2024空间转录组分析方向和文献分享

作者，Evil Genius~~

这一篇我们来汇总一下最新的空间转录组的分析思路，这些方面目前都是‘荒漠’，是机遇，更是机会。

空间转录组的运用方向：转录、蛋白

ST需要解决的生物问题：细胞空间排布、邻域通讯、细胞“社区”等等

空间转录组数据探索：重点围绕“细胞组成-空间结构-区域功能”这一分析过程。

方向一、空间VDJ方向

细胞免疫疗法TCR-T和空间VDJ测序

一文汇总TCR-T细胞免疫疗法在肿瘤治疗的运用

10X空间转录组VDJ分析引入日程与临床免疫疗法

全球首篇FFPE空间转录组分析揭示了肾细胞癌中三级淋巴结构抗肿瘤机制

10X空间转录组技术创新之同时测RNA和TCR（BCR）

10X空间转录组之免疫组库分析

实验方案也是现成的，Localization of T cell clonotypes using the Visium spatial transcriptomics platform

原理么，也很简单

在文章Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer（Immunity IF32.4）也要用到空间BCR。

空间VDJ方向是我认为最有前景的方向，无论是科研还是临床都会有很大的运用。

方向二、空间微生物方向

GATK的人类宿主的微生物检测流程PathSeq和在空转上的运用（检测流程）

单细胞空间宏基因组揭秘微生物群对癌症空间和细胞异质性的作用2

单细胞空间宏基因组揭秘微生物群对癌症空间和细胞异质性的作用

这个方向也是值得关注的方向，微生物作为人类的第二套基因组，研究价值不言而喻，尤其是口腔和肠道。原理也很简单：

Schematic showing the experimental approach: RNAscope imaging was implemented to identify tumour areas positive for bacteria or F. nucleatum from OSCC and CRC tumours embedded in OCT blocks. Tumour tissues were trimmed to fit the capture area (6.5 mm x 6.5 mm) on the 10x Visium slide. Following tissue permeabilization, RNA is released from cells and bind to an array of probes that are attached to the surface of the slide within capture spots. Each probe has a unique molecular identifier (UMI) and a barcode sequence providing the spatial coordinates for each transcript. cDNA is generated from the captured RNA through a reverse transcription reaction. The barcoded cDNA is denatured and pooled and then further processed to generate cDNA libraries. All transcripts are aligned against the human transcriptome to map the human gene-expression profile across the sample. The unmapped reads are then aligned against microbial databases through GATK PathSeq to identify the microbiome composition.

具体的做法

The bam files generated by SpaceRanger Count v1.3.0 (10x Genomics) were processed via GATK PathSeq v4.1.3.0 Pathogen discovery pipeline (Broad institute, Cambridge, MA, USA) to identify and taxonomically classify microbial reads

为什么这么做是可以的，因为微生物的核糖体RNA（16sRNA）拥有类似真核的结构，捕获的微生物转录物，主要由核糖体RNA组成，微生物转录本的测序读数包含一个独特的分子标识符(UMI)，能够量化这些组织切片中细菌转录负荷。

由此可以获得肿瘤组织空间微生物分布信息

方向三、空间邻域方向

10X空间转录组之构建邻域通讯网络

空间组学邻域分析方法更新之BANKSY

空转数据分析之细胞“社区”

10X空间转录组数据分析之细胞单元

空间转录组的几个分析要点及经典文献分享

空转数据分析之关于细胞空间邻近的分析文章总结

10X空间转录组数据分析之同型分数（细胞网络）和异型分数（临近网络）计算

邻域分析包括分子邻域和细胞邻域，对应的空间分子niche矩阵和细胞niche矩阵，下图的T细胞的分子邻域

接下来是细胞邻域

邻域分析是空间分析必备的，与共定位类似，或者叫生态位，总结如下：

1、组织都是有序的系统，细胞之间的相互距离是其有序性的体现
2、空间细胞的交流是有距离限制的，通常认为在200um的范围内。
3、空间细胞的共定位是细胞之间相互需要的结果。
4、空间上细胞的位置错乱是引起疾病的重要原因之一，尤其在分析肿瘤异质性的时候。
5、空间位置上的细胞临近关系，往往存在1 + 1的小团伙共同起作用，实现1 + 1 > 2 的生物学作用。
6、空间位置上细胞之间的手拉手关系也受到各种调节，相互作用后改变自身的形态，在研究区域的细胞差异的时候，手拉手的关系尤其重要。

方向四、单细胞 + 突变方向

最近讲了很多，单细胞是可以call 部分的突变信息的，如有单细胞联合突变信息的分析，必然会提升文章的档次。

系统整理10X单细胞空间数据中可检测到的有害突变位点（OncoKB）

单细胞、空间、外显子解析TP53突变重构肺腺癌细胞图谱

多组学（单细胞、空间转录+蛋白、外显子、甲基化）揭示神经母细胞瘤异质性图谱

单细胞、空间、外显子分析方法更新

单细胞、空间、外显子多组学分析探讨

在文章An atlas of epithelial cell states and plasticity in lung adenocarcinoma中给了单细胞数据检测突变的方法。

突变信息匹配到单细胞级别
Mapping KRAS codon 12 mutations. To map somatic KRAS mutations at single-cell resolution, alignment records were extracted from the corresponding BAM files using mutation location information. Unique mapping alignments (MAPQ = 255) labelled as either PCR duplication or secondary mapping were filtered out. The resulting somatic variant carrying reads were evaluated using Integrative Genomics Viewer (IGV) and the CB tags were used to identify cell identities of mutation-carrying reads. To estimate the VAF of KRAS^G12D mutation and cell fraction of KRAS^G12D-carrying cells within malignant and non-malignant epithelial cell subpopulations (for example, malignant cells from all LUADs, malignant cells from KM-LUADs, KACs from KM-LUADs), reads were first extracted based on their unique cell barcodes and BAM files were generated for each subpopulation using samtools. Mutations were then visualized using IGV, and VAFs were calculated by dividing the number of KRAS^G12D-carrying reads by the total number of uniquely aligned reads for each subpopulation. A similar approach was used to visualize KRAS^G12D-carrying reads and to calculate the VAF of KRAS^G12D in KACs of normal tissues from KM-LUAD cases. To calculate the mutation-carrying cell fraction, extracted reads were mapped to the KRASG12D locus from BAM files using AlignmentFile and fetch functions in pysam package. Extracted reads were further filtered using the ‘Duplicate’ and ‘Quality’ tags to remove PCR duplicates and low-quality mappings. The number of reads with or without KRAS^G12D mutation in each cell was summarized using the CB tag in read barcodes. Mutation-carrying cell fractions were then calculated as the ratio of the number of cells with at least one KRAS^G12D read over the number of cells with at least one high-quality read mapped to the locus.

这样的话，大家在转录组信息上再添加突变信息，多组学的发文章当然会更高。

生活就是这样，逆水行舟，不进则退，都是非常好的方向，方法都是现成的，而且做的人很少，是很好的机遇，卷起来以后那就难了。

生活很好，有你更好