Gene trajectory inference for single-cell data by optimal transport metrics.

Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.

HIV-1-infected T cell clones are shared across cerebrospinal fluid and blood during ART.

The central nervous system HIV reservoir is incompletely understood and is a major barrier to HIV cure. We profiled people with HIV (PWH) and uninfected controls through single-cell transcriptomic and T cell receptor (TCR) sequencing to understand the dynamics of HIV persistence in the CNS. In PWH on ART, we found that most participants had single cells containing HIV-1 RNA, which was found predominantly in CD4 central memory T cells, in both cerebrospinal fluid (CSF) and blood. HIV-1 RNA-containing cells were found more frequently in CSF than blood, indicating a higher burden of reservoir cells in the CNS than blood for some PWH. Most CD4 T cell clones containing infected cells were compartment specific, while some (22%) - including rare clones with members of the clone containing detectable HIV RNA in both blood and CSF - were found in both CSF and blood. These results suggest that infected T cells trafficked between tissue compartments and that maintenance and expansion of infected T cell clones contributed to the CNS reservoir in PWH on ART.

Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation.

Ensuring robustness of image classifiers against adversarial attacks and spurious correlation has been challenging. One of the most effective methods for adversarial robustness is a type of data augmentation that uses adversarial examples during training. Here, inspired by computational models of human vision, we explore a synthesis of this approach by leveraging a structured prior over image formation: the 3D geometry of objects and how it projects to images. We combine adversarial training with a weight initialization that implicitly encodes such a prior about 3D objects via 3D reconstruction pre-training. We evaluate our approach using two different datasets and compare it to alternative pre-training protocols that do not encode a prior about 3D shape. To systematically explore the effect of 3D pre-training, we introduce a novel dataset called Geon3D, which consists of simple shapes that nevertheless capture variation in multiple distinct dimensions of geometry. We find that while 3D reconstruction pre-training does not improve robustness for the simplest dataset setting, we consider (Geon3D on a clean background) that it improves upon adversarial training in more realistic (Geon3D with textured background and ShapeNet) conditions. We also find that 3D pre-training coupled with adversarial training improves the robustness to spurious correlations between shape and background textures. Furthermore, we show that the benefit of using 3D-based pre-training outperforms 2D-based pre-training on ShapeNet. We hope that these results encourage further investigation of the benefits of structured, 3D-based models of vision for adversarial robustness.

Circulating Tumor Reactive KIR+CD8+ T cells Suppress Anti-Tumor Immunity in Patients with Melanoma.

Effective anti-tumor immunity is largely driven by cytotoxic CD8+ T cells that can specifically recognize tumor antigens. However, the factors which ultimately dictate successful tumor rejection remain poorly understood. Here we identify a subpopulation of CD8+ T cells which are tumor antigen-specific in patients with melanoma but resemble KIR+CD8+ T cells with a regulatory function (Tregs). These tumor antigen-specific KIR+CD8+ T cells are detectable in both the tumor and the blood, and higher levels of this population are associated with worse overall survival. Our findings therefore suggest that KIR+CD8+ Tregs are tumor antigen-specific but uniquely suppress anti-tumor immunity in patients with melanoma.

IL-10 constrains sphingolipid metabolism to limit inflammation.

Interleukin-10 (IL-10) is a key anti-inflammatory cytokine that can limit immune cell activation and cytokine production in innate immune cell types1. Loss of IL-10 signalling results in life-threatening inflammatory bowel disease in humans and mice-however, the exact mechanism by which IL-10 signalling subdues inflammation remains unclear2-5. Here we find that increased saturated very long chain (VLC) ceramides are critical for the heightened inflammatory gene expression that is a hallmark of IL-10 deficiency. Accordingly, genetic deletion of ceramide synthase 2 (encoded by Cers2), the enzyme responsible for VLC ceramide production, limited the exacerbated inflammatory gene expression programme associated with IL-10 deficiency both in vitro and in vivo. The accumulation of saturated VLC ceramides was regulated by a decrease in metabolic flux through the de novo mono-unsaturated fatty acid synthesis pathway. Restoring mono-unsaturated fatty acid availability to cells deficient in IL-10 signalling limited saturated VLC ceramide production and the associated inflammation. Mechanistically, we find that persistent inflammation mediated by VLC ceramides is largely dependent on sustained activity of REL, an immuno-modulatory transcription factor. Together, these data indicate that an IL-10-driven fatty acid desaturation programme rewires VLC ceramide accumulation and aberrant activation of REL. These studies support the idea that fatty acid homeostasis in innate immune cells serves as a key regulatory node to control pathologic inflammation and suggests that 'metabolic correction' of VLC homeostasis could be an important strategy to normalize dysregulated inflammation caused by the absence of IL-10.

SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN.

Spatially resolved transcriptomics or proteomics data have the potential to contribute fundamental insights into the mechanisms underlying physiologic and pathological processes. However, analysis of these data capable of relating spatial information, multiplexed markers, and their observed phenotypes remains technically challenging. To analyze these relationships, we developed SORBET, a deep learning framework that leverages recent advances in graph neural networks (GNN). We apply SORBET to predict tissue phenotypes, such as response to immunotherapy, across different disease processes and different technologies including both spatial proteomics and transcriptomics methods. Our results show that SORBET accurately learns biologically meaningful relationships across distinct tissue structures and data acquisition methods. Furthermore, we demonstrate that SORBET facilitates understanding of the spatially-resolved biological mechanisms underlying the inferred phenotypes. In sum, our method facilitates mapping between the rich spatial and marker information acquired from spatial 'omics technologies to emergent biological phenotypes. Moreover, we provide novel techniques for identifying the biological processes that comprise the predicted phenotypes.

Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity.

High throughput sequencing of B cell receptors (BCRs) is increasingly applied to study the immense diversity of antibodies. Learning biologically meaningful embeddings of BCR sequences is beneficial for predictive modeling. Several embedding methods have been developed for BCRs, but no direct performance benchmarking exists. Moreover, the impact of the input sequence length and paired-chain information on the prediction remains to be explored. We evaluated the performance of multiple embedding models to predict BCR sequence properties and receptor specificity. Despite the differences in model architectures, most embeddings effectively capture BCR sequence properties and specificity. BCR-specific embeddings slightly outperform general protein language models in predicting specificity. In addition, incorporating full-length heavy chains and paired light chain sequences improves the prediction performance of all embeddings. This study provides insights into the properties of BCR embeddings to improve downstream prediction applications for antibody analysis and discovery.

A bedside to bench study of anti-PD-1, anti-CD40, and anti-CSF1R indicates that more is not necessarily better.

BACKGROUND: Stimulating inflammatory tumor associated macrophages can overcome resistance to PD-(L)1 blockade. We previously conducted a phase I trial of cabiralizumab (anti-CSF1R), sotigalimab (CD40-agonist) and nivolumab. Our current purpose was to study the activity and cellular effects of this three-drug regimen in anti-PD-1-resistant melanoma. METHODS: We employed a Simon's two-stage design and analyzed circulating immune cells from patients treated with this regimen for treatment-related changes. We assessed various dose levels of anti-CSF1R in murine melanoma models and studied the cellular and molecular effects. RESULTS: Thirteen patients were enrolled in the first stage. We observed one (7.7%) confirmed and one (7.7%) unconfirmed partial response, 5 patients had stable disease (38.5%) and 6 disease progression (42.6%). We elected not to proceed to the second stage. CyTOF analysis revealed a reduction in non-classical monocytes. Patients with prolonged stable disease or partial response who remained on study for longer had increased markers of antigen presentation after treatment compared to patients whose disease progressed rapidly. In a murine model, higher anti-CSF1R doses resulted in increased tumor growth and worse survival. Using single-cell RNA-sequencing, we identified a suppressive monocyte/macrophage population in murine tumors exposed to higher doses. CONCLUSIONS: Higher anti-CSF1R doses are inferior to lower doses in a preclinical model, inducing a suppressive macrophage population, and potentially explaining the disappointing results observed in patients. While it is impossible to directly infer human doses from murine studies, careful intra-species evaluation can provide important insight. Cabiralizumab dose optimization is necessary for this patient population with limited treatment options. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03502330.

LMD: Multiscale Marker Identification in Single-cell RNA-seq Data.

