Integrated analysis of glycan and RNA in single cells

A detection system using sensing motif-tethered oligodeoxynucleotides for multiplex biomolecular analysis

We developed a system to detect multiple target biomolecules through sensing motif-tethered oligodeoxynucleotides. DNA-based molecular probes gave the primary amine motif upon reaction with the target biomolecules, glutathione (GSH) and H2O2. After labelling with biotin, the product DNAs were selectively collected to be quantified by qPCR.

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.

Droplet-Based Glycan and RNA Sequencing for Profiling the Distinct Cellular Glyco-States in Single Cells

Plate-based single-cell glycan and RNA sequencing (scGR-seq) is previously developed to realize the integrated analysis of glycome and transcriptome in single cells. However, the sample size is limited to only a few hundred cells. Here, a droplet-based scGR-seq is developed to address this issue by adopting a 10x Chromium platform to simultaneously profile ten thousand cells' glycome and transcriptome in single cells. To establish droplet-based scGR-seq, a comparative analysis of two distinct cell lines is performed: pancreatic ductal adenocarcinoma cells and normal pancreatic duct cells. Droplet-based scGR-seq revealed distinct glycan profiles between the two cell lines that showed a strong correlation with the results obtained by flow cytometry. Next, droplet-based scGR-seq is applied to a more complex sample: peripheral blood mononuclear cells (PBMC) containing various immune cells. The method can systematically map the glycan signature for each immune cell in PBMC as well as glycan alterations by cell lineage. Prediction of the association between the glycan expression and the gene expression using regression analysis ultimately leads to the identification of a glycan epitope that impacts cellular functions. In conclusion, the droplet-based scGR-seq realizes the high-throughput profiling of the distinct cellular glyco-states in single cells.

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling

Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed LeGenD, a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns. We trained the LeGenD model using 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Our approach accurately reconstructed experimentally-measured N-glycoprofiles of bovine Fetuin B and IgG from human sera. Explanatory AI analysis with SHapley Additive exPlanations (SHAP) helped identify the critical lectins for glycoprofile predictions. Our LeGenD approach thus presents an alternative approach for N-glycan analysis.

In Situ Glycan Analysis and Editing in Living Systems

Besides proteins and nucleic acids, carbohydrates are also ubiquitous building blocks of living systems. Approximately 70% of mammalian proteins are glycosylated. Glycans not only provide structural support for living systems but also act as crucial regulators of cellular functions. As a result, they are considered essential pieces of the life science puzzle. However, research on glycans has lagged far behind that on proteins and nucleic acids. The main reason is that glycans are not direct products of gene coding, and their synthesis is nontemplated. In addition, the diversity of monosaccharide species and their linkage patterns contribute to the complexity of the glycan structures, which is the molecular basis for their diverse functions. Research in glycobiology is extremely challenging, especially for the in situ elucidation of glycan structures and functions. There is an urgent need to develop highly specific glycan labeling tools and imaging methods and devise glycan editing strategies. This Perspective focuses on the challenges of in situ analysis of glycans in living systems at three spatial levels (i.e., cell, tissue, and in vivo) and highlights recent advances and directions in glycan labeling, imaging, and editing tools. We believe that examining the current development landscape and the existing bottlenecks can drive the evolution of in situ glycan analysis and intervention strategies and provide glycan-based insights for clinical diagnosis and therapeutics.

Elucidating the glycan-binding specificity and structure of Cucumis melo agglutinin, a new R-type lectin

Plant lectins have garnered attention for their roles as laboratory probes and potential therapeutics. Here, we report the discovery and characterization of Cucumis melo agglutinin (CMA1), a new R-type lectin from melon. Our findings reveal CMA1's unique glycan-binding profile, mechanistically explained by its 3D structure, augmenting our understanding of R-type lectins. We expressed CMA1 recombinantly and assessed its binding specificity using multiple glycan arrays, covering 1,046 unique sequences. This resulted in a complex binding profile, strongly preferring C2-substituted, beta-linked galactose (both GalNAc and Fuca1-2Gal), which we contrasted with the established R-type lectin Ricinus communis agglutinin 1 (RCA1). We also report binding of specific glycosaminoglycan subtypes and a general enhancement of binding by sulfation. Further validation using agglutination, thermal shift assays, and surface plasmon resonance confirmed and quantified this binding specificity in solution. Finally, we solved the high-resolution structure of the CMA1 N-terminal domain using X-ray crystallography, supporting our functional findings at the molecular level. Our study provides a comprehensive understanding of CMA1, laying the groundwork for further exploration of its biological and therapeutic potential.

