Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor

Transcription factor dynamics, oscillation, and functions in human enteroendocrine cell differentiation

Enteroendocrine cells (EECs) secrete serotonin (enterochromaffin [EC] cells) or specific peptide hormones (non-EC cells) that serve vital metabolic functions. The basis for terminal EEC diversity remains obscure. By forcing activity of the transcription factor (TF) NEUROG3 in 2D cultures of human intestinal stem cells, we replicated physiologic EEC differentiation and examined transcriptional and cis-regulatory dynamics that culminate in discrete cell types. Abundant EEC precursors expressed stage-specific genes and TFs. Before expressing pre-terminal NEUROD1, post-mitotic precursors oscillated between transcriptionally distinct ASCL1+ and HES6hi cell states. Loss of either factor accelerated EEC differentiation substantially and disrupted EEC individuality; ASCL1 or NEUROD1 deficiency had opposing consequences on EC and non-EC cell features. These TFs mainly bind cis-elements that are accessible in undifferentiated stem cells, and they tailor subsequent expression of TF combinations that underlie discrete EEC identities. Thus, early TF oscillations retard EEC maturation to enable accurate diversity within a medically important cell lineage.

A single-cell transcriptomic study of heterogeneity in human embryonic tanycytes

Disruptions in energy homeostasis can lead to diseases like obesity and diabetes, affecting millions of people each year. Tanycytes, the adult stem cells in the hypothalamus, play crucial roles in assisting hypothalamic neurons in maintaining energy balance. Although tanycytes have been extensively studied in rodents, our understanding of human tanycytes remains limited. In this study, we utilized single-cell transcriptomics data to explore the heterogeneity of human embryonic tanycytes, investigate their gene regulatory networks, analyze their intercellular communication, and examine their developmental trajectory. Our analysis revealed the presence of two clusters of β tanycytes and three clusters of α tanycytes in our dataset. Surprisingly, human embryonic tanycytes displayed significant similarities to mouse tanycytes in terms of marker gene expression and transcription factor activities. Trajectory analysis indicated that α tanycytes were the first to be generated, giving rise to β tanycytes in a dorsal-ventral direction along the third ventricle. Furthermore, our CellChat analyses demonstrated that tanycytes generated earlier along the developmental lineages exhibited increased intercellular communication compared to those generated later. In summary, we have thoroughly characterized the heterogeneity of human embryonic tanycytes from various angles. We are confident that our findings will serve as a foundation for future research on human tanycytes.

Coexpression enhances cross-species integration of single-cell RNA sequencing across diverse plant species

Single-cell RNA sequencing is increasingly used to investigate cross-species differences driven by gene expression and cell-type composition in plants. However, the frequent expansion of plant gene families due to whole-genome duplications makes identification of one-to-one orthologues difficult, complicating integration. Here we demonstrate that coexpression can be used to trim many-to-many orthology families down to identify one-to-one gene pairs with proxy expression profiles, improving the performance of traditional integration methods and reducing barriers to integration across a diverse array of plant species.

The single-cell opioid responses in the context of HIV (SCORCH) consortium

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

Coordinated chemokine expression defines macrophage subsets across tissues

Lung-resident macrophages, which include alveolar macrophages and interstitial macrophages (IMs), exhibit a high degree of diversity, generally attributed to different activation states, and often complicated by the influx of monocytes into the pool of tissue-resident macrophages. To gain a deeper insight into the functional diversity of IMs, here we perform comprehensive transcriptional profiling of resident IMs and reveal ten distinct chemokine-expressing IM subsets at steady state and during inflammation. Similar IM subsets that exhibited coordinated chemokine signatures and differentially expressed genes were observed across various tissues and species, indicating conserved specialized functional roles. Other macrophage types shared specific IM chemokine profiles, while also presenting their own unique chemokine signatures. Depletion of CD206hi IMs in Pf4creR26EYFP+DTR and Pf4creR26EYFPCx3cr1DTR mice led to diminished inflammatory cell recruitment, reduced tertiary lymphoid structure formation and fewer germinal center B cells in models of allergen- and infection-driven inflammation. These observations highlight the specialized roles of IMs, defined by their coordinated chemokine production, in regulating immune cell influx and organizing tertiary lymphoid tissue architecture.

Single-cell transcriptome profiling reveals cell type-specific variation and development in HLA expression of human skin

Skin, the largest organ of body, is a highly immunogenic tissue with a diverse collection of immune cells. Highly polymorphic human leukocyte antigen (HLA) molecules have a central role in coordinating immune responses as recognition molecules. Nevertheless, HLA gene expression patterns among diverse cell types within a specific organ, like the skin, have yet to be thoroughly investigated, with stromal cells attracting much less attention than immune cells. To illustrate HLA expression profiles across different cell types in the skin, we performed single-cell RNA sequencing (scRNA-seq) analyses on skin datasets, covering adult and fetal skin, and hair follicles as the skin appendages. We revealed the variation in HLA expression between different skin populations by examining normal adult skin datasets. Moreover, we evaluated the potential immunogenicity of multiple skin populations based on the expression of classical HLA class I genes, which were well represented in all cell types. Furthermore, we generated scRNA-seq data of developing skin from fetuses of 15 post conception weeks (PCW), 17 PCW, and 22 PCW, delineating the dynamic expression of HLA genes with cell type-dependent variation among various cell types during development. Notably, the pseudotime trajectory analysis unraveled the significant variance in HLA genes during the evolution of vascular endothelial cells. Moreover, we uncovered the immune-privileged properties of hair follicles at single-cell resolution. Our study presents a comprehensive single-cell transcriptomic landscape of HLA genes in the skin, which provides new insights into variation in HLA molecules and offers a clue for allogeneic skin transplantation.

Improving replicability in single-cell RNA-Seq cell type discovery with Dune

BACKGROUND

Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable.

