Combinatorial Analysis of mRNA Expression Patterns in Mouse Embryos Using Hybridization Chain Reaction

Huss, David; Choi, Harry M. T.; Readhead, Carol; Fraser, Scott E.; Pierce, Niles A.; Lansford, Rusty

doi:10.1101/pdb.prot083832

Cited by 20 publications

(23 citation statements)

References 4 publications

(6 reference statements)

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“…The fact that amplification polymers carry up to hundreds of fluorophores (Choi et al, 2014) makes it possible to achieve high signal-to-background even when autofluorescence is high [e.g. in whole-mount vertebrate embryos (Choi et al, 2014;McLennan et al, 2015;Huss et al, 2015), in thick mouse brain sections (Sylwestrak et al, 2016) or in bacteria contained within environmental samples or other organisms (Rosenthal et al, 2013;Yamaguchi et al, 2015;Nikolakakis et al, 2015)]. The resulting HCR signal is stable for at least 1 week in zebrafish embryos stored in solution (Fig.…”

Section: Resultsmentioning

confidence: 99%

Mapping a multiplexed zoo of mRNA expression

et al. 2016

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In situ hybridization methods are used across the biological sciences to map mRNA expression within intact specimens. Multiplexed experiments, in which multiple target mRNAs are mapped in a single sample, are essential for studying regulatory interactions, but remain cumbersome in most model organisms. Programmable in situ amplifiers based on the mechanism of hybridization chain reaction (HCR) overcome this longstanding challenge by operating independently within a sample, enabling multiplexed experiments to be performed with an experimental timeline independent of the number of target mRNAs. To assist biologists working across a broad spectrum of organisms, we demonstrate multiplexed in situ HCR in diverse imaging settings: bacteria, whole-mount nematode larvae, whole-mount fruit fly embryos, whole-mount sea urchin embryos, whole-mount zebrafish larvae, whole-mount chicken embryos, whole-mount mouse embryos and formalin-fixed paraffinembedded human tissue sections. In addition to straightforward multiplexing, in situ HCR enables deep sample penetration, high contrast and subcellular resolution, providing an incisive tool for the study of interlaced and overlapping expression patterns, with implications for research communities across the biological sciences.

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Section: Resultsmentioning

confidence: 99%

Mapping a multiplexed zoo of mRNA expression

et al. 2016

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“…Recently, several highly sensitive FISH approaches have been were reported, including as branched DNA ISH (Player et al 2001;Kenny et al 2002), RNAscope (Wang et al 2012, 2014Grabinski et al 2015), and ISH chain reaction (Choi et al 2010(Choi et al , 2014. ISH chain reaction was used with the highly sensitive multiple fluorescence ISH technique, which enabled the simultaneous mapping of multiple target mRNAs within intact zebrafish embryos (Choi et al 2014), and within intact mouse embryos (Huss et al 2015) simultaneously. With this approach, DNA/RNA probes complementary to target mRNAs trigger chain reactions in which metastable fluorophore-labeled DNA/RNA hairpins self-assemble into fluorescent amplification polymers.…”

Section: Discussionmentioning

confidence: 99%

“…This new generation ISH protocol was designed to detect mRNAs in intact zebrafish embryos or mouse embryos and employed significantly milder conditions than previous HCR-amplified protocols. The length of the DNA oligonucleotide probe and hairpin set was 132bp and 72bp, respectively (Choi et al 2014;Huss et al 2015).of sare The sensitivity of in situ HCR depends on the number of labeled molecules number within the copolymers thatwhich are formed by the hairpins. The greater the number ofmore labeled molecules, the greater the more sensitivitye of the in situ HCR.…”

Section: This New Ish Technique Enabled Simultaneous Detecting Of Mulmentioning

confidence: 99%

A modified protocol for the detection of three different mRNAs with a new-generation in situ hybridization chain reaction on frozen sections

Sui

Zhu

et al. 2016

J Mol Hist

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A new multiple fluorescence in situ hybridization method based on hybridization chain reaction was recently reported, enabling simultaneous mapping of multiple target mRNAs within intact zebrafish and mouse embryos. With this approach, DNA probes complementary to target mRNAs trigger chain reactions in which metastable fluorophore-labeled DNA hairpins self-assemble into fluorescent amplification polymers. The formation of the specific polymers enhances greatly the sensitivity of multiple fluorescence in situ hybridization. In this study we describe the optimal parameters (hybridization chain reaction time and temperature, hairpin and salt concentration) for multiple fluorescence in situ hybridization via amplification of hybridization chain reaction for frozen tissue sections. The combined use of fluorescence in situ hybridization and immunofluorescence, together with other control experiments (sense probe, neutralization and competition, RNase treatment, and anti-sense probe without initiator) confirmed the high specificity of the fluorescence in situ hybridization used in this study. Two sets of three different mRNAs for oxytocin, vasopressin and somatostatin or oxytocin, vasopressin and thyrotropin releasing hormone were successfully visualized via this new method. We believe that this modified protocol for multiple fluorescence in situ hybridization via hybridization chain reaction would allow researchers to visualize multiple target nucleic acids in the future.

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“…However scarce, such data sets are also increasingly information-rich, often consisting of stacks of high-resolution z-slices that together make up a complete 3D image of the embryo or region under study. Moreover, advances in the multi-plexed detection of mRNA distribution means that such 3D structural information can be coupled with information about the distribution and quantification of multiple mRNA species from the tissue to sub-cellular level [49][50][51][52]. With ML approaches it is often unclear at the onset, exactly how much data is going to be required to achieve a certain test accuracy.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for staging embryonic tissue isolates with small data

Pond

Hwang

Verd

et al. 2020

Preprint

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Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches.Author summaryThe application of machine learning approaches currently hinges on the availability of large data sets to train the models with. However, recent research has shown that large data sets might not always be required. In this work we set out to see whether we could use small confocal microscopy image data sets to train a convolutional neural network (CNN) to stage zebrafish tail buds at four different stages in their development. We found that high test accuracies can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a CNN. This work also shows that we can robustly stage the embryonic development of isolated structures, without the need to refer back to landmarks in the tail bud. This constitutes an important methodological advance for staging organoids and other 3D culture in vitro systems. This work proves that prohibitively large data sets are not always required to train CNNs, and we hope will encourage others to apply the power of machine learning to their areas of study even if data are scarce.

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Combinatorial Analysis of mRNA Expression Patterns in Mouse Embryos Using Hybridization Chain Reaction

Cited by 20 publications

References 4 publications

Mapping a multiplexed zoo of mRNA expression

Mapping a multiplexed zoo of mRNA expression

A modified protocol for the detection of three different mRNAs with a new-generation in situ hybridization chain reaction on frozen sections

A deep learning approach for staging embryonic tissue isolates with small data

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