今天分享一篇發(fā)表于cell的文章书蚪，其中很多方法十分的經(jīng)典喇澡，希望大家可以借鑒。
summary
Acute myeloid leukemia (AML)（急性粒細胞白血彩庑！）是一種異質(zhì)性的疾病晴玖， 位于復雜的微環(huán)境中，使得了解不同細胞類型如何促進疾病進展的工作復雜化为流。結(jié)合單細胞轉(zhuǎn)錄組和基因分型技術------16個病人和5個正撑皇海患者，然后敬察，我們應用機器學習分類器來區(qū)分惡性細胞類型的頻譜秀睛，這些惡性細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。細胞類型組成與原型遺傳性損傷相關莲祸，包括FLT3-ITD與大量祖細胞樣細胞相關蹂安。原始AML細胞表現(xiàn)出轉(zhuǎn)錄程序失調(diào)，with co-expression of stemness and
myeloid priming genes and had prognostic significance锐帜。
介紹
1田盈、AML是一種惡性腫瘤（其特征在于髓系譜系的未成熟細胞的積累），5年內(nèi)復發(fā)率達到75%缴阎，對于AML的復發(fā)與遺傳性的抗體克隆成果有限允瞧，所以對功能異質(zhì)性的非遺傳驅(qū)動因素進入了研究的視野。
2蛮拔、Normal hematopoietic stem cells（HSCs）形成成熟的細胞類型（髓系述暂，淋系，erythroid/megakaryocyte lineages）语泽。AML也包括原始和分化的細胞贸典，原始的細胞（白血病干細胞）LSCs，sustain the disease和干細胞屬性（自我更新踱卵，靜息廊驼，和therapy resistance）据过，分化的AML細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)境或造血功能妒挎。
3绳锅、 AML受正常細胞的影響，免疫系統(tǒng)限制腫瘤的擴展直到免疫逃逸或者抑制宿主免疫系統(tǒng)亞群的出現(xiàn)酝掩。AML的內(nèi)在特性鳞芙，包括免疫調(diào)節(jié)因子的表達，外在微環(huán)境的改變可以導致加強對T-reg和CTL細胞活性的抑制期虾。增強T細胞介導的AML細胞清除率是一種有吸引力的治療策略原朝，但免疫治療試驗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)境中免疫抑制基礎的細胞成分和機制的關鍵需求镶苞。
4喳坠、在AML種，單細胞轉(zhuǎn)錄組技術可以潛在的解決stemness茂蚓，發(fā)展層次壕鹉，惡性細胞和免疫細胞的相互聯(lián)系，然而聋涨，AML面臨著與其復雜的分化層次以及微環(huán)境中惡性細胞和正常細胞之間的相似性相關的獨特挑戰(zhàn)晾浴。為了全面分析AML異質(zhì)性，我們必須通過對基因數(shù)據(jù)進行基因分型以區(qū)分惡性腫瘤與正常細胞來補充數(shù)千個細胞的轉(zhuǎn)錄數(shù)據(jù)牍白。捕獲全長轉(zhuǎn)錄本的基于標準板的scRNA-seq方法缺乏足夠的通量脊凰。最近的droplet- and nanowell-based methods提供了更高的通量，但是但所得的測序數(shù)據(jù)偏向3‘轉(zhuǎn)錄本末端茂腥，無法有效檢測惡性細胞特異的突變笙各，這些考慮因素強調(diào)需要結(jié)合使用單細胞轉(zhuǎn)錄和基因譜分析方法來表征AML環(huán)境。
5础芍、運用基于納米孔的技術，以獲取來自骨髓（BM）吸出物的數(shù)千個單細胞的轉(zhuǎn)錄和突變數(shù)據(jù)数尿，我們通過scRNA-seq對來自16名AML患者的30,712個細胞和來自五個健康供體的7,698個細胞進行了分析仑性，并獲得了3,799個細胞的基因分型信息。 我們還結(jié)合了長期讀取的納米孔測序技術來進行phase mutations右蹦，檢測插入和融合以及區(qū)分亞克隆诊杆。我們將這些數(shù)據(jù)整合到了機器學習分類器中，該分類器將惡性腫瘤與正常細胞區(qū)分開來何陆，并確定了六種沿HSC投射至髓樣分化軸的惡性AML細胞類型晨汹。我們使用此資源將發(fā)展層次結(jié)構與基因型相關聯(lián)，以評估原始AML細胞的特性和預后意義贷盲，并鑒定具有免疫調(diào)節(jié)特性的分化AML細胞淘这。
每篇文章的前沿是信息量最大的剥扣，也是最難讀的（專業(yè)詞匯太多），但是會對作者的研究有了一個背景的初步了解铝穷，所以讀文獻钠怯，靜下心來最重要
主要結(jié)果
（1）Identification of Cell Populations in Healthy BM Samples
運用scRNA（nanowell-based protocol, termed Seq-Well）技術來表征正常BM（骨髓）的細胞多樣性，
然后做細胞定義（基于marker）曙聂，All 15 cell types were identified in at least three donors（這里的細胞比例需要我們注意）晦炊。
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宁脊、作者無監(jiān)督聚類采用的R包的是BackSPIN断国，不同于Seurat，感興趣的可以查閱一下榆苞。
2稳衬、這里的多樣本整合的矯正問題，這個問題我們在后面的方法部分進行討論语稠。