Accurate cell marker identification in single-cell RNA-seq data is crucial for understanding cellular diversity and function. An ideal marker is highly specific in identifying cells that are similar in terms of function and state. Current marker identification methods, commonly based on clustering and differential expression, capture general cell-type markers but often miss markers for subtypes or functional cell subsets, with their performance largely dependent on clustering quality. Moreover, cluster-independent approaches tend to favor genes that lack the specificity required to characterize regions within the transcriptomic space at multiple scales. Here we introduce Localized Marker Detector (LMD), a novel tool to identify "localized genes" - genes with expression profiles specific to certain groups of highly similar cells - thereby characterizing cellular diversity in a multi-resolution and fine-grained manner. LMD's strategy involves building a cell-cell affinity graph, diffusing the gene expression value across the cell graph, and assigning a score to each gene based on its diffusion dynamics. We show that LMD exhibits superior accuracy in recovering known cell-type markers in the Tabula Muris bone marrow dataset relative to other methods for marker identification. Notably, markers favored by LMD exhibit localized expression, whereas markers prioritized by other clustering-free algorithms are often dispersed in the transcriptomic space. We further group the markers suggested by LMD into functional gene modules to improve the separation of cell types and subtypes in a more fine-grained manner. These modules also identify other sources of variation, such as cell cycle status. In conclusion, LMD is a novel algorithm that can identify fine-grained markers for cell subtypes or functional states without relying on clustering or differential expression analysis. LMD exploits the complex interactions among cells and reveals cellular diversity at high resolution.

Deep identifiable modeling of single-cell atlases enables zero-shot query of cellular states.

With the emerging single-cell RNA-seq datasets at atlas levels, the potential of a universal model built on existing atlas that can extrapolate to new data remains unclear.A fundamental yet challenging problem for such a model is to identify the underlying biological and batch variations in a zero-shot manner, which is crucial for characterizing scRNA-seq datasets with new biological states. In this work, we present scShift, a mechanistic model that learns batch and biological patterns from atlas-level scRNA-seq data as well as perturbation scRNA-seq data. scShift models genes as functions of latent biological processes, with sparse shifts induced by batch effects and biological perturbations, leveraging recent advances of causal representation learning. Through benchmarking in holdout real datasets, we show scShift reveals unified cell type representations as well as underlying biological variations for query data in zero-shot manners, outperforming widely-used atlas integration, batch correction, and perturbation modeling approaches. scShift enables mapping of gene expression profiles to perturbation labels, and predicts meaningful targets for exhausted T cells as well as a list of diseases in the CellxGene blood atlas.

Maternal CXCR4 deletion results in placental defects and pregnancy loss mediated by immune dysregulation.

CXCR4 is a key regulator of the development of NK cells and DCs, both of which play an important role in early placental development and immune tolerance at the maternal-fetal interface. However, the role of CXCR4 in pregnancy is not well understood. Our study demonstrates that adult-induced global genetic CXCR4 deletion, but not uterine-specific CXCR4 deletion, was associated with increased pregnancy resorptions and decreased litter size. CXCR4-deficient mice had decreased NK cells and increased granulocytes in the decidua, along with increased leukocyte numbers in peripheral blood. We found that CXCR4-deficient mice had abnormal decidual NK cell aggregates and NK cell infiltration into trophoblast areas beyond the giant cell layer. This was associated with low NK cell expression of granzyme B, a NK cell granule effector, indicative of NK cell dysfunction. Pregnancy failure in these mice was associated with abnormalities in placental vascular development and increased placental expression of inflammatory genes. Importantly, adoptive BM transfer of WT CXCR4+ BM cells into CXCR4-deficient mice rescued the reproductive deficits by normalizing NK cell function and mediating normal placental vascular development. Collectively, our study found an important role for maternal CXCR4 expression in immune cell function, placental development, and pregnancy maintenance.

Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms.

Recent advances in sample preparation and sequencing technology have made it possible to profile the transcriptomes of individual cells using single-cell RNA sequencing (scRNA-Seq). Compared to bulk RNA-Seq data, single-cell data often contain a higher percentage of zero reads, mainly due to lower sequencing depth per cell, which affects mostly measurements of low-expression genes. However, discrepancies between platforms are observed regardless of expression level. Using four paired datasets with multiple samples each, we investigated technical and biological factors that can contribute to this expression shift. Using two separate machine learning models we found that, in addition to expression level, RNA integrity, gene or UTR3 length, and the number of transcripts potentially also influence the occurrence of zeros. These findings could enable the development of novel analytical methods for cross-platform expression shift correction. We also identified genes and biological pathways in our diverse datasets that consistently showed differences when assessed at the single cell versus bulk level to assist in interpreting analysis across transcriptomic platforms. At the gene level, 25 genes (0.12%) were found in all datasets as discordant, but at the pathway level, 7 pathways (2.02%) showed shared enrichment in discordant genes.

