scholarly journals Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data

2020 ◽  
Vol 3 (11) ◽  
pp. e202000867 ◽  
Author(s):  
Jovan Tanevski ◽  
Thin Nguyen ◽  
Buu Truong ◽  
Nikos Karaiskos ◽  
Mehmet Eren Ahsen ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Gene Selection ◽  
Spatial Information ◽  
Spatial Clustering ◽  
Spatial Location ◽  
Spatial Arrangement ◽  
Fish Embryo ◽  
Hybridization Data ◽  
Silver Standard

Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues.

scholarly journals Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

2019 ◽  
Author(s):  
Jovan Tanevski ◽  
Thin Nguyen ◽  
Buu Truong ◽  
Nikos Karaiskos ◽  
Mehmet Eren Ahsen ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Gene Selection ◽  
Spatial Information ◽  
Spatial Clustering ◽  
Spatial Location ◽  
Drosophila Embryo ◽  
Rna Seq ◽  
Hybridization Data ◽  
Transcriptomics Data

AbstractSingle-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

scholarly journals Spatial charting of single cell transcriptomes in tissues

2021 ◽  
Author(s):  
Nicholas Navin ◽  
Runmin Wei ◽  
Siyuan He ◽  
Shanshan Bai ◽  
Emi Sei ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Spatial Information ◽  
Ductal Carcinoma ◽  
Single Cells ◽  
Cell Types ◽  
Spatial Structures ◽  
Tissue Sections ◽  
Normal Mouse Brain

Single cell RNA sequencing (scRNA-seq) methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics (ST) assays can profile spatial regions in tissue sections, but do not have single cell genomic resolution. Here, we developed a computational approach called SChart, that combines these two datasets to achieve single cell spatial mapping of cell types, cell states and continuous phenotypes. We applied SChart to reconstruct cellular spatial structures in existing datasets from normal mouse brain and kidney tissues to validate our approach. We also performed scRNA-seq and ST experiments on two ductal carcinoma in situ (DCIS) tissues and applied SChart to identify subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data shows that SChart can accurately map single cells in diverse tissue types to resolve their spatial organization into cellular neighborhoods and tissue structures.

scholarly journals Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 28
Author(s):  
Shruti Gupta ◽  
Ajay Kumar Verma ◽  
Shandar Ahmad
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Single Cell ◽  
Gene Selection ◽  
Spatial Information ◽  
Single Cells ◽  
Drosophila Embryo ◽  
Main Challenge ◽  
Selection For

Single-cell transcriptomics data, when combined with in situ hybridization patterns of specific genes, can help in recovering the spatial information lost during cell isolation. Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium conducted a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC) to predict the masked locations of single cells from a set of 60, 40 and 20 genes out of 84 in situ gene patterns known in Drosophila embryo. We applied a genetic algorithm (GA) to predict the most important genes that carry positional and proximity information of the single-cell origins, in combination with the base distance mapping algorithm DistMap. Resulting gene selection was found to perform well and was ranked among top 10 in two of the three sub-challenges. However, the details of the method did not make it to the main challenge publication, due to an intricate aggregation ranking. In this work, we discuss the detailed implementation of GA and its post-challenge parameterization, with a view to identify potential areas where GA-based approaches of gene-set selection for topological association prediction may be improved, to be more effective. We believe this work provides additional insights into the feature-selection strategies and their relevance to single-cell similarity prediction and will form a strong addendum to the recently published work from the consortium.

scholarly journals Spatial organization of the somatosensory cortex revealed by cyclic smFISH

2018 ◽  
Author(s):  
Simone Codeluppi ◽  
Lars E. Borm ◽  
Amit Zeisel ◽  
Gioele La Manno ◽  
Josina A. van Lunteren ◽  
...  
Keyword(s):  
Single Cell ◽  
Somatosensory Cortex ◽  
Single Molecule ◽  
Spatial Organization ◽  
Spatial Information ◽  
Cell Types ◽  
Cellular Organization ◽  
New Methods ◽  
The Brain

