The Human Cell Atlas bone marrow single-cell interactive web portal

Transcriptomes of MPO-deficient patients with generalized pustular psoriasis reveals expansion of CD4+ cytotoxic T cells and an involvement of the complement system

Generalized pustular psoriasis (GPP) is a severe psoriatic subtype characterized by epidermal neutrophil infiltration. Although variants in IL36RN and MPO have been shown to affect immune cells, a systematic analysis of neutrophils and peripheral blood mononuclear cells (PBMCs) subsets and their differential gene expression dependent on MPO genotypes was not performed yet. We assessed transcriptomes of MPO-deficient patients using single cell RNA-sequencing (scRNAseq) of PBMCs and RNA-sequencing of neutrophils in stable disease state. Cell type annotation by multimodal reference mapping of scRNAseq data was verified by flow cytometry of surface and intracellular markers; proportions of CD4⁺ cytotoxic T-lymphocytes (CTLs) and other CD4⁺ effector cells were increased in GPP, while frequencies of naïve CD4⁺ T cells were significantly lower. The expression of FGFBP2 marking CD4⁺ CTLs and CD8⁺ effector memory T-cells (TEMs) was elevated in GPP patients with disease-contributing variants compared to non-carriers (p=0.0015). In neutrophils, differentially expressed genes (DEGs) were significantly enriched in genes of the classical complement activation pathway. Future studies assessing affected cell-types and pathways will show their contribution to GPP's pathogenesis, and indicate whether findings can be transferred to the acute epidermal situation and whether depletion or inactivation of CD4⁺ CTLs may be a reasonable therapeutic approach.

HLF expression defines the human hematopoietic stem cell state

Hematopoietic stem cells (HSCs) sustain blood cell homeostasis throughout life and can regenerate all blood lineages after transplantation. Despite this clear functional definition, highly enriched isolation of human HSCs can currently only be achieved through combinatorial assessment of multiple surface antigens. Although several transgenic HSC reporter mouse strains have been described, no analogous approach to prospectively isolate human HSCs has been reported. To identify genes with the most selective expression in human HSCs, we profiled population and single-cell transcriptomes of unexpanded and ex vivo cultured cord blood-derived hematopoietic stem and progenitor cells as well as peripheral blood, adult bone marrow, and fetal liver. On the basis of these analyses, we propose the master transcription factor HLF (hepatic leukemia factor) as one of the most specific HSC marker genes. To directly track its expression in human hematopoietic cells, we developed a genomic HLF reporter strategy, capable of selectively labeling the most immature blood cells on the basis of a single engineered parameter. Most importantly, HLF-expressing cells comprise all stem cell activity in culture and in vivo during serial transplantation. Taken together, these results experimentally establish HLF as a defining gene of the human HSC state and outline a new approach to continuously mark these cells with high fidelity.

The Autoimmune Risk R262W Variant of the Adaptor SH2B3 Improves Survival in Sepsis

The single-nucleotide polymorphism (SNP) rs3184504 is broadly associated with increased risk for multiple autoimmune and cardiovascular diseases. Although the allele is uniquely enriched in European descent, the mechanism for the widespread selective sweep is not clear. In this study, we find the rs3184504*T allele had a strong association with reduced mortality in a human sepsis cohort. The rs3184504*T allele associates with a loss-of-function amino acid change (p.R262W) in the adaptor protein SH2B3, a likely causal variant. To better understand the role of SH2B3 in sepsis, we used mouse modeling and challenged SH2B3-deficient mice with a polymicrobial cecal-ligation puncture (CLP) procedure. We found SH2B3 deficiency improved survival and morbidity with less organ damage and earlier bacterial clearance compared with control mice. The peritoneal infiltrating cells exhibited augmented phagocytosis in Sh2b3 ^-/- mice with enriched recruitment of Ly6C^hi inflammatory monocytes despite equivalent or reduced chemokine expression. Rapid cycling of monocytes and progenitors occurred uniquely in the Sh2b3 ^-/- mice following CLP, suggesting augmented myelopoiesis. To model the hypomorphic autoimmune risk allele, we created a novel knockin mouse harboring a similar point mutation in the murine pleckstrin homology domain of SH2B3. At baseline, phenotypic changes suggested a hypomorphic allele. In the CLP model, homozygous knockin mice displayed improved mortality and morbidity compared with wild-type or heterozygous mice. Collectively, these data suggest that hypomorphic SH2B3 improves the sepsis response and that balancing selection likely contributed to the relative frequency of the autoimmune risk variant.

Clinical importance of high-mannose, fucosylated and complex N-glycans in breast cancermetastasis

BACKGROUND. Although aberrant glycosylation is recognized as a hallmark of cancer, glycosylation in clinical breast cancer (BC) metastasis has not yet been studied. While preclinical studies show that the glycocalyx coating of cancer cells is involved in adhesion, migration, and metastasis, glycosylation changes from primary tumor (PT) to various metastatic sites remain unknown in patients.

METHODS. We investigated N-glycosylation profiles in 17 metastatic BC patients from our rapid autopsy program. Primary breast tumor, lymph node metastases, multiple systemic metastases, and various normal tissue cores from each patient were arranged on unique single-patient tissue microarrays (TMAs). We performed mass spectrometry imaging (MSI) combined with extensive pathology annotation of these TMAs, and this process enabled spatially differentiated cell-based analysis of N-glycosylation patterns in metastatic BC.

RESULTS. N-glycan abundance increased during metastatic progression independently of BC subtype and treatment regimen, with high-mannose glycans most frequently elevated in BC metastases, followed by fucosylated and complex glycans. Bone metastasis, however, displayed increased core-fucosylation and decreased high-mannose glycans. Consistently, N-glycosylated proteins and N-glycan biosynthesis genes were differentially expressed during metastatic BC progression, with reduced expression of mannose-trimming enzymes and with elevated EpCAM, N-glycan branching, and sialyation enzymes in BC metastases versus PT.

CONCLUSION. We show in patients that N-glycosylation of breast cancer cells undergoing metastasis occurs in a metastatic site–specific manner, supporting the clinical importance of high-mannose, fucosylated, and complex N-glycans as future diagnostic markers and therapeutic targets in metastatic BC.

FUNDING. NIH grants R01CA213428, R01CA213492, R01CA264901, T32CA193145, Dutch Province Limburg “LINK”, European Union ERA-NET TRANSCAN2-643638.

Longitudinal single-cell analysis of a myeloma mouse model identifies subclonal molecular programs associated with progression

Molecular programs that underlie precursor progression in multiple myeloma are incompletely understood. Here, we report a disease spectrum-spanning, single-cell analysis of the Vκ*MYC myeloma mouse model. Using samples obtained from mice with serologically undetectable disease, we identify malignant cells as early as 30 weeks of age and show that these tumours contain subclonal copy number variations that persist throughout progression. We detect intratumoural heterogeneity driven by transcriptional variability during active disease and show that subclonal expression programs are enriched at different times throughout early disease. We then show how one subclonal program related to GCN2 stress response is progressively activated during progression in myeloma patients. Finally, we use chemical and genetic perturbation of GCN2 in vitro to support this pathway as a therapeutic target in myeloma. These findings therefore present a model of precursor progression in Vκ*MYC mice, nominate an adaptive mechanism important for myeloma survival, and highlight the need for single-cell analyses to understand the biological underpinnings of disease progression.

