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SUMMARYThe HIV-1 accessory protein, Vpr, is an enigmatic protein required for efficient spread of HIV from macrophages to T cells, a necessary step for propagation of infection. To illuminate the role of Vpr in HIV-infection of primary macrophages, we used single-cell RNA sequencing to capture the transcriptional changes during an HIV-1 spreading infection plus and minus Vpr. We found that Vpr reprogramed HIV-infected macrophage gene expression by targeting the master transcriptional regulator, PU.1. PU.1 was required for efficient induction of the host innate immune response to HIV, including upregulation ofISG15,LY96,andIFI6. In contrast, we did not observe direct effects of PU.1 on HIV gene transcription. Single cell gene expression analysis also revealed Vpr countered an innate immune response to HIV-infection within bystander macrophages via a PU.1-independent mechanism. The capacity of Vpr to target PU.1 and disrupt the anti-viral response was highly conserved across primate lentiviruses including HIV-2 and several SIVs. By demonstrating how Vpr overcomes a critical early warning system of infection, we identify a crucial reason why Vpr is necessary for HIV infection and spread.

Gene expression and morphology both play a key role in determining the types and functions of cells, but the relationship between molecular and morphological features is largely uncharacterized. We present MorphNet, a computational approach that can draw pictures of a cell’s morphology from its gene expression profile. Our approach leverages paired morphology and molecular data to train a neural network that can predict nuclear or whole-cell morphology from gene expression. We employ state-of-the-art data augmentation techniques that allow training using as few as 103images. We find that MorphNet can generate novel, realistic morphological images that retain the complex relationship between gene expression and cell appearance. We then train MorphNet to generate nuclear morphology from gene expression using brain-wide MERFISH data. In addition, we show that MorphNet can generate neuron morphologies with realistic axonal and dendritic structures. MorphNet generalizes to unseen brain regions, allowing prediction of neuron morphologies across the entire mouse isocortex and even non-cortical regions. We show that MorphNet performs meaningful latent space interpolation, allowing prediction of the effects of gene expression variation on morphology. Finally, we provide a web server that allows users to predict neuron morphologies for their own scRNA-seq data. MorphNet represents a powerful new approach for linking gene expression and morphology.

Small molecule treatment and gene knockout or overexpression induce complex changes in the molecular states of cells, and the space of possible perturbations is too large to measure exhaustively. We present PerturbNet, a deep generative model for predicting the distribution of cell states induced by unseen chemical or genetic perturbations. Our key innovation is to use high-throughput perturbation response data such as Perturb-Seq to learn a continuous mapping between the space of possible perturbations and the space of possible cell states.Using Sci-Plex and LINCS datasets, PerturbNet can accurately predict the distribution of gene expression changes induced by unseen small molecules given only their chemical structures. PerturbNet also accurately predicts gene expression changes induced by shRNA, CRISPRi, or CRISPRa perturbations using a perturbation network trained on gene functional annotations. Furthermore, self-supervised sequence embeddings allow PerturbNet to predict gene expression changes induced by missense mutations. We also use PerturbNet to attribute cell state shifts to specific perturbation features, including atoms and functional gene annotations. Finally, we leverage PerturbNet to design perturbations that achieve a desired cell state distribution. PerturbNet holds great promise for understanding perturbation responses and ultimately designing novel chemical and genetic interventions.

Experimental approaches for measuring single-cell gene expression can observe each cell at only one time point, requiring computational approaches for reconstructing the dynamics of gene expression during cell fate transitions. RNA velocity is a promising computational approach for this problem, but existing inference methods fail to capture key aspects of real data, limiting their utility. To address these limitations, we developed VeloVAE, a Bayesian model for RNA velocity inference. VeloVAE uses variational Bayesian inference to estimate the posterior distribution of latent time, latent cell state, and kinetic rate parameters for each cell. Our approach addresses key limitations of previous methods by inferring a global time and cell state value for each cell; explicitly modeling the emergence of multiple cell types; incorporating prior information such as time point labels; using scalable minibatch optimization; and quantifying parameter uncertainty. We show that VeloVAE significantly outperforms previous approaches in terms of data fit and accuracy of inferred differentiation directions. VeloVAE can also capture qualitative features of expression dynamics neglected by previous methods, including late induction, early repression, transcriptional boosts, and bifurcations. These improvements allow VeloVAE to accurately model gene expression dynamics in complex biological systems, including hematopoiesis, induced pluripotent stem cell reprogramming, neurogenesis, and organogenesis. We find that the latent time automatically inferred using all cells can even outperform pseudotime inferred using manually chosen cell subsets and root cells. We further use the inferred parameters to construct cell type transition graphs and fit branching differential equation models that describe the effects of cell type bifurcations on kinetic rate parameters.

Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large, diverse and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. Iterative data addition can also be used to map new data to a reference dataset. Comparisons with previous methods indicate that the improvements in efficiency do not sacrifice dataset alignment and cluster preservation performance. We demonstrate the effectiveness of online iNMF by integrating more than 1 million cells on a standard laptop, integrating large single-cell RNA sequencing and spatial transcriptomic datasets, and iteratively constructing a single-cell multi-omic atlas of the mouse motor cortex.

High-throughput single-cell sequencing technologies hold tremendous potential for defining cell types in an unbiased fashion using gene expression and epigenomic state. A key challenge in realizing this potential is integrating single-cell datasets from multiple protocols, biological contexts, and data modalities into a joint definition of cellular identity. We previously developed an approach, called linked inference of genomic experimental relationships (LIGER), that uses integrative nonnegative matrix factorization to address this challenge. Here, we provide a step-by-step protocol for using LIGER to jointly define cell types from multiple single-cell datasets. The main stages of the protocol are data preprocessing and normalization, joint factorization, quantile normalization and joint clustering, and visualization. We describe how to jointly define cell types from single-cell RNA-seq (scRNA-seq) and single-nucleus ATAC-seq (snATAC-seq) data, but similar steps apply across a wide range of other settings and data types, including cross-species analysis, single-nucleus DNA methylation, and spatial transcriptomics. Our protocol contains examples of expected results, describes common pitfalls, and relies only on our freely available, open-source R implementation of LIGER. We also provide R Markdown tutorials showing the outputs from each individual code segment. The analysis process can be performed in 1–4 h, depending on dataset size, and assumes no specialized bioinformatics training. Here, the authors describe step-by-step procedures for integrating single-cell sequencing datasets from different experiments or modalities to identify common and distinct cell types using the R-based software tool LIGER.

Bone marrow stromal cells (BMSCs) are versatile mesenchymal cell populations underpinning the major functions of the skeleton, a majority of which adjoin sinusoidal blood vessels and express C-X-C motif chemokine ligand 12 (CXCL12). However, how these cells are activated during regeneration and facilitate osteogenesis remains largely unknown. Cell-lineage analysis using Cxcl12-creER mice reveals that quiescent Cxcl12-creER+ perisinusoidal BMSCs differentiate into cortical bone osteoblasts solely during regeneration. A combined single cell RNA-seq analysis demonstrate that these cells convert their identity into a skeletal stem cell-like state in response to injury, associated with upregulation of osteoblast-signature genes and activation of canonical Wnt signaling components along the single-cell trajectory. β-catenin deficiency in these cells indeed causes insufficiency in cortical bone regeneration. Therefore, quiescent Cxcl12-creER+ BMSCs transform into osteoblast precursor cells in a manner mediated by canonical Wnt signaling, highlighting a unique mechanism by which dormant stromal cells are enlisted for skeletal regeneration., Bone marrow stromal cells (BMSCs) lining sinusoidal blood vessels are mesenchymal cells whose function is critical for the skeleton. Here the authors show that quiescent CXCL12-expressing BMSCs can convert into a skeletal stem cell-like state, and differentiate into cortical bone osteoblasts only in response to injury.

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.

Single-cell multi-omic datasets, in which multiple molecular modalities are profiled within the same cell, provide a unique opportunity to discover the relationships between cellular epigenomic and transcriptomic changes. To realize this potential, we developed MultiVelo, a mechanistic 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 from single-cell data, providing a quantitative summary of the temporal relationship between epigenomic and transcriptomic changes. Incorporating chromatin accessibility data significantly improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Fitting MultiVelo on single-cell multi-omic datasets from brain, skin, and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. Our model also identifies four types of cell states–two states in which epigenome and transcriptome are coupled and two distinct decoupled states. The parameters inferred by MultiVelo quantify the length of time for which genes occupy each of the four states, ranking genes by the degree of coupling between transcriptome and epigenome. 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. We provide an open-source Python implementation of MultiVelo on PyPI and GitHub (https://github.com/welch-lab/MultiVelo).

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 ).

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.