3宋彼、KNN算法，臨近點算法仙畦，把相近的細胞放在一起（擬時分析的原理也是這樣）输涕。
（2）Single-Cell Profiling of AML Tumor Ecosystems
16個病人的骨髓提取物，靶基因測序驗證了基因組上的突變結(jié)果（符合預期）
對每一個病人的細胞樣本進行tSNE展示慨畸，展示了不同的細胞類型在不同的臨床階段比例發(fā)生了很大的變化莱坎。
除了惡性細胞外，這些數(shù)據(jù)還揭示了腫瘤生態(tài)系統(tǒng)中表達譜系特異性基因的正常造血細胞類型寸士，例如血紅蛋白（類紅細胞）和CD3（T細胞）檐什。誘導化療后收集的樣本中主要是T /自然殺傷（NK）細胞，這與AML原始細胞的清除和組織學染色顯示淋巴細胞頻繁一致弱卡。盡管其他細胞群體也表達與特定造血細胞類型相關的標志物乃正，但它們的正常或惡性身份無法從其表達程序中事先區(qū)分出來婶博。因而需要額外的方法來識別惡性AML細胞
值得注意的地方
1瓮具、作者在統(tǒng)計細胞比例的過程中是單個樣本進行聚類，細胞定義后進行比例的統(tǒng)計凡人，而不是通常我們采用的多樣本整合統(tǒng)計
3名党、Single-Cell Genotyping by Short-Read and Nanopore Sequencing
（短讀和納米孔測序的單細胞基因分型）
之前的腫瘤的單細胞數(shù)據(jù)已經(jīng)檢測了基因的突變（轉(zhuǎn)錄組全長）CNVs來識別惡性細胞，而3‘端高通常的測序方法挠轴，限制了突變的檢測传睹，而且，AML缺少CNVs信息岸晦，因此欧啤，我們采用了Seq-Well來擴增和測序包含AML突變的轉(zhuǎn)錄本部分睛藻，
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.我們設計了43個引物，與通過目標DNA測序在我們的隊列中檢測到的所有突變相鄰堂油，并生成了包含附在CB上的突變位點的擴增子修档。這些產(chǎn)品的測序使我們能夠?qū)⑼蛔儬顟B(tài)疊加到我們的scRNA-seq數(shù)據(jù)上。我們對35個AML樣本中的每一個樣本都應用了特定于突變的單細胞基因分型府框。We successfully detected wild-type and/or mutant transcripts at 27 of the 43 targeted sites吱窝。我們在16例患者中的14例中檢測到轉(zhuǎn)錄本，平均355份轉(zhuǎn)錄本映射到每位患者258個細胞迫靖。Mutations near 30 transcript ends of highly expressed genes were more efficiently detected院峡。
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系宜。此外照激，我們檢測到的突變頻率與通過靶向DNA測序獲得的變異等位基因頻率（VAF）密切相關。
全長轉(zhuǎn)錄組測序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）盹牧。擴增了代表性的致癌基因俩垃，腫瘤抑制物，以及來自三名AML患者的CB融合汰寓，并使用Oxford Nanopore Technologies MinION對擴增子進行測序口柳，納米孔數(shù)據(jù)補充了illumina data，檢測突變的能力有了很大的提升
TP53等位基因的分階段顯示有滑，每個突變均影響不同的轉(zhuǎn)錄本跃闹，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毛好。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 望艺。
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。
突出了三代全長的優(yōu)勢
（4）Machine Learning Classifier Distinguishes Malignant from Normal Cells
一肌访、First, we selected all AML cells for which single-cell genotyping detected mutations in the assessed genes找默。
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吼驶。
The vast majority of cells with mutations resembled one of six normal cell types along the HSC to myeloid axis啡莉。
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。
總體上旨剥，我們檢測到13489例惡性AML細胞。對于任何給定的腫瘤浅缸，分類為惡性的單個細胞的比例與臨床blast計數(shù)一致轨帜。這些數(shù)據(jù)共同證明了我們區(qū)分AML腫瘤中正常細胞和惡性腫瘤的方法的準確性。
機器學習衩椒，分類器蚌父，隨機森林
（5）Intra-Tumoral Heterogeneity of Malignant AML Cells
腫瘤內(nèi)異質(zhì)性已使用細胞表面標記物進行了廣泛研究茎辐，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褪猛，
