TET2 promotes tumor antigen presentation and T cell IFN-γ which is enhanced by vitamin C.

Immune evasion by tumors is promoted by low T cell infiltration, ineffective T cell activity directed against the tumor and reduced tumor antigen presentation. The TET2 DNA dioxygenase gene is frequently mutated in hematopoietic malignancies and loss of TET enzymatic activity is found in a variety of solid tumors. We showed previously that vitamin C (VC), a co-factor of TET2, enhances tumor-associated T cell recruitment and checkpoint inhibitor therapy responses in a TET2-dependent manner. Using single-cell RNA sequencing (scRNA-seq) analysis performed on B16-OVA melanoma tumors, we have shown here that an additional function for TET2 in tumors is to promote expression of certain antigen presentation machinery genes, which is potently enhanced by VC. Consistently, VC promoted antigen presentation in cell-based and tumor assays in a TET2-dependent manner. Quantifying intercellular signaling from the scRNA-seq dataset showed that T cell-derived IFNγ-induced signaling within the tumor and tumor microenvironment requires tumor-associated TET2 expression which is enhanced by VC treatment. Analysis of patient tumor samples indicated that TET activity directly correlates with antigen-presentation gene expression and with patient outcomes. Our results demonstrate the importance of tumor-associated TET2 activity as a critical mediator of tumor immunity which is augmented by high-dose VC therapy.

Linking transcriptome and morphology in bone cells at cellular resolution with generative AI.

Recent advancements in deep learning (DL) have revolutionized the capability of artificial intelligence (AI) by enabling the analysis of large-scale, complex datasets that are difficult for humans to interpret. However, large amounts of high-quality data are required to train such generative AI models successfully. With the rapid commercialization of single-cell sequencing and spatial transcriptomics platforms, the field is increasingly producing large-scale datasets such as histological images, single-cell molecular data, and spatial transcriptomic data. These molecular and morphological datasets parallel the multimodal text and image data used to train highly successful generative AI models for natural language processing and computer vision. Thus, these emerging data types offer great potential to train generative AI models that uncover intricate biological processes of bone cells at a cellular level. In this Perspective, we summarize the progress and prospects of generative AI applied to these datasets and their potential applications to bone research. In particular, we highlight three AI applications: predicting cell differentiation dynamics, linking molecular and morphological features, and predicting cellular responses to perturbations. To make generative AI models beneficial for bone research, important issues, such as technical biases in bone single-cell datasets, lack of profiling of important bone cell types, and lack of spatial information, need to be addressed. Realizing the potential of generative AI for bone biology will also likely require generating large-scale, high-quality cellular-resolution spatial transcriptomics datasets, improving the sensitivity of current spatial transcriptomics datasets, and thorough experimental validation of model predictions.

Imagine if pathologists could infer the whole transcriptomes of individual cells from a standard histological section of a bone biopsy, identify molecular defects compared to healthy cells, and predict how those cells would respond to various chemical or genetic treatments. The ability to model the relationship between transcriptomic profiles and morphological or functional properties based on limited biopsy samples would revolutionize diagnosis and treatment decisions in clinical practice. Such modeling seemed impossible only a few years ago, and comprehensive molecular diagnosis is currently impractical, as it requires extensive and expensive laboratory tests. However, rapid advances in artificial intelligence (AI) may soon make this dream a reality. In this Perspective, we discuss the promise of generative AI for linking transcriptomes and morphology at cellular resolution to benefit bone research and potential clinical application. We argue that there is a plausible path toward AI-assisted diagnosis using the whole transcriptome in a cellular and spatial context, which will lead to breakthroughs in our understanding of bone biology and bone disease.

HIV-1 Vpr combats the PU.1-driven antiviral response in primary human macrophages.

HIV-1 Vpr promotes efficient spread of HIV-1 from macrophages to T cells by transcriptionally downmodulating restriction factors that target HIV-1 Envelope protein (Env). Here we find that Vpr induces broad transcriptomic changes by targeting PU.1, a transcription factor necessary for expression of host innate immune response genes, including those that target Env. Consistent with this, we find silencing PU.1 in infected macrophages lacking Vpr rescues Env. Vpr downmodulates PU.1 through a proteasomal degradation pathway that depends on physical interactions with PU.1 and DCAF1, a component of the Cul4A E3 ubiquitin ligase. The capacity for Vpr to target PU.1 is highly conserved across primate lentiviruses. In addition to impacting infected cells, we find that Vpr suppresses expression of innate immune response genes in uninfected bystander cells, and that virion-associated Vpr can degrade PU.1. Together, we demonstrate Vpr counteracts PU.1 in macrophages to blunt antiviral immune responses and promote viral spread.

