Alignment of single-cell trajectories by tuMap enables high-resolution quantitative comparison of cancer samples.
Alignment of single-cell trajectories by tuMap enables high-resolution quantitative comparison of cancer samples.
复制标题
DOI：
10.1016/j.cels.2021.09.003
复制
发表时间：
2022-01-19
期刊：
影响因子：
9.3
通讯作者：
Shen-Orr SS
中科院分区：
文献类型：
--
作者：
Alpert A;Nahman O;Starosvetsky E;Hayun M;Curiel TJ;Ofran Y;Shen-Orr SS
单细胞技术允许将癌症样品表征为连续的发展轨迹。然而，由于患者之间存在较大的表型异质性，因此无法利用获得的时间分辨率进行比较分析。在这里，我们提出了tuMap算法，该算法利用癌症样本的高维单细胞数据，这些数据显示出潜在的发育结构，使它们与健康发育相一致，从而产生tuMap伪时间轴，使它们能够进行系统的、有意义的比较。我们将tuMap应用于急性淋巴细胞和髓性白血病的单细胞团细胞计数数据，以揭示tuMap伪时间轴与临床之间的关联，其优于发育人群的细胞分配。tuMap算法在单细胞RNA测序数据上的应用进一步鉴定了位于癌症轨迹非常早期部分的干细胞的基因特征。tuMap提供的定量框架允许生成用于癌症患者评估的指标。单细胞技术提供了前所未有的大量数据，其中只有一小部分被利用。轨迹推理方法利用详细的信息来获得过程表征的时间分辨率。然而，在癌症中，患者的异质性阻止了对不同患者中发生的过程进行定量比较，这是临床推理和机械理解的重要步骤。我们提出了tuMap算法，该算法克服了患者间的差异性，并将癌症患者的轨迹与单轴对齐，从而为癌症比较提供了连续的框架。
Single-cell technologies allow characterization of cancer samples as continuous developmental trajectories. Yet, the obtained temporal resolution cannot be leveraged for a comparative analysis due to the large phenotypic heterogeneity existing between patients. Here we present the tuMap algorithm that exploits high-dimensional single-cell data of cancer samples exhibiting an underlying developmental structure to align them with the healthy development, yielding the tuMap pseudotime axis that allows their systematic, meaningful comparison. We applied tuMap on single-cell mass cytometry data of acute lymphoblastic and myeloid leukemia to reveal associations between the tuMap pseudotime axis and clinics that outperforms cellular assignment into developmental populations. Application of the tuMap algorithm on single-cell RNA sequencing data further identified gene signatures of stem cells residing at the very early parts of the cancer trajectories. The quantitative framework provided by tuMap allows generation of metrics for cancer patients evaluation. Single-cell technologies provide unprecedentedly large amounts of data, of which only a small fraction is utilized. Trajectory inference methodologies leverage the detailed information to gain temporal resolution in process characterization. Yet, in cancer, patient heterogeneity prevents quantitative comparison of the processes occurring in different patients, an essential step for clinical reasoning and mechanistic understanding. We present the tuMap algorithm that overcomes inter-patient variability and aligns cancer patient trajectories to a single axis, thus providing a continuous framework for cancer comparison.
登录
查看更多内容
影响因子：
4.3
作者：
Chen H;Lau MC;Wong MT;Newell EW;Poidinger M;Chen J
通讯作者：
Chen J
DOI：
10.1126/science.1198704
发表时间：
2011-05-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bendall SC;Simonds EF;Qiu P;Amir el-AD;Krutzik PO;Finck R;Bruggner RV;Melamed R;Trejo A;Ornatsky OI;Balderas RS;Plevritis SK;Sachs K;Pe'er D;Tanner SD;Nolan GP
通讯作者：
Nolan GP
影响因子：
48
作者：
Chen, William S.;Zivanovic, Nevena;Krishnaswamy, Smita
通讯作者：
Krishnaswamy, Smita
影响因子：
30.8
作者：
Corces, M. Ryan;Buenrostro, Jason D.;Wu, Beijing;Greenside, Peyton G.;Chan, Steven M.;Koenig, Julie L.;Snyder, Michael P.;Pritchard, Jonathan K.;Kundaje, Anshul;Gkeenleaf, William J.;Majeti, Ravindra;Chang, Howard Y.
通讯作者：
Chang, Howard Y.
影响因子：
12.8
作者：
De Kouchkovsky I;Abdul-Hay M
通讯作者：
Abdul-Hay M