Single-Cell Proteomics

Proteoform profiling of endogenous single cells from rat hippocampus at scale

We perform intact proteoform profiling of 10,809 endogenous single cells from the rat hippocampus using single-cell proteoform imaging mass spectrometry (scPiMS). scPiMS directly extracts whole proteins and demonstrates high throughput for MS-based single-cell proteomics compared with existing approaches. We develop an informatics workflow dedicated to this datatype and use it to assign neurons, astrocytes or microglia cell types according to their proteoform signatures.

The metabolic dialogue between intratumoural microbes and cancer: implications for immunotherapy

The tumour microenvironment (TME) exerts a profound influence on cancer progression and treatment outcomes. Recent investigations have elucidated the crucial role of intratumoural microbiota and their metabolites in shaping the TME and modulating anti-tumour immunity. This review critically assesses the influence of intratumoural microbial metabolites on the TME and cancer immunotherapy. We systematically analyse how microbial-derived glucose, amino acid, and lipid metabolites modulate immune cell function, cytokine secretion, and tumour growth. The roles of specific metabolites, including lactate, short-chain fatty acids, bile acids, and tryptophan derivatives, are comprehensively examined in regulating immune responses and tumour progression. Furthermore, we investigate the potential of these metabolites to augment the efficacy of cancer immunotherapies, with particular emphasis on immune checkpoint inhibitors. By delineating the mechanisms through which microbial metabolites influence the TME, this review provides insights into novel microbiome-based therapeutic strategies, thereby highlighting a promising frontier in personalised cancer medicine.

Microamount Phosphopeptide-Enrichment-System-Based Phosphoproteomic Analysis for Small Numbers of Cells and Single Cells

Protein phosphorylation plays an important role in cellular regulation and signal transduction. In this study, we developed a microamount phosphopeptide enrichment system (mPES) to achieve the enrichment of phosphopeptides in nanoliter-scale based on microfluidics and the solid-phase microextraction (SPME) technique. Based on mPES, we established a complete set of phosphoproteomic analysis workflow for trace cell samples, including operations of cell capture, cell lysis, protein proteolysis, phosphopeptide enrichment, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) detection, which can profile phosphoproteomics of complex samples as low as picogram amounts. Cells pretreatment and phosphopeptide enrichment were carried out in the nanoliter-scale volume range, which significantly increased the enrichment efficiency and reduced the sample loss by container adsorption. We applied the present system to the analysis of phosphorylation in 1, 3, 5, 10 HeLa cells, and 83, 117, 187, 227 phosphopeptides were identified after enrichment, respectively. The intensities of phosphopeptides increased by 2.91, 2.80, 8.81, and 9.95 times compared with the control groups. The mPES was further applied to the phosphoproteomic analysis of single mouse oocytes, with an average of 221 phosphoproteins identified in single germinal vesicle (GV) oocytes and 460 in single metaphase II (MII) oocytes, which provided a single-cell perspective for further elucidating the mechanism of oocyte maturation.

Single Cell Untargeted Lipidomics Using Liquid Chromatography Ion Mobility-Mass Spectrometry

Advancements in technology over the years have propelled omics analysis to the level of single cell resolution. Following the breakthroughs in single cell transcriptomics and genomics, single cell proteomics has recently rapidly progressed, aided by highly sensitive mass spectrometry instrumentation. However, there is currently a paucity of studies and methodologies for single cell lipidomics, aside from imaging-based approaches. Profiling lipids at the single cell level holds promise for providing novel insights into the complex heterogeneity of cells in various human disorders. Further, by integrating single cell lipidomics with other single cell omics including proteomics, it becomes possible to achieve single cell multiomics, enabling the discovery of novel molecular signatures. We developed untargeted single cell lipidomics using nanoflow liquid chromatography-ion mobility spectrometry-mass spectrometry. To enhance lipid coverage at the single cell level, the method was conducted in both positive and negative ion modes. We identified an average of 161 lipids spanning phospholipids, sphingolipids, cholesteryl esters, and glycerides in positive ion mode from single cells of human cholangiocarcinoma cells based on a rule-based lipid annotation. Additionally, an average of 20 species of phospholipids was identified in the negative ion mode. These preliminary data demonstrate a new methodology to profile lipids at a single or low input of cells.

Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics

The development of micro- and nano-fabrication technologies has greatly advanced single-cell and spatial omics technologies. With the advantages of integration and compartmentalization, microfluidic chips are capable of generating high-throughput parallel reaction systems for single-cell screening and analysis. As omics technologies improve, microfluidic chips can now integrate promising transcriptomics technologies, providing new insights from molecular characterization for tissue gene expression profiles and further revealing the static and even dynamic processes of tissues in homeostasis and disease. Here, we survey the current landscape of microfluidic methods in the field of single-cell and spatial multi-omics, as well as assessing their relative advantages and limitations. We highlight how microfluidics has been adapted and improved to provide new insights into multi-omics over the past decade. Last, we emphasize the contributions of microfluidic-based omics methods in development, neuroscience, and disease mechanisms, as well as further revealing some perspectives for technological advances in translational and clinical medicine.

Risk of Alzheimer's disease in Down syndrome: Insights gained by multi-omics

Individuals with Down syndrome (DS) are highly susceptible to Alzheimer's disease (AD). The integration of genomics, transcriptomics, epigenomics, proteomics, and metabolomics enables unprecedented understanding of DS-AD, offering a detailed picture of this complex issue. The vast -omics data also present challenges that reflect the complexity of genetic information flow. These studies nonetheless reveal critical mechanisms behind AD risk, including unique observations in DS that differ from those seen in the general population and familial dominant AD. In addition, the correlations between the AD polygenic risk score and proteins related to female infertility and autoimmune thyroiditis corroborate clinical observations. Metabolomic data reveal disrupted metabolic networks, offering prospects for a dynamic score to create specialized nutritional interventions. By adopting a multidimensional perspective with integrated reductionism, the evolving landscape presents an opportunity to identify promising directions for developing precision strategies to mitigate the impact of AD in the DS population. HIGHLIGHTS: Individuals with Down syndrome (DS) are highly susceptible to Alzheimer's disease (AD). DS-AD is characterized by its polygenic nature, extending beyond chromosome 21 with significant contributions from various chromosomes. DS-AD also presents unique features that differ from those observed in the general population and familial dominant AD. Our review consolidates key findings from genomics, transcriptomics, epigenomics, proteomics, and metabolomics, providing a comprehensive view of the molecular mechanisms underlying DS-AD. We highlight promising research directions to further elucidate the pathogenesis of DS-AD.

