Identifying cell types from spatially referenced single-cell expression datasets

Redefining Mucosal Inflammation with Spatial Genomics

The human oral mucosa contains one of the most complex cellular systems that are essential for normal physiology and defense against a wide variety of local pathogens. Evolving techniques and experimental systems have helped refine our understanding of this complex cellular network. Current single-cell RNA sequencing methods can resolve subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the oral mucosa in health and disease. However, it requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss the contribution of spatial technologies in shaping our understanding of this complex system. We consider the impact on identifying disease cellular neighborhoods and how space defines cell state. We also discuss the limitations and future directions of spatial sequencing technologies with recent advances in machine learning. Finally, we offer a perspective on open questions about mucosal homeostasis that these technologies are well placed to address.

CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data

Identifying cell types is crucial for understanding the functional units of an organism. Machine learning has shown promising performance in identifying cell types, but many existing methods lack biological significance due to poor interpretability. However, it is of the utmost importance to understand what makes cells share the same function and form a specific cell type, motivating us to propose a biologically interpretable method. CellTICS prioritizes marker genes with cell-type-specific expression, using a hierarchy of biological pathways for neural network construction, and applying a multi-predictive-layer strategy to predict cell and sub-cell types. CellTICS usually outperforms existing methods in prediction accuracy. Moreover, CellTICS can reveal pathways that define a cell type or a cell type under specific physiological conditions, such as disease or aging. The nonlinear nature of neural networks enables us to identify many novel pathways. Interestingly, some of the pathways identified by CellTICS exhibit differential expression "variability" rather than differential expression across cell types, indicating that expression stochasticity within a pathway could be an important feature characteristic of a cell type. Overall, CellTICS provides a biologically interpretable method for identifying and characterizing cell types, shedding light on the underlying pathways that define cellular heterogeneity and its role in organismal function. CellTICS is available at https://github.com/qyyin0516/CellTICS.

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell's marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions.

Exploring tissue architecture using spatial transcriptomics

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

STARCH: copy number and clone inference from spatial transcriptomics data

Tumors are highly heterogeneous, consisting of cell populations with both transcriptional and genetic diversity. These diverse cell populations are spatially organized within a tumor, creating a distinct tumor microenvironment. A new technology called spatial transcriptomics can measure spatial patterns of gene expression within a tissue by sequencing RNA transcripts from a grid of spots, each containing a small number of cells. In tumor cells, these gene expression patterns represent the combined contribution of regulatory mechanisms, which alter the rate at which a gene is transcribed, and genetic diversity, particularly copy number aberrations (CNAs) which alter the number of copies of a gene in the genome. CNAs are common in tumors and often promote cancer growth through upregulation of oncogenes or downregulation of tumor-suppressor genes. We introduce a new method STARCH (spatial transcriptomics algorithm reconstructing copy-number heterogeneity) to infer CNAs from spatial transcriptomics data. STARCH overcomes challenges in inferring CNAs from RNA-sequencing data by leveraging the observation that cells located nearby in a tumor are likely to share similar CNAs. We find that STARCH outperforms existing methods for inferring CNAs from RNA-sequencing data without incorporating spatial information.

SpatialDE: identification of spatially variable genes

Technological advances have made it possible to measure spatially resolved gene expression at high throughput. However, methods to analyze these data are not established. Here, we develop SpatialDE, a statistical test to identify genes with spatial patterns of expression variation from multiplexed imaging or spatial RNA sequencing data. SpatialDE also implements “automatic expression histology”, a spatial gene clustering approach that enables expression-based tissue histology.

Data Analysis in Single-Cell Transcriptome Sequencing

Single-cell transcriptome sequencing, often referred to as single-cell RNA sequencing (scRNA-seq), is used to measure gene expression at the single-cell level and provides a higher resolution of cellular differences than bulk RNA-seq. With more detailed and accurate information, scRNA-seq will greatly promote the understanding of cell functions, disease progression, and treatment response. Although the scRNA-seq experimental protocols have been improved very quickly, many challenges in the scRNA-seq data analysis still need to be overcome. In this chapter, we focus on the introduction and discussion of the research status in the field of scRNA-seq data normalization and cluster analysis, which are the two most important challenges in the scRNA-seq data analysis. Particularly, we present a protocol to discover and validate cancer stem cells (CSCs) using scRNA-seq. Suggestions have also been made to help researchers rationally design their scRNA-seq experiments and data analysis in their future studies.

Programming Morphogenesis through Systems and Synthetic Biology

Mammalian tissue development is an intricate, spatiotemporal process of self-organization that emerges from gene regulatory networks of differentiating stem cells. A major goal in stem cell biology is to gain a sufficient understanding of gene regulatory networks and cell-cell interactions to enable the reliable and robust engineering of morphogenesis. Here, we review advances in synthetic biology, single cell genomics, and multiscale modeling, which, when synthesized, provide a framework to achieve the ambitious goal of programming morphogenesis in complex tissues and organoids.

