Data used in eMCI

Zebrafish melanoma datasets

The ST data from three samples (A, B, and C, with 2425, 2179, and 2677 spots, respectively) and scRNA-seq data from two samples (E and F, with 1911 and 1085 cells, respectively) of zebrafish melanoma were available from the Gene Expression Omnibus (GEO) database under accession code GSE159709 [1]. 

Soybean datasets

For soybean, the snRNA-seq data, including 8229 cells from 12 dpi and 12004 cells from 21 dpi, along with ST data from four tissue slices at 12 dpi and 21 dpi with 2229, 1871, 1593, and 1981 spots per slice, was available from the Open Archive for Miscellaneous Data under accession code OMIX002290 [2].

Lung development datasets

The scRNA-seq data for human embryonic lung utilized in this study consisted of samples across seven developmental stages, with cell counts ranging from 3781 to 15845, available from GEO (GSE215895). Similarly, the ST data used in this study comprised samples across seven developmental stages, with spot counts ranging from 355 to 4400, accessible from GEO (GSE215897) [3].

Human fetal pancreas datasets

The scRNA-seq data of human fetal pancreases at 12 and 20 PCW, with 4321 and 2122 cells, respectively, used for this study, were available on GEO with accession numbers GSE197064. The paired ST data at 12 and 20 PCW, with 294 and 2126 spots, respectively, were available on GEO with accession number GSE197317 [4].

PDAC dataset

The scRNA-seq data of pancreatic ductal adenocarcinoma containing 1926 cells and the ST data containing 428 spots can be accessed at GEO under accession number GSE111672 [5].

BRCA dataset

The scRNA-seq data of BRCA, originally consisting of 100064 cells, was publicly available from GEO under accession code GSE176078 [6], while the ST data of BRCA, containing 4727 spots, was publicly available from the 10X Genomics website (https://support.10xgenomics.com/spatialgene-expression/datasets).

Reference

[1] Hunter MV, Moncada R, Weiss JM, Yanai I, White RM. Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface. Nat. Commun. 2021;12:6278. DOI:10.1038/s41467-021-26614-z

[2]Liu Z, Kong X, Long Y, Liu S, Zhang H, Jia J, Cui W, Zhang Z, Song X, Qiu L. Integrated single-nucleus and spatial transcriptomics captures transitional states in soybean nodule maturation. Nat. Plants. 2023;9:515-524. DOI:10.1038/s41477-023-01387-z

[3]Sountoulidis A, Marco Salas S, Braun E, Avenel C, Bergenstråhle J, Theelke J, Vicari M, Czarnewski P, Liontos A, Abalo X. A topographic atlas defines developmental origins of cell heterogeneity in the human embryonic lung. Nat. Cell Biol. 2023;25:351-365. DOI:10.1038/s41556-022-01064-x

[4]Olaniru OE, Kadolsky U, Kannambath S, Vaikkinen H, Fung K, Dhami P, Persaud SJ. Single-cell transcriptomic and spatial landscapes of the developing human pancreas. Cell Metab. 2023;35:184-199. DOI:10.1016/j.cmet.2022.11.009

[5]Moncada R, Barkley D, Wagner F, Chiodin M, Devlin JC, Baron M, Hajdu CH, Simeone DM, Yanai I. Integrating microarray-based spatial transcriptomics and single-cell rna-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 2020;38:333-342. DOI:10.1038/s41587-019-0392-8

[6]Wu SZ, Al-Eryani G, Roden DL, Junankar S, Harvey K, Andersson A, Thennavan A, Wang C, Torpy JR, Bartonicek N. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 2021;53:1334-1347. DOI:10.1038/s41588-021-00911-1