Anatomical structures, cell types and biomarkers of the Human Reference Atlas

The Human Reference Atlas (HRA) aims to map all of the cells of the human body to advance biomedical research and clinical practice. This Perspective presents collaborative work by members of 16 international consortia on two essential and interlinked parts of the HRA: (1) three-dimensional representations of anatomy that are linked to (2) tables that name and interlink major anatomical structures, cell types, plus biomarkers (ASCT+B). We discuss four examples that demonstrate the practical utility of the HRA.

Dynamic transcriptional reprogramming leads to immunotherapeutic vulnerabilities in myeloma

While there is extensive evidence for genetic variation as a basis for treatment resistance, other sources of variation result from cellular plasticity. Using multiple myeloma as an example of an incurable lymphoid malignancy, we show how cancer cells modulate lineage restriction, adapt their enhancer usage and employ cell-intrinsic diversity for survival and treatment escape. By using single-cell transcriptome and chromatin accessibility profiling, we show that distinct transcriptional states co-exist in individual cancer cells and that differential transcriptional regulon usage and enhancer rewiring underlie these alternative transcriptional states. We demonstrate that exposure to standard treatment further promotes transcriptional reprogramming and differential enhancer recruitment while simultaneously reducing developmental potential. Importantly, treatment generates a distinct complement of actionable immunotherapy targets, such as CXCR4, which can be exploited to overcome treatment resistance. Our studies therefore delineate how to transform the cellular plasticity that underlies drug resistance into immuno-oncologic therapeutic opportunities.

Defining the fetal origin of MLL-AF4 infant leukemia highlights specific fatty acid requirements

Infant MLL-AF4-driven acute lymphoblastic leukemia (ALL) is a devastating disease with dismal prognosis. A lack of understanding of the unique biology of this disease, particularly its prenatal origin, has hindered improvement of survival. We perform multiple RNA sequencing experiments on fetal, neonatal, and adult hematopoietic stem and progenitor cells from human and mouse. This allows definition of a conserved fetal transcriptional signature characterized by a prominent proliferative and oncogenic nature that persists in infant ALL blasts. From this signature, we identify a number of genes in functional validation studies that are critical for survival of MLL-AF4+ ALL cells. Of particular interest are PLK1 because of the readily available inhibitor and ELOVL1, which highlights altered fatty acid metabolism as a feature of infant ALL. We identify which aspects of the disease are residues of its fetal origin and potential disease vulnerabilities.

Germline POT1 Deregulation Can Predispose to Myeloid Malignancies in Childhood

While the shelterin complex guards and coordinates the mechanism of telomere regulation, deregulation of this process is tightly linked to malignant transformation and cancer. Here, we present the novel finding of a germline stop-gain variant (p.Q199*) in the shelterin complex gene POT1, which was identified in a child with acute myeloid leukemia. We show that the cells overexpressing the mutated POT1 display increased DNA damage and chromosomal instabilities compared to the wildtype counterpart. Protein and mRNA expression analyses in the primary patient cells further confirm that, physiologically, the variant leads to a nonfunctional POT1 allele in the patient. Subsequent telomere length measurements in the primary cells carrying heterozygous POT1 p.Q199* as well as POT1 knockdown AML cells revealed telomeric elongation as the main functional effect. These results show a connection between POT1 p.Q199* and telomeric dysregulation and highlight POT1 germline deficiency as a predisposition to myeloid malignancies in childhood.

Mutation signatures of pediatric acute myeloid leukemia and normal blood progenitors associated with differential patient outcomes

A subset of pediatric AML cases harbors more somatic mutations in their genomes compared to normal blood progenitors. This subset displays expression profiles that resemble more committed progenitors and associates with better patient survival.

Acquisition of oncogenic mutations with age is believed to be rate limiting for carcinogenesis. However, the incidence of leukemia in children is higher than in young adults. Here we compare somatic mutations across pediatric acute myeloid leukemia (pAML) patient-matched leukemic blasts and hematopoietic stem and progenitor cells (HSPC), as well as HSPCs from age-matched healthy donors. HSPCs in the leukemic bone marrow have limited genetic relatedness and share few somatic mutations with the cell of origin of the malignant blasts, suggesting polyclonal hematopoiesis in patients with pAML. Compared with normal HSPCs, a subset of pAML cases harbored more somatic mutations and a distinct composition of mutational process signatures. We hypothesize that these cases might have arisen from a more committed progenitor. This subset had better outcomes than pAML cases with mutation burden comparable with age-matched healthy HSPCs. Our study provides insights into the etiology and patient stratification of pAML.

A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder

Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoencoder (scGAE), a dimensionality reduction method that preserves topological structure in scRNA-seq data. scGAE builds a cell graph and uses a multitask-oriented graph autoencoder to preserve topological structure information and feature information in scRNA-seq data simultaneously. We further extended scGAE for scRNA-seq data visualization, clustering, and trajectory inference. Analyses of simulated data showed that scGAE accurately reconstructs developmental trajectory and separates discrete cell clusters under different scenarios, outperforming recently developed deep learning methods. Furthermore, implementation of scGAE on empirical data showed scGAE provided novel insights into cell developmental lineages and preserved inter-cluster distances.

Detailed characterization of the transcriptome of single B cells in mantle cell lymphoma suggesting a potential use for SOX4

Mantle cell lymphoma (MCL) is a malignancy arising from naive B lymphocytes with common bone marrow (BM) involvement. Although t(11;14) is a primary event in MCL development, the highly diverse molecular etiology and causal genomic events are still being explored. We investigated the transcriptome of CD19⁺ BM cells from eight MCL patients at single-cell level. The transcriptomes revealed marked heterogeneity across patients, while general homogeneity and clonal continuity was observed within the patients with no clear evidence of subclonal involvement. All patients were SOX11⁺CCND1⁺CD20⁺. Despite monotypic surface immunoglobulin (Ig) κ or λ protein expression in MCL, 10.9% of the SOX11 + malignant cells expressed both light chain transcripts. The early lymphocyte transcription factor SOX4 was expressed in a fraction of SOX11 + cells in two patients and co-expressed with the precursor lymphoblastic marker, FAT1, in a blastoid case, suggesting a potential prognostic role. Additionally, SOX4 was found to identify non-malignant SOX11^– pro-/pre-B cell populations. Altogether, the observed expression of markers such as SOX4, CD27, IgA and IgG in the SOX11⁺ MCL cells, may suggest that the malignant cells are not fixed in the differentiation state of naïve mature B cells, but instead the patients carry B lymphocytes of different differentiation stages.

Transitions in lineage specification and gene regulatory networks in hematopoietic stem/progenitor cells over human development

Human hematopoiesis is a dynamic process that starts in utero 18-21 days post-conception. Understanding the site- and stage-specific variation in hematopoiesis is important if we are to understand the origin of hematological disorders, many of which occur at specific points in the human lifespan. To unravel how the hematopoietic stem/progenitor cell (HSPC) compartment changes during human ontogeny and the underlying gene regulatory mechanisms, we compare 57,489 HSPCs from 5 different tissues spanning 4 developmental stages through the human lifetime. Single-cell transcriptomic analysis identifies significant site- and developmental stage-specific transitions in cellular architecture and gene regulatory networks. Hematopoietic stem cells show progression from cycling to quiescence and increased inflammatory signaling during ontogeny. We demonstrate the utility of this dataset for understanding aberrant hematopoiesis through comparison to two cancers that present at distinct time points in postnatal life-juvenile myelomonocytic leukemia, a childhood cancer, and myelofibrosis, which classically presents in older adults.

