Understanding Cell Identity With Single Cell Transcriptomics

In my thesis work, I use single-cell whole-transcriptome sequencing to reveal new insights into cell identity: when cell types arise in development, how cell types are patterned in the adult, how splicing and transcription factors are modulated by cell identity, and the molecules that may be responsible for generating these patterns. In the first study, I sequenced neurons from the mouse striatum, a large brain region involved in Parkinsons and Huntingtons, in collaboration with Ozgun Gokce and Thomas Sudhof. I created a well-resolved classification of striatal cell type of the mouse striatum; transcriptome analysis revealed 10 differentiated distinct cell types, including neurons, astrocytes, oligodendrocytes, ependymal, immune, and vascular cells, and enabled the discovery of numerous novel marker genes. I further explored neuronal heterogeneity in the adult murine striatum by combining single-cell RNA-seq of SPNs combined with quantitative RNA in situ hybridization (ISH) using the RNAscope platform. I developed a novel computational algorithm that distinguishes discrete versus continuous cell identities in scRNA-seq data, and used it to show that SPNs in the striatum can be classified into four major discrete types with little overlap and no implied spatial relationship. I found that these discrete classes that continuously vary along multiple spatial gradients axes of expression; these gradients define anatomical location by a combinatorial mechanism. I used this information to support the description of a novel region of the striatum. Broadly, our results suggest that neuronal circuitry has a substructure at far higher resolution than is typically interrogated which is defined by the precise identity and location of a neuron. In a collaboration with Rahul Sinha and Irving Weissman, I discovered and investigated an artifact in Illumina sequencing data. Illumina-based next generation sequencing (NGS) has accelerated biomedical discovery through its ability to generate thousands of gigabases of sequencing output at low cost. In 2015, a new chemistry of cluster generation was introduced in the newer Illumina machines called exclusion amplification (ExAmp). This advance has been widely adopted for genome sequencing because greater sequencing depth can be achieved for lower cost without compromising the quality of longer reads. We show that this promising chemistry is problematic, however, when multiplexing samples. We discovered that up to 0.4-10% of sequencing reads (or signals) are incorrectly assigned from a given sample to other samples in a multiplexed pool. We provide evidence that this "spreading-of-signals" arises from low levels of free index primers present in the pool. The rate of signal spreading depending on the level of free index primers present in a library pool, and therefore, variable among experiments. In a collaboration with Tianying Su, Rahul Sinha, and Kristy Red-Horse, I investigated the development of mouse coronary arteries using scRNA-Seq and mouse genetics. I developed a statistical test that categorizes subpopulations within scRNA-Seq datasets as continuous or discrete to identify candidate developmental transitions. I analyzed the transitions between coronary progenitors and artery cells computationally and in vivo, which revealed that the progenitor cells of the mouse heart undergo a gradual conversion from vein to artery before a subset crosses a threshold to differentiate into pre-artery cells. I showed that pre-artery cells in scRNA-Seq data appear prior to blood flow, contrary to previous assumptions about how the heart develops. We showed that a venous transcription factor, COUP-TFII, blocked progression to the pre-artery state through activation of cell cycle genes. I was also interested in how transcription factors maintained cell identity. I therefore analyzed a dataset composed of more than 100,000 cells from 20 organs and tissues, produced by the Tabula Muris Consortium, to understand the transcription factor codes specifying cell identity in the mouse. One of the challenges of scRNA-Seq data is that nearly all studies are specific to a single organ, and it is challenging to compare data collected from different animals by independent labs with varying experimental techniques. To understand which TFs were most informative for specifying cell types, we used random forest machine learning to show that 136 TFs are needed to simultaneously define all cell types across all organs. I collected a compendium of transcription factor reprogramming protocols and showed that for nearly all reprogramming protocols, the TFs used also specified the targeted cell type in our data, suggesting that whole-organism scRNA-Seq data can inform novel reprogramming schemes.