RESULTS

Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html .

CONCLUSIONS

Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

A spatial transcriptome map of the developing maize ear

A comprehensive understanding of inflorescence development is crucial for crop genetic improvement, as inflorescence meristems give rise to reproductive organs and determine grain yield. However, dissecting inflorescence development at the cellular level has been challenging owing to a lack of specific marker genes to distinguish among cell types, particularly in different types of meristems that are vital for organ formation. In this study, we used spatial enhanced resolution omics-sequencing (Stereo-seq) to construct a precise spatial transcriptome map of the developing maize ear primordium, identifying 12 cell types, including 4 newly defined cell types found mainly in the inflorescence meristem. By extracting the meristem components for detailed clustering, we identified three subtypes of meristem and validated two MADS-box genes that were specifically expressed at the apex of determinate meristems and involved in stem cell determinacy. Furthermore, by integrating single-cell RNA transcriptomes, we identified a series of spatially specific networks and hub genes that may provide new insights into the formation of different tissues. In summary, this study provides a valuable resource for research on cereal inflorescence development, offering new clues for yield improvement.

Whole-cortex in situ sequencing reveals input-dependent area identity

The cerebral cortex is composed of neuronal types with diverse gene expression that are organized into specialized cortical areas. These areas, each with characteristic cytoarchitecture1,2, connectivity3,4 and neuronal activity5,6, are wired into modular networks3,4,7. However, it remains unclear whether these spatial organizations are reflected in neuronal transcriptomic signatures and how such signatures are established in development. Here we used BARseq, a high-throughput in situ sequencing technique, to interrogate the expression of 104 cell-type marker genes in 10.3 million cells, including 4,194,658 cortical neurons over nine mouse forebrain hemispheres, at cellular resolution. De novo clustering of gene expression in single neurons revealed transcriptomic types consistent with previous single-cell RNA sequencing studies8,9. The composition of transcriptomic types is highly predictive of cortical area identity. Moreover, areas with similar compositions of transcriptomic types, which we defined as cortical modules, overlap with areas that are highly connected, suggesting that the same modular organization is reflected in both transcriptomic signatures and connectivity. To explore how the transcriptomic profiles of cortical neurons depend on development, we assessed cell-type distributions after neonatal binocular enucleation. Notably, binocular enucleation caused the shifting of the cell-type compositional profiles of visual areas towards neighbouring cortical areas within the same module, suggesting that peripheral inputs sharpen the distinct transcriptomic identities of areas within cortical modules. Enabled by the high throughput, low cost and reproducibility of BARseq, our study provides a proof of principle for the use of large-scale in situ sequencing to both reveal brain-wide molecular architecture and understand its development.

Reconstruction of macroglia and adult neurogenesis evolution through cross-species single-cell transcriptomic analyses

Macroglia fulfill essential functions in the adult vertebrate brain, producing and maintaining neurons and regulating neuronal communication. However, we still know little about their emergence and diversification. We used the zebrafish D. rerio as a distant vertebrate model with moderate glial diversity as anchor to reanalyze datasets covering over 600 million years of evolution. We identify core features of adult neurogenesis and innovations in the mammalian lineage with a potential link to the rarity of radial glia-like cells in adult humans. Our results also suggest that functions associated with astrocytes originated in a multifunctional cell type fulfilling both neural stem cell and astrocytic functions before these diverged. Finally, we identify conserved elements of macroglial cell identity and function and their time of emergence during evolution.

Insights for disease modeling from single-cell transcriptomics of iPSC-derived Ngn2-induced neurons and astrocytes across differentiation time and co-culture

BACKGROUND

Trans-differentiation of human-induced pluripotent stem cells into neurons via Ngn2-induction (hiPSC-N) has become an efficient system to quickly generate neurons a likely significant advance for disease modeling and in vitro assay development. Recent single-cell interrogation of Ngn2-induced neurons, however, has revealed some similarities to unexpected neuronal lineages. Similarly, a straightforward method to generate hiPSC-derived astrocytes (hiPSC-A) for the study of neuropsychiatric disorders has also been described.

RESULTS

Here, we examine the homogeneity and similarity of hiPSC-N and hiPSC-A to their in vivo counterparts, the impact of different lengths of time post Ngn2 induction on hiPSC-N (15 or 21 days), and the impact of hiPSC-N/hiPSC-A co-culture. Leveraging the wealth of existing public single-cell RNA-seq (scRNA-seq) data in Ngn2-induced neurons and in vivo data from the developing brain, we provide perspectives on the lineage origins and maturation of hiPSC-N and hiPSC-A. While induction protocols in different labs produce consistent cell type profiles, both hiPSC-N and hiPSC-A show significant heterogeneity and similarity to multiple in vivo cell fates, and both more precisely approximate their in vivo counterparts when co-cultured. Gene expression data from the hiPSC-N show enrichment of genes linked to schizophrenia (SZ) and autism spectrum disorders (ASD) as has been previously shown for neural stem cells and neurons. These overrepresentations of disease genes are strongest in our system at early times (day 15) in Ngn2-induction/maturation of neurons, when we also observe the greatest similarity to early in vivo excitatory neurons. We have assembled this new scRNA-seq data along with the public data explored here as an integrated biologist-friendly web-resource for researchers seeking to understand this system more deeply: https://nemoanalytics.org/p?l=DasEtAlNGN2&g=NES .

CONCLUSIONS

While overall we support the use of the investigated cellular models for the study of neuropsychiatric disease, we also identify important limitations. We hope that this work will contribute to understanding and optimizing cellular modeling for complex brain disorders.

