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scDOT: optimal transport for mapping senescent cells in spatial transcriptomics

by Jiang, Peiran , Li, Dongmei , Finkel, Toren , Lugo-Martinez, Jose , Rojas, Mauricio , Mora, Ana L. , Königshoff, Melanie , Bar-Joseph, Ziv , Bueno, Marta , Rosas, Lorena , Ovando-Ricardez, Jose A. , Ma, Qin , Hasanaj, Euxhen , Pryhuber, Gloria S. , Nguyen, Nam D. , Khaliullin, Timur , Rahman, Irfan , Eickelberg, Oliver

in Animal Genetics and Genomics / Bioinformatics / Biomedical and Life Sciences / Cell interactions / Cells / Cellular Senescence - genetics / Datasets / Enzymes / Evolutionary Biology / Gene expression / Gene Expression Profiling - methods / genome / Human Genetics / Humans / Life Sciences / Lung - cytology / Lung - metabolism / lungs / Method / Microbial Genetics and Genomics / Performance evaluation / Plant Genetics and Genomics / Probability / RNA / Senescence / Sequence Analysis, RNA - methods / Simulation / Single-Cell Analysis - methods / Transcriptome / Transcriptomics

2024

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2024

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2024

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Journal Article

scDOT: optimal transport for mapping senescent cells in spatial transcriptomics

Jiang, Peiran,

Li, Dongmei,

Finkel, Toren,

Lugo-Martinez, Jose,

Rojas, Mauricio,

Mora, Ana L.,

Königshoff, Melanie,

Bar-Joseph, Ziv,

Bueno, Marta,

Rosas, Lorena,

Ovando-Ricardez, Jose A.,

Ma, Qin,

Hasanaj, Euxhen,

Pryhuber, Gloria S.,

Nguyen, Nam D.,

Khaliullin, Timur,

Rahman, Irfan,

Eickelberg, Oliver

2024

Overview

The low resolution of spatial transcriptomics data necessitates additional information for optimal use. We developed scDOT, which combines spatial transcriptomics and single cell RNA sequencing to improve the ability to reconstruct single cell resolved spatial maps and identify senescent cells. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to lung spatial transcriptomics data improves on prior methods and allows the identification of the spatial organization of senescent cells, their neighboring cells and novel genes involved in cell-cell interactions that may be driving senescence.

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Publisher

BioMed Central,Springer Nature B.V,BMC

Subject

Animal Genetics and Genomics

/ Bioinformatics

/ Biomedical and Life Sciences

/ Cell interactions

/ Cells

/ Cellular Senescence - genetics

/ Datasets