Age-related epithelial defects limit thymic function and regeneration.

The thymus is essential for establishing adaptive immunity yet undergoes age-related involution that leads to compromised immune responsiveness. The thymus is also extremely sensitive to acute insult and although capable of regeneration, this capacity declines with age for unknown reasons. We applied single-cell and spatial transcriptomics, lineage-tracing and advanced imaging to define age-related changes in nonhematopoietic stromal cells and discovered the emergence of two atypical thymic epithelial cell (TEC) states. These age-associated TECs (aaTECs) formed high-density peri-medullary epithelial clusters that were devoid of thymocytes; an accretion of nonproductive thymic tissue that worsened with age, exhibited features of epithelial-to-mesenchymal transition and was associated with downregulation of FOXN1. Interaction analysis revealed that the emergence of aaTECs drew tonic signals from other functional TEC populations at baseline acting as a sink for TEC growth factors. Following acute injury, aaTECs expanded substantially, further perturbing trophic regeneration pathways and correlating with defective repair of the involuted thymus. These findings therefore define a unique feature of thymic involution linked to immune aging and could have implications for developing immune-boosting therapies in older individuals.

DUX4 expression in cancer induces a metastable early embryonic totipotent program.

The transcription factor DUX4 regulates a portion of the zygotic gene activation (ZGA) program in the early embryo. Many cancers express DUX4 but it is unknown whether this generates cells similar to early embryonic stem cells. Here we identified cancer cell lines that express DUX4 and showed that DUX4 is transiently expressed in a small subset of the cells. DUX4 expression activates the DUX4-regulated ZGA transcriptional program, the subsequent 8C-like program, and markers of early embryonic lineages, while suppressing steady-state and interferon-induced MHC class I expression. Although DUX4 was expressed in a small number of cells under standard culture conditions, DNA damage or changes in growth conditions increased the fraction of cells expressing DUX4 and its downstream programs. Our demonstration that transient expression of endogenous DUX4 in cancer cells induces a metastable early embryonic stem cell program and suppresses antigen presentation has implications for cancer growth, progression, and immune evasion.

Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon.

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive cellular differentiation, regeneration, and disease. Here, we present Mellon, a novel computational algorithm for high-resolution estimation of cell-state densities from single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of various differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. Utilizing hematopoietic stem cell fate specification to B-cells as a case study, we present evidence implicating enhancer priming and the activation of master regulators in the emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during the inherently continuous developmental processes. Scalable and adaptable, Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide new insights into the regulatory mechanisms guiding cellular fate decisions.

SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data.

Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.

Defining cellular population dynamics at single-cell resolution during prostate cancer progression.

Advanced prostate malignancies are a leading cause of cancer-related deaths in men, in large part due to our incomplete understanding of cellular drivers of disease progression. We investigate prostate cancer cell dynamics at single-cell resolution from disease onset to the development of androgen independence in an in vivo murine model. We observe an expansion of a castration-resistant intermediate luminal cell type that correlates with treatment resistance and poor prognosis in human patients. Moreover, transformed epithelial cells and associated fibroblasts create a microenvironment conducive to pro-tumorigenic immune infiltration, which is partially androgen responsive. Androgen-independent prostate cancer leads to significant diversification of intermediate luminal cell populations characterized by a range of androgen signaling activity, which is inversely correlated with proliferation and mRNA translation. Accordingly, distinct epithelial populations are exquisitely sensitive to translation inhibition, which leads to epithelial cell death, loss of pro-tumorigenic signaling, and decreased tumor heterogeneity. Our findings reveal a complex tumor environment largely dominated by castration-resistant luminal cells and immunosuppressive infiltrates.

Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling.

Drug resistance in cancer is often linked to changes in tumor cell state or lineage, but the molecular mechanisms driving this plasticity remain unclear. Using murine organoid and genetically engineered mouse models, we investigated the causes of lineage plasticity in prostate cancer and its relationship to antiandrogen resistance. We found that plasticity initiates in an epithelial population defined by mixed luminal-basal phenotype and that it depends on increased Janus kinase (JAK) and fibroblast growth factor receptor (FGFR) activity. Organoid cultures from patients with castration-resistant disease harboring mixed-lineage cells reproduce the dependency observed in mice by up-regulating luminal gene expression upon JAK and FGFR inhibitor treatment. Single-cell analysis confirms the presence of mixed-lineage cells with increased JAK/STAT (signal transducer and activator of transcription) and FGFR signaling in a subset of patients with metastatic disease, with implications for stratifying patients for clinical trials.

