hello戏售，大家好侨核，今天我們繼續(xù)來分享關(guān)于10X空間轉(zhuǎn)錄組在腫瘤方面的研究，其實(shí)也和大家分享過很多次了灌灾，強(qiáng)調(diào)腫瘤正常邊界的重要醫(yī)學(xué)意義搓译，而如果實(shí)現(xiàn)這個(gè)，就必須借助空間轉(zhuǎn)錄組技術(shù)來實(shí)現(xiàn)相應(yīng)的研究锋喜，今天我們參考的文獻(xiàn)在Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface些己，2021年11月發(fā)表于NC，影響因子14分嘿般，其實(shí)不知道大家注意到?jīng)]有段标，隨著時(shí)間的推移，借助空間轉(zhuǎn)錄組發(fā)表的文章影響因子也慢慢降低了炉奴，更多的要求多組學(xué)的分析逼庞，單細(xì)胞、空間瞻赶、免疫熒光等要多技術(shù)結(jié)合才能發(fā)表好的文章赛糟，希望有條件的科研人員能夠珍惜這個(gè)窗口期吧。

圖片.png

Abstract

During tumor progression, cancer cells come into contact with various non-tumor cell types（其實(shí)就是腫瘤微環(huán)境）, but it is unclear how tumors adapt to these new environments. Here, we integrate spatially resolved transcriptomics, single-cell RNA-seq, and single-nucleus RNA-seq to characterize tumor-microenvironment interactions at the tumor boundary（運(yùn)用到的技術(shù)包括單細(xì)胞轉(zhuǎn)錄組砸逊、單細(xì)胞核轉(zhuǎn)錄組璧南、空間轉(zhuǎn)錄組技術(shù)，關(guān)于三種技術(shù)的聯(lián)合運(yùn)用师逸，大家可以參考我之前的文章10X單細(xì)胞轉(zhuǎn)錄組司倚、單細(xì)胞核轉(zhuǎn)錄組、VDJ篓像、空間轉(zhuǎn)錄組聯(lián)合分析識(shí)別人肺組織的免疫細(xì)胞生態(tài)位）. Using a zebrafish model of melanoma（黑色素瘤）, we identify a distinct “interface” cell state where the tumor contacts neighboring tissues（腫瘤和正常區(qū)域的交界處动知，這個(gè)地方非常值得深入研究）. This interface is composed of specialized tumor and microenvironment cells that upregulate a common set of cilia genes（纖毛基因）, and cilia proteins are enriched only where the tumor contacts the microenvironment（在交界出富集）. Cilia gene expression is regulated by ETS-family transcription factors, which normally act to suppress cilia genes outside of the interface. A ciliaenriched interface is conserved in human patient samples, suggesting it is a conserved feature of human melanoma. Our results demonstrate the power of spatially resolved transcriptomics in uncovering mechanisms that allow tumors to adapt to new environments.（腫瘤如何適應(yīng)新環(huán)境的研究）。

Introduction

As tumors grow and invade into new tissues, they come into contact with various new cell types（腫瘤不斷擴(kuò)展的同時(shí)也不斷接觸新的細(xì)胞類型）, but it is poorly understood how these cell–cell interactions allow for successful invasion and tumor progression. In melanoma, these interactions can occur between the tumor cells and a diverse number of cell types（其實(shí)就是腫瘤細(xì)胞跟各種細(xì)胞類型之間的交流差異是什么员辩，為什么可以在眾多其他細(xì)胞類型存在的條件下不斷invade）. In many cases, the tumor cells interact directly with stromal cells such as fibroblasts or immune cells.However, increasing evidence suggests that the repertoire of such
interactions is considerably broader, and can include cell types including adipocytes and keratinocytes. Many of these cell interactions can influence tumor cell behavior（看來是一個(gè)相互影響的過程）拍柒。

There are likely at least two levels of cell–cell interactions that are relevant to cancer: “microenvironmental” interactions in which the tumor cell directly interacts with adjacent non-tumor cells, and “macroenvironmental” interactions, in which the tumor cell indirectly interacts with more distant cells（兩種模式，直接接觸和遠(yuǎn)程調(diào)控屈暗，兩種之間肯定存在很多差異）. The microenvironment is increasingly appreciated to play a major role in cancer phenotypes, including proliferation, invasion, metastasis,and drug resistance. However, it is debatable whether every cell type that a tumor interacts with is truly part of the microenvironment, since the mechanisms by which these cells influence tumor cell behavior are often unclear（各種細(xì)胞類型對(duì)腫瘤細(xì)胞行為的影響）. This uncertainty is compounded by the fact that tumor cells themselves are highly heterogeneous（腫瘤的高度異質(zhì)性）, making it challenging to determine which subset of tumor cells are directly interacting with surrounding nontumor cells. The macroenvironment may also influence tumor progression, since the tumor cells can interact with other cells in the body at a distance, as recently demonstrated for metabolic coupling between melanoma cells and distant cells in the liver（看來腫瘤異質(zhì)性是關(guān)鍵點(diǎn)拆讯，一些腫瘤細(xì)胞直接接觸正常細(xì)胞脂男，而另外一些腫瘤細(xì)胞遠(yuǎn)程調(diào)控細(xì)胞間的交流和維持自己的形態(tài)）。

A better understanding of the nature of these cell–cell interactions requires high resolution imaging and analyses of genes expressed by tumor cells as they interact with different cell types（單細(xì)胞空間兩種技術(shù)都必不可少）. While bulk and single-cell RNA-sequencing approaches have improved our ability to understand cell–cell interactions, these techniques require dissociation of the tissue of interest, resulting in a loss of spatial information. Thus, a comprehensive understanding of how tumor and surrounding cells interact in situ is lacking, at least in part due to the limitations of current RNAsequencing technologies（細(xì)胞之間的臨近交流和空間位置种呐，非常重要的研究手段）宰翅。

