Python for gene expression

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.</p

Python for gene expression

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.</p

Python for gene expression

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.</p

Python for gene expression

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.</p

Python for gene expression

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.</p

Python for gene expression

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.</p

Measures of Clonal Hematopoiesis:Are We Missing Something?

Clonal Hematopoiesis (CH) is a common, age-related phenomenon of growing scientific interest, due to its association with hematologic malignancy, cardiovascular disease and decreased overall survival. CH is commonly attributed to the preferential outgrowth of a mutant hematopoietic stem cell (HSC) with enhanced fitness, resulting in clonal imbalance. In-depth understanding of the relation between HSC clonal dynamics, CH and hematologic malignancy requires integration of fundamental lineage tracing studies with clinical data. However, this is hampered by lack of a uniform definition of CH and by inconsistency in the analytical methods used for its quantification. Here, we propose a conceptual and analytical framework for the definition and measurement of CH. First, we transformed the conceptual definition of CH into the CH index, which provides a quantitative measure of clone numbers and sizes. Next, we generated a set of synthetic data, based on the beta-distribution, to simulate clonal populations with different degrees of imbalance. Using these clonal distributions and the CH index as a reference, we tested several established indices of clonal diversity and (in-)equality for their ability to detect and quantify CH. We found that the CH index was distinct from any of the other tested indices. Nonetheless, the diversity indices (Shannon, Simpson) more closely resembled the CH index than the inequality indices (Gini, Pielou). Notably, whereas the inequality indices mainly responded to changes in clone sizes, the CH index and the tested diversity indices also responded to changes in the number of clones in a sample. Accordingly, these simulations indicate that CH can result not only by skewing clonal abundancies, but also by variation in their overall numbers. Altogether, our model-based approach illustrates how a formalized definition and quantification of CH can provide insights into its pathogenesis. In the future, use of the CH index or Shannon index to quantify clonal diversity in fundamental as well as clinical clone-tracing studies will promote cross-disciplinary discussion and progress in the field.</p

Clonal analysis of patient-derived samples using cellular barcodes

Cellular barcoding is a relatively simple method that allows quantitative assessment of the clonal dynamics of normal, nonmalignant hematopoietic stem cells and of leukemia. Cellular barcodes are (semi-)random synthetic DNA sequences of a fixed length, which are used to uniquely mark and track cells over time. A successful barcoding experiment consists of several essential steps, including library production, transfection, transduction, barcode retrieval, and barcode data analysis. Key challenges are to obtain sufficient number of barcoded cells to conduct experiments and reliable barcode data analysis. This is especially relevant for experiments using primary leukemia cells (which are of limited availability and difficult to transduce), when studying low levels of chimerism, or when the barcoded cell population is sorted in different smaller subpopulations (e.g., lineage contribution of normal hematopoietic stem cells in murine xenografts). In these settings, retrieving accurate barcode data from low input material using standard PCR amplification techniques might be challenging and more sophisticated approaches are required. In this chapter we describe the procedures to transfect and transduce patient-derived leukemia cells, to retrieve barcoded data from both high and low input material, and to filter barcode data from sequencing noise prior to quantitative clonal analysis

Left or right? Directions to stem cell engraftment:Directions to stem cell engraftment

In this issue of JEM, Wu et al. (https://doi.org/10.1084/jem.20171341) use genetic barcoding of macaque hematopoietic stem cells to demonstrate that, after transplantation, HSCs are very asymmetrically distributed and uncover a thymus-independent pathway for mature T cell production in the bone marrow

Donor-to-Donor Heterogeneity in the Clonal Dynamics of Transplanted Human Cord Blood Stem Cells in Murine Xenografts

Umbilical cord blood (UCB) provides an alternative source of hematopoietic stem cells (HSCs) for allogeneic transplantation. Administration of sufficient donor HSCs is critical to restore recipient hematopoiesis and to maintain long-term polyclonal blood formation. However, due to lack of unique markers, the frequency of HSCs among UCB CD34(+) cells is the subject of ongoing debate, urging for reproducible strategies for their counting. Here, we used cellular barcoding to determine the frequency and clonal dynamics of human UCB HSCs and to determine how data analysis methods affect these parameters. We transplanted lentivirally barcoded CD34(+) cells from 20 UCB donors into Nod/Scid/IL2Ry(-/-) (NSG) mice (n = 30). Twelve recipients (of 8 UCB donors) engrafted with >1% GFP(+) cells, allowing for clonal analysis by multiplexed barcode deep sequencing. Using multiple definitions of clonal diversity and strategies for data filtering, we demonstrate that differences in data analysis can change clonal counts by several orders of magnitude and propose methods to improve their consistency. Using these methods, we show that the frequency of NSG-repopulating cells was low (median similar to 1 HSC/10(4) CD34(+) UCB cells) and could vary up to 10-fold between donors. Clonal patterns in blood became increasingly consistent over time, likely reflecting initial output of transient progenitors, followed by long-term HSCs with stable hierarchies. The majority of long-term clones displayed multilineage output, yet clones with lymphoid- or myeloid-biased output were also observed. Altogether, this study uncovers substantial interdonor and analysis-induced variability in the frequency of UCB CD34(+) clones that contribute to post-transplant hematopoiesis. As clone tracing is increasingly relevant, we urge for universal and transparent methods to count HSC clones during normal aging and upon transplantation. (C) 2019 American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc