今天分享一篇發(fā)表于cell的文章，其中很多方法十分的經(jīng)典，希望大家可以借鑒。
summary
Acute myeloid leukemia (AML)（急性粒細(xì)胞白血?。┦且环N異質(zhì)性的疾病， 位于復(fù)雜的微環(huán)境中，使得了解不同細(xì)胞類型如何促進(jìn)疾病進(jìn)展的工作復(fù)雜化。結(jié)合單細(xì)胞轉(zhuǎn)錄組和基因分型技術(shù)------16個(gè)病人和5個(gè)正?；颊?，然后，我們應(yīng)用機(jī)器學(xué)習(xí)分類器來區(qū)分惡性細(xì)胞類型的頻譜，這些惡性細(xì)胞類型的豐度在患者之間以及同一腫瘤的亞克隆之間變化。細(xì)胞類型組成與原型遺傳性損傷相關(guān)，包括FLT3-ITD與大量祖細(xì)胞樣細(xì)胞相關(guān)。原始AML細(xì)胞表現(xiàn)出轉(zhuǎn)錄程序失調(diào)，with co-expression of stemness and
myeloid priming genes and had prognostic significance。
介紹
1、AML是一種惡性腫瘤（其特征在于髓系譜系的未成熟細(xì)胞的積累），5年內(nèi)復(fù)發(fā)率達(dá)到75%，對(duì)于AML的復(fù)發(fā)與遺傳性的抗體克隆成果有限，所以對(duì)功能異質(zhì)性的非遺傳驅(qū)動(dòng)因素進(jìn)入了研究的視野。
2、Normal hematopoietic stem cells（HSCs）形成成熟的細(xì)胞類型（髓系，淋系，erythroid/megakaryocyte lineages）。AML也包括原始和分化的細(xì)胞，原始的細(xì)胞（白血病干細(xì)胞）LSCs，sustain the disease和干細(xì)胞屬性（自我更新，靜息，和therapy resistance），分化的AML細(xì)胞缺少了自我更新的能力，但是可以通過病理的特征來影響腫瘤微環(huán)境或造血功能。
3、 AML受正常細(xì)胞的影響，免疫系統(tǒng)限制腫瘤的擴(kuò)展直到免疫逃逸或者抑制宿主免疫系統(tǒng)亞群的出現(xiàn)。AML的內(nèi)在特性，包括免疫調(diào)節(jié)因子的表達(dá)，外在微環(huán)境的改變可以導(dǎo)致加強(qiáng)對(duì)T-reg和CTL細(xì)胞活性的抑制。增強(qiáng)T細(xì)胞介導(dǎo)的AML細(xì)胞清除率是一種有吸引力的治療策略，但免疫治療試驗(yàn)的成功率不及其他癌癥。這突出顯示了更好地了解AML微環(huán)境中免疫抑制基礎(chǔ)的細(xì)胞成分和機(jī)制的關(guān)鍵需求。
4、在AML種，單細(xì)胞轉(zhuǎn)錄組技術(shù)可以潛在的解決stemness，發(fā)展層次，惡性細(xì)胞和免疫細(xì)胞的相互聯(lián)系，然而，AML面臨著與其復(fù)雜的分化層次以及微環(huán)境中惡性細(xì)胞和正常細(xì)胞之間的相似性相關(guān)的獨(dú)特挑戰(zhàn)。為了全面分析AML異質(zhì)性，我們必須通過對(duì)基因數(shù)據(jù)進(jìn)行基因分型以區(qū)分惡性腫瘤與正常細(xì)胞來補(bǔ)充數(shù)千個(gè)細(xì)胞的轉(zhuǎn)錄數(shù)據(jù)。捕獲全長(zhǎng)轉(zhuǎn)錄本的基于標(biāo)準(zhǔn)板的scRNA-seq方法缺乏足夠的通量。最近的droplet- and nanowell-based methods提供了更高的通量，但是但所得的測(cè)序數(shù)據(jù)偏向3‘轉(zhuǎn)錄本末端，無法有效檢測(cè)惡性細(xì)胞特異的突變，這些考慮因素強(qiáng)調(diào)需要結(jié)合使用單細(xì)胞轉(zhuǎn)錄和基因譜分析方法來表征AML環(huán)境。
5、運(yùn)用基于納米孔的技術(shù)，以獲取來自骨髓（BM）吸出物的數(shù)千個(gè)單細(xì)胞的轉(zhuǎn)錄和突變數(shù)據(jù)，我們通過scRNA-seq對(duì)來自16名AML患者的30,712個(gè)細(xì)胞和來自五個(gè)健康供體的7,698個(gè)細(xì)胞進(jìn)行了分析，并獲得了3,799個(gè)細(xì)胞的基因分型信息。 我們還結(jié)合了長(zhǎng)期讀取的納米孔測(cè)序技術(shù)來進(jìn)行phase mutations，檢測(cè)插入和融合以及區(qū)分亞克隆。我們將這些數(shù)據(jù)整合到了機(jī)器學(xué)習(xí)分類器中，該分類器將惡性腫瘤與正常細(xì)胞區(qū)分開來，并確定了六種沿HSC投射至髓樣分化軸的惡性AML細(xì)胞類型。我們使用此資源將發(fā)展層次結(jié)構(gòu)與基因型相關(guān)聯(lián)，以評(píng)估原始AML細(xì)胞的特性和預(yù)后意義，并鑒定具有免疫調(diào)節(jié)特性的分化AML細(xì)胞。
每篇文章的前沿是信息量最大的，也是最難讀的（專業(yè)詞匯太多），但是會(huì)對(duì)作者的研究有了一個(gè)背景的初步了解，所以讀文獻(xiàn)，靜下心來最重要
主要結(jié)果