Spatially resolved transcriptomics (SRT) has recently emerged as a way to address the limitations of both bulk and single-cell RNA-seq by preserving tissue architecture, while still profiling the genes expressed by the cell or tissue at high resolution. Current SRT techniques typically either use spatially-barcoded probes to capture and sequence mRNA from tissue sections, or multiple rounds of in situ hybridization, sequencing, and imaging to computationally reconstruct the transcriptional landscape of the cell. In situ hybridization-based SRT techniques allow the user to profile the transcriptional landscape of the cell at cellular or even subcellular resolution, whereas the resolution of techniques that capture and sequence mRNA from sections is limited by the diameter of each capture spot on the SRT array (for example, 55 μm with the current 10× Genomics Visium SRT technology, with a 45 μm gap between spots). However, to overcome the limited spatial resolution of SRT arrays, a number of computational methods to infer single cell resolved gene expression profiles have recently been developed, including SPOTlight（關(guān)于SPOTlight，大家可以參考我的文章10X單細(xì)胞空間聯(lián)合分析之三----Spotlight,關(guān)于單細(xì)胞空間聯(lián)合的方法匯總爽室，大家可以參考我的文章10X單細(xì)胞空間聯(lián)合分析之十一（CellTrek）) and Stereoscope. We recently developed a technique to integrate capture probe-based SRT and scRNA-seq to map the transcriptomic and cellular architecture of tumors. This provides a unique opportunity to understand the mechanisms that are driving the cell–cell interactions that occur between the tumor and its immediately adjacent microenvironment（這里還是要強(qiáng)調(diào)臨近通訊汁讼，單細(xì)胞做通訊分析最大的問題就在這里）。

Here, we integrate SRT, single-cell RNA-seq, and single-nucleus RNA-seq to characterize the transcriptional landscape of melanoma cells as they interact with the immediately adjacent microenvironment（近鄰細(xì)胞交流阔墩，這個(gè)強(qiáng)調(diào)過多多次了嘿架，希望引起大家的足夠重視）. Using a zebrafish model of melanoma, we construct a spatially-resolved gene expression atlas of transcriptomic heterogeneity within tumors and surrounding tissues. We discover a histologically invisible but transcriptionally distinct “interface” region where tumors contact neighboring tissues, composed of cells in specialized tumor-like and microenvironment-like states（交界處的細(xì)胞類型的組成）. We uncover enrichment of cilia genes and proteins at the tumor boundary, and find that ETS-family transcription factors regulate cilia gene expression specifically at the interface. We further demonstrate that this distinct “interface” transcriptional state may be conserved in human melanoma, suggesting a conserved mechanism that presents opportunities for halting melanoma invasion and progression.

Results

Spatially resolved transcriptomics reveals the architecture of the melanoma–microenvironment interface.

To investigate the transcriptional landscape of tumors and neighboring tissues in situ with spatial resolution, we processed frozen sections from three adult zebrafish with large, invasive BRAF^V600E-driven melanomas for capture probe-based spatially resolved transcriptomics (SRT), using the 10× Genomics Visium platform(還是用的10X平臺(tái))。Although the size of the tissue section used is limited by the size of the Visium array (6.5mm²), zebrafish allow us the
unique advantage that a transverse section through an adult fish (~5mm diameter) fits in its entirety on the array. Zebrafish are thus one of the only vertebrate animals that can be used to study both the tumor and all surrounding tissues(腫瘤和其鄰近區(qū)域) in their intact forms, without any need for dissection. Our SRT dataset contained transcriptomes for 7281 barcoded array spots across three samples, encompassing 17,317 unique genes. We detected approximately 1000–15,000 transcripts (unique molecular identifiers, UMIs) and 500–3000 unique genes per spot, with somewhat fewer UMIs/genes detected in sample C. Visium array spots within the tumor region typically contained more UMIs than spots in the rest of the tissue, likely at least in part due to higher density of cells within the tumor region（空間細(xì)胞密度）啸箫。

圖片.png

圖片.png

We first combined our expression matrices using an anchoring framework to identify common cell states across different datasets（這里是對(duì)空間轉(zhuǎn)錄組數(shù)據(jù)進(jìn)行的整合分析）.

圖片.png

圖片.png

圖片.png

The tumor–microenvironment interface is transcriptionally distinct from the surrounding tissues.

We noticed in all of the samples a transcriptionally distinct cluster of array spots that
localized to the border between the tumor and the adjacent microenvironment（有單獨(dú)的cluster位于腫瘤和正常組織的交界處）, in which specific biological pathways were upregulated（這個(gè)就是之前一直講到的基因開關(guān)）. This “interface” cluster was present in all three samples.

圖片.png

圖片.png

• 注：The tumor–microenvironment interface is transcriptionally distinct from the surrounding microenvironment. a Interface and muscle-annotated cluster spots projected onto tissue image (n = 3 sections). Insets show the tissue underlying the interface spots (1) and muscle spots (2). b Correlation matrix between average expression profile of SRT clusters across all three datasets. Clusters are ordered by hierarchical clustering of the Pearson’s correlation coefficients and bubble sizes correspond to p-value (?log10) of correlation (two-sided), with p-values < 10^?3 omitted. Clustering of tumor and interface together is highlighted in the dendrogram (red). c Volcano plot of differentially expressed genes between the interface cluster versus the muscle and tumor clusters. p-values were obtained from the Wilcoxon’s rank sum test (two-sided).

The upregulation of most of these genes was subtle (though statistically significant;), which may be due to the somewhat lower cellular resolution of the Visium technology and number of UMIs detected per spot (note: to address this, we further compare the magnitude of changes for these genes in our single cell datasets below(看來對(duì)于空間轉(zhuǎn)錄組來講胸遇，精度仍然是個(gè)問題)). To identify gene expression programs that are enriched specifically at the interface and provide further evidence for the interface as a transcriptionally distinct tissue region, we
performed non-negative matrix factorization (NMF) on all microenvironment spots (including both interface and muscle clusters) across all samples（NMF識(shí)別轉(zhuǎn)錄程序荧呐，這個(gè)我分享了很多了，大家可以參考我的文章10X單細(xì)胞（10X空間轉(zhuǎn)錄組）分析之尋找目標(biāo)bases基因集（factors）（PNMF））. When we projected the NMF factor scores onto each spot, we found that some factors were enriched across all three samples (e.g., factor 2)纸镊， whereas some were only enriched in one or two of the samples (e.g., factors 4, 11).

圖片.png

圖片.png

圖片.png

• 注：d Non-negative matrix factorization (NMF) of the microenvironment spots (muscle and interface clusters). Shown are the standardized factor scores for interface-specific NMF factor 7, projected onto microenvironment spots. Arrows denote areas with higher factor scores. e Enriched GO terms for the top 150 scoring genes in NMF factor 7.

The tumor–microenvironment interface is composed of specialized cell states.（交界處的細(xì)胞狀態(tài)）。

Our SRT results so far detail a transcriptionally distinct “interface” region where tumors contact the microenvironment. However, spatially resolved transcriptomics data is limited in resolution by the diameter of each spot on the Visium array (55 μm with current technology). Thus, each array spot probably captures transcripts from multiple cells. As the interface region is, by nature, likely a mixture of tumor and microenvironment cells, we performed single-cell RNA-seq (scRNA-seq薄腻，借助單細(xì)胞數(shù)據(jù)提高精度) on tumor and non-tumor cells from three adult zebrafish with large melanomas in order to better define the cell states present in the interface. We detected approximately 10,000–75,000 transcripts and 1000–5000 unique genes per cell.

