Novel cell-type specific RNA-seq analysis provides new insights into the molecular processes of receptive phase endometrium and identifies sensitive biomarkers

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

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CellO: Comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

10.1101/634097 ◽

2019 ◽

Cited By ~ 1

Author(s):

Matthew N. Bernstein ◽

Zhongjie Ma ◽

Michael Gleicher ◽

Colin N. Dewey

Keyword(s):

Single Cell ◽

Web Application ◽

Cell Types ◽

Rna Seq ◽

Cell Type ◽

Training Set ◽

Sequence Read Archive ◽

Cell Ontology ◽

Cell Type Specific ◽

Type Classification

SummaryCell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical structure of known cell types, a source of prior knowledge that is not utilized by existing methods. Furthemore, CellO comes pre-trained on a novel, comprehensive dataset of human, healthy, untreated primary samples in the Sequence Read Archive, which to the best of our knowledge, is the most diverse curated collection of primary cell data to date. CellO’s comprehensive training set enables it to run out-of-the-box on diverse cell types and achieves superior or competitive performance when compared to existing state-of-the-art methods. Lastly, CellO’s linear models are easily interpreted, thereby enabling exploration of cell type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO’s models across the ontology.HighlightWe present CellO, a tool for hierarchically classifying cell type from single-cell RNA-seq data against the graph-structured Cell OntologyCellO is pre-trained on a comprehensive dataset comprising nearly all bulk RNA-seq primary cell samples in the Sequence Read ArchiveCellO achieves superior or comparable performance with existing methods while featuring a more comprehensive pre-packaged training setCellO is built with easily interpretable models which we expose through a novel web application, the CellO Viewer, for exploring cell type-specific signatures across the Cell OntologyGraphical Abstract

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Development of a multiomics and functional drug sensitivity platform to investigate cell type specific drug effects in AML.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e19013 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e19013-e19013

Author(s):

Marianne T. Santaguida ◽

Ryosuke Kita ◽

Steven A. Schaffert ◽

Erica K. Anderson ◽

Kamran A Ali ◽

...

Keyword(s):

Flow Cytometry ◽

Drug Response ◽

Drug Sensitivity ◽

Ex Vivo ◽

Cell Types ◽

Specific Cell ◽

Rna Seq ◽

Cell Type ◽

Drug Sensitivity Assay ◽

Cell Type Specific

e19013 Background: Understanding the heterogeneity of AML is necessary for developing targeted drugs and diagnostics. A key measure of heterogeneity is the variance in response to treatments. Previously, we developed an ex vivo flow cytometry drug sensitivity assay (DSA) that predicted response to treatments in myelodysplastic syndrome. Unlike bulk cell viability measures of other drug sensitivity assays, our flow cytometry assay provides single cell resolution. The assay measures a drug’s effect on the viability or functional state of specific cell types. Here we present the development of this technology for AML, with additional measurements of DNA-Seq and RNA-Seq. Using the data from this assay, we aim to characterize the heterogeneity in AML drug sensitivity and the molecular mechanisms that drive it. Methods: As an initial feasibility analysis, we assayed 1 bone marrow and 3 peripheral blood AML patient samples. For the DSA, the samples were cultured with six AML standard of care (SOC) compounds across seven doses, in addition to two combinations. The cells were stained to detect multiple cell types including tumor blasts, and drug response was measured by flow cytometry. For the multi-omics, the cells were magnetically sorted to enrich for blasts and then assayed using a targeted 400 gene DNA-Seq panel and whole bulk transcriptome RNA-Seq. For comparison with BeatAML, Pearson correlations between gene expression and venetoclax sensitivity were investigated. Results: In our drug sensitivity assay, we measured dose response curves for the six SOC compounds, for each different cell type across each sample. The dose responses had cell type specific effects, including differences in drug response between CD11b+ blasts, CD11b- blasts, and other non-blast populations. Integrating with the DNA-Seq and RNA-Seq data, known associations between ex vivo drug response and gene expression were identified with additional cell type specificity. For example, BCL2A1 expression was negatively correlated with venetoclax sensitivity in CD11b- blasts but not in CD11b+ blasts. To further corroborate, among the top 1000 genes associated with venetoclax sensitivity in BeatAML, 93.7% had concordant directionality in effect. Conclusions: Here we describe the development of an integrated ex vivo drug sensitivity assay and multi-omics dataset. The data demonstrated that ex vivo responses to compounds differ between cell types, highlighting the importance of measuring drug response in specific cell types. In addition, we demonstrated that integrating these data will provide unique insights on molecular mechanisms that affect cell type specific drug response. As we continue to expand the number of patient samples evaluated with our multi-dimensional platform, this dataset will provide insights for novel drug target discovery, biomarker development, and, in the future, informing treatment decisions.

