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Grant Details

Grant Number: 4UH3CA255134-03 Interpret this number
Primary Investigator: Cai, Long
Organization: California Institute Of Technology
Project Title: In Situ Transcriptome Profiling in Single Cells
Fiscal Year: 2020


Summary We have recently developed intron seqFISH (sequential Fluorescence in situ hybridization) to multiplex 10,421 genes directly in single cells. We showed that the 10,421 gene nascent transcriptome profile can identify cell types as well as capture the trajectory of the cells. We further demonstrated that we can perform mRNA seqFISH as well as immunostaining in the same cells following the 10,421 gene intron seqFISH measurement. We propose to develop this technology as a potential alternative approach to single cell RNAseq for the HuBMAP to characterize cell types directly in situ in tissues. In particular, we will adept in situ amplification methods such as hybridization chain reaction (HCR) to intron seqFISH. We had previously shown that mRNA seqFISH with HCR amplification performs exceptionally in tissues in overcoming autofluorescence background and enable robust decoding seqFISH barcodes. We will validate the integrated intron and mRNA seqFISH protocol in the mouse hippocampus in the UG3 phase of the project. Also in UG3 phase, we will develop computational tools to integrate intron seqFISH data with mRNA seqFISH as well as single cell RNAseq data. In the UH3 phase, we will translate the technology to human tissues, with a focus on human mammary tissues provided by Dr. Seewaldt at City of Hope. We will also work with the tissue mapping centers in the HuBMAP program to accelerate the translation of this technology to many tissue types. In the UH3 phase, we will generate million cell spatial atlas of human tissues containing intron profiles, mRNA profiles and protein abundances in each single cell. We will further develop computational tools to analyze for spatial enrichment of genes in the tissue and generate a pseudotime of developmental trajectories using the nascent transcriptome data. Taken together, we will develop a high throughput in situ imaging based platform to characterize cell types and future trajectories of cells using intron and mRNA seqFISH technologies.


Analyzing Spatial Transcriptomics Data Using Giotto.
Authors: Del Rossi N. , Chen J.G. , Yuan G.C. , Dries R. .
Source: Current protocols, 2022 Apr; 2(4), p. e405.
PMID: 35384407
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Advances in spatial transcriptomic data analysis.
Authors: Dries R. , Chen J. , Del Rossi N. , Khan M.M. , Sistig A. , Yuan G.C. .
Source: Genome research, 2021 Oct; 31(10), p. 1706-1718.
PMID: 34599004
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Community-wide hackathons to identify central themes in single-cell multi-omics.
Authors: LĂȘ Cao K.A. , Abadi A.J. , Davis-Marcisak E.F. , Hsu L. , Arora A. , Coullomb A. , Deshpande A. , Feng Y. , Jeganathan P. , Loth M. , et al. .
Source: Genome biology, 2021-08-05; 22(1), p. 220.
EPub date: 2021-08-05.
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Giotto: a toolbox for integrative analysis and visualization of spatial expression data.
Authors: Dries R. , Zhu Q. , Dong R. , Eng C.L. , Li H. , Liu K. , Fu Y. , Zhao T. , Sarkar A. , Bao F. , et al. .
Source: Genome biology, 2021-03-08; 22(1), p. 78.
EPub date: 2021-03-08.
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