It determines the immune response and results in surprisingly diverse symptoms, even in response to similar external stimuli. However, the detailed mechanisms underlying such diverse immune responses have remained mostly elusive. The utilization of recently developed single-cell multimodal analysis platforms has started to answer this question. Emerging studies have elucidated several molecular networks that may explain diversity with respect to age or other factors. An elaborate interplay between inherent physical conditions and environmental conditions has been demonstrated. Furthermore, the importance of modifications by the epigenome resulting in transcriptome variation among individuals is gradually being revealed. Accordingly, epigenomes and transcriptomes are direct indicators of the medical history and dynamic interactions with environmental factors. Coronavirus disease 2019 (COVID-19) has recently become one of the most remarkable examples of the necessity of in-depth analyses of diverse responses with respect to various factors to improve treatment in severe cases and to prevent viral transmission from asymptomatic carriers. In fact, determining why some patients develop serious symptoms is still a pressing issue. Here, we review the current "state of the art" in single-cell analytical technologies and their broad applications to healthy individuals and representative diseases, including COVID-19.

Advances in single-cell omics and multiomics for high-resolution molecular profiling

Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

Dynamics of Whole Transcriptome Analysis (WTA) and Surface markers expression (AbSeq) in Immune Cells of COVID-19 Patients and Recovered captured through Single Cell Genomics

Introduction

Single-cell multi-omics studies, such as multidimensional transcriptomics (whole transcriptomic analysis, WTA), and surface marker analysis (antibody sequencing, AbSeq), have turned out to be valuable techniques that offer inaccessible possibilities for single-cell profiling of mRNA, lncRNA, and proteins.

Methods

We used this technique to understand the dynamics of mRNA and protein-level differences in healthy, COVID-19-infected and recovered individuals using peripheral blood mononuclear cells (PBMCs). Our results demonstrate that compared to mRNA expression, protein abundance is a better indicator of the disease state.

Results

We demonstrate that compared to mRNA expression, protein abundance is a better indicator of the disease state. We observed high levels of cell identity and regulatory markers, CD3E, CD4, CD8A, CD5, CD7, GITR, and KLRB1 in healthy individuals, whereas markers related to cell activation, CD38, CD28, CD69, CD62L, CD14, and CD16 elevated in the SARS-CoV-2 infected patients at both WTA and AbSeq levels. Curiously, in recovered individuals, there was a high expression of cytokine and chemokine receptors (CCR5, CCR7, CCR4, CXCR3, and PTGRD2). We also observed variations in the expression of markers within cell populations under different states.

Discussion

Furthermore, our study emphasizes the significance of employing an oligo-based method (AbSeq) that can help in diagnosis, prognosis, and protection from disease/s by identifying cell surface markers that are unique to different cell types or states. It also allows simultaneous study of a vast array of markers, surpassing the constraints of techniques like FACS to query the vast repertoire of proteins.

Single-cell RNA sequencing combined with proteomics of infected macrophages reveals prothymosin-α as a target for treatment of apical periodontitis

INTRODUCTION

Chronic apical periodontitis (CAP) is a common infectious disease of the oral cavity. Immune responses and osteoclastogenesis of monocytes/macrophages play a crucial role in CAP progression, and this study want to clarify role of monocytes/macrophages in CAP, which will contribute to treatment of CAP.

OBJECTIVES

We aim to explore the heterogeneity of monocyte populations in periapical lesion of CAP tissues and healthy control (HC) periodontal tissues by single-cell RNA sequencing (scRNA-seq), search novel targets for alleviating CAP, and further validate it by proteomics and in vitro and in vivo evaluations.

METHODS

ScRNA-seq was used to analyze the heterogeneity of monocyte populations in CAP, and proteomics of THP-1-derived macrophages with porphyromonas gingivalis infection were intersected with the differentially expressed genes (DEGs) of macrophages between CAP and HC tissues. The upregulated PTMA (prothymosin-α) were validated by immunofluorescence staining and quantitative real time polymerase chain reaction. We evaluated the effect of thymosin α1 (an amino-terminal proteolytic cleavage product of PTMA protein) on inflammatory factors and osteoclast differentiation of macrophages infected by P. gingivalis. Furthermore, we constructed mouse and rat mandibular bone lesions caused by apical periodontitis, and estimated treatment of systemic and topical administration of PTMA for CAP. Statistical analyses were performed using GraphPad Prism software (v9.2) RESULTS: Monocytes were divided into seven sub-clusters comprising monocyte-macrophage-osteoclast (MMO) differentiation in CAP. 14 up-regulated and 21 down-regulated genes and proteins were intersected between the DEGs of scRNA-seq data and proteomics, including the high expression of PTMA. Thymosin α1 may decrease several inflammatory cytokine expressions and osteoclastogenesis of THP-1-derived macrophages. Both systemic administration in mice and topical administration in the pulp chamber of rats alleviated periapical lesions.