The functional roles of protein glycosylation in human maternal-fetal crosstalk

BACKGROUND

The establishment of maternal-fetal crosstalk is vital to a successful pregnancy. Glycosylation is a post-translational modification in which glycans (monosaccharide chains) are attached to an organic molecule. Glycans are involved in many physiological and pathological processes. Human endometrial epithelium, endometrial gland secretions, decidual immune cells, and trophoblasts are highly enriched with glycoconjugates and glycan-binding molecules important for a healthy pregnancy. Aberrant glycosylation in the placenta and uterus has been linked to repeated implantation failure and various pregnancy complications, but there is no recent review summarizing the functional roles of glycosylation at the maternal-fetal interface and their associations with pathological processes.

OBJECTIVE AND RATIONALE

This review aims to summarize recent findings on glycosylation, glycosyltransferases, and glycan-binding receptors at the maternal-fetal interface, and their involvement in regulating the biology and pathological conditions associated with endometrial receptivity, placentation and maternal-fetal immunotolerance. Current knowledge limitations and future insights into the study of glycobiology in reproduction are discussed.

SEARCH METHODS

A comprehensive PubMed search was conducted using the following keywords: glycosylation, glycosyltransferases, glycan-binding proteins, endometrium, trophoblasts, maternal-fetal immunotolerance, siglec, selectin, galectin, repeated implantation failure, early pregnancy loss, recurrent pregnancy loss, preeclampsia, and fetal growth restriction. Relevant reports published between 1980 and 2023 and studies related to these reports were retrieved and reviewed. Only publications written in English were included.

OUTCOMES

The application of ultrasensitive mass spectrometry tools and lectin-based glycan profiling has enabled characterization of glycans present at the maternal-fetal interface and in maternal serum. The endometrial luminal epithelium is covered with highly glycosylated mucin that regulates blastocyst adhesion during implantation. In the placenta, fucose and sialic acid residues are abundantly presented on the villous membrane and are essential for proper placentation and establishment of maternal-fetal immunotolerance. Glycan-binding receptors, including selectins, sialic-acid-binding immunoglobulin-like lectins (siglecs) and galectins, also modulate implantation, trophoblast functions and maternal-fetal immunotolerance. Aberrant glycosylation is associated with repeated implantation failure, early pregnancy loss and various pregnancy complications. The current limitation in the field is that most glycobiological research relies on association studies, with few studies revealing the specific functions of glycans. Technological advancements in analytic, synthetic and functional glycobiology have laid the groundwork for further exploration of glycans in reproductive biology under both physiological and pathological conditions.

WIDER IMPLICATIONS

A deep understanding of the functions of glycan structures would provide insights into the molecular mechanisms underlying their involvement in the physiological and pathological regulation of early pregnancy. Glycans may also potentially serve as novel early predictive markers and therapeutic targets for repeated implantation failure, pregnancy loss, and other pregnancy complications.

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.

Auto-Lectin Dotcoding by Two Octopuses: Rapid Analysis of Fluorescence-Labeled Glycoproteins by an 8-channel Fully-Automatic Bead Array Scanner with a Rolling-Circle Detector

Protein glycosylation is a crucial factor that must be evaluated in biological pharmaceuticals. The glycoform profile of a protein can vary depending on the conditions of the cultivation, purification process, and the selection of a host cell. Lectin microarrays are reliable bioanalytical methods used in the early phases of bioprocesses for the detection of glycosylation. The concept of a fully automated glycan detection with a bead array has been previously reported; however, no simple system has been constructed on fluorescence-based detection using a microarray. Here, we present a fully automated detection system equipped with a novel fluorescence detector for a 13-lectin bead array with a single tip. The lattice-like arrangement of a set of fibers proximate to the tip of the light emitting diode and photomultiplier tube detector minimized the noise caused by the reflection of incident light on the plastic capillary tip and bead. A unique rolling-circle fiber unit with quadruple lattices stacked in two layers realizes the 8-parallel automeasurement with a drastic reduction in scanning time and machine size. The 8-glycan profiles obtained automatically within 25 min were identical with those obtained with the conventional lectin microarray after overnight incubation. The signals obtained were represented as lectin dotcodes. Therefore, autolectin dotcoding assisted by the twin 8 legs named as "detection and irradiation octopuses" may be a rapid glyco-evaluation system during the production and development of biopharmaceuticals.

