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Cell type-specific inference of differential expression in spatial transcriptomics

Abstract

A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE’s framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer’s disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package https://github.com/dmcable/spacexr.

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Fig. 1: C-SIDE learns cell type-specific DE from spatial transcriptomics data.
Fig. 2: C-SIDE provides unbiased estimates of cell type-specific DE in simulated data.
Fig. 3: C-SIDE’s estimated cell type-specific DE is validated by HCR-FISH.
Fig. 4: C-SIDE discovers cell type-specific DE in a diverse set of problems on testes, Alzheimer’s hippocampus and hypothalamus datasets.
Fig. 5: C-SIDE enables DE discovery on diverse spatial transcriptomics technologies including Visium and MERFISH.
Fig. 6: C-SIDE enables the discovery of DE pathways in a KrasG12D/+Trp53−/− (KP) mouse model.

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Data availability

Slide-seq V2 data generated for this study and additional data are available at the Broad Institute Single Cell Portal https://singlecell.broadinstitute.org/single_cell/study/SCP1663. We also used the following publicly available datasets in our study. MERFISH hypothalamus dataset was accessed from Dryad https://doi.org/10.5061/dryad.8t8s248. Visium human lymph node is available at https://www.10xgenomics.com/resources/datasets/human-lymph-node-1-standard-1-1-0. Testes Slide-seq data can be accessed at https://www.dropbox.com/s/ygzpj0d0oh67br0/Testis_Slideseq_Data.zip?dl=0. Cancer Slide-seq data are available at https://singlecell.broadinstitute.org/single_cell/study/SCP1278. Hallmark gene sets were accessed from https://www.gsea-msigdb.org/.

Code availability

C-SIDE is implemented in the open-source R package spacexr, with source code freely available at https://github.com/dmcable/spacexr. Additional code used for analysis in this paper is available at https://github.com/dmcable/spacexr/tree/master/AnalysisCSIDE.

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Acknowledgements

We thank R. Stickels for providing valuable input on the analysis. We thank T. Zhao and Z. Chiang for generously providing the cancer Slide-seq data. We thank S. Marsh (Harvard Medical School/Boston Children’s Hospital) for kindly providing mouse J20 Alzheimer’s model samples. We thank members of the Chen laboratory, Irizarry laboratory and Macosko laboratory including T. Kamath for helpful discussions and feedback. D.M.C. was supported by a Fannie and John Hertz Foundation Fellowship and an National Science Federation Graduate Research Fellowship. This work was supported by an National Institutes of Health (NIH) Early Independence Award (DP5, 1DP5OD024583 to F.C.), the NHGRI (R01, R01HG010647 to F.C. and E.Z.M.), as well as the Burroughs Wellcome Fund, the Searle Scholars Award, and the Merkin Institute to F.C. R.A.I. was supported by NIH grant nos. R35GM131802 and R01HG005220.

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Authors

Contributions

D.M.C., R.A.I. and F.C. conceived the study. F.C., E.M., E.Z.M. and D.M.C. designed the Slide-seq, antibody stain and HCR experiments. E.M. generated the Slide-seq, antibody stain and HCR data. D.M.C., R.A.I. and F.C. developed the statistical methods. D.M.C., F.C. and R.A.I. designed the analysis. D.M.C., S.Z., L.S.Z., M.D., R.A.I. and F.C. analyzed the data. D.M.C., F.C., R.A.I., V.S. and H.C. interpreted biological results. V.S. annotated the tumor H&E stain. D.M.C., F.C. and R.A.I. wrote the manuscript and all authors read and approved the final manuscript.

Corresponding authors

Correspondence to Rafael A. Irizarry or Fei Chen.

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Competing interests

E.Z.M. and F.C. are listed as inventors on a patent application related to Slide-seq. F.C. and E.Z.M. are paid consultants of Atlas Bio. The remaining authors declare no competing interests.

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Nature Methods thanks Pengyi Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

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Supplementary information

Supplementary Information

Supplementary Methods and Figs. 1–12.

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Supplementary Tables

Supplementary Table 1 Slide-seq cerebellum population-level C-SIDE significant results across three experimental replicates. Columns include cell type, mean_est (estimated loge fold-change), sd_est (standard error), Z_est (Z-score), p (P value), q_val (q-val) and sig_p (estimated standard deviation of technical and biological variation across samples). Also contains the log fold-change and standard errors for each of the three datasets. Supplementary Table 2 Slide-seq testes C-SIDE significant results. Columns include cell type, log_fc (estimated loge fold-change across two stages with maximal DE), sd (standard error), Xi (estimated log-e expression in stage i), p_val (P value). Supplementary Table 3 MERFISH hypothalamus linear C-SIDE significant results. Columns include cell type, log_fc (estimated DE loge fold-change), Z_score (Z-score), p_val (P value) and conv (convergence). Supplementary Table 4 MERFISH hypothalamus quadratic C-SIDE significant results. Columns include cell type, log_fc (estimated DE loge fold-change), Z_score (Z-score), p_val (P value) and conv (convergence). Supplementary Table 5 Visium lymph node C-SIDE significant results. Columns include cell type, log_fc (estimated DE loge fold-change), Z_score (Z-score), p_val (P value) and conv (convergence). Supplementary Table 6 Slide-seq J20 Hippocampus population-level C-SIDE significant results across four experimental replicates. Columns include cell type, mean_est (estimated log-e-fold-change), sd_est (standard error), Z_est (Z-score), p (P value), q_val (q-val) and sig_p (estimated standard deviation of technical and biological variation across samples). Also, contains the log fold-change and standard errors for each of the four datasets. Supplementary Table 7 Slide-seq tumor nonparametric C-SIDE significant results. Columns include cell type, Z_score (Z-score), p_val (P value) and conv (convergence). Supplementary Table 8 Gene set testing results on the Slide-seq tumor. Significant gene sets are shown. Supplementary Table 9 Slide-seq tumor parametric C-SIDE significant results. Columns include cell type, log_fc (estimated DE loge fold-change), Z_score (Z-score), p_val (P value) and conv (convergence). Supplementary Table 10 HCR probes used for validation experiments on the cerebellum. Supplementary Table 11 Metadata about datasets analyzed in this C-SIDE paper.

Supplementary Software 1

spacexr package manual, v.2.0.0.

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Cable, D.M., Murray, E., Shanmugam, V. et al. Cell type-specific inference of differential expression in spatial transcriptomics. Nat Methods 19, 1076–1087 (2022). https://doi.org/10.1038/s41592-022-01575-3

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