Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo

Abstract High-content imaging and single-cell genomics are two of the most prominent high-throughput technologies for studying cellular properties and functions at scale. Recent studies have demonstrated that information in large imaging datasets can be used to estimate gene mutations and to predict the cell-cycle state and the cellular decision making directly from cellular morphology. Thus, high-throughput imaging methodologies, such as imaging flow cytometry can potentially aim beyond simple sorting of cell-populations. We introduce IFC-seq, a machine learning methodology for predicting the expression profile of every cell in an imaging flow cytometry experiment. Since it is to-date unfeasible to observe single-cell gene expression and morphology in flow, we integrate uncoupled imaging data with an independent transcriptomics dataset by leveraging common surface markers. We demonstrate that IFC-seq successfully models gene expression of a moderate number of key gene-markers for two independent imaging flow cytometry datasets: (i) human blood mononuclear cells and (ii) mouse myeloid progenitor cells. In the case of mouse myeloid progenitor cells IFC-seq can predict gene expression directly from brightfield images in a label-free manner, using a convolutional neural network. The proposed method promises to add gene expression information to existing and new imaging flow cytometry datasets, at no additional cost.

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Identification of genes driving lineage divergence from single-cell gene expression data in C. elegans

Developmental Biology ◽

10.1016/j.ydbio.2014.07.009 ◽

2014 ◽

Vol 393 (2) ◽

pp. 236-244

Author(s):

Chen Xu ◽

Zhengchang Su

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Expression Data ◽

C Elegans ◽

Cell Gene Expression ◽

Lineage Divergence ◽

Cell Gene

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Gene expression-based identification of antigen-responsive CD8+ T cells on a single-cell level

10.1101/673707 ◽

2019 ◽

Author(s):

Yannick F. Fuchs ◽

Virag Sharma ◽

Anne Eugster ◽

Gloria Kraus ◽

Robert Morgenstern ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

T Cells ◽

Single Cell ◽

Single Cell Level ◽

Cell Level ◽

Large Gene ◽

Gene Sets ◽

Cell Gene Expression ◽

Cell Gene

AbstractCD8+ T cells are important effectors of adaptive immunity against pathogens, tumors and self antigens. Here, we asked how human cognate antigen-responsive CD8+ T cells and their receptors could be identified in unselected single-cell gene expression data. Single-cell RNA sequencing and qPCR of dye-labelled antigen-specific cells identified large gene sets that were congruently up- or downregulated in virus-responsive CD8+ T cells under different antigen presentation conditions. Combined expression of TNFRSF9, XCL1, XCL2, and CRTAM was the most distinct marker of virus-responsive cells on a single-cell level. Using transcriptomic data, we developed a machine learning-based classifier that provides sensitive and specific detection of virus-responsive CD8+ T cells from unselected populations. Gene response profiles of CD8+ T cells specific for the autoantigen islet-specific glucose-6-phosphatase catalytic subunit-related protein differed markedly from virus-specific cells. These findings provide single-cell gene expression parameters for comprehensive identification of rare antigen-responsive cells and T cell receptors.One-sentence summaryIdentification of genes, gene sets, and development of a machine learning-based classifier that distinguishes antigen-responsive CD8+ T cells on a single-cell level.

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