Dictionary learning for integrative, multimodal and scalable single-cell analysis

Genomic structural variation contributes to evolved changes in gene expression in high-altitude Tibetan sheep

Tibetan sheep were introduced to the Qinghai Tibet plateau roughly 3,000 B.P., making this species a good model for investigating genetic mechanisms of high-altitude adaptation over a relatively short timescale. Here, we characterize genomic structural variants (SVs) that distinguish Tibetan sheep from closely related, low-altitude Hu sheep, and we examine associated changes in tissue-specific gene expression. We document differentiation between the two sheep breeds in frequencies of SVs associated with genes involved in cardiac function and circulation. In Tibetan sheep, we identified high-frequency SVs in a total of 462 genes, including EPAS1, PAPSS2, and PTPRD. Single-cell RNA-Seq data and luciferase reporter assays revealed that the SVs had cis-acting effects on the expression levels of these three genes in specific tissues and cell types. In Tibetan sheep, we identified a high-frequency chromosomal inversion that exhibited modified chromatin architectures relative to the noninverted allele that predominates in Hu sheep. The inversion harbors several genes with altered expression patterns related to heart protection, brown adipocyte proliferation, angiogenesis, and DNA repair. These findings indicate that SVs represent an important source of genetic variation in gene expression and may have contributed to high-altitude adaptation in Tibetan sheep.

Semaglutide mitigates testicular damage in diabetes by inhibiting ferroptosis

Diabetes is linked to male infertility, but the mechanisms and therapeutic options remain unclear. This study investigates the effects of semaglutide on testicular function in a diabetes mouse model. Clinical data shows that diabetes affects blood glucose, lipid levels, and sperm quality. Single-cell and transcriptome analyses reveal changes in testicular tissue cell proportions and activation of ferroptosis pathways in diabetic patients/rats. In the diabetes mouse model, sperm quality decreases significantly. Treatment with semaglutide (Sem) and the ferroptosis inhibitor ferrostatin-1 (Fer-1) alleviates testicular damage, as evidenced by improved lipid peroxidation and ferroptosis markers. Moreover, the diabetes-induced decrease in the TM-3 cell line's vitality, increased lipid peroxidation, ROS, ferrous ions, and mitochondrial membrane potential damage are all improved by semaglutide and ferrostatin-1 intervention. Overall, these findings highlight semaglutide's potential as a therapeutic approach for mitigating diabetes-induced testicular damage through modulation of the ferroptosis pathway.

Genomic barcoding for clonal diversity monitoring and control in cell-based complex antibody production

Engineered mammalian cells are key for biotechnology by enabling broad applications ranging from in vitro model systems to therapeutic biofactories. Engineered cell lines exist as a population containing sub-lineages of cell clones that exhibit substantial genetic and phenotypic heterogeneity. There is still a limited understanding of the source of this inter-clonal heterogeneity as well as its implications for biotechnological applications. Here, we developed a genomic barcoding strategy for a targeted integration (TI)-based CHO antibody producer cell line development process. This technology provided novel insights about clone diversity during stable cell line selection on pool level, enabled an imaging-independent monoclonality assessment after single cell cloning, and eventually improved hit-picking of antibody producer clones by monitoring of cellular lineages during the cell line development (CLD) process. Specifically, we observed that CHO producer pools generated by TI of two plasmids at a single genomic site displayed a low diversity (< 0.1% RMCE efficiency), which further depends on the expressed molecules, and underwent rapid population skewing towards dominant clones during routine cultivation. Clonal cell lines from one individual TI event demonstrated a significantly lower variance regarding production-relevant and phenotypic parameters as compared to cell lines from distinct TI events. This implies that the observed cellular diversity lies within pre-existing cell-intrinsic factors and that the majority of clonal variation did not develop during the CLD process, especially during single cell cloning. Using cellular barcodes as a proxy for cellular diversity, we improved our CLD screening workflow and enriched diversity of production-relevant parameters substantially. This work, by enabling clonal diversity monitoring and control, paves the way for an economically valuable and data-driven CLD process.

Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease

Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.

scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis

Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method designed to reconstruct single-cell spatial neighborhoods and facilitate 3D tissue visualization using spatial and single-cell RNA sequencing data. scHolography employs a high-dimensional transcriptome-to-space projection that infers spatial relationships among cells, defining spatial neighborhoods and enhancing analyses of cell-cell communication. When applied to both human and mouse datasets, scHolography enables quantitative assessments of spatial cell neighborhoods, cell-cell interactions, and tumor-immune microenvironment. Together, scHolography offers a robust computational framework for elucidating 3D tissue organization and analyzing spatial dynamics at the cellular level.

snRNA-seq of human cutaneous neurofibromas before and after selumetinib treatment implicates role of altered Schwann cell states, inter-cellular signaling, and extracellular matrix in treatment response

Neurofibromatosis Type 1 (NF1) is caused by loss of function variants in the NF1 gene. Most patients with NF1 develop skin lesions called cutaneous neurofibromas (cNFs). Currently the only approved therapeutic for NF1 is selumetinib, a mitogen -activated protein kinase (MEK) inhibitor. The purpose of this study was to analyze the transcriptome of cNF tumors before and on selumetinib treatment to understand both tumor composition and response. We obtained biopsy sets of tumors both pre- and on- selumetinib treatment from the same individuals and were able to collect sets from four separate individuals. We sequenced mRNA from 5844 nuclei and identified 30,442 genes in the untreated group and sequenced 5701 nuclei and identified 30,127 genes in the selumetinib treated group. We identified and quantified distinct populations of cells (Schwann cells, fibroblasts, pericytes, myeloid cells, melanocytes, keratinocytes, and two populations of endothelial cells). While we anticipated that cell proportions might change with treatment, we did not identify any one cell population that changed significantly, likely due to an inherent level of variability between tumors. We also evaluated differential gene expression based on drug treatment in each cell type. Ingenuity pathway analysis (IPA) was also used to identify pathways that differ on treatment. As anticipated, we identified a significant decrease in ERK/MAPK signaling in cells including Schwann cells but most specifically in myeloid cells. Interestingly, there is a significant decrease in opioid signaling in myeloid and endothelial cells; this downward trend is also observed in Schwann cells and fibroblasts. Cell communication was assessed by RNA velocity, Scriabin, and CellChat analyses which indicated that Schwann cells and fibroblasts have dramatically altered cell states defined by specific gene expression signatures following treatment (RNA velocity). There are dramatic changes in receptor-ligand pairs following treatment (Scriabin), and robust intercellular signaling between virtually all cell types associated with extracellular matrix (ECM) pathways (Collagen, Laminin, Fibronectin, and Nectin) is downregulated after treatment. These response specific gene signatures and interaction pathways could provide clues for understanding treatment outcomes or inform future therapies.

