Immune single-cell RNA-seq data from PyMT-M tumor and its peripheral blood samples

<p><em>Immune single-cell RNA-seq data collection and preprocessing</em></p> <p>Blood and tumor samples were harvested from PyMT-M tumor-bearing mice. Blood samples (n=3) are collected retro-orbitally using caliper tubes and processed with red blood cell lysis buffer (Tonbo Biosciences) before library preparation. A tumor sample are dissociated with Tumor Dissociation Kit following the manufacturer&rsquo;s instructions (Miltenyi Biotec). After isolation and filtering through a 70&micro;m filter, CD45+DAPI- cells were sorted using FACSAria cell sorter (BD Biosciences) at the Cytometry and Cell Sorting Core. The single-cell libraries were prepared using Chromium Controller (10X Genomics) at the Single Cell Genomics Core and sequenced using NovaSeq 6000 at the Genomics and RNA Profiling Core of Baylor College of Medicine. The FASTQ files were processed using Cell Ranger pipelines (10X Genomics) to generate feature-barcode matrices.</p> <p><em>Integrating immune single-cell RNA-seq data from the blood and tumor of PyMT-M mouse</em></p> <p>We followed the Seurat tutorial on single-cell RNA integration from <a href="https://satijalab.org/seurat/articles/integration_introduction.html">https://satijalab.org/seurat/articles/integration_introduction.html</a>. Specifically, both datasets were library-size normalized and log-scaled. Then, variable genes from both datasets were extracted, and overlapped variable genes were used as anchors to integrate the two datasets to generate a combined immune single-cell RNA-seq dataset.</p> <p>&nbsp;</p> <p><em>Immune cell type annotation using SingleR</em></p> <p>After having the integrated immune single-cell RNA-sequencing data, we performed the standard pipeline for clustering, including scaling the expression data, performing dimension reduction using PCA and UMAP, and finding clusters by a shared nearest neighbor (SNN) modularity optimization (<a href="https://satijalab.org/seurat/articles/integration_introduction.html">https://satijalab.org/seurat/articles/integration_introduction.html</a>). Then, we used the R package <em>SingleR&nbsp;</em>to assign cell-type labels to each identified cluster using the <em>ImmGen</em> reference data from the Immunological Genome Project.</p> <p>&nbsp;</p>