Plasma Exosomal-Derived SERPINA1 and GNAI2 Downregulation as Potential Diagnostic Biomarkers of Kawasaki Disease with Coronary Artery Aneurysms

Kawasaki disease (KD) with coronary artery aneurysms (CAAs) is currently the primary cause of childhood acquired heart disease with an unclear pathogenesis. We established five groups for the discovery of differentially expressed proteins (DEPs): healthy control, febrile control, KD without CAAs, KD with small and medium CAAs, and KD with giant CAAs (n = 8 in each group). The validation of selected DEPs was conducted in another five groups (n = 4 in each group). We conducted comprehensive bioinformatics analyses to elucidate the functional roles of the DEPs in the groups of KD with CAAs and KD without CAAs. A total of 104 DEPs were identified in KD patients, which were primarily associated with complement-related pathways. A trend analysis of these 104 DEPs revealed 54 significantly changed DEPs associated with increased disease severity, which were primarily associated with G-protein-related functions. The alterations in α-1-antitrypsin short peptide (SERPINA1) and guanine nucleotide-binding protein G(i) subunit alpha-2 (GNAI2), which were selected from complement-related and G-protein-related pathways, respectively, were validated by Western blotting, and they were significantly decreased in KD patients with vs. without CAAs. In addition, we conducted an analysis of the DEPs in the groups of KD with CAAs and KD without CAAs, separately. There were 91 DEPs specifically expressed in KD patients with CAAs, associated with the neutrophil extracellular trap and complement pathways, while 16 DEPs were specific to those without CAAs, associated with viral infection and immunity pathways. Additionally, for DEPs among different severities of CAAs, there were 102 DEPs in KD patients with small and medium CAAs, associated with complement pathways and platelet activation pathways, whereas 34 DEPs were specific to giant CAAs, associated with the Rap1 signaling pathway and cell functions. In conclusion, this study provides plasmatic exosomal protein profiles in KD patients with CAAs, suggesting that SERPINA1 and GNIA2 might serve as novel potential diagnostic biomarkers for KD with CAAs.

Middle-throughput LC-MS-based Platelet Proteomics with Minute Sample Amounts Using Semiautomated Positive Pressure FASP in 384-Well Format

Comprehensive characterization of platelets requires various functional assays and analytical techniques, including omics disciplines, each demanding a separate aliquot of the given sample. Consequently, sample material for each assay is often highly limited, necessitating the downscaling of methods to work with just a few micrograms of platelet protein.Here, we present a novel sample preparation platform for proteomics analysis using only 3 μg of purified platelet protein, corresponding to 2 × 106 platelets, which can be obtained from approximately 2 to 8 μL of blood from a healthy individual (1.5 × 105-4.5 × 105 platelets/μL) or approximately 100 μL of blood from a patient with severe thrombocytopenia (<2 × 104 platelets/µL).Using this platform, we detected a significant fraction of key players in the platelet activation cascade and, most importantly, identified 36 clinically relevant platelet disease markers even with a non-state-of-the art instrument. This makes LC-MS-based proteomics a highly attractive alternative to conventional assays, which often require milliliters of blood. Our platform transitions from our previously established 96-well proteomics workflow (PF96), which has been successfully employed in numerous platelet proteomics studies, into the 384-well format. This transition is accompanied by (1) a more than two-fold increase in sensitivity, (2) improved reproducibility, (3) a four-fold increase in throughput, allowing 1,536 samples to be processed per lab worker per week, and (4) reduced sample preparation costs.Thus, LC-MS-based platelet proteomics offers a compelling alternative to immunoaffinity assays (which depend on antibody availability and quality), as well as to genomic assays (which can only reveal genotypes). In summary, in conjunction with recent advances in LC-MS instrumentation, our platform represents a highly valuable tool for rapid phenotyping of platelets in research with extraordinary potential for future employment in companion or routine diagnostics.

Graph neural networks for single-cell omics data: a review of approaches and applications

Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data have introduced substantial difficulties to traditional computational methods. The intricate non-Euclidean networks of intracellular and intercellular signaling molecules within single-cell datasets, coupled with the complex, multimodal structures arising from multi-omics joint analysis, pose significant challenges to conventional deep learning operations reliant on Euclidean geometries. Graph neural networks (GNNs) have extended deep learning to non-Euclidean data, allowing cells and their features in single-cell datasets to be modeled as nodes within a graph structure. GNNs have been successfully applied across a broad range of tasks in single-cell data analysis. In this survey, we systematically review 107 successful applications of GNNs and their six variants in various single-cell omics tasks. We begin by outlining the fundamental principles of GNNs and their six variants, followed by a systematic review of GNN-based models applied in single-cell epigenomics, transcriptomics, spatial transcriptomics, proteomics, and multi-omics. In each section dedicated to a specific omics type, we have summarized the publicly available single-cell datasets commonly utilized in the articles reviewed in that section, totaling 77 datasets. Finally, we summarize the potential shortcomings of current research and explore directions for future studies. We anticipate that this review will serve as a guiding resource for researchers to deepen the application of GNNs in single-cell omics.

Comparative proteomic landscapes elucidate human preimplantation development and failure

Understanding mammalian preimplantation development, particularly in humans, at the proteomic level remains limited. Here, we applied our comprehensive solution of ultrasensitive proteomic technology to measure the proteomic profiles of oocytes and early embryos and identified nearly 8,000 proteins in humans and over 6,300 proteins in mice. We observed distinct proteomic dynamics before and around zygotic genome activation (ZGA) between the two species. Integrative analysis with translatomic data revealed extensive divergence between translation activation and protein accumulation. Multi-omic analysis indicated that ZGA transcripts often contribute to protein accumulation in blastocysts. Using mouse embryos, we identified several transcriptional regulators critical for early development, thereby linking ZGA to the first lineage specification. Furthermore, single-embryo proteomics of poor-quality embryos from over 100 patient couples provided insights into preimplantation development failure. Our study may contribute to reshaping the framework of mammalian preimplantation development and opening avenues for addressing human infertility.