How has the AI boom impacted algorithmic biology?

This Voices piece will highlight the impact of artificial intelligence on algorithm development among computational biologists. How has worldwide focus on AI changed the path of research in computational biology? What is the impact on the algorithmic biology research community?

Autophagy and machine learning: Unanswered questions.

Autophagy is a critical conserved cellular process in maintaining cellular homeostasis by clearing and recycling damaged organelles and intracellular components in lysosomes and vacuoles. Autophagy plays a vital role in cell survival, bioenergetic homeostasis, organism development, and cell death regulation. Malfunctions in autophagy are associated with various human diseases and health disorders, such as cancers and neurodegenerative diseases. Significant effort has been devoted to autophagy-related research in the context of genes, proteins, diagnosis, etc. In recent years, there has been a surge of studies utilizing state of the art machine learning (ML) tools to analyze and understand the roles of autophagy in various biological processes. We taxonomize ML techniques that are applicable in an autophagy context, comprehensively review existing efforts being taken in this direction, and outline principles to consider in a biomedical context. In recognition of recent groundbreaking advances in the deep-learning community, we discuss new opportunities in interdisciplinary collaborations and seek to engage autophagy and computer science researchers to promote autophagy research with joint efforts.

Integrating single-cell multimodal epigenomic data using 1D-convolutional neural networks.

Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary together across cell types and states. A pivotal step in using this type of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type. Our key insight is to model single-cell multimodal epigenome data as a multi-channel sequential signal. Based on this insight, we developed ConvNet-VAEs, a novel framework that uses 1D-convolutional variational autoencoders (VAEs) for single-cell multimodal epigenomic data integration. We evaluated ConvNet-VAEs on nano-CT and scNTT-seq data generated from juvenile mouse brain and human bone marrow. We found that ConvNet-VAEs can perform dimension reduction and batch correction better than previous architectures while using significantly fewer parameters. Furthermore, the performance gap between convolutional and fully-connected architectures increases with the number of modalities, and deeper convolutional architectures can increase performance while performance degrades for deeper fully-connected architectures. Our results indicate that convolutional autoencoders are a promising method for integrating current and future single-cell multimodal epigenomic datasets.

HIV-1 Vpr combats the PU.1-driven antiviral response in primary human macrophages.

HIV-1 Vpr promotes efficient spread of HIV-1 from macrophages to T cells by transcriptionally downmodulating restriction factors that target HIV-1 Envelope protein (Env). Here we find that Vpr induces broad transcriptomic changes by targeting PU.1, a transcription factor necessary for expression of host innate immune response genes, including those that target Env. Consistent with this, we find silencing PU.1 in infected macrophages lacking Vpr rescues Env. Vpr downmodulates PU.1 through a proteasomal degradation pathway that depends on physical interactions with PU.1 and DCAF1, a component of the Cul4A E3 ubiquitin ligase. The capacity for Vpr to target PU.1 is highly conserved across primate lentiviruses. In addition to impacting infected cells, we find that Vpr suppresses expression of innate immune response genes in uninfected bystander cells, and that virion-associated Vpr can degrade PU.1. Together, we demonstrate Vpr counteracts PU.1 in macrophages to blunt antiviral immune responses and promote viral spread.

Endosteal stem cells at the bone-blood interface: A double-edged sword for rapid bone formation: Bone marrow endosteal stem cells provide a robust source of bone-making osteoblasts both in normal and abnormal bone formation.

Endosteal stem cells are a subclass of bone marrow skeletal stem cell populations that are particularly important for rapid bone formation occurring in growth and regeneration. These stem cells are strategically located near the bone surface in a specialized microenvironment of the endosteal niche. These stem cells are abundant in young stages but eventually depleted and replaced by other stem cell types residing in a non-endosteal perisinusoidal niche. Single-cell molecular profiling and in vivo cell lineage analyses play key roles in discovering endosteal stem cells. Importantly, endosteal stem cells can transform into bone tumor-making cells when deleterious mutations occur in tumor suppressor genes. The emerging hypothesis is that osteoblast-chondrocyte transitional identities confer a special subset of endosteal stromal cells with stem cell-like properties, which may make them susceptible for tumorigenic transformation. Endosteal stem cells are likely to represent an important therapeutic target of bone diseases caused by aberrant bone formation.

