ABSTRACTThe lateral septum (LS) is a nucleus in the ventral forebrain that modulates complex social and affective behaviors. Several distinct neuronal types have been described in the LS; however, the full extent of this cellular and molecular diversity remains unclear. We address this gap by profiling the transcriptional identity of mature LS neurons originating from two progenitor lineages defined by their anatomical location and expression of the transcription factorNkx2.1. We describe 12 molecularly distinct subtypes of LS neurons that fall into two main groups: those with a history ofNkx2.1expression and those without. We discovered that LS neurons from theNkx2.1lineage share an enrichment of select cell adhesion and communication molecules. Despite this, we found that LS neurons that have distinct developmental origins can exhibit significant transcriptional similarities. We then examined the spatial relationships among neurons in the LS, revealing that each subtype occupies a discrete anatomical domain. These anatomical domains are defined by graded patterns of gene expression that correlate with the molecular taxonomy of LS neuron subtypes and encode proteins involved in synaptic signaling. Lastly, we genetically labeled non-overlapping subgroups of LS neurons, and detailed their connective, morphological, and electrophysiological properties. Our findings offer a deeper understanding of neuronal heterogeneity in the LS, paving the way for future studies into how these neuronal types contribute to regulating emotional and motivated behaviors.

Chemoresistance, a leading cause of treatment failure in cancer, is commonly explained by Darwinian selection of treatment-resistant cell clones. However, recurring resistant tumors invariably display complex phenotypes that contribute to increased malignancy, unlikely to have been selected for by chemotherapy. The growing awareness of (non - genetic) phenotypic plasticity led to the hypothesis that chemotherapy, while reducing tumor burden, also inflicts cell stress that induces stem-like states in cells that survive treatment. Here we examined the transcriptomes of HL-60 leukemic cancer cells that survived exposure to three commonly used drugs at submaximal doses for two to four days, and compared differentially expressed genes to those associated with prognosis in public transcriptome databases of acute myeloid leukemia cohorts. While among the genes differentially upregulated in surviving cells, some reflect the therapeutic effect of chemotherapy as they were associated with favorable outcomes in cohort data, many genes upregulated were associated with poor survival, notably genes involved in stemness, epithelial-mesenchymal transition (EMT), inflammation, drug resistance, and apoptosis evasion. These findings support the idea that treatment effectiveness is the net result of an intrinsic tradeoff: Cytocidal treatment, intended to quantitatively reduce cancer cells, also qualitatively increases the malignancy of non-killed cells, which could contribute to residual disease and relapse. This result has implications for drug screening of new therapeutics, as well as in vitro profiling of patient-derived tumor cell susceptibility to existing drugs, which only assess suppression of cancer cell growth and survival. Cells surviving chemotherapy upregulate many genes that may affect prognosis. Therefore, drug screening must embrace a more holistic assessment of the biological quality of cell response beyond the rate of cell killing.

Mouse lemurs (Microcebus spp.) are an emerging primate model organism, but their genetics, cellular and molecular biology remain largely unexplored. In an accompanying paper1, we performed large-scale single-cell RNA sequencing of 27 organs from mouse lemurs. We identified more than 750 molecular cell types, characterized their transcriptomic profiles and provided insight into primate evolution of cell types. Here we use the generated atlas to characterize mouse lemur genes, physiology, disease and mutations. We uncover thousands of previously unidentified lemur genes and hundreds of thousands of new splice junctions including over 85,000 primate splice junctions missing in mice. We systematically explore the lemur immune system by comparing global expression profiles of key immune genes in health and disease, and by mapping immune cell development, trafficking and activation. We characterize primate-specific and lemur-specific physiology and disease, including molecular features of the immune program, lemur adipocytes and metastatic endometrial cancer that resembles the human malignancy. We present expression patterns of more than 400 primate genes missing in mice, many with similar expression patterns to humans and some implicated in human disease. Finally, we provide an experimental framework for reverse genetic analysis by identifying naturally occurring nonsense mutations in three primate immune genes missing in mice and by analysing their transcriptional phenotypes. This work establishes a foundation for molecular and genetic analyses of mouse lemurs and prioritizes primate genes, isoforms, physiology and disease for future study.

