Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

Download Full-text

Phenotypic convergence in the brain: distinct transcription factors regulate common terminal neuronal characters

10.1101/243113 ◽

2018 ◽

Cited By ~ 2

Author(s):

Nikos Konstantinides ◽

Katarina Kapuralin ◽

Chaimaa Fadil ◽

Luendreo Barboza ◽

Rahul Satija ◽

...

Keyword(s):

Transcription Factors ◽

Single Cell ◽

Large Scale ◽

Single Cells ◽

Deep Understanding ◽

Cell Types ◽

Marker Genes ◽

Cell Type ◽

Functional Specification ◽

Phenotypic Convergence

SummaryTranscription factors regulate the molecular, morphological, and physiological characters of neurons and generate their impressive cell type diversity. To gain insight into general principles that govern how transcription factors regulate cell type diversity, we used large-scale single-cell mRNA sequencing to characterize the extensive cellular diversity in the Drosophila optic lobes. We sequenced 55,000 single optic lobe neurons and glia and assigned them to 52 clusters of transcriptionally distinct single cells. We validated the clustering and annotated many of the clusters using RNA sequencing of characterized FACS-sorted single cell types, as well as marker genes specific to given clusters. To identify transcription factors responsible for inducing specific terminal differentiation features, we used machine-learning to generate a ‘random forest’ model. The predictive power of the model was confirmed by showing that two transcription factors expressed specifically in cholinergic (apterous) and glutamatergic (traffic-jam) neurons are necessary for the expression of ChAT and VGlut in many, but not all, cholinergic or glutamatergic neurons, respectively. We used a transcriptome-wide approach to show that the same terminal characters, including but not restricted to neurotransmitter identity, can be regulated by different transcription factors in different cell types, arguing for extensive phenotypic convergence. Our data provide a deep understanding of the developmental and functional specification of a complex brain structure.

Download Full-text

Single-cell transcriptomics characterizes cell types in the subventricular zone and uncovers molecular defects underlying impaired adult neurogenesis

10.1101/365619 ◽

2018 ◽

Author(s):

Vera Zywitza ◽

Aristotelis Misios ◽

Lena Bunatyan ◽

Thomas E. Willnow ◽

Nikolaus Rajewsky

Keyword(s):

Single Cell ◽

Adult Neurogenesis ◽

Subventricular Zone ◽

Single Cells ◽

Cell Types ◽

Neural Cell ◽

Adult Brain ◽

List Type ◽

Cell Type ◽

Cell Type Specific

SUMMARYNeural stem cells (NSCs) contribute to plasticity and repair of the adult brain. Niches harboring NSCs are crucial for regulating stem cell self-renewal and differentiation. We used single-cell RNA profiling to generate an unbiased molecular atlas of all cell types in the largest neurogenic niche of the adult mouse brain, the subventricular zone (SVZ). We characterized > 20 neural and non-neural cell types and gained insights into the dynamics of neurogenesis by predicting future cell states based on computational analysis of RNA kinetics. Furthermore, we apply our single-cell approach to mice lacking LRP2, an endocytic receptor required for SVZ maintenance. The number of NSCs and proliferating progenitors was significantly reduced. Moreover, Wnt and BMP4 signaling was perturbed. We provide a valuable resource for adult neurogenesis, insights into SVZ neurogenesis regulation by LRP2, and a proof-of-principle demonstrating the power of single-cell RNA-seq in pinpointing neural cell type-specific functions in loss-of-function models.HIGHLIGHTSunbiased single-cell transcriptomics characterizes adult NSCs and their nichecell type-specific signatures and marker genes for 22 SVZ cell typesFree online tool to assess gene expression across 9,804 single cellscell type-specific dysfunctions underlying impaired adult neurogenesis

Download Full-text

920 A single-cell spatially resolved MERFISH map of the colorectal tumor immune microenvironment

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-sitc2021.920 ◽

2021 ◽

Vol 9 (Suppl 3) ◽

pp. A965-A965

Author(s):

Colles Price ◽

Jonathan Chen ◽

Karin Pelka ◽

Sherry Chao ◽

Jiang He ◽

...