Cardiac regeneration occurs primarily through proliferation of existing cardiomyocytes, but also involves complex interactions between distinct cardiac cell types including non-cardiomyocytes (non-CMs). However, the subpopulations, distinguishing molecular features, cellular functions, and intercellular interactions of non-CMs in heart regeneration remain largely unexplored. Using the LIGER algorithm, we assemble an atlas of cell states from 61,977 individual non-CM scRNA-seq profiles isolated at multiple time points during regeneration. This analysis reveals extensive non-CM cell diversity, including multiple macrophage (MC), fibroblast (FB), and endothelial cell (EC) subpopulations with unique spatiotemporal distributions, and suggests an important role for MC in inducing the activated FB and EC subpopulations. Indeed, pharmacological perturbation of MC function compromises the induction of the unique FB and EC subpopulations. Furthermore, we developed computational algorithm Topologizer to map the topological relationships and dynamic transitions between functional states. We uncover dynamic transitions between MC functional states and identify factors involved in mRNA processing and transcriptional regulation associated with the transition. Together, our single-cell transcriptomic analysis of non-CMs during cardiac regeneration provides a blueprint for interrogating the molecular and cellular basis of this process.

Description, Mutant IDH1 gliomas are infiltrated by nonsuppressive immature myeloid cells, resulting in enhanced response to 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 glioma–bearing 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.

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.

Beginner's Guides each cover a key technique and offer the scientifically literate but not necessarily expert audience a background briefing on the underlying science of a technique that is (or will be) widely used in molecular bioscience. The series covers a mixture of techniques, including some that are well established amongst a subset of our readership but not necessarily familiar to those in different specialisms, or the reverse. This is our Beginner's Guide to single-cell transcriptomics.

Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-cell gene expression data. We also develop MichiGAN, a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. We learn disentangled representations of three large single-cell RNA-seq datasets and use MichiGAN to sample from these representations. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment. Supplementary Information The online version contains supplementary material available at (10.1186/s13059-021-02373-4).

Single-cell genomic technologies provide an unprecedented opportunity to define molecular cell types in a data-driven fashion, but present unique data integration challenges. Integration analyses often involve datasets with partially overlapping features, including both shared features that occur in all 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 novel 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, SHARE-seq, and cross-species datasets. We have incorporated the UINMF algorithm into the open-source LIGER R package (https://github.com/welch-lab/liger).

Bone marrow houses a multifunctional stromal cell population expressing C-X-C motif chemokine ligand 12 (CXCL12), termed CXCL12-abundant reticular (CAR) cells, that regulates osteogenesis and adipogenesis. The quiescent pre-adipocyte-like subset of CXCL12+ stromal cells ("Adipo-CAR" cells) is localized to sinusoidal surfaces and particularly enriched for hematopoiesis-supporting cytokines. However, detailed characteristics of these CXCL12+ pre-adipocyte-like stromal cells and how they contribute to marrow adipogenesis remain largely unknown. Here we highlight CXCL12-dependent physical coupling with hematopoietic cells as a potential mechanism regulating the adipogenic potential of CXCL12+ stromal cells. Single-cell computational analyses of RNA velocity and cell signaling reveal that Adipo-CAR cells exuberantly communicate with hematopoietic cells through CXCL12-CXCR4 ligand-receptor interactions but do not interconvert with Osteo-CAR cells. Consistent with this computational prediction, a substantial fraction of Cxcl12-creER+ pre-adipocyte-like cells intertwines with hematopoietic cells in vivo and in single-cell preparation in a protease-sensitive manner. Deletion of CXCL12 in these cells using Col2a1-cre leads to a reduction of stromal-hematopoietic coupling and extensive marrow adipogenesis in adult bone marrow, which appears to involve direct conversion of CXCL12+ cells to lipid-laden marrow adipocytes without altering mesenchymal progenitor cell fates. Therefore, these findings suggest that CXCL12+ pre-adipocyte-like marrow stromal cells prevent their premature differentiation by maintaining physical coupling with hematopoietic cells in a CXCL12-dependent manner, highlighting a possible cell-non-autonomous mechanism that regulates marrow adipogenesis. © 2021 American Society for Bone and Mineral Research (ASBMR).

Deep generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), have achieved remarkable successes in generating and manipulating highdimensional images. VAEs excel at learning disentangled image representations, while GANs excel at generating realistic images. Here, we systematically assess disentanglement and generation performance on single-cell gene expression data and find that these strengths and weaknesses of VAEs and GANs apply to single-cell gene expression data in a similar way. We also develop MichiGAN1, a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. We learn disentangled representations of two large singlecell RNA-seq datasets [13, 68] and use MichiGAN to sample from these representations. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment.

Oxford Nanopore sequencers provide results in real time as DNA passes through a nanopore and can eject a molecule after it has been partly sequenced. However, the computational challenge of deciding whether to keep or reject a molecule in real time has limited the application of this capability. We present SquiggleNet, the first deep learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than the DNA passes through the pore, allowing real-time classification and read ejection. When given the amount of sequencing data generated in one second, 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 approaches based on alignment. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen species, identified bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.

