Wishbone identifies bifurcating developmental trajectories from single-cell data.

Recent single-cell analysis technologies offer an unprecedented opportunity to elucidate developmental pathways. Here we present Wishbone, an algorithm for positioning single cells along bifurcating developmental trajectories with high resolution. Wishbone uses multi-dimensional single-cell data, such as mass cytometry or RNA-Seq data, as input and orders cells according to their developmental progression, and it pinpoints bifurcation points by labeling each cell as pre-bifurcation or as one of two post-bifurcation cell fates. Using 30-channel mass cytometry data, we show that Wishbone accurately recovers the known stages of T-cell development in the mouse thymus, including the bifurcation point. We also apply the algorithm to mouse myeloid differentiation and demonstrate its generalization to additional lineages. A comparison of Wishbone to diffusion maps, SCUBA and Monocle shows that it outperforms these methods both in the accuracy of ordering cells and in the correct identification of branch points.

Trajectories of cell-cycle progression from fixed cell populations.

An accurate dissection of sources of cell-to-cell variability is crucial for quantitative biology at the single-cell level but has been challenging for the cell cycle. We present Cycler, a robust method that constructs a continuous trajectory of cell-cycle progression from images of fixed cells. Cycler handles heterogeneous microenvironments and does not require perturbations or genetic markers, making it generally applicable to quantifying multiple sources of cell-to-cell variability in mammalian cells.

Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.

Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology.

Detection of minimal residual disease in B lymphoblastic leukemia using viSNE.

BACKGROUND: Minimal residual disease (MRD) following treatment is a robust prognostic marker in B lymphoblastic leukemia. However, the detection of MRD by flow cytometric immunophenotyping is technically challenging, and an automated method to detect MRD is therefore desirable. viSNE, a recently developed computational tool based on the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, has been shown to be capable of detecting synthetic "MRD-like" populations of leukemic cells created in vitro, but whether viSNE can facilitate the immunophenotypic detection of MRD in clinical samples has not been evaluated. METHODS: We applied viSNE retrospectively to 8-color flow cytometric immunophenotyping data from normal bone marrow samples, and samples from B lymphoblastic leukemia patients with or without suspected MRD on the basis of conventional manual gating. RESULTS: In each of 14 bone marrow specimens containing MRD or suspected MRD, viSNE identified a putative MRD population; an abnormal composite immunophenotype was confirmed for the putative MRD in each case. MRD populations were not identified by viSNE in control bone marrow samples from patients with increased normal B-cell precursors, or in post-treatment samples from B lymphoblastic leukemia patients who did not have detectable MRD by manual gating. CONCLUSION: viSNE shows promise as an automated method to facilitate immunophenotypic MRD detection in patients treated for B lymphoblastic leukemia.

Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development.

Tissue regeneration is an orchestrated progression of cells from an immature state to a mature one, conventionally represented as distinctive cell subsets. A continuum of transitional cell states exists between these discrete stages. We combine the depth of single-cell mass cytometry and an algorithm developed to leverage this continuum by aligning single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. Applied to human B cell lymphopoiesis, the algorithm (termed Wanderlust) constructed trajectories spanning from hematopoietic stem cells through to naive B cells. This trajectory revealed nascent fractions of B cell progenitors and aligned them with developmentally cued regulatory signaling including IL-7/STAT5 and cellular events such as immunoglobulin rearrangement, highlighting checkpoints across which regulatory signals are rewired paralleling changes in cellular state. This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and "corrupted" developmental processes including cancer.

viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia.

New high-dimensional, single-cell technologies offer unprecedented resolution in the analysis of heterogeneous tissues. However, because these technologies can measure dozens of parameters simultaneously in individual cells, data interpretation can be challenging. Here we present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the high-dimensional structure of the data. viSNE plots individual cells in a visual similar to a scatter plot, while using all pairwise distances in high dimension to determine each cell's location in the plot. We integrated mass cytometry with viSNE to map healthy and cancerous bone marrow samples. Healthy bone marrow automatically maps into a consistent shape, whereas leukemia samples map into malformed shapes that are distinct from healthy bone marrow and from each other. We also use viSNE and mass cytometry to compare leukemia diagnosis and relapse samples, and to identify a rare leukemia population reminiscent of minimal residual disease. viSNE can be applied to any multi-dimensional single-cell technology.