SplicingFactory-splicing diversity analysis for transcriptome data

MOTIVATION

Alternative splicing contributes to the diversity of RNA found in biological samples. Current tools investigating patterns of alternative splicing check for coordinated changes in the expression or relative ratio of RNA isoforms where specific isoforms are up- or down-regulated in a condition. However, the molecular process of splicing is stochastic and changes in RNA isoform diversity for a gene might arise between samples or conditions. A specific condition can be dominated by a single isoform, while multiple isoforms with similar expression levels can be present in a different condition. These changes might be the result of mutations, drug treatments or differences in the cellular or tissue environment. Here, we present a tool for the characterization and analysis of RNA isoform diversity using isoform level expression measurements.

RESULTS

We developed an R package called SplicingFactory, to calculate various RNA isoform diversity metrics, and compare them across conditions. Using the package, we tested the effect of RNA-seq quantification tools, quantification uncertainty, gene expression levels and isoform numbers on the isoform diversity calculation. We analyzed a set of CD34+ hematopoietic stem cells and myelodysplastic syndrome samples and found a set of genes whose isoform diversity change is associated with SF3B1 mutations.

AVAILABILITY AND IMPLEMENTATION

The SplicingFactory package is freely available under the GPL-3.0 license from Bioconductor for the Windows, MacOS and Linux operating systems (https://www.bioconductor.org/packages/release/bioc/html/SplicingFactory.html).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Landscape of Bone Marrow Metastasis in Human Neuroblastoma Unraveled by Transcriptomics and Deep Multiplex Imaging

While the bone marrow attracts tumor cells in many solid cancers leading to poor outcome in affected patients, comprehensive analyses of bone marrow metastases have not been performed on a single-cell level. We here set out to capture tumor heterogeneity and unravel microenvironmental changes in neuroblastoma, a solid cancer with bone marrow involvement. To this end, we employed a multi-omics data mining approach to define a multiplex imaging panel and developed DeepFLEX, a pipeline for subsequent multiplex image analysis, whereby we constructed a single-cell atlas of over 35,000 disseminated tumor cells (DTCs) and cells of their microenvironment in the metastatic bone marrow niche. Further, we independently profiled the transcriptome of a cohort of 38 patients with and without bone marrow metastasis. Our results revealed vast diversity among DTCs and suggest that FAIM2 can act as a complementary marker to capture DTC heterogeneity. Importantly, we demonstrate that malignant bone marrow infiltration is associated with an inflammatory response and at the same time the presence of immuno-suppressive cell types, most prominently an immature neutrophil/granulocytic myeloid-derived suppressor-like cell type. The presented findings indicate that metastatic tumor cells shape the bone marrow microenvironment, warranting deeper investigations of spatio-temporal dynamics at the single-cell level and their clinical relevance.

Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor

Single-cell RNA-sequencing data have significantly advanced the characterization of cell-type diversity and composition. However, cell-type definitions vary across data and analysis pipelines, raising concerns about cell-type validity and generalizability. With MetaNeighbor, we proposed an efficient and robust quantification of cell-type replicability that preserves dataset independence and is highly scalable compared to dataset integration. In this protocol, we show how MetaNeighbor can be used to characterize cell-type replicability by following a simple three-step procedure: gene filtering, neighbor voting and visualization. We show how these steps can be tailored to quantify cell-type replicability, determine gene sets that contribute to cell-type identity and pretrain a model on a reference taxonomy to rapidly assess newly generated data. The protocol is based on an open-source R package available from Bioconductor and GitHub, requires basic familiarity with Rstudio or the R command line and can typically be run in <5 min for millions of cells.

Abcc1 and Ggt5 support lymphocyte guidance through export and catabolism of S-geranylgeranyl-l-glutathione

P2RY8 promotes the confinement and growth regulation of germinal center (GC) B cells, and loss of human P2RY8 is associated with B cell lymphomagenesis. The metabolite S-geranylgeranyl-l-glutathione (GGG) is a P2RY8 ligand. The mechanisms controlling GGG distribution are poorly understood. Here, we show that gamma-glutamyltransferase-5 (Ggt5) expression in stromal cells was required for GGG catabolism and confinement of P2RY8-expressing cells to GCs. We identified the ATP-binding cassette subfamily C member 1 (Abcc1) as a GGG transporter and showed that Abcc1 expression by hematopoietic cells was necessary for P2RY8-mediated GC confinement. Furthermore, we discovered that P2RY8 and GGG negatively regulated trafficking of B and T cells to the bone marrow (BM). P2RY8 loss-of-function human T cells increased their BM homing. By defining how GGG distribution was determined and identifying sites of P2RY8 activity, this work helps establish how disruptions in P2RY8 function contribute to lymphomagenesis and other disease states.

Neural G0: a quiescent-like state found in neuroepithelial-derived cells and glioma

Single-cell RNA sequencing has emerged as a powerful tool for resolving cellular states associated with normal and maligned developmental processes. Here, we used scRNA-seq to examine the cell cycle states of expanding human neural stem cells (hNSCs). From these data, we constructed a cell cycle classifier that identifies traditional cell cycle phases and a putative quiescent-like state in neuroepithelial-derived cell types during mammalian neurogenesis and in gliomas. The Neural G0 markers are enriched with quiescent NSC genes and other neurodevelopmental markers found in non-dividing neural progenitors. Putative glioblastoma stem-like cells were significantly enriched in the Neural G0 cell population. Neural G0 cell populations and gene expression are significantly associated with less aggressive tumors and extended patient survival for gliomas. Genetic screens to identify modulators of Neural G0 revealed that knockout of genes associated with the Hippo/Yap and p53 pathways diminished Neural G0 in vitro, resulting in faster G1 transit, down-regulation of quiescence-associated markers, and loss of Neural G0 gene expression. Thus, Neural G0 represents a dynamic quiescent-like state found in neuroepithelial-derived cells and gliomas.

An integrated analysis of human myeloid cells identifies gaps in in vitro models of in vivo biology

The Stemformatics myeloid atlas is an integrated transcriptome atlas of human macrophages and dendritic cells that systematically compares freshly isolated tissue-resident, cultured, and pluripotent stem cell–derived myeloid cells. Three classes of tissue-resident macrophage were identified: Kupffer cells and microglia; monocyte-associated; and tumor-associated macrophages. Culture had a major impact on all primary cell phenotypes. Pluripotent stem cell–derived macrophages were characterized by atypical expression of collagen and a highly efferocytotic phenotype. Myeloid subsets, and phenotypes associated with derivation, were reproducible across experimental series including data projected from single-cell studies, demonstrating that the atlas provides a robust reference for myeloid phenotypes. Implementation in Stemformatics.org allows users to visualize patterns of sample grouping or gene expression for user-selected conditions and supports temporary upload of your own microarray or RNA sequencing samples, including single-cell data, to benchmark against the atlas.

