Characterization of cell fate probabilities in single-cell data with Palantir.

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir's resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.

Impact of Mycobacterium tuberculosis complex lineages as a determinant of disease phenotypes from an immigrant rich moderate tuberculosis burden country.

BACKGROUND: Growing evidences suggested that the Mycobacterium tuberculosis complex (MTBC) lineages can determine the clinical outcome of pulmonary and extra-pulmonary tuberculosis. However, limited data only available revealing such association of bacterial genotypes and clinical phenotypes from immigrant rich countries. METHODS: A multicenter study has been carried out on a collection of 2092 (1003 extrapulmonary and 1089 pulmonary) MTBC isolates. Genotyping of all the isolates were carried out by spoligotyping and 24 loci based MIRU-VNTR typing. RESULTS: Demographically domination of young Saudi nationals (61.4%) and men (61.2%) were found in this cohort. Lymph nodes (62.4%) and gastrointestinal sites (16.7%) were the most common anatomical sites of infection. The predominant lineages were Delhi/CAS (26.9%), EAI (14.2%) and Ghana (9.9%). Mycobacterium africanum type I and II were reported for the first time in the country among extrapulmonary cases. 'Ancestral' lineages M.bovis (OR-5.22; 95% CI-2.23-8.22, p- < 0.001) and Delhi/CAS (OR-0.57; 95% CI-0.411-0.734, p- < 0.001) were directly associated with lymph node tuberculosis and gastrointestinal tuberculosis (M. bovis-OR-0.33; 95% CI-0.085-0.567, p-0.001 and Delhi/CAS-OR-1.87; 95% CI-1.22-2.53, p- < 0.001) respectively. Among the 'Modern' lineages, EAI showed significant association to central nervous system tuberculosis (OR-1.98; 95% CI-0.76-3.19, p-0.04) and Uganda-I to gastrointestinal tuberculosis (OR-2.41; 95% CI-0.77-4.06, p-0.02). CONCLUSIONS: The findings substantially contribute to the emerging evidences that MTBC lineages influence disease phenotypes and epidemiological consequences.

Single-cell mutation identification via phylogenetic inference.

Reconstructing the evolution of tumors is a key aspect towards the identification of appropriate cancer therapies. The task is challenging because tumors evolve as heterogeneous cell populations. Single-cell sequencing holds the promise of resolving the heterogeneity of tumors; however, it has its own challenges including elevated error rates, allelic drop-out, and uneven coverage. Here, we develop a new approach to mutation detection in individual tumor cells by leveraging the evolutionary relationship among cells. Our method, called SCIΦ, jointly calls mutations in individual cells and estimates the tumor phylogeny among these cells. Employing a Markov Chain Monte Carlo scheme enables us to reliably call mutations in each single cell even in experiments with high drop-out rates and missing data. We show that SCIΦ outperforms existing methods on simulated data and applied it to different real-world datasets, namely a whole exome breast cancer as well as a panel acute lymphoblastic leukemia dataset.

Aldehyde dehydrogenase activity in cryopreserved cord blood cells for quality assessment prior to transplantation.

In umbilical cord blood transplantation (UCBT), the number of cluster of differentiation (CD)34+ cells and colony­forming units (CFUs) in the cord blood (CB) graft positively correlate with patient survival. Therefore, these parameters are currently used for quality assessment of the cryopreserved CB cells in the attached segment that is considered representative of the CB in the main bag prior to UCBT. Since aldehyde dehydrogenase (ALDH) activity is high in hematopoietic stem cells, the number of ALDH­bright (ALDHbr) cells was examined in comparison with the number of CD34+ cells and CFUs for the quality assessment of CB units. In the cryopreserved main bag, the number of ALDHbr cells in the CB unit exhibited positive correlation with the number of CD34+ cells, and with CFU­granulocytes/macrophages and total CFU counts. Furthermore, the concentration of ALDHbr cells in the cryopreserved attached segment was not significantly different compared to that of the main bag, suggesting that the attached segment is representative of the main bag. In conclusion, the present study suggested that ALDHbr cell counts in the cryopreserved attached segments may serve as a quality assessment indicator for CB units prior to UCBT.

Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm.

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, an algorithm designed to assess protein activity in tissue-independent fashion by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactomes. We confirm the algorithm's value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

Differentiation of functional endothelial cells from human induced pluripotent stem cells: A novel, highly efficient and cost effective method.

Endothelial cells derived from human induced pluripotent stem cells (hiPSC- EC) are of significant value for research on human vascular development, in vitro disease models and drug screening. Here we report an alternative, highly efficient and cost-effective simple three step method (mesoderm induction, endothelial cell differentiation and endothelial cell expansion) to differentiate hiPSC directly into endothelial cells. We demonstrate that efficiency of described method to derive CD31+ and VE-Cadherin+ double positive cells is higher than 80% in 12 days. Most notably we established that hiPSC-EC differentiation efficacy depends on optimization of both mesoderm differentiation and endothelial cell differentiation steps.

Chronic interleukin-1 exposure drives haematopoietic stem cells towards precocious myeloid differentiation at the expense of self-renewal.