（1）Identification of Cell Populations in Healthy BM Samples
運(yùn)用scRNA（nanowell-based protocol, termed Seq-Well）技術(shù)來表征正常BM（骨髓）的細(xì)胞多樣性，
然后做細(xì)胞定義（基于marker），All 15 cell types were identified in at least three donors（這里的細(xì)胞比例需要我們注意）。
Next, we explored the relationships between these cell types by visualizing K-nearest-neighbor (KNN) graphs that connected all single cells in our dataset to their five nearest neighbors in gene expression space
這揭示了假定的分化軌跡，Thus, scRNA-seq of normal BM reveals diverse hematopoietic cell types and implies differentiation trajectories consistent with current views of hematopoiesis.
值得注意的地方
1、作者無監(jiān)督聚類采用的R包的是BackSPIN，不同于Seurat，感興趣的可以查閱一下。
2、這里的多樣本整合的矯正問題，這個(gè)問題我們?cè)诤竺娴姆椒ú糠诌M(jìn)行討論。
3、KNN算法，臨近點(diǎn)算法，把相近的細(xì)胞放在一起（擬時(shí)分析的原理也是這樣）。
（2）Single-Cell Profiling of AML Tumor Ecosystems
16個(gè)病人的骨髓提取物，靶基因測(cè)序驗(yàn)證了基因組上的突變結(jié)果（符合預(yù)期）
對(duì)每一個(gè)病人的細(xì)胞樣本進(jìn)行tSNE展示，展示了不同的細(xì)胞類型在不同的臨床階段比例發(fā)生了很大的變化。
除了惡性細(xì)胞外，這些數(shù)據(jù)還揭示了腫瘤生態(tài)系統(tǒng)中表達(dá)譜系特異性基因的正常造血細(xì)胞類型，例如血紅蛋白（類紅細(xì)胞）和CD3（T細(xì)胞）。誘導(dǎo)化療后收集的樣本中主要是T /自然殺傷（NK）細(xì)胞，這與AML原始細(xì)胞的清除和組織學(xué)染色顯示淋巴細(xì)胞頻繁一致。盡管其他細(xì)胞群體也表達(dá)與特定造血細(xì)胞類型相關(guān)的標(biāo)志物，但它們的正常或惡性身份無法從其表達(dá)程序中事先區(qū)分出來。因而需要額外的方法來識(shí)別惡性AML細(xì)胞
值得注意的地方
1、作者在統(tǒng)計(jì)細(xì)胞比例的過程中是單個(gè)樣本進(jìn)行聚類，細(xì)胞定義后進(jìn)行比例的統(tǒng)計(jì)，而不是通常我們采用的多樣本整合統(tǒng)計(jì)
3、Single-Cell Genotyping by Short-Read and Nanopore Sequencing
（短讀和納米孔測(cè)序的單細(xì)胞基因分型）
之前的腫瘤的單細(xì)胞數(shù)據(jù)已經(jīng)檢測(cè)了基因的突變（轉(zhuǎn)錄組全長(zhǎng)）CNVs來識(shí)別惡性細(xì)胞，而3‘端高通常的測(cè)序方法，限制了突變的檢測(cè)，而且，AML缺少CNVs信息，因此，我們采用了Seq-Well來擴(kuò)增和測(cè)序包含AML突變的轉(zhuǎn)錄本部分，
We took advantage of an intermediate whole-transcriptome amplification (WTA) step that yields full-length cDNAs with cell barcodes (CBs) appended to their 30 ends.我們?cè)O(shè)計(jì)了43個(gè)引物，與通過目標(biāo)DNA測(cè)序在我們的隊(duì)列中檢測(cè)到的所有突變相鄰，并生成了包含附在CB上的突變位點(diǎn)的擴(kuò)增子。這些產(chǎn)品的測(cè)序使我們能夠?qū)⑼蛔儬顟B(tài)疊加到我們的scRNA-seq數(shù)據(jù)上。我們對(duì)35個(gè)AML樣本中的每一個(gè)樣本都應(yīng)用了特定于突變的單細(xì)胞基因分型。We successfully detected wild-type and/or mutant transcripts at 27 of the 43 targeted sites。我們?cè)?6例患者中的14例中檢測(cè)到轉(zhuǎn)錄本，平均355份轉(zhuǎn)錄本映射到每位患者258個(gè)細(xì)胞。Mutations near 30 transcript ends of highly expressed genes were more efficiently detected。
