Single-cell Profiling Technologies Research Articles

Dual Recombinase-Based Mouse Models Help Decipher Cancer Biology and Targets for Therapy.

The advent of next-generation sequencing (NGS) and single-cell profiling technologies has revealed the complex and heterogenous ecosystem of human tumors under steady-state and therapeutic perturbation. Breakthroughs in the development of genetically engineered mouse models (GEMM) of human cancers that are based on the combination of two site-specific recombinase systems [dual-recombinase system (DRS)] offer fundamental new possibilities to elucidate and understand critical drivers of the diverse tumor phenotypes and validate potential targets for therapy. Here, we discuss opportunities DRS-based cancer GEMMs offer to model, trace, manipulate, and functionally investigate established cancers, their interactions with the host, and their response to therapy.

Applying single cell multi-omic analyses to understand treatment resistance in pediatric high grade glioma.

Despite improvements in cancer patient outcomes seen in the past decade, tumor resistance to therapy remains a major impediment to achieving durable clinical responses. Intratumoral heterogeneity related to genetic, epigenetic, transcriptomic, proteomic, and metabolic differences between individual cancer cells has emerged as a driver of therapeutic resistance. This cell to cell heterogeneity can be assessed using single cell profiling technologies that enable the identification of tumor cell clones that exhibit similar defining features like specific mutations or patterns of DNA methylation. Single cell profiling of tumors before and after treatment can generate new insights into the cancer cell characteristics that confer therapeutic resistance by identifying intrinsically resistant sub-populations that survive treatment and by describing new cellular features that emerge post-treatment due to tumor cell evolution. Integrative, single cell analytical approaches have already proven advantageous in studies characterizing treatment-resistant clones in cancers where pre- and post-treatment patient samples are readily available, such as leukemia. In contrast, little is known about other cancer subtypes like pediatric high grade glioma, a class of heterogeneous, malignant brain tumors in children that rapidly develop resistance to multiple therapeutic modalities, including chemotherapy, immunotherapy, and radiation. Leveraging single cell multi-omic technologies to analyze naïve and therapy-resistant glioma may lead to the discovery of novel strategies to overcome treatment resistance in brain tumors with dismal clinical outcomes. In this review, we explore the potential for single cell multi-omic analyses to reveal mechanisms of glioma resistance to therapy and discuss opportunities to apply these approaches to improve long-term therapeutic response in pediatric high grade glioma and other brain tumors with limited treatment options.

Quantifying Cell-Type-Specific Differences of Single-Cell Datasets Using Uniform Manifold Approximation and Projection for Dimension Reduction and Shapley Additive exPlanations.

With rapid advances in single-cell profiling technologies, larger-scale investigations that require comparisons of multiple single-cell datasets can lead to novel findings. Specifically, quantifying cell-type-specific responses to different conditions across single-cell datasets could be useful in understanding how the difference in conditions is induced at a cellular level. In this study, we present a computational pipeline that quantifies cell-type-specific differences and identifies genes responsible for the differences. We quantify differences observed in a low-dimensional uniform manifold approximation and projection for dimension reduction space as a proxy for the difference present in the high-dimensional space and use SHapley Additive exPlanations to quantify genes driving the differences. In this study, we applied our algorithm to the Iris flower dataset, single-cell RNA sequencing dataset, and mass cytometry dataset and demonstrate that it can robustly quantify cell-type-specific differences and it can also identify genes that are responsible for the differences.

Tissue Niches Formed by Intestinal Mesenchymal Stromal Cells in Mucosal Homeostasis and Immunity.

The gastrointestinal tract is the largest mucosal surface in our body and accommodates the majority of the total lymphocyte population. Being continuously exposed to both harmless antigens and potentially threatening pathogens, the intestinal mucosa requires the integration of multiple signals for balancing immune responses. This integration is certainly supported by tissue-resident intestinal mesenchymal cells (IMCs), yet the molecular mechanisms whereby IMCs contribute to these events remain largely undefined. Recent studies using single-cell profiling technologies indicated a previously unappreciated heterogeneity of IMCs and provided further knowledge which will help to understand dynamic interactions between IMCs and hematopoietic cells of the intestinal mucosa. In this review, we focus on recent findings on the immunological functions of IMCs: On one hand, we discuss the steady-state interactions of IMCs with epithelial cells and hematopoietic cells. On the other hand, we summarize our current knowledge about the contribution of IMCs to the development of intestinal inflammatory conditions, such as infections, inflammatory bowel disease, and fibrosis. By providing a comprehensive list of cytokines and chemokines produced by IMCs under homeostatic and inflammatory conditions, we highlight the significant immunomodulatory and tissue niche forming capacities of IMCs.

