Mapping human cell phenotypes to genotypes with single-cell genomics

Barcodes based on nucleic acid sequences: Applications and challenges (Review)

Cells are the fundamental structural and functional units of living organisms and the study of these entities has remained a central focus throughout the history of biological sciences. Traditional cell research techniques, including fluorescent protein tagging and microscopy, have provided preliminary insights into the lineage history and clonal relationships between progenitor and descendant cells. However, these techniques exhibit inherent limitations in tracking the full developmental trajectory of cells and elucidating their heterogeneity, including sensitivity, stability and barcode drift. In developmental biology, nucleic acid barcode technology has introduced an innovative approach to cell lineage tracing. By assigning unique barcodes to individual cells, researchers can accurately identify and trace the origin and differentiation pathways of cells at various developmental stages, thereby illuminating the dynamic processes underlying tissue development and organogenesis. In cancer research, nucleic acid barcoding has played a pivotal role in analyzing the clonal architecture of tumor cells, exploring their heterogeneity and resistance mechanisms and enhancing our understanding of cancer evolution and inter‑clonal interactions. Furthermore, nucleic acid barcodes play a crucial role in stem cell research, enabling the tracking of stem cells from diverse origins and their derived progeny. This has offered novel perspectives on the mechanisms of stem cell self‑renewal and differentiation. The present review presented a comprehensive examination of the principles, applications and challenges associated with nucleic acid barcode technology.

BEscreen: a versatile toolkit to design base editing libraries

Base editing enables the high-throughput screening of genetic variants for phenotypic effects. Base editing screens require the design of single guide RNA (sgRNA) libraries to enable either gene- or variant-centric approaches. While computational tools supporting the design of sgRNAs exist, no solution offers versatile and scalable library design enabling all major use cases. Here, we introduce BEscreen, a comprehensive base editing guide design tool provided as a web server (bescreen.ostendorflab.org) and as a command line tool. BEscreen provides variant-, gene-, and region-centric modes to accommodate various screening approaches. The variant mode accepts genomic coordinates, amino acid changes, or rsIDs as input. The gene mode designs near-saturation libraries covering the entire coding sequence of given genes or transcripts, and the region mode designs all possible guides for given genomic regions. BEscreen enables selection of guides by biological consequence, it features comprehensive customization of base editor characteristics, and it offers optional annotation using Ensembl's Variant Effect Predictor. In sum, BEscreen is a highly versatile tool to design base editing screens for a wide range of use cases with seamless scalability from individual variants to large, near-saturation libraries.

A novel coarsened graph learning method for scalable single-cell data analysis

The emergence of single-cell technologies, including flow and mass cytometry, as well as single-cell RNA sequencing, has revolutionized the study of cellular heterogeneity, generating vast datasets rich in biological insights. Despite the effectiveness of graph-based analyses in deciphering the complexities of these datasets, managing large-scale graph representations of single-cell data remains computationally challenging. Coarsening has been employed to tackle this difficulty. However, current coarsening techniques such as Cytocoarsening, Heavy Edge Matching (HEM), and Locally Variable Edges (LVE) often suffer from slow processing speeds and limited adaptability. To address these challenges, we propose a novel approach utilizing Feature-Aware Graph Coarsening via Hashing (FACH), which integrates locality-sensitive hashing for scalable and efficient single-cell data analysis. This method directly extracts informative, low-dimensional cell representations from raw single-cell RNA sequencing and mass cytometry data, significantly improving processing speed while preserving essential data features. We demonstrate its effectiveness in downstream tasks, such as scalable graph neural network training on coarsened single-cell data, highlighting its ability to retain crucial biological features and enable efficient, accurate analyses. Our method directly extracts informative, low-dimensional cell representations from raw single-cell RNA sequencing and mass cytometry data, significantly improving processing speed and preserving critical biological features, such as transcriptional signatures and network topology. It reduces computational time by at least 50% compared to existing methods and achieves superior classification accuracy, such as 88.1% on the Baron Human dataset, underscoring its efficiency and precision in large-scale single-cell analysis.