Novel hypoxia- and lactate metabolism-related molecular subtyping and prognostic signature for colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a serious global health burden because of its high morbidity and mortality rates. Hypoxia and massive lactate production are hallmarks of the CRC microenvironment. However, the effects of hypoxia and lactate metabolism on CRC have not been fully elucidated. This study aimed to develop a novel molecular subtyping based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs) and construct a signature to predict the prognosis of patients with CRC and treatment efficacy.

METHODS

Bulk and single-cell RNA-sequencing and clinical data of CRC were downloaded from the TCGA and GEO databases. HRGs and LMRGs were obtained from the Molecular Signatures Database. The R software package DESeq2 was used to perform differential expression analysis. Molecular subtyping was performed using unsupervised clustering. A predictive signature was developed using univariate Cox regression, random forest model, LASSO, and multivariate Cox regression analyses. Finally, the sensitivity of tumor cells to chemotherapeutic agents before and after hypoxia was verified using in vitro experiments.

RESULTS

We classified 575 patients with CRC into three molecular subtypes and were able to distinguish their prognoses clearly. The C1 subtype, which exhibits high levels of hypoxia, has a low proportion of CD8 + T cells and a high proportion of macrophages. The expression of immune checkpoint genes is generally elevated in C1 patients with severe immune dysfunction. Subsequently, we constructed a predictive model, the HLM score, which effectively predicts the prognosis of patients with CRC and the efficacy of immunotherapy. The HLM score was validated in GSE39582, GSE106584, GSE17536, and IMvigor210 datasets. Patients with high HLM scores exhibit high infiltration of CD8 + exhausted T cells (Tex), especially terminal Tex, and oxidative phosphorylation (OXPHOS)-Tex in the immune microenvironment. Finally, in vitro experiments confirmed that CRC cell lines were less sensitive to 5-fluorouracil, oxaliplatin, and irinotecan under hypoxic conditions.

CONCLUSION

We constructed novel hypoxia- and lactate metabolism-related molecular subtypes and revealed their immunological and genetic characteristics. We also developed an HLM scoring system that could be used to predict the prognosis and efficacy of immunotherapy in patients with CRC.

The primitive endoderm supports lineage plasticity to enable regulative development

Mammalian blastocyst formation involves the specification of the trophectoderm followed by the differentiation of the inner cell mass into embryonic epiblast and extra-embryonic primitive endoderm (PrE). During this time, the embryo maintains a window of plasticity and can redirect its cellular fate when challenged experimentally. In this context, we found that the PrE alone was sufficient to regenerate a complete blastocyst and continue post-implantation development. We identify an in vitro population similar to the early PrE in vivo that exhibits the same embryonic and extra-embryonic potency and can form complete stem cell-based embryo models, termed blastoids. Commitment in the PrE is suppressed by JAK/STAT signaling, collaborating with OCT4 and the sustained expression of a subset of pluripotency-related transcription factors that safeguard an enhancer landscape permissive for multi-lineage differentiation. Our observations support the notion that transcription factor persistence underlies plasticity in regulative development and highlight the importance of the PrE in perturbed development.

Primitive macrophages enable long-term vascularization of human heart-on-a-chip platforms

The intricate anatomical structure and high cellular density of the myocardium complicate the bioengineering of perfusable vascular networks within cardiac tissues. In vivo neonatal studies highlight the key role of resident cardiac macrophages in post-injury regeneration and angiogenesis. Here, we integrate human pluripotent stem-cell-derived primitive yolk-sac-like macrophages within vascularized heart-on-chip platforms. Macrophage incorporation profoundly impacted the functionality and perfusability of microvascularized cardiac tissues up to 2 weeks of culture. Macrophages mitigated tissue cytotoxicity and the release of cell-free mitochondrial DNA (mtDNA), while upregulating the secretion of pro-angiogenic, matrix remodeling, and cardioprotective cytokines. Bulk RNA sequencing (RNA-seq) revealed an upregulation of cardiac maturation and angiogenesis genes. Further, single-nuclei RNA sequencing (snRNA-seq) and secretome data suggest that macrophages may prime stromal cells for vascular development by inducing insulin like growth factor binding protein 7 (IGFBP7) and hepatocyte growth factor (HGF) expression. Our results underscore the vital role of primitive macrophages in the long-term vascularization of cardiac tissues, offering insights for therapy and advancing heart-on-a-chip technologies.

A single-cell atlas of pig gastrulation as a resource for comparative embryology

Cell-fate decisions during mammalian gastrulation are poorly understood outside of rodent embryos. The embryonic disc of pig embryos mirrors humans, making them a useful proxy for studying gastrulation. Here we present a single-cell transcriptomic atlas of pig gastrulation, revealing cell-fate emergence dynamics, as well as conserved and divergent gene programs governing early porcine, primate, and murine development. We highlight heterochronicity in extraembryonic cell-types, despite the broad conservation of cell-type-specific transcriptional programs. We apply these findings in combination with functional investigations, to outline conserved spatial, molecular, and temporal events during definitive endoderm specification. We find early FOXA2 + /TBXT- embryonic disc cells directly form definitive endoderm, contrasting later-emerging FOXA2/TBXT+ node/notochord progenitors. Unlike mesoderm, none of these progenitors undergo epithelial-to-mesenchymal transition. Endoderm/Node fate hinges on balanced WNT and hypoblast-derived NODAL, which is extinguished upon endodermal differentiation. These findings emphasise the interplay between temporal and topological signalling in fate determination during gastrulation.

Chemokine-mediated cell migration into the central nervous system in progressive multifocal leukoencephalopathy

Progressive multifocal leukoencephalopathy (PML) has been associated with different forms of immune compromise. This study analyzes the chemokine signals and attracted immune cells in cerebrospinal fluid (CSF) during PML to define immune cell subpopulations relevant for the PML immune response. In addition to chemokines that indicate a general state of inflammation, like CCL5 and CXCL10, the CSF of PML patients specifically contains CCL2 and CCL4. Single-cell transcriptomics of CSF cells suggests an enrichment of distinct CD4+ and CD8+ T cells expressing chemokine receptors CCR2, CCR5, and CXCR3, in addition to ITGA4 and the genetic PML risk genes STXBP2 and LY9. This suggests that specific immune cell subpopulations migrate into the central nervous system to mitigate PML, and their absence might coincide with PML development. Monitoring them might hold clues for PML risk, and boosting their recruitment or function before therapeutic immune reconstitution might improve its risk-benefit ratio.