Single-cell omics is a progressing frontier that stems from the sequencing of the human genome and the development of omics technologies, particularly genomics, transcriptomics, epigenomics and proteomics, but the sensitivity is now improved to single-cell level. The new generation of methodologies, especially the next generation sequencing (NGS) technology, plays a leading role in genomics related fields; however, the conventional techniques of omics require number of cells to be large, usually on the order of millions of cells, which is hardly accessible in some cases. More importantly, harnessing the power of omics technologies and applying those at the single-cell level are crucial since every cell is specific and unique, and almost every cell population in every systems, derived in either vivo or in vitro, is heterogeneous. Deciphering the heterogeneity of the cell population hence becomes critical for recognizing the mechanism and significance of the system. However, without an extensive examination of individual cells, a massive analysis of cell population would only give an average output of the cells, but neglect the differences among cells. Single-cell omics seeks to study a number of individual cells in parallel for their different dimensions of molecular profile on genome-wide scale, providing unprecedented resolution for the interpretation of both the structure and function of an organ, tissue or other system, as well as the interaction (and communication) and dynamics of single cells or subpopulations of cells and their lineages. Importantly single-cell omics enables the identification of a minor subpopulation of cells that may play a critical role in biological process over a dominant subpolulation such as a cancer and a developing organ. It provides an ultra-sensitive tool for us to clarify specific molecular mechanisms and pathways and reveal the nature of cell heterogeneity. Besides, it also empowers the clinical investigation of patients when facing a very low quantity of cell available for analysis, such as noninvasive cancer screening with circulating tumor cells (CTC), noninvasive prenatal diagnostics (NIPD) and preimplantation genetic test (PGT) for in vitro fertilization. Single-cell omics greatly promotes the understanding of life at a more fundamental level, bring vast applications in medicine. Accordingly, single-cell omics is also called as single-cell analysis or single-cell biology. Within only a couple of years, single-cell omics, especially transcriptomic sequencing (scRNA-seq), whole genome and exome sequencing (scWGS, scWES), has become robust and broadly accessible. Besides the existing technologies, recently, multiplexing barcode design and combinatorial indexing technology, in combination with microfluidic platform exampled by Drop-seq, or even being independent of microfluidic platform but using a regular PCR-plate, enable us a greater capacity of single cell analysis, switching from one single cell to thousands of single cells in a single test. The unique molecular identifiers (UMIs) allow the amplification bias among the original molecules to be corrected faithfully, resulting in a reliable quantitative measurement of omics in single cells. Of late, a variety of single-cell epigenomics analyses are becoming sophisticated, particularly single cell chromatin accessibility (scATAC-seq) and CpG methylation profiling (scBS-seq, scRRBS-seq). High resolution single molecular Fluorescence in situ hybridization (smFISH) and its revolutionary versions (ex. seqFISH, MERFISH, and so on), in addition to the spatial transcriptome sequencing, make the native relationship of the individual cells of a tissue to be in 3D or 4D format visually and quantitatively clarified. On the other hand, CRISPR/cas9 editing-based In vivo lineage tracing methods enable dynamic profile of a whole developmental process to be accurately displayed. Multi-omics analysis facilitates the study of multi-dimensional regulation and relationship of different elements of the central dogma in a single cell, as well as permitting a clear dissection of the complicated omics heterogeneity of a system. Last but not the least, the technology, biological noise, sequence dropout, and batch effect bring a huge challenge to the bioinformatics of single cell omics. While significant progress in the data analysis has been made since then, revolutionary theory and algorithm logics for single cell omics are expected. Indeed, single-cell analysis exert considerable impacts on the fields of biological studies, particularly cancers, neuron and neural system, stem cells, embryo development and immune system; other than that, it also tremendously motivates pharmaceutic RD, clinical diagnosis and monitoring, as well as precision medicine. This book hereby summarizes the recent developments and general considerations of single-cell analysis, with a detailed presentation on selected technologies and applications. Starting with the experimental design on single-cell omics, the book then emphasizes the consideration on heterogeneity of cancer and other systems. It also gives an introduction of the basic methods and key facts for bioinformatics analysis. Secondary, this book provides a summary of two types of popular technologies, the fundamental tools on single-cell isolation, and the developments of single cell multi-omics, followed by descriptions of FISH technologies, though other popular technologies are not covered here due to the fact that they are intensively described here and there recently. Finally, the book illustrates an elastomer-based integrated fluidic circuit that allows a connection between single cell functional studies combining stimulation, response, imaging and measurement, and corresponding single cell sequencing. This is a model system for single cell functional genomics. In addition, it reports a pipeline for single-cell proteomics with an analysis of the early development of Xenopus embryo, a single-cell qRT-PCR application that defined the subpopulations related to cell cycling, and a new method for synergistic assembly of single cell genome with sequencing of amplification product by phi29 DNA polymerase. Due to the tremendous progresses of single-cell omics in recent years, the topics covered here are incomplete, but each individual topic is excellently addressed, significantly interesting and beneficial to scientists working in or affiliated with this field.