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework

MOTIVATION

Removal of batch effect between multiple datasets from different experimental platforms has become an urgent problem, since single-cell RNA sequencing (scRNA-seq) techniques developed rapidly. Although there have been some methods for this problem, most of them still face the challenge of under-correction or over-correction. Specifically, handling batch effect in highly nonlinear scRNA-seq data requires a more powerful model to address under-correction. In the meantime, some previous methods focus too much on removing difference between batches, which may disturb the biological signal heterogeneity of datasets generated from different experiments, thereby leading to over-correction.

RESULTS

In this article, we propose a novel multi-layer adaptation autoencoder with dual-channel framework to address the under-correction and over-correction problems in batch effect removal, which is called BERMAD and can achieve better results of scRNA-seq data integration and joint analysis. First, we design a multi-layer adaptation architecture to model distribution difference between batches from different feature granularities. The distribution matching on various layers of autoencoder with different feature dimensions can result in more accurate batch correction outcome. Second, we propose a dual-channel framework, where the deep autoencoder processing each single dataset is independently trained. Hence, the heterogeneous information that is not shared between different batches can be retained more completely, which can alleviate over-correction. Comprehensive experiments on multiple scRNA-seq datasets demonstrate the effectiveness and superiority of our method over the state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

The code implemented in Python and the data used for experiments have been released on GitHub (https://github.com/zhanglabNKU/BERMAD) and Zenodo (https://zenodo.org/records/10695073) with detailed instructions.

A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution

The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed the unique marker genes of many neuronal subtypes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard ( http://harvard.heavy.ai:6273/ ) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.

Dissecting the immune discrepancies in mouse liver allograft tolerance and heart/kidney allograft rejection

The liver is the most tolerogenic of transplanted organs. However, the mechanisms underlying liver transplant tolerance are not well understood. The comparison between liver transplantation tolerance and heart/kidney transplantation rejection will deepen our understanding of tolerance and rejection in solid organs. Here, we built a mouse model of liver, heart and kidney allograft and performed single-cell RNA sequencing of 66,393 cells to describe the cell composition and immune cell interactions at the early stage of tolerance or rejection. We also performed bulk RNA-seq of mouse liver allografts from Day 7 to Day 60 post-transplantation to map the dynamic transcriptional variation in spontaneous tolerance. The transcriptome of lymphocytes and myeloid cells were characterized and compared in three types of organ allografts. Cell-cell interaction networks reveal the coordinated function of Kupffer cells, macrophages and their associated metabolic processes, including insulin receptor signalling and oxidative phosphorylation in tolerance induction. Cd11b+ dendritic cells (DCs) in liver allografts were found to inhibit cytotoxic T cells by secreting anti-inflammatory cytokines such as Il10. In summary, we profiled single-cell transcriptome analysis of mouse solid organ allografts. We characterized the immune microenvironment of mouse organ allografts in the acute rejection state (heart, kidney) and tolerance state (liver).

Cortical somatostatin long-range projection neurons and interneurons exhibit divergent developmental trajectories

The mammalian cerebral cortex contains an extraordinary diversity of cell types that emerge by implementing different developmental programs. Delineating when and how cellular diversification occurs is particularly challenging for cortical inhibitory neurons because they represent a small proportion of all cortical cells and have a protracted development. Here, we combine single-cell RNA sequencing and spatial transcriptomics to characterize the emergence of neuronal diversity among somatostatin-expressing (SST+) cells in mice. We found that SST+ inhibitory neurons segregate during embryonic stages into long-range projection (LRP) neurons and two types of interneurons, Martinotti cells and non-Martinotti cells, following distinct developmental trajectories. Two main subtypes of LRP neurons and several subtypes of interneurons are readily distinguishable in the embryo, although interneuron diversity is likely refined during early postnatal life. Our results suggest that the timing for cellular diversification is unique for different subtypes of SST+ neurons and particularly divergent for LRP neurons and interneurons.

A model of human neural networks reveals NPTX2 pathology in ALS and FTLD

Human cellular models of neurodegeneration require reproducibility and longevity, which is necessary for simulating age-dependent diseases. Such systems are particularly needed for TDP-43 proteinopathies1, which involve human-specific mechanisms2-5 that cannot be directly studied in animal models. Here, to explore the emergence and consequences of TDP-43 pathologies, we generated induced pluripotent stem cell-derived, colony morphology neural stem cells (iCoMoNSCs) via manual selection of neural precursors6. Single-cell transcriptomics and comparison to independent neural stem cells7 showed that iCoMoNSCs are uniquely homogenous and self-renewing. Differentiated iCoMoNSCs formed a self-organized multicellular system consisting of synaptically connected and electrophysiologically active neurons, which matured into long-lived functional networks (which we designate iNets). Neuronal and glial maturation in iNets was similar to that of cortical organoids8. Overexpression of wild-type TDP-43 in a minority of neurons within iNets led to progressive fragmentation and aggregation of the protein, resulting in a partial loss of function and neurotoxicity. Single-cell transcriptomics revealed a novel set of misregulated RNA targets in TDP-43-overexpressing neurons and in patients with TDP-43 proteinopathies exhibiting a loss of nuclear TDP-43. The strongest misregulated target encoded the synaptic protein NPTX2, the levels of which are controlled by TDP-43 binding on its 3' untranslated region. When NPTX2 was overexpressed in iNets, it exhibited neurotoxicity, whereas correcting NPTX2 misregulation partially rescued neurons from TDP-43-induced neurodegeneration. Notably, NPTX2 was consistently misaccumulated in neurons from patients with amyotrophic lateral sclerosis and frontotemporal lobar degeneration with TDP-43 pathology. Our work directly links TDP-43 misregulation and NPTX2 accumulation, thereby revealing a TDP-43-dependent pathway of neurotoxicity.