Bone marrow endosteal stem cells dictate active osteogenesis and aggressive tumorigenesis.

The bone marrow contains various populations of skeletal stem cells (SSCs) in the stromal compartment, which are important regulators of bone formation. It is well-described that leptin receptor (LepR)+ perivascular stromal cells provide a major source of bone-forming osteoblasts in adult and aged bone marrow. However, the identity of SSCs in young bone marrow and how they coordinate active bone formation remains unclear. Here we show that bone marrow endosteal SSCs are defined by fibroblast growth factor receptor 3 (Fgfr3) and osteoblast-chondrocyte transitional (OCT) identities with some characteristics of bone osteoblasts and chondrocytes. These Fgfr3-creER-marked endosteal stromal cells contribute to a stem cell fraction in young stages, which is later replaced by Lepr-cre-marked stromal cells in adult stages. Further, Fgfr3+ endosteal stromal cells give rise to aggressive osteosarcoma-like lesions upon loss of p53 tumor suppressor through unregulated self-renewal and aberrant osteogenic fates. Therefore, Fgfr3+ endosteal SSCs are abundant in young bone marrow and provide a robust source of osteoblasts, contributing to both normal and aberrant osteogenesis.

Multiplying insights from perturbation experiments: predicting new perturbation combinations.

Experimentally exploring the effect of all perturbation combinations is not feasible. In their recent study, Theis and colleagues (Lotfollahi et al, 2023) present an approach that uses deep generative models to predict the effects of new perturbations from high-throughput single perturbation experiments.

Multi-omic single-cell velocity models epigenome-transcriptome interactions and improves cell fate prediction.

Multi-omic single-cell datasets, in which multiple molecular modalities are profiled within the same cell, offer an opportunity to understand the temporal relationship between epigenome and transcriptome. To realize this potential, we developed MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression and improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Application to multi-omic single-cell datasets from brain, skin and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. We also find four types of cell states: two states in which epigenome and transcriptome are coupled and two distinct decoupled states. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. MultiVelo is available on PyPI, Bioconda and GitHub ( https://github.com/welch-lab/MultiVelo ).

Predicting the Structural Impact of Human Alternative Splicing.

Protein structure prediction with neural networks is a powerful new method for linking protein sequence, structure, and function, but structures have generally been predicted for only a single isoform of each gene, neglecting splice variants. To investigate the structural implications of alternative splicing, we used AlphaFold2 to predict the structures of more than 11,000 human isoforms. We employed multiple metrics to identify splicing-induced structural alterations, including template matching score, secondary structure composition, surface charge distribution, radius of gyration, accessibility of post-translational modification sites, and structure-based function prediction. We identified examples of how alternative splicing induced clear changes in each of these properties. Structural similarity between isoforms largely correlated with degree of sequence identity, but we identified a subset of isoforms with low structural similarity despite high sequence similarity. Exon skipping and alternative last exons tended to increase the surface charge and radius of gyration. Splicing also buried or exposed numerous post-translational modification sites, most notably among the isoforms of BAX. Functional prediction nominated numerous functional differences among isoforms of the same gene, with loss of function compared to the reference predominating. Finally, we used single-cell RNA-seq data from the Tabula Sapiens to determine the cell types in which each structure is expressed. Our work represents an important resource for studying the structure and function of splice isoforms across the cell types of the human body.

Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation.

Skeletal muscle, the largest human organ by weight, is relevant to several polygenic metabolic traits and diseases including type 2 diabetes (T2D). Identifying genetic mechanisms underlying these traits requires pinpointing the relevant cell types, regulatory elements, target genes, and causal variants. Here, we used genetic multiplexing to generate population-scale single nucleus (sn) chromatin accessibility (snATAC-seq) and transcriptome (snRNA-seq) maps across 287 frozen human skeletal muscle biopsies representing 456,880 nuclei. We identified 13 cell types that collectively represented 983,155 ATAC summits. We integrated genetic variation to discover 6,866 expression quantitative trait loci (eQTL) and 100,928 chromatin accessibility QTL (caQTL) (5% FDR) across the five most abundant cell types, cataloging caQTL peaks that atlas-level snATAC maps often miss. We identified 1,973 eGenes colocalized with caQTL and used mediation analyses to construct causal directional maps for chromatin accessibility and gene expression. 3,378 genome-wide association study (GWAS) signals across 43 relevant traits colocalized with sn-e/caQTL, 52% in a cell-specific manner. 77% of GWAS signals colocalized with caQTL and not eQTL, highlighting the critical importance of population-scale chromatin profiling for GWAS functional studies. GWAS-caQTL colocalization showed distinct cell-specific regulatory paradigms. For example, a C2CD4A/B T2D GWAS signal colocalized with caQTL in muscle fibers and multiple chromatin loop models nominated VPS13C, a glucose uptake gene. Sequence of the caQTL peak overlapping caSNP rs7163757 showed allelic regulatory activity differences in a human myocyte cell line massively parallel reporter assay. These results illuminate the genetic regulatory architecture of human skeletal muscle at high-resolution epigenomic, transcriptomic, and cell state scales and serve as a template for population-scale multi-omic mapping in complex tissues and traits.

