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 A comparison between single cell RNA sequencing and single molecule RNA FISH for rare cell analysis

2017 ◽  
Author(s):  
Eduardo Torre ◽  
Hannah Dueck ◽  
Sydney Shaffer ◽  
Janko Gospocic ◽  
Rohit Gupte ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Molecule ◽  
Single Cells ◽  
Cycle Phase ◽  
Expression Variability ◽  
Sequencing Technologies ◽  
Rna Fish ◽  
Single Cell Rna Sequencing ◽  
Sequencing Platforms

AbstractThe development of single cell RNA sequencing technologies has emerged as a powerful means of profiling the transcriptional behavior of single cells, leveraging the breadth of sequencing measurements to make inferences about cell type. However, there is still little understanding of how well these methods perform at measuring single cell variability for small sets of genes and what “transcriptome coverage” (e.g. genes detected per cell) is needed for accurate measurements. Here, we use single molecule RNA FISH measurements of 26 genes in thousands of melanoma cells to provide an independent reference dataset to assess the performance of the DropSeq and Fluidigm single cell RNA sequencing platforms. We quantified the Gini coefficient, a measure of rare-cell expression variability, and find that the correspondence between RNA FISH and single cell RNA sequencing for Gini, unlike for mean, increases markedly with per-cell library complexity up to a threshold of ∼2000 genes detected. A similar complexity threshold also allows for robust assignment of multi-genic cell states such as cell cycle phase. Our results provide guidelines for selecting sequencing depth and complexity thresholds for single cell RNA sequencing. More generally, our results suggest that if the number of genes whose expression levels are required to answer any given biological question is small, then greater transcriptome complexity per cell is likely more important than obtaining very large numbers of cells.

Sign in / Sign up

Export Citation Format

Share Document