圖片.png

Consistent with our SRT results, clustering of our scRNA-seq data revealed a distinct “interface” cell cluster, which we identified based on the fact that cells in this cluster significantly upregulated the same genes that were upregulated in our SRT interface cluster (p= 1.83 × 10^?26). The distinct clustering of the interface population was not due to the presence of a significant number of cell doublets(多細(xì)胞去除，用到的方法是DoubleFinder楣颠，大家可以參考我的文章DoubletFinder) within this cluster尽纽。

圖片.png

Strikingly, UMAP and principal component analysis of the interface cluster revealed two distinct cell populations, one expressing tumor markers such as BRAF^V600E and the other expressing muscle genes such as ckba, with other genes such as the centromere gene stra13 upregulated in both populations. This result suggests that the transcriptionally distinct “interface” region we identified in our SRT data is actually composed of at least two similar, but distinct cell states: a “tumor-like interface” and a “muscle-like interface”(借助單細(xì)胞數(shù)據(jù)識(shí)別交界區(qū)域的細(xì)胞類型的組成). The interface region may not be limited to only tumorlike and muscle-like cell states; however, since zebrafish melanomas frequently invade into muscle, this likely contributes to the presence of muscle-like interface cells in our data.

圖片.png

Based on this, we separated the interface cluster into two subclusters（再分群分析）, and confirmed that the two subclusters express anticorrelated（負(fù)相關(guān)） levels of tumor markers such as BRAF^V600E, , and , and muscle markers such as , , and . A common set of genes, including many genes related to the microtubule cytoskeleton and cell proliferation such as , , , and , were upregulated in both subclusters. Both the tumor-like and muscle-like interface cell states were present in both scRNA-seq samples.

圖片.png

Interface cell states are distinct from neighboring tissues.

Our results so far indicate that we have uncovered an “interface” cell state localized to where the tumor contacts neighboring tissues. However, our scRNA-seq dataset does not contain a muscle cell cluster due to the fact that muscle fibers cannot be encapsulated for scRNA-seq（單細(xì)胞轉(zhuǎn)錄組無法捕獲某種細(xì)胞類型）. This makes it difficult to assess whether the specialized muscle cell state found in the interface is truly distinct from muscle that is not in proximity to the tumor. Thus, to effectively compare the interface cell state(s) to other microenvironment cell types/states that cannot be captured with scRNA-seq, we validated our scRNA-seq results by performing single-nucleus RNA-seq (snRNA-seq) on nuclei extracted from three adult zebrafish（借助單細(xì)胞核轉(zhuǎn)錄組的手段進(jìn)行分析）, all with large transgenic melanomas. Although snRNA-seq captures only nascent transcripts in the nucleus, which contains only 10–20% of the cell’s mRNA, scRNA-seq, and snRNA-seq typically recover the same cell states/types, albeit sometimes in different proportions. After quality control and filtering, our dataset encompassed transcriptomes for 10,527 individual nuclei。We also identified an “interface” cluster in our snRNA-seq dataset (看來單細(xì)胞和單核可以相互驗(yàn)證). We identified the interface cluster based on the fact that nuclei in this cluster strongly upregulated genes that were strongly upregulated in the interface cluster in our scRNA-seq dataset, including stmn1a, stra13, plk1, and haus4, and that the interface cluster from our snRNAseq dataset clustered with the interface cluster from our scRNAseq dataset when the two datasets were integrated（單細(xì)胞轉(zhuǎn)錄組和單核轉(zhuǎn)錄組的整合分析童漩，后續(xù)我們看看方法是什么）弄贿。

圖片.png

• 注：Single-nucleus RNA-seq demonstrates that the interface cell states are distinct from the rest of the microenvironment. a snRNA-seq cluster assignments plotted in UMAP space. b Expression of marker genes from the scRNA-seq interface cluster in the snRNA-seq dataset. c Integrated UMAP of the snRNA-seq and scRNA-seq datasets (labeled, top plot) showing colocalization of the two interface clusters (bottom plot).

To interrogate the types of nuclei present in the interface cluster in our snRNA-seq dataset, we performed dimensionality reduction and clustering on the nuclei from the interface cluster, which identified five discrete subclusters. Similar to our scRNA-seq dataset, within the interface cluster in our snRNA-seq dataset we identified subclusters of nuclei that upregulated tumor-specific or muscle-specific genes。 The interface cluster in our snRNA-seq dataset also contained other subclusters that did not express tumor-specific or muscle-specific genes.（單細(xì)胞轉(zhuǎn)錄組和單核轉(zhuǎn)錄組的相互驗(yàn)證）矫膨。Nuclei in these subclusters expressed genes related to other cell types in our snRNA-seq dataset, including immune cells (ctss2.1), liver (fabp10a), and digestive system (ela2) . This is in line with recent work showing that melanomas can reprogram microenvironmental cells such as liver cells even when not in physical contact. However, similar to our scRNA-seq and SRT datasets, there were many genes that were specifically upregulated across the interface subclusters that were not upregulated in any other cell type in the snRNA-seq dataset, further suggesting that the “interface” cell state is a distinct transcriptional entity（三種技術(shù)數(shù)據(jù)的相互驗(yàn)證差凹，看來下了相當(dāng)大的功夫）期奔。

圖片.png

• 注：d Subcluster assignments and expression of marker genes from the snRNA-seq interface cluster. e Dotplot showing expression of microenvironment cell-type specific genes within the interface subclusters. f Heatmap showing expression of the top 100 genes upregulated across all of the interface subclusters.

Although our snRNA-seq analysis workflow includes multiple processing steps to exclude doublets, including filtering steps based on the number of UMIs per nucleus and removing possible doublets identified by DoubletFinder, to further interrogate whether these tumor-like and microenvironment-like interface nuclei could be attributed to doublets with the corresponding cell type, we quantified the number of UMIs/genes expressed by interface cells/nuclei relative to other cells/nuclei in the dataset. The results were inconclusive: in some cases we quantified significantly more UMIs/genes in interface cells, in some cases we quantified significantly less UMIs/genes in interface cells, but in other cases there was no significant difference (單核數(shù)據(jù)是否需要進(jìn)行雙細(xì)胞的去除，這里給出了答案).

圖片.png

Thus, to further investigate the presence of doublets in the interface, we calculated the degree of overlap between genes expressed by the tumor/microenvironment nuclei and genes expressed by the corresponding interface nuclei (進(jìn)一步驗(yàn)證).