SIMVI reveals intrinsic and spatial-induced states in spatial omics data.

Spatial omics analyze gene expression and interaction dynamics in relation to tissue structure and function. However, existing methods cannot model the intrinsic and spatial-induced variation in spatial omics data, thus failing to identify true spatial interaction effects. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free framework that disentangles cell intrinsic and spatial-induced latent variables for modeling gene expression in spatial omics data. SIMVI enables novel downstream analyses, such as clustering and differential expression analysis based on disentangled representations, spatial effect (SE) identification, SE interpretation, and transfer learning on new measurements / modalities. We benchmarked SIMVI on both simulated and real datasets and show that SIMVI uniquely generates highly accurate SE inferences in synthetic datasets and unveils intrinsic variation in complex real datasets. We applied SIMVI to spatial omics data from diverse platforms and tissues (MERFISH human cortex / mouse liver, Slide-seqv2 mouse hippocampus, Spatial-ATAC-RNA-seq) and revealed various region-specific and cell-type-specific spatial interactions. In addition, our experiments on MERFISH human cortex and spatial-ATAC-RNA-seq showcased SIMVI's power in identifying SEs for new samples / modalities. Finally, we applied SIMVI on a newly collected CosMx melanoma dataset. Using SIMVI, we identified immune cells associated with spatial-dependent interactions and revealed the underlying spatial variations associated with patient outcomes.

IL-6 trans-signaling in a humanized mouse model of scleroderma.

Fibrosis is regulated by interactions between immune and mesenchymal cells. However, the capacity of cell types to modulate human fibrosis pathology is poorly understood due to lack of a fully humanized model system. MISTRG6 mice were engineered by homologous mouse/human gene replacement to develop an immune system like humans when engrafted with human hematopoietic stem cells (HSCs). We utilized MISTRG6 mice to model scleroderma by transplantation of healthy or scleroderma skin from a patient with pansclerotic morphea to humanized mice engrafted with unmatched allogeneic HSC. We identified that scleroderma skin grafts contained both skin and bone marrow-derived human CD4 and CD8 T cells along with human endothelial cells and pericytes. Unlike healthy skin, fibroblasts in scleroderma skin were depleted and replaced by mouse fibroblasts. Furthermore, HSC engraftment alleviated multiple signatures of fibrosis, including expression of collagen and interferon genes, and proliferation and activation of human T cells. Fibrosis improvement correlated with reduced markers of T cell activation and expression of human IL-6 by mesenchymal cells. Mechanistic studies supported a model whereby IL-6 trans-signaling driven by CD4 T cell-derived soluble IL-6 receptor complexed with fibroblast-derived IL-6 promoted excess extracellular matrix gene expression. Thus, MISTRG6 mice transplanted with scleroderma skin demonstrated multiple fibrotic responses centered around human IL-6 signaling, which was improved by the presence of healthy bone marrow-derived immune cells. Our results highlight the importance of IL-6 trans-signaling in pathogenesis of scleroderma and the ability of healthy bone marrow-derived immune cells to mitigate disease.

Spectral top-down recovery of latent tree models.

Modeling the distribution of high-dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying tree structure, given only observations of its terminal nodes. Many algorithms for tree recovery are computationally intensive, which limits their applicability to trees of moderate size. For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps. First, separately recover the structure of multiple, possibly random subsets of the terminal nodes. Second, merge the resulting subtrees to form a full tree. Here, we develop spectral top-down recovery (STDR), a deterministic divide-and-conquer approach to infer large latent tree models. Unlike previous methods, STDR partitions the terminal nodes in a non random way, based on the Fiedler vector of a suitable Laplacian matrix related to the observed nodes. We prove that under certain conditions, this partitioning is consistent with the tree structure. This, in turn, leads to a significantly simpler merging procedure of the small subtrees. We prove that STDR is statistically consistent and bound the number of samples required to accurately recover the tree with high probability. Using simulated data from several common tree models in phylogenetics, we demonstrate that STDR has a significant advantage in terms of runtime, with improved or similar accuracy.

Autologous humanized PDX modeling for immuno-oncology recapitulates features of the human tumor microenvironment.