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A reference transcriptome atlas for human macrophage biology

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Culture alters primary myeloid phenotypes

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Pluripotent stem cell–derived macrophages retain a common stromal signature

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FLT3L-derived cord blood DCs lack expression of key pattern recognition receptors

RUNX-1 haploinsufficiency causes a marked deficiency of megakaryocyte-biased hematopoietic progenitor cells

Patients with familial platelet disorder with a predisposition to myeloid malignancy (FPDMM) harbor germline monoallelic mutations in a key hematopoietic transcription factor, RUNX-1. Previous studies of FPDMM have focused on megakaryocyte (Mk) differentiation and platelet production and signaling. However, the effects of RUNX-1 haploinsufficiency on hematopoietic progenitor cells (HPCs) and subsequent megakaryopoiesis remains incomplete. We studied induced pluripotent stem cell (iPSC)-derived HPCs (iHPCs) and Mks (iMks) from both patient-derived lines and a wild-type (WT) line modified to be RUNX-1 haploinsufficient (RUNX-1+/-), each compared with their isogenic WT control. All RUNX-1+/- lines showed decreased iMk yield and depletion of an Mk-biased iHPC subpopulation. To investigate global and local gene expression changes underlying this iHPC shift, single-cell RNA sequencing was performed on sorted FPDMM and control iHPCs. We defined several cell subpopulations in the Mk-biased iHPCs. Analyses of gene sets upregulated in FPDMM iHPCs indicated enrichment for response to stress, regulation of signal transduction, and immune signaling-related gene sets. Immunoblot analyses in FPDMM iMks were consistent with these findings, but also identified augmented baseline c-Jun N-terminal kinase (JNK) phosphorylation, known to be activated by transforming growth factor-β1 (TGF-β1) and cellular stressors. These findings were confirmed in adult human CD34+-derived stem and progenitor cells (HSPCs) transduced with lentiviral RUNX1 short hairpin RNA to mimic RUNX-1+/-. In both iHPCs and CD34+-derived HSPCs, targeted inhibitors of JNK and TGF-β1 pathways corrected the megakaryopoietic defect. We propose that such intervention may correct the thrombocytopenia in patients with FPDMM.

Single-cell technologies and analyses in hematopoiesis and hematological malignancies

In recent years, single-cell technologies have emerged as breakthrough techniques that enable the characterization of hematopoietic cell populations of normal and malignant tissue samples and will be combined in the near future with bulk technologies, currently used in clinical practice, to improve diagnosis, prognosis, and the search for novel molecular targets. These single-cell methods have the advantage of not masking cell-to-cell variation features and involve the study of genetic, epigenetic, transcriptional, and proteomic landscapes from a single-cell perspective. Latest advances in this field have enabled the development of novel strategies that significantly increase both sensitivity and high throughput. In this review, we emphasize emerging techniques aimed at assessing individual or multiomic parameters at single-cell resolution and analyze how these technologies have helped us understand hematopoietic variability and identify unknown and/or rare subpopulations. We also summarize the impact of these single-cell profiling strategies on the characterization of cell diversity within the tumor and the clonal evolution of multiple hematological malignancies in samples from untreated and treated patients, which provide valuable information for diagnosis, prognosis, and future treatments and explain why current therapies may fail. However, despite these improvements, new challenges lie ahead.

A latent subset of human hematopoietic stem cells resists regenerative stress to preserve stemness

Continuous supply of immune cells throughout life relies on the delicate balance in the hematopoietic stem cell (HSC) pool between long-term maintenance and meeting the demands of both normal blood production and unexpected stress conditions. Here we identified distinct subsets of human long-term (LT)-HSCs that responded differently to regeneration-mediated stress: an immune checkpoint ligand CD112^lo subset that exhibited a transient engraftment restraint (termed latency) before contributing to hematopoietic reconstitution and a primed CD112^hi subset that responded rapidly. This functional heterogeneity and CD112 expression are regulated by INKA1 through direct interaction with PAK4 and SIRT1, inducing epigenetic changes and defining an alternative state of LT-HSC quiescence that serves to preserve self-renewal and regenerative capacity upon regeneration-mediated stress. Collectively, our data uncovered the molecular intricacies underlying HSC heterogeneity and self-renewal regulation and point to latency as an orchestrated physiological response that balances blood cell demands with preserving a stem cell reservoir.

Single-cell multi-omics analysis of the immune response in COVID-19

Analysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy.

Genetically engineered myeloid cells rebalance the core immune suppression program in metastasis

Metastasis is the leading cause of cancer-related deaths, and greater knowledge of the metastatic microenvironment is necessary to effectively target this process. Microenvironmental changes occur at distant sites prior to clinically detectable metastatic disease; however, the key niche regulatory signals during metastatic progression remain poorly characterized. Here, we identify a core immune suppression gene signature in pre-metastatic niche formation that is expressed predominantly by myeloid cells. We target this immune suppression program by utilizing genetically engineered myeloid cells (GEMys) to deliver IL-12 to modulate the metastatic microenvironment. Our data demonstrate that IL12-GEMy treatment reverses immune suppression in the pre-metastatic niche by activating antigen presentation and T cell activation, resulting in reduced metastatic and primary tumor burden and improved survival of tumor-bearing mice. We demonstrate that IL12-GEMys can functionally modulate the core program of immune suppression in the pre-metastatic niche to successfully rebalance the dysregulated metastatic microenvironment in cancer.

Strategies to Identify Mesenchymal Stromal Cells in Minimally Manipulated Human Bone Marrow Aspirate Concentrate Lack Consensus

BACKGROUND

There is a need to identify and quantify mesenchymal stromal cells (MSCs) in human bone marrow aspirate concentrate (BMAC) source tissues, but current methods to do so were established in cultured cell populations. Given that surface marker and gene expression change in cultured cells, it is doubtful that these strategies are valid to quantify MSCs in fresh BMAC.

PURPOSE

To establish the presence, quantity, and heterogeneity of BMAC-derived MSCs in minimally manipulated BMAC using currently available strategies.

STUDY DESIGN

Descriptive laboratory study.

METHODS

Five published strategies to identify MSCs were compared for suitability and efficiency to quantify clinical-grade BMAC-MSCs and cultured MSCs at the single cell transcriptome level on BMAC samples being used clinically from 15 orthopaedic patients and on 1 cultured MSC sample. Strategies included (1) the guidelines by the International Society for Cellular Therapy (ISCT), (2) CD271 expression, (3) the Ghazanfari et al transcriptional profile, (4) the Jia et al transcriptional profile, and (5) the Silva et al transcriptional profile.

RESULTS

ISCT guidelines did not identify any MSCs in BMAC at the transcriptional level and only 1 in 9 million cells at the protein level. Of 12,850 BMAC cells, 9 expressed the CD271 gene. Only 116 of 396 Ghazanfari genes were detected in BMAC, whereas no cells expressed all of them. No cells expressed all Jia genes, but 25 cells expressed at least 13 of 22. No cells expressed all Silva genes, but 19 cells expressed at least 8 of 23. Most importantly, the liberalized strategies tended to identify different cells and most of them clustered with immune cells.