Chemoenzymatic Measurement of LacNAc in Single-Cell Multiomics Reveals It as a Cell-Surface Indicator of Glycolytic Activity of CD8+ T Cells

Despite the rich information about the physiological state of a cell encoded in the dynamic changes of cell-surface glycans, chemical methods to capture specific glycan epitopes at the single-cell level are quite limited. Here, we report a chemoenzymatic method for the single-cell detection of N-acetyllactosamine (LacNAc) by labeling LacNAc with a specific DNA barcode. The chemoenzymatic labeling does not alter the transcriptional status of immune cells and is compatible with multiple scRNA-seq platforms. Integrated analysis of LacNAc and the transcriptome of T cells at the single-cell level reveals that the amount of cell-surface LacNAc is significantly upregulated in activated CD8+ T cells but maintained at basal levels in resting CD8+ T cells (i.e., naive and central memory T cells). Further analysis confirms that LacNAc levels are positively correlated with the glycolytic activity of CD8+ T cells during differentiation. Taken together, our study demonstrates the feasibility of the chemoenzymatic detection of cell-surface glycan in single-cell RNA sequencing-based multiomics with TCR sequence and cell-surface epitope information (i.e., scTCR and CITE-seq), and provides a new way to characterize the biological role of glycan in diverse physiological states.

Glycoinformatics in the Artificial Intelligence Era

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

Single cell metabolism: current and future trends

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Reading the glyco-code: New approaches to studying protein-carbohydrate interactions

The surface of all living cells is decorated with carbohydrate molecules. Hundreds of functional proteins bind to these glycosylated ligands; such binding events subsequently modulate many aspects of protein and cell function. Identifying ligands for glycan-binding proteins (GBPs) is a defining challenge of glycoscience research. Here, we review recent advances that are allowing protein-carbohydrate interactions to be dissected with an unprecedented level of precision. We specifically highlight how cell-based glycan arrays and glyco-genomic profiling are being used to define the structural determinants of glycan-protein interactions in living cells. Going forward, these methods create exciting new opportunities for the study of glycans in physiology and disease.

Concerted Regulation of Glycosylation Factors Sustains Tissue Identity and Function

Glycosylation is a fundamental cellular process affecting human development and health. Complex machinery establishes the glycan structures whose heterogeneity provides greater structural diversity than other post-translational modifications. Although known to present spatial and temporal diversity, the evolution of glycosylation and its role at the tissue-specific level is poorly understood. In this study, we combined genome and transcriptome profiles of healthy and diseased tissues to uncover novel insights into the complex role of glycosylation in humans. We constructed a catalogue of human glycosylation factors, including transferases, hydrolases and other genes directly involved in glycosylation. These were categorized as involved in N-, O- and lipid-linked glycosylation, glypiation, and glycosaminoglycan synthesis. Our data showed that these glycosylation factors constitute an ancient family of genes, where evolutionary constraints suppressed large gene duplications, except for genes involved in O-linked and lipid glycosylation. The transcriptome profiles of 30 healthy human tissues revealed tissue-specific expression patterns preserved across mammals. In addition, clusters of tightly co-expressed genes suggest a glycosylation code underlying tissue identity. Interestingly, several glycosylation factors showed tissue-specific profiles varying with age, suggesting a role in ageing-related disorders. In cancer, our analysis revealed that glycosylation factors are highly perturbed, at the genome and transcriptome levels, with a strong predominance of copy number alterations. Moreover, glycosylation factor dysregulation was associated with distinct cellular compositions of the tumor microenvironment, reinforcing the impact of glycosylation in modulating the immune system. Overall, this work provides genome-wide evidence that the glycosylation machinery is tightly regulated in healthy tissues and impaired in ageing and tumorigenesis, unveiling novel potential roles as prognostic biomarkers or therapeutic targets.

Tools for mammalian glycoscience research

Cellular carbohydrates or glycans are critical mediators of biological function. Their remarkably diverse structures and varied activities present exciting opportunities for understanding many areas of biology. In this primer, we discuss key methods and recent breakthrough technologies for identifying, monitoring, and manipulating glycans in mammalian systems.

Quantitative evaluation of glycan-binding specificity of recombinant concanavalin A produced in lettuce (Lactuca sativa)

Concanavalin A (ConA), a mannose (Man)-specific leguminous lectin isolated from the jack bean (Canavalia ensiformis) seed extracts, was discovered over a century ago. Although ConA has been extensively applied in various life science research, recombinant mature ConA expression has not been fully established. Here, we aimed to produce recombinant ConA (rConA) in lettuce (Lactuca sativa) using an Agrobacterium tumefaciens-mediated transient expression system. rConA could be produced as a fully active form from soluble fractions of lettuce leaves and purified by affinity chromatography. From 12 g wet weight of lettuce leaves, 0.9 mg rConA could be purified. The glycan-binding properties of rConA were then compared with that of the native ConA isolated from jack bean using glycoconjugate microarray and frontal affinity chromatography. rConA demonstrated a glycan-binding specificity similar to nConA. Both molecules bound to N-glycans containing a terminal Man residue. Consistent with previous reports, terminal Manα1-6Man was found to be an essential unit for the high-affinity binding of rConA and nConA, while bisecting GlcNAc diminished the binding of rConA and nConA to Manα1-6Man-terminated N-glycans. These results demonstrate that the fully active rConA could be produced using the A. tumefaciens-mediated transient expression system and used as a recombinant substitute for nConA.