圖片.png

Although the tumor-like and microenvironment-like interface clusters expressed some tumor-specific and microenvironment-specific genes, as expected, in most cases there was not a significant degree of overlap between all genes upregulated between both cell states, suggesting that these interface cell states are not caused by doublets（雙細(xì)胞的效應(yīng)排除）. We did observe some overlap between all genes expressed by NK cells and macrophages relative to the immune-like interface cells, suggesting that some doublets could be present within the immune-like interface cluster. Notably, tumor-immune cell fusion has been reported in melanoma. Determining whether these potential doublets result from technical or biological reasons will be an important area of future study.

Since our snRNA-seq dataset contained more cells/nuclei and a greater breadth of cell types than our scRNA-seq dataset, we integrated our snRNA-seq data with our Visium SRT data using our recently developed multimodal intersection analysis (MIA) method（MIA整合單核和空間轉(zhuǎn)錄組數(shù)據(jù)危尿，大家可以參考我之前的文章MIA用于單細(xì)胞和空間的聯(lián)合分析） to confirm the presence of tumor-like and microenvironment-like cell states within the interface region. Notably, our MIA results suggested that the interface regions in our SRT dataset were enriched in cell types including muscle, macrophages, and tumor, in line with our scRNA-seq and snRNA-seq results. The cluster that was most significantly enriched in the interface region was the muscle-like interface cell state, in accordance with the histology of our SRT samples that showed that the interface region closely resembles the surrounding muscle. Together, these results suggest that the interface is composed of tumor and microenvironment cells which upregulate a common gene program that may contribute to tumor-microenvironment cell interactions at the tumor boundary（看來交界處確實(shí)是一個(gè)關(guān)鍵的轉(zhuǎn)換開關(guān)）.

圖片.png

As our SRT results suggest that the interface cell state may be modulated by direct cell–cell interactions between tumor and microenvironment cells, we used NicheNet（NicheNet是一個(gè)很好的細(xì)胞通訊的分析軟件呐萌，大家可以參考我的文章10X單細(xì)胞（10X空間轉(zhuǎn)錄組）通訊分析之NicheNet、10X單細(xì)胞（10X空間轉(zhuǎn)錄組）空間相關(guān)性分析和cellphoneDB與NicheNet聯(lián)合進(jìn)行細(xì)胞通訊分析） to computationally infer interactions between interface cells and the rest of the cells in our snRNA-seq dataset（注意這里作者運(yùn)用的是單核數(shù)據(jù)而不是單細(xì)胞轉(zhuǎn)錄組數(shù)據(jù)） by identifying potential ligands expressed by interface cells and receptors and target genes in the other cell types. As the NicheNet model is currently designed to work with human genes, we performed this analysis with the human orthologs of the zebrafish genes in our dataset (物種之間的基因轉(zhuǎn)換）. The top ligand predicted to be active in interface nuclei was HMGB2, of which there are two zebrafish orthologs: hmgb2a and hmgb2b. These genes were highly expressed in the interface clusters across our snRNA-seq, scRNA-seq, and SRT datasets. Interestingly, HMGB2 expression has been reported to be correlated with tumor aggressiveness. The predicted receptors for HMGB2 were AR, ITPR1, and CDH1 (fish orthologs: ar, itpr1a, itpr1b, cdh1). Of these potential receptors, cdh1 was the most highly expressed in general across the three datasets. cdh1 was expressed in various microenvironment cell types, including intestinal cells, keratinocytes, and also in some interface cell states. cdh1 (Ecadherin) is a core component of adherens junctions along with α-catenin and β-catenin（這個(gè)地方一定要注意谊娇，一般有adherens junctions說明空間上細(xì)胞類型存在共定位現(xiàn)象）. Interestingly, HMGB2 and β-catenin have been reported to cooperate to promote melanoma progression. These data demonstrate one of likely many signaling interactions that occur between interface cells and other cells adjacent to the tumor. Taken together, our results suggest that we have identified a putative “interface” cell state in each of our SRT, scRNA-seq, and snRNA-seq datasets, composed of tumor and microenvironment cells which upregulate a common gene program that may contribute to tumor–microenvironment cell interactions at the tumor boundary.（這里對(duì)于細(xì)胞類型之間相互作用的方法值得大家好好借鑒）肺孤。

圖片.png

Cilia genes and pathways are upregulated at the interface.

To gain further insight into the biological processes underlying the specialized “interface” region identified in our SRT and scRNAseq data, we performed pre-ranked gene set enrichment analysis (GSEA，這個(gè)分析大家應(yīng)該都熟悉济欢，但是現(xiàn)在一般GSVA用的多一點(diǎn)), using differentially expressed genes in the scRNA-seq interface cluster, to identify conserved pathways that may be active in interface cells（這個(gè)時(shí)候做富集又用到單細(xì)胞轉(zhuǎn)錄組了赠堵，O(∩_∩)O）. We noticed that many cilia-related pathways were enriched in the combined interface cluster. This enrichment of cilia-related pathways occurred in both the muscle-like and tumor-like interface cell states. Cilia-related GO terms were also enriched in the SRT interface, as were GO terms related to membrane-bound organelles in the genes contributing to NMF factor 7, which localized to the interface. When we calculated a list of common genes upregulated across the SRT, scRNA-seq, and snRNA-seq interface clusters, several cilia genes were present on this list including ran, tubb4b, stmn1a, and tuba8l4 (三種技術(shù)共有的上調(diào)基因，這個(gè)說明基因上調(diào)具有普遍性).

圖片.png

Several recent studies have implicated cilia in an important role in melanoma initiation and progression, although the mechanism by which cilia mediate melanoma progression is unclear. To further investigate a role for cilia at the tumor–microenvironment interface, we scored each cell from our scRNA-seq dataset for relative enrichment of cilia genes, using the “gold standard” SYSCILIA gene list, and quantified a significant upregulation of cilia genes in both interface cell states in our scRNA-seq data, with a particularly strong upregulation in the muscle-like interface cluster. Although cilia genes generally were expressed at relatively low levels in our snRNA-seq dataset, in line with the overall lower expression of most genes in our snRNA-seq data relative to our scRNA-seq data, the most highly upregulated cilia genes in the scRNA-seq interface cluster were also upregulated across the tumor-like and muscle-like cell states in the snRNA-seq interface cluster relative to the tumor and muscle clusters.

圖片.png

The tumor–microenvironment interface is ciliated.

