FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles

Abstract The advent of single-cell open-chromatin profiling technology has facilitated the analysis of heterogeneity of activity of regulatory regions at single-cell resolution. However, stochasticity and availability of low amount of relevant DNA, cause high drop-out rate and noise in single-cell open-chromatin profiles. We introduce here a robust method called as forest of imputation trees (FITs) to recover original signals from highly sparse and noisy single-cell open-chromatin profiles. FITs makes multiple imputation trees to avoid bias during the restoration of read-count matrices. It resolves the challenging issue of recovering open chromatin signals without blurring out information at genomic sites with cell-type-specific activity. Besides visualization and classification, FITs-based imputation also improved accuracy in the detection of enhancers, calculating pathway enrichment score and prediction of chromatin-interactions. FITs is generalized for wider applicability, especially for highly sparse read-count matrices. The superiority of FITs in recovering signals of minority cells also makes it highly useful for single-cell open-chromatin profile from in vivo samples. The software is freely available at https://reggenlab.github.io/FITs/.

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FITs: Forest of imputation trees for recovering true signals in single-cell open chromatin profiles

10.1101/2020.08.12.243121 ◽

2020 ◽

Author(s):

Rachesh Sharma ◽

Neetesh Pandey ◽

Anchal Mongia ◽

Shreya Mishra ◽

Angshul Majumdar ◽

...

Keyword(s):

Single Cell ◽

Specific Activity ◽

Read Count ◽

Drop Out ◽

Open Chromatin ◽

Drop Out Rate ◽

Cell Type Specific ◽

Chromatin Profiling ◽

Thesis Work

AbstractThe advent of single-cell open-chromatin profiling technology has facilitated the analysis of heterogeneity of activity of regulatory regions at single-cell resolution. However, stochasticity and availability of low amount of relevant DNA cause high drop-out rate and noise in single-cell open-chromatin profiles. We introduce here a robust method called as Forest of Imputation Trees (FITs) to recover original signals from highly sparse and noisy single-cell open-chromatin profiles. FITs makes a forest of imputation trees to avoid bias during the restoration of read-count matrices. It resolves the challenging issue of recovering open chromatin signals without blurring out information at genomic sites with cell-type-specific activity. FITs is generalized for wider applicability, especially for highly sparse read-count matrices. The superiority of FITs in recovering signals of minority cells also makes it highly useful for single-cell open-chromatin profile from in vivo samples.First made online as thesis work at https://repository.iiitd.edu.in/xmlui/handle/123456789/807

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Discovery of Transcription Factors and Regulatory Regions Driving In Vivo Tumor Development by ATAC-seq and FAIRE-seq Open Chromatin Profiling

PLoS Genetics ◽

10.1371/journal.pgen.1004994 ◽

2015 ◽

Vol 11 (2) ◽

pp. e1004994 ◽

Cited By ~ 97

Author(s):

Kristofer Davie ◽

Jelle Jacobs ◽

Mardelle Atkins ◽

Delphine Potier ◽

Valerie Christiaens ◽

...

Keyword(s):

Transcription Factors ◽

Tumor Development ◽

Open Chromatin ◽

Regulatory Regions ◽

Chromatin Profiling

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Systematic Comparison of High-Throughput Single-Cell RNAseq Methods for Immune Cell Profiling

10.21203/rs.3.rs-72851/v1 ◽

2020 ◽

Author(s):

Chi-Ming Kevin Li ◽

Tracy M Yamawaki ◽

Daniel R Lu ◽

Daniel C Ellwanger ◽

Dev Bhatt ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Immune Cell ◽

Cell Types ◽

Drop Out ◽

Detection Sensitivity ◽

Rna Seq ◽

Cell Recovery

Abstract Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the fieldof immunology by deepening the characterization of immune heterogeneity and leading to thediscovery of new subtypes. However, single-cell methods inherently suffer from limitations in therecovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropoutevents. This issue is often compounded by limited sample availability and limited prior knowledge ofheterogeneity, which can confound data interpretation.Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. Weprepared 21 libraries under identical conditions of a defined mixture of two human and two murinelymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluatemethods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expressionsignatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events whichfacilitates the identification of differentially-expressed genes and improves the concordance of singlecellprofiles to immune bulk RNA-seq signatures.Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which canguide selection of a high-throughput single-cell RNA-seq method for profiling more complex immunecellheterogeneity usually found in vivo.

