Dynamics and Spatial Genomics of the Nascent Transcriptome by Intron seqFISH

Shah, Sheel; Takei, Yodai; Zhou, Wen; Lubeck, Eric; Yun, Jina; Eng, Chee Huat; Koulena, Noushin; Cronin, Christopher J.; Karp, Christoph; Liaw, Eric J.; Amin, Mina; Cai, Long

doi:10.1016/j.cell.2018.05.035

Cited by 250 publications

(258 citation statements)

References 51 publications

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“…Furthermore, the two alleles of TTF1 showed coordination between these bursts suggesting that the observed transcriptional events are coupled through trans-regulatory factors. Finally, temporal changes in global rates of transcriptions (Skinner et al, 2016;Shah et al, 2018) can also make the interpretation of a single allele temporal reporter challenging. It is important to note that our work focuses on genes that encode for calcium signaling activity and might not represent all genes, such as reporters controlled by viral promoters (Singh et al, 2010;Dar et al, 2012) and genes that are key to cellular differentiation (Hansen & van Oudenaarden, 2013;Ochiai et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Mammalian gene expression variability is explained by underlying cell state

Foreman

Wollman

2020

Molecular Systems Biology

105

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Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele‐specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.

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Section: Discussionmentioning

confidence: 99%

Mammalian gene expression variability is explained by underlying cell state

Foreman

Wollman

2020

Molecular Systems Biology

105

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“…3E, 3G). Further validation was performed by interrogating whether the genes identified as being close to the β -actin-MBS locus had their transcription sites in spatial proximity as has been provided by the intron seq-FISH data (Shah et al, 2018). Of the RNAs edited by MCP TRIBE, 85 of 86 transcription sites interacted with chromosome five as determined by the previously reported seq-FISH results (closer than 500nm).…”

Section: Origin Of Mcp Tribe Off-target Signals Can Be Correlated Witmentioning

confidence: 93%

“…Inter-chromosomal contacts have been identified by Hi-C (Battulin et al, 2015;Lieberman-Aiden et al, 2009) and further supported by transcriptome wide RNA FISH studies in MEFs that observed nascent transcripts looping away from the chromosomal DNA (on average 0.8 ± 1.1µm away). This allowed intermingling with other nascent transcripts even from multiple chromosomes (Shah et al, 2018). Due to the limited resolution of ligation based approaches (Hi-C) and optical microscopy (intron seq-FISH), specific interactions at the nucleotide level could not be previously resolved.…”

Section: Identification Of Chromatin Contacts and Transcription Domainsmentioning

confidence: 99%

MS2-TRIBE evaluates protein-RNA interactions and nuclear organization of transcription by RNA editing

Biswas

Rahman

Gupta³

et al. 2019

Preprint

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Nearly every step of RNA regulation is mediated by binding proteins (RBPs). The most common method to identify specific RBP target transcripts in vivo is by crosslinking ("CLIP" and its variants), which rely on protein-RNA crosslinking and specific antibodies. Another recently introduced method exploits RNA editing, with the catalytic domain of ADAR covalently attached to a specific RBP ("TRIBE"). Both approaches suffer from difficulties in distinguishing real RNA targets from false negative and especially false positive signals. To critically evaluate this problem, we used fibroblasts from a mouse where every endogenous β -actin mRNA molecule was tagged with the bacteriophage MS2 RNA stem loops; hence there is only a single bona fide target mRNA for the MS2 capsid protein (MCP). CLIP and TRIBE could both detect the single RNA target, albeit with some false positives (transcripts lacking the MS2 stem loops). Consistent false positive CLIP signals could be attributed to nonspecific antibody crosslinking. To our surprise, the supposed false positive TRIBE targets correlated with the location of genes spatially proximal to the β -actin gene. This result indicates that MCP-ADAR bound to β -actin mRNA contacted and edited nearby nascent transcripts, as evidenced by frequent intronic editing. Importantly, nascent transcripts on nearby chromosomes were also edited, agreeing with the interchromosomal contacts observed in chromosome paint and Hi-C. The identification of nascent RNA-RNA contacts imply that RNA-regulatory proteins such as splicing factors can associate with multiple nascent transcripts and thereby form domains of post-transcriptional activity, which increase their local concentrations. These results more generally indicate that TRIBE combined with the MS2 system, MS2-TRIBE, is a new tool to study nuclear RNA organization and regulation.3 Introduction:

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“…Also, the modular nature of the SSAM framework allows for easy incorporation of new features such as spatial differential gene expression analysis (Svensson et al, 2018b), pseudo-time analysis to infer differentiation trajectories (Angerer et al, 2016;Qiu et al, 2017;Trapnell et al, 2014) and RNA velocity analysis to analyze the speed of transcriptional reprogramming or flux (La that is particularly applicable to the recently published intronSEQFISH technique (Shah et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Cell segmentation-free inference of cell types from in situ transcriptomics data

Park

Choi

Tiesmeyer

et al. 2019

Preprint

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Multiplexed fluorescence in situ hybridization techniques have enabled cell class or type identification by mRNA quantification in situ. However, inaccurate cell segmentation can result in incomplete cell-type and tissue characterization. Here, we present a robust segmentation-free computational framework, applicable to a variety of in situ transcriptomics platforms, called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM). SSAM assumes that spatial distribution of mRNAs relates to organization of higher complexity structures (e.g. cells or tissue layers) and performs de novo cell-type and tissue domain identification. Optionally, SSAM can also integrate prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. SSAM outperforms segmentation-based results, demonstrating that segmentation of cells is not required for inferring cell-type signatures, cell-type organization or tissue domains. KeywordsIn situ transcriptomics, spatial cell-type calling, segmentation-free, multiplexed FISH, SSAM, osmFISH, mFISH, multiplexed smFISH, MERFISH, spatially resolved RNA profiling Lignell et al., 2017), or total poly-A RNA (Codeluppi et al., 2018;Moffitt et al., 2018).Accurate cell segmentation, however, is difficult to achieve due to tightly apposed or overlapping cells, uneven cell borders, varying cell and nuclear shapes, signal intensity variation, probe fluorescence emission efficiency variation, and tiling artifacts (Thomas and John, 2017). The underlying problem is that the cellular structures one would want to segment are much smaller than the resolution of a diffraction-limited microscope. Therefore, there is a need for robust segmentation-independent methods for identification of cell-type signatures, cell-type organization, and tissue domains from multidimensional mRNA expression data in complex tissues. These methods could be used for datasets lacking landmarks or to validate segmentation-based approaches and identify associated artifacts.Here we introduce a novel computational framework named Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a multi-platform segmentation-free computational framework for identifying cell-type signatures and reconstructing cell-type and tissue domain maps from both 2D-and 3D-spatially resolved in situ transcriptomics data. We apply SSAM to three mouse brain tissue images obtained by different techniques: the somatosensory cortex by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. We demonstrate the performance of SSAM in identifying 1) cell types in situ, 2) spatial distribution of cell types, 3) spatial relationships between cell types, and 4) tissue domains (e.g., cortical layers) based on the local composition of cell types without having even segmented a single cell. ResultsThe SSAM computational fra...

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Dynamics and Spatial Genomics of the Nascent Transcriptome by Intron seqFISH

Cited by 250 publications

References 51 publications

Mammalian gene expression variability is explained by underlying cell state

Mammalian gene expression variability is explained by underlying cell state

MS2-TRIBE evaluates protein-RNA interactions and nuclear organization of transcription by RNA editing

Cell segmentation-free inference of cell types from in situ transcriptomics data

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