Interestingly, previous studies have shown that human and mouse melanomas
are not ciliated, although they express cilia genes. To reconcile these models, we stained sections through adult zebrafish with invasive BRAF^V600E-driven melanomas for acetylated tubulin, a common cilia marker30. Strikingly, we found that although the bulk of the tumor was not ciliated as expected, there was a specific enrichment of cilia at the invasive front of the tumor, where it contacts the muscle(纖毛和肌肉組織的關(guān)系很密切).

圖片.png

We observed long, acetylated tubulinpositive tubulinpositive projections that were often found in the extracellular space spanning tumor and adjacent muscle cells. These projections were not found in the bulk of the tumor or in muscle that was not adjacent to the tumor （說明了什么挖胃？轉(zhuǎn)錄組和蛋白組的豐度不能劃等號(hào)). These structures did not resemble typical cilia, which we occasionally see on cultured zebrafish melanoma cells expressing a transgenic cilia reporter, as the acetylated tubulin-positive structures we see in vivo are longer and structurally distinct from typical cilia. Determining the nature and function of these structures will be an exciting area of future study.

圖片.png

圖片.png

• 注：g Immunofluorescent images of sections through adult zebrafish with invasive melanomas, stained for GFP (tumor cells), acetylated tubulin (cilia), and Hoescht (nuclei), showing the tumor-muscle interface (left), center of the tumor (middle), and distant muscle (right). Arrows denote cilia at the interface. Scale bars, 100 μm. Images are representative from at least three independent experiments. h Inset of region highlighted in g (left). Scale bars, 25 μm.

ETS-family transcription factors regulate cilia gene expression at the interface.

To identify potential regulators of gene expression within the interface, we performed HOMER motif analysis(這個(gè)地方我們需要注意) to identify conserved transcription factor (TF) binding motifs enriched in genes differentially expressed in the interface. When we performed de novo motif enrichment analysis on genes differentially expressed in the SRT interface compared to normal muscle, the top-ranked motif was the highly conserved ETS DNA-binding domain, containing a core GGAA/T sequence (p = 1 × 10^?22). The ETS domain was also the top-ranked motif enriched in genes differentially expressed in the SRT interface compared to all other SRT spots (p = 1 × 10^?15), and was the second-ranked motif enriched in genes differentially expressed in the interface cluster identified in our scRNA-seq dataset (p = 1 × 10^?13) and in genes differentially expressed in our snRNA-seq interface cluster (p = 1 × 10^?13). Furthermore, ETS motifs were frequently enriched in both the tumor-like and muscle-like interface subclusters in our scRNA-seq dataset, along with, notably, motifs for RFX-family transcription factors which regulate ciliogenesis.Although ETS-family transcription factors have not been widely studied in melanoma, they have been reported to function in melanoma invasion and phenotype switching, and are aberrantly upregulated in many types of solid tumors. Interestingly, zebrafish ETS-family transcription factors were downregulated in the interface in each of our scRNA-seq, snRNA-seq, and SRT datasets.(motif分析目前是一個(gè)很重要的分析點(diǎn)酱鸭，不過分析的方法我們還是重點(diǎn)關(guān)注一下)吗垮。

圖片.png

• 注：ETS transcription factors may regulate cilia gene expression at the interface. a Results from HOMER de novo motif analysis of differentially expressed genes in the SRT, scRNA-seq, and snRNA-seq interface clusters. b Top ten enriched motifs from HOMER known motif analysis of the scRNAseq tumor-like (left) and muscle-like (right) interface cell states. a, b p-values calculated using the hypergeometric test (one-tailed). c–e Relative expression of zebrafish ETS genes across the clusters in the scRNA-seq (c), SRT (d), and snRNA-seq (e) datasets. p-values are noted (Wilcoxon rank sum test, two-sided, with Bonferroni’s correction).

To identify potential biological processes that could be regulated by ETS transcription factors at the tumor–microenvironment interface, we investigated putative target genes containing an ETS motif in their promoter. We queried the zebrafish genome for genes with an ETS motif within 500 bp of the transcription start site, filtered these genes to include only those differentially expressed in the tissue/cell state of interest, and performed GSEA on the resulting target gene lists. Surprisingly, within the ETS-target genes in both the SRT and scRNA-seq interface clusters, we again found an enrichment of pathways related to cilia. As ETS TFs are downregulated specifically in the interface, this suggests that ETS-family TFs may act as a transcriptional repressor of cilia genes. ETS TFs can act as transcriptional activators and/or repressors depending on gene and context. In support of this model, when we scored each cell in the interface for relative expression of both ETS genes and ETS-target genes, the two were strongly anti-correlated (R=?0.625, p=9.02 × 10^?27). Collectively, these data suggest that ETS-family transcription factors act as transcriptional repressors of cilia genes in cells at the interface between tumors and fold in our scRNA-seq interface cluster, and classified cells that upregulated these genes as an “interface” population. Similar to our snRNAseq results, “interface” cells were found across all the major cell types in the human melanoma dataset. For the purposes of statistical power, we focused on interface-like cells from the three largest clusters (tumor, myeloid cells, and T/NK cells). Human cells in an interface-like cell state upregulated many of the same genes upregulated in the interface in our zebrafish datasets, including PLK1, HMGB2, TUBB4B and TPX2. Cilia genes were significantly upregulated across all of the interface cell states, relative to their corresponding tumor/TME cell types. This suggests that a transcriptionally distinct “interface” gene signature may be found in human melanoma. Identifying which human melanoma subtypes (e.g., BRAF, NRAS, c-KIT, etc) in which an interface cell state is found awaits larger datasets of freshly isolated tumors subjected to scRNA-seq and/or SRT. Follow-up analyses determining the roles of specific types of immune and myeloid cells in the interface would also be an interesting area of future study. Together, our results suggest that cell-cell interactions at the tumor–microenvironment interface are accomplished by a subset of specialized tumor and muscle cells, which together upregulate a conserved common gene program characterized by upregulation of cilia genes and downregulation of ETS transcription factors.(總之，我們的結(jié)果表明凹髓，腫瘤-微環(huán)境interface上的細(xì)胞-細(xì)胞相互作用是由一組特化的腫瘤和肌肉細(xì)胞完成的烁登，它們共同上調(diào)了一個(gè)保守的共同基因程序，其特征是纖毛基因的上調(diào)和 ETS 轉(zhuǎn)錄因子的下調(diào)蔚舀。)