Rotenone impairs brain glial energetics and locomotor behavior in bumblebees

Bumblebees are essential pollinators of both wildflowers and crops and face multiple anthropogenic stressors, particularly the utilization of pesticides. Rotenone is an extensively applied neurotoxic pesticide that possesses insecticidal activities against a wide range of pests. However, whether environmentally realistic exposure levels of rotenone can damage neurons in bumblebee brains is still uncertain. Using single-cell RNA-seq, we revealed that rotenone induced cell-specific responses in bumblebee brains, emphasizing the disruption of energy metabolism and mitochondrial dysfunction in glial cells. Correspondingly, the gene regulatory network associated with neurotransmission was also suppressed. Notably, rotenone could specially reduce the number of dopaminergic neurons, impairing bumblebee's ability to fly and crawl. We also found impaired intestinal motility in rotenone-treated bumblebees. Finally, we demonstrated that many differentially expressed genes in our snRNA-seq data overlapped with rotenone-induced Parkinson's disease risk genes, especially in glial cells. Although rotenone is widely used owing to its hypotoxicity, we found that environmentally realistic exposure levels of rotenone induced disturbed glial energetics and locomotor dysfunction in bumblebees, which may lead to an indirect decline in this essential pollinator.

Historical perspective and future directions: computational science in immuno-oncology

Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.

Single-cell transcriptomics and chromatin accessibility profiling elucidate the kidney-protective mechanism of mineralocorticoid receptor antagonists

Mineralocorticoid excess commonly leads to hypertension (HTN) and kidney disease. In our study, we used single-cell expression and chromatin accessibility tools to characterize the mineralocorticoid target genes and cell types. We demonstrated that mineralocorticoid effects were established through open chromatin and target gene expression, primarily in principal and connecting tubule cells and, to a lesser extent, in segments of the distal convoluted tubule cells. We examined the kidney-protective effects of steroidal and nonsteroidal mineralocorticoid antagonists (MRAs), as well as of amiloride, an epithelial sodium channel inhibitor, in a rat model of deoxycorticosterone acetate, unilateral nephrectomy, and high-salt consumption-induced HTN and cardiorenal damage. All antihypertensive therapies protected against cardiorenal damage. However, finerenone was particularly effective in reducing albuminuria and improving gene expression changes in podocytes and proximal tubule cells, even with an equivalent reduction in blood pressure. We noted a strong correlation between the accumulation of injured/profibrotic tubule cells expressing secreted posphoprotein 1 (Spp1), Il34, and platelet-derived growth factor subunit b (Pdgfb) and the degree of fibrosis in rat kidneys. This gene signature also showed a potential for classifying human kidney samples. Our multiomics approach provides fresh insights into the possible mechanisms underlying HTN-associated kidney disease, the target cell types, the protective effects of steroidal and nonsteroidal MRAs, and amiloride.

Automatic cell-type harmonization and integration across Human Cell Atlas datasets

Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues.

Characterization of human pluripotent stem cell differentiation by single-cell dual-omics analyses

In vivo differentiation of human pluripotent stem cells (hPSCs) has unique advantages, such as multilineage differentiation, angiogenesis, and close cell-cell interactions. To systematically investigate multilineage differentiation mechanisms of hPSCs, we constructed the in vivo hPSC differentiation landscape containing 239,670 cells using teratoma models. We identified 43 cell types, inferred 18 cell differentiation trajectories, and characterized common and specific gene regulation patterns during hPSC differentiation at both transcriptional and epigenetic levels. Additionally, we developed the developmental single-cell Basic Local Alignment Search Tool (dscBLAST), an R-based cell identification tool, to simplify the identification processes of developmental cells. Using dscBLAST, we aligned cells in multiple differentiation models to normally developing cells to further understand their differentiation states. Overall, our study offers new insights into stem cell differentiation and human embryonic development; dscBLAST shows favorable cell identification performance, providing a powerful identification tool for developmental cells.

Unraveling the Phenotypic States of Human innate-like T Cells: Comparative Insights with Conventional T Cells and Mouse Models

The "innate-like" T cell compartment, known as Tinn, represents a diverse group of T cells that straddle the boundary between innate and adaptive immunity, having the ability to mount rapid responses following activation. In mice, this ability is acquired during thymic development. We explored the transcriptional landscape of Tinn compared to conventional T cells (Tconv) in the human thymus and blood using single cell RNA sequencing and flow cytometry. We reveal that in human blood, the majority of Tinn cells, including iNKT, MAIT, and Vδ2+Vγ9+ T cells, share an effector program characterized by the expression of unique chemokine and cytokine receptors, and cytotoxic molecules. This program is driven by specific transcription factors, distinct from those governing Tconv cells. Conversely, only a fraction of thymic Tinn cells displays an effector phenotype, while others share transcriptional features with developing Tconv cells, indicating potential divergent developmental pathways. Unlike the mouse, human Tinn cells do not differentiate into multiple effector subsets but develop a mixed type I/type III effector potential. To conduct a comprehensive cross-species analysis, we constructed a murine Tinn developmental atlas and uncovered additional species-specific distinctions, including the absence of type II Tinn cells in humans, which implies distinct immune regulatory mechanisms across species. The study provides insights into the development and functionality of Tinn cells, emphasizing their role in immune responses and their potential as targets for therapeutic interventions.

Comparative Analysis of Single-cell and Single-nucleus RNA-sequencing in a Rabbit Model of Retinal Detachment-related Proliferative Vitreoretinopathy

Purpose

Proliferative vitreoretinopathy (PVR) is the most common cause of failure of retinal reattachment surgery, and the molecular changes leading to this aberrant wound healing process are currently unknown. Our ultimate goal is to study PVR pathogenesis by employing single-cell transcriptomics to dissect cellular heterogeneity.

Design

Here we aimed to compare single-cell RNA sequencing (scRNA-seq)  and single-nucleus RNA-sequencing (snRNA-seq) of retinal PVR samples in the rabbit model.

