Detection of mRNA Transfer Between Mammalian Cells in Coculture by Single-Molecule Fluorescent In Situ Hybridization (smFISH)

2019 ◽  
pp. 109-129 ◽  
Author(s):  
Gal Haimovich ◽  
Jeffrey E. Gerst
Keyword(s):  
In Situ Hybridization ◽  
Single Molecule ◽  
Mammalian Cells ◽  
Fluorescent In Situ Hybridization

scholarly journals smiFISH and embryo segmentation for single-cell multi-gene RNA quantification in arthropods

2020 ◽  
Author(s):  
Llilians Calvo ◽  
Matthew Ronshaugen ◽  
Tom Pettini
Keyword(s):  
In Situ Hybridization ◽  
Single Cell ◽  
Single Molecule ◽  
Fluorescent In Situ Hybridization ◽  
Single Cells ◽  
Fano Factor ◽  
Confocal Imaging ◽  
Cell Segmentation ◽  
Expression Variability

ABSTRACTRecently, advances in fluorescent in-situ hybridization techniques and in imaging technology have enabled visualisation and counting of individual RNA molecules in single cells. This has greatly enhanced the resolution in our understanding of transcriptional processes. Here, we adapt a recently published smiFISH protocol (single-molecule inexpensive fluorescent in-situ hybridization) to whole embryos across a range of arthropod model species, and also to non-embryonic tissues. Using multiple fluorophores with distinct spectra and white light laser confocal imaging, we simultaneously detect and separate single RNAs from up to eight different genes in a whole embryo. We also combine smiFISH with cell membrane immunofluorescence, and present an imaging and analysis pipeline for 3D cell segmentation and single-cell RNA counting in whole blastoderm embryos. Finally, using whole embryo single-cell RNA count data, we propose two alternative single-cell variability measures to the commonly used Fano factor, and compare the capacity of these three measures to address different aspects of single-cell expression variability.

scholarly journals smiFISH and embryo segmentation for single-cell multi-gene RNA quantification in arthropods

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Llilians Calvo ◽  
Matthew Ronshaugen ◽  
Tom Pettini
Keyword(s):  
In Situ Hybridization ◽  
Single Cell ◽  
Single Molecule ◽  
Fluorescent In Situ Hybridization ◽  
Single Cells ◽  
Fano Factor ◽  
Confocal Imaging ◽  
Cell Segmentation ◽  
Expression Variability

AbstractRecently, advances in fluorescent in-situ hybridization techniques and in imaging technology have enabled visualization and counting of individual RNA molecules in single cells. This has greatly enhanced the resolution in our understanding of transcriptional processes. Here, we adapt a recently published smiFISH protocol (single-molecule inexpensive fluorescent in-situ hybridization) to whole embryos across a range of arthropod model species, and also to non-embryonic tissues. Using multiple fluorophores with distinct spectra and white light laser confocal imaging, we simultaneously detect and separate single RNAs from up to eight different genes in a whole embryo. We also combine smiFISH with cell membrane immunofluorescence, and present an imaging and analysis pipeline for 3D cell segmentation and single-cell RNA counting in whole blastoderm embryos. Finally, using whole embryo single-cell RNA count data, we propose two alternative single-cell variability measures to the commonly used Fano factor, and compare the capacity of these three measures to address different aspects of single-cell expression variability.

scholarly journals dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues

2019 ◽  
Author(s):  
Kristen R. Maynard ◽  
Madhavi Tippani ◽  
Yoichiro Takahashi ◽  
BaDoi N. Phan ◽  
Thomas M. Hyde ◽  
...  
Keyword(s):  
In Situ Hybridization ◽  
Differential Expression ◽  
Expression Analysis ◽  
Single Molecule ◽  
Fluorescent In Situ Hybridization ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Specific Expression ◽  
Dorsolateral Prefrontal

ABSTRACTMultiplex single-molecule fluorescent in situ hybridization (smFISH) is a powerful method for validating RNA sequencing and emerging spatial transcriptomic data, but quantification remains a computational challenge. We present a framework for generating and analyzing smFISH data in complex tissues while overcoming autofluorescence and increasing multiplexing capacity. We developed dotdotdot (https://github.com/LieberInstitute/dotdotdot) as a corresponding software package to quantify RNA transcripts in single nuclei and perform differential expression analysis. We first demonstrate robustness of our platform in single mouse neurons by quantifying differential expression of activity-regulated genes. We then quantify spatial gene expression in human dorsolateral prefrontal cortex (DLPFC) using spectral imaging and dotdotdot to mask lipofuscin autofluorescence. We lastly apply machine learning to predict cell types and perform downstream cell type-specific expression analysis. In summary, we provide experimental workflows, imaging acquisition and analytic strategies for quantification and biological interpretation of smFISH data in complex tissues.

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