Single-cell Omics Technologies Research Articles

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics

Abstract Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies such as transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.

Unveiling the molecular complexity of intestinal ischemia-reperfusion injury through omics technologies.

Intestinal ischemia-reperfusion injury (IR) is implicated in various clinical conditions and causes damage to the intestinal epithelium resulting in intestinal barrier loss. This presents a substantial clinical challenge, emphasizing the importance of gaining a comprehensive understanding of molecular events to aid in the identification of novel therapeutic targets. This review systematically explores the extent to which omics technologies-transcriptomics, proteomics, metabolomics, and metagenomics-have already contributed to deciphering the molecular mechanisms contributing to intestinal IR injury, in in vivo and in vitro animal and human models, and in clinical samples. Recent breakthroughs involve applying omics methodologies on exosomes, organoids, and single cells, shedding light on promising avenues and valuable targets to reduce intestinal IR injury. Future directions aimed at expediting clinical translation are discussed as well and include multi-omics data integration to facilitate the identification of key regulatory nodes driving intestinal IR injury and advancing human organoid models based on the novel insights by single-cell omics technologies, offering hope for clinical application of therapeutic strategies in the years to come.

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

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

A fast, scalable and versatile tool for analysis of single-cell omics data.

Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.

The Application of Single-Cell Omics Technologies in Neuroscientific Research

The Application of Single-Cell Omics Technologies in Neuroscientific Research

Engineering custom morpho- and chemotypes of Populus for sustainable production of biofuels, bioproducts, and biomaterials.

Humans have been modifying plant traits for thousands of years, first through selection (i.e., domestication) then modern breeding, and in the last 30 years, through biotechnology. These modifications have resulted in increased yield, more efficient agronomic practices, and enhanced quality traits. Precision knowledge of gene regulation and function through high-resolution single-cell omics technologies, coupled with the ability to engineer plant genomes at the DNA sequence, chromatin accessibility, and gene expression levels, can enable engineering of complex and complementary traits at the biosystem level. Populus spp., the primary genetic model system for woody perennials, are among the fastest growing trees in temperate zones and are important for both carbon sequestration and global carbon cycling. Ample genomic and transcriptomic resources for poplar are available including emerging single-cell omics datasets. To expand use of poplar outside of valorization of woody biomass, chassis with novel morphotypes in which stem branching and tree height are modified can be fabricated thereby leading to trees with altered leaf to wood ratios. These morphotypes can then be engineered into customized chemotypes that produce high value biofuels, bioproducts, and biomaterials not only in specific organs but also in a cell-type-specific manner. For example, the recent discovery of triterpene production in poplar leaf trichomes can be exploited using cell-type specific regulatory sequences to synthesize high value terpenes such as the jet fuel precursor bisabolene specifically in the trichomes. By spatially and temporally controlling expression, not only can pools of abundant precursors be exploited but engineered molecules can be sequestered in discrete cell structures in the leaf. The structural diversity of the hemicellulose xylan is a barrier to fully utilizing lignocellulose in biomaterial production and by leveraging cell-type-specific omics data, cell wall composition can be modified in a tailored and targeted specific manner to generate poplar wood with novel chemical features that are amenable for processing or advanced manufacturing. Precision engineering poplar as a multi-purpose sustainable feedstock highlights how genome engineering can be used to re-imagine a crop species.

Single-nucleotide variant calling in single-cell sequencing data with Monopogen.

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterization of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy. We propose SnapCCESS for clustering cells by integrating data modalities in multimodal single-cell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterization of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data. SnapCCESS is implemented as a Python package and is freely available from https://github.com/PYangLab/SnapCCESS under the open-source license of GPL-3. The data used in this study are publicly available (see section 'Data availability').

Stabilized mosaic single-cell data integration using unshared features.

Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates 'multi-hop' mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference.

Perspective: Can early embryonic losses be reduced in lactating dairy cows?

Perspective: Can early embryonic losses be reduced in lactating dairy cows?

CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data.

Single-cell omics technologies have made it possible to analyze the individual cells within a biological sample, providing a more detailed understanding of biological systems. Accurately determining the cell type of each cell is a crucial goal in single-cell RNA-seq (scRNA-seq) analysis. Apart from overcoming the batch effects arising from various factors, single-cell annotation methods also face the challenge of effectively processing large-scale datasets. With the availability of an increase in the scRNA-seq datasets, integrating multiple datasets and addressing batch effects originating from diverse sources are also challenges in cell-type annotation. In this work, to overcome the challenges, we developed a supervised method called CIForm based on the Transformer for cell-type annotation of large-scale scRNA-seq data. To assess the effectiveness and robustness of CIForm, we have compared it with some leading tools on benchmark datasets. Through the systematic comparisons under various cell-type annotation scenarios, we exhibit that the effectiveness of CIForm is particularly pronounced in cell-type annotation. The source code and data are available at https://github.com/zhanglab-wbgcas/CIForm.