Transcriptomic and proteomic effects of gene deletion are not evolutionarily conserved

Although the textbook definition of gene function is the effect for which the gene was selected and/or by which it is maintained, gene function is commonly inferred from the phenotypic effects of deleting the gene. Because some of the deletion effects are byproducts of other effects, they may not reflect the gene's selected-effect function. To evaluate the degree to which the phenotypic effects of gene deletion inform gene function, we compare the transcriptomic and proteomic effects of systematic gene deletions in budding yeast (Saccharomyces cerevisiae) with those effects in fission yeast (Schizosaccharomyces pombe). Despite evidence for functional conservation of orthologous genes, their deletions result in no more sharing of transcriptomic or proteomic effects than that from deleting nonorthologous genes. Because the wild-type mRNA and protein levels of orthologous genes are significantly correlated between the two yeasts and because transcriptomic effects of deleting the same gene strongly overlap between studies in the same S. cerevisiae strain by different laboratories, our observation cannot be explained by rapid evolution or large measurement error of gene expression. Analysis of transcriptomic and proteomic effects of gene deletions in multiple S. cerevisiae strains by the same laboratory reveals a high sensitivity of these effects to the genetic background, explaining why these effects are not evolutionarily conserved. Together, our results suggest that most transcriptomic and proteomic effects of gene deletion do not inform selected-effect function. This finding has important implications for assessing and/or understanding gene function, pleiotropy, and biological complexity.

Organoids from pluripotent stem cells and human tissues: When two cultures meet each other

Human organoids are a widely used tool in cell biology to study homeostatic processes, disease, and development. The term organoids covers a plethora of model systems from different cellular origins that each have unique features and applications but bring their own challenges. This review discusses the basic principles underlying organoids generated from pluripotent stem cells (PSCs) as well as those derived from tissue stem cells (TSCs). We consider how well PSC- and TSC-organoids mimic the different intended organs in terms of cellular complexity, maturity, functionality, and the ongoing efforts to constitute predictive complex models of in vivo situations. We discuss the advantages and limitations associated with each system to answer different biological questions including in the field of cancer and developmental biology, and with respect to implementing emerging advanced technologies, such as (spatial) -omics analyses, CRISPR screens, and high-content imaging screens. We postulate how the two fields may move forward together, integrating advantages of one to the other.

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.

Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives

BACKGROUND

Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.

AIM OF REVIEW

This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.

Label-free active single-cell encapsulation enabled by microvalve-based on-demand droplet generation and real-time image processing

Droplet microfluidics-based single-cell encapsulation is a critical technology that enables large-scale parallel single-cell analysis by capturing and processing thousands of individual cells. As the efficiency of passive single-cell encapsulation is limited by Poisson distribution, active single-cell encapsulation has been developed to theoretically ensure that each droplet contains one cell. However, existing active single-cell encapsulation technologies still face issues related to fluorescence labeling and low throughput. Here, we present an active single-cell encapsulation technique by using microvalve-based drop-on-demand technology and real-time image processing to encapsulate single cells with high throughput in a label-free manner. Our experiments demonstrated that the single-cell encapsulation system can encapsulate individual polystyrene beads with 96.3 % efficiency and HeLa cells with 94.9 % efficiency. The flow speed of cells in this system can reach 150 mm/s, resulting in a corresponding theoretical encapsulation throughput of 150 Hz. This technology has significant potential in various biomedical applications, including single-cell omics, secretion detection, and drug screening.

Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.

Getting around the roundworms: Identifying knowledge gaps and research priorities for the ascarids

The ascarids are a large group of parasitic nematodes that infect a wide range of animal species. In humans, they cause neglected diseases of poverty; many animal parasites also cause zoonotic infections in people. Control measures include hygiene and anthelmintic treatments, but they are not always appropriate or effective and this creates a continuing need to search for better ways to reduce the human, welfare and economic costs of these infections. To this end, Le Studium Institute of Advanced Studies organized a two-day conference to identify major gaps in our understanding of ascarid parasites with a view to setting research priorities that would allow for improved control. The participants identified several key areas for future focus, comprising of advances in genomic analysis and the use of model organisms, especially Caenorhabditis elegans, a more thorough appreciation of the complexity of host-parasite (and parasite-parasite) communications, a search for novel anthelmintic drugs and the development of effective vaccines. The participants agreed to try and maintain informal links in the future that could form the basis for collaborative projects, and to co-operate to organize future meetings and workshops to promote ascarid research.

Natural products in osteoarthritis treatment: bridging basic research to clinical applications

Osteoarthritis (OA) is the most prevalent degenerative musculoskeletal disease, severely impacting the function of patients and potentially leading to disability, especially among the elderly population. Natural products (NPs), obtained from components or metabolites of plants, animals, microorganisms etc., have gained significant attention as important conservative treatments for various diseases. Recently, NPs have been well studied in preclinical and clinical researches, showing promising potential in the treatment of OA. In this review, we summed up the main signaling pathways affected by NPs in OA treatment, including NF-κB, MAPKs, PI3K/AKT, SIRT1, and other pathways, which are related to inflammation, anabolism and catabolism, and cell death. In addition, we described the therapeutic effects of NPs in different OA animal models and the current clinical studies in OA patients. At last, we discussed the potential research directions including in-depth analysis of the mechanisms and new application strategies of NPs for the OA treatment, so as to promote the basic research and clinical transformation in the future. We hope that this review may allow us to get a better understanding about the potential bioeffects and mechanisms of NPs in OA therapy, and ultimately improve the effectiveness of NPs-based clinical conservative treatment for OA patients.