A computational approach to assessing the prognostic implications of BRAF and RAS mutations in patients with papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer, posing a growing clinical challenge. PTC exhibits two age-related peaks, with established risk factors including family history and radiation exposure. Managing even low-risk, localized PTC cases remain complex, with growing interest in active surveillance as an alternative to immediate surgery. This study employed single-cell RNA sequencing (scRNA-Seq) to explore the predictive value of BRAF and RAS mutations in PTC, shedding light on their impact on disease progression and outcomes. The analyses emphasized the significance of BRAF and RAS mutations in tumor advancement, particularly the unique BRAF V600E mutation associated with aggressive features. The methodology involved scRNA-Seq analysis of PTC and normal samples, unveiling distinct cell clusters and indicating upregulated BRAF and RAS genes. Pathway enrichment analysis highlighted altered biological processes and immune-related pathways in PTC. The study consolidated previous research showing the prevalence of BRAF and RAS mutations in PTC, subtypes with distinct molecular profiles, and the impact of TERT promoter mutations on disease severity. In summary, this study unveils the complex interplay of genetic mutations and the cellular microenvironment in PTC through scRNA-Seq. The upregulated BRAF and RAS genes suggest their roles as PTC drivers, and pathway enrichment reveals alterations in immune-related processes. This synthesis of prior research enhances our understanding of PTC's molecular foundations, informing better prognosis and personalized treatment approaches. These insights advance the landscape of PTC management and provide directions for further research.

A microdeletion event at 19q13.43 in IDH-mutant astrocytomas is strongly correlated with MYC overexpression

MYC dysregulation is pivotal in the onset and progression of IDH-mutant gliomas, mostly driven by copy-number alterations, regulatory element alterations, or epigenetic changes. Our pilot analysis uncovered instances of relative MYC overexpression without alterations in the proximal MYC network (PMN), prompting a deeper investigation into potential novel oncogenic mechanisms. Analysing comprehensive genomics profiles of 236 "IDH-mutant 1p/19q non-co-deleted" lower-grade gliomas from The Cancer Genome Atlas, we identified somatic genomic alterations within the PMN. In tumours without PMN-alterations but with MYC-overexpression, genes correlated with MYC-overexpression were identified. Our analyses yielded that 86/236 of astrocytomas exhibited no PMN-alterations, a subset of 21/86 displaying relative MYC overexpression. Within this subset, we discovered 42 genes inversely correlated with relative MYC expression, all on 19q. Further analysis pinpointed a minimal common region at 19q13.43, encompassing 15 genes. The inverse correlations of these 15 genes with relative MYC overexpression were re-confirmed using independent scRNAseq data. Further, the micro-deleted astrocytoma subset displayed significantly higher genomic instability compared to WT cases, but lower instability compared to PMN-hit cases. This newly identified 19q micro-deletion represents a potential novel mechanism underlying MYC dysregulation in astrocytomas. Given the prominence of 19q loss in IDH-mutant gliomas, our findings bear significant implications for understanding gliomagenesis.

Neuronal identity control at the resolution of a single transcription factor isoform

The brain exhibits remarkable neuronal diversity which is critical for its functional integrity. From the sheer number of cell types emerging from extensive transcriptional, morphological, and connectome datasets, the question arises of how the brain is capable of generating so many unique identities. 'Terminal selectors' are transcription factors hypothesized to determine the final identity characteristics in post-mitotic cells. Which transcription factors function as terminal selectors and the level of control they exert over different terminal characteristics are not well defined. Here, we establish a novel role for the transcription factor broad as a terminal selector in Drosophila melanogaster . We capitalize on existing large sequencing and connectomics datasets and employ a comprehensive characterization of terminal characteristics including Perturb-seq and whole-cell electrophysiology. We find a single isoform broad-z4 serves as the switch between the identity of two visual projection neurons LPLC1 and LPLC2. Broad-z4 is natively expressed in LPLC1, and is capable of transforming the transcriptome, morphology, and functional connectivity of LPLC2 cells into LPLC1 cells when perturbed. Our comprehensive work establishes a single isoform as the smallest unit underlying an identity switch, which may serve as a conserved strategy replicated across developmental programs.

Eomes expression identifies the early bone marrow precursor to classical NK cells

Natural killer (NK) cells traffic through the blood and mount cytolytic and interferon-γ (IFNγ)-focused responses to intracellular pathogens and tumors. Type 1 innate lymphoid cells (ILC1s) also produce type 1 cytokines but reside in tissues and are not cytotoxic. Whether these differences reflect discrete lineages or distinct states of a common cell type is not understood. Using single-cell RNA sequencing and flow cytometry, we focused on populations of TCF7+ cells that contained precursors for NK cells and ILC1s and identified a subset of bone marrow lineage-negative NK receptor-negative cells that expressed the transcription factor Eomes, termed EomeshiNKneg cells. Transfer of EomeshiNKneg cells into Rag2-/-Il2rg-/- recipients generated functional NK cells capable of preventing metastatic disease. By contrast, transfer of PLZF+ ILC precursors generated a mixture of ILC1s, ILC2s and ILC3s that lacked cytotoxic potential. These findings identified EomeshiNKneg cells as the bone marrow precursor to classical NK cells and demonstrated that the NK and ILC1 lineages diverged early during development.

Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies

Motivation

Biomarker detection plays a pivotal role in biomedical research. Integrating omics studies from multiple cohorts can enhance statistical power, accuracy and robustness of the detection results. However, existing methods for horizontally combining omics studies are mostly designed for two-class scenarios (e.g., cases versus controls) and are not directly applicable for studies with multi-class design (e.g., samples from multiple disease subtypes, treatments, tissues, or cell types).

Results

We propose a statistical framework, namely Mutual Information Concordance Analysis (MICA), to detect biomarkers with concordant multi-class expression pattern across multiple omics studies from an information theoretic perspective. Our approach first detects biomarkers with con-cordant multi-class patterns across partial or all of the omics studies using a global test by mutual information. A post hoc analysis is then performed for each detected biomarkers and identify studies with concordant pattern. Extensive simulations demonstrate improved accuracy and successful false discovery rate control of MICA compared to an existing MCC method. The method is then applied to two practical scenarios: four tissues of mouse metabolism-related transcriptomic studies, and three sources of estrogen treatment expression profiles. Detected biomarkers by MICA show intriguing biological insights and functional annotations. Additionally, we implemented MICA for single-cell RNA-Seq data for tumor progression biomarkers, highlighting critical roles of ribosomal function in the tumor microenvironment of triple-negative breast cancer and underscoring the potential of MICA for detecting novel therapeutic targets.

Availability

https://github.com/jianzou75/MICA.