CONCLUSIONS

PTMA upregulation in CAP moderates the inflammatory response and prevents the osteoclastogenesis of macrophages, which provides a basis for targeted therapeutic strategies for CAP.

Emerging multianalyte biosensors for the simultaneous detection of protein and nucleic acid biomarkers

Traditionally, biosensors are designed to detect one specific analyte. Nevertheless, disease progression is regulated in a highly interactive way by different classes of biomolecules like proteins and nucleic acids. Therefore, a more comprehensive analysis of biomarkers from a single sample is of utmost importance to further improve both, the accuracy of diagnosis as well as the therapeutic success. This review summarizes fundamentals like biorecognition and sensing strategies for the simultaneous detection of proteins and nucleic acids and discusses challenges related to multianalyte biosensor development. We present an overview of the current state of biosensors for the combined detection of protein and nucleic acid biomarkers associated with widespread diseases, among them cancer and infectious diseases. Furthermore, we outline the multianalyte analysis in the rapidly evolving field of single-cell multiomics, to stress its significance for the future discovery and validation of biomarkers. Finally, we provide a critical perspective on the performance and translation potential of multianalyte biosensors for medical diagnostics.

DNA mismatch repair system regulates the expression of PD-L1 through DNMTs in cervical cancer

BACKGROUND

Cervical cancer (CC) is a potential clinical application of PD-1/PD-L1 inhibitor. We aimed to study the mechanism of DNA mismatch repair (MMR) system regulating the expression of PD-L1 in CC through DNA methyltransferase (DNMTs).

METHODS

We collected pathological specimens from 118 cases of CC to analyze the relationship between PD-L1 expression and DNMTs in different MMR states. RNA interference (RNAi) technique was used to simulate the formation of CC cell line with MMR deficiency (dMMR) state, and subcutaneous tumor formation experiment was carried out in nude mice to verify the relationship between PD-L1 expression and DNMTs in MMR state.

RESULTS

The PD-L1 positive rate in 118 cases of CC was 58.47%, while the microsatellite instability (MSI) status accounted for 5.93%. There was a significant difference in the expression of PD-L1 between patients within the dMMR and MMR proficient (pMMR) groups (χ2 = 21.405, P < 0.001). Subcutaneous inoculation after infection of Siha cells led to successful tumorigenesis in nude mice, accompanied by a significant increase in the level of PD-L1 expression in the mouse tumors, while the expression level of MLH1 and MSH2 protein decreased significantly. We also found that PD-L1 expression was closely related to the expression of DNMTs.

CONCLUSION

PD-L1 is universal expressed on the surface of CC cells, dMMR status enhances the expression of PD-L1 on the surface of CC cells, dMMR states of CC are related to the demethylation status of the PD-L1 gene promoter region.

Current annotation strategies for T cell phenotyping of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) has become a popular technique for interrogating the diversity and dynamic nature of cellular gene expression and has numerous advantages in immunology. For example, scRNA-seq, in contrast to bulk RNA sequencing, can discern cellular subtypes within a population, which is important for heterogenous populations such as T cells. Moreover, recent advancements in the technology allow the parallel capturing of the highly diverse T-cell receptor (TCR) sequence with the gene expression. However, the field of single-cell RNA sequencing data analysis is still hampered by a lack of gold-standard cell phenotype annotation. This problem is particularly evident in the case of T cells due to the heterogeneity in both their gene expression and their TCR. While current cell phenotype annotation tools can differentiate major cell populations from each other, labelling T-cell subtypes remains problematic. In this review, we identify the common automated strategy for annotating T cells and their subpopulations, and also describe what crucial information is still missing from these tools.

Programming human cell fate: overcoming challenges and unlocking potential through technological breakthroughs

In recent years, there have been notable advancements in the ability to programme human cell identity, enabling us to design and manipulate cell function in a Petri dish. However, current protocols for generating target cell types often lack efficiency and precision, resulting in engineered cells that do not fully replicate the desired identity or functional output. This applies to different methods of cell programming, which face similar challenges that hinder progress and delay the achievement of a more favourable outcome. However, recent technological and analytical breakthroughs have provided us with unprecedented opportunities to advance the way we programme cell fate. The Company of Biologists' 2023 workshop on 'Novel Technologies for Programming Human Cell Fate' brought together experts in human cell fate engineering and experts in single-cell genomics, manipulation and characterisation of cells on a single (sub)cellular level. Here, we summarise the main points that emerged during the workshop's themed discussions. Furthermore, we provide specific examples highlighting the current state of the field as well as its trajectory, offering insights into the potential outcomes resulting from the application of these breakthrough technologies in precisely engineering the identity and function of clinically valuable human cells.

