2020
DOI: 10.1101/2020.05.13.094268
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Expansion Sequencing: Spatially PreciseIn SituTranscriptomics in Intact Biological Systems

Abstract: Methods for highly multiplexed RNA imaging are limited in spatial resolution, and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt 10 expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to mouse brain, yielding readout of thousands of genes, including splice variants and novel transcripts. Targeted ExSeq yielded nanoscale-resolution m… Show more

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Cited by 63 publications
(66 citation statements)
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References 126 publications
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“…Currently, alternative protocols that involve the direct probing of RNA with PLPs and RCA, such as SCRINSHOT (16) and targeted ExSeq (17) have also exhibited improved transcript detection efficiency. However, PBCV-1 DNA ligase used in these protocols have shown high tolerance for mismatches in ligation and extensive optimization would have to be undertaken for different tissue types to prevent off-target detection (13,16) .…”
Section: Discussionmentioning
confidence: 99%
“…Currently, alternative protocols that involve the direct probing of RNA with PLPs and RCA, such as SCRINSHOT (16) and targeted ExSeq (17) have also exhibited improved transcript detection efficiency. However, PBCV-1 DNA ligase used in these protocols have shown high tolerance for mismatches in ligation and extensive optimization would have to be undertaken for different tissue types to prevent off-target detection (13,16) .…”
Section: Discussionmentioning
confidence: 99%
“…Next, we segmented our images to generate a custom mask for our images using the same color scheme adopted by the Allen ontology. For this, we applied semantic segmentation 18 , and segmented five classes in our histological image ( Fig. 7a): background, cortex, cerebellum, white matter and other grey matter.…”
Section: Integration Of Histological and Anatomical Atlas Supportmentioning
confidence: 99%
“…We used a semantic segmentation model from the Tensorflow Keras version of the segmentation_models library (https://github.com/qubvel/segmentation_models). Specifically, we chose a U-NET architecture 18 with a ResNet50 backbone 26 . All weights have been randomly initialized following the He scheme, with the exception of the ResNet50 encoder which was pretrained on ImageNet.…”
Section: Semantic Segmentation Model For Anatomical Region Callingmentioning
confidence: 99%
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“…However, fully exploiting this new data type can be challenging, for many reasons. Insufficient optical resolution can cause parts of multiple rolonies to appear in the same imaging voxel, resulting in a ‘mixed’ signal [ 3 , 4 ]. Tissue can deform or drift over multiple rounds of imaging [ 5 ], and the signal from individual rolonies can vary slightly between imaging rounds [ 6 ].…”
Section: Introductionmentioning
confidence: 99%