The global efforts towards the creation of a molecular census of the brain using single-cell transcriptomics is generating a large catalog of molecularly defined cell types lacking spatial information. Thus, new methods are needed to map a large number of cell-specific markers simultaneously on large tissue areas. Here, we developed a cyclic single molecule fluorescence in situ hybridization methodology and defined the cellular organization of the somatosensory cortex using markers identified by single-cell transcriptomics.

scholarly journals Short-range interactions govern cellular dynamics in microbial multi-genotype systems

2019 ◽  
Author(s):  
A. Dal Co ◽  
S. van Vliet ◽  
D. J. Kiviet ◽  
S. Schlegel ◽  
M. Ackermann
Keyword(s):  
Microbial Communities ◽  
Single Cell ◽  
Spatial Organization ◽  
Single Cell Analysis ◽  
Spatial Interaction ◽  
Interaction Network ◽  
Spatial Arrangement ◽  
Emergent Properties ◽  
Interaction Range ◽  
Microbial Systems

AbstractEcosystem processes result from interaction between organisms. When interactions are local, the spatial organization of organisms defines their network of interactions, and thus influences the system’s functioning. This can be especially relevant for microbial systems, which often consist of spatially structured communities of cells connected by a dense interaction network. Here we measured the spatial interaction network between cells in microbial systems and identify the factors that determine it. Combining quantitative single-cell analysis of synthetic bacterial communities with mathematical modeling, we find that cells only interact with other cells in their immediate neighbourhood. This short interaction range impacts the functioning of the whole system by reducing its ability to perform metabolic processes collectively. Our experiments and models demonstrate that the spatial scale of cell-to-cell interaction plays a fundamental role in understanding and controlling natural communities, and in engineering microbial systems for specific purposes.Significance StatementCommunities of interacting microbes perform fundamental processes on earth. We do not understand well how these processes emerge from the interactions between individual microbial cells. Our work investigates how strongly individual cells interact and how the strength of the interaction depends on the distance between cells. The discovery that individual cells ‘live in a small world’, i.e. they only interact with a small number of cells around them, changes our understanding of how cells in natural microbial communities are metabolically coupled and how their spatial arrangement determines emergent properties at the community level. Our quantitative single-cell approach allows to address central questions on systems composed of interacting genotypes and to increase our understanding and ability to control microbial communities.

scholarly journals scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

2021 ◽  
Author(s):  
Dongyuan Song ◽  
Kexin Aileen Li ◽  
Zachary Hemminger ◽  
Roy Wollman ◽  
Jingyi Jessica Li
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Spatial Information ◽  
Dimensional Space ◽  
Single Cells ◽  
High Sensitivity ◽  
Cell Types ◽  
Gene Profiling ◽  
Selection Methods ◽  
Low Dimensional

AbstractSingle-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity, and extra (e.g., spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Here we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and cell-type annotation on targeted gene profiling data.

scholarly journals SpiceMix: Integrative single-cell spatial modeling for inferring cell identity

2020 ◽  
Author(s):  
Benjamin Chidester ◽  
Tianming Zhou ◽  
Jian Ma
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Spatial Information ◽  
Single Cells ◽  
Joint Analysis ◽  
Transcriptome Data ◽  
Cell Type ◽  
Cell Identity ◽  
Cell Type Composition ◽  
Type Composition

AbstractSpatial transcriptomics technologies promise to reveal spatial relationships of cell-type composition in complex tissues. However, the development of computational methods that capture the unique properties of single-cell spatial transcriptome data to unveil cell identities remains a challenge. Here, we report SpiceMix, a new probabilistic model that enables effective joint analysis of spatial information and gene expression of single cells based on spatial transcriptome data. Both simulation and real data evaluations demonstrate that SpiceMix consistently improves upon the inference of the intrinsic cell types compared with existing approaches. As a proof-of-principle, we use SpiceMix to analyze single-cell spatial transcriptome data of the mouse primary visual cortex acquired by seqFISH+ and STARmap. We find that SpiceMix can improve cell identity assignments and uncover potentially new cell subtypes. SpiceMix is a generalizable framework for analyzing spatial transcriptome data that may provide critical insights into the cell-type composition and spatial organization of cells in complex tissues.

