# 2.2.5 研究 | 2020-原汁原味读--单细胞肿瘤免疫图谱 > 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） > > 其中不会有特别复杂的词语和语法，而且我会尽可能把文章逻辑层级写清楚 ## 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: Data Link (Restricted Access): Code Link: ## 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 ![image-20201030104337329](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-024337.png) * Immune cells clustered by cell identity rather than patient origin: integrated **317,111** immune cells using **canonical correlation analysis** => **25** clusters ![image-20201030104248148](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-024248.png) ### **GET2: 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 ![image-20201030105045062](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-025045.png) * 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) ![image-20201030105413983](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-025414.png) * **For future**: to facilitate the classification of immune profiles * trained an **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**) ![image-20201030111226885](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-031227.png) * **Third**: Check correlation (**Fig. C**) ![image-20201030112148461](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-032149.png) * 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** ![image-20201030112314187](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-032314.png) ### **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) ![image-20201030113032968](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-033033.png) * **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.** ![image-20201030132328526](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-052329.png) * Analysis of ductal breast carcinoma (**BC**) * also get a cancer-specific regional distribution: ![image-20201030134113951](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-054114.png) * subclonal was directly associated with local enrichment of distinct immune cell states ![image-20201030133721287](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2020-10-30-053721.png) * **Foresee** the regional distribution of immune cell types to become an important feature for the prediction of immuno-therapy outcome. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://jieandze1314.osca.top/02/2.2.5-yan-jiu-yuan-zhi-yuan-wei-du-dan-xi-bao-zhong-liu-mian-yi-tu-pu.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.