CONCLUSION

Currently available methods need to be liberalized to identify any MSCs in fresh human BMAC and lack consensus at the single cell transcriptome and protein expression levels. These different cells should be isolated and challenged to establish phenotypic differences.

CLINICAL RELEVANCE

This study demonstrated that improved strategies to quantify MSC concentrations in BMAC for clinical applications are urgently needed. Until then, injected minimally manipulated MSC doses should be reported as rough estimates or as unknown.

Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms

Networks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have been proposed. However, published benchmarks on single-cell network inference are mostly based on simulated data. Once applied to real data, these benchmarks take into account only a small set of genes and only compare the inferred networks with an imposed ground-truth. Here, we benchmark six single-cell network inference methods based on their reproducibility, i.e., their ability to infer similar networks when applied to two independent datasets for the same biological condition. We tested each of these methods on real data from three biological conditions: human retina, T-cells in colorectal cancer, and human hematopoiesis. Once taking into account networks with up to 100,000 links, GENIE3 results to be the most reproducible algorithm and, together with GRNBoost2, show higher intersection with ground-truth biological interactions. These results are independent from the single-cell sequencing platform, the cell type annotation system and the number of cells constituting the dataset. Finally, GRNBoost2 and CLR show more reproducible performance once a more stringent thresholding is applied to the networks (1,000–100 links). In order to ensure the reproducibility and ease extensions of this benchmark study, we implemented all the analyses in scNET, a Jupyter notebook available at https://github.com/ComputationalSystemsBiology/scNET.

Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data

Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface markers. However, scRNA-Seq is currently limited due to the need to manually classify each immune cell from its transcriptional profile. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells versus macrophages), making fine distinctions (e.g. CD8+ effector memory T cells) remains a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that its predictions are highly concordant with flow-based markers from CITE-seq and outperforms other tools (+15% recall, +14% precision) in distinguishing fine-grained cell types with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

The Origin of B-cells: Human Fetal B Cell Development and Implications for the Pathogenesis of Childhood Acute Lymphoblastic Leukemia

Human B-lymphopoiesis is a dynamic life-long process that starts in utero by around six post-conception weeks. A detailed understanding of human fetal B-lymphopoiesis and how it changes in postnatal life is vital for building a complete picture of normal B-lymphoid development through ontogeny, and its relevance in disease. B-cell acute lymphoblastic leukemia (B-ALL) is one of the most common cancers in children, with many of the leukemia-initiating events originating in utero. It is likely that the biology of B-ALL, including leukemia initiation, maintenance and progression depends on the developmental stage and type of B-lymphoid cell in which it originates. This is particularly important for early life leukemias, where specific characteristics of fetal B-cells might be key to determining how the disease behaves, including response to treatment. These cellular, molecular and/or epigenetic features are likely to change with age in a cell intrinsic and/or microenvironment directed manner. Most of our understanding of fetal B-lymphopoiesis has been based on murine data, but many recent studies have focussed on characterizing human fetal B-cell development, including functional and molecular assays at a single cell level. In this mini-review we will give a short overview of the recent advances in the understanding of human fetal B-lymphopoiesis, including its relevance to infant/childhood leukemia, and highlight future questions in the field.

Mutated clones driving leukemic transformation are already detectable at the single-cell level in CD34-positive cells in the chronic phase of primary myelofibrosis

Disease progression of myeloproliferative neoplasms is the result of increased genomic complexity. Since the ability to predict disease evolution is crucial for clinical decisions, we studied single-cell genomics and transcriptomics of CD34-positive cells from a primary myelofibrosis (PMF) patient who progressed to acute myeloid leukemia (AML) while receiving Ruxolitinib. Single-cell genomics allowed the reconstruction of clonal hierarchy and demonstrated that TET2 was the first mutated gene while FLT3 was the last one. Disease evolution was accompanied by increased clonal heterogeneity and mutational rate, but clones carrying TP53 and FLT3 mutations were already present in the chronic phase. Single-cell transcriptomics unraveled repression of interferon signaling suggesting an immunosuppressive effect exerted by Ruxolitinib. Moreover, AML transformation was associated with a differentiative block and immune escape. These results suggest that single-cell analysis can unmask tumor heterogeneity and provide meaningful insights about PMF progression that might guide personalized therapy.

Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication

Single-cell RNA sequencing provides exciting opportunities to unbiasedly study hematopoiesis. However, our understanding of leukemogenesis was limited due to the high individual differences. Integrated analyses of hematopoiesis and leukemogenesis potentially provides new insights. Here we analyzed ~200,000 single-cell transcriptomes of bone marrow mononuclear cells (BMMCs) and its subsets from 23 clinical samples. We constructed a comprehensive cell atlas as hematopoietic reference. We developed counterpart composite index (CCI; available at GitHub: https://github.com/pengfeeei/cci) to search for the healthy counterpart of each leukemia cell subpopulation, by integrating multiple statistics to map leukemia cells onto reference hematopoietic cells. Interestingly, we found leukemia cell subpopulations from each patient had different healthy counterparts. Analysis showed the trajectories of leukemia cell subpopulations were similar to that of their healthy counterparts, indicating that developmental termination of leukemia initiating cells at different phases leads to different leukemia cell subpopulations thus explained the origin of leukemia heterogeneity. CCI further predicts leukemia subtypes, cellular heterogeneity, and cellular stemness of each leukemia patient. Analyses of leukemia patient at diagnosis, refractory, remission and relapse vividly presented dynamics of cell population during leukemia treatment. CCI analyses showed the healthy counterparts of relapsed leukemia cells were closer to the root of hematopoietic tree than that of other leukemia cells, although single-cell transcriptomic genetic variants and haplotype tracing analyses showed the relapsed leukemia cell were derived from an early minor leukemia cell population. In summary, this study developed a unified framework for understanding leukemogenesis with hematopoiesis reference, which provided novel biological and medical implication.

Spatiotemporal analysis of human intestinal development at single-cell resolution

Development of the human intestine is not well understood. Here, we link single-cell RNA sequencing and spatial transcriptomics to characterize intestinal morphogenesis through time. We identify 101 cell states including epithelial and mesenchymal progenitor populations and programs linked to key morphogenetic milestones. We describe principles of crypt-villus axis formation; neural, vascular, mesenchymal morphogenesis, and immune population of the developing gut. We identify the differentiation hierarchies of developing fibroblast and myofibroblast subtypes and describe diverse functions for these including as vascular niche cells. We pinpoint the origins of Peyer’s patches and gut-associated lymphoid tissue (GALT) and describe location-specific immune programs. We use our resource to present an unbiased analysis of morphogen gradients that direct sequential waves of cellular differentiation and define cells and locations linked to rare developmental intestinal disorders. We compile a publicly available online resource, spatio-temporal analysis resource of fetal intestinal development (STAR-FINDer), to facilitate further work.

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Multimodal atlas of human intestinal development maps 101 cell types onto tissue

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Charts developmental origins of diverse cellular compartments and their progenitors

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Functional diversity of fibroblasts in stem cell, vasculature, and GALT formation

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Resource applied to interrogate pathology of in utero intestinal diseases

A primer on single-cell genomics in myeloid biology

PURPOSE OF REVIEW

Understanding the fast-moving field of single-cell technologies, as applied to myeloid biology, requires an appreciation of basic molecular, informatics, and biological concepts. Here, we highlight both key and recent articles to illustrate basic concepts for those new to molecular single-cell analyses in myeloid hematology.