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Cell type-specific gene expression profiling in brain tissue: comparison between TRAP, LCM and RNA-seq

BMB Reports ◽

10.5483/bmbrep.2015.48.7.218 ◽

2015 ◽

Vol 48 (7) ◽

pp. 388-394 ◽

Cited By ~ 12

Author(s):

TaeHyun Kim ◽

Chae-Seok Lim ◽

Bong-Kiun Kaang

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Brain Tissue ◽

Expression Profiling ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Specific Gene Expression ◽

Cell Type Specific

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Single-nucleus RNA-seq reveals dysregulation of striatal cell identity due to Huntington’s disease mutations

10.1101/2020.07.08.192880 ◽

2020 ◽

Author(s):

Sonia Malaiya ◽

Marcia Cortes-Gutierrez ◽

Brian R. Herb ◽

Sydney R. Coffey ◽

Samuel R.W. Legg ◽

...

Keyword(s):

Huntington's Disease ◽

Huntington’S Disease ◽

Neurodegenerative Disorder ◽

Cell Types ◽

Transcriptional Networks ◽

Rna Seq ◽

Cell Type ◽

Cell Identity ◽

Transcriptional Changes ◽

Cell Type Specific

ABSTRACTHuntington’s disease (HD) is a dominantly inherited neurodegenerative disorder caused by a trinucleotide expansion in exon 1 of the huntingtin (Htt) gene. Cell death in HD occurs primarily in striatal medium spiny neurons (MSNs), but the involvement of specific MSN subtypes and of other striatal cell types remains poorly understood. To gain insight into cell type-specific disease processes, we studied the nuclear transcriptomes of 4,524 cells from the striatum of a genetically precise knock-in mouse model of the HD mutation, HttQ175/+, and from wildtype controls. We used 14-15-month-old mice, a time point roughly equivalent to an early stage of symptomatic human disease. Cell type distributions indicated selective loss of D2 MSNs and increased microglia in aged HttQ175/+ mice. Thousands of differentially expressed genes were distributed across most striatal cell types, including transcriptional changes in glial populations that are not apparent from RNA-seq of bulk tissue. Reconstruction of cell typespecific transcriptional networks revealed a striking pattern of bidirectional dysregulation for many cell type-specific genes. Typically, these genes were repressed in their primary cell type, yet de-repressed in other striatal cell types. Integration with existing epigenomic and transcriptomic data suggest that partial loss-of-function of the Polycomb Repressive Complex 2 (PRC2) may underlie many of these transcriptional changes, leading to deficits in the maintenance of cell identity across virtually all cell types in the adult striatum.

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Improved estimation of cell type-specific gene expression through deconvolution of bulk tissues with matrix completion

10.1101/2021.06.30.450493 ◽

2021 ◽

Author(s):

Weixu Wang ◽

Jun Yao ◽

Yi Wang ◽

Chao Zhang ◽

Wei Tao ◽

...

Keyword(s):

Gene Expression ◽

Single Cells ◽

Matrix Completion ◽

Superior Performance ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Specific Gene Expression ◽

Large Patient ◽

Cell Type Specific

Cell type-specific gene expression (CSE) brings novel biological insights into both physiological and pathological processes compared with bulk tissue gene expression. Although fluorescence-activated cell sorting (FACS) and single-cell RNA sequencing (scRNA-seq) are two widely used techniques to detect gene expression in a cell type-specific manner, the constraints of cost and labor force make it impractical as a routine on large patient cohorts. Here, we present ENIGMA, an algorithm that deconvolutes bulk RNA-seq into cell type-specific expression matrices and cell type fraction matrices without the need of physical sorting or sequencing of single cells. ENIGMA used cell type signature matrix generated from either FACS RNA-seq or scRNA-seq as reference, and applied matrix completion technique to achieve fast and accurate deconvolution. We demonstrated the superior performance of ENIGMA to previously published algorithms (TCA, bMIND and CIBERSORTx) while requiring much less running time on both simulated and realistic datasets. To prove its value in biological discovery, we applied ENIGMA to bulk RNA-seq from arthritis patients and revealed a pseudo-differentiation trajectory that could reflect monocyte to macrophage transition. We also applied ENIGMA to bulk RNA-seq data of pancreatic islet tissue from type 2 diabetes (T2D) patients and discovered a beta cell-specific gene co-expression module related to senescence and apoptosis that possibly contributed to the pathogenesis of T2D. Together, ENIGMA provides a new framework to improve the CSE estimation by integrating FACS RNA-seq and scRNA-seq with tissue bulk RNA-seq data, and will extend our understandings about cell heterogeneity on population level with no need for experimental tissue disaggregation.