The Society for Immunotherapy of Cancer Perspective on Tissue-Based Technologies for Immuno-Oncology Biomarker Discovery and Application

With immuno-oncology becoming the standard of care for a variety of cancers, identifying biomarkers that reliably classify patient response, resistance, or toxicity becomes the next critical barrier toward improving care. Multiparametric, multi-omics, and computational platforms generating an unprecedented depth of data are poised to usher in the discovery of increasingly robust biomarkers for enhanced patient selection and personalized treatment approaches. Deciding which developing technologies to implement in clinical settings ultimately, applied either alone or in combination, relies on weighing pros and cons, from minimizing patient sampling to maximizing data outputs, and assessing the reproducibility and representativeness of findings, while lessening data fragmentation toward harmonization. These factors are all assessed while taking into consideration the shortest turnaround time. The Society for Immunotherapy of Cancer Biomarkers Committee convened to identify important advances in biomarker technologies and to address advances in biomarker discovery using multiplexed IHC and immunofluorescence, their coupling to single-cell transcriptomics, along with mass spectrometry-based quantitative and spatially resolved proteomics imaging technologies. We summarize key metrics obtained, ease of interpretation, limitations and dependencies, technical improvements, and outward comparisons of these technologies. By highlighting the most interesting recent data contributed by these technologies and by providing ways to improve their outputs, we hope to guide correlative research directions and assist in their evolution toward becoming clinically useful in immuno-oncology.

Benchmark of Data Integration in Single-Cell Proteomics

Single-cell proteomics (SCP) detected based on different technologies always involves batch-specific variations because of differences in sample processing and other potential biases. How to integrate SCP data effectively has become a great challenge. Integration of SCP data not only requires the conservation of true biological variances, but also realizes the removal of unwanted batch effects. In this study, benchmarking analysis of popular data integration methods was conducted to determine the most suitable method for SCP data. To comprehensively evaluate the performance of these integration methods, a novel evaluation system was proposed for integrating SCP data. This evaluation system consists of three objective measures from different perspectives: category (a), the efficacy of correcting batch effects; category (b), the power of conserving biological variances; and category (c), the ability to identify consistent markers. For this comprehensive evaluation, five benchmark data sets under different scenarios (containing substantial proteins, substantial cells, multiple batches, multiple cell types, and unbalanced data) were utilized for selecting the most suitable data integration method. As a result, three methods, ComBat, Scanorama, and Seurat version 3 CCA, were identified as the most recommended methods for integrating SCP data. Overall, this systematic evaluation might provide valuable guidance in choosing the appropriate method for data integration in the SCP.

Chitinase-3-like-1: a multifaceted player in neuroinflammation and degenerative pathologies with therapeutic implications

Chitinase-3-like-1 (CHI3L1) is an evolutionarily conserved protein involved in key biological processes, including tissue remodeling, angiogenesis, and neuroinflammation. It has emerged as a significant player in various neurodegenerative diseases and brain disorders. Elevated CHI3L1 levels have been observed in neurological conditions such as traumatic brain injury (TBI), Alzheimer's disease (AD), Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS), Creutzfeldt-Jakob disease (CJD), multiple sclerosis (MS), Neuromyelitis optica (NMO), HIV-associated dementia (HAD), Cerebral ischemic stroke (CIS), and brain tumors. This review explores the role of CHI3L1 in the pathogenesis of these disorders, with a focus on its contributions to neuroinflammation, immune cell infiltration, and neuronal degeneration. As a key regulator of neuroinflammation, CHI3L1 modulates microglia and astrocyte activity, driving the release of proinflammatory cytokines that exacerbate disease progression. In addition to its role in disease pathology, CHI3L1 has emerged as a promising biomarker for the diagnosis and monitoring of brain disorders. Elevated cerebrospinal fluid (CSF) levels of CHI3L1 have been linked to disease severity and cognitive decline, particularly in AD and MS, highlighting its potential for clinical diagnostics. Furthermore, therapeutic strategies targeting CHI3L1, such as small-molecule inhibitors and neutralizing antibodies, have shown promise in preclinical studies, demonstrating reduced neuroinflammation, amyloid plaque accumulation, and improved neuronal survival. Despite its therapeutic potential, challenges remain in developing selective and safe CHI3L1-targeted therapies, particularly in ensuring effective delivery across the blood-brain barrier and mitigating off-target effects. This review addresses the complexities of targeting CHI3L1, highlights its potential in precision medicine, and outlines future research directions aimed at unlocking its full therapeutic potential in treating neurodegenerative diseases and brain pathologies.

Immunoaffinity Depletion of High-Abundance Proteins from Serum/Plasma for Proteomic Analysis

Mass spectrometry-based proteomics is widely applied to human blood serum or plasma in the search of biomarkers for various diseases. However, the enormous complexity and dynamic range of protein concentrations in these samples render a significant analytical challenge, particularly for detecting low-abundance candidate biomarkers. As a result, strategies for enriching low-abundance proteins and improving their identification in serum or plasma proteomics are commonly used. Here, we describe an immunodepletion technique that is routinely used in our lab for removing high-abundance proteins from human serum/plasma.

scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP

Single-cell proteomics by mass spectrometry (MS) allows the quantification of proteins with high specificity and sensitivity. To increase its throughput, we developed nano-proteomic sample preparation (nPOP), a method for parallel preparation of thousands of single cells in nanoliter-volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation using the plexDIA MS multiplexing method, which uses non-isobaric mass tags to barcode peptides from different samples for data-independent acquisition, demonstrates accurate quantification of ~3,000-3,700 proteins per human cell. A separate implementation with isobaric mass tags and prioritized data acquisition demonstrates analysis of 1,827 single cells at a rate of >1,000 single cells per day at a depth of 800-1,200 proteins per human cell. The protocol is implemented by using a cell-dispensing and liquid-handling robot-the CellenONE instrument-and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 d to prepare >3,000 single cells. We provide metrics and software (the QuantQC R package) for quality control and data exploration. QuantQC supports the robust scaling of nPOP to higher plex reagents for achieving reliable and scalable single-cell proteomics.