Participants

Unilateral induction of PVR lesions in rabbit eyes with contralateral eyes serving as controls.

Methods

Proliferative vitreoretinopathy was induced unilaterally in Dutch Belted rabbits. At different timepoints after PVR induction, retinas were dissociated into either cells or nuclei suspension and processed for scRNA-seq or snRNA-seq.

Main Outcome Measures

Single cell and nuclei transcriptomic profiles of retinas after PVR induction.

Results

Single-cell RNA sequencing and snRNA-seq were conducted on retinas at 4 hours and 14 days after disease induction. Although the capture rate of unique molecular identifiers and genes were greater in scRNA-seq samples, overall gene expression profiles of individual cell types were highly correlated between scRNA-seq and snRNA-seq. A major disparity between the 2 sequencing modalities was the cell type capture rate, however, with glial cell types overrepresented in scRNA-seq, and inner retinal neurons were enriched by snRNA-seq. Furthermore, fibrotic Müller glia were overrepresented in snRNA-seq samples, whereas reactive Müller glia were overrepresented in scRNA-seq samples. Trajectory analyses were similar between the 2 methods, allowing for the combined analysis of the scRNA-seq and snRNA-seq data sets.

Conclusions

These findings highlight limitations of both scRNA-seq and snRNA-seq analysis and imply that use of both techniques together can more accurately identify transcriptional networks critical for aberrant fibrogenesis in PVR than using either in isolation.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Molecular traces of Drosophila hemocytes reveal transcriptomic conservation with vertebrate myeloid cells

Drosophila hemocytes serve as the primary defense system against harmful threats, allowing the animals to thrive. Hemocytes are often compared to vertebrate innate immune system cells due to the observed functional similarities between the two. However, the similarities have primarily been established based on a limited number of genes and their functional homologies. Thus, a systematic analysis using transcriptomic data could offer novel insights into Drosophila hemocyte function and provide new perspectives on the evolution of the immune system. Here, we performed cross-species comparative analyses using single-cell RNA sequencing data from Drosophila and vertebrate immune cells. We found several conserved markers for the cluster of differentiation (CD) genes in Drosophila hemocytes and validated the role of CG8501 (CD59) in phagocytosis by plasmatocytes, which function much like macrophages in vertebrates. By comparing whole transcriptome profiles in both supervised and unsupervised analyses, we showed that Drosophila hemocytes are largely homologous to vertebrate myeloid cells, especially plasmatocytes to monocytes/macrophages and prohemocyte 1 (PH1) to hematopoietic stem cells. Furthermore, a small subset of prohemocytes with hematopoietic potential displayed homology with hematopoietic progenitor populations in vertebrates. Overall, our results provide a deeper understanding of molecular conservation in the Drosophila immune system.

Coexpression enhances cross-species integration of scRNA-seq across diverse plant species

Single-cell RNA sequencing is increasingly used to investigate cross-species differences driven by gene expression and cell-type composition in plants. However, the frequent expansion of plant gene families due to whole genome duplications makes identification of one-to-one orthologs difficult, complicating integration. Here, we demonstrate that coexpression can be used to identify non-orthologous gene pairs with proxy expression profiles, improving the performance of traditional integration methods and reducing barriers to integration across a diverse array of plant species.

The utilization of single-cell sequencing technology in investigating the immune microenvironment of ccRCC

The growth and advancement of ccRCC are strongly associated with the presence of immune infiltration and the tumor microenvironment, comprising tumor cells, immune cells, stromal cells, vascular cells, myeloid-derived cells, and extracellular matrix (ECM). Nevertheless, as a result of the diverse and constantly evolving characteristics of the tumor microenvironment, prior advanced sequencing methods have frequently disregarded specific less prevalent cellular traits at varying intervals, thereby concealing their significance. The advancement and widespread use of single-cell sequencing technology enable us to comprehend the source of individual tumor cells and the characteristics of a greater number of individual cells. This, in turn, minimizes the impact of intercellular heterogeneity and temporal heterogeneity of the same cell on experimental outcomes. This review examines the attributes of the tumor microenvironment in ccRCC and provides an overview of the progress made in single-cell sequencing technology and its particular uses in the current focus of immune infiltration in ccRCC.

A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution

The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed many neuronal subtypes' unique marker genes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard (http://harvard.heavy.ai:6273/) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.

Integrated multi-omics single cell atlas of the human retina

Single-cell sequencing has revolutionized the scale and resolution of molecular profiling of tissues and organs. Here, we present an integrated multimodal reference atlas of the most accessible portion of the mammalian central nervous system, the retina. We compiled around 2.4 million cells from 55 donors, including 1.4 million unpublished data points, to create a comprehensive human retina cell atlas (HRCA) of transcriptome and chromatin accessibility, unveiling over 110 types. Engaging the retina community, we annotated each cluster, refined the Cell Ontology for the retina, identified distinct marker genes, and characterized cis-regulatory elements and gene regulatory networks (GRNs) for these cell types. Our analysis uncovered intriguing differences in transcriptome, chromatin, and GRNs across cell types. In addition, we modeled changes in gene expression and chromatin openness across gender and age. This integrated atlas also enabled the fine-mapping of GWAS and eQTL variants. Accessible through interactive browsers, this multimodal cross-donor and cross-lab HRCA, can facilitate a better understanding of retinal function and pathology.

Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus

The development and diversity of neuronal subtypes in the human hypothalamus has been insufficiently characterized. To address this, we integrated transcriptomic data from 241,096 cells (126,840 newly generated) in the prenatal and adult human hypothalamus to reveal a temporal trajectory from proliferative stem cell populations to mature hypothalamic cell types. Iterative clustering of the adult neurons identified 108 robust transcriptionally distinct neuronal subtypes representing 10 hypothalamic nuclei. Pseudotime trajectories provided insights into the genes driving formation of these nuclei. Comparisons to single-cell transcriptomic data from the mouse hypothalamus suggested extensive conservation of neuronal subtypes despite certain differences in species-enriched gene expression. The uniqueness of hypothalamic neuronal lineages was examined developmentally by comparing excitatory lineages present in cortex and inhibitory lineages in ganglionic eminence, revealing both distinct and shared drivers of neuronal maturation across the human forebrain. These results provide a comprehensive transcriptomic view of human hypothalamus development through gestation and adulthood at cellular resolution.

Comparative single-cell transcriptomic analysis of primate brains highlights human-specific regulatory evolution

Enhanced cognitive function in humans is hypothesized to result from cortical expansion and increased cellular diversity. However, the mechanisms that drive these phenotypic innovations remain poorly understood, in part because of the lack of high-quality cellular resolution data in human and non-human primates. Here, we take advantage of single-cell expression data from the middle temporal gyrus of five primates (human, chimp, gorilla, macaque and marmoset) to identify 57 homologous cell types and generate cell type-specific gene co-expression networks for comparative analysis. Although orthologue expression patterns are generally well conserved, we find 24% of genes with extensive differences between human and non-human primates (3,383 out of 14,131), which are also associated with multiple brain disorders. To assess the functional significance of gene expression differences in an evolutionary context, we evaluate changes in network connectivity across meta-analytic co-expression networks from 19 animals. We find that a subset of these genes has deeply conserved co-expression across all non-human animals, and strongly divergent co-expression relationships in humans (139 out of 3,383, <1% of primate orthologues). Genes with human-specific cellular expression and co-expression profiles (such as NHEJ1, GTF2H2, C2 and BBS5) typically evolve under relaxed selective constraints and may drive rapid evolutionary change in brain function.

Comparative transcriptomics reveals human-specific cortical features

The cognitive abilities of humans are distinctive among primates, but their molecular and cellular substrates are poorly understood. We used comparative single-nucleus transcriptomics to analyze samples of the middle temporal gyrus (MTG) from adult humans, chimpanzees, gorillas, rhesus macaques, and common marmosets to understand human-specific features of the neocortex. Human, chimpanzee, and gorilla MTG showed highly similar cell-type composition and laminar organization as well as a large shift in proportions of deep-layer intratelencephalic-projecting neurons compared with macaque and marmoset MTG. Microglia, astrocytes, and oligodendrocytes had more-divergent expression across species compared with neurons or oligodendrocyte precursor cells, and neuronal expression diverged more rapidly on the human lineage. Only a few hundred genes showed human-specific patterning, suggesting that relatively few cellular and molecular changes distinctively define adult human cortical structure.

Multi-species atlas resolves an axolotl limb development and regeneration paradox

Humans and other tetrapods are considered to require apical-ectodermal-ridge (AER) cells for limb development, and AER-like cells are suggested to be re-formed to initiate limb regeneration. Paradoxically, the presence of AER in the axolotl, a primary model organism for regeneration, remains controversial. Here, by leveraging a single-cell transcriptomics-based multi-species atlas, composed of axolotl, human, mouse, chicken, and frog cells, we first establish that axolotls contain cells with AER characteristics. Further analyses and spatial transcriptomics reveal that axolotl limbs do not fully re-form AER cells during regeneration. Moreover, the axolotl mesoderm displays part of the AER machinery, revealing a program for limb (re)growth. These results clarify the debate about the axolotl AER and the extent to which the limb developmental program is recapitulated during regeneration.

scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention

In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating cellular heterogeneity and structure. However, analyzing scRNA-seq data remains challenging, especially in the context of COVID-19 research. Single-cell clustering is a key step in analyzing scRNA-seq data, and deep learning methods have shown great potential in this area. In this work, we propose a novel scRNA-seq analysis framework called scAAGA. Specifically, we utilize an asymmetric autoencoder with a gene attention module to learn important gene features adaptively from scRNA-seq data, with the aim of improving the clustering effect. We apply scAAGA to COVID-19 peripheral blood mononuclear cell (PBMC) scRNA-seq data and compare its performance with state-of-the-art methods. Our results consistently demonstrate that scAAGA outperforms existing methods in terms of adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) scores, achieving improvements ranging from 2.8% to 27.8% in NMI scores. Additionally, we discuss a data augmentation technology to expand the datasets and improve the accuracy of scAAGA. Overall, scAAGA presents a robust tool for scRNA-seq data analysis, enhancing the accuracy and reliability of clustering results in COVID-19 research.

Single-cell reference mapping to construct and extend cell-type hierarchies

Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such large-scale atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups specializing in different cell type compartments, often using distinct terminology. Understanding how these distinct sets of annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge in the field. Whereas recent computational techniques, referred to as 'reference mapping' methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e. queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present 'treeArches', a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases for treeArches, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases.

Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex

Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.

Cell type matching across species using protein embeddings and transfer learning

MOTIVATION

Knowing the relation between cell types is crucial for translating experimental results from mice to humans. Establishing cell type matches, however, is hindered by the biological differences between the species. A substantial amount of evolutionary information between genes that could be used to align the species is discarded by most of the current methods since they only use one-to-one orthologous genes. Some methods try to retain the information by explicitly including the relation between genes, however, not without caveats.

RESULTS

In this work, we present a model to transfer and align cell types in cross-species analysis (TACTiCS). First, TACTiCS uses a natural language processing model to match genes using their protein sequences. Next, TACTiCS employs a neural network to classify cell types within a species. Afterward, TACTiCS uses transfer learning to propagate cell type labels between species. We applied TACTiCS on scRNA-seq data of the primary motor cortex of human, mouse, and marmoset. Our model can accurately match and align cell types on these datasets. Moreover, our model outperforms Seurat and the state-of-the-art method SAMap. Finally, we show that our gene matching method results in better cell type matches than BLAST in our model.