惡性腫瘤細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冬三。
單細胞數(shù)據(jù)與臨床數(shù)據(jù)的吻合和擴展
（6）AML419A包含具有不同細胞類型成分的亞克隆
接下來，我們考慮了惡性細胞類型豐度變異的根本原因喘落，AML419A contained two malignant cell types at opposite ends of the developmental axis懈叹，Genotyping of AML419A revealed three activating FLT3 mutations: FLT3-ITD, FLT3-A680V and
FLT3-N841K。納米孔測序讀數(shù)的分析使我們能夠?qū)⒚總€突變分配給不同的等位基因缤削，而第四個等位基因是野生型
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窘哈。
由于這些突變通過不同的機制賦予FLT3功能增強，
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)果表明滚婉，替代的FLT3基因型可以深刻影響單個腫瘤內(nèi)AML亞克隆的細胞層次.
基因上的突變對細胞進行分類，不同的角度看待細胞類型
（7）AML細胞層次結(jié)構與基礎遺傳變異相關
we used the scRNA-seq data to derive gene signatures for each of the six malignant cell types帅刀，設計這些特征以平均權衡每種惡性細胞類型让腹，并排除在AML細胞中普遍存在的正常細胞類型中表達的基因，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)別開來扣溺。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骇窍。
These inferences indicate marked variability in cell-type compositions and developmental hierarchies。
接下來娇妓，我們檢查了這些推斷的層次結(jié)構與基礎基因型之間的關系像鸡。值得注意的是，僅源自細胞類型豐度的cluster與AML的遺傳學密切相關哈恰，
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只估，基因改變對于層次的影響
（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着绷。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蛔钙。我們還評估了優(yōu)先表達的表面標記，因為它們?yōu)榘邢蛑委熖峁┝藱C會荠医。
為了進一步聯(lián)系原始惡性和正常細胞的分化狀態(tài)吁脱，我們生成了代表正常造血發(fā)育連續(xù)階段的三個基因標記：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彬向。
However, a distinct pattern emerged when we applied these signatures to malignant
AML cells兼贡，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)合緊密废封，需要惡補
（10）T Cell Signatures Are Suppressed in AML Patients
從干細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州泊，T細胞原則上可以消除AML細胞，但在AML中可能會受到損害漂洋，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)遥皂，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T-reg標記的細胞的一部分，但其數(shù)量有限氮发，無法進行進一步分析渴肉。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爽冕。
Conversely, the tumors had relatively greater numbers of T-regs, consistent with prior reports that this suppressive subset is increased in AML仇祭。因此，scRNA-seq和IHC顯示出T細胞數(shù)量和組成的一致變化颈畸，表明存在免疫抑制性腫瘤環(huán)境乌奇。
（11）分化的AML細胞體外抑制T細胞活化
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眯娱。
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礁苗。
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徙缴、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
