The Human Cell Atlas

Complexin vitromodels positioned for impact to drug testing in pharma: a review

Recent years have seen the creation and popularization of various complexin vitromodels (CIVMs), such as organoids and organs-on-chip, as a technology with the potential to reduce animal usage in pharma while also enhancing our ability to create safe and efficacious drugs for patients. Public awareness of CIVMs has increased, in part, due to the recent passage of the FDA Modernization Act 2.0. This visibility is expected to spur deeper investment in and adoption of such models. Thus, end-users and model developers alike require a framework to both understand the readiness of current models to enter the drug development process, and to assess upcoming models for the same. This review presents such a framework for model selection based on comparative -omics data (which we term model-omics), and metrics for qualification of specific test assays that a model may support that we term context-of-use (COU) assays. We surveyed existing healthy tissue models and assays for ten drug development-critical organs of the body, and provide evaluations of readiness and suggestions for improving model-omics and COU assays for each. In whole, this review comes from a pharma perspective, and seeks to provide an evaluation of where CIVMs are poised for maximum impact in the drug development process, and a roadmap for realizing that potential.

Spatiotemporal omics for biology and medicine

The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.

Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas

Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.

Small data methods in omics: the power of one

Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Transcriptomic analysis unveils bona fide molecular signatures of microglia under conditions of homeostasis and viral encephalitis

Microglia serve as a front-line defense against neuroinvasive viral infection, however, determination of their actual transcriptional profiles under conditions of health and disease is challenging. Here, we used various experimental approaches to delineate the transcriptional landscape of microglia during viral infection. Intriguingly, multiple activation genes were found to be artificially induced in sorted microglia and we demonstrated that shear stress encountered during cell sorting was one of the key inducers. Post-hoc analysis revealed that publicly available large-scale single-cell RNA sequencing datasets were significantly tainted by aberrant signatures that are associated with cell sorting. By exploiting the ribosomal tagging approach, we developed a strategy to enrich microglia-specific transcripts by comparing immunoprecipitated RNA with total RNA. Such enriched transcripts were instrumental in defining bona fide signatures of microglia under conditions of health and virus infection. These unified microglial signatures may serve as a benchmark to retrospectively assess ex vivo artefacts from available atlases. Leveraging the microglial translatome, we found enrichment of genes implicated in T-cell activation and cytokine production during the course of VSV infection. These data linked microglia with T-cell re-stimulation and further underscored that microglia are involved in shaping antiviral T-cell responses in the brain. Collectively, our study defines the transcriptional landscape of microglia under steady state and during viral encephalitis and highlights cellular interactions between microglia and T cells that contribute to the control of virus dissemination.

Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model

A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for molecular and cellular studies. Studies have shown that cells exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data augmentation. We developed UniCoord, a specially-tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The latent dimensions can be easily reconfigured to generate pseudo transcriptomic profiles with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.

Comprehensive transcription factor perturbations recapitulate fibroblast transcriptional states

Cell atlas projects have nominated recurrent transcriptional states as drivers of biological processes and disease, but their origins, regulation, and properties remain unclear. To enable complementary functional studies, we developed a scalable approach for recapitulating cell states in vitro using CRISPR activation (CRISPRa) Perturb-seq. Aided by a novel multiplexing method, we activated 1,836 transcription factors in two cell types. Measuring 21,958 perturbations showed that CRISPRa activated targets within physiological ranges, that epigenetic features predicted activatable genes, and that the protospacer seed region drove an off-target effect. Perturbations recapitulated in vivo fibroblast states, including universal and inflammatory states, and identified KLF4 and KLF5 as key regulators of the universal state. Inducing the universal state suppressed disease-associated states, highlighting its therapeutic potential. Our findings cement CRISPRa as a tool for perturbing differentiated cells and indicate that in vivo states can be elicited via perturbation, enabling studies of clinically relevant states ex vivo.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

A brain cell atlas integrating single-cell transcriptomes across human brain regions

While single-cell technologies have greatly advanced our comprehension of human brain cell types and functions, studies including large numbers of donors and multiple brain regions are needed to extend our understanding of brain cell heterogeneity. Integrating atlas-level single-cell data presents a chance to reveal rare cell types and cellular heterogeneity across brain regions. Here we present the Brain Cell Atlas, a comprehensive reference atlas of brain cells, by assembling single-cell data from 70 human and 103 mouse studies of the brain throughout major developmental stages across brain regions, covering over 26.3 million cells or nuclei from both healthy and diseased tissues. Using machine-learning based algorithms, the Brain Cell Atlas provides a consensus cell type annotation, and it showcases the identification of putative neural progenitor cells and a cell subpopulation of PCDH9high microglia in the human brain. We demonstrate the gene regulatory difference of PCDH9high microglia between hippocampus and prefrontal cortex and elucidate the cell-cell communication network. The Brain Cell Atlas presents an atlas-level integrative resource for comparing brain cells in different environments and conditions within the Human Cell Atlas.

Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes' biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN can detect functionally related genes coexpressed across species, redefining differential expression for cross-species analysis. Applying SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets, we show that SATURN can effectively transfer annotations across species, even when they are evolutionarily remote. We also demonstrate that SATURN can be used to find potentially divergent gene functions between glaucoma-associated genes in humans and four other species.

Accessible high-speed image-activated cell sorting

Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.

Transformers in single-cell omics: a review and new perspectives

Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets. Thus, the question arises of whether transformers could set off a similar shift in the field of single-cell modeling. Here we first describe the transformer architecture and its single-cell adaptations and then present a comprehensive review of the existing applications of transformers in single-cell analysis and critically discuss their future potential for single-cell biology. By studying limitations and technical challenges, we aim to provide a structured outlook for future research directions at the intersection of machine learning and single-cell biology.

Single-nucleus and spatial transcriptome reveal adrenal homeostasis in normal and tumoural adrenal glands

The human adrenal gland is a complex endocrine tissue. Studies on adrenal renewal have been limited to animal models or human foetuses. Enhancing our understanding of adult human adrenal homeostasis is crucial for gaining insights into the pathogenesis of adrenal diseases, such as adrenocortical tumours. Here, we present a comprehensive cellular genomics analysis of the adult human normal adrenal gland, combining single-nuclei RNA sequencing and spatial transcriptome data to reconstruct adrenal gland homeostasis. As expected, we identified primary cells of the various zones of the adrenal cortex and medulla, but we also uncovered additional cell types. They constitute the adrenal microenvironment, including immune cells, mostly composed of a large population of M2 macrophages, and new cell populations, including different subpopulations of vascular-endothelial cells and cortical-neuroendocrine cells. Utilizing spatial transcriptome and pseudotime trajectory analysis, we support evidence of the centripetal dynamics of adrenocortical cell maintenance and the essential role played by Wnt/β-catenin, sonic hedgehog, and fibroblast growth factor pathways in the adult adrenocortical homeostasis. Furthermore, we compared single-nuclei transcriptional profiles obtained from six healthy adrenal glands and twelve adrenocortical adenomas. This analysis unveiled a notable heterogeneity in cell populations within the adenoma samples. In addition, we identified six distinct adenoma-specific clusters, each with varying distributions based on steroid profiles and tumour mutational status. Overall, our results provide novel insights into adrenal homeostasis and molecular mechanisms potentially underlying early adrenocortical tumorigenesis and/or autonomous steroid secretion. Our cell atlas represents a powerful resource to investigate other adrenal-related pathologies.

Endothelial cell diversity: the many facets of the crystal

Endothelial cells (ECs) form the inner lining of blood vessels and play crucial roles in angiogenesis. While it has been known for a long time that there are considerable differences among ECs from lymphatic and blood vessels, as well as among arteries, veins and capillaries, the full repertoire of endothelial diversity is only beginning to be elucidated. It has become apparent that the role of ECs is not just limited to their exchange functions. Indeed, a multitude of organ-specific functions, including release of growth factors, regulation of immune functions, have been linked to ECs. Recent years have seen a surge into the identification of spatiotemporal molecular and functional heterogeneity of ECs, supported by technologies such as single-cell RNA sequencing (scRNA-seq), lineage tracing and intersectional genetics. Together, these techniques have spurred the generation of epigenomic, transcriptomic and proteomic signatures of ECs. It is now clear that ECs across organs and in different vascular beds, but even within the same vessel, have unique molecular identities and employ specialized molecular mechanisms to fulfil highly specialized needs. Here, we focus on the molecular heterogeneity of the endothelium in different organs and pathological conditions.

Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data

Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Large-scale foundation model on single-cell transcriptomics

Large pretrained models have become foundation models leading to breakthroughs in natural language processing and related fields. Developing foundation models for deciphering the 'languages' of cells and facilitating biomedical research is promising yet challenging. Here we developed a large pretrained model scFoundation, also named 'xTrimoscFoundationα', with 100 million parameters covering about 20,000 genes, pretrained on over 50 million human single-cell transcriptomic profiles. scFoundation is a large-scale model in terms of the size of trainable parameters, dimensionality of genes and volume of training data. Its asymmetric transformer-like architecture and pretraining task design empower effectively capturing complex context relations among genes in a variety of cell types and states. Experiments showed its merit as a foundation model that achieved state-of-the-art performances in a diverse array of single-cell analysis tasks such as gene expression enhancement, tissue drug response prediction, single-cell drug response classification, single-cell perturbation prediction, cell type annotation and gene module inference.

scGPT: toward building a foundation model for single-cell multi-omics using generative AI

Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

RNA Sequencing in Disease Diagnosis

RNA sequencing (RNA-seq) enables the accurate measurement of multiple transcriptomic phenotypes for modeling the impacts of disease variants. Advances in technologies, experimental protocols, and analysis strategies are rapidly expanding the application of RNA-seq to identify disease biomarkers, tissue- and cell-type-specific impacts, and the spatial localization of disease-associated mechanisms. Ongoing international efforts to construct biobank-scale transcriptomic repositories with matched genomic data across diverse population groups are further increasing the utility of RNA-seq approaches by providing large-scale normative reference resources. The availability of these resources, combined with improved computational analysis pipelines, has enabled the detection of aberrant transcriptomic phenotypes underlying rare diseases. Further expansion of these resources, across both somatic and developmental tissues, is expected to soon provide unprecedented insights to resolve disease origin, mechanism of action, and causal gene contributions, suggesting the continued high utility of RNA-seq in disease diagnosis.

Data acquisition approaches for single cell proteomics

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.

MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data

Many datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While biospecimen and experimental information is often captured, detailed metadata standards related to data matrices and analysis workflows are currently lacking. To address this, we develop the matrix and analysis metadata standards (MAMS) to serve as a resource for data centers, repositories, and tool developers. We define metadata fields for matrices and parameters commonly utilized in analytical workflows and developed the rmams package to extract MAMS from single-cell objects. Overall, MAMS promotes the harmonization, integration, and reproducibility of single-cell data across platforms.

Bioinformatics tools and resources for cancer and application

ABSTRACT

Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.

Cell cycle-driven transcriptome maturation confers multilineage competence to cardiopharyngeal progenitors

During development, stem and progenitor cells divide and transition through germ layer- and lineage-specific multipotent states to generate the diverse cell types that compose an animal. Defined changes in biomolecular composition underlie the progressive loss of potency and acquisition of lineage-specific characteristics. For example, multipotent cardiopharyngeal progenitors display multilineage transcriptional priming, whereby both the cardiac and pharyngeal muscle programs are partially active and coexist in the same progenitor cells, while their daughter cells engage in a cardiac or pharyngeal muscle differentiation path only after cell division. Here, using the tunicate Ciona, we studied the acquisition of multilineage competence and the coupling between fate decisions and cell cycle progression. We showed that multipotent cardiopharyngeal progenitors acquire the competence to produce distinct Tbx1/10 (+) and (-) daughter cells shortly before mitosis, which is necessary for Tbx1/10 activation. By combining transgene-based sample barcoding with single cell RNA-seq (scRNA-seq), we uncovered transcriptome-wide dynamics in migrating cardiopharyngeal progenitors as cells progress through G1, S and G2 phases. We termed this process "transcriptome maturation", and identified candidate "mature genes", including the Rho GAP-coding gene Depdc1 , which peak in late G2. Functional assays indicated that transcriptome maturation fosters cardiopharyngeal competence, in part through multilineage priming and proper oriented and asymmetric division that influences subsequent fate decisions, illustrating the concept of "behavioral competence". Both classic feedforward circuits and coupling with cell cycle progression drive transcriptome maturation, uncovering distinct levels of coupling between cell cycle progression and fateful molecular transitions. We propose that coupling competence and fate decision with the G2 and G1 phases, respectively, ensures the timely deployment of lineage-specific programs.

Single-cell discovery of m6A RNA modifications in the hippocampus

N 6-Methyladenosine (m6A) is a prevalent and highly regulated RNA modification essential for RNA metabolism and normal brain function. It is particularly important in the hippocampus, where m6A is implicated in neurogenesis and learning. Although extensively studied, its presence in specific cell types remains poorly understood. We investigated m6A in the hippocampus at a single-cell resolution, revealing a comprehensive landscape of m6A modifications within individual cells. Through our analysis, we uncovered transcripts exhibiting a dense m6A profile, notably linked to neurological disorders such as Alzheimer's disease. Our findings suggest a pivotal role of m6A-containing transcripts, particularly in the context of CAMK2A neurons. Overall, this work provides new insights into the molecular mechanisms underlying hippocampal physiology and lays the foundation for future studies investigating the dynamic nature of m6A RNA methylation in the healthy and diseased brain.

Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology

Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.

Identifying cell states in single-cell RNA-seq data at statistically maximal resolution

Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with 'similar' expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods. Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstates which operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters. We show that, on synthetic datasets, Cellstates almost perfectly recovers optimal partitions. On real data, Cellstates robustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstates identifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstates tool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important differentially expressed genes at each branch of this hierarchy, and to visualize these results.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications

The recent technological and computational advances in mass spectrometry-based single-cell proteomics have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell remains challenging. To address some of those limitations, we present a dedicated sample preparation chip, the proteoCHIP EVO 96 that directly interfaces with the Evosep One. This, in combination with the Bruker timsTOF demonstrates double the identifications without manual sample handling and the newest generation timsTOF Ultra identifies up to 4000 with an average of 3500 protein groups per single HEK-293T without a carrier or match-between runs. Our workflow spans 4 orders of magnitude, identifies over 50 E3 ubiquitin-protein ligases, and profiles key regulatory proteins upon small molecule stimulation. This study demonstrates that the proteoCHIP EVO 96-based sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.

mosaicMPI: a framework for modular data integration across cohorts and -omics modalities

Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.

Scanpro is a tool for robust proportion analysis of single-cell resolution data

In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation and transcriptional adaptation. However, in addition to shifting the expression level of individual genes, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single-cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single-cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike.

Gene regulatory network structure informs the distribution of perturbation effects

Gene regulatory networks (GRNs) govern many core developmental and biological processes underlying human complex traits. Even with broad-scale efforts to characterize the effects of molecular perturbations and interpret gene coexpression, it remains challenging to infer the architecture of gene regulation in a precise and efficient manner. Key properties of GRNs, like hierarchical structure, modular organization, and sparsity, provide both challenges and opportunities for this objective. Here, we seek to better understand properties of GRNs using a new approach to simulate their structure and model their function. We produce realistic network structures with a novel generating algorithm based on insights from small-world network theory, and we model gene expression regulation using stochastic differential equations formulated to accommodate modeling molecular perturbations. With these tools, we systematically describe the effects of gene knockouts within and across GRNs, finding a subset of networks that recapitulate features of a recent genome-scale perturbation study. With deeper analysis of these exemplar networks, we consider future avenues to map the architecture of gene expression regulation using data from cells in perturbed and unperturbed states, finding that while perturbation data are critical to discover specific regulatory interactions, data from unperturbed cells may be sufficient to reveal regulatory programs.

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

Revealing the molecular landscape of human placenta: a systematic review and meta-analysis of single-cell RNA sequencing studies

BACKGROUND

With increasing significance of developmental programming effects associated with placental dysfunction, more investigations are devoted to improving the characterization and understanding of placental signatures in health and disease. The placenta is a transitory but dynamic organ adapting to the shifting demands of fetal development and available resources of the maternal supply throughout pregnancy. Trophoblasts (cytotrophoblasts, syncytiotrophoblasts, and extravillous trophoblasts) are placental-specific cell types responsible for the main placental exchanges and adaptations. Transcriptomic studies with single-cell resolution have led to advances in understanding the placenta's role in health and disease. These studies, however, often show discrepancies in characterization of the different placental cell types.

OBJECTIVE AND RATIONALE

We aim to review the knowledge regarding placental structure and function gained from the use of single-cell RNA sequencing (scRNAseq), followed by comparing cell-type-specific genes, highlighting their similarities and differences. Moreover, we intend to identify consensus marker genes for the various trophoblast cell types across studies. Finally, we will discuss the contributions and potential applications of scRNAseq in studying pregnancy-related diseases.

SEARCH METHODS

We conducted a comprehensive systematic literature review to identify different cell types and their functions at the human maternal-fetal interface, focusing on all original scRNAseq studies on placentas published before March 2023 and published reviews (total of 28 studies identified) using PubMed search. Our approach involved curating cell types and subtypes that had previously been defined using scRNAseq and comparing the genes used as markers or identified as potential new markers. Next, we reanalyzed expression matrices from the six available scRNAseq raw datasets with cell annotations (four from first trimester and two at term), using Wilcoxon rank-sum tests to compare gene expression among studies and annotate trophoblast cell markers in both first trimester and term placentas. Furthermore, we integrated scRNAseq raw data available from 18 healthy first trimester and nine term placentas, and performed clustering and differential gene expression analysis. We further compared markers obtained with the analysis of annotated and raw datasets with the literature to obtain a common signature gene list for major placental cell types.

