Transcription factor dynamics, oscillation, and functions in human enteroendocrine cell differentiation.

Enteroendocrine cells (EECs) secrete serotonin (enterochromaffin [EC] cells) or specific peptide hormones (non-EC cells) that serve vital metabolic functions. The basis for terminal EEC diversity remains obscure. By forcing activity of the transcription factor (TF) NEUROG3 in 2D cultures of human intestinal stem cells, we replicated physiologic EEC differentiation and examined transcriptional and cis-regulatory dynamics that culminate in discrete cell types. Abundant EEC precursors expressed stage-specific genes and TFs. Before expressing pre-terminal NEUROD1, post-mitotic precursors oscillated between transcriptionally distinct ASCL1+ and HES6hi cell states. Loss of either factor accelerated EEC differentiation substantially and disrupted EEC individuality; ASCL1 or NEUROD1 deficiency had opposing consequences on EC and non-EC cell features. These TFs mainly bind cis-elements that are accessible in undifferentiated stem cells, and they tailor subsequent expression of TF combinations that underlie discrete EEC identities. Thus, early TF oscillations retard EEC maturation to enable accurate diversity within a medically important cell lineage.

Transcription factor dynamics, oscillation, and functions in human enteroendocrine cell differentiation.

Enteroendocrine cells (EECs), which secrete serotonin (enterochromaffin cells, EC) or a dominant peptide hormone, serve vital physiologic functions. As with any adult human lineage, the basis for terminal cell diversity remains obscure. We replicated human EEC differentiation in vitro , mapped transcriptional and chromatin dynamics that culminate in discrete cell types, and studied abundant EEC precursors expressing selected transcription factors (TFs) and gene programs. Before expressing the pre-terminal factor NEUROD1, non-replicating precursors oscillated between epigenetically similar but transcriptionally distinct ASCL1 + and HES6 hi cell states. Loss of either factor substantially accelerated EEC differentiation and disrupted EEC individuality; ASCL1 or NEUROD1 deficiency had opposing consequences on EC and hormone-producing cell features. Expressed late in EEC differentiation, the latter TFs mainly bind cis -elements that are accessible in undifferentiated stem cells and tailor the subsequent expression of TF combinations that specify EEC types. Thus, TF oscillations retard EEC maturation to enable accurate EEC diversification.

scATAnno: Automated Cell Type Annotation for single-cell ATAC Sequencing Data.

Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates the reference atlases from publicly available datasets enabling accurate cell type annotation by integrating query data with reference atlases, without the use of scRNA-seq data. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compare and benchmark scATAnno against 7 other published approaches for cell annotation and show superior performance in multiple data sets and metrics. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), Triple Negative Breast Cancer (TNBC), and basal cell carcinoma (BCC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a useful tool for scATAC-seq reference building and cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems.

Multi-batch single-cell comparative atlas construction by deep learning disentanglement.

Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield new insights into cell state and trajectory alterations. Perturbation experiments often require that single-cell assays be carried out in multiple batches, which can introduce technical distortions that confound the comparison of biological quantities between different batches. Here we propose CODAL, a variational autoencoder-based statistical model which uses a mutual information regularization technique to explicitly disentangle factors related to technical and biological effects. We demonstrate CODAL's capacity for batch-confounded cell type discovery when applied to simulated datasets and embryonic development atlases with gene knockouts. CODAL improves the representation of RNA-seq and ATAC-seq modalities, yields interpretable modules of biological variation, and enables the generalization of other count-based generative models to multi-batched data.

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment.

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells.

Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of these mechanisms regulates fate commitment. Here we present MIRA, probabilistic multimodal models for integrated regulatory analysis, a comprehensive methodology that systematically contrasts transcription and accessibility to infer the regulatory circuitry driving cells along cell state trajectories. MIRA leverages topic modeling of cell states and regulatory potential modeling of individual gene loci. MIRA thereby represents cell states in an efficient and interpretable latent space, infers high-fidelity cell state trees, determines key regulators of fate decisions at branch points and exposes the variable influence of local accessibility on transcription at distinct loci. Applied to epidermal differentiation and embryonic brain development from two different multimodal platforms, MIRA revealed that early developmental genes were tightly regulated by local chromatin landscape whereas terminal fate genes were titrated without requiring extensive chromatin remodeling.

Tuberculosis genotyping in six low-incidence States, 2000-2003.

BACKGROUND: As tuberculosis incidence declines in the United States, a new tool for TB control efforts is Mycobacterium tuberculosis genotyping. Colorado, Iowa, Montana, New Hampshire, West Virginia, and Wisconsin began routine genotyping of all culture-confirmed TB cases in October 2000. METHODS: M. tuberculosis isolates from cases reported October 2000 through December 2003 were genotyped by spoligotyping, mycobacterial interspersed repetitive units, and IS6110-based restriction fragment length polymorphism methods. Genotyping results were linked to demographic variables from national surveillance records. Patients who were in genotype clusters were interviewed and their records reviewed to determine possible transmission links among clustered patients. Final analysis was completed during April 2004 through June 2005. RESULTS: Of 971 reported TB cases, 774 (80%) were culture-confirmed, of which 728 (94%) were genotyped. Most genotyped isolates (634 [87%]) were unique. Within 36 clusters linking 94 individuals, four clusters involved both U.S.- and foreign-born individuals. For eight clusters, genotyping results led to the discovery of previously unsuspected transmission. Transmission links between individuals were established in 21 (58%) of the 36 clusters. CONCLUSIONS: In these six low-incidence states, most isolates had unique genotypes, suggesting that most cases arose from activation of latent infection. Few TB clusters involved the foreign-born. For 58% of genotype clusters, epidemiologic investigation ascertained that clustering represented recent M. tuberculosis transmission.