Detecting Differences of Fluorescent Markers Distribution in Single Cell Microscopy: Textural or Pointillist Feature Space?

ABSTRACTEpigenetic single-cell measurements reveal a layer of regulatory information not accessible to single-cell transcriptomics, however single-cell-omics analysis tools mainly focus on gene expression data. To address this issue, we present epiScanpy, a computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy makes the many existing RNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities. We introduce and compare multiple feature space constructions for epigenetic data and show the feasibility of common clustering, dimension reduction and trajectory learning techniques. We benchmark epiScanpy by interrogating different single-cell brain mouse atlases of DNA methylation, ATAC-seq and transcriptomics. We find that differentially methylated and differentially open markers between cell clusters enrich transcriptome-based cell type labels by orthogonal epigenetic information.

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Improved Chemical-Genetic Fluorescent Markers for Live Cell Microscopy

Biochemistry ◽

10.1021/acs.biochem.8b00649 ◽

2018 ◽

Vol 57 (39) ◽

pp. 5648-5653 ◽

Cited By ~ 8

Author(s):

Alison G. Tebo ◽

Frederico M. Pimenta ◽

Yu Zhang ◽

Arnaud Gautier

Keyword(s):

Live Cell ◽

Chemical Genetic ◽

Fluorescent Markers ◽

Live Cell Microscopy ◽

Cell Microscopy

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A Combined Confocal-Total Internal Reflection Fluorescence (TIRF) Single-Cell Microscopy Investigation of CEACAM1 Dynamics

Biophysical Journal ◽

10.1016/j.bpj.2010.12.3548 ◽

2011 ◽

Vol 100 (3) ◽

pp. 616a

Author(s):

Laura N. Poloni ◽

Christopher M. Yip

Keyword(s):

Single Cell ◽

Total Internal Reflection ◽

Internal Reflection ◽

Total Internal Reflection Fluorescence ◽

Microscopy Investigation ◽

Cell Microscopy

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Whole slide imaging is a high-throughput method to assess Candida biofilm formation

10.1101/2020.03.15.992735 ◽

2020 ◽

Author(s):

Maximilian W. D. Raas ◽

Thiago P. Silva ◽

Jhamine C. O. Freitas ◽

Lara M. Campos ◽

Rodrigo L. Fabri ◽

...

Keyword(s):

Single Cell ◽

Biofilm Formation ◽

Large Scale ◽

Structural Features ◽

Whole Slide Imaging ◽

Bright Field ◽

Biofilm Growth ◽

Fluorescent Markers ◽

Golden Standard ◽

Important Addition

AbstractNew strategies that enable fast and accurate visualization of Candida biofilms are necessary to better study their structure and response to antifungals agents. Here, we applied whole slide imaging (WSI) to study biofilm formation of Candida species. Three relevant biofilm-forming Candida species (C. albicans ATCC 10231, C. glabrata ATCC 2001, and C. tropicalis ATCC 750) were cultivated on glass coverslips both in presence and absence of widely used antifungals. Accumulated biofilms were stained with fluorescent markers and scanned in both bright-field and fluorescence modes using a WSI digital scanner. WSI enabled clear assessment of both size and structural features of Candida biofilms. Quantitative analyses readily detected reductions in biofilm-covered surface area upon antifungal exposure. Furthermore, we show that the overall biofilm growth can be adequately assessed across both bright-field and fluorescence modes. At the single-cell level, WSI proved adequate, as morphometric parameters evaluated with WSI did not differ significantly from those obtained with scanning electron microscopy, considered as golden standard at single-cell resolution. Thus, WSI allows for reliable visualization of Candida biofilms enabling both large-scale growth assessment and morphometric characterization of single-cell features, making it an important addition to the available microscopic toolset to image and analyze fungal biofilm growth.