Keyword(s):

Colorectal Cancer ◽

Tumor Microenvironment ◽

Single Cell ◽

Spatial Organization ◽

Single Cells ◽

Cell Types ◽

List Type ◽

Cellular Composition ◽

Cell Type ◽

Spatially Resolved

BackgroundUnderstanding the tumor microenvironment (TIME) requires more than just a catalog of cell types and gene programs. It is critical to see the spatial organization of the cells are and where they form multicellular interaction networks. Here we present a single-cell spatially resolved transcriptomic analysis of human mismatch repair deficient (MMRd) and proficient (MMRp) colorectal cancer (CRC) specimens. High tumor mutational burden MMRd tumors are known to have an immune response characterized by higher cytolytic T cell infiltrates compared to MMRp tumors, making them an ideal system for spatial single-cell profiling and understanding how the immune-driven programs differ between these tumors.MethodsMERFISH is a massively multiplexed single molecule imaging technology which can simultaneously capture and measure the quantity and distribution of hundreds to thousands of RNA species within single cells across a tissue.1 We designed a MERFISH library of over 450 genes including genes important to proliferation, apoptosis, immune signaling, immune cell type pathways and other critical pathways in CRC. Patient samples were obtained commercially or through Massachusetts General Hospital. Samples were hybridized with the designed MERFISH library and stained with a cell boundary marker to delineate cells across the tissue. We performed unsupervised clustering to identify cell types and we explored calculated spatial statistics to characterize how the cell type distribution varied between MMRd and MMRp tumors. We identified the cellular composition of each tumor, including immune and stromal cells, and the spatial distribution of these cell types.ResultsUsing MERFISH, we were able to readily identify all cell types and states previously discovered by single-cell RNA sequencing2 in intact patient specimens, thus providing an accurate map of the cellular composition and spatial organization of these cells in the tumor microenvironment. Of note, previously predicted multicellular interaction networks2 appeared as spatially organized structures in the tissue and were distinct in MMRd versus MMRp tumor specimens. Our data provide a richness of concrete hypotheses about which cells are working together and how these cells function cooperatively, which will be critical in advancing immunotherapy in these immunologically distinct types of colorectal cancer.ConclusionsHere we present a single-cell resolved spatial map of the cell types and states in the tumor microenvironment of MMRd and MMRp cancer. This will aid the development of future immunotherapies for CRC patients.ReferencesChen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 2015;348:AAA 6090.Pelka K, Hofree M, Chen J, Sarkizova S, Pirl JD, Jorgji V, et al. Multicellular immune hubs and their organization in MMRd and MMRp colorectal cancer. BioRxiv 2021;426796.Ethics ApprovalAll samples not commercially purchased were collected in accordance with IRB protocol DF/HCC IRB 02-240.

Download Full-text

A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

10.1101/247114 ◽

2018 ◽

Cited By ~ 1

Author(s):

Douglas Abrams ◽

Parveen Kumar ◽

R. Krishna Murthy Karuturi ◽

Joshy George

Keyword(s):

Experimental Design ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

Cell Number ◽

Fold Change ◽

Computational Method ◽

Marker Genes ◽

Cell Type ◽

Estimate Sample Size

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.

Download Full-text

Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽

10.1177/0333102418762476 ◽

2018 ◽

Vol 38 (13) ◽

pp. 1976-1983 ◽

Cited By ~ 5

Author(s):

William Renthal

Keyword(s):

Human Brain ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Cell Types ◽

Susceptibility Genes ◽

Brain Cell ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

Download Full-text

Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells

10.1101/538553 ◽

2019 ◽

Cited By ~ 3

Author(s):

Arnav Moudgil ◽

Michael N. Wilkinson ◽

Xuhua Chen ◽

June He ◽

Alex J. Cammack ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Single Cell ◽

Binding Sites ◽

Expression Profiles ◽

Single Cells ◽

Gene Expression Profiles ◽

Cell Types ◽

Specific Cell

AbstractIn situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.

Download Full-text

RNA splicing programs define tissue compartments and cell types at single cell resolution

10.1101/2021.05.01.442281 ◽

2021 ◽

Author(s):

Julia Eve Olivieri ◽

Roozbeh Dehghannasiri ◽

Peter Wang ◽

SoRi Jang ◽

Antoine de Morree ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

High Throughput ◽

Rna Splicing ◽

Single Cells ◽

Cell Types ◽

Mouse Lemur ◽

Cell Type ◽

Multiple Organs ◽

Single Cell Pcr

More than 95% of human genes are alternatively spliced. Yet, the extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach that is agnostic to transcript annotation, to detect cell-type-specific regulated splicing in > 110K carefully annotated single cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type specifically spliced. These results are validated with RNA FISH, single cell PCR, and in high throughput with Smart-seq2. Regulated splicing is found in ubiquitously expressed genes such as actin light chain subunit MYL6 and ribosomal protein RPS24, which has an epithelial-specific microexon. 13% of the statistically most variable splice sites in cell-type specifically regulated genes are also most variable in mouse lemur or mouse. SpliZ analysis further reveals 170 genes with regulated splicing during sperm development using, 10 of which are conserved in mouse and mouse lemur. The statistical properties of the SpliZ allow model-based identification of subpopulations within otherwise indistinguishable cells based on gene expression, illustrated by subpopulations of classical monocytes with stereotyped splicing, including an un-annotated exon, in SAT1, a Diamine acetyltransferase. Together, this unsupervised and annotation-free analysis of differential splicing in ultra high throughput droplet-based sequencing of human cells across multiple organs establishes splicing is regulated cell-type-specifically independent of gene expression.