OUTCOMES

Variations in the sampling site, gestational age, fetal sex, and subsequent sequencing and analysis methods were observed between the studies. Although their proportions varied, the three trophoblast types were consistently identified across all scRNAseq studies, unlike other non-trophoblast cell types. Notably, no marker genes were shared by all studies for any of the investigated cell types. Moreover, most of the newly defined markers in one study were not observed in other studies. These discrepancies were confirmed by our analysis on trophoblast cell types, where hundreds of potential marker genes were identified in each study but with little overlap across studies. From 35 461 and 23 378 cells of high quality in the first trimester and term placentas, respectively, we obtained major placental cell types, including perivascular cells that previously had not been identified in the first trimester. Importantly, our meta-analysis provides marker genes for major placental cell types based on our extensive curation.

WIDER IMPLICATIONS

This review and meta-analysis emphasizes the need for establishing a consensus for annotating placental cell types from scRNAseq data. The marker genes identified here can be deployed for defining human placental cell types, thereby facilitating and improving the reproducibility of trophoblast cell annotation.

Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing

Technology for spatial multi-omics aids the discovery of new insights into cellular functions and disease mechanisms. Here we report the development and applicability of multi-omics in situ pairwise sequencing (MiP-seq), a method for the simultaneous detection of DNAs, RNAs, proteins and biomolecules at subcellular resolution. Compared with other in situ sequencing methods, MiP-seq enhances decoding capacity and reduces sequencing and imaging costs while maintaining the efficacy of detection of gene mutations, allele-specific expression and RNA modifications. MiP-seq can be integrated with in vivo calcium imaging and Raman imaging, which enabled us to generate a spatial multi-omics atlas of mouse brain tissues and to correlate gene expression with neuronal activity and cellular biochemical fingerprints. We also report a sequential dilution strategy for resolving optically crowded signals during in situ sequencing. High-throughput in situ pairwise sequencing may facilitate the multidimensional analysis of molecular and functional maps of tissues.

Building and analyzing metacells in single-cell genomics data

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.

Aging atlas reveals cell-type-specific effects of pro-longevity strategies

Organismal aging involves functional declines in both somatic and reproductive tissues. Multiple strategies have been discovered to extend lifespan across species. However, how age-related molecular changes differ among various tissues and how those lifespan-extending strategies slow tissue aging in distinct manners remain unclear. Here we generated the transcriptomic Cell Atlas of Worm Aging (CAWA, http://mengwanglab.org/atlas ) of wild-type and long-lived strains. We discovered cell-specific, age-related molecular and functional signatures across all somatic and germ cell types. We developed transcriptomic aging clocks for different tissues and quantitatively determined how three different pro-longevity strategies slow tissue aging distinctively. Furthermore, through genome-wide profiling of alternative polyadenylation (APA) events in different tissues, we discovered cell-type-specific APA changes during aging and revealed how these changes are differentially affected by the pro-longevity strategies. Together, this study offers fundamental molecular insights into both somatic and reproductive aging and provides a valuable resource for in-depth understanding of the diversity of pro-longevity mechanisms.

Metabolomic Analysis of Human Cirrhosis and Hepatocellular Carcinoma: A Pilot Study

BACKGROUND

Molecular changes in HCC development are largely unknown. As the liver plays a fundamental role in the body's metabolism, metabolic changes are to be expected.

AIMS

We aimed to identify metabolomic changes in HCC in comparison to liver cirrhosis (LC) patients, which could potentially serve as novel biomarkers for HCC diagnosis and prognosis.

METHODS

Metabolite expression from 38 HCC from the SORAMIC trial and 32 LC patients were analyzed by mass spectrometry. Metabolites with significant differences between LC and HCC at baseline were analyzed regarding expression over follow-up. In addition, association with overall survival was tested using univariate Cox proportional-hazard analysis.

