Regulation of hematopoiesis during human development remains poorly defined. Here we applied single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to over 8,000 human immunophenotypic blood cells from fetal liver and bone marrow. We inferred their differentiation trajectory and identified three highly proliferative oligopotent progenitor populations downstream of hematopoietic stem cells (HSCs)/multipotent progenitors (MPPs). Along this trajectory, we observed opposing patterns of chromatin accessibility and differentiation that coincided with dynamic changes in the activity of distinct lineage-specific transcription factors. Integrative analysis of chromatin accessibility and gene expression revealed extensive epigenetic but not transcriptional priming of HSCs/MPPs prior to their lineage commitment. Finally, we refined and functionally validated the sorting strategy for the HSCs/MPPs and achieved around 90% enrichment. Our study provides a useful framework for future investigation of human developmental hematopoiesis in the context of blood pathologies and regenerative medicine.

Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools.

Stereotactic neuro‐radiosurgery is a well‐established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft‐tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and growth. Therefore, particular attention must be deserved to these hypoxic areas that could lead to recurrent cancers and resistance to therapeutic damage. This article proposes a novel fully automatic method for necrosis extraction (NeXt), using the Fuzzy C‐Means algorithm, after the GTV segmentation. This unsupervised Machine Learning technique detects and delineates the necrotic regions also in heterogeneous cancers. The overall processing pipeline is an integrated two‐stage segmentation approach useful to support neuro‐radiosurgery.NeXtcan be exploited for dose escalation, allowing for a more selective strategy to increase radiation dose in hypoxic radioresistant areas. Moreover,NeXtanalyzes contrast‐enhanced T1‐weighted MR images alone and does not require multispectral MRI data, representing a clinically feasible solution. This study considers an MRI dataset composed of 32 brain metastatic cancers, wherein 20 tumors present necroses. The segmentation accuracy ofNeXtwas evaluated using both spatial overlap‐based and distance‐based metrics, achieving these average values: Dice similarity coefficient 95.93% ± 4.23% and mean absolute distance 0.225 ± 0.229 (pixels).