BACKGROUND: Interactions between immune and tumor cells are critical to determining cancer progression and response. In addition, preclinical prediction of immune-related drug efficacy is limited by interspecies differences between human and mouse, as well as inter-person germline and somatic variation. To address these gaps, we developed an autologous system that models the tumor microenvironment (TME) from individual patients with solid tumors. METHOD: With patient-derived bone marrow hematopoietic stem and progenitor cells (HSPCs), we engrafted a patient's hematopoietic system in MISTRG6 mice, followed by transfer of patient-derived xenograft (PDX) tissue, providing a fully genetically matched model to recapitulate the individual's TME. We used this system to prospectively study tumor-immune interactions in patients with solid tumor. RESULTS: Autologous PDX mice generated innate and adaptive immune populations; these cells populated the TME; and tumors from autologously engrafted mice grew larger than tumors from non-engrafted littermate controls. Single-cell transcriptomics revealed a prominent vascular endothelial growth factor A (VEGFA) signature in TME myeloid cells, and inhibition of human VEGF-A abrogated enhanced growth. CONCLUSIONS: Humanization of the interleukin 6 locus in MISTRG6 mice enhances HSPC engraftment, making it feasible to model tumor-immune interactions in an autologous manner from a bedside bone marrow aspirate. The TME from these autologous tumors display hallmarks of the human TME including innate and adaptive immune activation and provide a platform for preclinical drug testing.

IL-10 constrains sphingolipid metabolism via fatty acid desaturation to limit inflammation.

Unchecked chronic inflammation is the underlying cause of many diseases, ranging from inflammatory bowel disease to obesity and neurodegeneration. Given the deleterious nature of unregulated inflammation, it is not surprising that cells have acquired a diverse arsenal of tactics to limit inflammation. IL-10 is a key anti-inflammatory cytokine that can limit immune cell activation and cytokine production in innate immune cell types; however, the exact mechanism by which IL-10 signaling subdues inflammation remains unclear. Here, we find that IL-10 signaling constrains sphingolipid metabolism. Specifically, we find increased saturated very long chain (VLC) ceramides are critical for the heightened inflammatory gene expression that is a hallmark of IL-10-deficient macrophages. Genetic deletion of CerS2, the enzyme responsible for VLC ceramide production, limited exacerbated inflammatory gene expression associated with IL-10 deficiency both in vitro and in vivo , indicating that "metabolic correction" is able to reduce inflammation in the absence of IL-10. Surprisingly, accumulation of saturated VLC ceramides was regulated by flux through the de novo mono-unsaturated fatty acid (MUFA) synthesis pathway, where addition of exogenous MUFAs could limit both saturated VLC ceramide production and inflammatory gene expression in the absence of IL-10 signaling. Together, these studies mechanistically define how IL-10 signaling manipulates fatty acid metabolism as part of its molecular anti-inflammatory strategy and could lead to novel and inexpensive approaches to regulate aberrant inflammation.

Spatial epigenome-transcriptome co-profiling of mammalian tissues.

Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context1-5. However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.

The age of bone marrow dictates the clonality of smooth muscle-derived cells in atherosclerotic plaques.

Aging is the predominant risk factor for atherosclerosis, the leading cause of death. Rare smooth muscle cell (SMC) progenitors clonally expand giving rise to up to ~70% of atherosclerotic plaque cells; however, the effect of age on SMC clonality is not known. Our results indicate that aged bone marrow (BM)-derived cells non-cell autonomously induce SMC polyclonality and worsen atherosclerosis. Indeed, in myeloid cells from aged mice and humans, TET2 levels are reduced which epigenetically silences integrin ß3 resulting in increased tumor necrosis factor [TNF]-α signaling. TNFα signals through TNF receptor 1 on SMCs to promote proliferation and induces recruitment and expansion of multiple SMC progenitors into the atherosclerotic plaque. Notably, integrin ß3 overexpression in aged BM preserves dominance of the lineage of a single SMC progenitor and attenuates plaque burden. Our results demonstrate a molecular mechanism of aged macrophage-induced SMC polyclonality and atherogenesis and suggest novel therapeutic strategies.

Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES.

MOTIVATION: Recent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level. RESULTS: NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms, is compatible with existing single-cell packages, and allows rapid, flexible analysis of cell-cell signaling at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: NICHES is an open-source software implemented in R under academic free license v3.0 and it is available at http://github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.