RESULTS

41 metabolites showed differential expression between LC and HCC patients. 14 metabolites demonstrated significant changes in HCC patients during follow-up. Campesterol, lysophosphatidylcholine, octadecenoic and octadecadienoic acid, and furoylglycine showed a differential expression in the local ablation vs. palliative care group. High expression of eight metabolites (octadecenoic acid, 2-hydroxybutyrate, myo-inositol, isocitrate, erythronic acid, creatinine, pseudouridine, and erythrol) were associated with poor overall survival. The association between poor OS and octadecenoic acid and creatinine remained statistically significant even after adjusting for tumor burden and LC severity.

CONCLUSION

Our findings give promising insides into the metabolic changes during HCC carcinogenesis and provide candidate biomarkers for future studies. Campesterol and furoylglycine in particular were identified as possible biomarkers for HCC progression. Moreover, eight metabolites were detected as predictors for poor overall survival.

A new atlas to study embryonic cell types in Xenopus

This paper introduces a single-cell atlas for pivotal developmental stages in Xenopus, encompassing gastrulation, neurulation, and early tailbud. Notably surpassing its predecessors, the new atlas enhances gene mapping, read counts, and gene/cell type nomenclature. Leveraging the latest Xenopus tropicalis genome version, alongside advanced alignment pipelines and machine learning for cell type assignment, this release maintains consistency with previous cell type annotations while rectifying nomenclature issues. Employing an unbiased approach for cell type assignment proves especially apt for embryonic contexts, given the considerable number of non-terminally differentiated cell types. An alternative cell type attribution here adopts a fuzzy, non-deterministic stance, capturing the transient nature of early embryo progenitor cells by presenting an ensemble of types in superposition. The value of the new resource is emphasized through numerous examples, with a focus on previously unexplored germ cell populations where we uncover novel transcription onset features. Offering interactive exploration via a user-friendly web portal and facilitating complete data downloads, this atlas serves as a comprehensive and accessible reference.

Deep 3D histology powered by tissue clearing, omics and AI

To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.

Cold exposure increases circulating fibroblast growth factor 21 in the evening in males and females

Objectives

Cold exposure is linked to cardiometabolic benefits. Cold activates brown adipose tissue (BAT), increases energy expenditure, and induces secretion of the hormones fibroblast growth factor 21 (FGF21) and growth differentiation factor 15 (GDF15). The cold-induced increase in energy expenditure exhibits a diurnal rhythm in men. Therefore, we aimed to investigate the effect of cold exposure on serum FGF21 and GDF15 levels in humans and whether cold-induced changes in FGF21 and GDF15 levels differ between morning and evening in males and females.

Method

In this randomized cross-over study, serum FGF21 and GDF15 levels were measured in healthy lean males (n = 12) and females (n = 12) before, during, and after 90 min of stable cold exposure in the morning (07:45 h) and evening (19:45 h) with a 1-day washout period in between.

Results

Cold exposure increased FGF21 levels in the evening compared to the morning both in males (+61% vs -13%; P < 0.001) and in females (+58% vs +8%; P < 0.001). In contrast, cold exposure did not significantly modify serum GDF15 levels, and no diurnal variation was found. Changes in FGF21 and GDF15 levels did not correlate with changes in cold-induced energy expenditure in the morning and evening.

Conclusion

Cold exposure increased serum FGF21 levels in the evening, but not in the morning, in both males and females. GDF15 levels were not affected by cold exposure. Thus, this study suggests that the timing of cold exposure may influence cold-induced changes in FGF21 levels but not GDF15 levels and seems to be independent of changes in energy expenditure.

Discovery of optimal cell type classification marker genes from single cell RNA sequencing data

The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches at classification with significantly higher F-beta scores when applied to datasets from three human organs - brain, kidney, and lung.

Single-cell analysis in rheumatic and allergic diseases: insights for clinical practice

Since the advent of single-cell RNA sequencing (scRNA-seq) methodology, single-cell analysis has become a powerful tool for exploration of cellular networks and dysregulated immune responses in disease pathogenesis. Advanced bioinformatics tools have enabled the combined analysis of scRNA-seq data and information on various cell properties, such as cell surface molecular profiles, chromatin accessibility and spatial information, leading to a deeper understanding of pathology. This Review provides an overview of the achievements in single-cell analysis applied to clinical samples of rheumatic and allergic diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, allergic airway diseases and atopic dermatitis, with an expanded scope beyond peripheral blood cells to include local diseased tissues. Despite the valuable insights that single-cell analysis has provided into disease pathogenesis, challenges remain in translating single-cell findings into clinical practice and developing personalized treatment strategies. Beyond understanding the atlas of cellular diversity, we discuss the application of data obtained in each study to clinical practice, with a focus on identifying biomarkers and therapeutic targets.