RECENT FINDINGS

Recent studies apply single-cell omics to discover novel cell populations, construct relationships between cell populations, reconfigure the organization of hematopoiesis, and study hematopoietic lineage tree and fate choices. Accompanying development of technologies, new informatic tools have emerged, providing exciting new insights.

SUMMARY

Hematopoietic stem and progenitor cells are regulated by complex intrinsic and extrinsic factors to produce blood cell types. In this review, we discuss recent advances in single-cell omics to profile these cells, methods to infer cell type identify, and trajectories from molecular omics data to ultimately derive new insights into hematopoietic stem and progenitor cell biology. We further discuss future applications of these technologies to understand hematopoietic cell interactions, function, and development. The goal is to offer a comprehensive overview of current single-cell technologies and their impact on our understanding of myeloid cell development for those new to single-cell analyses.

HOXA9/IRX1 expression pattern defines two subgroups of infant MLL-AF4-driven acute lymphoblastic leukemia

Infant t(4;11) acute lymphoblastic leukemia is the most common leukemia in infant patients and has a highly aggressive nature. The patients have a dismal prognosis, which has not improved in more than a decade, suggesting that a better understanding of this disease is required. In the study described here, we analyzed two previously published RNA-sequencing data sets and gained further insights into the global transcriptomes of two known subgroups of this disease, which are characterized by the presence or absence of a homeobox gene expression signature. Specifically, we identified a remarkable mutually exclusive expression of the HOXA9/HOXA10 and IRX1 genes and termed the two subgroups iALL-HOXA9 and iALL-IRX1. This expression pattern is critical as it suggests that there is a fundamental difference between the two subgroups. Investigation of the transcriptomes of the two subgroups reveals a more aggressive nature for the iALL-IRX1 group, which is further supported by the fact that patients within this group have a worse prognosis and are also diagnosed at a younger age. This could be reflective of a developmentally earlier cell of origin for iALL-IRX1. Our analysis further uncovered critical differences between the two groups that may have an impact on treatment strategies. In summary, after a detailed investigation into the transcriptional profiles of iALL-HOXA9 and iALL-IRX1 patients, we highlight the importance of acknowledging that these two subgroups are different and that this is of clinical importance.

Single-Cell Analysis of Hematopoietic Stem Cells

The study of hematopoiesis has been revolutionized in recent years by the application of single-cell RNA sequencing technologies. The technique coupled with rapidly developing bioinformatic analysis has provided great insight into the cell type compositions of many populations previously defined by their cell surface phenotype. Moreover, transcriptomic information enables the identification of individual molecules and pathways which define novel cell populations and their transitions including cell lineage decisions. Combining single-cell transcriptional profiling with molecular perturbations allows functional analysis of individual factors in gene regulatory networks and better understanding of the earliest stages of malignant transformation. In this chapter we describe a comprehensive protocol for scRNA-Seq analysis of the mouse bone marrow, using both plate-based (low throughput) and droplet-based (high throughput) methods. The protocol includes instructions for sample preparation, an antibody panel for flow cytometric purification of hematopoietic progenitors with index sorting for plate-based analysis or in bulk for droplet-based methods. The plate-based protocol described in this chapter is a combination of the Smart-Seq2 and mcSCRB-Seq protocols, optimized in our laboratory. It utilizes off-the-shelf reagents for cDNA preparation, is amenable to automation using a liquid handler, and takes 4 days from preparation of the cells for sorting to producing a sequencing-ready library. The droplet-based method (using for instance the 10× Genomics platform) relies on the manufacturer's user guide and commercial reagents, and takes 3 days from isolation of the cells to the production of a library ready for sequencing.

Epigenetic changes in human model KMT2A leukemias highlight early events during leukemogenesis

Chromosomal translocations involving the KMT2A gene are among the most common genetic alterations found in pediatric acute myeloid leukemias although the molecular mechanisms that initiate the disease remain incompletely defined. To elucidate these initiating events we used a human model system of acute myeloid leukemia driven by the KMT2A-MLLT3 (KM3) fusion. More specifically, we investigated changes in DNA methylation, histone modifications, and chromatin accessibility at each stage of our model system and correlated these with expression changes. We observed the development of a pronounced hypomethyl - ation phenotype in the early stages of leukemic transformation after KM3 addition along with loss of expression of stem-cell-associated genes and skewed expression of other genes, such as S100A8/9, implicated in leukemogenesis. In addition, early increases in the expression of the lysine demethylase KDM4B was functionally linked to these expression changes as well as other key transcription factors. Remarkably, our ATAC-sequencing data showed that there were relatively few leukemia-specific changes and that the vast majority corresponded to open chromatin regions and transcription factor clusters previously observed in other cell types. Integration of the gene expression and epigenetic changes revealed that the adenylate cyclase gene ADCY9 is an essential gene in KM3-acute myeloid leukemia, and suggested the potential for autocrine signaling through the chemokine receptor CCR1 and CCL23 ligand. Collectively, our results suggest that KM3 induces subtle changes in the epigenome while co-opting the normal transcriptional machinery to drive leukemogenesis.

Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF

Motivation

The rapid proliferation of single-cell RNA-sequencing (scRNA-Seq) technologies has spurred the development of diverse computational approaches to detect transcriptionally coherent populations. While the complexity of the algorithms for detecting heterogeneity has increased, most require significant user-tuning, are heavily reliant on dimension reduction techniques and are not scalable to ultra-large datasets. We previously described a multi-step algorithm, Iterative Clustering and Guide-gene Selection (ICGS), which applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally coherent cell populations from an intuitive graphical user interface.

Results

We describe a new iteration of ICGS that outperforms state-of-the-art scRNA-Seq detection workflows when applied to well-established benchmarks. This approach combines multiple complementary subtype detection methods (HOPACH, sparse non-negative matrix factorization, cluster ‘fitness’, support vector machine) to resolve rare and common cell-states, while minimizing differences due to donor or batch effects. Using data from multiple cell atlases, we show that the PageRank algorithm effectively downsamples ultra-large scRNA-Seq datasets, without losing extremely rare or transcriptionally similar yet distinct cell types and while recovering novel transcriptionally distinct cell populations. We believe this new approach holds tremendous promise in reproducibly resolving hidden cell populations in complex datasets.

Availability and implementation

ICGS2 is implemented in Python. The source code and documentation are available at http://altanalyze.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Single-Cell Profiling of Ebola Virus Disease In Vivo Reveals Viral and Host Dynamics

Ebola virus (EBOV) causes epidemics with high mortality yet remains understudied due to the challenge of experimentation in high-containment and outbreak settings. Here, we used single-cell transcriptomics and CyTOF-based single-cell protein quantification to characterize peripheral immune cells during EBOV infection in rhesus monkeys. We obtained 100,000 transcriptomes and 15,000,000 protein profiles, finding that immature, proliferative monocyte-lineage cells with reduced antigen-presentation capacity replace conventional monocyte subsets, while lymphocytes upregulate apoptosis genes and decline in abundance. By quantifying intracellular viral RNA, we identify molecular determinants of tropism among circulating immune cells and examine temporal dynamics in viral and host gene expression. Within infected cells, EBOV downregulates STAT1 mRNA and interferon signaling, and it upregulates putative pro-viral genes (e.g., DYNLL1 and HSPA5), nominating pathways the virus manipulates for its replication. This study sheds light on EBOV tropism, replication dynamics, and elicited immune response and provides a framework for characterizing host-virus interactions under maximum containment.

Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities

BACKGROUND

Tight regulatory loops orchestrate commitment to B cell fate within bone marrow. Genetic lesions in this gene regulatory network underlie the emergence of the most common childhood cancer, acute lymphoblastic leukemia (ALL). The initial genetic hits, including the common translocation that fuses ETV6 and RUNX1 genes, lead to arrested cell differentiation. Here, we aimed to characterize transcription factor activities along the B-lineage differentiation trajectory as a reference to characterize the aberrant cell states present in leukemic bone marrow, and to identify those transcription factors that maintain cancer-specific cell states for more precise therapeutic intervention.

METHODS

We compared normal B-lineage differentiation and in vivo leukemic cell states using single cell RNA-sequencing (scRNA-seq) and several complementary genomics profiles. Based on statistical tools for scRNA-seq, we benchmarked a workflow to resolve transcription factor activities and gene expression distribution changes in healthy bone marrow lymphoid cell states. We compared these to ALL bone marrow at diagnosis and in vivo during chemotherapy, focusing on leukemias carrying the ETV6-RUNX1 fusion.

RESULTS

We show that lymphoid cell transcription factor activities uncovered from bone marrow scRNA-seq have high correspondence with independent ATAC- and ChIP-seq data. Using this comprehensive reference for regulatory factors coordinating B-lineage differentiation, our analysis of ETV6-RUNX1-positive ALL cases revealed elevated activity of multiple ETS-transcription factors in leukemic cells states, including the leukemia genome-wide association study hit ELK3. The accompanying gene expression changes associated with natural killer cell inactivation and depletion in the leukemic immune microenvironment. Moreover, our results suggest that the abundance of G1 cell cycle state at diagnosis and lack of differentiation-associated regulatory network changes during induction chemotherapy represent features of chemoresistance. To target the leukemic regulatory program and thereby overcome treatment resistance, we show that inhibition of ETS-transcription factors reduced cell viability and resolved pathways contributing to this using scRNA-seq.

CONCLUSIONS

Our data provide a detailed picture of the transcription factor activities characterizing both normal B-lineage differentiation and those acquired in leukemic bone marrow and provide a rational basis for new treatment strategies targeting the immune microenvironment and the active regulatory network in leukemia.

Single-cell sequencing in hematology

PURPOSE OF REVIEW

In this review, we highlight key recent insights into hematopoiesis and hematological malignancies through the application of novel single-cell approaches. We particularly focus on biological insights made through the study of stem/progenitors cells in myeloid malignancy at single-cell resolution.

RECENT FINDINGS

Bulk molecular profiling of hematological malignancies by next generation sequencing techniques has provided major insights into the molecular pathogenesis of blood cancers. This technology is now routinely implemented in advanced clinical diagnostics, leading to the development of novel targeted therapies. However, bulk genetic analysis can obscure key aspects of intratumoral heterogeneity which underlies critical disease events, such as treatment resistance and clonal evolution. The past few years have seen an explosion of novel techniques to analyze RNA, DNA, and protein expression at the single-cell level, providing unprecedented insight into cellular heterogeneity.

SUMMARY

Given the ease of accessibility of liquid tumor biopsies, hematology is well positioned to move novel single-cell techniques towards routine application in the clinic. The present review sets out to discuss current and potential future applications for this technology in the management of patients with hematological cancers.

Immunogenomic Landscape of Hematological Malignancies

Understanding factors that shape the immune landscape across hematological malignancies is essential for immunotherapy development. We integrated over 8,000 transcriptomes and 2,000 samples with multilevel genomics of hematological cancers to investigate how immunological features are linked to cancer subtypes, genetic and epigenetic alterations, and patient survival, and validated key findings experimentally. Infiltration of cytotoxic lymphocytes was associated with TP53 and myelodysplasia-related changes in acute myeloid leukemia, and activated B cell-like phenotype and interferon-γ response in lymphoma. CIITA methylation regulating antigen presentation, cancer type-specific immune checkpoints, such as VISTA in myeloid malignancies, and variation in cancer antigen expression further contributed to immune heterogeneity and predicted survival. Our study provides a resource linking immunology with cancer subtypes and genomics in hematological malignancies.

Decoding Susceptibility to Respiratory Viral Infections and Asthma Inception in Children

Human Respiratory Syncytial Virus and Human Rhinovirus are the most frequent cause of respiratory tract infections in infants and children and are major triggers of acute viral bronchiolitis, wheezing and asthma exacerbations. Here, we will discuss the application of the powerful tools of systems biology to decode the molecular mechanisms that determine risk for infection and subsequent asthma. An important conceptual advance is the understanding that the innate immune system is governed by a Bow-tie architecture, where diverse input signals converge onto a few core pathways (e.g., IRF7), which in turn generate diverse outputs that orchestrate effector and regulatory functions. Molecular profiling studies in children with severe exacerbations of asthma/wheeze have identified two major immunological phenotypes. The IRF7hi phenotype is characterised by robust upregulation of antiviral response networks, and the IRF7lo phenotype is characterised by upregulation of markers of TGFβ signalling and type 2 inflammation. Similar phenotypes have been identified in infants and children with severe viral bronchiolitis. Notably, genome-wide association studies supported by experimental validation have identified key pathways that increase susceptibility to HRV infection (ORMDL3 and CHDR3) and modulate TGFβ signalling (GSDMB, TGFBR1, and SMAD3). Moreover, functional deficiencies in the activation of type I and III interferon responses are already evident at birth in children at risk of developing febrile lower respiratory tract infections and persistent asthma/wheeze, suggesting that the trajectory to asthma begins at birth or in utero. Finally, exposure to microbes and their products reprograms innate immunity and provides protection from the development of allergies and asthma in children, and therefore microbial products are logical candidates for the primary prevention of asthma.

Single-cell transcriptomic landscape of human blood cells

High throughput single-cell RNA-seq has been successfully implemented to dissect the cellular and molecular features underlying hematopoiesis. However, an elaborate and comprehensive transcriptome reference of the whole blood system is lacking. Here, we profiled the transcriptomes of 7551 human blood cells representing 32 immunophenotypic cell types, including hematopoietic stem cells, progenitors and mature blood cells derived from 21 healthy donors. With high sequencing depth and coverage, we constructed a single-cell transcriptional atlas of blood cells (ABC) on the basis of both protein-coding genes and long noncoding RNAs (lncRNAs), and showed a high consistence between them. Notably, putative lncRNAs and transcription factors regulating hematopoietic cell differentiation were identified. While common transcription factor regulatory networks were activated in neutrophils and monocytes, lymphoid cells dramatically changed their regulatory networks during differentiation. Furthermore, we showed a subset of nucleated erythrocytes actively expressing immune signals, suggesting the existence of erythroid precursors with immune functions. Finally, a web portal offering transcriptome browsing and blood cell type prediction has been established. Thus, our work provides a transcriptional map of human blood cells at single-cell resolution, thereby offering a comprehensive reference for the exploration of physiological and pathological hematopoiesis.