AVAILABILITY AND IMPLEMENTATION

The implementation is available on GitHub (https://github.com/kbiharie/TACTiCS). The preprocessed datasets and trained models can be downloaded from Zenodo (https://doi.org/10.5281/zenodo.7582460).

T cell-Mediated Development of Stromal Fibroblasts with an Immune-Enhancing Chemokine Profile

Stromal fibroblasts reside in inflammatory tissues that are characterized by either immune suppression or activation. Whether and how fibroblasts adapt to these contrasting microenvironments remains unknown. Cancer-associated fibroblasts (CAF) mediate immune quiescence by producing the chemokine CXCL12, which coats cancer cells to suppress T-cell infiltration. We examined whether CAFs can also adopt an immune-promoting chemokine profile. Single-cell RNA sequencing of CAFs from mouse pancreatic adenocarcinomas identified a subpopulation of CAFs with decreased expression of Cxcl12 and increased expression of the T cell-attracting chemokine Cxcl9 in association with T-cell infiltration. TNFα and IFNγ containing conditioned media from activated CD8+ T cells converted stromal fibroblasts from a CXCL12+/CXCL9- immune-suppressive phenotype into a CXCL12-/CXCL9+ immune-activating phenotype. Recombinant IFNγ and TNFα acted together to augment CXCL9 expression, whereas TNFα alone suppressed CXCL12 expression. This coordinated chemokine switch led to increased T-cell infiltration in an in vitro chemotaxis assay. Our study demonstrates that CAFs have a phenotypic plasticity that allows their adaptation to contrasting immune tissue microenvironments.

A guide to the BRAIN Initiative Cell Census Network data ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Transcriptional diversity in specific synaptic gene sets discriminates cortical neuronal identity

Synapse diversity has been described from different perspectives, ranging from the specific neurotransmitters released, to their diverse biophysical properties and proteome profiles. However, synapse diversity at the transcriptional level has not been systematically identified across all synapse populations in the brain. To quantify and identify specific synaptic features of neuronal cell types we combined the SynGO (Synaptic Gene Ontology) database with single-cell RNA sequencing data of the mouse neocortex. We show that cell types can be discriminated by synaptic genes alone with the same power as all genes. The cell type discriminatory power is not equally distributed across synaptic genes as we could identify functional categories and synaptic compartments with greater cell type specific expression. Synaptic genes, and specific SynGO categories, belonged to three different types of gene modules: gradient expression over all cell types, gradient expression in selected cell types and cell class- or type-specific profiles. This data provides a deeper understanding of synapse diversity in the neocortex and identifies potential markers to selectively identify synapses from specific neuronal populations.

A pan-grass transcriptome reveals patterns of cellular divergence in crops

Different plant species within the grasses were parallel targets of domestication, giving rise to crops with distinct evolutionary histories and traits1. Key traits that distinguish these species are mediated by specialized cell types2. Here we compare the transcriptomes of root cells in three grass species-Zea mays, Sorghum bicolor and Setaria viridis. We show that single-cell and single-nucleus RNA sequencing provide complementary readouts of cell identity in dicots and monocots, warranting a combined analysis. Cell types were mapped across species to identify robust, orthologous marker genes. The comparative cellular analysis shows that the transcriptomes of some cell types diverged more rapidly than those of others-driven, in part, by recruitment of gene modules from other cell types. The data also show that a recent whole-genome duplication provides a rich source of new, highly localized gene expression domains that favour fast-evolving cell types. Together, the cell-by-cell comparative analysis shows how fine-scale cellular profiling can extract conserved modules from a pan transcriptome and provide insight on the evolution of cells that mediate key functions in crops.

Single-cell RNA sequencing reveals maturation trajectory in human pluripotent stem cell-derived cardiomyocytes in engineered tissues

Cardiac in vitro models have become increasingly obtainable and affordable with the optimization of human pluripotent stem cell-derived cardiomyocyte (hPSC-CM) differentiation. However, these CMs are immature compared to their in vivo counterparts. Here we study the cellular phenotype of hPSC-CMs by comparing their single-cell gene expression and functional profiles in three engineered cardiac tissue configurations: human ventricular (hv) cardiac anisotropic sheet, cardiac tissue strip, and cardiac organoid chamber (hvCOC), with spontaneously aggregated 3D cardiac spheroids (CS) as control. The CM maturity was found to increase with increasing levels of complexity of the engineered tissues from CS to hvCOC. The contractile components are the first function to mature, followed by electrophysiology and oxidative metabolism. Notably, the 2D tissue constructs show a higher cellular organization whereas metabolic maturity preferentially increases in the 3D constructs. We conclude that the tissue engineering models resembling configurations of native tissues may be reliable for drug screening or disease modeling.

Smooth muscle contributes to the development and function of a layered intestinal stem cell niche

Wnt and Rspondin (RSPO) signaling drives proliferation, and bone morphogenetic protein inhibitors (BMPi) impede differentiation, of intestinal stem cells (ISCs). Here, we identify the mouse ISC niche as a complex, multi-layered structure that encompasses distinct mesenchymal and smooth muscle populations. In young and adult mice, diverse sub-cryptal cells provide redundant ISC-supportive factors; few of these are restricted to single cell types. Niche functions refine during postnatal crypt morphogenesis, in part to oppose the dense aggregation of differentiation-promoting BMP+ sub-epithelial myofibroblasts at crypt-villus junctions. Muscularis mucosae, a specialized muscle layer, first appears during this period and supplements neighboring RSPO and BMPi sources. Components of this developing niche are conserved in human fetuses. The in vivo ablation of mouse postnatal smooth muscle increases BMP signaling activity, potently limiting a pre-weaning burst of crypt fission. Thus, distinct and progressively specialized mesenchymal cells together create the milieu that is required to propagate crypts during rapid organ growth and to sustain adult ISCs.