Recent advances of osteoimmunology research in rheumatoid arthritis: From single-cell omics approach.

Cellular immune responses as well as generalized and periarticular bone loss are the key pathogenic features of rheumatoid arthritis (RA). Under the pathological conditions of RA, dysregulated inflammation and immune processes tightly interact with skeletal system, resulting in pathological bone damage via inhibition of bone formation or induction of bone resorption. Single-cell omics technologies are revolutionary tools in the field of modern biological research.They enable the display of the state and function of cells in various environments from a single-cell resolution, thus making it conducive to identify the dysregulated molecular mechanisms of bone destruction in RA as well as the discovery of potential therapeutic targets and biomarkers. Here, we summarize the latest findings of single-cell omics technologies in osteoimmunology research in RA. These results suggest that single-cell omics have made significant contributions to transcriptomics and dynamics of specific cells involved in bone remodeling, providing a new direction for our understanding of cellular heterogeneity in the study of osteoimmunology in RA.

Characterizing the Immune Cell Transcriptomic Response To Low-Dose Ionizing Radiation

Abstract Low-dose exposure to ionizing radiation is increasingly common in medical, industrial, and public settings worldwide. Previous studies examining the impacts of low-dose radiation (LDR) exposure on genetic material, cellular responses, and health have been inconclusive and conflicting. The National Academy of Science, Engineering and Medicine has outlined that future LDR research should utilize single-cell omics technologies and computational workflows to define the molecular signatures of LDR exposure. In partnership with the Canadian Nuclear Laboratories (CNL), we have used single-cell RNA sequencing (scRNA-seq) to characterize the in-vivo immune cell response to chronic LDR. Mature adult (18 weeks-old) C57BL/6 female mice were whole-body-exposed to 60Co gamma radiation for 7 days, at dose rates of 0.06 mGy/h or 0.6 mGy/h, to achieve cumulative absorbed doses of 10 mGy (n=6) or 100 mGy (n=6), respectively. scRNA-seq of cells isolated immediately post-irradiation identified subtle candidate transcriptomic changes caused by LDR exposure in splenic and bone marrow cell populations. As the first scRNA-seq study of in-vivo LDR exposure, we reveal a more detailed description of the cellular response to LDR exposure than available in the current literature. Therefore, dissemination of these results will advance radiation biology and will be valuable for evaluating Nuclear Safety guidelines. The research is funded by the CANDU Owner's Group and the Mitacs Accelerate program.

Spatially Guided and Single Cell Tools to Map the Microenvironment in Cutaneous T-Cell Lymphoma

Mycosis fungoides (MF) and Sézary syndrome (SS) are two closely related clinical variants of cutaneous T-cell lymphomas (CTCL). Previously demonstrated large patient-to-patient and intra-patient disease heterogeneity underpins the importance of personalized medicine in CTCL. Advanced stages of CTCL are characterized by dismal prognosis, and the early identification of patients who will progress remains a clinical unmet need. While the exact molecular events underlying disease progression are poorly resolved, the tumor microenvironment (TME) has emerged as an important driver. In particular, the Th1-to-Th2 shift in the immune response is now commonly identified across advanced-stage CTCL patients. Herein, we summarize the role of the TME in CTCL evolution and the latest studies in deciphering inter- and intra-patient heterogeneity. We introduce spatially resolved omics as a promising technology to advance immune-oncology efforts in CTCL. We propose the combined implementation of spatially guided and single-cell omics technologies in paired skin and blood samples. Such an approach will mediate in-depth profiling of phenotypic and molecular changes in reactive immune subpopulations and malignant T cells preceding the Th1-to-Th2 shift and reveal mechanisms underlying disease progression from skin-limited to systemic disease that collectively will lead to the discovery of novel biomarkers to improve patient prognostication and the design of personalized treatment strategies.

The Neurovasculome: Key Roles in Brain Health and Cognitive Impairment: A Scientific Statement From the American Heart Association/American Stroke Association.

Preservation of brain health has emerged as a leading public health priority for the aging world population. Advances in neurovascular biology have revealed an intricate relationship among brain cells, meninges, and the hematic and lymphatic vasculature (the neurovasculome) that is highly relevant to the maintenance of cognitive function. In this scientific statement, a multidisciplinary team of experts examines these advances, assesses their relevance to brain health and disease, identifies knowledge gaps, and provides future directions. Authors with relevant expertise were selected in accordance with the American Heart Association conflict-of-interest management policy. They were assigned topics pertaining to their areas of expertise, reviewed the literature, and summarized the available data. The neurovasculome, composed of extracranial, intracranial, and meningeal vessels, as well as lymphatics and associated cells, subserves critical homeostatic functions vital for brain health. These include delivering O2 and nutrients through blood flow and regulating immune trafficking, as well as clearing pathogenic proteins through perivascular spaces and dural lymphatics. Single-cell omics technologies have unveiled an unprecedented molecular heterogeneity in the cellular components of the neurovasculome and have identified novel reciprocal interactions with brain cells. The evidence suggests a previously unappreciated diversity of the pathogenic mechanisms by which disruption of the neurovasculome contributes to cognitive dysfunction in neurovascular and neurodegenerative diseases, providing new opportunities for the prevention, recognition, and treatment of these conditions. These advances shed new light on the symbiotic relationship between the brain and its vessels and promise to provide new diagnostic and therapeutic approaches for brain disorders associated with cognitive dysfunction.