Application of the method across our patient cohort identified 3,745 wild-type and 1,230 mutant transcripts。Mutations were not detected in healthy donor BM samples and were markedly decreased in AML patients in clinical remission。此外，我們檢測(cè)到的突變頻率與通過靶向DNA測(cè)序獲得的變異等位基因頻率（VAF）密切相關(guān)。
全長(zhǎng)轉(zhuǎn)錄組測(cè)序We reasoned that the long reads provided by this platform could enhance detection of mutations across transcripts and reveal long insertions, deletions, and fusion breakpoints（融合斷點(diǎn)）。擴(kuò)增了代表性的致癌基因，腫瘤抑制物，以及來自三名AML患者的CB融合，并使用Oxford Nanopore Technologies MinION對(duì)擴(kuò)增子進(jìn)行測(cè)序，納米孔數(shù)據(jù)補(bǔ)充了illumina data，檢測(cè)突變的能力有了很大的提升
TP53等位基因的分階段顯示，每個(gè)突變均影響不同的轉(zhuǎn)錄本，與該抑癌基因的雙等位基因失活相一致。Second, in the FLT3 mutant tumor AML328, long reads revealed a 60-bp FLT3 internal tandem duplication (ITD) that was missed by short-read sequencing。Finally, in the RUNX1 fusion tumor AML707B, long reads enabled detection of RUNX1-RUNX1T1 fusion transcripts in 32 cells and revealed the exact sequence of the junction 。
In conclusion, we present methods for amplifying barcoded transcripts of genes that are frequently mutated in AML. Shortread and nanopore sequencing enabled detection and phasing of point mutations, insertions, deletions, and fusions, thereby
genotyping individual cells from AML aspirates。
突出了三代全長(zhǎng)的優(yōu)勢(shì)
（4）Machine Learning Classifier Distinguishes Malignant from Normal Cells
一、First, we selected all AML cells for which single-cell genotyping detected mutations in the assessed genes。
二、used the random forest machine learning algorithm to classify these putatively malignant cells according to their similarity to all 15 normal BM cell types。
The vast majority of cells with mutations resembled one of six normal cell types along the HSC to myeloid axis。
These malignant cell types were then incorporated as additional classes in a second classifier that was used to annotate all AML cells in our dataset as malignant or normal。
總體上，我們檢測(cè)到13489例惡性AML細(xì)胞。對(duì)于任何給定的腫瘤，分類為惡性的單個(gè)細(xì)胞的比例與臨床blast計(jì)數(shù)一致。這些數(shù)據(jù)共同證明了我們區(qū)分AML腫瘤中正常細(xì)胞和惡性腫瘤的方法的準(zhǔn)確性。
機(jī)器學(xué)習(xí)，分類器，隨機(jī)森林
（5）Intra-Tumoral Heterogeneity of Malignant AML Cells
腫瘤內(nèi)異質(zhì)性已使用細(xì)胞表面標(biāo)記物進(jìn)行了廣泛研究，However, this approach relies on predefined markers that may not accurately represent underlying transcriptional programs and may be expressed by both malignant and normal cells，
惡性腫瘤細(xì)胞類型在不同病人的分布是不一樣的。
