Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust

Choi, Harry M. T.; Schwarzkopf, Maayan; Fornace, Mark E.; Acharya, Aneesh; Artavanis, Georgios; Stegmaier, Johannes; Cunha, Alexandre; Pierce, Niles A.

doi:10.1101/285213

Cited by 229 publications

(374 citation statements)

References 34 publications

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“…We excluded probes with complementarity to repetitive elements, pseudogenes or the vector backbone used to generate the barcode plasmid library. We then split each 42mer probe into 2 20mer sequences (removing the middle two nucleotides) and appended split-initiator HCR sequences using custom python scripts (See Supplementary Table 3 for sequences) (Choi et al, 2018) . For each 20mer sequence, we measured the maximum complementarity to the vector backbone and other barcodes present in the sample in order to manually exclude probes with potential for non-specific hybridization.…”

Section: Barcode Fish Probe Designmentioning

confidence: 99%

“…For in situ Barcode FISH using the Hybridization Chain Reaction (HCR) we adapted the protocol from (Choi et al, 2018) as follows. We used 1.2 pmol each of up to 240 barcode FISH probes per 0.3 mL hybridization buffer.…”

Section: Barcode Rna Hcrmentioning

confidence: 99%

“…For performing HCR in suspension, we adapted the published protocol as follows (Choi et al, 2018) . We fixed dissociated cells in suspension by washing the cells with 1xDPBS, resuspending the cell in ice cold 1xDPBS, adding equal volume of ice cold fixation buffer (3.7% formaldehyde 1x PBS) then incubating with rotation at room temperature for 10 minutes.…”

Section: Barcode Rna Hcrmentioning

confidence: 99%

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Variability within rare cell states enables multiple paths towards drug resistance

Emert

Coté

Torre

et al. 2020

Preprint

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Molecular differences between individual cells can lead to dramatic differences in cell fate following an applied treatment, such as the difference between death versus survival of cancer cells upon receiving anti-cancer drugs. However, current strategies to retrospectively identify the cells that give rise to distinct rare behaviors and determine their distinguishing molecular characteristics remain limited. Here we describe Rewind, a methodology that combines genetic barcoding with an RNA-based readout to directly capture rare cells that give rise to cellular behaviors of interest, specifically the emergence of resistance to targeted cancer therapy. Using Rewind, we analyzed over 5 million cells to identify differences in gene expression and MAP-kinase signaling in single melanoma cells that mark a rare subpopulation of drug-naive cells (initial frequency of ~1:1000-1:10,000 cells) that ultimately gives rise to drug resistant clones. We further show that even within this rare subpopulation, molecular differences between single cells before the application of drug predict future differences in drug resistant behavior. Similarly, we show that treatments that modify the frequency of resistance can allow otherwise non-resistant cells in the drug-naive population to become resistant, and that these new populations are marked by the variable expression of distinct genes. Together, our results reveal the presence of cryptic variability that can underlie a range of distinct rare-cell phenotypic outcomes upon drug exposure. Applying Rewind to other rare biological phenomena, such as cancer metastasis, tissue regeneration, and stem cell reprogramming, may provide a means to map rare cellular states to the unique cellular fates to which they give rise.

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Section: Barcode Fish Probe Designmentioning

confidence: 99%

Section: Barcode Rna Hcrmentioning

confidence: 99%

Section: Barcode Rna Hcrmentioning

confidence: 99%

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Variability within rare cell states enables multiple paths towards drug resistance

Emert

Coté

Torre

et al. 2020

Preprint

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“…Testing how combinatorial signals lead to such temporal expression kinetics will require experimental manipulation of morphogen gradients at specific times and locations such as with heat‐shock inducible transgenic constructs (Alexander et al, ; Zuniga et al, ). Testing their spatial correlates will require high‐resolution measurements of expression in situ with techniques such as hybridization chain reaction (Choi et al, ).…”

Section: Discussionmentioning

confidence: 99%

Transcriptomics reveals complex kinetics of dorsal–ventral patterning gene expression in the mandibular arch

Sharma

MacLean

Meinecke

et al. 2018

Genesis

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Summary The mandibular or first pharyngeal arch forms the upper and lower jaws in all gnathostomes. A gene regulatory network that defines ventral, intermediate, and dorsal domains along the dorsal-ventral (D-V) axis of the arch has emerged from studies in zebrafish and mice, but the temporal dynamics of this process remain unclear. To define cell fate trajectories in the arches we have performed quantitative gene expression analyses of D-V patterning genes in pharyngeal arch primordia in zebrafish and mice. Using NanoString technology to measure transcript numbers per cell directly we show that, in many cases, genes expressed in similar D-V domains and induced by similar signals vary dramatically in their temporal profiles. This suggests that cellular responses to D-V patterning signals are likely shaped by the baseline kinetics of target gene expression. Furthermore, similarities in the temporal dynamics of genes that occupy distinct pathways suggest novel shared modes of regulation. Incorporating these gene expression kinetics into our computational models for the mandibular arch improves the accuracy of patterning, and facilitates temporal comparisons between species. These data suggest that the magnitude and timing of target gene expression help diversify responses to patterning signals during craniofacial development.

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“…We next used hybridization chain reaction-based fluorescence in situ hybridization (HCR-FISH; Choi et al, 2018) to examine the spatial location and co-localization of marker expression among the PFC transcriptomic types. Focusing on dmPFC and vmPFC because of their clear laminar structure in the coronal plane, we quantified marker expression within Vglut1-labeled cell soma (Figures 2A, 2B, S2A).…”

Section: Anatomical Locations Of Transcriptomic Typesmentioning

confidence: 99%

Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks

Lui

Nguyen

Grutzner

et al. 2020

Preprint

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Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC→PAG (periaqueductal gray). By comparing Ca 2+ -activity of the molecularly homogeneous PFC→PAG type against two heterogeneous classes in several two-alternative choice tasks in freely-moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.

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Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust

Cited by 229 publications

References 34 publications

Variability within rare cell states enables multiple paths towards drug resistance

Variability within rare cell states enables multiple paths towards drug resistance

Transcriptomics reveals complex kinetics of dorsal–ventral patterning gene expression in the mandibular arch

Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks

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