Regenerative lineages and immune-mediated pruning in lung cancer metastasis.

Developmental processes underlying normal tissue regeneration have been implicated in cancer, but the degree of their enactment during tumor progression and under the selective pressures of immune surveillance, remain unknown. Here we show that human primary lung adenocarcinomas are characterized by the emergence of regenerative cell types, typically seen in response to lung injury, and by striking infidelity among transcription factors specifying most alveolar and bronchial epithelial lineages. In contrast, metastases are enriched for key endoderm and lung-specifying transcription factors, SOX2 and SOX9, and recapitulate more primitive transcriptional programs spanning stem-like to regenerative pulmonary epithelial progenitor states. This developmental continuum mirrors the progressive stages of spontaneous outbreak from metastatic dormancy in a mouse model and exhibits SOX9-dependent resistance to natural killer cells. Loss of developmental stage-specific constraint in macrometastases triggered by natural killer cell depletion suggests a dynamic interplay between developmental plasticity and immune-mediated pruning during metastasis.

Characterization of cell fate probabilities in single-cell data with Palantir.

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir's resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.

Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment.

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We profiled 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph nodes, using single-cell RNA-seq. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous phenotypic expansions specific to the tumor microenvironment. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer. Our results have important implications for characterizing tumor-infiltrating immune cells.

Epigenomic-Guided Mass Cytometry Profiling Reveals Disease-Specific Features of Exhausted CD8 T Cells.

Exhausted CD8 T (Tex) cells are immunotherapy targets in chronic infection and cancer, but a comprehensive assessment of Tex cell diversity in human disease is lacking. Here, we developed a transcriptomic- and epigenetic-guided mass cytometry approach to define core exhaustion-specific genes and disease-induced changes in Tex cells in HIV and human cancer. Single-cell proteomic profiling identified 9 distinct Tex cell clusters using phenotypic, functional, transcription factor, and inhibitory receptor co-expression patterns. An exhaustion severity metric was developed and integrated with high-dimensional phenotypes to define Tex cell clusters that were present in healthy subjects, common across chronic infection and cancer or enriched in either disease, linked to disease severity, and changed with HIV therapy. Combinatorial patterns of immunotherapy targets on different Tex cell clusters were also defined. This approach and associated datasets present a resource for investigating human Tex cell biology, with implications for immune monitoring and immunomodulation in chronic infections, autoimmunity, and cancer.

Integrated genomic profiling identifies microRNA-92a regulation of IQGAP2 in locally advanced rectal cancer.

Locally advanced rectal cancer (LARC) is treated with chemoradiation prior to surgical excision, leaving residual tumors altered or completely absent. Integrating layers of genomic profiling might identify regulatory pathways relevant to rectal tumorigenesis and inform therapeutic decisions and further research. We utilized formalin-fixed, paraffin-embedded pre-treatment LARC biopsies (n=138) and compared copy number, mRNA, and miRNA expression with matched normal rectal mucosa. An integrative model was used to predict regulatory interactions to explain gene expression changes. These predictions were evaluated in vitro using multiple colorectal cancer cell lines. The Cancer Genome Atlas (TCGA) was also used as an external cohort to validate our genomic profiling and predictions. We found differentially expressed mRNAs and miRNAs that characterize LARC. Our integrative model predicted the upregulation of miR-92a, miR-182, and miR-221 expression to be associated with downregulation of their target genes after adjusting for the effect of copy number alterations. Cell line studies using miR-92a mimics and inhibitors demonstrate that miR-92a expression regulates IQGAP2 expression. We show that endogenous miR-92a expression is inversely associated with endogenous KLF4 expression in multiple cell lines, and that this relationship is also present in rectal cancers of TCGA. Our integrative model predicted regulators of gene expression change in LARC using pre-treatment FFPE tissues. Our methodology implicated multiple regulatory interactions, some of which are corroborated by independent lines of study, while others indicate new opportunities for investigation.

Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation.