ioSearch: An approach for identifying interacting multiomics biomarkers using a novel algorithm with application on breast cancer data sets

Identification of biomarkers by integrating multiple omics together is important because complex diseases occur due to an intricate interplay of various genetic materials. Traditional single-omics association tests neither explore this crucial interomics dependence nor identify moderately weak signals due to the multiple-testing burden. Conversely, multiomics data integration imparts complementary information but suffers from an increased multiple-testing burden, data diversity inherent with different omics features, high-dimensionality, and so forth. Most of the available methods address subtype classification using dimension-reduction techniques to circumvent the sample size issue but interacting multiomics biomarker identification methods are unavailable. We propose a two-step model that first investigates phenotype-omics association using logistic regression. Then, selects disease-associated omics using sparse principal components which explores the interrelationship of multiple variables from two omics in a multivariate multiple regression framework. On the basis of this model, we developed a multiomics biomarker identification algorithm, interacting omics search (ioSearch), that jointly tests the effect of multiple omics with disease and between-omics associations by using pathway information that subsequently reduces the multiple-testing burden. Further, inference in terms of p values potentially makes it an easily interpretable biomarker identification tool. Extensive simulation demonstrates ioSearch as statistically powerful with a controlled Type-I error rate. Its application to publicly available breast cancer data sets identified relevant omics features in important pathways.

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.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Methods and applications for single-cell and spatial multi-omics

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.

Time-resolved single-cell RNAseq profiling identifies a novel Fabp5+ subpopulation of inflammatory myeloid cells with delayed cytotoxic profile in chronic spinal cord injury

Traumatic spinal cord injuries (SCI) are a group of highly debilitating pathologies affecting thousands annually, and adversely affecting quality of life. Currently, no fully restorative therapies exist, and SCI still results in significant personal, societal and financial burdens. Inflammation plays a major role in the evolution of SCI, with myeloid cells, including bone marrow derived macrophages (BMDMs) and microglia (MG) being primary drivers of both early secondary pathogenesis and delayed wound healing events. The precise role of myeloid cell subsets is unclear as upon crossing the blood-spinal cord barrier, infiltrating bone marrow derived macrophages (BMDMs) may take on the morphology of resident microglia, and upregulate canonical microglia markers, thus making the two populations difficult to distinguish. Here, we used time-resolved scRNAseq and transgenic fate-mapping to chart the transcriptional profiles of tissue-resident and -infiltrating myeloid cells in a mouse model of thoracic contusion SCI. Our work identifies a novel subpopulation of foam cell-like inflammatory myeloid cells with increased expression of Fatty Acid Binding Protein 5 (Fabp5) and comprise both tissue-resident and -infiltrating cells. Fabp5+ inflammatory myeloid cells display a delayed cytotoxic profile that is predominant at the lesion epicentre and extends into the chronic phase of SCI.

Omics Overview of the SPARC Gene in Mesothelioma

The SPARC gene plays multiple roles in extracellular matrix synthesis and cell shaping, associated with tumor cell migration, invasion, and metastasis. The SPARC gene is also involved in the epithelial-mesenchymal transition (EMT) process, which is a critical phenomenon leading to a more aggressive cancer cell phenotype. SPARC gene overexpression has shown to be associated with poor survival in the mesothelioma (MESO) cohort from the TCGA database, indicating that this gene may be a powerful prognostic factor in MESO. Its overexpression is correlated with the immunosuppressive tumor microenvironment. Here, we summarize the omics advances of the SPARC gene, including the summary of SPARC gene expression associated with prognosis in pancancer and MESO, the immunosuppressive microenvironment, and cancer cell stemness. In addition, SPARC might be targeted by microRNAs. Notably, despite the controversial functions on angiogenesis, SPARC may directly or indirectly contribute to tumor angiogenesis in MESO. In conclusion, SPARC is involved in tumor invasion, metastasis, immunosuppression, cancer cell stemness, and tumor angiogenesis, eventually impacting patient survival. Strategies targeting this gene may provide novel therapeutic approaches to the treatment of MESO.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but absolute proteomic data remain scarce compared to transcriptomics obtained by RNA sequencing. We addressed this persistent gap by relating mRNA to protein statistically using best-available data from quantitative proteomics-transcriptomics for 4366 genes in 369 cell lines. The approach starts with a central estimate of protein copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model that links mRNAs to protein. For dozens of independent cell lines and primary prostate samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, and empirical protein-to-mRNA ratios. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein interaction complexes, suggesting mechanistic relationships are embedded. We use the method to estimate viral-receptor abundances of CD55-CXADR from human heart transcriptomes and build 1489 systems-biology models of coxsackievirus B3 infection susceptibility. When applied to 796 RNA sequencing profiles of breast cancer from The Cancer Genome Atlas, inferred copy-number estimates collectively reclassify 26% of Luminal A and 29% of Luminal B tumors. Protein-based reassignments strongly involve a pharmacologic target for luminal breast cancer (CDK4) and an α-catenin that is often undetectable at the mRNA level (CTTNA2). Thus, by adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility limits of contemporary proteomics. The collection of gene-specific models is assembled as a web tool for users seeking mRNA-guided predictions of absolute protein abundance (http://janeslab.shinyapps.io/Pinferna).

