Expansion Sequencing: Spatially Precise <i>In Situ</i> Transcriptomics in Intact Biological Systems

Alon, Shahar; Goodwin, Daniel; Sinha, Anubhav; Wassie, Asmamaw T.; Chen, Fei; Daugharthy, Evan R; Bando, Yosuke; Kajita, Atsushi; Xue, Andrew G.; Marrett, Karl; Prior, Robert; Cui, Yi; Payne, Andrew; Yao, Chun-Chen; Suk, Ho‐Jun; Wang, Ru; Yu, C. C.; Tillberg, Paul W; Reginato, Paul; Pak, Nikita; Liu, Songlei; Punthambaker, Sukanya; Iyer, Eswar Prasad R.; Kohman, Richie E.; Miller, Jeremy A.; Lein, Ed; Lako, Ana; Cullen, Nicole; Rodig, Scott J.; Helvie, Karla; Abravanel, Daniel L.; Wagle, Nikhil; Johnson, Bruce E.; Klughammer, Johanna; Slyper, Michal; Waldman, Julia; Jané‐Valbuena, Judit; Rozenblatt-Rosen, Orit; Regev, Aviv; Church, George M.; Marblestone, Adam; Boyden, Edward S.

doi:10.1101/2020.05.13.094268

Cited by 57 publications

(52 citation statements)

References 116 publications

(67 reference statements)

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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%

Direct RNA targeted transcriptomic profiling in tissue using Hybridization-based RNA In Situ Sequencing (HybRISS)

Lee

Salas

Gyllborg

et al. 2020

Preprint

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Highly multiplexed spatial mapping of multiple transcripts within tissues allows for investigation of the transcriptomic and cellular diversity of mammalian organs previously unseen. Here we explore the possibilities of a direct RNA (dRNA) detection approach incorporating the use of padlock probes and rolling circle amplification in combination with hybridization-based in situ sequencing (HybISS) chemistry. We benchmark a dRNA targeting kit that circumvents the standard reverse transcription limiting, cDNA-based in situ sequencing (ISS). We found a five-fold increase in transcript detection efficiency when compared to cDNA-based ISS and also validated its multiplexing capability by targeting a curated panel of 50 genes from previous publications on mouse brain sections, leading to additional data interpretation such as de novo cell typing. With this increased efficiency, we maintain specificity, multiplexing capabilities and ease of implementation. Overall, the dRNA chemistry shows significant improvements in target detection efficiency, closing the gap between the gold standard of fluorescent in situ hybridization (FISH) based technologies and opens up possibilities to explore new biological questions previously not possible with cDNA-based ISS, nor with FISH.

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Section: Discussionmentioning

confidence: 99%

Direct RNA targeted transcriptomic profiling in tissue using Hybridization-based RNA In Situ Sequencing (HybRISS)

Lee

Salas

Gyllborg

et al. 2020

Preprint

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“…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%

“…Conversely, spatial technologies resolve transcriptomes in space, but are limited in either gene throughput or spatial resolution. In general, targeted in situ technologies (e.g., ISS 16 , MERFISH 1 , smFISH 3 , osmFISH 17 , STARMap 2 , Targeted ExSeq 18 , seqFISH+ 19 ) are limited in the number measured genes measured, which must be pre-selected and are typically in the hundreds to thousands range, whereas adding more probes can reduce accuracy for some genes 2 . Spatial Transcriptomics methods (e.g., Spatial Transcriptomics (ST) 4 (now available commercially as Visium), Slide-seq 20 , or High Definition Spatial Trascriptomics 21 ) spatially barcode entire transcriptomes, but with limited capture rate (and substantial "dropouts", which increase at higher resolution) and a spatial resolution larger than a single cell, ranging from 50-100 µm for ST to 10 µm for Slide-seq.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram

Biancalani

Scalia

Buffoni

et al. 2020

Preprint

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Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively5,6, but relating those to their histological and anatomical positions in the context of an organ’s common coordinate framework remains a major challenge and barrier to the construction of a cell atlas7–10. Conversely, Spatial Transcriptomics allows for in-situ measurements11–13 at the histological level, but at lower spatial resolution and with limited sensitivity. Targeted in situ technologies1–3 solve both issues, but are limited in gene throughput which impedes profiling of the entire transcriptome. Finally, as samples are collected for profiling, their registration to anatomical atlases often require human supervision, which is a major obstacle to build pipelines at scale. Here, we demonstrate spatial mapping of cells, histology, and anatomy in the somatomotor area and the visual area of the healthy adult mouse brain. We devise Tangram, a method that aligns snRNA-seq data to various forms of spatial data collected from the same brain region, including MERFISH1, STARmap2, smFISH3, and Spatial Transcriptomics4 (Visium), as well as histological images and public atlases. Tangram can map any type of sc/snRNA-seq data, including multi-modal data such as SHARE-seq data5, which we used to reveal spatial patterns of chromatin accessibility. We equipped Tangram with a deep learning computer vision pipeline, which allows for automatic identification of anatomical annotations on histological images of mouse brain. By doing so, Tangram reconstructs a genome-wide, anatomically-integrated, spatial map of the visual and somatomotor area with ∼30,000 genes at single-cell resolution, revealing spatial gene expression and chromatin accessibility patterning beyond current limitation of in-situ technologies.

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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%

BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

et al. 2021

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Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the ‘NeuroCAAS’ cloud platform.

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Expansion Sequencing: Spatially Precise In Situ Transcriptomics in Intact Biological Systems

Cited by 57 publications

References 116 publications

Direct RNA targeted transcriptomic profiling in tissue using Hybridization-based RNA In Situ Sequencing (HybRISS)

Direct RNA targeted transcriptomic profiling in tissue using Hybridization-based RNA In Situ Sequencing (HybRISS)

Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram

BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

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