Deep phosphotyrosine characterisation of primary murine T cells using broad spectrum optimisation of selective triggering

Sequencing the tyrosine phosphoproteome using MS-based proteomics is challenging due to the low abundance of tyrosine phosphorylation in cells, a challenge compounded in scarce samples like primary cells or clinical samples. The broad-spectrum optimisation of selective triggering (BOOST) method was recently developed to increase phosphotyrosine sequencing in low protein input samples by leveraging tandem mass tags (TMT), phosphotyrosine enrichment, and a phosphotyrosine-loaded carrier channel. Here, we demonstrate the viability of BOOST in T cell receptor (TCR)-stimulated primary murine T cells by benchmarking the accuracy and precision of the BOOST method and discerning significant alterations in the phosphoproteome associated with receptor stimulation. Using 1 mg of protein input (about 20 million cells) and BOOST, we identify and precisely quantify more than 2000 unique pY sites compared to about 300 unique pY sites in non-BOOST control samples. We show that although replicate variation increases when using the BOOST method, BOOST does not jeopardise quantitative precision or the ability to determine statistical significance for peptides measured in triplicate. Many pY previously uncharacterised sites on important T cell signalling proteins are quantified using BOOST, and we identify new TCR responsive pY sites observable only with BOOST. Finally, we determine that the phase-spectrum deconvolution method on Orbitrap instruments can impair pY quantitation in BOOST experiments.

Identifying therapeutic strategies for triple-negative breast cancer via phosphoproteomics

INTRODUCTION

Given the poor prognosis of patients with TNBC, it is urgent to identify new biomarkers and therapeutic targets to enable personalized treatment strategies and improve patient survival. Comprehensive insights beyond genomic and transcriptomic analysis are crucial to improved outcomes for patients. As proteins are the workhorses of cellular function with their activity primarily regulated by phosphorylation, advanced phosphoproteomics techniques, such as mass spectrometry and antibody arrays, are essential for elucidating kinase signaling pathways that drive TNBC progression and contribute to therapy resistance.

AREA COVERED

This review discusses the critical need to integrate phosphoproteomics into TNBC research, evaluates commonly used technologies and their applications, and explores their advantages and limitations. We highlight significant findings from phosphoproteomic analyses in TNBC and address the challenges of implementing these technologies into clinical practice.

EXPERT OPINION

Rapid advances in phosphoproteomics analysis facilitate subtype stratification, adaptive response monitoring, and identification of biomarkers and therapeutic targets in TNBC. However, challenges in analyzing protein phosphorylation, especially in deep spatially resolved analysis of malignant cells and the tumor ecosystem, hinder the translation of phosphoproteomics to the CLIA setting. Nonetheless, phosphoproteomics offers a powerful tool that, when integrated into routine clinical practice, has the potential to revolutionize patient care.

Investigation of human aging at the single-cell level

Human aging is characterized by a gradual decline in physiological functions and an increased susceptibility to various diseases. The complex mechanisms underlying human aging are still not fully elucidated. Single-cell sequencing (SCS) technologies have revolutionized aging research by providing unprecedented resolution and detailed insights into cellular diversity and dynamics. In this review, we discuss the application of various SCS technologies in human aging research, encompassing single-cell, genomics, transcriptomics, epigenomics, and proteomics. We also discuss the combination of multiple omics layers within single cells and the integration of SCS technologies with advanced methodologies like spatial transcriptomics and mass spectrometry. These approaches have been essential in identifying aging biomarkers, elucidating signaling pathways associated with aging, discovering novel aging cell subpopulations, uncovering tissue-specific aging characteristics, and investigating aging-related diseases. Furthermore, we provide an overview of aging-related databases that offer valuable resources for enhancing our understanding of the human aging process.

Characterization of Human Pancreatic Islet Cells Using a Single-Cell Western Blot Platform

OBJECTIVE

Islet transplantation is an effective treatment for type 1 diabetes. However, transplant success depends on quick islet assessment because islets deteriorate 2-3 days after isolation. A new tool, single-cell western blot (scWestern), offers results within 1 day. In this study, we aimed to test the suitability of scWestern to detect protein markers for beta (insulin), alpha (glucagon), and delta (somatostatin) cells, the 3 major endocrine cell types in islets.

MATERIALS AND METHODS

We characterized the antibody specificity, signal intensity, and cell identification on the scWestern platform and then compared the islet cell composition analysis between scWestern and immunohistochemistry performed by the Integrated Islet Distribution Program.

RESULTS

Islet cell composition is comparable for alpha and beta cells, but not delta cells. Protein expression levels of insulin, glucagon, and somatostatin in individual islet cells varied greatly, highlighting cell type heterogeneity. Surprisingly, scWestern revealed double-hormonal cells (~1%), co-expressing insulin and somatostatin or insulin and glucagon, in nondiabetic and nonobese adult human islets, which was confirmed by confocal immunofluorescence microscopy.

CONCLUSIONS

These results demonstrate that each alpha, beta, and delta cells express varying levels of peptide hormones, and a small subpopulation co-expresses double hormones in normal human islets. The scWestern platform will enable timely assessment of beta cell mass in isolated islets before clinical transplantation.

NanoPlex: a universal strategy for fluorescence microscopy multiplexing using nanobodies with erasable signals

Fluorescence microscopy has long been a transformative technique in biological sciences. Nevertheless, most implementations are limited to a few targets, which have been revealed using primary antibodies and fluorescently conjugated secondary antibodies. Super-resolution techniques such as Exchange-PAINT and, more recently, SUM-PAINT have increased multiplexing capabilities, but they require specialized equipment, software, and knowledge. To enable multiplexing for any imaging technique in any laboratory, we developed NanoPlex, a streamlined method based on conventional antibodies revealed by engineered secondary nanobodies that allow the selective removal of fluorescence signals. We develop three complementary signal removal strategies: OptoPlex (light-induced), EnzyPlex (enzymatic), and ChemiPlex (chemical). We showcase NanoPlex reaching 21 targets for 3D confocal analyses and 5-8 targets for dSTORM and STED super-resolution imaging. NanoPlex has the potential to revolutionize multi-target fluorescent imaging methods, potentially redefining the multiplexing capabilities of antibody-based assays.

Using long columns to quantify over 9200 unique protein groups from brain tissue in a single injection on an Orbitrap Exploris 480 mass spectrometer

The most exciting advancement in LC-MS/MS-based bottom-up proteomics has centered around enhancing mass spectrometers. Among these, the latest and most advanced mass spectrometer for bottom-up proteomics is the Orbitrap Astral that has the highest scan rate to accelerate throughput and the highest sensitivity to handle a very small amount of peptide samples and to achieve deeper proteomics. However, its affordability remains a challenge for most laboratories. While significant strides have been made in improving mass spectrometry, advancing liquid chromatography (LC) to achieve deeper proteomics has not achieved significant successes since the innovation of Multidimensional Protein Identification Technology (MudPIT) in 2001. To achieve deeper proteomics in a less labor-intensive and more reproducible approach while using a more cost-effective mass spectrometer, such as the Orbitrap Exploris 480, we evaluated trap columns as long as 40 cm and analytical column as long as 600 cm besides sample loading amount, gradient time, and analytical column particle size to enable a fractionation-free method for a single injection to obtain deeper proteomics. The length of trap and analytic columns is the key factor. Using a 30 cm trap column and 250 cm analytical column with other optimized LC conditions, we quantified over 9200 unique protein groups from brain tissue in a single injection using a 24-h gradient on an Orbitrap Exploris 480 mass spectrometer.