Endothelin Receptor-A Inhibition Decreases Ductular Reaction, Liver Fibrosis, and Angiogenesis in a Model of Cholangitis

BACKGROUND & AIMS

Primary sclerosing cholangitis (PSC) leads to ductular reaction and fibrosis and is complicated by vascular dysfunction. Cholangiocyte and endothelial cell crosstalk modulates their proliferation in cholestatic models. Endothelin (ET)-1 and ET-2 bind to their receptor, ET-A, and cholangiocytes are a key source of ET-1 after bile duct ligation. We aimed to evaluate the therapeutic potential of ET-A inhibition in PSC and biliary-endothelial crosstalk mediated by this pathway.

METHODS

Wild-type and multidrug resistance 2 knockout (Mdr2-/-) mice at 12 weeks of age were treated with vehicle or Ambrisentan (ET-A antagonist) for 1 week by daily intraperitoneal injections. Human control and PSC samples were used.

RESULTS

Mdr2-/- mice at 4, 8, and 12 weeks displayed angiogenesis that peaked at 12 weeks. Mdr2-/- mice at 12 weeks had enhanced biliary ET-1/ET-2/ET-A expression and secretion, whereas human PSC had enhanced ET-1/ET-A expression and secretion. Ambrisentan reduced biliary damage, immune cell infiltration, and fibrosis in Mdr2-/- mice. Mdr2-/- mice had squamous cholangiocytes with blunted microvilli and dilated arterioles lacking cilia; however, Ambrisentan reversed these alterations. Ambrisentan decreased cholangiocyte expression of pro-angiogenic factors, specifically midkine, through the regulation of cFOS. In vitro, ET-1/ET-A caused cholangiocyte senescence, endothelial cell angiogenesis, and macrophage inflammation. In vitro, human PSC cholangiocyte supernatants increased endothelial cell migration, which was blocked with Ambrisentan treatment.

CONCLUSIONS

ET-A inhibition reduced biliary and liver damage in Mdr2-/- mice. ET-A promotes biliary angiocrine signaling that may, in turn, enhance angiogenesis. Targeting ET-A may prove therapeutic for PSC, specifically patients displaying vascular dysfunction.

SEC-seq: association of molecular signatures with antibody secretion in thousands of single human plasma cells

The secreted products of cells drive many functions in vivo; however, methods to link this functional information to surface markers and transcriptomes have been lacking. By accumulating secretions close to secreting cells held within cavity-containing hydrogel nanovials, we demonstrate workflows to analyze the amount of IgG secreted from single human B cells and link this information to surface markers and transcriptomes from the same cells. Measurements using flow cytometry and imaging flow cytometry corroborate the association between IgG secretion and CD38/CD138. By using oligonucleotide-labeled antibodies we find that upregulation of pathways for protein localization to the endoplasmic reticulum and mitochondrial oxidative phosphorylation are most associated with high IgG secretion, and uncover surrogate plasma cell surface markers (e.g., CD59) defined by the ability to secrete IgG. Altogether, this method links quantity of secretion with single-cell sequencing (SEC-seq) and enables researchers to fully explore the links between genome and function, laying the foundation for discoveries in immunology, stem cell biology, and beyond.

Angiotensin-(1-7) ameliorates intestinal barrier dysfunction by activating the Keap1/Nrf2/HO-1 signaling pathway in acute pancreatitis

BACKGROUND

Intestinal barrier dysfunction is a serious complication associated with acute pancreatitis (AP). Angiotensin (Ang)-(1-7) plays a protective role in the intestinal barrier, but the underlying mechanism remains clear. This study investigated the impact of Ang-(1-7) on AP-induced intestinal dysfunction and its involvement in the Keap1/Nrf2/HO-1 pathway.