We carried out an integrative analysis of enhancer landscape and gene expression dynamics during hematopoietic differentiation using DNase-seq, histone mark ChIP-seq and RNA sequencing to model how the early establishment of enhancers and regulatory locus complexity govern gene expression changes at cell state transitions. We found that high-complexity genes-those with a large total number of DNase-mapped enhancers across the lineage-differ architecturally and functionally from low-complexity genes, achieve larger expression changes and are enriched for both cell type-specific and transition enhancers, which are established in hematopoietic stem and progenitor cells and maintained in one differentiated cell fate but lost in others. We then developed a quantitative model to accurately predict gene expression changes from the DNA sequence content and lineage history of active enhancers. Our method suggests a new mechanistic role for PU.1 at transition peaks during B cell specification and can be used to correct assignments of enhancers to genes.

SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps.

Genome-wide maps of transcription factor (TF) occupancy and regions of open chromatin implicitly contain DNA sequence signals for multiple factors. We present SeqGL, a novel de novo motif discovery algorithm to identify multiple TF sequence signals from ChIP-, DNase-, and ATAC-seq profiles. SeqGL trains a discriminative model using a k-mer feature representation together with group lasso regularization to extract a collection of sequence signals that distinguish peak sequences from flanking regions. Benchmarked on over 100 ChIP-seq experiments, SeqGL outperformed traditional motif discovery tools in discriminative accuracy. Furthermore, SeqGL can be naturally used with multitask learning to identify genomic and cell-type context determinants of TF binding. SeqGL successfully scales to the large multiplicity of sequence signals in DNase- or ATAC-seq maps. In particular, SeqGL was able to identify a number of ChIP-seq validated sequence signals that were not found by traditional motif discovery algorithms. Thus compared to widely used motif discovery algorithms, SeqGL demonstrates both greater discriminative accuracy and higher sensitivity for detecting the DNA sequence signals underlying regulatory element maps. SeqGL is available at http://cbio.mskcc.org/public/Leslie/SeqGL/.

MicroRNA 28 controls cell proliferation and is down-regulated in B-cell lymphomas.

Burkitt lymphoma (BL) is a highly aggressive B-cell non-Hodgkin lymphoma (B-NHL), which originates from germinal center (GC) B cells and harbors translocations deregulating v-myc avian myelocytomatosis viral oncogene homolog (MYC). A comparative analysis of microRNAs expressed in normal and malignant GC B cells identified microRNA 28 (miR-28) as significantly down-regulated in BL, as well as in other GC-derived B-NHL. We show that reexpression of miR-28 impairs cell proliferation and clonogenic properties of BL cells by modulating several targets including MAD2 mitotic arrest deficient-like 1, MAD2L1, a component of the spindle checkpoint whose down-regulation is essential in mediating miR-28-induced proliferation arrest, and BCL2-associated athanogene, BAG1, an activator of the ERK pathway. We identify the oncogene MYC as a negative regulator of miR-28 expression, suggesting that its deregulation by chromosomal translocation in BL leads to miR-28 suppression. In addition, we show that miR-28 can inhibit MYC-induced transformation by directly targeting genes up-regulated by MYC. Overall, our data suggest that miR-28 acts as a tumor suppressor in BL and that its repression by MYC contributes to B-cell lymphomagenesis.

CSF-1R inhibition alters macrophage polarization and blocks glioma progression.

Glioblastoma multiforme (GBM) comprises several molecular subtypes, including proneural GBM. Most therapeutic approaches targeting glioma cells have failed. An alternative strategy is to target cells in the glioma microenvironment, such as tumor-associated macrophages and microglia (TAMs). Macrophages depend on colony stimulating factor-1 (CSF-1) for differentiation and survival. We used an inhibitor of the CSF-1 receptor (CSF-1R) to target TAMs in a mouse proneural GBM model, which significantly increased survival and regressed established tumors. CSF-1R blockade additionally slowed intracranial growth of patient-derived glioma xenografts. Surprisingly, TAMs were not depleted in treated mice. Instead, glioma-secreted factors, including granulocyte-macrophage CSF (GM-CSF) and interferon-γ (IFN-γ), facilitated TAM survival in the context of CSF-1R inhibition. Expression of alternatively activated M2 markers decreased in surviving TAMs, which is consistent with impaired tumor-promoting functions. These gene signatures were associated with enhanced survival in patients with proneural GBM. Our results identify TAMs as a promising therapeutic target for proneural gliomas and establish the translational potential of CSF-1R inhibition for GBM.

BCL6 positively regulates AID and germinal center gene expression via repression of miR-155.