Artificial intelligence-enabled multipurpose smart detection in active-matrix electrowetting-on-dielectric digital microfluidics

An active-matrix electrowetting-on-dielectric (AM-EWOD) system integrates hundreds of thousands of active electrodes for sample droplet manipulation, which can enable simultaneous, automatic, and parallel on-chip biochemical reactions. A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps. In this work, we demonstrated an artificial intelligence (AI)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks. We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods. We used the YOLOv8 model to monitor the droplet-splitting process online. A 97.76% splitting success rate was observed with 18 different AM-EWOD chips. A 99.982% model precision rate and a 99.980% model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets. Compared with manual verification, the model achieved 99.260% and 99.193% precision and recall rates, respectively. In addition, single-cell droplet sorting and routing experiments were demonstrated. With an AI-based smart detection system, AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.

Miniaturized protein profiling permits targeted signaling pathway analysis in individual circulating tumor cells to improve personalized treatment

BACKGROUND

Traditional genomic profiling and mutation analysis of single cells like Circulating Tumor Cells (CTCs) fails to capture post-translational and functional alterations of proteins, often leading to limited treatment efficacy. To overcome this gap, we developed a miniaturized 'protein analysis on the single cell level' workflow-baptized ZeptoCTC. It integrates established technologies for single-cell isolation with sensitive Reverse Phase Protein Array (RPPA) analysis, thus enabling the comprehensive assessment of multiple protein expression and activation in individual CTCs.

METHODS

The ZeptoCTC workflow involves several critical steps. Firstly, individual cells are labeled and isolated. This is followed by cell lysis and the printing of true single cell lysate preparations onto a ZeptoChip using a modified micromanipulator, CellCelector™. The printed lysates then undergo fluorescence immunoassay RPPA protein detection using a ZeptoReader. Finally, signal quantification is carried out with Image J software, ensuring precise measurement of multiple protein levels.

RESULTS

The efficacy of ZeptoCTC was demonstrated through various applications. Initially, it was used for measuring EpCAM protein expression, a standard marker for CTC detection, revealing higher levels in single MCF-7 over MDA-MB-231 tumor cells. Furthermore, in Capivasertib (Akt-inhibitor)-treated MCF-7 single cells, ZeptoCTC detected a 2-fold increase in the pAkt/Akt ratio compared to control cells, and confirmed co-performed bulk-cell western blot analysis results. Notably, when applied to individual CTCs from metastasized breast cancer patients, ZeptoCTC revealed significant differences in protein activation levels, particularly in measured pAkt and pErk levels, compared to patient-matched WBCs. Moreover, it successfully differentiated between CTCs from patients with different Akt1 genotypes, highlighting its potential to determine the activation status of druggable cancer driving proteins for individual and targeted treatment decision making.

CONCLUSIONS

The ZeptoCTC workflow represents a valuable tool in single cell cancer research, crucial for personalized medicine. It permits detailed analysis of key proteins and their activation status of targeted, cancer-driven signaling pathways in single cell samples, aiding in understanding tumor response, progression, and treatment efficacy beyond bulk analysis. The method significantly advances clinical investigations in cancer, improving treatment precision and effectiveness. The workflow will be applicable to protein analysis on other types of single cells like relevant in stem cell, neuropathology and hemopoietic cell research.

Imputing abundance of over 2,500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles

Cell surface proteins serve as primary drug targets and cell identity markers. Techniques such as CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) have enabled the simultaneous quantification of surface protein abundance and transcript expression within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability of these computational approaches across diverse contexts (e.g., different tissues/disease states) impede their widespread adoption. Here, we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA sequencing), a context-agnostic zero-shot deep ensemble model, which enables large-scale protein abundance prediction and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2,500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER, including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer. A record of this paper's transparent peer review process is included in the supplemental information.

Characterizing the nonmonotonic behavior of mutual information along biochemical reaction cascades

Cells sense environmental signals and transmit information intracellularly through changes in the abundance of molecular components. Such molecular abundances can be measured in single cells and exhibit significant heterogeneity in clonal populations even in identical environments. Experimentally observed joint probability distributions can then be used to quantify the covariability and mutual information between molecular abundances along signaling cascades. However, because stationary state abundances along stochastic biochemical reaction cascades are not conditionally independent, their mutual information is not constrained by the data-processing inequality. Here, we report the conditions under which the mutual information between stationary state abundances increases along a cascade of biochemical reactions. This nonmonotonic behavior can be intuitively understood in terms of noise propagation and time-averaging stochastic fluctuations that are short-lived compared to an extrinsic signal. Our results reemphasize that mutual information measurements of stationary state distributions of cellular components may be of limited utility for characterizing cellular signaling processes because they do not measure information transfer.

Dissecting viral infections, one cell at a time, by single-cell technologies

The meteoric rise of single-cell genomic technologies, especially of single-cell RNA-sequencing (scRNA-seq), has revolutionized several fields of cellular biology, especially immunology, oncology, neuroscience and developmental biology. While the field of virology has been relatively slow to adopt these technological advances, many works have shed new light on the fascinating interactions of viruses with their hosts using single cell technologies. One clear example is the multitude of studies dissecting viral infections by single-cell sequencing technologies during the recent COVID-19 pandemic. In this review we will detail the advantages of studying viral infections at a single-cell level, how scRNA-seq technologies can be used to achieve this goal and the associated technical limitations, challenges and solutions. We will highlight recent biological discoveries and breakthroughs in virology enabled by single-cell analyses and will end by discussing possible future directions of the field. Given the rate of publications in this exciting new frontier of virology, we have likely missed some important works and we apologize in advance to the researchers whose work we have failed to cite.