METHODS AND RESULTS

We studied caerulein- and lipopolysaccharide (LPS)-induced AP in mice and an epithelial cell line (IEC-6) from the small intestinal crypt of rats. Ang-(1-7) was administered orally or via the tail vein. IEC-6 cells were divided into five groups: control; LPS; LPS + Ang-(1-7); LPS + Ang-(1-7) + ML385 (an Nrf2 inhibitor); and LPS + ML385. Pancreatic and intestinal histopathology scores were analyzed using the Schmidt and Chiu scores. The expression of intestinal barrier-associated proteins and Keap1/Nrf2/HO-1 pathway constituents was assessed by RT-PCR and western blotting. The peroxide and antioxidant activities in the IEC-6 cells were measured. Compared to those in AP mice, Ang-(1-7) diminished the intestinal levels of proinflammatory factors (interleukin-1β and tumor necrosis factor α) and serum levels of intestine permeability (D-lactate). Ang-(1-7) increased the expression of barrier-associated proteins (aquaporin-1, claudin-1, and occludin) compared to those in the AP and LPS group. Moreover, Ang-(1-7) promoted the Keap/Nrf2/HO-1 pathway, which resulted in significantly reduced malondialdehyde and increased superoxide dismutase levels.. However, ML385 abolished the effects of Ang-(1-7) on barrier-associated proteins and reversed the Keap1/Nrf2/HO-1 pathway.

CONCLUSIONS

Ang-(1-7) reduces AP-induced intestinal inflammation and oxidative injuries by activating the Keap1/Nrf2/HO-1 pathway.

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.

Global Molecular Response of Paracoccidioides brasiliensis to Zinc Deprivation: Analyses at Transcript, Protein and MicroRNA Levels

Zinc is one of the main micronutrients for all organisms. One of the defense mechanisms used by the host includes the sequestration of metals used in fungal metabolism, such as iron and zinc. There are several mechanisms that maintain the balance in the intracellular zinc supply. MicroRNAs are effector molecules of responses between the pathogen and host, favoring or preventing infection in many microorganisms. Fungi of the Paracoccidioides genus are thermodimorphic and the etiological agents of paracoccidioidomycosis (PCM). In the current pandemic scenario world mycosis studies continue to be highly important since a significant number of patients with COVID-19 developed systemic mycoses, co-infections that complicated their clinical condition. The objective was to identify transcriptomic and proteomic adaptations in Paracoccidioides brasiliensis during zinc deprivation. Nineteen microRNAs were identified, three of which were differentially regulated. Target genes regulated by those microRNAs are elements of zinc homeostasis such as ZRT1, ZRT3 and COT1 transporters. Transcription factors that have zinc in their structure are also targets of those miRNAs. Transcriptional and proteomic data suggest that P. brasiliensis undergoes metabolic remodeling to survive zinc deprivation and that miRNAs may be part of the regulatory process.

Integration of Candida albicans-induced single-cell gene expression data and secretory protein concentrations reveal genetic regulators of inflammation

Both gene expression and protein concentrations are regulated by genetic variants. Exploring the regulation of both eQTLs and pQTLs simultaneously in a context- and cell-type dependent manner may help to unravel mechanistic basis for genetic regulation of pQTLs. Here, we performed meta-analysis of Candida albicans-induced pQTLs from two population-based cohorts and intersected the results with Candida-induced cell-type specific expression association data (eQTL). This revealed systematic differences between the pQTLs and eQTL, where only 35% of the pQTLs significantly correlated with mRNA expressions at single cell level, indicating the limitation of eQTLs use as a proxy for pQTLs. By taking advantage of the tightly co-regulated pattern of the proteins, we also identified SNPs affecting protein network upon Candida stimulations. Colocalization of pQTLs and eQTLs signals implicated several genomic loci including MMP-1 and AMZ1. Analysis of Candida-induced single cell gene expression data implicated specific cell types that exhibit significant expression QTLs upon stimulation. By highlighting the role of trans-regulatory networks in determining the abundance of secretory proteins, our study serve as a framework to gain insights into the mechanisms of genetic regulation of protein levels in a context-dependent manner.

Small intestine and colon tissue-resident memory CD8+ T cells exhibit molecular heterogeneity and differential dependence on Eomes

Tissue-resident memory CD8+ T (TRM) cells are a subset of memory T cells that play a critical role in limiting early pathogen spread and controlling infection. TRM cells exhibit differences across tissues, but their potential heterogeneity among distinct anatomic compartments within the small intestine and colon has not been well recognized. Here, by analyzing TRM cells from the lamina propria and epithelial compartments of the small intestine and colon, we showed that intestinal TRM cells exhibited distinctive patterns of cytokine and granzyme expression along with substantial transcriptional, epigenetic, and functional heterogeneity. The T-box transcription factor Eomes, which represses TRM cell formation in some tissues, exhibited unexpected context-specific regulatory roles in supporting the maintenance of established TRM cells in the small intestine, but not in the colon. Taken together, these data provide previously unappreciated insights into the heterogeneity and differential requirements for the formation vs. maintenance of intestinal TRM cells.

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.