Single-nucleus sequencing deciphers developmental trajectories in rice pistils

Angiosperms possess a life cycle with an alternation of sporophyte and gametophyte generations, which happens in plant organs like pistils. Rice pistils contain ovules and receive pollen for successful fertilization to produce grains. The cellular expression profile in rice pistils is largely unknown. Here, we show a cell census of rice pistils before fertilization through the use of droplet-based single-nucleus RNA sequencing. The ab initio marker identification validated by in situ hybridization assists with cell-type annotation, revealing cell heterogeneity between ovule- and carpel-originated cells. A comparison of 1N (gametophyte) and 2N (sporophyte) nuclei identifies the developmental path of germ cells in ovules with typical resetting of pluripotency before the sporophyte-gametophyte transition, while trajectory analysis of carpel-originated cells suggests previously neglected features of epidermis specification and style function. These findings gain a systems-level view of cellular differentiation and development of rice pistils before flowering and lay a foundation for understanding female reproductive development in plants.

Mapping of clonal lineages across developmental stages in human neural differentiation

The cell lineages across developmental stages remain to be elucidated. Here, we developed single-cell split barcoding (SISBAR) that allows clonal tracking of single-cell transcriptomes across stages in an in vitro model of human ventral midbrain-hindbrain differentiation. We developed "potential-spective" and "origin-spective" analyses to investigate the cross-stage lineage relationships and mapped a multi-level clonal lineage landscape depicting the whole differentiation process. We uncovered many previously uncharacterized converging and diverging trajectories. Furthermore, we demonstrate that a transcriptome-defined cell type can arise from distinct lineages that leave molecular imprints on their progenies, and the multilineage fates of a progenitor cell-type represent the collective results of distinct rather than similar clonal fates of individual progenitors, each with distinct molecular signatures. Specifically, we uncovered a ventral midbrain progenitor cluster as the common clonal origin of midbrain dopaminergic (mDA) neurons, midbrain glutamatergic neurons, and vascular and leptomeningeal cells and identified a surface marker that can improve graft outcomes.

BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids

Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, revealing conserved and specific developmental trajectories across brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than with other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental single-cell RNA sequencing (scRNA-seq) data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes through the use of immunofluorescence. BOMA is open-source available as a web tool for community use.

Batch alignment of single-cell transcriptomics data using deep metric learning

scRNA-seq has uncovered previously unappreciated levels of heterogeneity. With the increasing scale of scRNA-seq studies, the major challenge is correcting batch effect and accurately detecting the number of cell types, which is inevitable in human studies. The majority of scRNA-seq algorithms have been specifically designed to remove batch effect firstly and then conduct clustering, which may miss some rare cell types. Here we develop scDML, a deep metric learning model to remove batch effect in scRNA-seq data, guided by the initial clusters and the nearest neighbor information intra and inter batches. Comprehensive evaluations spanning different species and tissues demonstrated that scDML can remove batch effect, improve clustering performance, accurately recover true cell types and consistently outperform popular methods such as Seurat 3, scVI, Scanorama, BBKNN, Harmony et al. Most importantly, scDML preserves subtle cell types in raw data and enables discovery of new cell subtypes that are hard to extract by analyzing each batch individually. We also show that scDML is scalable to large datasets with lower peak memory usage, and we believe that scDML offers a valuable tool to study complex cellular heterogeneity.

Molecular and cellular evolution of the amygdala across species analyzed by single-nucleus transcriptome profiling

The amygdala, or an amygdala-like structure, is found in the brains of all vertebrates and plays a critical role in survival and reproduction. However, the cellular architecture of the amygdala and how it has evolved remain elusive. Here, we generated single-nucleus RNA-sequencing data for more than 200,000 cells in the amygdala of humans, macaques, mice, and chickens. Abundant neuronal cell types from different amygdala subnuclei were identified in all datasets. Cross-species analysis revealed that inhibitory neurons and inhibitory neuron-enriched subnuclei of the amygdala were well-conserved in cellular composition and marker gene expression, whereas excitatory neuron-enriched subnuclei were relatively divergent. Furthermore, LAMP5⁺ interneurons were much more abundant in primates, while DRD2⁺ inhibitory neurons and LAMP5⁺SATB2⁺ excitatory neurons were dominant in the human central amygdalar nucleus (CEA) and basolateral amygdalar complex (BLA), respectively. We also identified CEA-like neurons and their species-specific distribution patterns in chickens. This study highlights the extreme cell-type diversity in the amygdala and reveals the conservation and divergence of cell types and gene expression patterns across species that may contribute to species-specific adaptations.

Differential cell composition and split epidermal differentiation in human palm, sole, and hip skin

Palmoplantar skin is structurally and functionally unique, but the transcriptional programs driving this specialization are unclear. Here, we use bulk and single-cell RNA sequencing of human palm, sole, and hip skin to describe the distinguishing characteristics of palmoplantar and non-palmoplantar skin while also uncovering differences between palmar and plantar sites. Our approach reveals an altered immune environment in palmoplantar skin, with downregulation of diverse immunological processes and decreased immune cell populations. Further, we identify specific fibroblast populations that appear to orchestrate key differences in cell-cell communication in palm, sole, and hip. Dedicated keratinocyte analysis highlights major differences in basal cell fraction among the three sites and demonstrates the existence of two spinous keratinocyte populations constituting parallel, site-selective epidermal differentiation trajectories. In summary, this deep characterization of highly adapted palmoplantar skin contributes key insights into the fundamental biology of human skin and provides a valuable data resource for further investigation.