Download Full-text

Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

10.1101/104844 ◽

2017 ◽

Cited By ~ 8

Author(s):

Junyue Cao ◽

Jonathan S. Packer ◽

Vijay Ramani ◽

Darren A. Cusanovich ◽

Chau Huynh ◽

...

Keyword(s):

Single Cell ◽

Expression Profiles ◽

Single Cells ◽

Transcriptional Profiling ◽

Cost Effective ◽

Cell Types ◽

Cell Capture ◽

Rna Seq ◽

Major Step ◽

C Elegans

AbstractConventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold “shotgun cellular coverage” of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

Download Full-text

A human cell atlas of fetal chromatin accessibility

Science ◽

10.1126/science.aba7612 ◽

2020 ◽

Vol 370 (6518) ◽

pp. eaba7612 ◽

Cited By ~ 1

Author(s):

Silvia Domcke ◽

Andrew J. Hill ◽

Riza M. Daza ◽

Junyue Cao ◽

Diana R. O’Day ◽

...

Keyword(s):

Gene Expression ◽

Human Cell ◽

Single Cells ◽

Complex Trait ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Cell Type ◽

Cell Type Specific

The chromatin landscape underlying the specification of human cell types is of fundamental interest. We generated human cell atlases of chromatin accessibility and gene expression in fetal tissues. For chromatin accessibility, we devised a three-level combinatorial indexing assay and applied it to 53 samples representing 15 organs, profiling ~800,000 single cells. We leveraged cell types defined by gene expression to annotate these data and cataloged hundreds of thousands of candidate regulatory elements that exhibit cell type–specific chromatin accessibility. We investigated the properties of lineage-specific transcription factors (such as POU2F1 in neurons), organ-specific specializations of broadly distributed cell types (such as blood and endothelial), and cell type–specific enrichments of complex trait heritability. These data represent a rich resource for the exploration of in vivo human gene regulation in diverse tissues and cell types.

Download Full-text

Saturating Single-Cell atlas Datasets

10.1101/218370 ◽

2017 ◽

Cited By ~ 2

Author(s):

Aparna Bhaduri ◽

Tomasz J. Nowakowski ◽

Alex A. Pollen ◽

Arnold R. Kriegstein

Keyword(s):

Population Structure ◽

Single Cell ◽

Mouse Brain ◽

Large Scale ◽

Single Cells ◽

Cost Effective ◽

Cell Types ◽

Cell Number ◽

Cell Type ◽

The Relationship

AbstractHigh throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. Efficient generation of such an atlas will depend on sufficient sampling of the diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals. To examine the relationship between cell number and transcriptional heterogeneity in the context of unbiased cell type classification, we explicitly explored the population structure of a publically available 1.3 million cell dataset from the E18.5 mouse brain. We propose a computational framework for inferring the saturation point of cluster discovery in a single cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a “complexity index”, which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells (20,000). Together, these findings suggest that most of the biologically interpretable insights from the 1.3 million cells can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high “cellular coverage”, the much anticipated cell atlasing studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage.Recent efforts seek to create a comprehensive cell atlas of the human body1,2 Current technology, however, makes it precipitously expensive to perform analysis of every cell. Therefore, designing effective sampling strategies be critical to generate a working atlas in an efficient, cost-effective, and streamlined manner. The advent of single cell and single nucleus mRNA sequencing (RNAseq) in droplet format3,4 now enables large scale sampling of cells from any tissue, and a recently released publicly available dataset of 1.3 million single cells from the E18.5 mouse brain generated with the 10X Chromium5 provides an opportunity to explore the relationship between population structure and the number of sampled cells necessary to reveal the underlying diversity of cell types. Here, we present a framework for how researchers can evaluate whether a dataset has reached saturation, and we estimate how many cells would be required to generate an atlas of the sample analyzed here. This framework can be applied to any organ or cell type specific atlas for any organism.

Download Full-text