Mapping cell fate during hematopoiesis Biologists have long attempted to understand how stem and progenitor cells in regenerating and embryonic tissues differentiate into mature cell types. Through the use of recent technical advances to sequence the genes expressed in thousands of individual cells, differentiation mechanisms are being revealed. Weinreb et al. extended these methods to track clones of cells (cell families) across time. Their approach reveals differences in cellular gene expression as cells progress through hematopoiesis, which is the process of blood production. Using machine learning, they tested how well gene expression measurements account for the choices that cells make. This work reveals that a considerable gap still exists in understanding differentiation mechanisms, and future methods are needed to fully understand—and ultimately control—cell differentiation. Science, this issue p. eaaw3381 Single-cell barcoding allows the tracking of gene expression states over time as blood cells differentiate during hematopoiesis. INTRODUCTION During tissue turnover, stem and progenitor cells differentiate to produce mature cell types. To understand and ultimately control differentiation, it is important to establish how initial differences between cells influence their ultimate choice of cell fate. This challenge is exemplified in hematopoiesis, the ongoing process of blood regeneration in bone marrow, in which multipotent progenitors give rise to red cells of the blood, as well as myeloid and lymphoid immune cell types. In hematopoiesis, progenitor cell states have been canonically defined by their expression of several antigens. However, as in several other tissues, recent transcriptome analysis by single-cell RNA sequencing (scSeq) showed that the canonically defined intermediate cell types are not uniform, but rather contain cells in a variety of gene expression states. scSeq also showed that the states of hematopoietic progenitors form a continuum, differing from classic depictions of a discrete stepwise hierarchy. RATIONALE In this study, we set out to establish how variation in transcriptional state biases future cell fate and whether scSeq is sufficient to completely distinguish cells with distinct fate biases. Directly linking whole-transcriptome descriptions of cells to their future fate is challenging because cells are destroyed during scSeq measurement. We therefore developed a tool we call LARRY (lineage and RNA recovery) that clonally tags cells with DNA barcodes that can be read using scSeq. Using LARRY, we aimed to reconstruct the genome-wide transcriptional trajectories of cells as they differentiate. RESULTS We linked transcriptional progenitor states with their clonal fates by barcoding heterogeneous cells, allowing cell division, and then sampling cells for scSeq immediately or at later time points after differentiation in culture or in transplanted mice. We profiled >300,000 cells in total, comprising 10,968 clones that gave information on lineage relationships at single time points and 2632 clones spanning multiple time points in culture or in mice. We confirmed that clonal trajectories over time approximated the trajectories of single cells and were thus able to identify states of primed fate potential on the continuous transcriptional landscape. From this analysis, we identified genes correlating with fate, established a lineage hierarchy for hematopoiesis in culture and after transplantation, and revealed two routes of monocyte differentiation that give rise to distinct gene expression programs in mature cells. The data made it possible to test state-of the-art algorithms of scSeq analysis, and we found that fate choice occurs earlier than predicted algorithmically but that computationally predicted pseudotime orderings faithfully describe clonal dynamics. We investigated whether there are stable cellular properties that have a cell-autonomous influence on fate choice yet are not detected by scSeq. By analyzing clones split between wells or transplanted into separate mice, we found that the variance in cell fate choice attributable to cell-autonomous fate bias was greater than what could be explained by initial transcriptional state. Less formally, sister cells tended to be far more similar in their fate choice than pairs of cells with similar transcriptomes. These results suggest that current scSeq measurements cannot fully separate progenitor cells with distinct fate bias. The missing signature of future fate choice might be detectable in the RNA that is not sampled during scSeq. Alternatively, other stable cellular properties such as chromatin state could encode the missing information. CONCLUSION By integrating transcriptome and lineage measurements, we established a map of clonal fate on a continuous transcriptional landscape. The map revealed transcriptional correlates of fate among putatively multipotent cells, convergent differentiation trajectories, and fate boundaries that could be not be predicted using current trajectory inference methods. However, the map is far from complete because scSeq cannot separate cells with distinct fate bias. Our results argue for looking beyond scSeq to define cellular maps of stem and progenitor cells and offer an approach for linking cell state and fate in other tissues. Lineage and transcriptome measurements allow fate mapping on continuous cell state landscapes. A tool we named LARRY labels cell clones with an scSeq-compatible barcode. By barcoding cells, letting them divide, and then sampling them immediately or after differentiation, it is possible to link the initial states of cells with their differentiation outcomes and produce a map of cell fate bias on a continuous transcriptional landscape. A challenge in biology is to associate molecular differences among progenitor cells with their capacity to generate mature cell types. Here, we used expressed DNA barcodes to clonally trace transcriptomes over time and applied this to study fate determination in hematopoiesis. We identified states of primed fate potential and located them on a continuous transcriptional landscape. We identified two routes of monocyte differentiation that leave an imprint on mature cells. Analysis of sister cells also revealed cells to have intrinsic fate biases not detectable by single-cell RNA sequencing. Finally, we benchmarked computational methods of dynamic inference from single-cell snapshots, showing that fate choice occurs earlier than is detected by state-of the-art algorithms and that cells progress steadily through pseudotime with precise and consistent dynamics.

