如今在整個(gè)生物領(lǐng)域中正被用來研究一個(gè)共同的問題：如何研究異質(zhì)細(xì)胞群體中的細(xì)胞多樣性。這種多樣性能夠?qū)?xì)胞存活和增殖、對(duì)藥物療法和干預(yù)作出的反應(yīng)以及很多其他的生物過程產(chǎn)生深刻影響。大規(guī)模單細(xì)胞轉(zhuǎn)錄組測序可用來研究黑色素瘤組織多樣性的復(fù)雜細(xì)胞微環(huán)境。

2研究方法

? 通過流式分選了來自于19個(gè)患者的4,645細(xì)胞進(jìn)行單細(xì)胞轉(zhuǎn)錄組測序；每個(gè)細(xì)胞15萬Reads數(shù)。
? 熒光免疫組化分析。

3研究結(jié)果

? 單細(xì)胞轉(zhuǎn)錄譜區(qū)分惡性細(xì)胞和非惡性細(xì)胞的狀態(tài)
通過CNV分析確定為惡性的細(xì)胞，然后根據(jù)其基因表達(dá)情況進(jìn)行了TSNE降維分析，惡性細(xì)胞分為6個(gè)亞型（圖1），表明了腫瘤的異質(zhì)性。而非惡性細(xì)胞則相反，主要是通過細(xì)胞類型進(jìn)行了區(qū)分，比如T細(xì)胞、B細(xì)胞、巨噬細(xì)胞、內(nèi)皮細(xì)胞、癌相關(guān)成纖維細(xì)胞（CAFs）和NK細(xì)胞。

? 惡性細(xì)胞的分析揭示了細(xì)胞周期的異質(zhì)性

我們使用G1或S／M期的標(biāo)志性基因來分析惡性細(xì)胞的不同亞群，該分析揭示了在腫瘤組織中，循環(huán)的細(xì)胞所占的比例平均為13.5%；也區(qū)分低循環(huán)狀態(tài)的細(xì)胞和高循環(huán)狀態(tài)的細(xì)胞，比如Mel79處于低循環(huán)、而Mel78則處于高循環(huán)狀態(tài)的腫瘤。KDM5B基因的表達(dá)與非循環(huán)狀態(tài)的腫瘤有高度相關(guān)性，它在小鼠模型中編碼H3K4組蛋白去甲基酶，這個(gè)酶與低循環(huán)相關(guān)，同時(shí)也有類似于黑色素瘤干細(xì)胞的耐藥性的特征。

? 非惡性細(xì)胞以及他們?cè)诤谏亓鑫h(huán)境中的相互作用

各種各樣的非惡性細(xì)胞組成了腫瘤的微環(huán)境，微環(huán)境的組成對(duì)腫瘤的發(fā)生和調(diào)節(jié)有重要影響。通過單細(xì)胞測序的數(shù)據(jù)區(qū)分了微環(huán)境中不同細(xì)胞的類型（圖4），包括了T細(xì)胞、B細(xì)胞、巨噬細(xì)胞、內(nèi)皮細(xì)胞和腫瘤相關(guān)的成纖維細(xì)胞（CAFs），并通過特征標(biāo)簽揭示了這些細(xì)胞在微環(huán)境中的相對(duì)豐富程度。
接下來我們分析細(xì)胞在腫瘤微環(huán)境中的作用關(guān)系，有很多新的發(fā)現(xiàn)，比如一群在CAF細(xì)胞中特征表達(dá)的基因與T細(xì)胞的浸潤有高度相關(guān)性。

大量黑色素瘤細(xì)胞之間的數(shù)據(jù)拆分解揭示了細(xì)胞與細(xì)胞間的相互作用

? 腫瘤浸潤性T淋巴細(xì)胞的多樣性及其功能狀態(tài)
腫瘤浸潤淋巴細(xì)胞（TILs），尤其是CD8 + T細(xì)胞是免疫監(jiān)視的主要決定因素。單細(xì)胞測序分析來推斷各種類型T細(xì)胞的功能，從而有助于研究對(duì)免疫檢查抑制劑的腫瘤反應(yīng)和抵抗：
通過特征性標(biāo)記定義了T細(xì)胞的主要亞群，比如CD4+,Tregs，CD8+等。然后通過耗竭基因分析了細(xì)胞的耗竭狀態(tài)，并通過免疫組化驗(yàn)證（圖5B和5C）。
zui后分析了細(xì)胞毒T細(xì)胞中耗竭T細(xì)胞的特征表達(dá)狀態(tài)，以及高耗竭和低耗竭細(xì)胞的基因表達(dá)情況，并進(jìn)行了TCR分析

It is now being used in the whole biological field to study a common problem: how to study the cell diversity in the heterogeneous cell population. This diversity can have a profound impact on cell survival and proliferation, response to drug therapy and intervention, and many other biological processes. The sequencing of a large single cell transcriptome can be used to study the complex cell microenvironment of the diversity of melanoma tissue.

2 research methods

By FACS from single cell transcriptome sequencing in 4645 cells in 19 patients; 150 thousand Reads per cell number.
Analysis of fluorescence immunohistochemistry.

3 research results

Single cell transcriptional profiling to distinguish between malignant and nonmalignant cells state
The malignant cells were identified by CNV analysis, and then TSNE dimension reduction analysis was performed according to their gene expression. The malignant cells were divided into 6 subtypes (Fig. 1), indicating the heterogeneity of tumors. Instead of malignant cells, they are mainly differentiated by cell types, such as T cells, B cells, macrophages, endothelial cells, cancer associated fibroblasts (CAFs) and NK cells.

Analysis of malignant cells revealed the heterogeneity of cell cycle

Marker genes, we use G1 or S / M phase analysis of malignant cells in different subgroup, the analysis revealed in tumor tissue, cell cycle accounted for the proportion of the average of 13.5%; also distinguish between low cycle state and high cell cycle state of cells, such as Mel79 in the low cycle, and Mel78 in the high cycle of tumor. The expression of KDM5B gene is highly correlated with the acyclic tumor. It encodes H3K4 histone demethylation enzyme in mouse model, which is related to low circulation and similar to the drug-resistance of melanoma stem cells.

Non malignant cells and their interactions in melanoma microenvironment.

Various non malignant cells constitute the microenvironment of the tumor, and the composition of the microenvironment has an important influence on the occurrence and regulation of the tumor. The single cell sequencing data to distinguish the different types of cells in the microenvironment (Figure 4), including T cells, B cells, macrophages, endothelial cells and tumor associated fibroblasts (CAFs), and reveals that these cells in the microenvironment of the relative abundance through the feature label.
Next, we analyze the role of cells in tumor microenvironment, and there are many new discoveries. For example, a group of genes expressed in CAF cells are highly correlated with the infiltration of T cells.

Figure 4 the disassembly of a large number of melanoma cells reveals the interaction between cells and cells

Of tumor infiltrating T lymphocytes in the diversity and function of state
Tumor infiltrating lymphocytes (TILs), especially CD8 + T cells, are the main determinants of immune surveillance. Single cell sequencing analysis is used to infer the functions of various types of T cells, thus helping to study the tumor response and resistance to immunoassay inhibitors:
The main subgroups of T cells, such as CD4+, Tregs, CD8+, are defined by characteristic markers. Then the depletion of the cells was analyzed by the exhaustion gene and verified by immunohistochemistry (FIGS. 5B and 5C).
Finally, we analyzed the characteristic expression state of depleted T cells in cytotoxic T cells, and the gene expression of high exhaustion and low depletion cells (map 5D, E, F), and carried out TCR analysis.

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