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UniPath: a uniform approach for pathway and gene-set based analysis of heterogeneity in single-cell epigenome and transcriptome profiles

Nucleic Acids Research ◽

10.1093/nar/gkaa1138 ◽

2020 ◽

Author(s):

Smriti Chawla ◽

Sudhagar Samydurai ◽

Say Li Kong ◽

Zhenxun Wang ◽

Wai Leong TAM ◽

...

Keyword(s):

Single Cell ◽

Expression Profiles ◽

Single Cells ◽

Gene Expression Profiles ◽

Transcriptome Profiling ◽

Mouse Cell ◽

Drop Out ◽

Open Chromatin ◽

Gene Set Enrichment ◽

Gene Set

Abstract Recent advances in single-cell open-chromatin and transcriptome profiling have created a challenge of exploring novel applications with a meaningful transformation of read-counts, which often have high variability in noise and drop-out among cells. Here, we introduce UniPath, for representing single-cells using pathway and gene-set enrichment scores by a transformation of their open-chromatin or gene-expression profiles. The robust statistical approach of UniPath provides high accuracy, consistency and scalability in estimating gene-set enrichment scores for every cell. Its framework provides an easy solution for handling variability in drop-out rate, which can sometimes create artefact due to systematic patterns. UniPath provides an alternative approach of dimension reduction of single-cell open-chromatin profiles. UniPath's approach of predicting temporal-order of single-cells using their pathway enrichment scores enables suppression of covariates to achieve correct order of cells. Analysis of mouse cell atlas using our approach yielded surprising, albeit biologically-meaningful co-clustering of cell-types from distant organs. By enabling an unconventional method of exploiting pathway co-occurrence to compare two groups of cells, our approach also proves to be useful in inferring context-specific regulations in cancer cells. Available at https://reggenlab.github.io/UniPathWeb/.

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Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

10.21203/rs.3.rs-72851/v2 ◽

2020 ◽

Author(s):

Tracy M Yamawaki ◽

Daniel R Lu ◽

Daniel C Ellwanger ◽

Dev Bhatt ◽

Paolo Manzanillo ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Immune Cell ◽

Cell Types ◽

Drop Out ◽

Detection Sensitivity ◽

Rna Seq ◽

Cell Recovery

Abstract Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’ v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

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Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

10.1101/2020.09.04.283499 ◽

2020 ◽

Author(s):

Tracy M. Yamawaki ◽

Daniel R. Lu ◽

Daniel C. Ellwanger ◽

Dev Bhatt ◽

Paolo Manzanillo ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Immune Cell ◽

Cell Types ◽

Drop Out ◽

Detection Sensitivity ◽

Rna Seq ◽

Cell Recovery

AbstractBackgroundElucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation.ResultsHere, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’ v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.ConclusionOverall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

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Dissection of a Complex Enhancer Element: Maintenance of Keratinocyte Specificity but Loss of Differentiation Specificity

Molecular and Cellular Biology ◽

10.1128/mcb.22.12.4293-4308.2002 ◽

2002 ◽

Vol 22 (12) ◽

pp. 4293-4308 ◽

Cited By ~ 26

Author(s):

Charles K. Kaufman ◽

Satrajit Sinha ◽

Diana Bolotin ◽

Jie Fan ◽

Elaine Fuchs

Keyword(s):

