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A method for cell type marker discovery by high-throughput gene expression analysis of mixed cell populations.

ABSTRACT: BACKGROUND: Gene transcripts specifically expressed in a particular cell type (cell-type specific gene markers) are useful for its detection and isolation from a tissue or other cell mixtures. However, finding informative marker genes can be problematic when working with a poorly characterized cell type, as markers can only be unequivocally determined once the cell type has been isolated. We propose a method that could identify marker genes of an uncharacterized cell type within a mixed cell population, provided that the proportion of the cell type of interest in the mixture can be estimated by some indirect method, such as a functional assay. RESULTS: We show that cell-type specific gene markers can be identified from the global gene expression of several cell mixtures that contain the cell type of interest in a known proportion by their high correlation to the concentration of the corresponding cell type across the mixtures. CONCLUSIONS: Genes detected using this high-throughput strategy would be candidate markers that may be useful in detecting or purifying a cell type from a particular biological context. We present an experimental proof-of-concept of this method using cell mixtures of various well-characterized hematopoietic cell types, and we evaluate the performance of the method in a benchmark that explores the requirements and range of validity of the approach.

SUBMITTER: Andrade-Navarro MA

PROVIDER: S-EPMC3853712 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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A method for cell type marker discovery by high-throughput gene expression analysis of mixed cell populations.

Andrade-Navarro Miguel A MA Kanji Femina F Palii Carmen G CG Brand Marjorie M Atkins Harold H Perez-Iratxeta Carol C

BMC biotechnology 20131003

<h4>Background</h4>Gene transcripts specifically expressed in a particular cell type (cell-type specific gene markers) are useful for its detection and isolation from a tissue or other cell mixtures. However, finding informative marker genes can be problematic when working with a poorly characterized cell type, as markers can only be unequivocally determined once the cell type has been isolated. We propose a method that could identify marker genes of an uncharacterized cell type within a mixed c ...[more]

PMID: 24090206

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Project description:BackgroundHigh throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner.ResultsWe introduce RANKCORR, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RANKCORR proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RANKCORR to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells.ConclusionsAccording to the metrics introduced in this work, RANKCORR is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RANKCORR is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RANKCORR software is available for download at https://github.com/ahsv/RankCorr with extensive documentation.

Project description:BackgroundStaphylococcus epidermidis bacteria are a major cause of biomaterial-associated infections in modern medicine. Yet there is little known about the host responses against this normally innocent bacterium in the context of infection of biomaterials. In order to better understand the factors involved in this process, a whole animal model with high throughput screening possibilities and markers for studying the host response to S. epidermidis infection are required.ResultsWe have used a zebrafish yolk injection system to study bacterial proliferation and the host response in a time course experiment of S. epidermidis infection. By combining an automated microinjection system with complex object parametric analysis and sorting (COPAS) technology we have quantified bacterial proliferation. This system was used together with transcriptome analysis at several time points during the infection period. We show that bacterial colony forming unit (CFU) counting can be replaced by high throughput flow-based fluorescence analysis of embryos enabling high throughput readout. Comparison of the host transcriptome response to S. epidermidis and Mycobacterium marinum infection in the same system showed that M. marinum has a far stronger effect on host gene regulation than S. epidermidis. However, multiple genes responded differently to S. epidermidis infection than to M. marinum, including a cell adhesion gene linked to specific infection by staphylococci in mammals.ConclusionsOur zebrafish embryo infection model allowed (i) quantitative assessment of bacterial proliferation, (ii) identification of zebrafish genes serving as markers for infection with the opportunistic pathogen S. epidermidis, and (iii) comparison of the transcriptome response of infection with S. epidermidis and with the pathogen M. marinum. As a result we have identified markers that can be used to distinguish common and specific responses to S. epidermidis. These markers enable the future integration of our high throughput screening technology with functional analyses of immune response genes and immune modulating factors.

Project description:Magnetic cell sorting technology stands out because of its speed, simplicity, and ability to process large cell numbers. However, it also suffers from a number of drawbacks, in particular low discrimination power, which results in all-or-none selection outcomes limited to a bulk separation of cell populations into positive and negative fractions, as well as the modest purity of the selected cells and the inability to select subpopulations of cells with high expression of a surface marker. In the present study, we developed a simple solution to this problem and confirmed the effectiveness of this approach by multiple experiments with the magnetic selection of transduced cell populations. Murine NIH 3T3 cells were transduced with the bicistronic retroviral vector constructs co-expressing fluorescent reporter proteins EGFP (enhanced green fluorescent protein) or DsRed-Express 2 and LNGFR (low-affinity nerve growth factor receptor) as surface selection markers. The effects of the magnetic selection of transduced cells with anti-LNGFR Micro Bead (MB) doses ranging from 0.5 to 80 µL have been assessed. Low doses of MBs favored the depletion of weakly positive cells from the population, resulting in the higher expression levels of EGFP or DsRed-Express2 reporters in the selected cell fractions. Low MB doses also contributed to the increased purity of the selected population, even for samples with a low initial percentage of positive cells. At the same time, high MB doses resulted in the increased yield and a more faithful representation of the original expression profiles following selection. We further demonstrate that for populations with fairly narrow distribution of expression levels, it is possible to achieve separation into high- and low-expressing subsets using the two-stage selection scheme based on the sequential use of low and high doses of MBs. For populations with broad expression distribution, a one-stage selection with low or high doses of MBs is sufficient for a clear separation of low- and high-expressing subsets in the column-retained and flow-through fractions, respectively. This study substantially extends the potential of magnetic cell sorting, and may open new possibilities in a number of biomedical applications.

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A method for cell type marker discovery by high-throughput gene expression analysis of mixed cell populations.

Publications

A method for cell type marker discovery by high-throughput gene expression analysis of mixed cell populations.

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