Mycobacterium tuberculosis (MTB) ESX-1, a type VII secretion system, is a key virulence determinant contributing to MTB's survival within lung mononuclear phagocytes (MNPs), but its effect on MNP recruitment and differentiation remains unknown. Here, using multiple single-cell RNA sequencing techniques, we studied the role of ESX-1 in MNP heterogeneity and response in mice and murine bone marrow-derived macrophages (BMDM). We found that ESX-1 is required for MTB to recruit diverse MNP subsets with high MTB burden. Further, MTB induces a transcriptional signature of immune evasion in lung macrophages and BMDM in an ESX-1-dependent manner. Spatial transcriptomics revealed an up-regulation of permissive features within MTB lesions, where monocyte-derived macrophages concentrate near MTB-infected cells. Together, our findings suggest that MTB ESX-1 facilitates the recruitment and differentiation of MNPs, which MTB can infect and manipulate for survival. Our dataset across various models and methods could contribute to the broader understanding of recruited cell heterogeneity during MTB lung infection.

Rationale: Two molecular phenotypes of the acute respiratory distress syndrome (ARDS) with divergent clinical trajectories and responses to therapy have been identified. Classification as "hyperinflammatory" or "hypoinflammatory" depends on plasma biomarker profiling. Limited data are available about the differences in the pulmonary biology of the molecular phenotypes. Objectives: To identify differences in the pulmonary biology of ARDS molecular phenotypes Methods: We compared tracheal aspirate gene expression between hyperinflammatory and hypoinflammatory phenotypes in bulk RNA sequencing (RNASeq) from coronavirus disease (COVID-19) and non-COVID-19 ARDS and single-cell RNASeq from non-COVID-19 ARDS. In a subset of subjects, we also compared plasma proteomic data. Measurements and Main Results: In bulk RNASeq analyses, 1,157 genes were differentially expressed (false discovery rate < 0.1) between phenotypes in non-COVID-19 ARDS, and 85 genes were differentially expressed between phenotypes in COVID-19 ARDS. Eighteen genes were reproducibly differentially expressed between phenotypes in both cohorts, including greater expression of IL32, HSPA8, and PPP3CC in hyperinflammatory ARDS. A total of 195 pathways were reproducibly enriched across the two cohorts by gene set enrichment analysis, including greater expression of granulopoiesis, T-cell and IFN signaling, and integrated stress response pathways in hyperinflammatory ARDS. Network analysis of single-cell RNASeq in a third group of patients identified greater T-cell signaling to other immune cells in hyperinflammatory ARDS. Conclusions: Hyperinflammatory and hypoinflammatory ARDS molecular phenotypes have distinct respiratory biology. Hyperinflammatory ARDS is characterized by an increased IFN-stimulated gene expression and T-cell activation in the lungs.

Mouse lemurs are the smallest and fastest reproducing primates, as well as one of the most abundant, and they are emerging as a model organism for primate biology, behaviour, health and conservation. Although much has been learnt about their ecology and phylogeny in Madagascar and their physiology, little is known about their cellular and molecular biology. Here we used droplet-based and plate-based single-cell RNA sequencing to create Tabula Microcebus, a transcriptomic atlas of 226,000 cells from 27 mouse lemur organs opportunistically obtained from four donors clinically and histologically characterized. Using computational cell clustering, integration and expert cell annotation, we define and biologically organize more than 750 lemur molecular cell types and their full gene expression profiles. This includes cognates of most classical human cell types, including stem and progenitor cells, and differentiating cells along the developmental trajectories of spermatogenesis, haematopoiesis and other adult tissues. We also describe dozens of previously unidentified or sparsely characterized cell types. We globally compare expression profiles to define the molecular relationships of cell types across the body, and explore primate cell and gene expression evolution by comparing lemur transcriptomes to those of human, mouse and macaque. This reveals cell-type-specific patterns of primate specialization and many cell types and genes for which the mouse lemur provides a better human model than mouse 1 . The atlas provides a cellular and molecular foundation for studying this model primate and establishes a general approach for characterizing other emerging model organisms.

Artificial intelligence holds immense promise for transforming biology, yet a lack of standardized, cross domain, benchmarks undermines our ability to build robust, trustworthy models. Here, we present insights from a recent workshop that convened machine learning and computational biology experts across imaging, transcriptomics, proteomics, and genomics to tackle this gap. We identify major technical and systemic bottlenecks such as data heterogeneity and noise, reproducibility challenges, biases, and the fragmented ecosystem of publicly available resources and propose a set of recommendations for building benchmarking frameworks that can efficiently compare ML models of biological systems across tasks and data modalities. By promoting high quality data curation, standardized tooling, comprehensive evaluation metrics, and open, collaborative platforms, we aim to accelerate the development of robust benchmarks for AI driven Virtual Cells. These benchmarks are crucial for ensuring rigor, reproducibility, and biological relevance, and will ultimately advance the field toward integrated models that drive new discoveries, therapeutic insights, and a deeper understanding of cellular systems.

Cell-type classification is a crucial step in single-cell sequencing analysis. Various methods have been proposed for transferring a cell-type label from an annotated reference atlas to unannotated query datasets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness. To address this, we propose popular Vote (popV), an ensemble of prediction models with an ontology-based voting scheme. PopV achieves accurate cell-type labeling and provides uncertainty scores. In multiple case studies, popV confidently annotates the majority of cells while highlighting cell populations that are challenging to annotate by label transfer. This additional step helps to reduce the load of manual inspection, which is often a necessary component of the annotation process, and enables one to focus on the most problematic parts of the annotation, streamlining the overall annotation process. Popular Vote (popV) is a simple, ensemble popular vote approach for cell type annotation in single-cell omic data, flexibly incorporating various methods in an open-source Python framework. Across various challenging input datasets, popV offers consistent, accurate performance.

Summary Amyotrophic lateral sclerosis (ALS) is a devastating condition with very limited treatment options. It is a heterogeneous disease with complex genetics and unclear etiology, making the discovery of disease-modifying interventions very challenging. To discover novel mechanisms underlying ALS, we leverage a unique platform that combines isogenic, induced pluripotent stem cell (iPSC)-derived models of disease-causing mutations with rich phenotyping via high-content imaging and deep learning models. We introduced eight mutations that cause familial ALS (fALS) into multiple donor iPSC lines, and differentiated them into motor neurons to create multiple isogenic pairs of healthy (wild-type) and sick (mutant) motor neurons. We collected extensive high-content imaging data and used machine learning (ML) to process the images, segment the cells, and learn phenotypes. Self-supervised ML was used to create a concise embedding that captured significant, ALS-relevant biological information in these images. We demonstrate that ML models trained on core cell morphology alone can accurately predict TDP-43 mislocalization, a known phenotypic feature related to ALS. In addition, we were able to impute RNA expression from these image embeddings, in a way that elucidates molecular differences between mutants and wild-type cells. Finally, predictors leveraging these embeddings are able to distinguish between mutant and wild-type both within and across donors, defining cellular, ML-derived disease models for diverse fALS mutations. These disease models are the foundation for a novel screening approach to discover disease-modifying targets for familial ALS.

Elucidating organismal developmental processes requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by reconstructions of cellular trajectories. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate-mapping experiments. To demonstrate the versatility of our multimodal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of neuro-mesodermal progenitors (NMPs) and the origins of a joint kidney-hemangioblast progenitor population.

Mycobacterium tuberculosis (MTB) infects and replicates in lung mononuclear phagocytes (MNPs) with astounding ability to evade elimination. ESX-1, a type VII secretion system, acts as a virulence determinant that contributes to MTB's ability to survive within MNPs, but its effect on MNP recruitment and/or differentiation remains unknown. Here, using single-cell RNA sequencing, we studied the role of ESX-1 in MNP heterogeneity and response in mice and murine bone marrow-derived macrophages (BMDM). We found that ESX-1 is required for MTB to recruit diverse MNP subsets with high MTB burden. Further, MTB induces an anti-inflammatory signature in MNPs and BMDM in an ESX-1 dependent manner. Similarly, spatial transcriptomics revealed an upregulation of anti-inflammatory signals in MTB lesions, where monocyte-derived macrophages concentrate near MTB-infected cells. Together, our findings suggest that MTB ESX-1 mediates the recruitment and differentiation of anti-inflammatory MNPs, which MTB can infect and manipulate for survival.