scholarly journals Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Madhav Mantri ◽  
Gaetano J. Scuderi ◽  
Roozbeh Abedini-Nassab ◽  
Michael F. Z. Wang ◽  
David McKellar ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Differentiation ◽  
Spatial Organization ◽  
Spatial Information ◽  
Cellular Interactions ◽  
Chicken Heart ◽  
Spatially Resolved ◽  
Expression Of Genes ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing is a powerful tool to study developmental biology but does not preserve spatial information about tissue morphology and cellular interactions. Here, we combine single-cell and spatial transcriptomics with algorithms for data integration to study the development of the chicken heart from the early to late four-chambered heart stage. We create a census of the diverse cellular lineages in developing hearts, their spatial organization, and their interactions during development. Spatial mapping of differentiation transitions in cardiac lineages defines transcriptional differences between epithelial and mesenchymal cells within the epicardial lineage. Using spatially resolved expression analysis, we identify anatomically restricted expression programs, including expression of genes implicated in congenital heart disease. Last, we discover a persistent enrichment of the small, secreted peptide, thymosin beta-4, throughout coronary vascular development. Overall, our study identifies an intricate interplay between cellular differentiation and morphogenesis.

scholarly journals DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Floyd Maseda ◽  
Zixuan Cang ◽  
Qing Nie
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Cell Fate ◽  
Spatial Information ◽  
Expression Patterns ◽  
Spatial Arrangement ◽  
Small Subset ◽  
Imaging Data ◽  
Cell Fate Decisions ◽  
Data Adaptive

Single-cell RNA sequencing (scRNA-seq) data provides unprecedented information on cell fate decisions; however, the spatial arrangement of cells is often lost. Several recent computational methods have been developed to impute spatial information onto a scRNA-seq dataset through analyzing known spatial expression patterns of a small subset of genes known as a reference atlas. However, there is a lack of comprehensive analysis of the accuracy, precision, and robustness of the mappings, along with the generalizability of these methods, which are often designed for specific systems. We present a system-adaptive deep learning-based method (DEEPsc) to impute spatial information onto a scRNA-seq dataset from a given spatial reference atlas. By introducing a comprehensive set of metrics that evaluate the spatial mapping methods, we compare DEEPsc with four existing methods on four biological systems. We find that while DEEPsc has comparable accuracy to other methods, an improved balance between precision and robustness is achieved. DEEPsc provides a data-adaptive tool to connect scRNA-seq datasets and spatial imaging datasets to analyze cell fate decisions. Our implementation with a uniform API can serve as a portal with access to all the methods investigated in this work for spatial exploration of cell fate decisions in scRNA-seq data. All methods evaluated in this work are implemented as an open-source software with a uniform interface.

scholarly journals Spatiotemporal single-cell RNA sequencing of developing hearts reveals interplay between cellular differentiation and morphogenesis

2020 ◽  
Author(s):  
Madhav Mantri ◽  
Gaetano J. Scuderi ◽  
Roozbeh Abedini Nassab ◽  
Michael F.Z. Wang ◽  
David McKellar ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Differentiation ◽  
Spatial Organization ◽  
Spatial Information ◽  
Cardiac Development ◽  
Cellular Interactions ◽  
Chicken Heart ◽  
Spatially Resolved ◽  
Single Cell Rna Sequencing

ABSTRACTSingle-cell RNA sequencing is a powerful tool to study developmental biology but does not preserve spatial information about cellular interactions and tissue morphology. Here, we combined single-cell and spatial transcriptomics with new algorithms for data integration to study the early development of the chicken heart. We collected data from four key ventricular development stages, ranging from the early chamber formation stage to the late four-chambered stage. We created an atlas of the diverse cellular lineages in developing hearts, their spatial organization, and their interactions during development. Spatial mapping of differentiation transitions revealed the intricate interplay between cellular differentiation and morphogenesis in cardiac cellular lineages. Using spatially resolved expression analysis, we identified anatomically restricted gene expression programs. Last, we discovered a stage-dependent role for the small secreted peptide, thymosin beta-4, in the coordination of multi-lineage cellular populations. Overall, our study identifies key stage-specific regulatory programs that govern cardiac development.

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