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Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

10.1101/354944 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xuran Wang ◽

Jihwan Park ◽

Katalin Susztak ◽

Nancy R. Zhang ◽

Mingyao Li

Keyword(s):

Single Cell ◽

Cell Types ◽

Cellular Heterogeneity ◽

Tissue Cell ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Cell Type Specific ◽

Cell Expression

AbstractWe present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms.

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A transcriptional toolbox for exploring peripheral neuro-immune interactions

10.1101/813980 ◽

2019 ◽

Cited By ~ 2

Author(s):

Zhi Liang ◽

Zoe Hore ◽

Peter Harley ◽

Federico Uchenna Stanley ◽

Aleksandra Michrowska ◽

...

Keyword(s):

Rna Seq ◽

Cell Type ◽

Web Based ◽

Peripheral Neurons ◽

Bladder Disease ◽

Monocytes And Macrophages ◽

Initial Injury ◽

Cell Type Specific ◽

Inflammatory Bowel ◽

Correct Communication

AbstractCorrect communication between immune cells and peripheral neurons is crucial for the protection of our bodies. Its breakdown is observed in many common, often painful conditions, including arthritis, neuropathies and inflammatory bowel or bladder disease. Here, we have characterised the immune response in a mouse model of neuropathic pain using flow cytometry and cell-type specific RNA sequencing (RNA-seq). We found few striking sex differences, but a very persistent inflammatory response, with increased numbers of monocytes and macrophages up to 3½ months after the initial injury. This raises the question of whether the commonly used categorisation of pain into “inflammatory” and “neuropathic” is one that is mechanistically appropriate. Finally, we collated our data with other published RNA-seq datasets on neurons, macrophages and Schwann cells in naïve and nerve injury states. The result is a practical web-based tool for the transcriptional data-mining of peripheral neuroimmune interactions.http://rna-seq-browser.herokuapp.com/

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Chromatin-enriched RNAs mark active and repressive cis-regulation: an analysis of nuclear RNA-seq

10.1101/646950 ◽

2019 ◽

Author(s):

Xiangying Sun ◽

Zhezhen Wang ◽

Carlos Perez-Cervantes ◽

Alex Ruthenburg ◽

Ivan Moskowitz ◽

...

Keyword(s):

Gene Expression ◽

Noncoding Rna ◽

Molecular Mechanisms ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Neighboring Gene ◽

Cis Regulation ◽

Nuclear Rna ◽

Cell Type Specific

AbstractLong noncoding RNAs (lncRNAs) localize in the cell nucleus and influence gene expression through a variety of molecular mechanisms. RNA sequencing of two biochemical fractions of nuclei reveals a unique class of lncRNAs, termed chromatin-enriched nuclear RNAs (cheRNAs) that are tightly bound to chromatin and putatively function to cis-activate gene expression. Until now, a rigorous analytic pipeline for nuclear RNA-seq has been lacking. In this study, we survey four computational strategies for nuclear RNA-seq data analysis and show that a new pipeline, Tuxedo, outperforms other approaches. Tuxedo not only assembles a more complete transcriptome, but also identifies cheRNA with higher accuracy. We have used Tuxedo to analyze gold-standard K562 cell datasets and further characterize the genomic features of intergenic cheRNA (icheRNA) and their similarity to those of enhancer RNA (eRNA). Moreover, we quantify the transcriptional correlation of icheRNA and adjacent genes, and suggest that icheRNA may be the cis-acting transcriptional regulator that is more positively associated with neighboring gene expression than eRNA predicted by state-of-art method or CAGE signal. We also explore two novel genomic associations, suggesting cheRNA may have diverse functions. A possible new role of H3K9me3 modification coincident with icheRNA may be associated with active enhancer derived from ancient mobile elements, while a potential cis-repressive function of antisense cheRNA (as-cheRNA) is likely to be involved in transiently modulating cell type-specific cis-regulation.Author SummaryChromatin-enriched nuclear RNA (cheRNA) is a class of gene regulatory non-coding RNAs. CheRNA provides a powerful way to profile the nuclear transcriptional landscape, especially to profile the noncoding transcriptome. The computational framework presented here provides a reliable approach to identifying cheRNA, and for studying cell-type specific gene regulation. We found that intergenic cheRNA, including intergenic cheRNA with high levels of H3K9me3 (a mark associated with closed/repressed chromatin), may act as a transcriptional activator. In contrast, antisense cheRNA, which originates from the complementary strand of the protein-coding gene, may interact with diverse chromatin modulators to repress local transcription. With our new pipeline, one future challenge will be refining the functional mechanisms of these noncoding RNA classes through exploring their regulatory roles, which are involved in diverse molecular and cellular processes in human and other organisms.

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