We wish to acknowledge the following funding bodies: National Health of Medical Research Council Career Development Fellowship AU, (KALC); Chan Zuckerberg initiative (AJA, VJC, ACC); Australian Research Council (AJA, KALC, MER); National Institute of Health, (EFDM, AD, SH); National Cancer Institute, (EFDM, VJC, CSG, GCY, EJF); Chair of bioinformatics in oncology of the CRCT (INSERM, Fondation Toulouse Cancer Santé, Pierre Fabre Research Institute, (AC, VP); Fondation Toulouse Cancer Santé, (AC, VP); Pierre Fabre Research Institute, (AC, VP); Michael Smith Foundation for Health Research, (AS); Mitacs, (AS); Kavli Neuroscience Discovery Institute, (GLSO); Johns Hopkins Provost Postdoctoral Fellowship, (GLSO); National Human Genome Research Institute, NIH, (JDW, VJC, CSG, WM, MIL); National Institute of Allergy and Infectious Diseases, NIH, (JDW, SH); National Institute of Mental Health, NIH, (JDW, MIL); EMBL PhD program, (RA); National Institutes of Health, National Institute on Aging, (GCY); DoD, (ACC); National Institute of Health, National Institute of Dental and Craniofacial Research, (EJF); Lustgarten Foundation, (EJF); Emerson Foundation, (EJF); Allegheny Health Network, (EJF). Peer Reviewed "Article signat per 31 autors/es: Kim-Anh Lê Cao, Al J. Abadi, Emily F. Davis-Marcisak, Lauren Hsu, Arshi Arora, Alexis Coullomb, Atul Deshpande, Yuzhou Feng, Pratheepa Jeganathan, Melanie Loth, Chen Meng, Wancen Mu, Vera Pancaldi, Kris Sankaran, Dario Righelli, Amrit Singh, Joshua S. Sodicoff, Genevieve L. Stein-O’Brien, Ayshwarya Subramanian, Joshua D. Welch, Yue You, Ricard Argelaguet, Vincent J. Carey, Ruben Dries, Casey S. Greene, Susan Holmes, Michael I. Love, Matthew E. Ritchie, Guo-Cheng Yuan, Aedin C. Culhane & Elana Fertig "

Mutation in isocitrate dehydrogenase (mIDH) is a gain of function mutation resulting in the production of the oncometabolite, R-2-hydroxyglutarate, that inhibits DNA and histone demethylases. The resultant hypermethylation phenotype reprograms the glioma cells’ transcriptome and elicits profound effects on glioma immunity. We report that in mouse models and human gliomas, mIDH1 in the context of ATRX and TP53 inactivation results in global expansion of the granulocytic myeloid cells’ compartment. Single-cell RNA-sequencing coupled with mass cytometry analysis revealed that these granulocytes are mainly non-immunosuppressive neutrophils and pre-neutrophils; with a small fraction of polymorphonuclear myeloid-derived suppressor cells. The mechanism of mIDH1 mediated pre-neutrophils expansion involves epigenetic reprogramming which leads to enhanced expression of the granulocyte colony-stimulating factor (G-CSF). Blocking G-CSF restored the inhibitory potential of PMN-MDSCs and enhanced tumor progression. Thus, G-CSF induces remodeling of the inhibitory PMN-MDSCs in mIDH1 glioma rendering them non-immunosuppressive; and having significant therapeutic implications.SIGNIFICANCEmIDH1 is the most common mutation in gliomas associated with improved prognosis. Gliomas harboring mIDH1, together with ATRX and TP53 inactivation, exhibit higher circulating levels of G-CSF, ensuing the recruitment and expansion of non-suppressive neutrophils, pre-neutrophils and small fraction of PMN-MDSCs to the TME leading to an immune permissive phenotype.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1–3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis., The authors describe an integrated atlas of the diverse cell types in the mouse primary motor cortex.

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.

Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large, diverse and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. Iterative data addition can also be used to map new data to a reference dataset. Comparisons with previous methods indicate that the improvements in efficiency do not sacrifice dataset alignment and cluster preservation performance. We demonstrate the effectiveness of online iNMF by integrating more than 1 million cells on a standard laptop, integrating large single-cell RNA sequencing and spatial transcriptomic datasets, and iteratively constructing a single-cell multi-omic atlas of the mouse motor cortex.

