Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census.

Motivation: Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin.

Results: We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin.

Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues.

Existing genomic sequencing data can be used to study host-microbiome ecosystems, however distinguishing signals originating from truly present microbes versus contaminating species and artifacts is a substantial and often prohibitive challenge. Here we show that emerging sequencing technologies definitely capture reads from present microbes. We developed SAHMI, a computational resource to identify truly present microbial nucleic acids and filter contaminants and spurious false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues.

Tumor microbiome links cellular programs and immunity in pancreatic cancer.

Microorganisms are detected in multiple cancer types, including in putatively sterile organs, but the contexts in which they influence oncogenesis or anti-tumor responses in humans remain unclear. We recently developed single-cell analysis of host-microbiome interactions (SAHMI), a computational pipeline to recover and denoise microbial signals from single-cell sequencing of host tissues. Here we use SAHMI to interrogate tumor-microbiome interactions in two human pancreatic cancer cohorts.

Inference on spatial heterogeneity in tumor microenvironment using spatial transcriptomics data.

In the tumor microenvironment (TME), functional interactions among tumor, immune, and stromal cells and the extracellular matrix play key roles in tumor progression, invasion, immune modulation, and response to treatment. Intratumor heterogeneity is ubiquitous not only at the genetic and transcriptomic levels but also in the composition and characteristics of TME. However, quantitative inference on spatial heterogeneity in the TME is still limited.

Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.

Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell-cell interactions and relevant ligand-receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp.

Multiplexed Profiling of Single Extracellular Vesicles.

Extracellular vesicles (EV) are a family of cell-originating, membrane-enveloped nanoparticles with diverse biological function, diagnostic potential, and therapeutic applications. While EV can be abundant in circulation, their small size (∼4 order of magnitude smaller than cells) has necessitated bulk analyses, making many more nuanced biological explorations, cell of origin questions, or heterogeneity investigations impossible. Here we describe a single EV analysis (SEA) technique which is simple, sensitive, multiplexable, and practical.

Analysis of transcription factor network underlying 3T3-L1 adipocyte differentiation.

Lipid accumulation in adipocytes reflects a balance between enzymatic pathways leading to the formation and breakdown of esterified lipids, primarily triglycerides. This balance is extremely important, as both high and low lipid levels in adipocytes can have deleterious consequences. The enzymes responsible for lipid synthesis and breakdown (lipogenesis and lipolysis, respectively) are regulated through the coordinated actions of several transcription factors (TFs).