Multimodal spatiotemporal phenotyping of human retinal organoid development

Organoids generated from human pluripotent stem cells provide experimental systems to study development and disease, but quantitative measurements across different spatial scales and molecular modalities are lacking. In this study, we generated multiplexed protein maps over a retinal organoid time course and primary adult human retinal tissue. We developed a toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components and global patterning in each organoid and primary tissue. In addition, we generated a single-cell transcriptome and chromatin accessibility timecourse dataset and inferred a gene regulatory network underlying organoid development. We integrated genomic data with spatially segmented nuclei into a multimodal atlas to explore organoid patterning and retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death and showing that mosaic genetic perturbations in retinal organoids provide insight into cell fate regulation.

Single-cell multiomics of the human retina reveals hierarchical transcription factor collaboration in mediating cell type-specific effects of genetic variants on gene regulation

BACKGROUND

Systematic characterization of how  genetic variation modulates gene regulation in a cell type-specific context is essential for understanding complex traits. To address this question, we profile gene expression and chromatin accessibility in cells from healthy retinae of 20 human donors through single-cell multiomics and genomic sequencing.

RESULTS

We map eQTL, caQTL, allelic-specific expression, and allelic-specific chromatin accessibility in major retinal cell types. By integrating these results, we identify and characterize regulatory elements and genetic variants effective on gene regulation in individual cell types. The majority of identified sc-eQTLs and sc-caQTLs display cell type-specific effects, while the cis-elements containing genetic variants with cell type-specific effects are often accessible in multiple cell types. Furthermore, the transcription factors whose binding sites are perturbed by genetic variants tend to have higher expression levels in the cell types where the variants exert their effects, compared to the cell types where the variants have no impact. We further validate our findings with high-throughput reporter assays. Lastly, we identify the enriched cell types, candidate causal variants and genes, and cell type-specific regulatory mechanism underlying GWAS loci.

CONCLUSIONS

Overall, genetic effects on gene regulation are highly context dependent. Our results suggest that cell type-dependent genetic effect is driven by precise modulation of both trans-factor expression and chromatin accessibility of cis-elements. Our findings indicate hierarchical collaboration among transcription factors plays a crucial role in mediating cell type-specific effects of genetic variants on gene regulation.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

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

Human-specific genetics: new tools to explore the molecular and cellular basis of human evolution

Our ancestors acquired morphological, cognitive and metabolic modifications that enabled humans to colonize diverse habitats, develop extraordinary technologies and reshape the biosphere. Understanding the genetic, developmental and molecular bases for these changes will provide insights into how we became human. Connecting human-specific genetic changes to species differences has been challenging owing to an abundance of low-effect size genetic changes, limited descriptions of phenotypic differences across development at the level of cell types and lack of experimental models. Emerging approaches for single-cell sequencing, genetic manipulation and stem cell culture now support descriptive and functional studies in defined cell types with a human or ape genetic background. In this Review, we describe how the sequencing of genomes from modern and archaic hominins, great apes and other primates is revealing human-specific genetic changes and how new molecular and cellular approaches - including cell atlases and organoids - are enabling exploration of the candidate causal factors that underlie human-specific traits.

A Roadmap for the Human Gut Cell Atlas

The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.

What is a cell type?

A next step for cell atlases should be to chart perturbations in human model systems.

High sensitivity top-down proteomics captures single muscle cell heterogeneity in large proteoforms

Single-cell proteomics has emerged as a powerful method to characterize cellular phenotypic heterogeneity and the cell-specific functional networks underlying biological processes. However, significant challenges remain in single-cell proteomics for the analysis of proteoforms arising from genetic mutations, alternative splicing, and post-translational modifications. Herein, we have developed a highly sensitive functionally integrated top-down proteomics method for the comprehensive analysis of proteoforms from single cells. We applied this method to single muscle fibers (SMFs) to resolve their heterogeneous functional and proteomic properties at the single-cell level. Notably, we have detected single-cell heterogeneity in large proteoforms (>200 kDa) from the SMFs. Using SMFs obtained from three functionally distinct muscles, we found fiber-to-fiber heterogeneity among the sarcomeric proteoforms which can be related to the functional heterogeneity. Importantly, we detected multiple isoforms of myosin heavy chain (~223 kDa), a motor protein that drives muscle contraction, with high reproducibility to enable the classification of individual fiber types. This study reveals single muscle cell heterogeneity in large proteoforms and establishes a direct relationship between sarcomeric proteoforms and muscle fiber types, highlighting the potential of top-down proteomics for uncovering the molecular underpinnings of cell-to-cell variation in complex systems.