Sopa: a technology-invariant pipeline for analyses of image-based spatial omics

Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents analytical challenges and demands substantial computing resources. Moreover, the proliferation of diverse spatial omics technologies, such as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, and MACSima and PhenoCycler in multiplex imaging, hinders the generality of existing tools. We introduce Sopa ( https://github.com/gustaveroussy/sopa ), a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics. Built upon the universal SpatialData framework, Sopa optimizes tasks like segmentation, transcript/channel aggregation, annotation, and geometric/spatial analysis. Its output includes user-friendly web reports and visualizer files, as well as comprehensive data files for in-depth analysis. Overall, Sopa represents a significant step toward unifying spatial data analysis, enabling a more comprehensive understanding of cellular interactions and tissue organization in biological systems.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

The temporal progression of lung immune remodeling during breast cancer metastasis

Tumor metastasis requires systemic remodeling of distant organ microenvironments that impacts immune cell phenotypes, population structure, and intercellular communication. However, our understanding of immune phenotypic dynamics in the metastatic niche remains incomplete. Here, we longitudinally assayed lung immune transcriptional profiles in the polyomavirus middle T antigen (PyMT) and 4T1 metastatic breast cancer models from primary tumorigenesis, through pre-metastatic niche formation, to the final stages of metastatic outgrowth at single-cell resolution. Computational analyses of these data revealed a TLR-NFκB inflammatory program enacted by both peripherally derived and tissue-resident myeloid cells that correlated with pre-metastatic niche formation and mirrored CD14+ "activated" myeloid cells in the primary tumor. Moreover, we observed that primary tumor and metastatic niche natural killer (NK) cells are differentially regulated in mice and human patient samples, with the metastatic niche featuring elevated cytotoxic NK cell proportions. Finally, we identified cell-type-specific dynamic regulation of IGF1 and CCL6 signaling during metastatic progression that represents anti-metastatic immunotherapy candidate pathways.

Molecular and spatial transcriptomic classification of midbrain dopamine neurons and their alterations in a LRRK2G2019S model of Parkinson's disease

Several studies have revealed that midbrain dopamine (DA) neurons, even within a single neuroanatomical area, display heterogeneous properties. In parallel, studies using single cell profiling techniques have begun to cluster DA neurons into subtypes based on their molecular signatures. Recent work has shown that molecularly defined DA subtypes within the substantia nigra (SNc) display distinctive anatomic and functional properties, and differential vulnerability in Parkinson's disease (PD). Based on these provocative results, a granular understanding of these putative subtypes and their alterations in PD models, is imperative. We developed an optimized pipeline for single-nuclear RNA sequencing (snRNA-seq) and generated a high-resolution hierarchically organized map revealing 20 molecularly distinct DA neuron subtypes belonging to three main families. We integrated this data with spatial MERFISH technology to map, with high definition, the location of these subtypes in the mouse midbrain, revealing heterogeneity even within neuroanatomical sub-structures. Finally, we demonstrate that in the preclinical LRRK2G2019S knock-in mouse model of PD, subtype organization and proportions are preserved. Transcriptional alterations occur in many subtypes including those localized to the ventral tier SNc, where differential expression is observed in synaptic pathways, which might account for previously described DA release deficits in this model. Our work provides an advancement of current taxonomic schemes of the mouse midbrain DA neuron subtypes, a high-resolution view of their spatial locations, and their alterations in a prodromal mouse model of PD.

Infiltrating lipid-rich macrophage subpopulations identified as a regulator of increasing prostate size in human benign prostatic hyperplasia

Macrophages exhibit marked phenotypic heterogeneity within and across disease states, with lipid metabolic reprogramming contributing to macrophage activation and heterogeneity. Chronic inflammation has been observed in human benign prostatic hyperplasia (BPH) tissues, however macrophage activation states and their contributions to this hyperplastic disease have not been defined. We postulated that a shift in macrophage phenotypes with increasing prostate size could involve metabolic alterations resulting in prostatic epithelial or stromal hyperplasia. Single-cell RNA-seq of CD45 + transition zone leukocytes from 10 large (>90 grams) and 10 small (<40 grams) human prostates was conducted. Macrophage subpopulations were defined using marker genes. BPH macrophages do not distinctly categorize into M1 and M2 phenotypes. Instead, macrophages with neither polarization signature preferentially accumulate in large versus small prostates. Specifically, macrophage subpopulations with altered lipid metabolism pathways, demarcated by TREM2 and MARCO expression, significantly accumulate with increased prostate volume. TREM2 + and MARCO + macrophage abundance positively correlates with patient body mass index and urinary symptom scores. TREM2 + macrophages have significantly higher neutral lipid than TREM2 - macrophages from BPH tissues. Lipid-rich macrophages were observed to localize within the stroma in BPH tissues. In vitro studies indicate that lipid-loaded macrophages increase prostate epithelial and stromal cell proliferation compared to control macrophages. These data define two new BPH immune subpopulations, TREM2 + and MARCO + macrophages, and suggest that lipid-rich macrophages may exacerbate lower urinary tract symptoms in patients with large prostates. Further investigation is needed to evaluate the therapeutic benefit of targeting these cells in BPH.

Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression

Motivation

Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This study describes expression-aware demultiplexing (EAD), a computational method that employs differential co-expression patterns between individuals to demultiplex pooled samples without any extra experimental steps.

Results

We use synthetic sample pools and show that the top interindividual differentially co-expressed genes provide a distinct cluster of cells per individual, significantly enriching the regulation of metabolism. Our application of EAD to samples of six isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve interindividual variations related to metabolic pathways. We utilized 30 samples from both sepsis and healthy individuals in six batches to assess the performance of classification approaches. The results indicate that combining genetic and EAD results can enhance the accuracy of assignments (Min. 0.94, Mean 0.98, Max. 1). The results were enhanced by an average of 1.4% when EAD and barcoding techniques were combined (Min. 1.25%, Median 1.33%, Max. 1.74%). Furthermore, we demonstrate that interindividual differential co-expression analysis within the same cell type can be used to identify cells from the same donor in different activation states. By analysing single-nuclei transcriptome profiles from the brain, we demonstrate that our method can be applied to nonimmune cells.

Availability and implementation

EAD workflow is available at https://isarnassiri.github.io/scDIV/ as an R package called scDIV (acronym for single-cell RNA-sequencing data demultiplexing using interindividual variations).

Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging

Non-hematopoietic cells are essential contributors to hematopoiesis. However, heterogeneity and spatial organization of these cells in human bone marrow remain largely uncharacterized. We used single-cell RNA sequencing (scRNA-seq) to profile 29,325 non-hematopoietic cells and discovered nine transcriptionally distinct subtypes. We simultaneously profiled 53,417 hematopoietic cells and predicted their interactions with non-hematopoietic subsets. We employed co-detection by indexing (CODEX) to spatially profile over 1.2 million cells. We integrated scRNA-seq and CODEX data to link predicted cellular signaling with spatial proximity. Our analysis revealed a hyperoxygenated arterio-endosteal neighborhood for early myelopoiesis, and an adipocytic localization for early hematopoietic stem and progenitor cells (HSPCs). We used our CODEX atlas to annotate new images and uncovered mesenchymal stromal cell (MSC) expansion and spatial neighborhoods co-enriched for leukemic blasts and MSCs in acute myeloid leukemia (AML) patient samples. This spatially resolved, multiomic atlas of human bone marrow provides a reference for investigation of cellular interactions that drive hematopoiesis.