中文翻译：

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转录景观的谱系追踪将分化过程中的状态与命运联系起来

在造血过程中绘制细胞命运图 生物学家长期以来一直试图了解再生和胚胎组织中的干细胞和祖细胞如何分化为成熟细胞类型。通过使用最近的技术进步对数千个单个细胞中表达的基因进行测序，分化机制正在被揭示。温雷布等人。扩展这些方法以跟踪细胞（细胞家族）的克隆。他们的方法揭示了细胞基因表达的差异，因为细胞通过造血（造血过程）进行。使用机器学习，他们测试了基因表达测量对细胞做出的选择的解释程度。这项工作表明，在理解分化机制方面仍然存在相当大的差距，需要未来的方法来完全理解并最终控制细胞分化。科学，这个问题 p。eaaw3381 单细胞条形码允许随着血细胞在造血过程中分化而随时间跟踪基因表达状态。介绍 在组织更新过程中，干细胞和祖细胞分化产生成熟的细胞类型。要了解并最终控制分化，重要的是要确定细胞之间的初始差异如何影响它们对细胞命运的最终选择。这一挑战体现在造血作用中，这是骨髓中血液再生的持续过程，其中多能祖细胞产生血液中的红细胞以及骨髓和淋巴免疫细胞类型。在造血过程中，祖细胞状态已通过其几种抗原的表达被规范地定义。然而，与其他几种组织一样，最近通过单细胞 RNA 测序 (scSeq) 进行的转录组分析表明，规范定义的中间细胞类型并不统一，而是包含处于各种基因表达状态的细胞。scSeq 还表明，造血祖细胞的状态形成一个连续体，与离散逐步层次结构的经典描述不同。基本原理在本研究中，我们着手确定转录状态的变化如何影响未来的细胞命运，以及 scSeq 是否足以完全区分具有不同命运偏见的细胞。将细胞的全转录组描述与其未来命运直接联系起来具有挑战性，因为细胞在 scSeq 测量期间被破坏。因此，我们开发了一种称为 LARRY（谱系和 RNA 恢复）的工具，该工具使用可以使用 scSeq 读取的 DNA 条形码克隆标记细胞。使用 LARRY，我们旨在重建细胞分化时的全基因组转录轨迹。结果我们通过对异质细胞进行条形码编码，允许细胞分裂，然后在培养或移植小鼠中分化后立即或在稍后的时间点对细胞进行 scSeq 采样，将转录祖细胞状态与其克隆命运联系起来。我们总共分析了 > 300,000 个细胞，包括 10,968 个提供单个时间点谱系关系信息的克隆，以及跨越培养或小鼠多个时间点的 2632 个克隆。我们证实，随着时间的推移，克隆轨迹近似于单细胞的轨迹，因此能够识别连续转录景观上的致敏命运潜力状态。通过这项分析，我们确定了与命运相关的基因，建立了培养和移植后造血的谱系层次结构，并揭示了在成熟细胞中产生不同基因表达程序的两种单核细胞分化途径。这些数据使测试最先进的 scSeq 分析算法成为可能，我们发现命运选择发生的时间早于算法预测，但计算预测的伪时间排序忠实地描述了克隆动态。我们调查了是否存在稳定的细胞特性，这些特性对命运选择具有细胞自主影响，但 scSeq 未检测到。通过分析在孔之间分裂或移植到单独小鼠中的克隆，我们发现可归因于细胞自主命运偏差的细胞命运选择的差异大于初始转录状态所能解释的差异。不太正式的是，姐妹细胞在命运选择上往往比具有相似转录组的成对细胞更加相似。这些结果表明，当前的 scSeq 测量不能完全分离具有明显命运偏差的祖细胞。在 scSeq 期间未采样的 RNA 中可能会检测到未来命运选择的缺失特征。或者，其他稳定的细胞特性，如染色质状态，可以对缺失的信息进行编码。结论通过整合转录组和谱系测量，我们在连续转录景观上建立了克隆命运图。该图揭示了假定的多能细胞之间命运的转录相关性、趋同分化轨迹和使用当前轨迹推断方法无法预测的命运边界。然而，该图还远未完成，因为 scSeq 无法分离具有明显命运偏差的细胞。我们的结果主张超越 scSeq 来定义干细胞和祖细胞的细胞图谱，并提供一种将其他组织中的细胞状态和命运联系起来的方法。谱系和转录组测量允许在连续细胞状态图上绘制命运图。我们命名为 LARRY 的工具使用与 scSeq 兼容的条形码标记细胞克隆。通过条形码细胞，让它们分裂，然后立即或在分化后对它们进行采样，可以将细胞的初始状态与其分化结果联系起来，并在连续转录图谱上生成细胞命运偏差图。生物学中的一个挑战是将祖细胞之间的分子差异与其产生成熟细胞类型的能力联系起来。在这里，我们使用表达的 DNA 条形码随着时间的推移对转录组进行克隆追踪，并将其应用于研究造血过程中的命运决定。我们确定了启动命运潜力的状态，并将它们定位在一个连续的转录环境中。我们确定了在成熟细胞上留下印记的两种单核细胞分化途径。对姐妹细胞的分析还揭示了细胞具有单细胞 RNA 测序无法检测到的内在命运偏差。最后，