Proximity analysis of native proteomes reveals phenotypic modifiers in a mouse model of autism and related neurodevelopmental conditions

One of the main drivers of autism spectrum disorder is risk alleles within hundreds of genes, which may interact within shared but unknown protein complexes. Here we develop a scalable genome-editing-mediated approach to target 14 high-confidence autism risk genes within the mouse brain for proximity-based endogenous proteomics, achieving the identification of high-specificity spatial proteomes. The resulting native proximity proteomes are enriched for human genes dysregulated in the brain of autistic individuals, and reveal proximity interactions between proteins from high-confidence risk genes with those of lower-confidence that may provide new avenues to prioritize genetic risk. Importantly, the datasets are enriched for shared cellular functions and genetic interactions that may underlie the condition. We test this notion by spatial proteomics and CRISPR-based regulation of expression in two autism models, demonstrating functional interactions that modulate mechanisms of their dysregulation. Together, these results reveal native proteome networks in vivo relevant to autism, providing new inroads for understanding and manipulating the cellular drivers underpinning its etiology.

Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles

Cell surface proteins serve as primary drug targets and cell identity markers. The emergence of techniques like CITE-seq has enabled simultaneous quantification of surface protein abundance and transcript expression for multimodal data analysis within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance based solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability for these computational approaches across diverse contexts, such as different tissues or disease states, impede their widespread adoption. Here we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA-seq), a context-agnostic zero-shot deep ensemble model, which enables the large-scale prediction of cell surface protein abundance and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer.

Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data

Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.

Single-cell technologies in psoriasis

Psoriasis is a chronic and recurrent inflammatory skin disorder. The primary manifestation of psoriasis arises from disturbances in the cutaneous immune microenvironment, but the specific functions of the cellular components within this microenvironment remain unknown. Recent advancements in single-cell technologies have enabled the detection of multi-omics at the level of individual cells, including single-cell transcriptome, proteome, and metabolome, which have been successfully applied in studying autoimmune diseases, and other pathologies. These techniques allow the identification of heterogeneous cell clusters and their varying contributions to disease development. Considering the immunological traits of psoriasis, an in-depth exploration of immune cells and their interactions with cutaneous parenchymal cells can markedly advance our comprehension of the mechanisms underlying the onset and recurrence of psoriasis. In this comprehensive review, we present an overview of recent applications of single-cell technologies in psoriasis, aiming to improve our understanding of the underlying mechanisms of this disorder.

Solid-Phase Microextraction-Aided Capillary Zone Electrophoresis-Mass Spectrometry: Toward Bottom-Up Proteomics of Single Human Cells

Capillary zone electrophoresis-mass spectrometry (CZE-MS) has been recognized as a valuable technique for the proteomics of mass-limited biological samples (i.e., single cells). However, its broad adoption for single cell proteomics (SCP) of human cells has been impeded by the low sample loading capacity of CZE, only allowing us to use less than 5% of the available peptide material for each measurement. Here we present a reversed-phase-based solid-phase microextraction (RP-SPME)-CZE-MS platform to solve the issue, paving the way for SCP of human cells using CZE-MS. The RP-SPME-CZE system was constructed in one fused silica capillary with zero dead volume for connection via in situ synthesis of a frit, followed by packing C8 beads into the capillary to form a roughly 2 mm long SPME section. Peptides captured by SPME were eluted with a buffer containing 30% (v/v) acetonitrile and 50 mM ammonium acetate (pH 6.5), followed by dynamic pH junction-based CZE-MS. The SPME-CZE-MS enabled the injection of nearly 40% of the available peptide sample for each measurement. The system identified 257 ± 24 proteins and 523 ± 69 peptides (N = 2) using a Q-Exactive HF mass spectrometer when only 0.25 ng of a commercial HeLa cell digest was available in the sample vial and 0.1 ng of the sample was injected. The amount of available peptide is equivalent to the protein mass of one HeLa cell. The data indicate that SPME-CZE-MS is ready for SCP of human cells.

Native N-glycome profiling of single cells and ng-level blood isolates using label-free capillary electrophoresis-mass spectrometry

The development of reliable single-cell dispensers and substantial sensitivity improvement in mass spectrometry made proteomic profiling of individual cells achievable. Yet, there are no established methods for single-cell glycome analysis due to the inability to amplify glycans and sample losses associated with sample processing and glycan labeling. In this work, we present an integrated platform coupling online in-capillary sample processing with high-sensitivity label-free capillary electrophoresis-mass spectrometry for N-glycan profiling of single mammalian cells. Direct and unbiased quantitative characterization of single-cell surface N-glycomes are demonstrated for HeLa and U87 cells, with the detection of up to 100 N-glycans per single cell. Interestingly, N-glycome alterations are unequivocally detected at the single-cell level in HeLa and U87 cells stimulated with lipopolysaccharide. The developed workflow is also applied to the profiling of ng-level amounts (5-500 ng) of blood-derived protein, extracellular vesicle, and total plasma isolates, resulting in over 170, 220, and 370 quantitated N-glycans, respectively.

A Tiny Viral Protein, SARS-CoV-2-ORF7b: Functional Molecular Mechanisms

This study presents the interaction with the human host metabolism of SARS-CoV-2 ORF7b protein (43 aa), using a protein-protein interaction network analysis. After pruning, we selected from BioGRID the 51 most significant proteins among 2753 proven interactions and 1708 interactors specific to ORF7b. We used these proteins as functional seeds, and we obtained a significant network of 551 nodes via STRING. We performed topological analysis and calculated topological distributions by Cytoscape. By following a hub-and-spoke network architectural model, we were able to identify seven proteins that ranked high as hubs and an additional seven as bottlenecks. Through this interaction model, we identified significant GO-processes (5057 terms in 15 categories) induced in human metabolism by ORF7b. We discovered high statistical significance processes of dysregulated molecular cell mechanisms caused by acting ORF7b. We detected disease-related human proteins and their involvement in metabolic roles, how they relate in a distorted way to signaling and/or functional systems, in particular intra- and inter-cellular signaling systems, and the molecular mechanisms that supervise programmed cell death, with mechanisms similar to that of cancer metastasis diffusion. A cluster analysis showed 10 compact and significant functional clusters, where two of them overlap in a Giant Connected Component core of 206 total nodes. These two clusters contain most of the high-rank nodes. ORF7b acts through these two clusters, inducing most of the metabolic dysregulation. We conducted a co-regulation and transcriptional analysis by hub and bottleneck proteins. This analysis allowed us to define the transcription factors and miRNAs that control the high-ranking proteins and the dysregulated processes within the limits of the poor knowledge that these sectors still impose.

