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

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeongbin Park ◽  
Wonyl Choi ◽  
Sebastian Tiesmeyer ◽  
Brian Long ◽  
Lars E. Borm ◽  
...  
Keyword(s):  
Tissue Characterization ◽  
Cell Types ◽  
Cell Segmentation ◽  
Cell Type ◽  
Preoptic Region ◽  
Computational Framework ◽  
Transcriptomics Data ◽  
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 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. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

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

2019 ◽  
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.

scholarly journals Cell Type Assignments for Spatial Transcriptomics Data

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]

scholarly journals Spatial mapping of cell types by integration of transcriptomics data

2019 ◽  
Author(s):  
Alma Andersson ◽  
Joseph Bergenstråhle ◽  
Michaela Asp ◽  
Ludvig Bergenstråhle ◽  
Aleksandra Jurek ◽  
...  
Keyword(s):  
Single Cell ◽  
Probabilistic Method ◽  
Cell Types ◽  
Spatial Mapping ◽  
Transcriptional Expression ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Different Types ◽  
Transcriptomics Data ◽  
Spatial Cell

Spatial transcriptomics and single cell RNA-sequencing offer complementary insights into the transcriptional expression landscape. We here present a probabilistic method that integrates data from both techniques, leveraging their respective strengths in such a way that we are able to spatially map cell types to a tissue. The method is applied to several different types of tissue where the spatial cell type topographies are successfully delineated.

scholarly journals Author Correction: Cell segmentation-free inference of cell types from in situ transcriptomics data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeongbin Park ◽  
Wonyl Choi ◽  
Sebastian Tiesmeyer ◽  
Brian Long ◽  
Lars E. Borm ◽  
...  
Keyword(s):  
Cell Types ◽  
Cell Segmentation ◽  
Transcriptomics Data

scholarly journals Jointly leveraging spatial transcriptomics and deep learning models for pathology image annotation improves cell type identification over either approach alone.

2021 ◽  
Author(s):  
Asif Zubair ◽  
Richard H. Chapple ◽  
Sivaraman Natarajan ◽  
William C. Wright ◽  
Min Pan ◽  
...  
Keyword(s):  
Immune Cell ◽  
Image Annotation ◽  
Cell Types ◽  
Tissue Cell ◽  
Cell Type ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Diagnostic Applications ◽  
The Many ◽  
Level Performance

The disorganization of cell types within tissues underlies many human diseases and has been studied for over a century using the conventional tools of pathology, including tissue-marking dyes such as the H&E stain. Recently, spatial transcriptomics technologies were developed that can measure spatially resolved gene expression directly in pathology-stained tissues sections, revealing cell types and their dysfunction in unprecedented detail. In parallel, artificial intelligence (AI) has approached pathologist-level performance in computationally annotating H&E images of tissue sections. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and AI-based pathology has performed less impressively outside their training datasets. Here, we describe a methodology that can computationally integrate AI-annotated pathology images with spatial transcriptomics data to markedly improve inferences of tissue cell type composition made over either class of data alone. We show that this methodology can identify regions of clinically relevant tumor immune cell infiltration, which is predictive of response to immunotherapy and was missed by an initial pathologist's manual annotation. Thus, combining spatial transcriptomics and AI-based image annotation has the potential to exceed pathologist-level performance in clinical diagnostic applications and to improve the many applications of spatial transcriptomics that rely on accurate cell type annotations.

scholarly journals SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data

2020 ◽  
Author(s):  
Xiaoyu Lu ◽  
Szu-Wei Tu ◽  
Wennan Chang ◽  
Changlin Wan ◽  
Jiashi Wang ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Cell Types ◽  
Small Sample ◽  
Training Data ◽  
Mouse Tissue ◽  
Marker Genes ◽  
Specific Cell ◽  
Cell Type ◽  
Data Set ◽  
Transcriptomics Data

Abstract Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD.

scholarly journals Multimodal cell type correspondence by intersectional mFISH in intact tissues

2019 ◽  
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.

scholarly journals A Statistical Framework to Identify Cell Types Whose Genetically Regulated Proportions are Associated with Complex Diseases

2021 ◽  
Author(s):  
Hongyu Zhao ◽  
Wei Liu ◽  
Wenxuan Deng ◽  
Ming Chen ◽  
Zihan Dong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Statistical Power ◽  
Predictive Biomarkers ◽  
Complex Diseases ◽  
Cell Types ◽  
Chronic Obstructive ◽  
Cell Type ◽  
Obstructive Pulmonary Disease ◽  
Statistical Framework ◽  
Transcriptomics Data

Abstract Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. Here, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed substantial statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblast in the lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

scholarly journals Comprehensive benchmarking of computational deconvolution of transcriptomics data

2020 ◽  
Author(s):  
Francisco Avila Cobos ◽  
José Alquicira-Hernandez ◽  
Joseph Powell ◽  
Pieter Mestdagh ◽  
Katleen De Preter
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Type ◽  
Factors Affecting ◽  
Marker Selection ◽  
Cell Type Composition ◽  
Type Composition ◽  
Comparable Performance ◽  
Transcriptomics Data ◽  
Combined Impact

AbstractMany computational methods to infer cell type proportions from bulk transcriptomics data have been developed. Attempts comparing these methods revealed that the choice of reference marker signatures is far more important than the method itself. However, a thorough evaluation of the combined impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the results is still lacking.Using different single-cell RNA-sequencing (scRNA-seq) datasets, we generated hundreds of pseudo-bulk mixtures to evaluate the combined impact of these factors on the deconvolution results. Along with methods to perform deconvolution of bulk RNA-seq data we also included five methods specifically designed to infer the cell type composition of bulk data using scRNA-seq data as reference.Both bulk and single-cell deconvolution methods perform best when applied to data in linear scale and the choice of normalization can have a dramatic impact on the performance of some, but not all methods. Overall, single-cell methods have comparable performance to the best performing bulk methods and bulk methods based on semi-supervised approaches showed higher error and lower correlation values between the computed and the expected proportions. Moreover, failure to include cell types in the reference that are present in a mixture always led to substantially worse results, regardless of any of the previous choices. Taken together, we provide a thorough evaluation of the combined impact of the different factors affecting the computational deconvolution task across different datasets and propose general guidelines to maximize its performance.

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