CRISPR for neuroscientists

Genome engineering technologies provide an entry point into understanding and controlling the function of genetic elements in health and disease. The discovery and development of the microbial defense system CRISPR-Cas yielded a treasure trove of genome engineering technologies and revolutionized the biomedical sciences. Comprising diverse RNA-guided enzymes and effector proteins that evolved or were engineered to manipulate nucleic acids and cellular processes, the CRISPR toolbox provides precise control over biology. Virtually all biological systems are amenable to genome engineering-from cancer cells to the brains of model organisms to human patients-galvanizing research and innovation and giving rise to fundamental insights into health and powerful strategies for detecting and correcting disease. In the field of neuroscience, these tools are being leveraged across a wide range of applications, including engineering traditional and non-traditional transgenic animal models, modeling disease, testing genomic therapies, unbiased screening, programming cell states, and recording cellular lineages and other biological processes. In this primer, we describe the development and applications of CRISPR technologies while highlighting outstanding limitations and opportunities.

Single-cell multi-omics and lineage tracing to dissect cell fate decision-making

The concept of cell fate relates to the future identity of a cell, and its daughters, which is obtained via cell differentiation and division. Understanding, predicting, and manipulating cell fate has been a long-sought goal of developmental and regenerative biology. Recent insights obtained from single-cell genomic and integrative lineage-tracing approaches have further aided to identify molecular features predictive of cell fate. In this perspective, we discuss these approaches with a focus on theoretical concepts and future directions of the field to dissect molecular mechanisms underlying cell fate.

Single-cell profiling to transform immunotherapy usage and target discovery in immune-mediated inflammatory diseases

Immunotherapy drugs are transforming the clinical care landscape of major human diseases from cancer, to inflammatory diseases, cardiovascular diseases, neurodegenerative diseases and even aging. In polygenic immune-mediated inflammatory diseases (IMIDs), the clinical benefits of immunotherapy have nevertheless remained limited to a subset of patients. Yet the identification of new actionable molecular candidates has remained challenging, and the use of standard of care imaging and/or histological diagnostic assays has failed to stratify potential responders from non-responders to biotherapies already available. We argue that these limitations partly stem from a poor understanding of disease pathophysiology and insufficient characterization of the roles assumed by candidate targets during disease initiation, progression and treatment. By transforming the resolution and scale of tissue cell mapping, high-resolution profiling strategies offer unprecedented opportunities to the understanding of immunopathogenic events in human IMID lesions. Here we discuss the potential for single-cell technologies to reveal relevant pathogenic cellular programs in IMIDs and to enhance patient stratification to guide biotherapy eligibility and clinical trial design.

Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy

The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.

Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.

Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq

A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. We use transcriptional phenotypes to predict the function of poorly characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena-from RNA processing to differentiation. We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function.

SRS-FISH: A high-throughput platform linking microbiome metabolism to identity at the single-cell level

One of the biggest challenges in microbiome research in environmental and medical samples is to better understand functional properties of microbial community members at a single-cell level. Single-cell isotope probing has become a key tool for this purpose, but the current detection methods for determination of isotope incorporation into single cells do not allow high-throughput analyses. Here, we report on the development of an imaging-based approach termed stimulated Raman scattering-two-photon fluorescence in situ hybridization (SRS-FISH) for high-throughput metabolism and identity analyses of microbial communities with single-cell resolution. SRS-FISH offers an imaging speed of 10 to 100 ms per cell, which is two to three orders of magnitude faster than achievable by state-of-the-art methods. Using this technique, we delineated metabolic responses of 30,000 individual cells to various mucosal sugars in the human gut microbiome via incorporation of deuterium from heavy water as an activity marker. Application of SRS-FISH to investigate the utilization of host-derived nutrients by two major human gut microbiome taxa revealed that response to mucosal sugars tends to be dominated by Bacteroidales, with an unexpected finding that Clostridia can outperform Bacteroidales at foraging fucose. With high sensitivity and speed, SRS-FISH will enable researchers to probe the fine-scale temporal, spatial, and individual activity patterns of microbial cells in complex communities with unprecedented detail.

Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.