The Mouse Nervous System provides a comprehensive account of the central nervous system of the mouse. The book is aimed at molecular biologists who need a book that introduces them to the anatomy of the mouse brain and spinal cord, but also takes them into the relevant details of development and organization of the area they have chosen to study. The Mouse Nervous System offers a wealth of new information for experienced anatomists who work on mice. The book serves as a valuable resource for researchers and graduate students in neuroscience. Systematic consideration of the anatomy and connections of all regions of the brain and spinal cord by the authors of the most cited rodent brain atlases A major section (12 chapters) on functional systems related to motor control, sensation, and behavioral and emotional states A detailed analysis of gene expression during development of the forebrain by Luis Puelles, the leading researcher in this area Full coverage of the role of gene expression during development and the new field of genetic neuroanatomy using site-specific recombinases Examples of the use of mouse models in the study of neurological illness

Provides background information and detailed protocols for developing a mouse colony and using the animals in transgenic and gene-targeting experiments. The protocols list the animals, equipment, and reagents required and step-by-step procedures. Topics include in vitro culture of preimplantation embryos, surgical procedures, the production of chimeras, and the analysis of genome alterations. The third edition adds protocols for cloning mice, modifying embryonic stem cells, intracytoplasmic sperm injection, and cryopreservation of embryos.

This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.

Cell identity used to be viewed as something static and each generation of organisms had their primordial germ cells separated early so that the somatic cells are fated to differentiate into the rest of the body. Pluripotency was a one-way street where things went from pluripotent to unipotent. However, today's understanding of cell identity has been massively shifted as the field of reprogramming has matured to a point where changing from one cell type to another is possible. Attempts at dissecting the process of iPS reprogramming has helped to improve efficiency of reprogramming and lower tumorigenicity, but improving reprogramming speed proves to be a stronger barrier. And yet, many different cell types reprogram into iPS faster than with fibroblasts. By analyzing and comparing the gene expression profiles of two human cell types that reprogram quickly (Amniotic-Fluid Derived Cells and Adipose Derived Stem Cells) and two human cell types that reprogram slowly (Human Foreskin Fibroblasts and keratinocytes), I found two sets of genes that could explain the differences in reprogramming speeds. PIAS3 and STAT3 seem to be the most promising candidates, but experiments with these two factors are inconclusive. There has also been major progress in direct reprogramming or transdifferentiation between two lineage-specified cell types. One of the first cell types to be successfully transdifferentiated into in this new revitalization of reprogramming was the induced neurons. However, the neurobiology field is no longer dominated solely by the neuron as astrocytes have become more important in disease pathology as well. Therefore, I use the tried and true reprogramming approach that successfully reprogrammed fibroblasts into iPSCs and iNs to reprogram mouse and human fibroblasts into functional induced astrocytes. Mouse iAs require only the transcription factor, NFIA while human iA reprogramming requires NFIA in addition to TET2 and NOTCH. As a proof-of-principle, I apply the technology to reprogram Alexander's Disease patient-specific fibroblasts to show that the induced astrocytes can be used to create disease models in vitro. Finally, I use shRNA knockdown of the mutant protein that causes the mutant phenotype to show that the iA model is tractable to therapeutic testing.

The gb-glucuronidase (GUS) gene is extremely useful as a reporter of the expression of introduced genes and can be used in organisms where other reporter genes are useless. Thus, the GUS gene is the reporter gene of choice for transgenic plant research. Not only can this assay be used to detect whether a gene is being expressed, but it can be used to determine the location of the gene product within cells. Low cost is another advantage of this assay. GUS Protocols provides instructions and essential background information that will enable researchers to effectively use the GUS gene as a reporter of the expression of introduced genes. First book on the GUS reporter system Up-to-date reference lists following each of the fourteen chapters Comb-bound for convenient bench-top use Written by leading authorities including R.A. Jefferson, inventor of the GUS assay Illustrated with color GUS detection by fluorometric, spectrophotometric, and histochemical methods Fast, automated assays

Discover how the creative brain works across musical, literary, visual artistic, kinesthetic and scientific spheres, and how to study it.

This volume provides a comprehensive overview for investigating biology at the level of individual cells. Chapters are organized into eight parts detailing a single-cell lab, single cell DNA-seq, RNA-seq, single cell proteomic and epigenetic, single cell multi-omics, single cell screening, and single cell live imaging. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Single Cell Methods: Sequencing and Proteomics aims to make each experiment easily reproducible in every lab.

Intercellular Communication in Plants provides an overview of intercellular signaling systems, capitalizing on the results of contemporary molecular biology. Many biological phenomena are controlled by intercellular signaling systems, initiated by messenger molecules. For example, intercellular communication channels are thought to be associated with a plant's growth and dormancy development - an important adaptive strategy for the survival and regrowth of temperate perennials. This volume is directed at researchers and professionals in plant biochemistry, physiology, cell biology and molecular biology, in both the academic and industrial sectors.