Statistical and Computational Methods for Proteogenomic Data Analysis

Proteins are the functional molecules for almost all cellular and biological processes. They are also the targets of most drugs. Proteins employ complex, multilevel regulations, so their abundance levels do not well correlated with their mRNA expression levels. The structure, activity, and functional roles of proteins are affected by posttranslational modifications (PTM), which are even less correlated with mRNA expression levels than protein abundances. Comprehensive characterization of the proteomics data is critical for understanding the molecular and cellular mechanisms of biological systems and developing news therapeutics. Current large-scale proteomic profiling technologies, such as mass spectrometry, provide relative identification of peptides and proteins, with data vulnerable to outliers, batch effects, and nonrandom missingness. In order to perform high-quality proteomic data analysis, we will first introduce a data preprocessing and quality control pipeline that includes normalization, outlier detection and removal, batch effect identification and handling, and missing data imputation. Then, we will describe several statistical methods that leverage well-processed proteomic data to generate scientific discoveries, especially with an integration with genomics and transcriptomics. These methods cover topics like association analysis, network construction, clustering, and cell-type deconvolution. To demonstrate these methods, we will use the proteogenomic data from the lung squamous cell carcinoma study of the Clinical Proteomic Tumor Analysis Consortium and provide sample codes for data access and analyses.

Spatial proteomics in three-dimensional intact specimens

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT.

XCVATR: detection and characterization of variant impact on the Embeddings of single -cell and bulk RNA-sequencing samples

BACKGROUND

RNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq can be used for detecting variants, including single nucleotide polymorphisms, small insertions/deletions, and larger variants, such as copy number variants. Notably, joint analysis of variants with cellular transcriptional states may provide insights into the impact of mutations, especially for complex and heterogeneous samples. However, this analysis is often challenging due to a prohibitively high number of variants and cells, which are difficult to summarize and visualize. Further, there is a dearth of methods that assess and summarize the association between detected variants and cellular transcriptional states.

RESULTS

Here, we introduce XCVATR (eXpressed Clusters of Variant Alleles in Transcriptome pRofiles), a method that identifies variants and detects local enrichment of expressed variants within embedding of samples and cells in single-cell and bulk RNA-seq datasets. XCVATR visualizes local "clumps" of small and large-scale variants and searches for patterns of association between each variant and cellular states, as described by the coordinates of cell embedding, which can be computed independently using any type of distance metrics, such as principal component analysis or t-distributed stochastic neighbor embedding. Through simulations and analysis of real datasets, we demonstrate that XCVATR can detect enrichment of expressed variants and provide insight into the transcriptional states of cells and samples. We next sequenced 2 new single cell RNA-seq tumor samples and applied XCVATR. XCVATR revealed subtle differences in CNV impact on tumors.

CONCLUSIONS

XCVATR is publicly available to download from https://github.com/harmancilab/XCVATR .

Deep proteome profiling reveals signatures of age and sex differences in paw skin and sciatic nerve of naïve mice

The age and sex of studied animals profoundly impact experimental outcomes in biomedical research. However, most preclinical studies in mice use a wide-spanning age range from 4 to 20 weeks and do not assess male and female mice in parallel. This raises concerns regarding reproducibility and neglects potentially relevant age and sex differences, which are largely unknown at the molecular level in naïve mice. Here, we employed an optimized quantitative proteomics workflow in order to deeply profile mouse paw skin and sciatic nerves (SCN) - two tissues implicated in nociception and pain as well as diseases linked to inflammation, injury, and demyelination. Remarkably, we uncovered significant differences when comparing male and female mice at adolescent (4 weeks) and adult (14 weeks) age. Our analysis deciphered protein subsets and networks that were correlated with the age and/or sex of mice. Notably, among these were proteins/biological pathways with known (patho)physiological relevance, e.g., homeostasis and epidermal signaling in skin, and, in SCN, multiple myelin proteins and regulators of neuronal development. Extensive comparisons with available databases revealed that various proteins associated with distinct skin diseases and pain exhibited significant abundance changes in dependence on age and/or sex. Taken together, our study uncovers hitherto unknown sex and age differences at the level of proteins and protein networks. Overall, we provide a unique proteome resource that facilitates mechanistic insights into somatosensory and skin biology, and integrates age and sex as biological variables - a prerequisite for successful preclinical studies in mouse disease models.