Identifying in vivo genetic dependencies of melanocyte and melanoma development

The advent of large-scale sequencing in both development and disease has identified large numbers of candidate genes that may be linked to important phenotypes. Validating the function of these candidates in vivo is challenging, due to low efficiency and low throughput of most model systems. We have developed a rapid, scalable system for assessing the role of candidate genes using zebrafish. We generated transgenic zebrafish in which Cas9 was knocked-in to the endogenous mitfa locus, a master transcription factor of the melanocyte lineage. We used this system to identify both cell-autonomous and non-cell autonomous regulators of normal melanocyte development. We then applied this to the melanoma setting to demonstrate that loss of genes required for melanocyte survival can paradoxically promote more aggressive phenotypes, highlighting that in vitro screens can mask in vivo phenotypes. Our high-efficiency genetic approach offers a versatile tool for exploring developmental processes and disease mechanisms that can readily be applied to other cell lineages.

Liam tackles complex multimodal single-cell data integration challenges

Multi-omics characterization of single cells holds outstanding potential for profiling the dynamics and relations of gene regulatory states of thousands of cells. How to integrate multimodal data is an open problem, especially when aiming to combine data from multiple sources or conditions containing both biological and technical variation. We introduce liam, a flexible model for the simultaneous horizontal and vertical integration of paired single-cell multimodal data and mosaic integration of paired with unimodal data. Liam learns a joint low-dimensional representation of the measured modalities, which proves beneficial when the information content or quality of the modalities differ. Its integration accounts for complex batch effects using a tunable combination of conditional and adversarial training, which can be optimized using replicate information while retaining selected biological variation. We demonstrate liam's superior performance on multiple paired multimodal data types, including Multiome and CITE-seq data, and in mosaic integration scenarios. Our detailed benchmarking experiments illustrate the complexities and challenges remaining for integration and the meaningful assessment of its success.

Modeling blood-brain barrier formation and cerebral cavernous malformations in human PSC-derived organoids

The human blood-brain barrier (hBBB) is a highly specialized structure that regulates passage across blood and central nervous system (CNS) compartments. Despite its critical physiological role, there are no reliable in vitro models that can mimic hBBB development and function. Here, we constructed hBBB assembloids from brain and blood vessel organoids derived from human pluripotent stem cells. We validated the acquisition of blood-brain barrier (BBB)-specific molecular, cellular, transcriptomic, and functional characteristics and uncovered an extensive neuro-vascular crosstalk with a spatial pattern within hBBB assembloids. When we used patient-derived hBBB assembloids to model cerebral cavernous malformations (CCMs), we found that these assembloids recapitulated the cavernoma anatomy and BBB breakdown observed in patients. Upon comparison of phenotypes and transcriptome between patient-derived hBBB assembloids and primary human cavernoma tissues, we uncovered CCM-related molecular and cellular alterations. Taken together, we report hBBB assembloids that mimic the core properties of the hBBB and identify a potentially underlying cause of CCMs.

Characterization of a Human Respiratory Mucosa Model to Study Odorant Metabolism

Nasal xenobiotic metabolizing enzymes (XMEs) are important for the sense of smell because they influence odorant availability and quality. Since the major part of the human nasal cavity is lined by a respiratory mucosa, we hypothesized that this tissue contributed to nasal odorant metabolism through XME activity. Thus, we built human respiratory tissue models and characterized the XME profiles using single-cell RNA sequencing. We focused on the XMEs dicarbonyl and l-xylulose reductase, aldehyde dehydrogenase (ALDH) 1A1, and ALDH3A1, which play a role in food odorant metabolism. We demonstrated protein abundance and localization in the tissue models and showed the metabolic activity of the corresponding enzyme families by exposing the models to the odorants 3,4-hexandione and benzaldehyde. Using gas chromatography coupled with mass spectrometry, we observed, for example, a significantly higher formation of the corresponding metabolites 4-hydroxy-3-hexanone (39.03 ± 1.5%, p = 0.0022), benzyl alcohol (10.05 ± 0.88%, p = 0.0008), and benzoic acid (8.49 ± 0.57%, p = 0.0004) in odorant-treated tissue models compared to untreated controls (0 ± 0, 0.12 ± 0.12, and 0.18 ± 0.18%, respectively). This is the first study that reveals the XME profile of tissue-engineered human respiratory mucosa models and demonstrates their suitability to study nasal odorant metabolism.

Single-cell and spatial transcriptomics reveal a high glycolysis B cell and tumor-associated macrophages cluster correlated with poor prognosis and exhausted immune microenvironment in diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous malignancy characterized by varied responses to treatment and prognoses. Understanding the metabolic characteristics driving DLBCL progression is crucial for developing personalized therapies.

METHODS

This study utilized multiple omics technologies including single-cell transcriptomics (n = 5), bulk transcriptomics (n = 966), spatial transcriptomics (n = 10), immunohistochemistry (n = 34), multiple immunofluorescence (n = 20) and to elucidate the metabolic features of highly malignant DLBCL cells and tumor-associated macrophages (TAMs), along with their associated tumor microenvironment. Metabolic pathway analysis facilitated by scMetabolism, and integrated analysis via hdWGCNA, identified glycolysis genes correlating with malignancy, and the prognostic value of glycolysis genes (STMN1, ENO1, PKM, and CDK1) and TAMs were verified.

RESULTS

High-glycolysis malignant DLBCL tissues exhibited an immunosuppressive microenvironment characterized by abundant IFN_TAMs (CD68+CXCL10+PD-L1+) and diminished CD8+ T cell infiltration. Glycolysis genes were positively correlated with malignancy degree. IFN_TAMs exhibited high glycolysis activity and closely communicating with high-malignancy DLBCL cells identified within datasets. The glycolysis score, evaluated by seven genes, emerged as an independent prognostic factor (HR = 1.796, 95% CI: 1.077-2.995, p = 0.025 and HR = 2.631, 95% CI: 1.207-5.735, p = 0.015) along with IFN_TAMs were positively correlated with poor survival (p < 0.05) in DLBCL. Immunohistochemical validation of glycolysis markers (STMN1, ENO1, PKM, and CDK1) and multiple immunofluorescence validation of IFN_TAMs underscored their prognostic value (p < 0.05) in DLBCL.

CONCLUSIONS

This study underscores the significance of glycolysis in tumor progression and modulation of the immune microenvironment. The identified glycolysis genes and IFN_TAMs represent potential prognostic markers and therapeutic targets in DLBCL.

