Multimodal cell type correspondence by intersectional mFISH in intact tissues

Mapping Intimacies ◽

10.1101/525451 ◽

2019 ◽

Cited By ~ 6

Author(s):

Philip R. Nicovich ◽

Michael J. Taormina ◽

Christopher A. Baker ◽

Thuc Nghi Nguyen ◽

Elliot R. Thomsen ◽

...

Keyword(s):

Single Cells ◽

Morphological Characteristics ◽

Cell Types ◽

Synaptic Connectivity ◽

Cell Type ◽

New Approach ◽

Two Photon ◽

Mouse Cortex ◽

Complete Set

AbstractDefining a complete set of cell types within the cortex requires reconciling disparate results achieved through diverging methodologies. To address this correspondence problem, multiple methodologies must be applied to the same cells across multiple single-cell experiments. Here we present a new approach applying spatial transcriptomics using multiplexed fluorescencein situhybridization, (mFISH) to brain tissue previously interrogated through two photon optogenetic mapping of synaptic connectivity. This approach can resolve the anatomical, transcriptomic, connectomic, electrophysiological, and morphological characteristics of single cells within the mouse cortex.

Cell Type Assignments for Spatial Transcriptomics Data

10.1101/2021.02.25.432887 ◽

2021 ◽

Author(s):

Haotian Teng ◽

Ye Yuan ◽

Ziv Bar-Joseph

Keyword(s):

Gene Expression ◽

Single Cells ◽

Cell Types ◽

New Method ◽

Inhibitory Neurons ◽

Cell Type ◽

Inference Algorithms ◽

Probabilistic Function ◽

Transcriptomics Data

ABSTRACTMotivationRecent advancements in fluorescence in situ hybridization (FISH) techniques enable them to concurrently obtain information on the location and gene expression of single cells. A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types. To date, most studies used methods that only rely on the expression levels of the genes in each cell for such assignments. To fully utilize the data and to improve the ability to identify novel sub-types we developed a new method, FICT, which combines both expression and neighborhood information when assigning cell types.ResultsFICT optimizes a probabilistic function that we formalize and for which we provide learning and inference algorithms. We used FICT to analyze both simulated and several real spatial transcriptomics data. As we show, FICT can accurately identify cell types and sub-types improving on expression only methods and other methods proposed for clustering spatial transcriptomics data. Some of the spatial sub-types identified by FICT provide novel hypotheses about the new functions for excitatory and inhibitory neurons.AvailabilityFICT is available at: https://github.com/haotianteng/[email protected]

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

10.1101/2020.04.12.038000 ◽

2020 ◽

Author(s):

Feng Tian ◽

Fan Zhou ◽

Xiang Li ◽

Wenping Ma ◽

Honggui Wu ◽

...

Keyword(s):

Transcription Factors ◽

Single Cell ◽

Human Cell ◽

Expression Profiles ◽

Single Cells ◽

Cell Types ◽

List Type ◽

Cell Type ◽

Genomic Architecture ◽

Gene Modules

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

CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

10.1101/2022.01.12.476034 ◽

2022 ◽

Author(s):

Takaho Tsuchiya ◽

Hiroki Hori ◽

Haruka Ozaki

Keyword(s):

Gene Expression ◽

Single Cells ◽

Cell Types ◽

Regression Modeling ◽

Transcriptome Data ◽

Cell Type ◽

Neighboring Cell ◽

Expression Variability ◽

Cell Expression ◽

Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

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.

Modeling and Visualizing Cell Type Switching

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/293980 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Ahmadreza Ghaffarizadeh ◽

Gregory J. Podgorski ◽

Nicholas S. Flann

Keyword(s):

Expression Profiles ◽

Control Cell ◽

Three Dimensional ◽

Myeloid Cell ◽

Cell Types ◽

State Transitions ◽

Cell Type ◽

New Approach ◽

Gene Expression Noise ◽

Lineage Trees

Understanding cellular differentiation is critical in explaining development and for taming diseases such as cancer. Differentiation is conventionally represented using bifurcating lineage trees. However, these lineage trees cannot readily capture or quantify all the types of transitions now known to occur between cell types, including transdifferentiation or differentiation off standard paths. This work introduces a new analysis and visualization technique that is capable of representing all possible transitions between cell states compactly, quantitatively, and intuitively. This method considers the regulatory network of transcription factors that control cell type determination and then performs an analysis of network dynamics to identify stable expression profiles and the potential cell types that they represent. A visualization tool calledCellDiff3Dcreates an intuitive three-dimensional graph that shows the overall direction and probability of transitions between all pairs of cell types within a lineage. In this study, the influence of gene expression noise and mutational changes during myeloid cell differentiation are presented as a demonstration of theCellDiff3Dtechnique, a new approach to quantify and envision all possible cell state transitions in any lineage network.

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.

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.

Cell segmentation-free inference of cell types from in situ transcriptomics data

10.1101/800748 ◽

2019 ◽

Cited By ~ 5

Author(s):

Jeongbin Park ◽

Wonyl Choi ◽

Sebastian Tiesmeyer ◽

Brian Long ◽

Lars E. Borm ◽

...

Keyword(s):

Tissue Characterization ◽

Cell Types ◽

Cell Segmentation ◽

Cell Type ◽

Computational Framework ◽

Transcriptomics Data ◽

Novel Method ◽

2D And 3D ◽

Spatial Cell

AbstractMultiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a novel method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. We found that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

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.

Probing transcription factor combinatorics in different promoter classes and in enhancers

10.1101/197418 ◽

2017 ◽

Author(s):

Jimmy Vandel ◽

Océane Cassan ◽

Sophie Lèbre ◽

Charles-Henri Lecellier ◽

Laurent Bréhélin

Keyword(s):

Binding Sites ◽

Target Genes ◽

Cell Types ◽

Model Parameters ◽

Binding Motif ◽

Cell Type ◽

Regulatory Regions ◽

New Approach ◽

Cell Type Specific ◽

Common Target

In eukaryotic cells, transcription factors (TFs) are thought to act in a combinatorial way, by competing and collaborating to regulate common target genes. However, several questions remain regarding the conservation of these combina-tions among different gene classes, regulatory regions and cell types. We propose a new approach named TFcoop to infer the TF combinations involved in the binding of a tar-get TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combina-tions involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for pre-dicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers. First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in pro-moters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and en-hancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the dif-ferent targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately dis-tinguish promoters associated with specific biological processes.