The BCL6 proto-oncogene encodes a transcriptional repressor that is required for germinal center (GC) formation and whose de-regulation is involved in lymphomagenesis. Although substantial evidence indicates that BCL6 exerts its function by repressing the transcription of hundreds of protein-coding genes, its potential role in regulating gene expression via microRNAs (miRNAs) is not known. We have identified a core of 15 miRNAs that show binding of BCL6 in their genomic loci and are down-regulated in GC B cells. Among BCL6 validated targets, miR-155 and miR-361 directly modulate AID expression, indicating that via repression of these miRNAs, BCL6 up-regulates AID. Similarly, the expression of additional genes relevant for the GC phenotype, including SPI1, IRF8, and MYB, appears to be sustained via BCL6-mediated repression of miR-155. These findings identify a novel mechanism by which BCL6, in addition to repressing protein coding genes, promotes the expression of important GC functions by repressing specific miRNAs.

Identification of global alteration of translational regulation in glioma in vivo.

Post-transcriptional regulation of gene expression contributes to the protein output of a cell, however, methods for measuring translational regulation in complex in vivo systems are lacking. Here, we describe a sensitive method for measuring translational regulation in defined cell populations from heterogeneous tissue in vivo. We adapted the translating ribosome affinity purification (TRAP) methodology to measure the relative occupancy of individual mRNA transcripts in translating ribosomes in the Olig2-positive tumor cell population in a genetically engineered mouse model (GEM) of glioma. Global measurement of paired ribosome-bound and total cellular mRNA populations from tumor cells in vivo identified a broad distribution of relative ribosome occupancies amongst mRNA species that was highly reproducible across biological samples. Comparison of the translation state of glioma cells to non-transformed oligodendrocyte progenitor cells in normal brain identified global alteration of translation in tumor, and specifically of genes involved in cell division and synthetic metabolism. Furthermore, investigation of alteration in steady state translational efficiencies upon loss of PTEN, one of the most frequently mutated and deleted tumor suppressors in glioma, identified differential translation of proteins involved in cellular respiration, canonically regulated by PI3K/Akt signaling, and cellular glycosylation profiles, deregulation of which is known to be associated with tumor progression. Application of the translation efficiency profiling method described here to other biological contexts and conditions would extend our knowledge of the scope and impact of this important mode of gene regulation in complex in vivo systems.

Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma.

Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.

A cooperative microRNA-tumor suppressor gene network in acute T-cell lymphoblastic leukemia (T-ALL).

The importance of individual microRNAs (miRNAs) has been established in specific cancers. However, a comprehensive analysis of the contribution of miRNAs to the pathogenesis of any specific cancer is lacking. Here we show that in T-cell acute lymphoblastic leukemia (T-ALL), a small set of miRNAs is responsible for the cooperative suppression of several tumor suppressor genes. Cross-comparison of miRNA expression profiles in human T-ALL with the results of an unbiased miRNA library screen allowed us to identify five miRNAs (miR-19b, miR-20a, miR-26a, miR-92 and miR-223) that are capable of promoting T-ALL development in a mouse model and which account for the majority of miRNA expression in human T-ALL. Moreover, these miRNAs produce overlapping and cooperative effects on tumor suppressor genes implicated in the pathogenesis of T-ALL, including IKAROS (also known as IKZF1), PTEN, BIM, PHF6, NF1 and FBXW7. Thus, a comprehensive and unbiased analysis of miRNA action in T-ALL reveals a striking pattern of miRNA-tumor suppressor gene interactions in this cancer.

Genomic safe harbors permit high ß-globin transgene expression in thalassemia induced pluripotent stem cells.

Realizing the therapeutic potential of human induced pluripotent stem (iPS) cells will require robust, precise and safe strategies for genetic modification, as cell therapies that rely on randomly integrated transgenes pose oncogenic risks. Here we describe a strategy to genetically modify human iPS cells at 'safe harbor' sites in the genome, which fulfill five criteria based on their position relative to contiguous coding genes, microRNAs and ultraconserved regions. We demonstrate that ∼10% of integrations of a lentivirally encoded ß-globin transgene in ß-thalassemia-patient iPS cell clones meet our safe harbor criteria and permit high-level ß-globin expression upon erythroid differentiation without perturbation of neighboring gene expression. This approach, combining bioinformatics and functional analyses, should be broadly applicable to introducing therapeutic or suicide genes into patient-specific iPS cells for use in cell therapy.