The cell-type abundances estimated by our classifier corresponded closely to clinical parameters determined by cell morphology and surface phenotypes。Thus, scRNA-seq data are consistent with clinical parameters, but provide more detailed information on AML cell types and differentiation states。
單細(xì)胞數(shù)據(jù)與臨床數(shù)據(jù)的吻合和擴(kuò)展
（6）AML419A包含具有不同細(xì)胞類型成分的亞克隆
接下來，我們考慮了惡性細(xì)胞類型豐度變異的根本原因，AML419A contained two malignant cell types at opposite ends of the developmental axis，Genotyping of AML419A revealed three activating FLT3 mutations: FLT3-ITD, FLT3-A680V and
FLT3-N841K。納米孔測(cè)序讀數(shù)的分析使我們能夠?qū)⒚總€(gè)突變分配給不同的等位基因，而第四個(gè)等位基因是野生型
the FLT3-N841K mutation never co-occurred with other mutant alleles in the same cell
Integration of these data with VAFs from bulk DNA sequencing enabled us to infer a putative phylogeny of AML419A: that it evolved one subclone ‘‘A’’ with a FLT3-A680V mutation, a second subclone ‘‘B’’ with an additional FLT3-ITD mutation on the opposite allele, and an independent third subclone ‘‘C’’ with a FLT3-N841K mutation only。
由于這些突變通過不同的機(jī)制賦予FLT3功能增強(qiáng)，
A majority of cells in subclones A/B expressed signature genes associated with progenitor-like cells . In contrast, nearly all subclone C cells expressed genes associated with differentiated monocyte-like or cDC-like cells.這些結(jié)果表明，替代的FLT3基因型可以深刻影響單個(gè)腫瘤內(nèi)AML亞克隆的細(xì)胞層次.
基因上的突變對(duì)細(xì)胞進(jìn)行分類，不同的角度看待細(xì)胞類型
（7）AML細(xì)胞層次結(jié)構(gòu)與基礎(chǔ)遺傳變異相關(guān)
we used the scRNA-seq data to derive gene signatures for each of the six malignant cell types，設(shè)計(jì)這些特征以平均權(quán)衡每種惡性細(xì)胞類型，并排除在AML細(xì)胞中普遍存在的正常細(xì)胞類型中表達(dá)的基因，從而將我們的方法與以前的研究（通過可變基因或分類人群的特征將AML分層）區(qū)別開來。We used our signatures to score bulk expression profiles of 179 diagnostic AML aspirates from the Cancer Genome Atlas (TCGA) and thereby infer their cell-type compositions.
Hierarchical clustering of the TCGA AMLs by these signatures revealed seven clusters of tumors with distinct malignant celltype compositions。
These inferences indicate marked variability in cell-type compositions and developmental hierarchies。
接下來，我們檢查了這些推斷的層次結(jié)構(gòu)與基礎(chǔ)基因型之間的關(guān)系。值得注意的是，僅源自細(xì)胞類型豐度的cluster與AML的遺傳學(xué)密切相關(guān)，
Taken together, our analyses reveal striking variability in the abundances of malignant cell types across AMLs and suggest a prominent role for genotype in determining the cell-type composition and hierarchy of a given tumor.