New insights into the bone marrow niche in multiple myeloma revealed by single-cell profiling technologies

Several types of nonhematopoietic cells, including mesenchymal stem or progenitor cells, mature mesenchymal cells, and endothelial cells, and mature hematopoietic cells such as monocytes, macrophages, NK cells, T cells, and B cells regulate the proliferation and survival of myeloma cells. The cell functions adjacent to myeloma cells is specialized by the location of these cells, called a niche. This review focuses on the role and interaction of cellular components of the myeloma-specific niche revealed by studies that utilized the recently developed experimental techniques of single-cell RNA sequencing and high-parameter cytometry. In the myeloma niche, immune cells and mesenchymal cells regulate tumor proliferation. Bone marrow inflammation induced by multiple myeloma, tumor cells, mesenchymal stromal cells, and immune cells interact, results in the dysregulation of anti-tumor immunity. This complex network could affect tumorigenesis, disease progression, and treatment resistance of multiple myeloma.

Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field Model.

Advanced single-cell profiling technologies promote exploration of cell heterogeneity, and clustering of single-cell RNA (scRNA-seq) data enables discovery of coexpression genes and network relationships between genes. In particular, single-cell profiling of circulating tumor cells (CTCs) can provide unique insights into tumor heterogeneity (including in triple-negative breast cancer (TNBC)), while scRNA-seq leads to better understanding of subclonal architecture and biological function. Despite numerous reports suggesting a direct correlation between circulating tumor cells (CTCs) and poor clinical outcomes, few studies have provided a thorough heterogeneity characterization of CTCs. In addition, TNBC is a disease with not only intertumor but also intratumor heterogeneity and represents various biological distinct subgroups that may have relationships with immune functions that are not clearly established yet. In this article, we introduce a new scheme for detecting genotypic characterization of single-cell heterogeneities and apply it to CTC and TNBC single-cell RNA-seq data. First, we use an existing mixture exponential family graph model to partition the cell-cell network; then, with the Markov random field model, we obtain more flexible network rewiring. Finally, we find the cell heterogeneity and network relationships according to different high coexpression gene modules in different cell subsets. Our results demonstrate that this scheme provides a reasonable and effective way to model different cell clusters and different biological enrichment gene clusters. Thus, using different internal coexpression genes of different cell clusters, we can infer the differences in tumor composition and diversity.

Recording development with single cell dynamic lineage tracing.

ABSTRACTEvery animal grows from a single fertilized egg into an intricate network of cell types and organ systems. This process is captured in a lineage tree: a diagram of every cell's ancestry back to the founding zygote. Biologists have long sought to trace this cell lineage tree in individual organisms and have developed a variety of technologies to map the progeny of specific cells. However, there are billions to trillions of cells in complex organisms, and conventional approaches can only map a limited number of clonal populations per experiment. A new generation of tools that use molecular recording methods integrated with single cell profiling technologies may provide a solution. Here, we summarize recent breakthroughs in these technologies, outline experimental and computational challenges, and discuss biological questions that can be addressed using single cell dynamic lineage tracing.

Stemformatics: visualize and download curated stem cell data.

Stemformatics is an established gene expression data portal containing over 420 public gene expression datasets derived from microarray, RNA sequencing and single cell profiling technologies. Developed for the stem cell community, it has a major focus on pluripotency, tissue stem cells, and staged differentiation. Stemformatics includes curated ‘collections’ of data relevant to cell reprogramming, as well as hematopoiesis and leukaemia. Rather than simply rehosting datasets as they appear in public repositories, Stemformatics uses a stringent set of quality control metrics and its own pipelines to process handpicked datasets from raw files. This means that about 30% of datasets processed by Stemformatics fail the quality control metrics and never make it to the portal, ensuring that Stemformatics data are of high quality and have been processed in a consistent manner. Stemformatics provides easy-to-use and intuitive tools for biologists to visually explore the data, including interactive gene expression profiles, principal component analysis plots and hierarchical clusters, among others. The addition of tools that facilitate cross-dataset comparisons provides users with snapshots of gene expression in multiple cell and tissues, assisting the identification of cell-type restricted genes, or potential housekeeping genes. Stemformatics is freely available at stemformatics.org.

A Branch Point on Differentiation Trajectory is the Bifurcating Event Revealed by Dynamical Network Biomarker Analysis of Single-Cell Data

The advance in single-cell profiling technologies and the development in computational algorithms provide the opportunity to reconstruct pseudo temporal trajectory with branch point of cellular development. On the other hand, theories such as dynamical network biomarkers (DNB) theory have been recently proposed to characterize the pre-transition state in biological systems. Few studies have validated whether the branch point identified in pseudo time is the critical point in dynamical system. In this study, the dynamical behavior of the branch point on the pseudo trajectory has been investigated. We study the pseudo temporal trajectories reconstructed by Wishbone and diffusion pseudotime analysis (DPT) algorithms, as well as the simulated trajectory. DNB theory is applied to justify the bifurcating event on the pseudo trajectories. Our results demonstrate that the branch point recovered by Wishbone and DPT algorithms is confirmed as a transition state in cell differentiation process by DNB theory. Furthermore, we show that an appropriate DNB group will amplify the comprehensive index of critical event as defined in DNB theory. Our study provides biological insights on pseudo trajectory with branch point in a dynamical view and also indicates that DNB theory may serve as a benchmark to check the validity of branch point.