Haematopoietic stem cells (HSCs) maintain lifelong blood production and increase blood cell numbers in response to chronic and acute injury. However, the mechanism(s) by which inflammatory insults are communicated to HSCs and their consequences for HSC activity remain largely unknown. Here, we demonstrate that interleukin-1 (IL-1), which functions as a key pro-inflammatory 'emergency' signal, directly accelerates cell division and myeloid differentiation of HSCs through precocious activation of a PU.1-dependent gene program. Although this effect is essential for rapid myeloid recovery following acute injury to the bone marrow, chronic IL-1 exposure restricts HSC lineage output, severely erodes HSC self-renewal capacity, and primes IL-1-exposed HSCs to fail massive replicative challenges such as transplantation. Importantly, these damaging effects are transient and fully reversible on IL-1 withdrawal. Our results identify a critical regulatory circuit that tailors HSC responses to acute needs, and is likely to underlie deregulated blood homeostasis in chronic inflammation conditions.

Comparing the biological impact of glatiramer acetate with the biological impact of a generic.

For decades, policies regarding generic medicines have sought to provide patients with economical access to safe and effective drugs, while encouraging the development of new therapies. This balance is becoming more challenging for physicians and regulators as biologics and non-biological complex drugs (NBCDs) such as glatiramer acetate demonstrate remarkable efficacy, because generics for these medicines are more difficult to assess. We sought to develop computational methods that use transcriptional profiles to compare branded medicines to generics, robustly characterizing differences in biological impact. We combined multiple computational methods to determine whether differentially expressed genes result from random variation, or point to consistent differences in biological impact of the generic compared to the branded medicine. We applied these methods to analyze gene expression data from mouse splenocytes exposed to either branded glatiramer acetate or a generic. The computational methods identified extensive evidence that branded glatiramer acetate has a more consistent biological impact across batches than the generic, and has a distinct impact on regulatory T cells and myeloid lineage cells. In summary, we developed a computational pipeline that integrates multiple methods to compare two medicines in an innovative way. This pipeline, and the specific findings distinguishing branded glatiramer acetate from a generic, can help physicians and regulators take appropriate steps to ensure safety and efficacy.

Do cancer cells undergo phenotypic switching? The case for imperfect cancer stem cell markers.

The identification of cancer stem cells in vivo and in vitro relies on specific surface markers that should allow to sort cancer cells in phenotypically distinct subpopulations. Experiments report that sorted cancer cell populations after some time tend to express again all the original markers, leading to the hypothesis of phenotypic switching, according to which cancer cells can transform stochastically into cancer stem cells. Here we explore an alternative explanation based on the hypothesis that markers are not perfect and are thus unable to identify all cancer stem cells. Our analysis is based on a mathematical model for cancer cell proliferation that takes into account phenotypic switching, imperfect markers and error in the sorting process. Our conclusion is that the observation of reversible expression of surface markers after sorting does not provide sufficient evidence in support of phenotypic switching.

An early decrease in Notch activation is required for human TCR-alphabeta lineage differentiation at the expense of TCR-gammadelta T cells.

Although well characterized in the mouse, the role of Notch signaling in the human T-cell receptor alphabeta (TCR-alphabeta) versus TCR-gammadelta lineage decision is still unclear. Although it is clear in the mouse that TCR-gammadelta development is less Notch dependent compared with TCR-alphabeta differentiation, retroviral overexpression studies in human have suggested an opposing role for Notch during human T-cell development. Using the OP9-coculture system, we demonstrate that changes in Notch activation are differentially required during human T-cell development. High Notch activation promotes the generation of T-lineage precursors and gammadelta T cells but inhibits differentiation toward the alphabeta lineage. Reducing the amount of Notch activation rescues alphabeta-lineage differentiation, also at the single-cell level. Gene expression analysis suggests that this is mediated by differential sensitivities of Notch target genes in response to changes in Notch activation. High Notch activity increases DTX1, NRARP, and RUNX3 expression, genes that are down-regulated during alphabeta-lineage differentiation. Furthermore, increased interleukin-7 levels cannot compensate for the Notch dependent TCR-gammadelta development. Our results reveal stage-dependent molecular changes in Notch signaling that are critical for normal human T-cell development and reveal fundamental molecular differences between mouse and human.

Robustness of coalescent estimators to between-lineage mutation rate variation.

Data from HIV and from human neoplastic cells can show substantial between-lineage mutation rate variation even within a single population. Such variation may affect estimators of population quantities such as Theta = 4N(e)mu. Using simulated DNA data, I measured the effect of rate variation on recovery of Theta by the summary-statistic estimator of Watterson (Watterson GA. 1975. On the number of segregating sites in genetical systems without recombination. Theor Popul Biol. 7:256-276) and the coalescent maximum likelihood algorithm LAMARC (Kuhner MK. 2006. LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters. Bioinformatics. Advance Access doi: 10.1093/bioinformatics/btk051). Watterson's estimator showed a downward bias, as expected, with high values of Theta. LAMARC's mean estimate was accurate for all tested values of Theta and rate variation except for a downward bias when rate variation was maximal (i.e., the slow rate was zero). LAMARC had consistently narrower confidence intervals (CIs) than Watterson's estimator. Both methods tended to reject the truth too often when rate variation was 8x or greater and independent among branches, as well as when variation was 4x or greater and correlated among branches. In the case of Watterson's estimate, this excess rejection was fully attributable to variation among genealogies in the amount of total branch length associated with the fast and slow rates. However, in the case of LAMARC, some excess rejection was still observed even when between-genealogy variation was taken into account. Both estimators are robust to modest rate variation; however, their use should be coupled with a statistical test to rule out extreme rate variation as the resulting CIs may not be reliable.