The fate of early perichondrial cells in developing bones.

In endochondral bone development, bone-forming osteoblasts and bone marrow stromal cells have dual origins in the fetal cartilage and its surrounding perichondrium. However, how early perichondrial cells distinctively contribute to developing bones remain unidentified. Here we show using in vivo cell-lineage analyses that Dlx5+ fetal perichondrial cells marked by Dlx5-creER do not generate cartilage but sustainably contribute to cortical bone and marrow stromal compartments in a manner complementary to fetal chondrocyte derivatives under the regulation of Hedgehog signaling. Postnatally, Dlx5+ fetal perichondrial cell derivatives preferentially populate the diaphyseal marrow stroma with a dormant adipocyte-biased state and are refractory to parathyroid hormone-induced bone anabolism. Therefore, early perichondrial cells of the fetal cartilage are destined to become an adipogenic subset of stromal cells in postnatal diaphyseal bone marrow, supporting the theory that the adult bone marrow stromal compartments are developmentally prescribed within the two distinct cells-of-origins of the fetal bone anlage.

PyLiger: scalable single-cell multi-omic data integration in Python.

MOTIVATION: LIGER (Linked Inference of Genomic Experimental Relationships) is a widely used R package for single-cell multi-omic data integration. However, many users prefer to analyze their single-cell datasets in Python, which offers an attractive syntax and highly optimized scientific computing libraries for increased efficiency. RESULTS: We developed PyLiger, a Python package for integrating single-cell multi-omic datasets. PyLiger offers faster performance than the previous R implementation (2-5× speedup), interoperability with AnnData format, flexible on-disk or in-memory analysis capability and new functionality for gene ontology enrichment analysis. The on-disk capability enables analysis of arbitrarily large single-cell datasets using fixed memory. AVAILABILITY AND IMPLEMENTATION: PyLiger is available on Github at https://github.com/welch-lab/pyliger and on the Python Package Index. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization.

Single-cell genomic technologies provide an unprecedented opportunity to define molecular cell types in a data-driven fashion, but present unique data integration challenges. Many analyses require "mosaic integration", including both features shared across datasets and features exclusive to a single experiment. Previous computational integration approaches require that the input matrices share the same number of either genes or cells, and thus can use only shared features. To address this limitation, we derive a nonnegative matrix factorization algorithm for integrating single-cell datasets containing both shared and unshared features. The key advance is incorporating an additional metagene matrix that allows unshared features to inform the factorization. We demonstrate that incorporating unshared features significantly improves integration of single-cell RNA-seq, spatial transcriptomic, SNARE-seq, and cross-species datasets. We have incorporated the UINMF algorithm into the open-source LIGER R package ( https://github.com/welch-lab/liger ).

Single-Cell Transcriptomic Analysis Reveals Developmental Relationships and Specific Markers of Mouse Periodontium Cellular Subsets.