Intrinsic Gata4 expression sensitizes the aortic root to dilation in a Loeys-Dietz syndrome mouse model

Loeys-Dietz syndrome (LDS) is an aneurysm disorder caused by mutations that decrease transforming growth factor-β (TGF-β) signaling. Although aneurysms develop throughout the arterial tree, the aortic root is a site of heightened risk. To identify molecular determinants of this vulnerability, we investigated the heterogeneity of vascular smooth muscle cells (VSMCs) in the aorta of Tgfbr1 M318R/+ LDS mice by single cell and spatial transcriptomics. Reduced expression of components of the extracellular matrix-receptor apparatus and upregulation of stress and inflammatory pathways were observed in all LDS VSMCs. However, regardless of genotype, a subset of Gata4-expressing VSMCs predominantly located in the aortic root intrinsically displayed a less differentiated, proinflammatory profile. A similar population was also identified among aortic VSMCs in a human scRNAseq dataset. Postnatal VSMC-specific Gata4 deletion reduced aortic root dilation in LDS mice, suggesting that this factor sensitizes the aortic root to the effects of impaired TGF-β signaling.

Single-cell and spatiotemporal profile of ovulation in the mouse ovary

Ovulation is a spatiotemporally coordinated process that involves several tightly controlled events, including oocyte meiotic maturation, cumulus expansion, follicle wall rupture and repair, and ovarian stroma remodeling. To date, no studies have detailed the precise window of ovulation at single-cell resolution. Here, we performed parallel single-cell RNA-seq and spatial transcriptomics on paired mouse ovaries across an ovulation time course to map the spatiotemporal profile of ovarian cell types. We show that major ovarian cell types exhibit time-dependent transcriptional states enriched for distinct functions and have specific localization profiles within the ovary. We also identified gene markers for ovulation-dependent cell states and validated these using orthogonal methods. Finally, we performed cell-cell interaction analyses to identify ligand-receptor pairs that may drive ovulation, revealing previously unappreciated interactions. Taken together, our data provides a rich and comprehensive resource of murine ovulation that can be mined for discovery by the scientific community.

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

APOE Christchurch enhances a disease-associated microglial response to plaque but suppresses response to tau pathology

Background

Apolipoprotein E ε4 (APOE4) is the strongest genetic risk factor for late-onset Alzheimer's disease (LOAD). A recent case report identified a rare variant in APOE, APOE3-R136S (Christchurch), proposed to confer resistance to autosomal dominant Alzheimer's Disease (AD). However, it remains unclear whether and how this variant exerts its protective effects.

Methods

We introduced the R136S variant into mouse Apoe ( ApoeCh ) and investigated its effect on the development of AD-related pathology using the 5xFAD model of amyloidosis and the PS19 model of tauopathy. We used immunohistochemical and biochemical analysis along with single-cell spatial transcriptomics and proteomics to explore the impact of the ApoeCh variant on AD pathological development and the brain's response to plaques and tau.

Results

In 5xFAD mice, ApoeCh enhances a Disease-Associated Microglia (DAM) phenotype in microglia surrounding plaques, and reduces plaque load, dystrophic neurites, and plasma neurofilament light chain. By contrast, in PS19 mice, ApoeCh suppresses the microglial and astrocytic responses to tau-laden neurons and does not reduce tau accumulation or phosphorylation, but partially rescues tau-induced synaptic and myelin loss. We compared how microglia responses differ between the two mouse models to elucidate the distinct DAM signatures induced by ApoeCh . We identified upregulation of antigen presentation-related genes in the DAM response in a PS19 compared to a 5xFAD background, suggesting a differential response to amyloid versus tau pathology that is modulated by the presence of ApoeCh .

Conclusions

These findings highlight the ability of the ApoeCh variant to modulate microglial responses based on the type of pathology, enhancing DAM reactivity in amyloid models and dampening neuroinflammation to promote protection in tau models. This suggests that the Christchurch variant's protective effects likely involve multiple mechanisms, including changes in receptor binding and microglial programming.

Changes in the cellular makeup of motor patterning circuits drive courtship song evolution in Drosophila

How evolutionary changes in genes and neurons encode species variation in complex motor behaviors is largely unknown. Here, we develop genetic tools that permit a neural circuit comparison between the model species Drosophila melanogaster and the closely related species D. yakuba, which has undergone a lineage-specific loss of sine song, one of the two major types of male courtship song in Drosophila. Neuroanatomical comparison of song-patterning neurons called TN1 across the phylogeny demonstrates a link between the loss of sine song and a reduction both in the number of TN1 neurons and the neurites supporting the sine circuit connectivity. Optogenetic activation confirms that TN1 neurons in D. yakuba have lost the ability to drive sine song, although they have maintained the ability to drive the singing wing posture. Single-cell transcriptomic comparison shows that D. yakuba specifically lacks a cell type corresponding to TN1A neurons, the TN1 subtype that is essential for sine song. Genetic and developmental manipulation reveals a functional divergence of the sex determination gene doublesex in D. yakuba to reduce TN1 number by promoting apoptosis. Our work illustrates the contribution of motor patterning circuits and cell type changes in behavioral evolution and uncovers the evolutionary lability of sex determination genes to reconfigure the cellular makeup of neural circuits.

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT

Identifying cell types and states remains a time-consuming and error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data, using unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integration of TACIT-identified cell with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

Neuropilin-1high monocytes protect against neonatal inflammation

Neonates are susceptible to inflammatory disorders such as necrotizing enterocolitis (NEC) due to their immature immune system. The timely appearance of regulatory immune cells in early life contributes to the control of inflammation in neonates, yet the underlying mechanisms of which remain poorly understood. In this study, we identified a subset of neonatal monocytes characterized by high levels of neuropilin-1 (Nrp1), termed Nrp1high monocytes. Compared with their Nrp1low counterparts, Nrp1high monocytes displayed potent immunosuppressive activity. Nrp1 deficiency in myeloid cells aggravated the severity of NEC, whereas adoptive transfer of Nrp1high monocytes led to remission of NEC. Mechanistic studies showed that Nrp1, by binding to its ligand Sema4a, induced intracellular p38-MAPK/mTOR signaling and activated the transcription factor KLF4. KLF4 transactivated Nos2 and enhanced the production of nitric oxide (NO), a key mediator of immunosuppression in monocytes. These findings reveal an important immunosuppressive axis in neonatal monocytes and provide a potential therapeutic strategy for treating inflammatory disorders in neonates.

Single-cell mapping of cell-type specific chromatin architecture in the central nervous system

Determining how chromatin is structured in the nucleus is critical to studying its role in gene regulation. Recent advances in the analysis of single-cell chromatin architecture have considerably improved our understanding of cell-type-specific chromosome conformation and nuclear architecture. In this review, we discuss the methods used for analysis of 3D chromatin conformation, including sequencing-based methods, imaging-based techniques, and computational approaches. We further review the application of these methods in the study of the role of chromatin topology in neural development and disorders.