腫瘤特征基因的層次聚類，基因改變對(duì)于層次的影響
（8）Differential Effects of FLT3 Genotypes on AML Differentiation
The remaining two TCGA clusters (D and E) both contained NPM1 mutant tumors, but markedly differed in their cell-type compositions。
our findings point to additional effects on cell differentiation that may help explain why FLT3-ITD AMLs have worse outcomes than FLT3-TKDmutant tumors
（9）原始AML細(xì)胞中轉(zhuǎn)錄程序的失調(diào)
Next, we turned our focus to primitive AML cell types, which fuel tumor growth。We found that primitive AML cells upregulate genes involved in stress response and redox signaling (XBP1, GPX1), proliferation (FLT3, PIM1, MYC), and self-renewal
(HOXA9, BMI1), relative to their normal counterparts。我們還評(píng)估了優(yōu)先表達(dá)的表面標(biāo)記，因?yàn)樗鼈優(yōu)榘邢蛑委熖峁┝藱C(jī)會(huì)。
為了進(jìn)一步聯(lián)系原始惡性和正常細(xì)胞的分化狀態(tài)，我們生成了代表正常造血發(fā)育連續(xù)階段的三個(gè)基因標(biāo)記：HSC / Prog(including MEIS1, NRIP1, MSI2), GMP (including
MPO, ELANE, AZU1), and differentiated myeloid (including LYZ,MNDA, CD14)。As expected, application of these signatures to single cells from normal BMs clearly distinguished major cellular subsets of HSC/Prog, GMP, and differentiated myeloid cells。
However, a distinct pattern emerged when we applied these signatures to malignant
AML cells，HSC/Prog signature genes and GMP signature genes were frequently co-expressed in the same malignant cells, markedly contrasting with their exclusivity in normal hematopoiesis.We found that patients with higherHSC/Prog-like signals, whose tumors presumably contain more primitive LSCs, had significantly worse outcomes。
這部分結(jié)果跟臨床結(jié)合緊密，需要惡補(bǔ)
（10）T Cell Signatures Are Suppressed in AML Patients
從干細(xì)胞移植后移植物抗白血病產(chǎn)生持久治愈的能力可以證明，T細(xì)胞原則上可以消除AML細(xì)胞，但在AML中可能會(huì)受到損害，In normal BM, we identified two T cell subsets,naive T cells (IL7R, CCR7) and CTLs (CD8A, GZMK), and a related population of NK cells (NCAM1/CD56, KLRD1)，We recovered the same three populations when we performed unsupervised clustering of all T- and NK cells from tumor and normal samples
監(jiān)督分析還鑒定了表達(dá)T-reg標(biāo)記的細(xì)胞的一部分，但其數(shù)量有限，無法進(jìn)行進(jìn)一步分析。AML aspirates tended to have proportionally fewer T cells and
CTLs than normal controls
we used immunohistochemistry (IHC) to quantify CD3+ T cells, CD8+ CTLs, and CD25+FOXP3+ T-regs in an additional cohort of 15 diagnostic AMLs and 15 normal BMs，We again found that AMLs contained significantly fewer T cells and CTLs and had a reduced CTL:T cell ratio。
Conversely, the tumors had relatively greater numbers of T-regs, consistent with prior reports that this suppressive subset is increased in AML。因此，scRNA-seq和IHC顯示出T細(xì)胞數(shù)量和組成的一致變化，表明存在免疫抑制性腫瘤環(huán)境。
（11）分化的AML細(xì)胞體外抑制T細(xì)胞活化
method
值得注意的地方
1、BackSPIN clustering
For clustering, we first determined the most variably expressed genes in the dataset. We performed a linear fit of the log-transformed average expression values and the log-transformed coefficients of variation (standard deviation divided by the average expression). Variably expressed genes were determined as genes associated with a residual larger than two times the standard deviation of all residuals. From these genes we excluded a set of genes that were associated with cell cycle (ASPM, CENPE, CENPF,DLGAP5, MKI67, NUSAP1, PCLAF, STMN1, TOP2A, TUBB). This yielded on the order of 1,000 to 2,000 variably expressed genes depending on the set of cells. Expression values were log-transformed (after addition of 1) before performing BackSPIN clustering.We used default settings and a maximum splitting depth of 5. In the healthy BM data this yielded a final set of 31 clusters。
In a first post-processing step we calculated the average expression level of each gene for each cluster. If gene expression of a single cell correlated higher to the average gene expression of another cluster than the cluster it was part of, we reassigned the cell to the cluster it was most highly correlated to. For the healthy BM data, we merged clusters if their average gene expression profiles were highly correlated and if they were characterized by similar cell type-specific marker genes. This yielded 15 cell types across the undifferentiated compartment and the three main lineages。
We independently clustered normal BM cells using SC3, a different clustering algorithm that is also designed for single cell analysis. We used a two-step strategy that first separates the main lineages (Undifferentiated, Myeloid, Erythroid, and Lymphoid), and then clustered again within each lineage. The results were concordant with our BackSPIN clustering results (data not shown). We conclude that the BackSPIN algorithm is an appropriate choice for clustering cell types in our scRNA-seq data
2、KNN and t-SNE visualization
3、Generation of the Random forest classifier
For our analysis we used the randomForest R package version 4.6-14.
[randomForest]https://cran.r-project.org/web/packages/randomForest/index.html