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FleN contributes to heterogeneous swimming at high temperatures inPseudomonas syringae

10.1101/372060 ◽

2018 ◽

Author(s):

Kevin L. Hockett ◽

Steven E. Lindow

Keyword(s):

Flow Cytometry ◽

Single Cell ◽

Pseudomonas Syringae ◽

Environmental Conditions ◽

Promoter Region ◽

Plant Pathogens ◽

Incubation Temperature ◽

Nutrient Depletion ◽

Synonymous Mutations ◽

Cell Microscopy

SUMMARYMotility is generally conserved among many animal and plant pathogens. Environmental conditions, however, significantly impact expression of the motile phenotype. In this study, we describe a novel heterogeneous motility phenotype inPseudomonas syringae, where under normally suppressive incubation conditions (30°C) punctate colonies arise that are spatially isolated from the point of inoculation, giving rise to a motility pattern we term constellation swimming (CS). We demonstrate that this phenotype is reproducible, reversible, and dependent on a functioning flagellum. Mirroring the heterogeneous motility phenotype, we demonstrate the existence of a sub-population of cells under non-permissive conditions that express flagellin (fliC) at levels similar to cells incubated under permissive conditions using both quantitative single cell microscopy and flow cytometry. To understand the genetics underlying the CS phenotype, we selected for naturally arising mutants that exhibited a normal swimming phenotype at the warmer incubation temperature. Sequencing these mutants recovered several independent non-synonymous mutations within FleN (also known as FlhG) as well as mutations within the promoter region of FleQ, the master flagellum regulator inPseudomonas. We further show that nutrient depletion is the likely underlying cause of CS, as reduced nutrients will stimulate bothfliCexpression and a normal swimming phenotype at 30 °C.

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DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data

10.1101/2020.10.07.330563 ◽

2020 ◽

Author(s):

Bobby Ranjan ◽

Wenjie Sun ◽

Jinyu Park ◽

Ronald Xie ◽

Fatemeh Alipour ◽

...

Keyword(s):

Feature Selection ◽

Single Cell ◽

Gene Selection ◽

Marker Gene ◽

Feature Space ◽

General Purpose ◽

Selection Marker ◽

Selection Methods ◽

Sequencing Data ◽

Data Types

Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. However, we found that the performance of existing feature selection methods was inconsistent across benchmark datasets, and occasionally even worse than without feature selection. Moreover, existing methods ignored information contained in gene-gene correlations. We there-fore developed DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUB-StepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. In a published scRNA-seq dataset from sorted monocytes, DUBStepR sensitively detected a rare and previously invisible population of contaminating basophils. DUBStepR is scalable to large datasets, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.

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Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data

10.1101/183863 ◽

2017 ◽

Cited By ~ 4

Author(s):

Sabrina Rashid ◽

Sohrab Shah ◽

Ziv Bar-Joseph ◽

Ravi Pandya

Keyword(s):

Gene Expression ◽

Single Cell ◽

Tumor Heterogeneity ◽

Genomic Data ◽

Feature Space ◽

Marker Genes ◽

Tumor Evolution ◽

Evolutionary Trajectory ◽

Link Type ◽

Variational Autoencoder

AbstractMotivationIntra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers, and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single cell genomic data in order to infer their evolutionary trajectory remains computationally challenging due to the extremely noisy and sparse nature of the data.ResultsHere we describe ‘Dhaka’, a variational autoencoder method which transforms single cell genomic data to a reduced dimension feature space that is more efficient in differentiating between (hidden) tumor subpopulations. Our method is general and can be applied to several different types of genomic data including copy number variation from scDNA-Seq and gene expression from scRNA-Seq experiments. We tested the method on synthetic and 6 single cell cancer datasets where the number of cells ranges from 250 to 6000 for each sample. Analysis of the resulting feature space revealed subpopulations of cells and their marker genes. The features are also able to infer the lineage and/or differentiation trajectory between cells greatly improving upon prior methods suggested for feature extraction and dimensionality reduction of such data.Availability and ImplementationAll the datasets used in the paper are publicly available and developed software package is available on Github https://github.com/MicrosoftGenomics/Dhaka.Supporting info and Software: https://github.com/MicrosoftGenomics/Dhaka

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