圖片.png

Discussion

在這里饵沧，結(jié)合空間分辨和單細(xì)胞和單核轉(zhuǎn)錄組學(xué)方法來表征腫瘤細(xì)胞如何與其周圍環(huán)境中的新組織相互作用，揭示這種interface如何形成的key regulators赌躺。分析了總共 49,944 個(gè)轉(zhuǎn)錄組狼牺，包括來自 7281 個(gè)空間spot、2889 個(gè)斑馬魚細(xì)胞礼患、10,527 個(gè)斑馬魚細(xì)胞核和 29,247 個(gè)人類細(xì)胞的 20,589 個(gè)獨(dú)特基因的表達(dá)是钥。分析的結(jié)果確定了一系列空間模式基因模塊，其中一些專門定位于腫瘤和周圍組織之間的interface缅叠。發(fā)現(xiàn)interface由專門的腫瘤和肌肉細(xì)胞狀態(tài)組成悄泥，其特征在于纖毛基因和蛋白質(zhì)的上調(diào)。進(jìn)一步表明肤粱，ETS 轉(zhuǎn)錄因子調(diào)節(jié)interface處纖毛基因的表達(dá)弹囚，并且在人類黑色素瘤患者樣本中，一個(gè)獨(dú)特的“interface”細(xì)胞群是保守的领曼∨葛模總之蛮穿，分析的結(jié)果揭示了可能介導(dǎo)黑色素瘤生長到周圍組織的“interface”轉(zhuǎn)錄狀態(tài)。
結(jié)果確定了 ETS 家族轉(zhuǎn)錄因子在介導(dǎo)interface纖毛基因表達(dá)中的作用宋舷。近年來绪撵，纖毛與黑色素瘤生物學(xué)的多個(gè)方面有關(guān)，但它們?cè)诤谏亓鲞M(jìn)展中的作用仍不清楚祝蝠。大部分黑色素瘤沒有纖毛音诈，事實(shí)上，“ciliation index”作為區(qū)分黑色素瘤和良性痣的診斷工具越來越受到重視绎狭。此外细溅，纖毛分解最近與黑色素瘤轉(zhuǎn)移有關(guān)，其中由 EZH2 調(diào)節(jié)的纖毛解構(gòu)驅(qū)動(dòng)轉(zhuǎn)移儡嘶。矛盾的是喇聊，雖然大多數(shù)黑色素瘤細(xì)胞沒有纖毛，但許多黑色素瘤仍然表達(dá)纖毛基因蹦狂。數(shù)據(jù)為這種復(fù)雜性增加了一層誓篱，因?yàn)槲覀儼l(fā)現(xiàn)纖毛基因不僅在腫瘤和微環(huán)境之間的interface上特異性上調(diào)，而且更重要的是凯楔，只有該interface的細(xì)胞表達(dá)高水平的纖毛蛋白窜骄。這提出了一個(gè)仍未完全回答的問題，即纖毛在黑色素瘤進(jìn)展的各個(gè)步驟中所起的作用摆屯。在原發(fā)性黑色素瘤生長中邻遏，很明顯大多數(shù)細(xì)胞是無纖毛的，并且 EZH2 會(huì)抑制這些基因虐骑。在這些模型中准验，通過 EZH2 丟失纖毛會(huì)通過增強(qiáng)的 Wnt/β-catenin 信號(hào)傳導(dǎo)增加轉(zhuǎn)移。關(guān)于大多數(shù)黑色素瘤細(xì)胞沒有纖毛的發(fā)現(xiàn)與這一發(fā)現(xiàn)一致廷没，但在interface處發(fā)現(xiàn)了一個(gè)特定的細(xì)胞亞群糊饱，這些細(xì)胞上調(diào)纖毛基因和蛋白質(zhì)，這些細(xì)胞似乎也存在于人類黑色素瘤中颠黎。
如何協(xié)調(diào)這些看似矛盾的數(shù)據(jù)另锋？分析的數(shù)據(jù)表明，當(dāng)它們第一次在鄰近環(huán)境中遇到新的異型細(xì)胞類型時(shí)盏缤，完整的纖毛可能是最重要的砰蠢。這里可以設(shè)想幾種不同的可能性來解釋為什么纖毛在這個(gè)interface上特別上調(diào)蓖扑。首先唉铜，interface處纖毛基因和蛋白質(zhì)的這種上調(diào)可能是暫時(shí)的，由腫瘤和肌肉之間的異型細(xì)胞 - 細(xì)胞相互作用誘導(dǎo)律杠。初級(jí)纖毛是細(xì)胞的關(guān)鍵信號(hào)傳導(dǎo)樞紐潭流，調(diào)節(jié)信號(hào)通路竞惋，如 Hedgehog 和 TGF-β/BMP，所有這些在癌癥進(jìn)展和細(xì)胞間通訊中都很重要灰嫉。NicheNet 分析表明拆宛，可能存在不同的配體/受體對(duì)，包括 HMG 家族蛋白讼撒，可能會(huì)介導(dǎo)此類信號(hào)浑厚。第二種可能性是初級(jí)纖毛充當(dāng)機(jī)械傳感器，并在細(xì)胞侵入新組織時(shí)在細(xì)胞定向遷移中發(fā)揮作用根盒。例如钳幅，對(duì)初級(jí)纖毛的開創(chuàng)性工作表明纖毛可以在 3T3 細(xì)胞中的遷移方向上定向，這也在傷口愈合的背景下被看到炎滞。最后敢艰，interface處纖毛的出現(xiàn)可能實(shí)際上是全身轉(zhuǎn)移性傳播的障礙，黑色素瘤和肌肉之間的異型相互作用可能會(huì)抑制進(jìn)展册赛。值得注意的是钠导，斑馬魚黑色素瘤的轉(zhuǎn)移率很低，實(shí)際上骨骼忌瘛（我們最容易看到interface的地方）是人類罕見的轉(zhuǎn)移部位牡属，這與這種可能性是一致的。未來研究的一個(gè)主要努力將是描繪纖毛在腫瘤發(fā)生的每個(gè)步驟中的作用柜砾、哪些信號(hào)節(jié)點(diǎn)是最關(guān)鍵的湃望，以及它們是否作為轉(zhuǎn)移的障礙或促成因素。