A State-of-the-Art Roadmap for Biomarker-Driven Drug Development in the Era of Personalized Therapies

Advances in biotechnology have enabled us to assay human tissue and cells to a depth and resolution that was never possible before, redefining what we know as the “biomarker”, and how we define a “disease”. This comes along with the shift of focus from a “one-drug-fits-all” to a “personalized approach”, placing the drug development industry in a highly dynamic landscape, having to navigate such disruptive trends. In response to this, innovative clinical trial designs have been key in realizing biomarker-driven drug development. Regulatory approvals of cancer genome sequencing panels and associated targeted therapies has brought personalized medicines to the clinic. Increasing availability of sophisticated biotechnologies such as next-generation sequencing (NGS) has also led to a massive outflux of real-world genomic data. This review summarizes the current state of biomarker-driven drug development and highlights examples showing the utility and importance of the application of real-world data in the process. We also propose that all stakeholders in drug development should (1) be conscious of and efficiently utilize real-world evidence and (2) re-vamp the way the industry approaches drug development in this era of personalized medicines.

Technique integration of single-cell RNA sequencing with spatially resolved transcriptomics in the tumor microenvironment

Background

The tumor microenvironment contributes to tumor initiation, growth, invasion, and metastasis. The tumor microenvironment is heterogeneous in cellular and acellular components, particularly structural features and their gene expression at the inter-and intra-tumor levels.

Main text

Single-cell RNA sequencing profiles single-cell transcriptomes to reveal cell proportions and trajectories while spatial information is lacking. Spatially resolved transcriptomics redeems this lack with limited coverage or depth of transcripts. Hence, the integration of single-cell RNA sequencing and spatial data makes the best use of their strengths, having insights into exploring diverse tissue architectures and interactions in a complicated network. We review applications of integrating the two methods, especially in cellular components in the tumor microenvironment, showing each role in cancer initiation and progression, which provides clinical relevance in prognosis, optimal treatment, and potential therapeutic targets.

Conclusion

The integration of two approaches may break the bottlenecks in the spatial resolution of neighboring cell subpopulations in cancer, and help to describe the signaling circuitry about the intercommunication and its exact mechanisms in producing different types and malignant stages of tumors.

Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study

An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.

Combined Measurement of RNA and Protein Expression on a Single-Cell Level

Single-cell RNA sequencing (sc-RNAseq) has become a critical approach for the analysis of immune cell function and heterogeneity. So far, the immune cell isolation, based on surface marker expression predicted by the RNA expression profiles, is often limited by the poor correlation between transcript and protein expression patterns. To overcome these difficulties, novel single-cell multi-omic approaches based on the combined analysis of transcript and surface protein expression have been developed. One of the major benefits of these technologies is the possibility to use a high number of antibodies conjugated with oligonucleotide (AbOs) for the surface marker detection, thus overcoming the limit of using few surface markers as occurs in flow cytometry. Here we describe the BD Rhapsody single-cell analysis system protocol for 3' mRNA whole transcriptome analysis (WTA), combined with AbO- and Sample Tag library preparation.

Adipocyte Heterogeneity Underlying Adipose Tissue Functions

Adipose tissue distribution in the human body is highly heterogeneous, and the relative mass of different depots is differentially associated with metabolic disease risk. Distinct functions of adipose depots are mediated by their content of specialized adipocyte subtypes, best exemplified by thermogenic adipocytes found in specific depots. Single-cell transcriptome profiling has been used to define the cellular composition of many tissues and organs, but the large size, buoyancy, and fragility of adipocytes have rendered it challenging to apply these techniques to understand the full complexity of adipocyte subtypes in different depots. Discussed here are strategies that have been recently developed for investigating adipocyte heterogeneity, including single-cell RNA-sequencing profiling of the stromal vascular fraction to identify diverse adipocyte progenitors, and single-nuclei profiling to characterize mature adipocytes. These efforts are yielding a more complete characterization of adipocyte subtypes in different depots, insights into the mechanisms of their development, and perturbations associated with different physiological states such as obesity. A better understanding of the adipocyte subtypes that compose different depots will help explain metabolic disease phenotypes associated with adipose tissue distribution and suggest new strategies for improving metabolic health.

Single Cell Technologies to Dissect Heterogenous Immune Cell Therapy Products

Single cell tools have dramatically transformed the life sciences; concurrently, autologous and allogeneic immune cell therapies have recently entered the clinic. Here we discuss methods, applications, and considerations for single cell technologies in the context of immune cell manufacturing. Molecular heterogeneity can be profiled at the level of the genome, epigenome, transcriptome, proteome, metabolome, and antigen receptor repertoire, in isolation or in tandem through multi-omic approaches. Such data inform heterogeneity within cell products and can be linked to potency readouts and clinical data, with the ultimate goal of identifying Critical Quality Attributes to predict patient outcomes. Non-destructive approaches hold promise for monitoring cell state and analyzing the impacts of gene edits within engineered products. Destructive omics approaches could be combined with non-destructive technologies to predict therapeutic potency. These technologies are poised to redefine cell manufacturing toward rapid, cost-effective, and high-throughput methods to detect and respond to dynamic cell states.