Nanoscale Methods for Longitudinal Extraction of Intracellular Contents

Longitudinal analysis of intracellular contents including gene and protein expression is crucial for deciphering the fundamentally dynamic nature of cells. This offers invaluable insights into complex tissue composition and behavior, and drives progress in disease diagnosis, biomarker discovery, and drug development. Traditional longitudinal analysis workflows, involving the destruction of cells at various timepoints, limit insights to singular moments and fail to account for cellular heterogeneity. Current non-destructive approaches, like temporal modeling with single-cell ribonucleic acid sequencing (RNA-seq) and live-cell fluorescence imaging, either rely on biological assumptions or possess the risk of cellular perturbation. Recent advances in nanoscale technologies for non-destructive intracellular content extraction offer a promising solution to these challenges. These novel methods work at the nanoscale to non-destructively access cellular membranes and can be broadly classified into three mechanisms: tip-facilitated aspiration, membrane-based, and probe-based methods. This perspective focuses on these emerging nanotechnologies for repeated intracellular content extraction. Their potential in longitudinal analysis is discussed, the critical requirements for effective repeated sampling are addressed, and the suitability of each technique for various applications is explored. Furthermore, unresolved challenges in repeated sampling are highlighted to encourage further research in this growing field.

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.

Aggressive high-grade NF2 mutant meningiomas downregulate oncogenic YAP signaling via the upregulation of VGLL4 and FAT3/4

Meningiomas are the most common primary brain tumors in adults. Although generally benign, a subset of meningiomas is of higher grade, shows aggressive growth behavior and recurs even after multiple surgeries. Around half of all meningiomas harbor inactivating mutations in NF2. While benign low-grade NF2 mutant meningiomas exhibit few genetic events in addition to NF2 inactivation, aggressive high-grade NF2 mutant meningiomas frequently harbor a highly aberrant genome. We and others have previously shown that NF2 inactivation leads to YAP1 activation and that YAP1 acts as the pivotal oncogenic driver in benign NF2 mutant meningiomas. Using bulk and single-cell RNA-Seq data from a large cohort of human meningiomas, we show that aggressive NF2 mutant meningiomas harbor decreased levels YAP1 activity compared to their benign counterparts. Decreased expression levels of YAP target genes are significantly associated with an increased risk of recurrence. We then identify the increased expression of the YAP1 competitor VGLL4 as well as the YAP1 upstream regulators FAT3/4 as a potential mechanism for the downregulation of YAP activity in aggressive NF2 mutant meningiomas. High expression of these genes is significantly associated with an increased risk of recurrence. In vitro, overexpression of VGLL4 resulted in the downregulation of YAP activity in benign NF2 mutant meningioma cells, confirming the direct link between VGLL4 expression and decreased levels of YAP activity observed in aggressive NF2 mutant meningiomas. Our results shed new insight on the biology of benign and aggressive NF2 mutant meningiomas and may have important implications for the efficacy of therapies targeting oncogenic YAP1 activity in NF2 mutant meningiomas.

Uncover spatially informed variations for single-cell spatial transcriptomics with STew

Motivation

The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge.

Results

We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease-relevant niches in complex tissues.

Availability and implementation

Source code and the R software tool STew are available from github.com/fanzhanglab/STew.

SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data

Multiplexed imaging data are revolutionizing our understanding of the composition and organization of tissues and tumors ("Catching up with Multiplexed Tissue Imaging," 2022). A critical aspect of such "tissue profiling" is quantifying the spatial relationships among cells at different scales from the interaction of neighboring cells to recurrent communities of cells of multiple types. This often involves statistical analysis of 107 or more cells in which up to 100 biomolecules (commonly proteins) have been measured. While software tools currently cater to the analysis of spatial transcriptomics data (Liu et al., 2022), there remains a need for toolkits explicitly tailored to the complexities of multiplexed imaging data including the need to seamlessly integrate image visualization with data analysis and exploration. We introduce SCIMAP, a Python package specifically crafted to address these challenges. With SCIMAP, users can efficiently preprocess, analyze, and visualize large datasets, facilitating the exploration of spatial relationships and their statistical significance. SCIMAP's modular design enables the integration of new algorithms, enhancing its capabilities for spatial analysis.

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 ).

TIGIT+Tfh show poor B-helper function and negatively correlate with SARS-CoV-2 antibody titre

Circulating follicular helper T cells (cTfh) can show phenotypic alterations in disease settings, including in the context of tissue-damaging autoimmune or anti-viral responses. Using severe COVID-19 as a paradigm of immune dysregulation, we have explored how cTfh phenotype relates to the titre and quality of antibody responses. Severe disease was associated with higher titres of neutralising S1 IgG and evidence of increased T cell activation. ICOS, CD38 and HLA-DR expressing cTfh correlated with serum S1 IgG titres and neutralising strength, and interestingly expression of TIGIT by cTfh showed a negative correlation. TIGIT+cTfh expressed increased IFNγ and decreased IL-17 compared to their TIGIT-cTfh counterparts, and showed reduced capacity to help B cells in vitro. Additionally, TIGIT+cTfh expressed lower levels of CD40L than TIGIT-cTfh, providing a potential explanation for their poor B-helper function. These data identify phenotypic changes in polyclonal cTfh that correlate with specific antibody responses and reveal TIGIT as a marker of cTfh with altered function.

Trans-omic profiling uncovers molecular controls of early human cerebral organoid formation

Defining the molecular networks orchestrating human brain formation is crucial for understanding neurodevelopment and neurological disorders. Challenges in acquiring early brain tissue have incentivized the use of three-dimensional human pluripotent stem cell (hPSC)-derived neural organoids to recapitulate neurodevelopment. To elucidate the molecular programs that drive this highly dynamic process, here, we generate a comprehensive trans-omic map of the phosphoproteome, proteome, and transcriptome of the exit of pluripotency and neural differentiation toward human cerebral organoids (hCOs). These data reveal key phospho-signaling events and their convergence on transcriptional factors to regulate hCO formation. Comparative analysis with developing human and mouse embryos demonstrates the fidelity of our hCOs in modeling embryonic brain development. Finally, we demonstrate that biochemical modulation of AKT signaling can control hCO differentiation. Together, our data provide a comprehensive resource to study molecular controls in human embryonic brain development and provide a guide for the future development of hCO differentiation protocols.