A developmental atlas of somatosensory diversification and maturation in the dorsal root ganglia by single-cell mass cytometry

Precisely controlled development of the somatosensory system is essential for detecting pain, itch, temperature, mechanical touch and body position. To investigate the protein-level changes that occur during somatosensory development, we performed single-cell mass cytometry on dorsal root ganglia from C57/BL6 mice of both sexes, with litter replicates collected daily from embryonic day 11.5 to postnatal day 4. Measuring nearly 3 million cells, we quantified 30 molecularly distinct somatosensory glial and 41 distinct neuronal states across all timepoints. Analysis of differentiation trajectories revealed rare cells that co-express two or more Trk receptors and over-express stem cell markers, suggesting that these neurotrophic factor receptors play a role in cell fate specification. Comparison to previous RNA-based studies identified substantial differences between many protein-mRNA pairs, demonstrating the importance of protein-level measurements to identify functional cell states. Overall, this study demonstrates that mass cytometry is a high-throughput, scalable platform to rapidly phenotype somatosensory tissues.

Non-coding antisense transcripts: fine regulation of gene expression in cancer

Natural antisense transcripts (NATs) are coding or non-coding RNA sequences transcribed on the opposite direction from the same genomic locus. NATs are widely distributed throughout the human genome and seem to play crucial roles in physiological and pathological processes, through newly described and targeted mechanisms. NATs represent the intricate complexity of the genome organization and constitute another layer of potential targets in disease. Here, we focus on the interesting and unique role of non-coding NATs in cancer, paying particular attention to those acting as miRNA sponges.

Inferring gene regulation from stochastic transcriptional variation across single cells at steady state

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

Full spectrum flow cytometry reveals mesenchymal heterogeneity in first trimester placentae and phenotypic convergence in culture, providing insight into the origins of placental mesenchymal stromal cells

Single-cell technologies (RNA-sequencing, flow cytometry) are critical tools to reveal how cell heterogeneity impacts developmental pathways. The placenta is a fetal exchange organ, containing a heterogeneous mix of mesenchymal cells (fibroblasts, myofibroblasts, perivascular, and progenitor cells). Placental mesenchymal stromal cells (pMSC) are also routinely isolated, for therapeutic and research purposes. However, our understanding of the diverse phenotypes of placental mesenchymal lineages, and their relationships remain unclear. We designed a 23-colour flow cytometry panel to assess mesenchymal heterogeneity in first-trimester human placentae. Four distinct mesenchymal subsets were identified; CD73⁺CD90⁺ mesenchymal cells, CD146⁺CD271⁺ perivascular cells, podoplanin⁺CD36⁺ stromal cells, and CD26⁺CD90⁺ myofibroblasts. CD73⁺CD90⁺ and podoplanin + CD36+ cells expressed markers consistent with cultured pMSCs, and were explored further. Despite their distinct ex-vivo phenotype, in culture CD73⁺CD90⁺ cells and podoplanin⁺CD36⁺ cells underwent phenotypic convergence, losing CD271 or CD36 expression respectively, and homogenously exhibiting a basic MSC phenotype (CD73⁺CD90⁺CD31^-CD144^-CD45^-). However, some markers (CD26, CD146) were not impacted, or differentially impacted by culture in different populations. Comparisons of cultured phenotypes to pMSCs further suggested cultured pMSCs originate from podoplanin⁺CD36⁺ cells. This highlights the importance of detailed cell phenotyping to optimise therapeutic capacity, and ensure use of relevant cells in functional assays.

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.

Single-cell sorting based on secreted products for functionally defined cell therapies

Cell therapies have emerged as a promising new class of "living" therapeutics over the last decade and have been particularly successful for treating hematological malignancies. Increasingly, cellular therapeutics are being developed with the aim of treating almost any disease, from solid tumors and autoimmune disorders to fibrosis, neurodegenerative disorders and even aging itself. However, their therapeutic potential has remained limited due to the fundamental differences in how molecular and cellular therapies function. While the structure of a molecular therapeutic is directly linked to biological function, cells with the same genetic blueprint can have vastly different functional properties (e.g., secretion, proliferation, cell killing, migration). Although there exists a vast array of analytical and preparative separation approaches for molecules, the functional differences among cells are exacerbated by a lack of functional potency-based sorting approaches. In this context, we describe the need for next-generation single-cell profiling microtechnologies that allow the direct evaluation and sorting of single cells based on functional properties, with a focus on secreted molecules, which are critical for the in vivo efficacy of current cell therapies. We first define three critical processes for single-cell secretion-based profiling technology: (1) partitioning individual cells into uniform compartments; (2) accumulating secretions and labeling via reporter molecules; and (3) measuring the signal associated with the reporter and, if sorting, triggering a sorting event based on these reporter signals. We summarize recent academic and commercial technologies for functional single-cell analysis in addition to sorting and industrial applications of these technologies. These approaches fall into three categories: microchamber, microfluidic droplet, and lab-on-a-particle technologies. Finally, we outline a number of unmet needs in terms of the discovery, design and manufacturing of cellular therapeutics and how the next generation of single-cell functional screening technologies could allow the realization of robust cellular therapeutics for all patients.

