Nature - 2019.08.29

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nature

research

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reporting

summary

October

2018

Data analysis

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Data

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All manuscripts must include adata availability statement. This statement should provide the following information, where applicable:

• Accession codes, unique identifiers, or web links for publicly available datasets


• A list of figures that have associated raw data


• A description of any restrictions on data availability

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Life sciences study design

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Sample size

Data exclusions

Replication

Randomization

Blinding

Reporting for specific materials, systems and methods

Statistics: analyses were performed using Prism software (version 7.0c, GraphPad Software, USA) with the exception of the qRTPCR data,

for which R was used.

Fluorescence imaging: FiJi (version 2.0.0-rc-68/1.52g, ImageJ) and Adobe Photoshop CC 2018 (version 19.0, Adobe, USA) were used to

analyse fluorescence images.

Immunohistochemistry: images were acquired using NIS-elements software (version 4.51, Nikon, Japan)

Flow cytometry: data analyses were carried out using FlowJo 10.4.2 (FlowJO, LCC 2006-2018, USA).

ImageStream: analyisis were performed using IDEA software (version 6.2, IDEAS Amnis, Merck, USA)

Proteomics: data analysis and differential protein expression was performed using Spectronaut Professional+. A detailed description of

sample processing, data acquisition and processing are available on request.

RNA sequencing: the RSEM package (version 1.2.29) and Bowtie2 were used to align reads to the mouse mm10 transcriptome.

Differential expression analysis was carried out with DESeq2 package9 (version 1.12.4) within R version 3.3.1 (https://www.rproject.org/).

Gene Set Enrichment Analysis, GSEA, (version 2.2.3) was carried out using ranked gene lists using the Wald statistic and the gene sets of

C2 canonical pathways and C5 biological processes. Heatmaps of differentially expressed genes were generated using the gplots (Gregory

et al., gplots: Various R Programming Tools for Plotting Data. R package version 3.0.1. (2016). https://CRAN.R-project.org/

package=gplots) CRAN package (version 3.0.1).

Single-cell RNA-sequencing: the Cell Ranger v2.1.1 pipeline was used to process raw reads, using STAR (v2.5.1b) to align to the mm10

transcriptome, deconvolve reads to their cell of origin using the UMI tags and report cell-specific gene expression count estimates. All

subsequent analyses were performed in R-3.4.1 using the cellrangerRkit, monocle and pheatmap packages.

See methods for further details

The RNA sequencing datasets (GSE117930) and the single cell RNA sequencing datasets (GEO13150) are deposited in the Gene Expression Omnibus (GEO, NCBI)

repository. The proteomic datasets are deposited in PRoteomics IDEntifications (PRIDE) repository (PXD010597).

Sample sizes were estimated based on previous experiments conducted in our laboratory, providing sufficient numbers of mice in each group

to yield a two-sided statistical test, with the potential to reject the null hypothesis with a power (1 - beta) of 80%, subject to alpha = 0.05.

No data was excluded

Unless otherwise specified in the figure legends, experiments were reproduced in at least two independent experiments.

The majority of the in vivo data generated in this study involved analysis between different areas of the same tissue in each mouse, therefore

both control and experiment cannot be randomized. The experiment involving a therapeutic treatment with the antibody was performed on

litter mice all injected with tumour cells and then randomized for the antibody treatment.

Investigators were not blinded for studies involving the analysis of the Niche versus distant lung cells as the cells were from the same samples

and the two subsets could only be discriminated by FACS analysis itself. Experiments using sorted and stained cells (niche versus distant lung),

scaffold assays and organoid assays were blinded at quantification. For the in vivo treatment experiment with antiWisp1, the quantification of

metastatic burden between the two group was performed blinded.