Glycan Profiling by Sequencing to Uncover Multicellular Communication: Launching Glycobiology in Single Cells and Microbiomes

Glycans are essential building blocks of life that are located at the outermost surface of all cells from mammals to bacteria and even viruses. Cell surface glycans mediate multicellular communication in diverse biological processes and are useful as “surface markers” to identify cells. Various single-cell sequencing technologies have already emerged that enable the high-throughput analysis of omics information, such as transcriptome and genome profiling on a cell-by-cell basis, which has advanced our understanding of complex multicellular interactions. However, there has been no robust technology to analyze the glycome in single cells, mainly because glycans with branched and heterogeneous structures cannot be readily amplified by polymerase chain reactions like nucleic acids. We hypothesized that the generation of lectins conjugated with DNA barcodes (DNA-barcoded lectins) would enable the conversion of glycan information to gene information, which may be amplified and measured using DNA sequencers. This technology will enable the simultaneous analysis of glycan and RNA in single cells. Based on this concept, we developed a technology to analyze glycans and RNA in single cells, which was referred to as scGR-seq. Using scGR-seq, we acquired glycan and gene expression profiles of individual cells constituting heterogeneous cell populations, such as tissues. We further extended Glycan-seq to the profiling of the surface glycans of bacteria and even gut microbiota. Glycan-seq and scGR-seq are new technologies that enable us to elucidate the function of glycans in cell–cell and cell–microorganism communication, which extends glycobiology to the level of single cells and microbiomes.

Applying transcriptomics to studyglycosylation at the cell type level

The complex multi-step process of glycosylation occurs in a single cell, yet current analytics generally cannot measure the output (the glycome) of a single cell. Here, we addressed this discordance by investigating how single cell RNA-seq data can be used to characterize the state of the glycosylation machinery and metabolic network in a single cell. The metabolic network involves 214 glycosylation and modification enzymes outlined in our previously built atlas of cellular glycosylation pathways. We studied differential mRNA regulation of enzymes at the organ and single cell level, finding that most of the general protein and lipid oligosaccharide scaffolds are produced by enzymes exhibiting limited transcriptional regulation among cells. We predict key enzymes within different glycosylation pathways to be highly transcriptionally regulated as regulatable hotspots of the cellular glycome. We designed the Glycopacity software that enables investigators to extract and interpret glycosylation information from transcriptome data and define hotspots of regulation.

•

RNA-seq can provide information on the glycosylation metabolic network state

•

It is possible to readout glycosylation capacity from single cell RNA-seq data

•

Genes regulating the biosynthesis of common glycan scaffolds show little regulation

•

Key enzymes in the glycosylation network are predicted to be regulatable hotspots

Bridging Glycomics and Genomics: New Uses of Functional Genetics in the Study of Cellular Glycosylation

All living cells are coated with a diverse collection of carbohydrate molecules called glycans. Glycans are key regulators of cell behavior and important therapeutic targets for human disease. Unlike proteins, glycans are not directly templated by discrete genes. Instead, they are produced through multi-gene pathways that generate a heterogenous array of glycoprotein and glycolipid antigens on the cell surface. This genetic complexity has sometimes made it challenging to understand how glycosylation is regulated and how it becomes altered in disease. Recent years, however, have seen the emergence of powerful new functional genomics technologies that allow high-throughput characterization of genetically complex cellular phenotypes. In this review, we discuss how these techniques are now being applied to achieve a deeper understanding of glyco-genomic regulation. We highlight specifically how methods like ChIP-seq, RNA-seq, CRISPR genomic screening and scRNA-seq are being used to map the genomic basis for various cell-surface glycosylation states in normal and diseased cell types. We also offer a perspective on how emerging functional genomics technologies are likely to create further opportunities for studying cellular glycobiology in the future. Taken together, we hope this review serves as a primer to recent developments at the glycomics-genomics interface.