A multi-platform analysis of human gingival crevicular fluid reveals ferroptosis as a relevant regulated cell death mechanism during the clinical progression of periodontitis

Ferroptosis is implicated in the pathogenesis of numerous chronic-inflammatory diseases, yet its association with progressive periodontitis remains unexplored. To investigate the involvement and significance of ferroptosis in periodontitis progression, we assessed sixteen periodontitis-diagnosed patients. Disease progression was clinically monitored over twelve weeks via weekly clinical evaluations and gingival crevicular fluid (GCF) collection was performed for further analyses. Clinical metrics, proteomic data, in silico methods, and bioinformatics tools were combined to identify protein profiles linked to periodontitis progression and to explore their potential connection with ferroptosis. Subsequent western blot analyses validated key findings. Finally, a single-cell RNA sequencing (scRNA-seq) dataset (GSE164241) for gingival tissues was analyzed to elucidate cellular dynamics during periodontitis progression. Periodontitis progression was identified as occurring at a faster rate than traditionally thought. GCF samples from progressing and non-progressing periodontal sites showed quantitative and qualitatively distinct proteomic profiles. In addition, specific biological processes and molecular functions during progressive periodontitis were revealed and a set of hub proteins, including SNCA, CA1, HBB, SLC4A1, and ANK1 was strongly associated with the clinical progression status of periodontitis. Moreover, we found specific proteins - drivers or suppressors - associated with ferroptosis (SNCA, FTH1, HSPB1, CD44, and GCLC), revealing the co-occurrence of this specific type of regulated cell death during the clinical progression of periodontitis. Additionally, the integration of quantitative proteomic data with scRNA-seq analysis suggested the susceptibility of fibroblasts to ferroptosis. Our analyses reveal proteins and processes linked to ferroptosis for the first time in periodontal patients, which offer new insights into the molecular mechanisms of progressive periodontal disease. These findings may lead to novel diagnostic and therapeutic strategies.

Spatial analysis of recurrent glioblastoma reveals perivascular niche organization

Tumor evolution is driven by genetic variation; however, it is the tumor microenvironment (TME) that provides the selective pressure contributing to evolution in cancer. Despite high histopathological heterogeneity within glioblastoma (GBM), the most aggressive brain tumor, the interactions between the genetically distinct GBM cells and the surrounding TME are not fully understood. To address this, we analyzed matched primary and recurrent GBM archival tumor tissues with imaging-based techniques aimed to simultaneously evaluate tumor tissues for the presence of hypoxic, angiogenic, and inflammatory niches, extracellular matrix (ECM) organization, TERT promoter mutational status, and several oncogenic amplifications on the same slide and location. We found that the relationships between genetic and TME diversity are different in primary and matched recurrent tumors. Interestingly, the texture of the ECM, identified by label-free reflectance imaging, was predictive of single-cell genetic traits present in the tissue. Moreover, reflectance of ECM revealed structured organization of the perivascular niche in recurrent GBM, enriched in immunosuppressive macrophages. Single-cell spatial transcriptomics further confirmed the presence of the niche-specific macrophage populations and identified interactions between endothelial cells, perivascular fibroblasts, and immunosuppressive macrophages. Our results underscore the importance of GBM tissue organization in tumor evolution and highlight genetic and spatial dependencies.

Single-cell RNA analysis of chromodomain-encoding genes in mesenchymal stromal cells of the mouse dental pulp

The chromodomain helicase DNA-binding (CHD) and chromobox (CBX) families of proteins play crucial roles in cell fate decisions, differentiation, and cell proliferation in a broad variety of tissues and cell types. CHD proteins are ATP-dependent epigenetic enzymes actively engaged in transcriptional regulation, DNA replication, and DNA damage repair, whereas CBX proteins are transcriptional repressors mainly involved in the formation of heterochromatin. The pleiotropic effects of CHD and CBX proteins are largely dependent on their versatility to interact with other key components of the epigenetic and transcriptional machinery. Although the function and regulatory modes of CHD and CBX factors are well established in many cell types, little is known about their roles during osteogenic differentiation. A single-cell RNA-sequencing analysis of the mouse incisor dental pulp revealed distinct spatiotemporal expression patterns of CHD- and CBX-encoding genes within different clusters of mesenchymal stromal cells (MSCs) representing various stages of osteogenic differentiation. Additionally, genes encoding interaction partners of CHD and CBX proteins, such as subunits of the trithorax-COMPASS and polycomb chromatin remodeling complexes, exhibited differential co-expression behaviors within MSC subpopulations. Thus, CHD- and CBX-encoding genes show partially overlapping but distinct expression patterns in MSCs, suggesting their differential roles in osteogenic cell fate decisions.

Multi-omic profiling reveals the endogenous and neoplastic responses to immunotherapies in cutaneous T cell lymphoma

Cutaneous T cell lymphomas (CTCLs) are skin cancers with poor survival rates and limited treatments. While immunotherapies have shown some efficacy, the immunological consequences of administering immune-activating agents to CTCL patients have not been systematically characterized. We apply a suite of high-dimensional technologies to investigate the local, cellular, and systemic responses in CTCL patients receiving either mono- or combination anti-PD-1 plus interferon-gamma (IFN-γ) therapy. Neoplastic T cells display no evidence of activation after immunotherapy. IFN-γ induces muted endogenous immunological responses, while anti-PD-1 elicits broader changes, including increased abundance of CLA+CD39+ T cells. We develop an unbiased multi-omic profiling approach enabling discovery of immune modules stratifying patients. We identify an enrichment of activated regulatory CLA+CD39+ T cells in non-responders and activated cytotoxic CLA+CD39+ T cells in leukemic patients. Our results provide insights into the effects of immunotherapy in CTCL patients and a generalizable framework for multi-omic analysis of clinical trials.

Transcriptomic profiling and regulatory pathways of cardiac resident macrophages in aging

Cardiovascular diseases are an array of age-related disorders, and accumulating evidence suggests a link between cardiac resident macrophages (CRMs) and the age-related disorders. However, how does CRMs alter with aging remains elusive. In the present study, aged mice (20 months old) have been employed to check for their cardiac structural and functional alterations, and the changes in the proportion of CRM subsets as well, followed by sorting of CRMs, including C-C Motif Chemokine Receptor 2 (CCR2)+ and CCR2- CRMs, which were subjected to Smart-Seq. Integrated analysis of the Smart-Seq data with three publicly available single-cell RNA-seq datasets revealed that inflammatory genes were drastic upregulated for both CCR2+ and CCR2- CRMs with aging, but genes germane to wound healing were downregulated for CCR2- CRMs, suggesting the differential functions of these two subsets. More importantly, inflammatory genes involved in damage sensing, complement cascades, and phagocytosis were largely upregulated in CCR2- CRMs, implying the imbalance of inflammatory response upon aging. Our work provides a comprehensive framework and transcriptional resource for assessing the impact of aging on CRMs with a potential for further understanding cardiac aging.

Applications of single-cell RNA sequencing in spermatogenesis and molecular evolution

Spermatogenic cell heterogeneity is determined by the complex process of spermatogenesis differentiation. However, effectively revealing the regulatory mechanisms underlying mammalian spermatogenic cell development and differentiation via traditional methods is difficult. Advances in technology have led to the emergence of many single-cell transcriptome sequencing protocols, which have partially addressed these challenges. In this review, we detail the principles of 10x Genomics technology and summarize the methods for downstream analysis of single-cell transcriptome sequencing data. Furthermore, we explore the role of single-cell transcriptome sequencing in revealing the heterogeneity of testicular ecological niche cells, delineating the establishment and disruption of testicular immune homeostasis during human spermatogenesis, investigating abnormal spermatogenesis in humans, and, ultimately, elucidating the molecular evolution of mammalian spermatogenesis.