另一個(gè)開放且相關(guān)的問題是哪些微環(huán)境細(xì)胞類型（肌肉除外）觸發(fā)腫瘤-微環(huán)境interface的纖毛化痰驱。snRNA-seq 數(shù)據(jù)和對(duì)人類患者數(shù)據(jù)的分析表明证芭，該interface不僅限于腫瘤和肌肉，而且其他細(xì)胞類型也可能被重新編程以采用這種細(xì)胞狀態(tài)担映，例如免疫細(xì)胞或肝細(xì)胞废士。最近的工作表明，在黑色素瘤中蝇完，腫瘤細(xì)胞可以在遠(yuǎn)處重新編程微環(huán)境細(xì)胞官硝，如肝細(xì)胞。目前尚不清楚是否需要腫瘤/微環(huán)境細(xì)胞之間的直接物理接觸來誘導(dǎo)類似interface的細(xì)胞狀態(tài)短蜕，或者更遠(yuǎn)距離的信號(hào)機(jī)制是否也可能在起作用氢架，但決定了這些腫瘤-微環(huán)境相互作用的性質(zhì)（代謝或表觀遺傳）是未來機(jī)制研究的一個(gè)令人興奮的領(lǐng)域。
分析的結(jié)果揭示了 ETS 家族轉(zhuǎn)錄因子在黑色素瘤中的作用朋魔，作為纖毛基因的潛在轉(zhuǎn)錄抑制因子岖研。盡管大多數(shù) ETS TF 可以作為轉(zhuǎn)錄激活因子，但已知至少有四種 ETS TF 具有阻遏活性。盡管 ETS TFs 在幾種類型的實(shí)體瘤中具有明確的作用孙援，但它們?cè)诤谏亓鲋械淖饔蒙形吹玫缴钊胙芯亢τ伲M管最近的一項(xiàng)研究發(fā)現(xiàn) ETS TFs 誘導(dǎo)紫外線損傷特征，這與增加的突變負(fù)荷相關(guān)人類黑色素瘤拓售。 ETS TFs 廣泛應(yīng)用于腫瘤發(fā)生的各個(gè)方面窥摄，包括 DNA 損傷、代謝础淤、自我更新和微環(huán)境重塑崭放。然而，大多數(shù)（如果不是全部）這些情況已被發(fā)現(xiàn)是由 ETS 基因的異常上調(diào)引起的鸽凶。相反莹菱，我們發(fā)現(xiàn)了 ETS TF 下調(diào)的作用，特別是在腫瘤接觸周圍組織的地方吱瘩。目前尚不清楚是什么觸發(fā)了這種空間受限區(qū)域中 ETS 基因的這種下調(diào)道伟。盡管它們作為轉(zhuǎn)錄因子，ETS 蛋白也參與廣泛的蛋白質(zhì)-蛋白質(zhì)相互作用使碾，并且它們的活性通過作為信號(hào)級(jí)聯(lián)結(jié)果的磷酸化進(jìn)行調(diào)節(jié)蜜徽。據(jù)報(bào)道，MAPK 信號(hào)可以調(diào)節(jié) ETS票摇，并且 MAPK 通路在黑色素瘤中經(jīng)常被激活拘鞋。目前尚不清楚 MAPK 或其他信號(hào)通路是否在腫瘤和/或微環(huán)境中顯示空間受限的激活模式，但 SRT 技術(shù)的出現(xiàn)將有助于解決這些問題矢门。
雖然這不是本文研究的重點(diǎn)盆色，但SRT 數(shù)據(jù)集也揭示了腫瘤本身內(nèi)空間組織的轉(zhuǎn)錄組異質(zhì)性砚嘴。近年來乔宿，單細(xì)胞轉(zhuǎn)錄組學(xué)方法的出現(xiàn)已經(jīng)在大多數(shù)（如果不是所有）類型的癌癥中確定了相當(dāng)程度的轉(zhuǎn)錄組異質(zhì)性岩喷。腫瘤異質(zhì)性通常隨著腫瘤的進(jìn)展而增加矢腻，并且可能是臨床結(jié)果不佳的預(yù)測(cè)因素，因?yàn)樗徽J(rèn)為是耐藥性的主要因素谜疤。研究不同腫瘤細(xì)胞亞型內(nèi)的根本原因和復(fù)雜的克隆關(guān)系已被證明具有挑戰(zhàn)性在张，原因有很多赠群，其中之一是缺乏關(guān)于這種異質(zhì)性的空間模式的信息叛薯。文中的數(shù)據(jù)集作為使用空間解析轉(zhuǎn)錄組學(xué)識(shí)別空間組織腫瘤異質(zhì)性的原理證明浑吟，并為使用數(shù)據(jù)集或其他數(shù)據(jù)集探索這種空間模式的基礎(chǔ)的未來研究奠定了基礎(chǔ)。
據(jù)目前所知耗溜，文章的研究是第一個(gè)空間分辨的腫瘤與其環(huán)境之間界面的基因表達(dá)圖譜组力。盡管發(fā)現(xiàn)了許多在腫瘤和/或環(huán)境中空間模式化的基因、通路和基因模塊抖拴，但數(shù)據(jù)集中可能還有更多有趣的生物現(xiàn)象尚未確定燎字。最近，深度學(xué)習(xí)方法已應(yīng)用于組織病理學(xué)圖像，以揭示分子改變轩触、突變和預(yù)后的空間分辨預(yù)測(cè)。合乎邏輯的下一步是擴(kuò)展這些方法家夺，將深度學(xué)習(xí)和模式識(shí)別算法與 SRT 數(shù)據(jù)相結(jié)合脱柱，以識(shí)別基因表達(dá)的有趣空間模式，并根據(jù)組織病理學(xué)預(yù)測(cè)轉(zhuǎn)錄組拉馋。最終榨为，轉(zhuǎn)錄組學(xué)、組織病理學(xué)和深度學(xué)習(xí)技術(shù)的整合將使我們能夠擴(kuò)展 SRT 和組織學(xué)數(shù)據(jù)集的實(shí)用性煌茴，并拓寬對(duì)體內(nèi)癌細(xì)胞相互作用的理解随闺。