Spatiotemporal diversity and regulation of glycosaminoglycans in cell homeostasis and human disease

Glycosaminoglycans (GAGs) are long, linear polysaccharides that are ubiquitously expressed on the cell surface and in the extracellular matrix of all animal cells. These complex carbohydrates play important roles in many cellular processes and have been implicated in many disease states, including cancer, inflammation, and genetic disorders. GAGs are among the most complex molecules in biology with enormous information content and extensive structural and functional heterogeneity. GAG biosynthesis is a nontemplate-driven process facilitated by a large group of biosynthetic enzymes that have been extensively characterized over the past few decades. Interestingly, the expression of the enzymes and the consequent structure and function of the polysaccharide chains can vary temporally and spatially during development and under certain pathophysiological conditions, suggesting their assembly is tightly regulated in cells. Due to their many key roles in cell homeostasis and disease, there is much interest in targeting the assembly and function of GAGs as a therapeutic approach. Recent advances in genomics and GAG analytical techniques have pushed the field and generated new perspectives on the regulation of mammalian glycosylation. This review highlights the spatiotemporal diversity of GAGs and the mechanisms guiding their assembly and function in human biology and disease.

The dynamic brain N-glycome

The attachment of carbohydrates to other macromolecules, such as proteins or lipids, is an important regulatory mechanism termed glycosylation. One subtype of protein glycosylation is asparagine-linked glycosylation (N-glycosylation) which plays a key role in the development and normal functioning of the vertebrate brain. To better understand the role of N-glycans in neurobiology, it's imperative we analyse not only the functional roles of individual structures, but also the collective impact of large-scale changes in the brain N-glycome. The systematic study of the brain N-glycome is still in its infancy and data are relatively scarce. Nevertheless, the prevailing view has been that the neuroglycome is inherently restricted with limited capacity for variation. The development of improved methods for N-glycomics analysis of brain tissue has facilitated comprehensive characterisation of the complete brain N-glycome under various experimental conditions on a larger scale. Consequently, accumulating data suggest that it's more dynamic than previously recognised and that, within a general framework, it has a given capacity to change in response to both intrinsic and extrinsic stimuli. Here, we provide an overview of the many factors that can alter the brain N-glycome, including neurodevelopment, ageing, diet, stress, neuroinflammation, injury, and disease. Given this emerging evidence, we propose that the neuroglycome has a hitherto underappreciated plasticity and we discuss the therapeutic implications of this regarding the possible reversal of pathological changes via interventions. We also briefly review the merits and limitations of N-glycomics as an analytical method before reflecting on some of the outstanding questions in the field.

scGR-seq: Integrated analysis of glycan and RNA in single cells

Glycans are structurally diverse molecules found on the surface of living cells. The protocol details a system developed for combined analysis of glycan and RNA in single cells (scGR-seq) using human induced pluripotent stem cells (hiPSCs) and hiPSC-derived neural progenitor cells (NPCs). scGR-seq consists of DNA-barcoded lectin-based glycan profiling by sequencing (scGlycan-seq) and single-cell transcriptome profiling (scRNA-seq). scGR-seq will be an essential technique to delineate the cellular heterogeneity of glycans across multicellular systems.

For complete details on the use and execution of this profile, please refer to Minoshima et al. (2021).

•

Protocols for combined analysis of glycan and RNA in single cells (scGR-seq)

•

scGR-seq is a DNA-barcoded lectin-based glycan profiling

•

scGR-seq analysis of hiPSCs and NPCs were shown as an example

•

scGR-seq is adaptable for any cells, tissues, and organs across organisms

New horizons in the stormy sea of multimodal single-cell data integration

While measurements of RNA expression have dominated the world of single-cell analyses, new single-cell techniques increasingly allow collection of different data modalities, measuring different molecules, structural connections, and intermolecular interactions. Integrating the resulting multimodal single-cell datasets is a new bioinformatics challenge. Equally important, it is a new experimental design challenge for the bench scientist, who is not only choosing from a myriad of techniques for each data modality but also faces new challenges in experimental design. The ultimate goal is to design, execute, and analyze multimodal single-cell experiments that are more than just descriptive but enable the learning of new causal and mechanistic biology. This objective requires strict consideration of the goals behind the analysis, which might range from mapping the heterogeneity of a cellular population to assembling system-wide causal networks that can further our understanding of cellular functions and eventually lead to models of tissues and organs. We review steps and challenges toward this goal. Single-cell transcriptomics is now a mature technology, and methods to measure proteins, lipids, small-molecule metabolites, and other molecular phenotypes at the single-cell level are rapidly developing. Integrating these single-cell readouts so that each cell has measurements of multiple types of data, e.g., transcriptomes, proteomes, and metabolomes, is expected to allow identification of highly specific cellular subpopulations and to provide the basis for inferring causal biological mechanisms.