Although each cell within an organism contains a nearly identical genome sequence, the three-dimensional (3D) packing of the genome varies among individual cells, influencing cell-type-specific gene expression. Genome Architecture Mapping (GAM) is the first genome-wide experimental method for capturing 3D proximities between any number of genomic loci without ligation. GAM overcomes several limitations of 3C-based methods by sequencing DNA from a large collection of thin sections sliced from individual nuclei. The GAM technique measures locus co-segregation, extracts radial positions, infers chromatin compaction, requires small numbers of cells, does not depend on ligation, and provides rich single-cell information. However, previous analyses of GAM data focused exclusively on population averages, neglecting the variation in 3D topology among individual cells.We present the first single-cell analysis of GAM data, demonstrating that the slices from individual cells reveal intercellular heterogeneity in chromosome conformation. By simultaneously clustering both slices and genomic loci, we identify topological variation among single cells, including differential compaction of cell cycle genes. We also develop a geometric model of the nucleus, allowing prediction of the 3D positions of each slice. Using GAM data from mouse embryonic stem cells, we make new discoveries about the structure of the major mammalian histone gene locus, which is incorporated into the Histone Locus Body (HLB), including structural fluctuations and putative causal molecular mechanisms. Our methods are packaged as SluiceBox, a toolkit for mining GAM data. Our approach represents a new method of investigating variation in 3D genome topology among individual cells across space and time.

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.

Recent experimental advances have enabled high-throughput single-cell measurement of gene expression, chromatin accessibility and DNA methylation. We previously used integrative non-negative matrix factorization (iNMF) to jointly learn interpretable low-dimensional representations from multiple single-cell datasets using dataset-specific and shared metagene factors. These factors provide a principled, quantitative definition of cellular identity and how it varies across biological contexts. However, datasets exceeding 1 million cells are now widely available, creating computational barriers to scientific discovery. For instance, it is no longer feasible to analyze large datasets using standard pipelines on a personal computer with limited memory capacity. Moreover, there is a need for an algorithm capable of iteratively refining the definition of cellular identity as efforts to create a comprehensive human cell atlas continually sequence new cells.To address these challenges, we developed an online learning algorithm for integrating large and continually arriving single-cell datasets. We extended previous online learning approaches for NMF to minimize the expected cost of a surrogate function for the iNMF objective. We also derived a novel hierarchical alternating least squares algorithm for iNMF and incorporated it into an efficient online algorithm. Our online approach accesses the training data as mini-batches, decoupling memory usage from dataset size and allowing on-the-fly incorporation of new datasets as they are generated. The online implementation of iNMF converges much more quickly using a fraction of the memory required for the batch implementation, without sacrificing solution quality. Our new approach processes 1.3 million single cells from the entire mouse embryo on a laptop in 25 minutes using less than 500 MB of RAM. We also analyze large datasets without downloading them to disk by streaming them over the internet on demand. Furthermore, we construct a single-cell multi-omic cell atlas of the mouse motor cortex by iteratively incorporating eight single-cell RNA-seq, single-nucleus RNA-seq, single-nucleus ATAC-seq, and single-nucleus DNA methylation datasets generated by the BRAIN Initiative Cell Census Network.Our approach obviates the need to recompute results each time additional cells are sequenced, dramatically increases convergence speed, and allows processing of datasets too large to fit in memory or on disk. Most importantly, it facilitates continual refinement of cell identity as new single-cell datasets from different biological contexts and data modalities are generated.

Recent experimental advances have enabled high-throughput single-cell measurement of gene expression, chromatin accessibility and DNA methylation. We previously employed integrative non-negative matrix factorization (iNMF) to jointly align multiple single-cell datasets (\(X_i\)) and learn interpretable low-dimensional representations using dataset-specific (\(V_i)\) and shared metagene factors (W) and cell factor loadings (\(H_i\)). We developed an alternating nonnegative least squares (ANLS) algorithm to solve the iNMF optimization problem [2]

The spatial organization of cells in tissue has a profound influence on their function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. Here, we introduce Slide-seq, a highly scalable method that enables facile generation of large volumes of unbiased spatial transcriptomes with 10 µm spatial resolution, comparable to the size of individual cells. In Slide-seq, RNA is transferred from freshly frozen tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the spatial locations of the RNA to be inferred by sequencing. To demonstrate Slide-seq’s utility, we localized cell types identified by large-scale scRNA-seq datasets within the cerebellum and hippocampus. We next systematically characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, identifying new axes of variation across Purkinje cell compartments. Finally, we used Slide-seq to define the temporal evolution of cell-type-specific responses in a mouse model of traumatic brain injury. Slide-seq will accelerate biological discovery by enabling routine, high-resolution spatial mapping of gene expression.One Sentence SummarySlide-seq measures genome-wide expression in complex tissues at 10-micron resolution.