Gene Expression ◽

Specific Activity ◽

Regulatory Elements ◽

Specific Gene ◽

Regulatory Sequence ◽

Open Chromatin ◽

Specific Expression ◽

Enhancer Activity ◽

Enhancer Element

ABSTRACT In this report, we explored the mechanisms underlying keratinocyte-specific and differentiation-specific gene expression in the skin. We have identified five keratinocyte-specific, open chromatin regions that exist within the 6 kb of 5′ upstream regulatory sequence known to faithfully recapitulate the strong endogenous keratin 5 (K5) promoter and/or enhancer activity. One of these, DNase I-hypersensitive site (HSs) 4, was unique in that it acted independently to drive abundant and keratinocyte-specific reporter gene activity in culture and in transgenic mice, despite the fact that it was not essential for K5 enhancer activity. We have identified evolutionarily conserved regulatory elements and a number of their associated proteins that bind to this compact and complex enhancer element. The 125-bp 3′ half of this element (referred to as 4.2) is by far the smallest known strong enhancer element possessing keratinocyte-specific activity in vivo. Interestingly, its activity is restricted to a subset of progeny of K5-expressing cells located within the sebaceous gland. The other half of HSs 4 (termed 4.1) possesses activity to suppress sebocyte-specific expression and induce expression in the channel (inner root sheath) cells surrounding the hair shaft. Our findings lead us to a view of keratinocyte gene expression which is determined by multiple regulatory modules, many of which contain AP-2 and/or Sp1/Sp3 binding sites for enhancing expression in skin epithelium, but which also harbor one or more unique sites for the binding of factors which determine specificity. Through mixing and matching of these modules, additional levels of specificity are obtained, indicating that both transcriptional repressors and activators govern the specificity.

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Improving chromatin-interaction prediction using single-cell open-chromatin profile and making insight about the cis-regulatory landscape of the human brain

10.1101/2020.12.29.424732 ◽

2020 ◽

Author(s):

Neetesh Pandey ◽

Omkar Chandra ◽

Shreya Mishra ◽

Vibhor Kumar

Keyword(s):

Human Brain ◽

Single Cell ◽

Long Range ◽

Target Genes ◽

Cell Types ◽

Chromatin Interaction ◽

Open Chromatin ◽

Range Interaction ◽

Chromatin Interactions ◽

A Cell

AbstractSingle-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell-type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin-interactions despite the fact that long-range interaction among genomic sites plays a significant role in gene regulation. Here, we propose a method that predicts both local and long-range interactions among genomic sites using single-cell open chromatin profiles. Using our method’s better sensitivity, we could predict almost 0.7 million interactions among genomic sites across 7 cell-types in the human brain. The chromatin-interactions estimated in the human brain revealed surprising but useful insight about target genes of human-accelerated-elements and disease-associated mutations.

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Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

10.21203/rs.3.rs-72851/v3 ◽

2021 ◽

Author(s):

Tracy M Yamawaki ◽

Daniel R Lu ◽

Daniel C Ellwanger ◽

Dev Bhatt ◽

Paolo Manzanillo ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Immune Cell ◽

Cell Types ◽

Drop Out ◽

Detection Sensitivity ◽

Rna Seq ◽

Cell Recovery

Abstract Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’ v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

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Soluble Thrombomodulin Purified from Human Urine Exhibits a Potent Anticoagulant Effect In Vitro and In Vivo

Thrombosis and Haemostasis ◽

10.1055/s-0038-1653872 ◽

1995 ◽

Vol 73 (05) ◽

pp. 805-811 ◽

Cited By ~ 12

Author(s):

Yasuo Takahashi ◽

Yoshitaka Hosaka ◽

Hiromi Niina ◽

Katsuaki Nagasawa ◽

Masaaki Naotsuka ◽

...

Keyword(s):

Human Urine ◽

Specific Activity ◽

Anticoagulant Activity ◽

Rabbit Lung ◽

Soluble Thrombomodulin ◽

Molecular Weights ◽

Dose Dependent Fashion ◽

Dose Dependent

SummaryWe examined the anticoagulant activity of two major molecules of soluble thrombomodulin purified from human urine. The apparent molecular weights of these urinary thrombomodulins (UTMs) were 72,000 and 79,000, respectively. Both UTMs showed more potent cofactor activity for protein C activation [specific activity >5,000 thrombomodulin units (TMU)/mg] than human placental thrombomodulin (2,180 TMU/mg) and rabbit lung thrombomodulin (1,980 TMU/mg). The UTMs prolonged thrombin-induced fibrinogen clotting time (>1 TMU/ml), APTT (>5 TMU/ml), TT (>5 TMU/ml) and PT (>40 TMU/ml) in a dose-dependent fashion. These effects appeared in the concentration range of soluble thrombomodulins present in human plasma and urine. In the rat DIC model induced by thromboplastin, administration of UTMs by infusion (300-3,000 TMU/kg) restored the hematological abnormalities derived from DIC in a dose-dependent fashion. These results demonstrate that UTMs exhibit potent anticoagulant and antithrombotic activities, and could play a physiologically important role in microcirculation.

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