Integrating single-cell RNA-seq and imaging with SCOPE-seq2

Abstract Live cell imaging allows direct observation and monitoring of phenotypes that are difficult to infer from transcriptomics. However, existing methods for linking microscopy and single-cell RNA-seq (scRNA-seq) have limited scalability. Here, we describe an upgraded version of Single Cell Optical Phenotyping and Expression (SCOPE-seq2) for combining single-cell imaging and expression profiling, with substantial improvements in throughput, molecular capture efficiency, linking accuracy, and compatibility with standard microscopy instrumentation. We introduce improved optically decodable mRNA capture beads and implement a more scalable and simplified optical decoding process. We demonstrate the utility of SCOPE-seq2 for fluorescence, morphological, and expression profiling of individual primary cells from a human glioblastoma (GBM) surgical sample, revealing relationships between simple imaging features and cellular identity, particularly among malignantly transformed tumor cells.

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Integrating single-cell RNA-seq and imaging with SCOPE-seq2

10.1101/2020.06.28.176404 ◽

2020 ◽

Author(s):

Zhouzerui Liu ◽

Jinzhou Yuan ◽

Anna Lasorella ◽

Antonio Iavarone ◽

Jeffrey N. Bruce ◽

...

Keyword(s):

Single Cell ◽

Expression Profiling ◽

Live Cell Imaging ◽

Cell Imaging ◽

Imaging Features ◽

Primary Cells ◽

Rna Seq ◽

Single Cell Imaging ◽

Cellular Identity ◽

Surgical Sample

AbstractLive cell imaging allows direct observation and monitoring of phenotypes that are difficult to infer from transcriptomics. However, existing methods for linking microscopy and single-cell RNA-seq (scRNA-seq) have limited scalability. Here, we describe an upgraded version of Single Cell Optical Phenotyping and Expression (SCOPE-seq2) for combining single-cell imaging and expression profiling, with substantial improvements in throughput, molecular capture efficiency, linking accuracy, and compatibility with standard microscopy instrumentation. We introduce improved optically decodable mRNA capture beads and implement a more scalable and simplified optical decoding process. We demonstrate the utility of SCOPE-seq2 for fluorescence, morphological, and expression profiling of individual primary cells from a human glioblastoma (GBM) surgical sample, revealing relationships between simple imaging features and cellular identity, particularly among malignantly transformed tumor cells.

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An Optically Decodable Bead Array for Linking Imaging and Sequencing with Single-Cell Resolution

10.1101/355677 ◽

2018 ◽

Cited By ~ 2

Author(s):

Jinzhou Yuan ◽

Jenny Sheng ◽

Peter A. Sims

Keyword(s):

Single Cell ◽

Expression Profiling ◽

Live Cell Imaging ◽

Cell Imaging ◽

Living Cells ◽

Live Cell ◽

Proof Of Concept ◽

Rna Seq ◽

Phenotypic Analysis

AbstractOptically decodable beads link the identity of an analyte or sample to a measurement through an optical barcode, enabling libraries of biomolecules to be captured on beads in solution and decoded by fluorescence. This approach has been foundational to microarray, sequencing, and flow-based expression profiling technologies. We have combined microfluidics with optically decodable beads to link phenotypic analysis of living cells to sequencing. As a proof-of-concept, we applied this to demonstrate an accurate and scalable tool for connecting live cell imaging to single-cell RNA-Seq called Single Cell Optical Phenotyping and Expression (SCOPE-Seq).

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Multi-domain translation between single-cell imaging and sequencing data using autoencoders

Nature Communications ◽

10.1038/s41467-020-20249-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Karren Dai Yang ◽

Anastasiya Belyaeva ◽

Saradha Venkatachalapathy ◽

Karthik Damodaran ◽

Abigail Katcoff ◽

...

Keyword(s):

T Cells ◽

Single Cell ◽

Cd4 T Cells ◽

Cell Imaging ◽

Cellular Heterogeneity ◽

Rna Seq ◽

Sequencing Data ◽

Single Cell Imaging ◽

Latent Space

AbstractThe development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on a population of cells, and their integration is critical for studying cellular heterogeneity and its function. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct subpopulations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

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Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders

10.1101/2019.12.13.875922 ◽

2019 ◽

Cited By ~ 1

Author(s):

Karren Dai Yang ◽

Anastasiya Belyaeva ◽

Saradha Venkatachalapathy ◽

Karthik Damodaran ◽

Adityanarayanan Radhakrishnan ◽

...

Keyword(s):

T Cells ◽

Single Cell ◽

Cd4 T Cells ◽

Cell Imaging ◽

Rna Seq ◽

Sequencing Data ◽

Single Cell Imaging ◽

Latent Space

The development of single-cell methods for capturing different data modalities including imaging and sequencing have revolutionized our ability to identify heterogeneous cell states. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct sub-populations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

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