Synaptic and peptidergic connectome of a neurosecretory center in the annelid brain

Neurosecretory centers in animal brains use peptidergic signaling to influence physiology and behavior. Understanding neurosecretory center function requires mapping cell types, synapses, and peptidergic networks. Here we use transmission electron microscopy and gene expression mapping to analyze the synaptic and peptidergic connectome of an entire neurosecretory center. We reconstructed 78 neurosecretory neurons and mapped their synaptic connectivity in the brain of larval Platynereis dumerilii, a marine annelid. These neurons form an anterior neurosecretory center expressing many neuropeptides, including hypothalamic peptide orthologs and their receptors. Analysis of peptide-receptor pairs in spatially mapped single-cell transcriptome data revealed sparsely connected networks linking specific neuronal subsets. We experimentally analyzed one peptide-receptor pair and found that a neuropeptide can couple neurosecretory and synaptic brain signaling. Our study uncovered extensive networks of peptidergic signaling within a neurosecretory center and its connection to the synaptic brain.

Interrogating islets in health and disease with single-cell technologies

BACKGROUND

Blood glucose levels are tightly controlled by the coordinated actions of hormone-producing endocrine cells that reside in pancreatic islets. Islet cell malfunction underlies diabetes development and progression. Due to the cellular heterogeneity within islets, it has been challenging to uncover how specific islet cells contribute to glucose homeostasis and diabetes pathogenesis. Recent advances in single-cell technologies and computational methods have opened up new avenues to resolve islet heterogeneity and study islet cell states in health and disease.

SCOPE OF REVIEW

In the past year, a multitude of studies have been published that used single-cell approaches to interrogate the transcriptome and proteome of the different islet cell types. Here, we summarize the conclusions of these studies, as well as discuss the technologies used and the challenges faced with computational analysis of single-cell data from islet studies.

MAJOR CONCLUSIONS

By analyzing single islet cells from rodents and humans at different ages and disease states, the studies reviewed here have provided new insight into endocrine cell function and facilitated a high resolution molecular characterization of poorly understood processes, including regeneration, maturation, and diabetes pathogenesis. Gene expression programs and pathways identified in these studies pave the way for the discovery of new targets and approaches to prevent, monitor, and treat diabetes.

Brain transcriptome atlases: a computational perspective

The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data. We provide a comprehensive overview of the various computational methods used to analyze brain transcriptome atlases.

Single-Cell Transcriptomics Bioinformatics and Computational Challenges

The emerging single-cell RNA-Seq (scRNA-Seq) technology holds the promise to revolutionize our understanding of diseases and associated biological processes at an unprecedented resolution. It opens the door to reveal intercellular heterogeneity and has been employed to a variety of applications, ranging from characterizing cancer cells subpopulations to elucidating tumor resistance mechanisms. Parallel to improving experimental protocols to deal with technological issues, deriving new analytical methods to interpret the complexity in scRNA-Seq data is just as challenging. Here, we review current state-of-the-art bioinformatics tools and methods for scRNA-Seq analysis, as well as addressing some critical analytical challenges that the field faces.

Single punch, double biopsy

OBJECTIVE

In lethal primary metastatic prostate cancer, biopsy material is often the only accessible cancer tissue. Lack of tissue quantity limited the use of biopsy cores for analyzing higher numbers of molecular markers and standard histopathologic evaluation for clinical diagnosis simultaneously. Recent advances in single cell analytics have paved the way to characterize a tumor in more depth from minute input material such as biopsies. We therefore aimed to develop a biopsy needle, which generates two cores side by side from the same punch: one for standard histopathologic analysis to allow for routine diagnostics and the second one for single cell analytics.

METHODS

On the basis of a conventional punch biopsy needle we have milled two parallel longitudinal rifts into the needles shat which are separated by a 100 µm thick metal sheet. Each rift can harbor a single tissue core.

RESULTS

Two cores from the same punch were generated reproducibly from a radical prostatectomy specimen and showed congruent results in histopathologic analysis. Both cores yielded equally sufficient material for standard H&E staining and histopathological evaluation.

CONCLUSION

Our modified biopsy system will allow for simultaneous acquisition of tissue cores for diagnostic and scientific analysis from solid tumors or metastatic sites.

Towards a systems-level understanding of development in the marine annelid Platynereis dumerilii

Platynereis dumerilii is a segmented marine worm from the phylum Annelida, a member of the Lophotrochozoans. Platynereis is easily maintained in the lab and exhibits a highly stereotypic development through spiral cleavage with a small, transparent, free-swimming larva highly suitable for microscopy studies. A protocol for embryo microinjection in Platynereis has enabled several genetic tools to be developed, paving the way for functional studies. Recent Platynereis studies have provided insights into the function of several signaling pathways in development. Platynereis has also proven a useful model system for comparative evolutionary developmental studies, allowing the formation of new hypotheses on the evolution of neuroendocrine signaling, body patterning, and organ development. Combining existing large datasets of spatial gene expression mapping, cell lineage mapping, and neuronal circuits with functional analyses of developmental genes represents a promising approach for future studies aiming at a systems-level understanding of development in Platynereis.

Statistical Methods in Integrative Genomics

Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and future research directions.