The periodontium is essential for supporting the functionality of the tooth, composed of diversity of mineralized and non-mineralized tissues such as the cementum, the periodontal ligament (PDL) and the alveolar bone. The periodontium is developmentally derived from the dental follicle (DF), a fibrous tissue surrounding the developing tooth bud. We previously showed through in vivo lineage-tracing experiments that DF contains mesenchymal progenitor cells expressing parathyroid hormone-related protein (PTHrP), which give rise to cells forming the periodontal attachment apparatus in a manner regulated by autocrine signaling through the PTH/PTHrP receptor. However, the developmental relationships between PTHrP+ DF cells and diverse cell populations constituting the periodontium remain undefined. Here, we performed single-cell RNA-sequencing (scRNA-seq) analyses of cells in the periodontium by integrating the two datasets, i.e. PTHrP-mCherry+ DF cells at P6 and 2.3kb Col1a1 promoter-driven GFP+ periodontal cells at P25 that include descendants of PTHrP+ DF cells, cementoblasts, osteoblasts and periodontal ligament cells. This integrative scRNA-seq analysis revealed heterogeneity of cells of the periodontium and their cell type-specific markers, as well as their relationships with DF cells. Most importantly, our analysis identified a cementoblast-specific metagene that discriminate cementoblasts from alveolar bone osteoblasts, including Pthlh (encoding PTHrP) and Tubb3. RNA velocity analysis indicated that cementoblasts were directly derived from PTHrP+ DF cells in the early developmental stage and did not interconvert with other cell types. Further, CellPhoneDB cell-cell communication analysis indicated that PTHrP derived from cementoblasts acts on diversity of cells in the periodontium in an autocrine and paracrine manner. Collectively, our findings provide insights into the lineage hierarchy and intercellular interactions of cells in the periodontium at a single-cell level, aiding to understand cellular and molecular basis of periodontal tissue formation.

SquiggleNet: real-time, direct classification of nanopore signals.

We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than DNA passes through the pore, allowing real-time classification and read ejection. Using 1 s of sequencing data, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than alignment-based approaches. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.

G-CSF secreted by mutant IDH1 glioma stem cells abolishes myeloid cell immunosuppression and enhances the efficacy of immunotherapy.

Mutant isocitrate-dehydrogenase 1 (mIDH1) synthesizes the oncometabolite 2-hydroxyglutarate (2HG), which elicits epigenetic reprogramming of the glioma cells' transcriptome by inhibiting DNA and histone demethylases. We show that the efficacy of immune-stimulatory gene therapy (TK/Flt3L) is enhanced in mIDH1 gliomas, due to the reprogramming of the myeloid cells' compartment infiltrating the tumor microenvironment (TME). We uncovered that the immature myeloid cells infiltrating the mIDH1 TME are mainly nonsuppressive neutrophils and preneutrophils. Myeloid cell reprogramming was triggered by granulocyte colony-stimulating factor (G-CSF) secreted by mIDH1 glioma stem/progenitor-like cells. Blocking G-CSF in mIDH1 glioma­bearing mice restores the inhibitory potential of the tumor-infiltrating myeloid cells, accelerating tumor progression. We demonstrate that G-CSF reprograms bone marrow granulopoiesis, resulting in noninhibitory myeloid cells within mIDH1 glioma TME and enhancing the efficacy of immune-stimulatory gene therapy.

Transcription factor FOXF1 identifies compartmentally distinct mesenchymal cells with a role in lung allograft fibrogenesis.

In this study, we demonstrate that forkhead box F1 (FOXF1), a mesenchymal transcriptional factor essential for lung development, was retained in a topographically distinct mesenchymal stromal cell population along the bronchovascular space in an adult lung and identify this distinct subset of collagen-expressing cells as key players in lung allograft remodeling and fibrosis. Using Foxf1-tdTomato BAC (Foxf1-tdTomato) and Foxf1-tdTomato Col1a1-GFP mice, we show that Lin-Foxf1+ cells encompassed the stem cell antigen 1+CD34+ (Sca1+CD34+) subset of collagen 1-expressing mesenchymal cells (MCs) with a capacity to generate CFU and lung epithelial organoids. Histologically, FOXF1-expressing MCs formed a 3D network along the conducting airways; FOXF1 was noted to be conspicuously absent in MCs in the alveolar compartment. Bulk and single-cell RNA-Seq confirmed distinct transcriptional signatures of Foxf1+ and Foxf1- MCs, with Foxf1-expressing cells delineated by their high expression of the transcription factor glioma-associated oncogene 1 (Gli1) and low expression of integrin α8 (Itga), versus other collagen-expressing MCs. FOXF1+Gli1+ MCs showed proximity to Sonic hedgehog-expressing (Shh-expressing) bronchial epithelium, and mesenchymal expression of Foxf1 and Gli1 was found to be dependent on paracrine Shh signaling in epithelial organoids. Using a murine lung transplant model, we show dysregulation of epithelial-mesenchymal SHH/GLI1/FOXF1 crosstalk and expansion of this specific peribronchial MC population in chronically rejecting fibrotic lung allografts.