Single-cell multi-omics sequencing and its applications in studying the nervous system

Single-cell sequencing has become one of the most powerful and popular techniques in dissecting molecular heterogeneity and modeling the cellular architecture of a biological system. During the past twenty years, the throughput of single-cell sequencing has increased from hundreds of cells to over tens of thousands of cells in parallel. Moreover, this technology has been developed from sequencing transcriptome to measure different omics such as DNA methylome, chromatin accessibility, and so on. Currently, multi-omics which can analyze different omics in the same cell is rapidly advancing. This work advances the study of many biosystems, including the nervous system. Here, we review current single-cell multi-omics sequencing techniques and describe how they improve our understanding of the nervous system. Finally, we discuss the open scientific questions in neural research that may be answered through further improvement of single-cell multi-omics sequencing technology.

Understanding the Phytoremediation Mechanisms of Potentially Toxic Elements: A Proteomic Overview of Recent Advances

Potentially toxic elements (PTEs) such as cadmium (Cd), lead (Pb), chromium (Cr), and arsenic (As), polluting the environment, pose a significant risk and cause a wide array of adverse changes in plant physiology. Above threshold accumulation of PTEs is alarming which makes them prone to ascend along the food chain, making their environmental prevention a critical intervention. On a global scale, current initiatives to remove the PTEs are costly and might lead to more pollution. An emerging technology that may help in the removal of PTEs is phytoremediation. Compared to traditional methods, phytoremediation is eco-friendly and less expensive. While many studies have reported several plants with high PTEs tolerance, uptake, and then storage capacity in their roots, stem, and leaves. However, the wide application of such a promising strategy still needs to be achieved, partly due to a poor understanding of the molecular mechanism at the proteome level controlling the phytoremediation process to optimize the plant's performance. The present study aims to discuss the detailed mechanism and proteomic response, which play pivotal roles in the uptake of PTEs from the environment into the plant's body, then scavenge/detoxify, and finally bioaccumulate the PTEs in different plant organs. In this review, the following aspects are highlighted as: (i) PTE's stress and phytoremediation strategies adopted by plants and (ii) PTEs induced expressional changes in the plant proteome more specifically with arsenic, cadmium, copper, chromium, mercury, and lead with models describing the metal uptake and plant proteome response. Recently, interest in the comparative proteomics study of plants exposed to PTEs toxicity results in appreciable progress in this area. This article overviews the proteomics approach to elucidate the mechanisms underlying plant's PTEs tolerance and bioaccumulation for optimized phytoremediation of polluted environments.

NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells

In this work, we describe NEAT-seq (sequencing of nuclear protein epitope abundance, chromatin accessibility and the transcriptome in single cells), enabling interrogation of regulatory mechanisms spanning the central dogma. We apply this technique to profile CD4 memory T cells using a panel of master transcription factors (TFs) that drive T cell subsets and identify examples of TFs with regulatory activity gated by transcription, translation and regulation of chromatin binding. We also link a noncoding genome-wide association study single-nucleotide polymorphism (SNP) within a GATA motif to a putative target gene, using NEAT-seq data to internally validate SNP impact on GATA3 regulation.

Similarities and differences between nigral and enteric dopaminergic neurons unravel distinctive involvement in Parkinson's disease

In addition to the well-known degeneration of midbrain dopaminergic neurons, enteric neurons can also be affected in neurodegenerative disorders such as Parkinson's disease (PD). Dopaminergic neurons have recently been identified in the enteric nervous system (ENS). While ENS dopaminergic neurons have been shown to degenerate in genetic mouse models of PD, analyses of their survival in enteric biopsies of PD patients have provided inconsistent results to date. In this context, this review seeks to highlight the distinctive and shared factors and properties that control the evolution of these two sets of dopaminergic neurons from neuronal precursors to aging neurons. Although their cellular sources and developmental times of origin differ, midbrain and ENS dopaminergic neurons express many transcription factors in common and their respective environments express similar neurotrophic molecules. For example, Foxa2 and Sox6 are expressed by both populations to promote the specification, differentiation, and long-term maintenance of the dopaminergic phenotype. Both populations exhibit sustained patterns of excitability that drive intrinsic vulnerability over time. In disorders such as PD, colon biopsies have revealed aggregation of alpha-synuclein in the submucosal plexus where dopaminergic neurons reside and lack blood barrier protection. Thus, these enteric neurons may be more susceptible to neurotoxic insults and aggregation of α-synuclein that spreads from gut to midbrain. Under sustained stress, inefficient autophagy leads to neurodegeneration, GI motility dysfunction, and PD symptoms. Recent findings suggest that novel neurotrophic factors such as CDNF have the potential to be used as neuroprotective agents to prevent and treat ENS symptoms of PD.