Myofibroblasts are distinguished from activated skin fibroblasts by the expression of AOC3 and other associated markers

Pericryptal myofibroblasts in the colon and rectum play an important role in regulating the normal colorectal stem cell niche and facilitating tumor progression. Myofibroblasts previously have been distinguished from normal fibroblasts mostly by the expression of α smooth muscle actin (αSMA). We now have identified AOC3 (amine oxidase, copper containing 3), a surface monoamine oxidase, as a new marker of myofibroblasts by showing that it is the target protein of the myofibroblast-reacting mAb PR2D3. The normal and tumor tissue distribution and the cell line reactivity of AOC3 match that expected for myofibroblasts. We have shown that the surface expression of AOC3 is sensitive to digestion by trypsin and collagenase and that anti-AOC3 antibodies can be used for FACS sorting of myofibroblasts obtained by nonenzymatic procedures. Whole-genome microarray mRNA-expression profiles of myofibroblasts and skin fibroblasts revealed four additional genes that are significantly differentially expressed in these two cell types: NKX2-3 and LRRC17 in myofibroblasts and SHOX2 and TBX5 in skin fibroblasts. TGFβ substantially down-regulated AOC3 expression in myofibroblasts but in skin fibroblasts it dramatically increased the expression of αSMA. A knockdown of NKX2-3 in myofibroblasts caused a decrease of myofibroblast-related gene expression and increased expression of the fibroblast-associated gene SHOX2, suggesting that NKX2-3 is a key mediator for maintaining myofibroblast characteristics. Our results show that colorectal myofibroblasts, as defined by the expression of AOC3, NKX2-3, and other markers, are a distinctly different cell type from TGFβ-activated fibroblasts.

Epigenome overlap measure (EPOM) for comparing tissue/cell types based on chromatin states

Background

The dynamics of epigenomic marks in their relevant chromatin states regulate distinct gene expression patterns, biological functions and phenotypic variations in biological processes. The availability of high-throughput epigenomic data generated by next-generation sequencing technologies allows a data-driven approach to evaluate the similarities and differences of diverse tissue and cell types in terms of epigenomic features. While ChromImpute has allowed for the imputation of large-scale epigenomic information to yield more robust data to capture meaningful relationships between biological samples, widely used methods such as hierarchical clustering and correlation analysis cannot adequately utilize epigenomic data to accurately reveal the distinction and grouping of different tissue and cell types.

Methods

We utilize a three-step testing procedure–ANOVA, t test and overlap test to identify tissue/cell-type- associated enhancers and promoters and to calculate a newly defined Epigenomic Overlap Measure (EPOM). EPOM results in a clear correspondence map of biological samples from different tissue and cell types through comparison of epigenomic marks evaluated in their relevant chromatin states.

Results

Correspondence maps by EPOM show strong capability in distinguishing and grouping different tissue and cell types and reveal biologically meaningful similarities between Heart and Muscle, Blood & T-cell and HSC & B-cell, Brain and Neurosphere, etc. The gene ontology enrichment analysis both supports and explains the discoveries made by EPOM and suggests that the associated enhancers and promoters demonstrate distinguishable functions across tissue and cell types. Moreover, the tissue/cell-type-associated enhancers and promoters show enrichment in the disease-related SNPs that are also associated with the corresponding tissue or cell types. This agreement suggests the potential of identifying causal genetic variants relevant to cell-type-specific diseases from our identified associated enhancers and promoters.

Conclusions

The proposed EPOM measure demonstrates superior capability in grouping and finding a clear correspondence map of biological samples from different tissue and cell types. The identified associated enhancers and promoters provide a comprehensive catalog to study distinct biological processes and disease variants in different tissue and cell types. Our results also find that the associated promoters exhibit more cell-type-specific functions than the associated enhancers do, suggesting that the non-associated promoters have more housekeeping functions than the non-associated enhancers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2303-9) contains supplementary material, which is available to authorized users.

SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis

A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html.

High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin

Understanding cell type identity in a multicellular organism requires the integration of gene expression profiles from individual cells with their spatial location in a particular tissue. Current technologies allow whole-transcriptome sequencing of spatially identified cells but lack the throughput needed to characterize complex tissues. Here we present a high-throughput method to identify the spatial origin of cells assayed by single-cell RNA-sequencing within a tissue of interest. Our approach is based on comparing complete, specificity-weighted mRNA profiles of a cell with positional gene expression profiles derived from a gene expression atlas. We show that this method allocates cells to precise locations in the brain of the marine annelid Platynereis dumerilii with a success rate of 81%. Our method is applicable to any system that has a reference gene expression database of sufficiently high resolution.

Computational and analytical challenges in single-cell transcriptomics

The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the study of global patterns of stochastic gene expression. Alongside the technological breakthroughs that have facilitated the large-scale generation of single-cell transcriptomic data, it is important to consider the specific computational and analytical challenges that still have to be overcome. Although some tools for analysing RNA-seq data from bulk cell populations can be readily applied to single-cell RNA-seq data, many new computational strategies are required to fully exploit this data type and to enable a comprehensive yet detailed study of gene expression at the single-cell level.