Method（我們關(guān)注一些關(guān)鍵的方法）

SRA Dimensionality reduction and clustering.

SRT data was processed using R version 3.6.3, Seurat version 3.1.417, Python version 3.6, and MATLAB 2019b. Data was normalized using SCTransform. The three SRT datasets were integrated using the Seurat SCTransform integration workflow, using 3000 integration features and including all common genes between the three datasets. Principal component analysis and UMAP dimensionality reduction were done using default parameters.Initial clustering was done using the FindClusters function implemented in the Seurat R package with the resolution parameter = 0.8. Tissue types of each cluster were inferred and clusters were further refined by plotting clusters onto the associated histology images and identifying marker genes using the Wilcoxon’s Rank Sum test. Expression scores for ETS and cilia gene sets were calculated using the Seurat function AddModuleScores with default parameters. A list of cilia genes was obtained from the SYSCILIA gold standard list33. A list of ETS genes was obtained from ref.（還是用的Seurat進(jìn)行的空間轉(zhuǎn)錄組數(shù)據(jù)的分析）。

Identification of genes enriched in the SRT interface.（SRA）

To identify genes that were enriched at the interface in the SRT data, we first used the Seurat function FindMarkers and the Wilcoxon rank sum test in order to calculate the average log2 fold change for each gene in our dataset within the interface cluster, relative to all other SRT array spots. We then used the same function to calculate the average log2 fold change of each of these genes within the tumor and muscle clusters. To account for the likely admixture of tumor and muscle cells within the interface region, we defined interface-upregulated genes as: genes with a log2 fold change > 0, log2 fold change in the interface > log2 fold change in the tumor, and log2 fold change in the interface > log2 fold change in the muscle. We defined interface-downregulated genes as: genes with a log2 fold change < 0, log2 fold change in the interface < log2 fold change in the tumor, and log2 fold change in the interface < log2 fold change in the muscle. Finally, we filtered the lists of genes upregulated and downregulated in the interface to only include genes with an adjusted p-value of <0.05.（還是找差異的分析方法）蔓腐。

Non-negative matrix factorization (NMF).（重點(diǎn)）

After normalization and integration of SRT data (see “Dimensionality reduction and clustering” section), negative values in the integrated expression matrix were set to zero. NMF was performed with a rank of 11. The optimal number of ranks was estimated using the function nmfEstimateRank based on the first rank for which cophenetic starts decreasing and for which RSS presents an inflection point. Factor scores were first z-scored across factors prior to plotting onto array spots.

Analysis of gene ontology (GO) terms with spatially coherent expression patterns.（這個(gè)空間的富集還是很值得注意的）

Danio rerio 的 GO terms注釋是從 Biomart 下載的矩乐。 對(duì)于每個(gè) GO terms，計(jì)算為該 GO terms注釋的基因的平均表達(dá)回论。 將高度表達(dá)該 GO terms的spot定義為spot散罕，這些基因的表達(dá)水平高于平均值加兩個(gè)標(biāo)準(zhǔn)差（要求這些spot的數(shù)量至少為 5 以進(jìn)行分析）。 然后計(jì)算了這些spot之間的歐幾里得距離傀蓉。 接下來欧漱，計(jì)算了相同數(shù)量的隨機(jī)spot之間的歐幾里德距離，并重復(fù)此計(jì)算 100 次以生成距離的零分布葬燎。 然后误甚，使用 Wilcoxon 秩和檢驗(yàn)將 GO terms spot距離與零分布進(jìn)行比較以計(jì)算 p 值。

Correlation between SRT spots and SRT clusters.(cluster相關(guān)性)

For computing the correlation across SRT clusters, we first computed the average expression of each tissue cluster in the integrated expression matrix of our three datasets. We then used the union of the ~1000 variably expressed genes in each individual dataset to obtain a list of ~2300 total variably expressed genes. We then used these genes to compute the Pearson’s correlation and associated p-values.

GSEA and pathway analysis.

Lists of differentially expressed genes for pathway analysis were created using the Seurat function FindMarkers using the Wilcoxon rank sum test. Ribosomal genes and genes with p-values above 0.05 were removed. Zebrafish genes were converted to their human orthologs using DIOPT, keeping only human orthologs with a DIOPT score >6. In cases where there were multiple zebrafish orthologs for one human gene, the gene with the highest log fold change in expression was used. Pathway analysis and GSEA68 was done using the fgsea R package, using the MSigDB GO biological processes and GO cellular component human genesets.

HOMER motif analysis.

Motif analysis was performed using HOMER（參考文章在Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities）, using the function findMotifs.pl. Motifs of lengths 8, 10, and 16 were queried within +/? 500 bp of the TSS of differentially expressed genes. Target genes containing the motif of interest were found by filtering the list of differentially expressed genes to contain only those with the desired motif. JASPAR73 was used to annotate motifs.

Multimodal intersection analysis (MIA).

To determine cell type enrichment in tissue regions we used MIA, which uses the hypergeometric cumulative distribution to determine the statistical significance of the overlap between cell type specific gene sets and tissue region specific gene sets. We used the intersect between all genes in the SRT count matrix and all genes in the snRNA-seq count matrix as the gene background to calculate the p-value. In parallel, we tested for cell type depletion by computing ?log10(1 ? p).

單細(xì)胞的分析

Data was processed using R version 3.6.3 and Seurat version 3.1.4. Cells with fewer than 200 unique genes or >20% mitochondrial reads were filtered out. Expression data was normalized using SCTransform. Datasets were integrated using the Seurat SCTransform integration workflow, with 3000 integration anchors and including all genes expressed in both datasets (15,154 genes). Principal component analysis, UMAP dimensionality reduction, HOMER analysis, GSEA, and pathway analysis were performed as described above. Cluster annotations were performed using the Seurat function FindAllMarkers, in conjunction with marker genes used in previous analyses. Doublets were detected using the doubletFinder R package, using 15 principal components.

Single-nucleus RNA-seq 分析

Data was processed using R version 3.6.3 and Seurat version 3.1.4.Nuclei with fewer than 200 unique genes, more than 1 million UMIs, predicted doublets and/or >20% mitochondrial reads were filtered out. A putative erythrocyte cluster was also filtered out for quality control reasons, due to the unusual nature of zebrafish erythrocyte nuclei. Expression data was normalized using SCTransform. PCA, UMAP, and HOMER analysis were performed as described above. Potential doublets were detected with doubletFinder and were filtered out before downstream analyses. Cluster annotations were performed using the Seurat function FindAllMarkers, in conjunction with marker genes used in previous analyses. Modeling of ligand–receptor interactions was performed using NicheNet and the nichenetR R package, with the combined interface cluster as the “sender” cell population and all other cells as “receiver”, using a cutoff of 0.1 for determining expressed genes and 0.5 for ligand-target scores. For NicheNet analysis, Zebrafish genes were converted to human as described above, using DIOPT, keeping only human orthologs with a DIOPT score >6. In cases where there were multiple zebrafish orthologs for one human gene, the gene with the highest log fold change in expression was used.

Calculation of an interface gene signature.

Genes significantly upregulated in the interface clusters of the SRT, scRNA-seq, and snRNA-seq datasets were calculated using the Seurat function FindMarkers and the Wilcoxon rank sum test. Ribosomal genes (starting with “rps” or “rpl”) were filtered out. The three genelists were then merged to only include common genes present on all three lists.

Statistical analysis.

Statistical analysis and figure generation were performed in MATLAB and R (R Foundation for Statistical Computing, 3.6.3). Image processing and analysis was performed in MATLAB and ImageJ (NIH). Unless otherwise noted, p-values were calculated using the Wilcoxon ranksum test, two-sided, with Bonferroni’s correction for multiple groups as necessary (R functions wilcox.test and pairwise.wilcox.test). Pearson correlation coefficients and corresponding p-values were calculated using the R function cor.test.

生活很好谱净，有你更好