探索一种新的文献阅读方法，可以更快去理解到作者的主体思路。下面我会从这几块介绍：SOURCE（文章来源）、WHY（作者为什么做这个项目）、HOW（作者怎么做的项目）、GET WHAT（作者得到了什么主要结论）

SOURCE

Title: A Single-Cell Tumor Immune Atlas for Precision Oncology

Date: 2020-10-26

Team: Barcelona Institute of Science and Technology (BIST), Barcelona, Spain

Paper Link: https://www.biorxiv.org/content/10.1101/2020.10.26.354829v1

Data Link (Restricted Access): https://zenodo.org/record/4036020#.X5uoHlMzaHF

Code Link: https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/Tumor-Immune-Cell-Atlas

WHY？

Immune microenvironments vary profoundly between patients and biomarkers for prognosis and treatment response lack precision
To pinpoint predictive cellular states of tumor immune cells and their spatial localization

HOW？

Analyzing >500,000 cells from 217 patients and 13 cancer types
Data projection: Seurat's anchor-transferring method
Using SPOTlight to combine single-cell and spatial transcriptomics data and identifying striking spatial immune cell patterns in tumor sections
ShinyApp (in progress) to project external data and to apply the immune classifier

GET WHAT?

GET 1: Generating a tumor immune cell atlas

Collected scRNA-seq datasets from 13 different cancer types, 217 patients and 526,261 cells
Immune cells clustered by cell identity rather than patient origin: integrated 317,111 immune cells using canonical correlation analysis => 25 clusters

Get 2: Tumor subtype classifier

For Current: to establish a pan-cancer immune classification system

used immune cell type and state frequencies of the reference atlas as input for similarity assessment across the 13 cancer types
A hierarchical k-means clustering using immune cell proportions as features defined six clusters with largely different compositions (almost all cancer types were presented in each cluster)

For future: to facilitate the classification of immune profiles

trained a RF(random forest) classifier with the 25 immune cell population achieving a highly accurate classification
using the classifier, the pan-cancer immune classification system could be extended to additional cancer types

GET 3: A resource for immune cell annotation

To demonstrate the potential value of the atlas

The applicability of the atlas as reference across different cancer types

First: Project cells onto atlas using a reference-based projection (Fig. A)
Next: Typical clustering matching (Fig. B)
Third: Check correlation (Fig. C)

The applicability of the atlas as reference across species

two liver metastases derived from mouse CRC organoids
main subtypes and specific subpopulations could also be assigned using the human reference

GET 4: Spatial localization of immune cells in tumor sections

Spatial distribution of immune cells is important for ICI (immune checkpoint inhibitors) response

Single-cell reference atlas immune profiles + Spatial transcriptome data

SPOTlight : non-negative matrix factorization (NMF) based spatial deconvolution framework

Analysis of oropharyngeal squamous cell carcinoma (SCC)

cluster 1/2 (cancer cells) is surrounded by cluster 0 (stroma) and cluster 3 (immune cells)

cluster1/2 presented a similar immune infiltration pattern, with an enrichment of proliferative T-cells and SPP1 macrophages
cluster 3 presented a distinct immune infiltration pattern characterized by an enriched presence of (proliferative) B-cells
cluster 0 harbored regulatory T-cells and terminally exhausted CD8 T-cells and was specifically enriched in M2 macrophages and naive T-cells.

Analysis of ductal breast carcinoma (BC)

also get a cancer-specific regional distribution:
subclonal was directly associated with local enrichment of distinct immune cell states
Foresee the regional distribution of immune cell types to become an important feature for the prediction of immuno-therapy outcome.

原汁原味读 | 单细胞肿瘤免疫图谱