Single cell transcriptome profiling of the human alcohol-dependent brain

Alcoholism remains a prevalent health concern throughout the world. Previous studies have identified transcriptomic patterns in the brain associated with alcohol dependence in both humans and animal models. But none of these studies have systematically investigated expression within the unique cell types present in the brain. We utilized single nucleus RNA sequencing (snRNA-seq) to examine the transcriptomes of over 16 000 nuclei isolated from the prefrontal cortex of alcoholic and control individuals. Each nucleus was assigned to one of seven major cell types by unsupervised clustering. Cell type enrichment patterns varied greatly among neuroinflammatory-related genes, which are known to play roles in alcohol dependence and neurodegeneration. Differential expression analysis identified cell type-specific genes with altered expression in alcoholics. The largest number of differentially expressed genes (DEGs), including both protein-coding and non-coding, were detected in astrocytes, oligodendrocytes and microglia. To our knowledge, this is the first single cell transcriptome analysis of alcohol-associated gene expression in any species and the first such analysis in humans for any addictive substance. These findings greatly advance the understanding of transcriptomic changes in the brain of alcohol-dependent individuals.

H3K79me2/3 controls enhancer-promoter interactions and activation of the pan-cancer stem cell marker PROM1/CD133 in MLL-AF4 leukemia cells

MLL gene rearrangements (MLLr) are a common cause of aggressive, incurable acute lymphoblastic leukemias (ALL) in infants and children, most of which originate in utero. The most common MLLr produces an MLL-AF4 fusion protein. MLL-AF4 promotes leukemogenesis by activating key target genes, mainly through recruitment of DOT1L and increased histone H3 lysine-79 methylation (H3K79me2/3). One key MLL-AF4 target gene is PROM1, which encodes CD133 (Prominin-1). CD133 is a pentaspan transmembrane glycoprotein that represents a potential pan-cancer target as it is found on multiple cancer stem cells. Here we demonstrate that aberrant PROM1/CD133 expression is essential for leukemic cell growth, mediated by direct binding of MLL-AF4. Activation is controlled by an intragenic H3K79me2/3 enhancer element (KEE) leading to increased enhancer–promoter interactions between PROM1 and the nearby gene TAPT1. This dual locus regulation is reflected in a strong correlation of expression in leukemia. We find that in PROM1/CD133 non-expressing cells, the PROM1 locus is repressed by polycomb repressive complex 2 (PRC2) binding, associated with reduced expression of TAPT1, partially due to loss of interactions with the PROM1 locus. Together, these results provide the first detailed analysis of PROM1/CD133 regulation that explains CD133 expression in MLLr ALL.

Fluorescence microscopy tensor imaging representations for large-scale dataset analysis

Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail.

Differences in the intrinsic spatial dynamics of the chromatin contribute to cell differentiation

Advances in chromosome conformation capture techniques as well as computational characterization of genomic loci structural dynamics open new opportunities for exploring the mechanistic aspects of genome-scale differences across different cell types. We examined here the dynamic basis of variabilities between different cell types by investigating their chromatin mobility profiles inferred from Hi-C data using an elastic network model representation of the chromatin. Our comparative analysis of sixteen cell lines reveals close similarities between chromosomal dynamics across different cell lines on a global scale, but notable cell-specific variations emerge in the detailed spatial mobilities of genomic loci. Closer examination reveals that the differences in spatial dynamics mainly originate from the difference in the frequencies of their intrinsically accessible modes of motion. Thus, even though the chromosomes of different types of cells have access to similar modes of collective movements, not all modes are deployed by all cells, such that the effective mobilities and cross-correlations of genomic loci are cell-type-specific. Comparison with RNA-seq expression data reveals a strong overlap between highly expressed genes and those distinguished by high mobilities in the present study, in support of the role of the intrinsic spatial dynamics of chromatin as a determinant of cell differentiation.

Pbx4 limits heart size and fosters arch artery formation by partitioning second heart field progenitors and restricting proliferation

Vertebrate heart development requires the integration of temporally distinct differentiating progenitors. However, few signals are understood that restrict the size of the later-differentiating outflow tract (OFT). We show that improper specification and proliferation of second heart field (SHF) progenitors in zebrafish lazarus (lzr) mutants, which lack the transcription factor Pbx4, produces enlarged hearts owing to an increase in ventricular and smooth muscle cells. Specifically, Pbx4 initially promotes the partitioning of the SHF into anterior progenitors, which contribute to the OFT, and adjacent endothelial cell progenitors, which contribute to posterior pharyngeal arches. Subsequently, Pbx4 limits SHF progenitor (SHFP) proliferation. Single cell RNA sequencing of nkx2.5+ cells revealed previously unappreciated distinct differentiation states and progenitor subpopulations that normally reside within the SHF and arterial pole of the heart. Specifically, the transcriptional profiles of Pbx4-deficient nkx2.5+ SHFPs are less distinct and display characteristics of normally discrete proliferative progenitor and anterior, differentiated cardiomyocyte populations. Therefore, our data indicate that the generation of proper OFT size and arch arteries requires Pbx-dependent stratification of unique differentiation states to facilitate both homeotic-like transformations and limit progenitor production within the SHF.

cellHarmony: cell-level matching and holistic comparison of single-cell transcriptomes

To understand the molecular pathogenesis of human disease, precision analyses to define alterations within and between disease-associated cell populations are desperately needed. Single-cell genomics represents an ideal platform to enable the identification and comparison of normal and diseased transcriptional cell populations. We created cellHarmony, an integrated solution for the unsupervised analysis, classification, and comparison of cell types from diverse single-cell RNA-Seq datasets. cellHarmony efficiently and accurately matches single-cell transcriptomes using a community-clustering and alignment strategy to compute differences in cell-type specific gene expression over potentially dozens of cell populations. Such transcriptional differences are used to automatically identify distinct and shared gene programs among cell-types and identify impacted pathways and transcriptional regulatory networks to understand the impact of perturbations at a systems level. cellHarmony is implemented as a python package and as an integrated workflow within the software AltAnalyze. We demonstrate that cellHarmony has improved or equivalent performance to alternative label projection methods, is able to identify the likely cellular origins of malignant states, stratify patients into clinical disease subtypes from identified gene programs, resolve discrete disease networks impacting specific cell-types, and illuminate therapeutic mechanisms. Thus, this approach holds tremendous promise in revealing the molecular and cellular origins of complex disease.

DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data

Methods for single-cell RNA sequencing (scRNA-seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-seq, these technologies readily produce technical artifacts, such as doublet cell captures. Doublets occurring between distinct cell types can appear as hybrid scRNA-seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic, mixed-species, genetic, and cell-hashing cell doublets from scRNA-seq datasets of varying cellular complexity with a high sensitivity relative to alternative approaches. Importantly, this algorithm prevents the prediction of valid mixed-lineage and transitional cell states as doublets by considering their unique gene expression. DoubletDecon has an easy-to-use graphical user interface and is compatible with diverse species and unsupervised population detection algorithms.