Multi-task learning from multimodal single-cell omics with Matilda.

Multimodal single-cell omics technologies enable multiple molecular programs to be simultaneously profiled at a global scale in individual cells, creating opportunities to study biological systems at a resolution that was previously inaccessible. However, the analysis of multimodal single-cell omics data is challenging due to the lack of methods that can integrate across multiple data modalities generated from such technologies. Here, we present Matilda, a multi-task learning method for integrative analysis of multimodal single-cell omics data. By leveraging the interrelationship among tasks, Matilda learns to perform data simulation, dimension reduction, cell type classification, and feature selection in a single unified framework. We compare Matilda with other state-of-the-art methods on datasets generated from some of the most popular multimodal single-cell omics technologies. Our results demonstrate the utility of Matilda for addressing multiple key tasks on integrative multimodal single-cell omics data analysis. Matilda is implemented in Pytorch and is freely available from https://github.com/PYangLab/Matilda.

Functional states of myeloid cells in cancer.

Myeloid cells, comprised of macrophages, dendritic cells, monocytes, and granulocytes, represent a major component of the tumor microenvironment (TME) and are critically involved in regulation of tumor progression and metastasis. In recent years, single-cell omics technologies have identified multiple phenotypically distinct subpopulations. In this review, we discuss recent data and concepts suggesting that the biology of myeloid cells is largely defined by a very limited number of functional states that transcend the narrowly defined cell populations. These functional states are primarily centered around classical and pathological states of activation, with the latter state commonly defined as myeloid-derived suppressor cells. We discuss the concept that lipid peroxidation of myeloid cells represents a major mechanism that governs their pathological state of activation in the TME. Lipid peroxidation is associated with ferroptosis mediating suppressive activity of these cells and thus could be considered an attractive target for therapeutic intervention.

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases.

Cardiovascular diseases (CVD) are the leading cause of death worldwide and display complex phenotypic heterogeneity caused by many convergent processes, including interactions between genetic variation and environmental factors. Despite the identification of a large number of associated genes and genetic loci, the precise mechanisms by which these genes systematically influence the phenotypic heterogeneity of CVD are not well understood. In addition to DNA sequence, understanding the molecular mechanisms of CVD requires data from other omics levels, including the epigenome, the transcriptome, the proteome, as well as the metabolome. Recent advances in multiomics technologies have opened new precision medicine opportunities beyond genomics that can guide precise diagnosis and personalized treatment. At the same time, network medicine has emerged as an interdisciplinary field that integrates systems biology and network science to focus on the interactions among biological components in health and disease, providing an unbiased framework through which to integrate systematically these multiomics data. In this review, we briefly present such multiomics technologies, including bulk omics and single-cell omics technologies, and discuss how they can contribute to precision medicine. We then highlight network medicine-based integration of multiomics data for precision medicine and therapeutics in CVD. We also include a discussion of current challenges, potential limitations, and future directions in the study of CVD using multiomics network medicine approaches.

Single-cell omics: A new direction for functional genetic research in human diseases and animal models.

Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.

Muscle fibro-adipogenic progenitors from a single-cell perspective: Focus on their “virtual” secretome

Skeletal muscle is a highly plastic tissue composed of a number of heterogeneous cell populations that, by interacting and communicating with each other, participate to the muscle homeostasis, and orchestrate regeneration and repair in healthy and diseased conditions. Although muscle regeneration relies on the activity of muscle stem cells (MuSCs), many other cellular players such as inflammatory, vascular and tissue-resident mesenchymal cells participate and communicate with MuSCs to sustain the regenerative process. Among them, Fibro-Adipogenic Progenitors (FAPs), a muscle interstitial stromal population, are crucial actors during muscle homeostasis and regeneration, interacting with MuSCs and other cellular players and dynamically producing and remodelling the extra-cellular matrix. Recent emerging single-cell omics technologies have resulted in the dissection of the heterogeneity of each cell populations within skeletal muscle. In this perspective we have reviewed the recent single-cell omics studies with a specific focus on FAPs in mouse and human muscle. More precisely, using the OutCyte prediction tool, we analysed the “virtual” secretome of FAPs, in resting and regenerating conditions, to highlight the potential of RNAseq data for the study of cellular communication.