ScGIST: Gene Panel Design for Spatial Transcriptomics with Prioritized Gene Sets

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2024 Feb 26

PMID 38408997

Authors

Mashrur Ahmed Yafi

Md Hasibul Husain Hisham

Francisco Grisanti

James F Martin

Atif Rahman

Md Abul Hassan Samee

Affiliations

Soon will be listed here.

Abstract

A critical challenge of single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, they are typically limited to panels of about a thousand genes. This constrains researchers to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or those in specific pathways. We propose scGIST, a constrained feature selection tool that designs sc-ST panels prioritizing user-specified genes without compromising cell type detection accuracy. We demonstrate scGIST's efficacy in diverse use cases, highlighting it as a valuable addition to sc-ST's algorithmic toolbox.

Citing Articles

Spatial-Omics Methods and Applications.

Kulasinghe A, Berrell N, Donovan M, Nilges B Methods Mol Biol. 2025; 2880:101-146.

PMID: 39900756 DOI: 10.1007/978-1-0716-4276-4_5.

References

He S, Bhatt R, Brown C, Brown E, Buhr D, Chantranuvatana K . High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat Biotechnol. 2022; 40(12):1794-1806. DOI: 10.1038/s41587-022-01483-z. View

Longo S, Guo M, Ji A, Khavari P . Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet. 2021; 22(10):627-644. PMC: 9888017. DOI: 10.1038/s41576-021-00370-8. View

Nelson M, Riva S, Cvejic A . SMaSH: a scalable, general marker gene identification framework for single-cell RNA-sequencing. BMC Bioinformatics. 2022; 23(1):328. PMC: 9361618. DOI: 10.1186/s12859-022-04860-2. View

Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay P, Swerdlow H . Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017; 14(9):865-868. PMC: 5669064. DOI: 10.1038/nmeth.4380. View

Liu J, Tran V, Vemuri V, Byrne A, Borja M, Kim Y . Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing. Life Sci Alliance. 2022; 6(1). PMC: 9760489. DOI: 10.26508/lsa.202201701. View

Allen W, Blosser T, Sullivan Z, Dulac C, Zhuang X . Molecular and spatial signatures of mouse brain aging at single-cell resolution. Cell. 2022; 186(1):194-208.e18. PMC: 10024607. DOI: 10.1016/j.cell.2022.12.010. View

Levsky J, Shenoy S, Pezo R, Singer R . Single-cell gene expression profiling. Science. 2002; 297(5582):836-40. DOI: 10.1126/science.1072241. View

Dumitrascu B, Villar S, Mixon D, Engelhardt B . Optimal marker gene selection for cell type discrimination in single cell analyses. Nat Commun. 2021; 12(1):1186. PMC: 7895823. DOI: 10.1038/s41467-021-21453-4. View

Armingol E, Officer A, Harismendy O, Lewis N . Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet. 2020; 22(2):71-88. PMC: 7649713. DOI: 10.1038/s41576-020-00292-x. View

10.

Wang G, Moffitt J, Zhuang X . Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci Rep. 2018; 8(1):4847. PMC: 5859009. DOI: 10.1038/s41598-018-22297-7. View

11.

Li Y, Chen C, Wasserman W . Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters. J Comput Biol. 2016; 23(5):322-36. DOI: 10.1089/cmb.2015.0189. View

12.

Lu Y, Liu M, Yang J, Weissman S, Pan X, Katz S . Spatial transcriptome profiling by MERFISH reveals fetal liver hematopoietic stem cell niche architecture. Cell Discov. 2021; 7(1):47. PMC: 8238952. DOI: 10.1038/s41421-021-00266-1. View

13.

Cleary B, Cong L, Cheung A, Lander E, Regev A . Efficient Generation of Transcriptomic Profiles by Random Composite Measurements. Cell. 2017; 171(6):1424-1436.e18. PMC: 5726792. DOI: 10.1016/j.cell.2017.10.023. View

14.

Chen K, Boettiger A, Moffitt J, Wang S, Zhuang X . RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015; 348(6233):aaa6090. PMC: 4662681. DOI: 10.1126/science.aaa6090. View

15.

Nowotschin S, Setty M, Kuo Y, Liu V, Garg V, Sharma R . The emergent landscape of the mouse gut endoderm at single-cell resolution. Nature. 2019; 569(7756):361-367. PMC: 6724221. DOI: 10.1038/s41586-019-1127-1. View

16.

Moffitt J, Lundberg E, Heyn H . The emerging landscape of spatial profiling technologies. Nat Rev Genet. 2022; 23(12):741-759. DOI: 10.1038/s41576-022-00515-3. View

17.

Lohoff T, Ghazanfar S, Missarova A, Koulena N, Pierson N, Griffiths J . Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nat Biotechnol. 2021; 40(1):74-85. PMC: 8763645. DOI: 10.1038/s41587-021-01006-2. View

18.

Moses L, Pachter L . Museum of spatial transcriptomics. Nat Methods. 2022; 19(5):534-546. DOI: 10.1038/s41592-022-01409-2. View

19.

Zhang M, Eichhorn S, Zingg B, Yao Z, Cotter K, Zeng H . Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature. 2021; 598(7879):137-143. PMC: 8494645. DOI: 10.1038/s41586-021-03705-x. View

20.

Eng C, Shah S, Thomassie J, Cai L . Profiling the transcriptome with RNA SPOTs. Nat Methods. 2017; 14(12):1153-1155. PMC: 5819366. DOI: 10.1038/nmeth.4500. View