Rare Does Not Mean Worthless: How Rare Diseases Have Shaped Neurodevelopment Research in the NGS Era

The advent of next-generation sequencing (NGS) is heavily changing both the diagnosis of human conditions and basic biological research. It is now possible to dig deep inside the genome of hundreds of thousands or even millions of people and find both common and rare genomic variants and to perform detailed phenotypic characterizations of both physiological organs and experimental models. Recent years have seen the introduction of multiple techniques using NGS to profile transcription, DNA and chromatin modifications, protein binding, etc., that are now allowing us to profile cells in bulk or even at a single-cell level. Although rare and ultra-rare diseases only affect a few people, each of these diseases represent scholarly cases from which a great deal can be learned about the pathological and physiological function of genes, pathways, and mechanisms. Therefore, for rare diseases, state-of-the-art investigations using NGS have double valence: their genomic cause (new variants) and the characterize the underlining the mechanisms associated with them (discovery of gene function) can be found. In a non-exhaustive manner, this review will outline the main usage of NGS-based techniques for the diagnosis and characterization of neurodevelopmental disorders (NDDs), under whose umbrella many rare and ultra-rare diseases fall.

Advances in Non-Coding RNA Sequencing

Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms.

Label-free single-cell isolation enabled by microfluidic impact printing and real-time cellular recognition

Analysis of cellular components at the single-cell level is important to reveal cellular heterogeneity. However, current technologies to isolate individual cells are either label-based or have low performance. Here, we present a novel technique by integrating real-time cellular recognition and microfluidic impact printing (MIP) to isolate single cells with high efficiency and high throughput in a label-free manner. Specifically, morphological characteristics of polystyrene beads and cells, computed by an efficient image processing algorithm, are utilized as selection criteria to identify target objects. Subsequently, each detected single-cell object in the suspension is ejected from the microfluidic channel by impact force. It has been demonstrated that the single-cell isolating system has the ability to encapsulate polystyrene beads in droplets with an efficiency of 95%, while for HeLa cells, this has been experimentally measured as 90.3%. Single-cell droplet arrays are generated at a throughput of 2 Hz and 96.6% of the cells remain alive after isolation. This technology has significant potential in various emerging applications, including single-cell omics, tissue engineering, and cell-line development.

Mapping genomic and epigenomic evolution in cancer ecosystems

[Figure: see text].

Intrinsically disordered protein biosensor tracks the physical-chemical effects of osmotic stress on cells

Cell homeostasis is perturbed when dramatic shifts in the external environment cause the physical-chemical properties inside the cell to change. Experimental approaches for dynamically monitoring these intracellular effects are currently lacking. Here, we leverage the environmental sensitivity and structural plasticity of intrinsically disordered protein regions (IDRs) to develop a FRET biosensor capable of monitoring rapid intracellular changes caused by osmotic stress. The biosensor, named SED1, utilizes the Arabidopsis intrinsically disordered AtLEA4-5 protein expressed in plants under water deficit. Computational modeling and in vitro studies reveal that SED1 is highly sensitive to macromolecular crowding. SED1 exhibits large and near-linear osmolarity-dependent changes in FRET inside living bacteria, yeast, plant, and human cells, demonstrating the broad utility of this tool for studying water-associated stress. This study demonstrates the remarkable ability of IDRs to sense the cellular environment across the tree of life and provides a blueprint for their use as environmentally-responsive molecular tools.

Intrinsically disordered protein biosensor tracks the physical-chemical effects of osmotic stress on cells

Cell homeostasis is perturbed when dramatic shifts in the external environment cause the physical-chemical properties inside the cell to change. Experimental approaches for dynamically monitoring these intracellular effects are currently lacking. Here, we leverage the environmental sensitivity and structural plasticity of intrinsically disordered protein regions (IDRs) to develop a FRET biosensor capable of monitoring rapid intracellular changes caused by osmotic stress. The biosensor, named SED1, utilizes the Arabidopsis intrinsically disordered AtLEA4-5 protein expressed in plants under water deficit. Computational modeling and in vitro studies reveal that SED1 is highly sensitive to macromolecular crowding. SED1 exhibits large and near-linear osmolarity-dependent changes in FRET inside living bacteria, yeast, plant, and human cells, demonstrating the broad utility of this tool for studying water-associated stress. This study demonstrates the remarkable ability of IDRs to sense the cellular environment across the tree of life and provides a blueprint for their use as environmentally-responsive molecular tools.