Harnessing atomic force microscopy-based single-cell analysis to advance physical oncology

Single-cell analysis is an emerging and promising frontier in the field of life sciences, which is expected to facilitate the exploration of fundamental laws of physiological and pathological processes. Single-cell analysis allows experimental access to cell-to-cell heterogeneity to reveal the distinctive behaviors of individual cells, offering novel opportunities to dissect the complexity of severe human diseases such as cancers. Among the single-cell analysis tools, atomic force microscopy (AFM) is a powerful and versatile one which is able to nondestructively image the fine topographies and quantitatively measure multiple mechanical properties of single living cancer cells in their native states under aqueous conditions with unprecedented spatiotemporal resolution. Over the past few decades, AFM has been widely utilized to detect the structural and mechanical behaviors of individual cancer cells during the process of tumor formation, invasion, and metastasis, yielding numerous unique insights into tumor pathogenesis from the biomechanical perspective and contributing much to the field of cancer mechanobiology. Here, the achievements of AFM-based analysis of single cancer cells to advance physical oncology are comprehensively summarized, and challenges and future perspectives are also discussed. RESEARCH HIGHLIGHTS: Achievements of AFM in characterizing the structural and mechanical behaviors of single cancer cells are summarized, and future directions are discussed. AFM is not only capable of visualizing cellular fine structures, but can also measure multiple cellular mechanical properties as well as cell-generated mechanical forces. There is still plenty of room for harnessing AFM-based single-cell analysis to advance physical oncology.

Beyond single cells: microfluidics empowering multiomics analysis

Single-cell multiomics technologies empower simultaneous measurement of multiple types of molecules within individual cells, providing a more profound comprehension compared with the analysis of discrete molecular layers from different cells. Microfluidic technology, on the other hand, has emerged as a pivotal facilitator for high-throughput single-cell analysis, offering precise control and manipulation of individual cells. The primary focus of this review encompasses an appraisal of cutting-edge microfluidic platforms employed in the realm of single-cell multiomics analysis. Furthermore, it discusses technological advancements in various single-cell omics such as genomics, transcriptomics, epigenomics, and proteomics, with their perspective applications. Finally, it provides future prospects of these integrated single-cell multiomics methodologies, shedding light on the possibilities for future biological research.

Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference

Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.

Induced-pluripotent stem cells and neuroproteomics as tools for studying neurodegeneration

The investigation of neurodegenerative diseases advanced significantly with the advent of cell-reprogramming technology, leading to the creation of new models of human illness. These models, derived from induced pluripotent stem cells (iPSCs), facilitate the study of sporadic as well as hereditary diseases and provide a comprehensive understanding of the molecular mechanisms involved with neurodegeneration. Through proteomics, a quantitative tool capable of identifying thousands of proteins from small sample volumes, researchers have attempted to identify disease mechanisms by detecting differentially expressed proteins and proteoforms in disease models, biofluids, and postmortem brain tissue. The integration of these two technologies allows for the identification of novel pathological targets within the realm of neurodegenerative diseases. Here, we highlight studies from the past 5 years on the contributions of iPSCs within neuroproteomic investigations, which uncover the molecular mechanisms behind these illnesses.

Protein translation: biological processes and therapeutic strategies for human diseases

Protein translation is a tightly regulated cellular process that is essential for gene expression and protein synthesis. The deregulation of this process is increasingly recognized as a critical factor in the pathogenesis of various human diseases. In this review, we discuss how deregulated translation can lead to aberrant protein synthesis, altered cellular functions, and disease progression. We explore the key mechanisms contributing to the deregulation of protein translation, including functional alterations in translation factors, tRNA, mRNA, and ribosome function. Deregulated translation leads to abnormal protein expression, disrupted cellular signaling, and perturbed cellular functions- all of which contribute to disease pathogenesis. The development of ribosome profiling techniques along with mass spectrometry-based proteomics, mRNA sequencing and single-cell approaches have opened new avenues for detecting diseases related to translation errors. Importantly, we highlight recent advances in therapies targeting translation-related disorders and their potential applications in neurodegenerative diseases, cancer, infectious diseases, and cardiovascular diseases. Moreover, the growing interest lies in targeted therapies aimed at restoring precise control over translation in diseased cells is discussed. In conclusion, this comprehensive review underscores the critical role of protein translation in disease and its potential as a therapeutic target. Advancements in understanding the molecular mechanisms of protein translation deregulation, coupled with the development of targeted therapies, offer promising avenues for improving disease outcomes in various human diseases. Additionally, it will unlock doors to the possibility of precision medicine by offering personalized therapies and a deeper understanding of the molecular underpinnings of diseases in the future.

Harnessing the Transcriptional Signatures of CAR-T-Cells and Leukemia/Lymphoma Using Single-Cell Sequencing Technologies

Chimeric antigen receptor (CAR)-T-cell therapy has greatly improved outcomes for patients with relapsed or refractory hematological malignancies. However, challenges such as treatment resistance, relapse, and severe toxicity still hinder its widespread clinical application. Traditional transcriptome analysis has provided limited insights into the complex transcriptional landscape of both leukemia cells and engineered CAR-T-cells, as well as their interactions within the tumor microenvironment. However, with the advent of single-cell sequencing techniques, a paradigm shift has occurred, providing robust tools to unravel the complexities of these factors. These techniques enable an unbiased analysis of cellular heterogeneity and molecular patterns. These insights are invaluable for precise receptor design, guiding gene-based T-cell modification, and optimizing manufacturing conditions. Consequently, this review utilizes modern single-cell sequencing techniques to clarify the transcriptional intricacies of leukemia cells and CAR-Ts. The aim of this manuscript is to discuss the potential mechanisms that contribute to the clinical failures of CAR-T immunotherapy. We examine the biological characteristics of CAR-Ts, the mechanisms that govern clinical responses, and the intricacies of adverse events. By exploring these aspects, we hope to gain a deeper understanding of CAR-T therapy, which will ultimately lead to improved clinical outcomes and broader therapeutic applications.