With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.

Metastasis is the leading cause of cancer-related deaths. It is unclear how intratumor heterogeneity (ITH) contributes to metastasis and how metastatic cells adapt to distant tissue environments. The study of these adaptations is challenged by the limited access to patient material and a lack of experimental models that appropriately recapitulate ITH. To investigate metastatic cell adaptations and the contribution of ITH to metastasis, we analyzed single-cell transcriptomes of matched primary tumors and metastases from patient-derived xenograft models of breast cancer. We found profound transcriptional differences between the primary tumor and metastatic cells. Primary tumors upregulated several metabolic genes, whereas motility pathway genes were upregulated in micrometastases, and stress response signaling was upregulated during progression. Additionally, we identified primary tumor gene signatures that were associated with increased metastatic potential and correlated with patient outcomes. Immune-regulatory control pathways were enriched in poorly metastatic primary tumors, whereas genes involved in epithelial-mesenchymal transition were upregulated in highly metastatic tumors. We found that ITH was dominated by epithelial-mesenchymal plasticity (EMP), which presented as a dynamic continuum with intermediate EMP cell states characterized by specific genes such as CRYAB and S100A2. . Elevated expression of an intermediate EMP signature correlated with worse patient outcomes. Our findings identified inhibition of the intermediate EMP cell state as a potential therapeutic target to block metastasis.

Background Colitis caused by checkpoint inhibitors (CPI) is frequent and is treated with empiric steroids, but CPI colitis mechanisms in steroid-experienced or refractory disease are unclear.Methods Using colon biopsies and blood from predominantly steroid-experienced CPI colitis patients, we performed multiplexed single-cell transcriptomics and proteomics to nominate contributing populations.Results CPI colitis biopsies showed enrichment of CD4+resident memory (RM) T cells in addition to CD8+ RM and cytotoxic CD8+ T cells. Matching T cell receptor (TCR) clonotypes suggested that both RMs are progenitors that yield cytotoxic effectors. Activated, CD38+ HLA-DR+ CD4+ RM and cytotoxic CD8+ T cells were enriched in steroid-experienced and a validation data set of steroid-naïve CPI colitis, underscoring their pathogenic potential across steroid exposure. Distinct from ulcerative colitis, CPI colitis exhibited perturbed stromal metabolism (NAD+, tryptophan) impacting epithelial survival and inflammation. Endothelial cells in CPI colitis after anti-TNF and anti-cytotoxic T-lymphocyte-associated antigen 4 (anti-CTLA-4) upregulated the integrin α4β7 ligand molecular vascular addressin cell adhesion molecule 1 (MAdCAM-1), which may preferentially respond to vedolizumab (anti-α4β7).Conclusions These findings nominate CD4+ RM and MAdCAM-1+ endothelial cells for targeting in specific subsets of CPI colitis patients.

Ulcerative colitis (UC) is driven by immune and stromal subsets, culminating in epithelial injury. Vedolizumab (VDZ) is an anti-integrin antibody that is effective for treating UC. VDZ is known to inhibit lymphocyte trafficking to the intestine, but its broader effects on other cell subsets are less defined. To identify the inflammatory cells that contribute to colitis and are affected by VDZ, we perform single-cell transcriptomic and proteomic analyses of peripheral blood and colonic biopsies in healthy controls and patients with UC on VDZ or other therapies. Here we show that VDZ treatment is associated with alterations in circulating and tissue mononuclear phagocyte (MNP) subsets, along with modest shifts in lymphocytes. Spatial multi-omics of formalin-fixed biopsies demonstrates trends towards increased abundance and proximity of MNP and fibroblast subsets in active colitis. Spatial transcriptomics of archived specimens pre-treatment identifies epithelial-, MNP-, and fibroblast-enriched genes related to VDZ responsiveness, highlighting important roles for these subsets in UC.