Vision, challenges and opportunities for a Plant Cell Atlas

With growing populations and pressing environmental problems, future economies will be increasingly plant-based. Now is the time to reimagine plant science as a critical component of fundamental science, agriculture, environmental stewardship, energy, technology and healthcare. This effort requires a conceptual and technological framework to identify and map all cell types, and to comprehensively annotate the localization and organization of molecules at cellular and tissue levels. This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses. A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA initiative can help to overcome them.

Charting human development using a multi-endodermal organ atlas and organoid models

Organs are composed of diverse cell types that traverse transient states during organogenesis. To interrogate this diversity during human development, we generate a single-cell transcriptome atlas from multiple developing endodermal organs of the respiratory and gastrointestinal tract. We illuminate cell states, transcription factors, and organ-specific epithelial stem cell and mesenchyme interactions across lineages. We implement the atlas as a high-dimensional search space to benchmark human pluripotent stem cell (hPSC)-derived intestinal organoids (HIOs) under multiple culture conditions. We show that HIOs recapitulate reference cell states and use HIOs to reconstruct the molecular dynamics of intestinal epithelium and mesenchyme emergence. We show that the mesenchyme-derived niche cue NRG1 enhances intestinal stem cell maturation in vitro and that the homeobox transcription factor CDX2 is required for regionalization of intestinal epithelium and mesenchyme in humans. This work combines cell atlases and organoid technologies to understand how human organ development is orchestrated.

Machine learning for perturbational single-cell omics

Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.

Single-cell RNA Sequencing Reveals Thoracolumbar Vertebra Heterogeneity and Rib-genesis in Pigs

Development of thoracolumbar vertebra (TLV) and rib primordium (RP) is a common evolutionary feature across vertebrates, although whole-organism analysis of the expression dynamics of TLV- and RP-related genes has been lacking. Here, we investigated the single-cell transcriptome landscape of thoracic vertebra (TV), lumbar vertebra (LV), and RP cells from a pig embryo at 27 days post-fertilization (dpf) and identified six cell types with distinct gene expression signatures. In-depth dissection of the gene expression dynamics and RNA velocity revealed a coupled process of osteogenesis and angiogenesis during TLV and RP development. Further analysis of cell type-specific and strand-specific expression uncovered the extremely high level of HOXA10 3'-UTR sequence specific to osteoblasts of LV cells, which may function as anti-HOXA10-antisense by counteracting the HOXA10-antisense effect to determine TLV transition. Thus, this work provides a valuable resource for understanding embryonic osteogenesis and angiogenesis underlying vertebrate TLV and RP development at the cell type-specific resolution, which serves as a comprehensive view on the transcriptional profile of animal embryo development.

Integration of Immunome With Disease-Gene Network Reveals Common Cellular Mechanisms Between IMIDs and Drug Repurposing Strategies

Objective

Development and progression of immune-mediated inflammatory diseases (IMIDs) involve intricate dysregulation of the disease-associated genes (DAGs) and their expressing immune cells. Identifying the crucial disease-associated cells (DACs) in IMIDs has been challenging due to the underlying complex molecular mechanism.

Methods

Using transcriptome profiles of 40 different immune cells, unsupervised machine learning, and disease-gene networks, we constructed the Disease-gene IMmune cell Expression (DIME) network and identified top DACs and DAGs of 12 phenotypically different IMIDs. We compared the DIME networks of IMIDs to identify common pathways between them. We used the common pathways and publicly available drug-gene network to identify promising drug repurposing targets.

Results

We found CD4+Treg, CD4+Th1, and NK cells as top DACs in inflammatory arthritis such as ankylosing spondylitis (AS), psoriatic arthritis, and rheumatoid arthritis (RA); neutrophils, granulocytes, and BDCA1+CD14+ cells in systemic lupus erythematosus and systemic scleroderma; ILC2, CD4+Th1, CD4+Treg, and NK cells in the inflammatory bowel diseases (IBDs). We identified lymphoid cells (CD4+Th1, CD4+Treg, and NK) and their associated pathways to be important in HLA-B27 type diseases (psoriasis, AS, and IBDs) and in primary-joint-inflammation-based inflammatory arthritis (AS and RA). Based on the common cellular mechanisms, we identified lifitegrast as a potential drug repurposing candidate for Crohn's disease and other IMIDs.

Conclusions

Existing methods are inadequate in capturing the intricate involvement of the crucial genes and cell types essential to IMIDs. Our approach identified the key DACs, DAGs, common mechanisms between IMIDs, and proposed potential drug repurposing targets using the DIME network. To extend our method to other diseases, we built the DIME tool (https://bitbucket.org/systemsimmunology/dime/) to help scientists uncover the etiology of complex and rare diseases to further drug development by better-determining drug targets, thereby mitigating the risk of failure in late clinical development.