Uncovering the Hidden World of Aqueous Humor Proteins for Discovery of Biomarkers for Marfan Syndrome

Ectopia lentis is a hallmark of Marfan syndrome (MFS), a genetic connective tissue disorder affecting 1/5000 to 1/10 000 individuals worldwide. Early detection in ophthalmology clinics and timely intervention of cardiovascular complications can be lifesaving. In this study, a modified proteomics workflow with liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based data-independent acquisition (DIA) and field asymmetric ion mobility spectrometry (FAIMS) to profile the proteomes of aqueous humor (AH) and lens tissue from MFS children with ectopia lentis is utilized. Over 2300 and 2938 comparable proteins are identified in AH and the lens capsule, respectively. Functional enrichment analyses uncovered dysregulation of complement and coagulation-related pathways, collagen binding, and cell adhesion in MFS. Through weighted correlation network analysis (WGCNA) and machine learning, distinct modules associated with clinical traits are constructed and a unique biomarker panel (Q14376, Q99972, P02760, Q07507; gene names: GALE, MYOC, AMBP, DPT) is defined. These biomarkers are further validated using advanced parallel reaction monitoring (PRM) in an independent patient cohort. The results provide novel insights into the proteome characterization of ectopia lentis and offer a promising approach for developing a valuable biomarker panel to aid in the early diagnosis of Marfan syndrome via AH proteome.

B-Cell Receptor Repertoire: Recent Advances in Autoimmune Diseases

In the field of contemporary medicine, autoimmune diseases (AIDs) are a prevalent and debilitating group of illnesses. However, they present extensive and profound challenges in terms of etiology, pathogenesis, and treatment. A major reason for this is the elusive pathophysiological mechanisms driving disease onset. Increasing evidence suggests the indispensable role of B cells in the pathogenesis of autoimmune diseases. Interestingly, B-cell receptor (BCR) repertoires in autoimmune diseases display a distinct skewing that can provide insights into disease pathogenesis. Over the past few years, advances in high-throughput sequencing have provided powerful tools for analyzing B-cell repertoire to understand the mechanisms during the period of B-cell immune response. In this paper, we have provided an overview of the mechanisms and analytical methods for generating BCR repertoire diversity and summarize the latest research progress on BCR repertoire in autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), primary Sjögren's syndrome (pSS), multiple sclerosis (MS), and type 1 diabetes (T1D). Overall, B-cell repertoire analysis is a potent tool to understand the involvement of B cells in autoimmune diseases, facilitating the creation of innovative therapeutic strategies targeting specific B-cell clones or subsets.

Novel Insights into the Initiation, Evolution, and Progression of Multiple Myeloma by Multi-Omics Investigation

The evolutionary history of multiple myeloma (MM) includes malignant transformation, followed by progression to pre-malignant stages and overt malignancy, ultimately leading to more aggressive and resistant forms. Over the past decade, large effort has been made to identify the potential therapeutic targets in MM. However, MM remains largely incurable. Most patients experience multiple relapses and inevitably become refractory to treatment. Tumor-initiating cell populations are the postulated population, leading to the recurrent relapses in many hematological malignancies. Clonal evolution of tumor cells in MM has been identified along with the disease progression. As a consequence of different responses to the treatment of heterogeneous MM cell clones, the more aggressive populations survive and evolve. In addition, the tumor microenvironment is a complex ecosystem which plays multifaceted roles in supporting tumor cell evolution. Emerging multi-omics research at single-cell resolution permits an integrative and comprehensive profiling of the tumor cells and microenvironment, deepening the understanding of biological features of MM. In this review, we intend to discuss the novel insights into tumor cell initiation, clonal evolution, drug resistance, and tumor microenvironment in MM, as revealed by emerging multi-omics investigations. These data suggest a promising strategy to unravel the pivotal mechanisms of MM progression and enable the improvement in treatment, both holistically and precisely.

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.

SingPro: a knowledge base providing single-cell proteomic data

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.

Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance

Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.

Single-Cell RNA-Sequencing of RVFV Infection

On the RNA level, viral infections are characterized by perturbations in the host cell transcriptome as well as the development of viral genetic information. Investigating the abundance and dynamic of RNA molecules can provide ample information to understand many aspects of the infection, from viral replication to pathogenesis. A key aspect therein is the resolution of the data, as infections are generally highly heterogeneous. Even in simple model systems such as cell lines, viral infections happen in a very asynchronous way. Quantifying RNAs at single-cell resolution can therefore substantially increase our understanding of these processes.Whereas measuring the RNA in bulk, that is, in samples containing thousands to hundreds of thousands of cells, is established and widely used since many years, methods for studying not only just a few different RNAs in individual cells became widely available only recently. Here, I outline and compare current concepts and methodologies for using single-cell RNA-sequencing to study virus infections. This covers sample preparation, cell preservation, biosafety considerations, and various experimental methods, with a special focus on the aspects that are important for studying virus infections. Since there is not "the one" method for doing single-cell RNA-sequencing, I will not provide a detailed protocol. Rather, this chapter should serve as a primer for getting started with single-cell RNA-sequencing experiments of virus infections and discusses the criteria that allow readers to choose the best procedures for their specific research question.

Proteogenomics-based functional genome research: approaches, applications, and perspectives in plants

Proteogenomics (PG) integrates the proteome with the genome and transcriptome to refine gene models and annotation. Coupled with single-cell (SC) assays, PG effectively distinguishes heterogeneity among cell groups. Affiliating spatial information to PG reveals the high-resolution circuitry within SC atlases. Additionally, PG can investigate dynamic changes in protein-coding genes in plants across growth and development as well as stress and external stimulation, significantly contributing to the functional genome. Here we summarize existing PG research in plants and introduce the technical features of various methods. Combining PG with other omics, such as metabolomics and peptidomics, can offer even deeper insights into gene functions. We argue that the application of PG will represent an important font of foundational knowledge for plants.

In-capillary sample processing coupled to label-free capillary electrophoresis-mass spectrometry to decipher the native N-glycome of single mammalian cells and ng-level blood isolates

The development of reliable single-cell dispensers and substantial sensitivity improvement in mass spectrometry made proteomic profiling of individual cells achievable. Yet, there are no established methods for single-cell glycome analysis due to the inability to amplify glycans and sample losses associated with sample processing and glycan labeling. In this work, we developed an integrated platform coupling online in-capillary sample processing with high-sensitivity label-free capillary electrophoresis-mass spectrometry for N-glycan profiling of single mammalian cells. Direct and unbiased characterization and quantification of single-cell surface N-glycomes were demonstrated for HeLa and U87 cells, with the detection of up to 100 N-glycans per single cell. Interestingly, N-glycome alterations were unequivocally detected at the single-cell level in HeLa and U87 cells stimulated with lipopolysaccharide. The developed workflow was also applied to the profiling of ng-level amounts of blood-derived protein, extracellular vesicle, and total plasma isolates, resulting in over 170, 220, and 370 quantitated N-glycans, respectively.