Astrocyte specification during development is influenced by both intrinsic and extrinsic factors, but the precise contribution of each remains poorly understood. Here we show that septal astrocytes from Nkx2.1 and Zic4 expressing progenitor zones are allocated into non-overlapping domains of the medial (MS) and lateral septal nuclei (LS) respectively. Astrocytes in these areas exhibit distinctive molecular and morphological features tailored to the unique cellular and synaptic circuit environment of each nucleus. Using single-nucleus (sn) RNA sequencing, we trace the developmental trajectories of cells in the septum and find that neurons and astrocytes undergo region and developmental stage-specific local cell-cell interactions. We show that expression of the classic morphogens Sonic hedgehog (Shh) and Fibroblast growth factors (Fgfs) by MS and LS neurons respectively, functions to promote the molecular specification of local astrocytes in each region. Finally, using heterotopic cell transplantation, we show that both morphological and molecular specifications of septal astrocytes are highly dependent on the local microenvironment, regardless of developmental origins. Our data highlights the complex interplay between intrinsic and extrinsic factors shaping astrocyte identities and illustrates the importance of the local environment in determining astrocyte functional specialization.

Dysregulation of intercellular communication is a hallmark of aging. To better quantify and explore changes in intercellular communication, we present scDiffCom and scAgeCom. scDiffCom is an R package, relying on approximately 5,000 curated ligand–receptor interactions, that performs differential intercellular communication analysis between two conditions from single-cell transcriptomics data. Built upon scDiffCom, scAgeCom is an atlas of age-related cell–cell communication changes covering 23 mouse tissues from 58 single-cell RNA sequencing datasets from Tabula Muris Senis and the Calico murine aging cell atlas. It offers a comprehensive resource of tissue-specific and sex-specific aging dysregulations and highlights age-related intercellular communication changes widespread across the whole body, such as the upregulation of immune system processes and inflammation, the downregulation of developmental processes, angiogenesis and extracellular matrix organization and the deregulation of lipid metabolism. Our analysis emphasizes the relevance of the specific ligands, receptors and cell types regulating these processes. The atlas is available online ( https://scagecom.org ).

Glioblastoma (GBM) is an aggressive tumor with very bad prognosis. The urgent need to find new effective therapies is challenged by the unique characteristics of GBM, including high intra and intertumoral heterogeneity. Using single-nucleus transcriptomics (snRNA-seq), we characterized the panorama of a preclinical immunocompetent murine model based in the implantation of mouse glioblastoma stem cells (GL261-GSCs) into the brain parenchyma. Additionally, we performed Visium spatial transcriptomics in the in vivo model to confirm the location of annotated cells. To understand the technical bias of this approach, we performed two scRNA-seq methods in GBM cells. We thoroughly characterized the tumor microenvironment (TME) at early and late stages of tumor development and upon treatment with temozolomide (TMZ), the standard of care for patients with GBM, and with Tat-Cx43 266-283 , a promising experimental treatment. We identified prominent GBM targets that can be addressed using this preclinical model, such as Grik2 , Nlgn3 , Gap43 or Kcnn4 , which are involved in electrical and synaptic integration of GBM cells into neural circuits, as well as the expression of Nt5e , Cd274 or Irf8 , which indicates the development of immune evasive properties in these GBM cells. In agreement, snRNA-seq unveiled high expression of several immunosuppressive-associated molecules in immune cells, such as Csf1r, Arg1, Mrc1 and Tgfb1 , suggesting the development of an immunosuppressive microenvironment. We also show the landscape of cytokines, cytokine receptors, checkpoint ligands and receptors in tumor and TME cells, which are crucial data for a rational design of immunotherapy studies. Thus, Mrc1, PD-L1, TIM-3 or B7-H3 are among the immunotherapy targets that can be addressed in this model. Finally, the comparison of the preclinical GL261-GSC GBM model with human GBM subtypes unveiled important similarities with the recently identified TMEmed human GBM, indicating that preclinical data obtained in GL261-GSC GBM model might be applied to TMEmed human GBM, improving patient stratification in clinical trials. In conclusion, this work provides crucial information for future preclinical studies in GBM improving their clinical application.

Research software engineering is central to data-driven biomedical research, but its role is often undervalued and poorly understood.