Computational principles and challenges in single-cell data integration

The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods.

Cerebral organoids and their potential for studies of brain diseases in domestic animals

The brain is a complex organ and any model for studying it in its normal and pathological aspects becomes a tool of choice for neuroscientists. The mastering and dissemination of protocols allowing brain organoids development have paved the way for a whole range of new studies in the field of brain development, modeling of neurodegenerative or neurodevelopmental diseases, understanding tumors as well as infectious diseases that affect the brain. While studies are so far limited to the use of human cerebral organoids, there is a growing interest in having similar models in other species. This review presents what is currently developed in this field, with a particular focus on the potential of cerebral organoids for studying neuro-infectious diseases in human and domestic animals.

Genomics of the Parasitic Nematode Ascaris and Its Relatives

Nematodes of the genus Ascaris are important parasites of humans and swine, and the phylogenetically related genera (Parascaris, Toxocara, and Baylisascaris) infect mammals of veterinary interest. Over the last decade, considerable genomic resources have been established for Ascaris, including complete germline and somatic genomes, comprehensive mRNA and small RNA transcriptomes, as well as genome-wide histone and chromatin data. These datasets provide a major resource for studies on the basic biology of these parasites and the host-parasite relationship. Ascaris and its relatives undergo programmed DNA elimination, a highly regulated process where chromosomes are fragmented and portions of the genome are lost in embryonic cells destined to adopt a somatic fate, whereas the genome remains intact in germ cells. Unlike many model organisms, Ascaris transcription drives early development beginning prior to pronuclear fusion. Studies on Ascaris demonstrated a complex small RNA network even in the absence of a piRNA pathway. Comparative genomics of these ascarids has provided perspectives on nematode sex chromosome evolution, programmed DNA elimination, and host-parasite coevolution. The genomic resources enable comparison of proteins across diverse species, revealing many new potential drug targets that could be used to control these parasitic nematodes.

Recent Advances in Trace Bioanalysis by Capillary Electrophoresis

Recently, single cell analysis is becoming more and more important to elucidate cellular heterogeneity. Except for nucleic acid that can be amplified by PCR, the required technical level for single cell analysis is extremely high and the appropriate design of sample preparation and a sensitive analytical system is necessary. Capillary/microchip electrophoresis (CE/MCE) can separate biomolecules in nL-scale solution with high resolution, and it is highly compatible with trace samples like a single cell. Coupled with highly sensitive detectors such as laser-induced fluorescence and nano-electrospray ionization-mass spectrometry, zmol level analytes can be detected. For further enhancing sensitivity, online sample preconcentration techniques can be employed. By integrating these high-sensitive techniques, single cell analysis of metabolites, proteins, and lipids have been achieved. This review paper highlights successful research on CE/MCE-based trace bioanalysis in recent 10 years. Firstly, an overview of basic knowledge on CE/MCE including sensitivity enhancement techniques is provided. Applications to trace bioanalysis are then introduced with discussion on current issues and future prospects.

Brain organoids: an ensemble of bioassays to investigate human neurodevelopment and disease

Understanding etiology of human neurological and psychiatric diseases is challenging. Genomic changes, protracted development, and histological features unique to human brain development limit the disease aspects that can be investigated using model organisms. Hence, in order to study phenotypes associated with human brain development, function, and disease, it is necessary to use alternative experimental systems that are accessible, ethically justified, and replicate human context. Human pluripotent stem cell (hPSC)-derived brain organoids offer such a system, which recapitulates features of early human neurodevelopment in vitro, including the generation, proliferation, and differentiation of neural progenitors into neurons and glial cells and the complex interactions among the diverse, emergent cell types of the developing brain in three-dimensions (3-D). In recent years, numerous brain organoid protocols and related techniques have been developed to recapitulate aspects of embryonic and fetal brain development in a reproducible and predictable manner. Altogether, these different organoid technologies provide distinct bioassays to unravel novel, disease-associated phenotypes and mechanisms. In this review, we summarize how the diverse brain organoid methods can be utilized to enhance our understanding of brain disorders. FACTS: Brain organoids offer an in vitro approach to study aspects of human brain development and disease. Diverse brain organoid techniques offer bioassays to investigate new phenotypes associated with human brain disorders that are difficult to study in monolayer cultures. Brain organoids have been particularly useful to study phenomena and diseases associated with neural progenitor morphology, survival, proliferation, and differentiation. OPEN QUESTION: Future brain organoid research needs to aim at later stages of neurodevelopment, linked with neuronal activity and connections, to unravel further disease-associated phenotypes. Continued improvement of existing organoid protocols is required to generate standardized methods that recapitulate in vivo-like spatial diversity and complexity.